Long-term changes in the timing and intensity of the pollen season in Slovenia (2002 – 2024)

Long-term changes in the timing and intensity of the pollen season in Slovenia (2002 – 2024) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Long-term changes in the timing and intensity of the pollen season in Slovenia (2002 – 2024) Rene Markovic, Vladimir Grubelnik, Anja Simčič, Andreja Kofol Seliger, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8731030/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Climate change is reshaping pollen season dynamics across Europe, with significant implications for allergic populations. This study presents the first comprehensive long-term analysis of pollen season trends in Slovenia, a region characterized by high phytogeographic diversity at the intersection of Mediterranean, Alpine, Dinaric, and Pannonian climatic influences. Daily airborne pollen concentration data from three monitoring stations were analyzed for 18 allergenic taxa over 23 years (2002-2024). Pollen season timing was determined using a normalized cumulative-sum approach, and linear regression quantified temporal trends in season onset, termination, duration, and total annual pollen load. Results reveal general advancement in season onset across most taxa, particularly pronounced for early-blooming arboreal species in the Mediterranean region. Season termination exhibited asymmetric patterns: earlier endings for spring-flowering trees and delayed termination for herbaceous taxa, especially Poaceae. Notably, early blooming trees showed compressed seasons due to faster advancement of season end relative to onset, potentially leading to more intense allergen exposure despite shorter duration. Significant increases in annual pollen load were detected for Poaceae, Urticaceae, and Plantago , while Artemisia showed widespread decline. The coastal Mediterranean site exhibited significantly earlier onset, later termination, and longer seasons compared to continental stations, which showed strong inter-site synchrony. These findings demonstrate asymmetric phenological responses to climate forcing across small spatial scales, with important implications for region-specific public health strategies. Airborne pollen Phenological trends Pollen season Slovenia Mediterranean climate Continental climate Allergenic exposure Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction The World Allergy Organization (WAO) identifies allergies as a major global public health concern (Pawankar et al. 2013 ). Pollen was one of the first allergens identified and is considered one of the most important triggers of allergic asthma and rhinoconjunctivitis (Sofiev et al. 2009 ). Consequently, airborne pollen is monitored worldwide to characterize pollen seasons and to support allergy prevention and treatment (Damialis et al. 2019 ). Airborne pollen is recognized as a sensitive indicator of climate change (Clot 2003 ), a driver that contributes to the increasing incidence of pollen allergies. Climate change can influence the timing, duration, and intensity of pollen seasons, with effects varying by region and plant species (Anderegg et al. 2021 ; Beggs 2021 ; D’Amato et al. 2016 ; Damialis et al. 2019 ; Rojo et al. 2021 ). Numerous studies have reported a general tendency toward an earlier start of pollen seasons for a wide range of taxa across Europe, with the most pronounced advances observed in early-season tree taxa such as Corylus , Alnus , Quercus , and members of the Cupressaceae family. Earlier season starts have also been documented for herbaceous taxa, particularly the Poaceae and Urticaceae families (Adams-Groom et al. 2022 ; Cristofolini et al. 2020 ; Glick et al. 2021 ; Hoebeke et al. 2018 ; Rojo et al. 2021 ). However, these responses show considerable spatial variability, as trends in the timing of pollen season onset differ among regions and are not statistically significant at all monitoring sites. Furthermore, long-term studies focusing on seasonal breakpoints indicate that trends for several species in Switzerland have been less pronounced or absent over the past two decades (Eeftens and Gehrig 2019 ). Comparable results have been reported for Betula pollen in France, Belgium, and Switzerland, where the timing of season onset shifted after the late 1990s and early 2000s, suggesting a trend toward a later start (Besancenot et al. 2019 ; Hoebeke et al. 2018 ; Jochner-Oette et al. 2019 ). Predominantly negative trends in pollen season end dates have been reported, with variations associated with the taxa examined and the definitions of the pollen season applied (Gehrig and Clot 2021 ). Earlier pollen season endings are reported mainly for arboreal taxa, whereas later end dates are more frequently observed for herbaceous species (Bogawski et al. 2014 ; Lind et al. 2016 ; Rojo et al. 2021 ). Alterations in pollen season start and end dates can impact the season’s overall duration. Several studies indicate that the pollen season has lengthened over time for various taxa (Anderegg et al. 2021 ; Tagliaferro et al. 2024 ; Ziska et al. 2019 ). Other studies, however, report generally stable season lengths with no significant trends, with late-flowering herbaceous taxa, such as Poaceae and Urticaceae, often emerging as exceptions and exhibiting a tendency toward season extension (Lind et al. 2016 ; Makra et al. 2011 ; Rojo et al. 2021 ). Despite regional variability, increases in the seasonal pollen integral ( SPIn ) or the annual pollen integral ( APIn ) were consistently observed across the Northern Hemisphere (Ziska et al. 2019 ). In their review article, Ziello et al. reported that the APIn value has been increasing across Europe, particularly for arboreal taxa, whereas many herbaceous species tend to show a decreasing trend (Ziello et al. 2012 ). Similar outcomes have also been reported in several later studies, with most research indicating increasing pollen trends among arboreal taxa of the Betulaceae, Fagaceae, Cupressaceae, and Oleaceae families, in contrast to declining or stable trends observed for herbaceous taxa such as Artemisia , Urticaceae, and Poaceae (de Weger et al. 2021 ; Galán et al. 2016 ; Rojo et al. 2021 ). Alterations in pollen season characteristics are driven by climate change and are linked to adverse health outcomes, as pollen exposure is a recognized trigger of symptoms in sensitized individuals (Beggs 2021 ; Schramm et al. 2021 ). Consistent with these observations, Sapkota et al. demonstrated that shifts in the timing of pollen season onset—whether earlier or later—are associated with increased hay fever prevalence (Sapkota et al. 2019 ). Moreover, increased pollen concentrations are also associated with negative health impacts, as they elevate the risk of allergies (Berezhanskiy et al. 2025 ) and exacerbate asthma symptoms (Guilbert et al. 2018 ). Rojo et al. and Hoebeke et al. provide further insight into the mechanistic factors influencing pollen seasonality, demonstrating how statistical modelling and long-term monitoring can enhance our understanding of these shifts (Hoebeke et al. 2018 ; Rojo et al. 2017 ). The results are strongly influenced by the specific years analyzed (Boullayali et al. 2025 ) and the criteria used to define the pollen season (Bastl et al. 2018 ; Gehrig and Clot 2021 ; Glick et al. 2021 ). The selection of the most suitable method depends on the main goal of the study (Galán et al., 2017). In this study, we aimed to identify changes in the timing and intensity of the pollen season for 18 clinically relevant taxa in Slovenia over the last twenty-three years (2002–2024). Using long-term daily pollen concentration data from three monitoring stations, we characterized regional seasonal patterns and assessed the degree of interregional synchrony. Slovenia represents a particularly informative study area, as it lies at the intersection of Mediterranean, Alpine, Dinaric, and Pannonian climatic and phytogeographical regions. Despite its small spatial extent, the country exhibits pronounced environmental heterogeneity, making it a natural laboratory for investigating climate-driven phenological responses across contrasting bioclimatic settings. In this context, our analysis focuses on the transition between the coastal Mediterranean region (Primorje) and the continental interior, represented by monitoring stations in Ljubljana and Maribor. This spatial configuration enables a direct comparison of pollen season dynamics across climatically distinct yet geographically proximate regions, allowing us to disentangle region-specific responses from broader, shared trends such as the general advancement of pollen season onset. To our knowledge, this is the first comprehensive long-term study of pollen season dynamics in Slovenia that explicitly addresses both regional differentiation and interregional coherence within a climatically transitional zone. By providing insights into asymmetric and taxa-specific phenological shifts across this contact region, the study contributes to a more nuanced understanding of how climate change affects aeroallergen exposure at fine spatial scales. The article is organized as follows: first, we describe the study region, monitoring sites, and analytical methods used to define pollen season characteristics and trends. This is followed by a presentation of the results and their discussion in the context of regional climate variability and previous European studies. Finally, we outline perspectives for future research, including the planned integration of aerobiological data with air quality measurements and health records from regional healthcare institutions. Such linkage is essential for assessing the clinical and societal impacts of changing pollen exposure, given the well-established association between airborne pollen, allergic disease burden, and public health relevance. 2. Methods 2.1 Study area, climate, and phytogeographic position Slovenia is situated within the temperate zone at the convergence of four major European geographic regions: the Mediterranean to the southwest, the Alps to the northwest, the Dinaric Alps to the south, and the Pannonian Plain to the northeast. Due to this unique geographical positioning, the country exhibits a transitional climate that can be broadly classified into moderate continental, moderate Mediterranean, and moderate mountain climate types (Ogrin 1996 ). The climatic conditions of the specific study areas are further differentiated by distinct environmental profiles. Primorje is characterized by a moderate Mediterranean climate, with a mean annual temperature of 14.1°C and annual precipitation of 968 mm. Ljubljana exhibits the moderate continental climate typical of western and southern Slovenia, with a mean temperature of 11.8°C and 1403 mm of precipitation. Maribor is defined by the moderate continental climate of eastern Slovenia, recording a mean temperature of 11.1°C and 956 mm of annual precipitation (“ARSO” 2026). The spatial distribution of these climatic zones and the locations of the sampling sites are illustrated in Fig. 1 . According to the phytogeographical classification, Slovenia comprises five floral provinces, all of which belong to the Euro-Siberian–North American Region. Regarding the specific study sites, Primorje is situated within the Illyrian–Adriatic floral province, Ljubljana within the Illyrian floral province, and Maribor within the Pre-Noricum–Slovenian floral province (Zupančič and Vreš 2019 ). 2.2 Aerobiological Data Acquisition and Preprocessing Daily airborne pollen concentration data were obtained from three permanent monitoring stations (see Fig. 1 ) located in Ljubljana (48.1482° N, 16.0385° E), Maribor (46.5646° N, 15.6460° E), and Primorje (45.5333° N, 13.6500° E), each equipped with a volumetric pollen sampler of the Hirst type (Hirst 1952 ). The dataset spans the period 2002–2024 and was provided by the National Laboratory of Health, Environment and Food (NLZOH), which operates the national aerobiological monitoring network following the European Aerobiology Society (EAS) methodological guidelines requirements (Galán et al. 2014 ). For this study, 18 pollen taxa were selected based on their allergic relevance and abundance: Alnus spp. (alder), Ambrosia sp. (ragweed), Artemisia spp. (mugwort), Betula spp. (birch), Castanea sp. (sweet chestnut), Corylus sp. (hazel), Cupressaceae/Taxaceae (cypress/yew), Fagus sp. (beech), Fraxinus spp. (ash), Carpinus spp. /Ostrya spp. (hornbeam/hop-hornbeam), Olea sp. (olive), Pinus spp. (pine), Plantago spp. (plantain), Platanus spp. (plane tree), Poaceae ( grasses), Quercus spp. (oak), Rumex spp. ( dock ) , and Urticaceae (nettle). It should be noted that a pollen taxon may comprise one species or an entire family, depending on the taxonomic level where morphological identification is possible (Dahl et al. 2013 ). Olea pollen detected in the Maribor and Ljubljana monitoring sites was primarily attributed to long-range transport from coastal regions and was therefore included among the analyzed taxa despite the limited local presence of olive trees at the continental monitoring sites. Pollen concentrations are expressed as grains per cubic meter of air (pollen grains/m³) and recorded on a daily basis throughout each year (ordinal days 1–365/366). To harmonize the temporal dimension, a fully automated Python preprocessing pipeline was developed using the Pandas library (The pandas development team 2026 ). In this step, ordinal day indices were systematically converted into ISO-compliant datetime objects. All records were merged into a single long-format (tidy) table comprising the following standardized fields: Date, Year, Location, Pollen taxa, and Value (pollen concentration). Non-numeric placeholders and textual markers (e.g., “x” or empty cells) were automatically converted to NaN values, ensuring proper handling of missing data in subsequent analyses. This preprocessing workflow guarantees full reproducibility and inter-regional comparability of the data, serving as a standardized foundation for the subsequent steps of temporal interpolation, normalization, and trend detection. 2.3 Data Completeness and Time Series Imputation Before statistical analyses were conducted, a detailed assessment of data completeness was performed for each unique combination of pollen taxon, year, and monitoring site. In the supplemental materials we provide detailed results showing how the completeness of the measurements over time (annually and monthly) and across different pollen taxa was assessed. A detailed assessment of monthly data completeness for the Maribor monitoring site revealed a pattern of persistent data gaps during the shoulder seasons. The annual data completeness by pollen taxon and year for the various sites is displayed in Figures S2a (Ljubljana), S6a (Maribor), and S10a (Primorje), while Figures S2b, S6b, and S10b present the corresponding monthly completeness over the same period. A detailed assessment of monthly data completeness for the Maribor monitoring site revealed a pattern of persistent data gaps during the shoulder seasons (Figure S6b). Specifically, we observed that the data for the months of January, November, and December were either completely missing or severely incomplete (indicated by low fractions ranging from 0.0 to 0.5) in the years prior to 2015 (Figure S6b). To ensure that our results were not biased or statistically unreliable due to this chronic lack of data during these specific periods, we decided to exclude all measurements corresponding to the months of January, November, and December for all analysis related to the Maribor region. Consequently, the analysis of the Maribor dataset was restricted to the core measuring period spanning from February to October. Because the typical pollination periods for Corylus and Alnus occur during these excluded months, these taxa were entirely excluded from the study for the Maribor region (Figure S7). To preserve the temporal integrity and biological seasonality of each series, data gaps were filled using the Prophet forecasting model (Taylor and Letham 2017 ). Prophet is a robust additive model designed for time series with pronounced seasonal components, making it particularly suitable for aerobiological data characterized by strong annual periodicity. The model was trained independently for each pollen taxon and year, thereby maintaining the seasonal dynamics profile of each taxon and avoiding the introduction of inter-annual bias. The imputation workflow followed three key steps. in step one, each daily time series was first sorted chronologically and converted into Prophet’s required input format (ds, y). In step 2, Prophet was configured with yearly seasonality enabled while daily and weekly periodicities were omitted, as short-term fluctuations are biologically irrelevant to pollen release dynamics. In step 3, missing values were predicted by the model and post-processed to ensure non-negativity—all negative estimates were replaced with zero, consistent with the physical interpretation of pollen concentration. When the available data for a given year were insufficient for stable model fitting (less than five valid points), a fallback mean-imputation procedure was applied, assigning the average of existing values to all missing entries. This hybrid strategy ensures that all annual records are continuous and comparable without artificially amplifying inter-seasonal variability. The imputation approach balances statistical rigor and biological plausibility, maintaining both the amplitude and phase characteristics of the original pollen signal. The resulting gap-free datasets form the basis for the subsequent normalization and delineation of pollen seasons (Section 2.4 ). 2.4 Determination of Pollen Season To objectively determine the onset and cessation of pollen seasons across taxa, years, and monitoring locations, we implemented a normalized cumulative-sum approach. For each unique combination of pollen taxon, year, and site, daily concentrations were first normalized by the annual pollen integral ( APIn ), the annual sum of all daily pollen concentrations. This normalization ensured that inter-annual variations in total pollen load did not bias the identification of pollen season descriptors. Following normalization, the cumulative proportion of the APIn was calculated for each day of the season. The start, T start , and end, T end , of the pollen season were then defined as the calendar days when the cumulative normalized curve reached 2.5% and 97.5% of the annual total, respectively. These percentile thresholds delineate the main pollen season (MPS) while excluding sporadic early or late pollen grains that may originate from long-range transport, secondary flowering, or resuspension of deposited pollen in the lower atmosphere. This approach is particularly recommended for ecological comparisons involving a large number of pollen taxa, including those with low APIn (Gehrig and Clot 2021 ; Rojo et al. 2020 ). This method offers several advantages over fixed-date or concentration-threshold approaches. By referencing each season to its own APIn , the approach is self-scaling, allowing valid comparison between years of high and low pollen productivity. It also preserves the seasonal integrity of each taxon by focusing on the relative timing of emission rather than absolute concentration magnitude. Furthermore, by applying the same percentile boundaries across all taxa and regions, the method supports standardized inter-regional and inter-annual comparisons, which are essential for multi-site trend analysis. The selection of 95% method, effectively characterizing the biologically relevant core of the season (Bastl et al. 2018 ; de Weger et al. 2021 ; Gehrig and Clot 2021 ; Rojo et al. 2017 ; Ziska et al. 2019 ). By adopting this normalized cumulative approach, pollen season shifts can be interpreted independently of inter-annual fluctuations in APIn , thereby isolating genuine climatic or ecological effects from variations in emission intensity. The implementation of the pollen season analysis pipeline is demonstrated in Fig. 2 , using Cupressaceae/Taxaceae data from the Ljubljana monitoring site as a representative example. The implementation of the analysis pipeline for all other taxa across the three regions is provided in the supplementary material (Figures S4a–r, S8a–r, and S12a–r). The bottom row in Fig. 2 shows the corresponding inter-annual trends in T start , end T end , and total annual pollen integral \(\:APIn\) . Linear regressions and coefficients of determination quantify the direction and strength of the temporal trends, demonstrating how the season’s onset, termination, and intensity evolve over the 23-year period. The normalized cumulative-sum method, combined with regression-based trend analysis, provides a rigorous and reproducible framework for detecting climate-driven pollen season shifts. The importance of explicitly stating the chosen limits is underscored by research showing that different methods can significantly influence aerobiological results, thus affecting the comparability of studies (Bastl et al. 2018 ; Gehrig and Clot 2021 ; Glick et al. 2021 ). Adherence to a standardized Hirst method is critical for ensuring data is reproducible and comparable across different monitoring stations, a principle outlined in the minimum requirements for pollen monitoring. Furthermore, for robust trend analysis, the use of a standardized baseline is essential for isolating seasonal shifts from annual variations in pollen production (Galán et al. 2014 ). 2.5 Inter-regional Correlation Analysis To assess the spatial coherence of pollen dynamics in Slovenia, pairwise Pearson correlation coefficients ( r ) were computed between the complete annual time series of daily pollen concentrations for the three monitoring sites (Ljubljana, Maribor, Primorje). All results are presented in supplementary material (see Figures S13a–S13r for the full suite of inter-regional correlation matrices and time series comparison for each taxon). This analysis quantified inter-regional synchrony of temporal fluctuations rather than long-term trends. Correlations were calculated for each taxon independently, using non-detrended time series to preserve the biological structure of year-to-year variability. 3. Results 3.1. Pollen Season Characteristics 3.1.1 Season Start To assess spatial differences and characteristics in the temporal aspects of pollen season across Slovenia, the start of the season, T start was compared among the three monitoring sites—Ljubljana, Maribor, and Primorje—for all pollen taxa. Figure 3 presents the distribution of T start values using boxplots that illustrate interannual variability and median onset dates for each taxon and region. The visual comparison reveals clear regional clustering patterns, particularly between the continental (Ljubljana, Maribor) and coastal (Primorje) stations. Comparison among monitoring sites revealed that the Primorje region exhibited an earlier onset of the pollen season for most arboreal taxa (e.g., Pinus , Cupressaceae/Taxaceae, Corylus ). The boxplot distribution further indicates lower variability in Primorje. To identify regional differences, a pairwise Mann–Whitney U test was applied to the distribution of the computed start times for each pollen taxon. Bonferroni-corrected p -values confirmed statistically significant regional differences ( p < 0.05) for multiple taxa. The strongest effects were observed for Pinus , Cupressaceae/Taxaceae, Urticaceae, Poaceae and Olea . In all cases, the coastal site exhibited significantly earlier pollen season onset compared to both continental stations. In continuation, we investigated temporal trends in the onset of the pollen season ( T start ) for each pollen taxon and monitoring location. The trends of the regression lines (Table 1 ) represent the annual rate of change in season onset (days per year), with negative values indicating an earlier start overtime. Corresponding coefficients of determination ( R ²) quantify the strength of the trend. Across Slovenia, a general tendency toward earlier pollen season onset was observed for most taxa. Based on the analysis (see Table 1 ), statistically significant advancements (negative trends) were confirmed for two key arboreal taxa in Primorje. Platanus showed a strong advancement of -0.7 days/yr ( p = 0.005, R 2 = 0.34). Carpinus/Ostrya also exhibited an advancement of -0.8 days/yr ( p = 0.019, R 2 = 0.24). Although not statistically significant ( p > 0.05), other arboreal taxa consistently showed a strong trend towards season advancement, with the most pronounced non-significant trends observed for Fraxinus (-1.27 days/yr, R 2 = 0.15) and Pinus (-1.01 days/yr, R 2 = 0.06). In Ljubljana, significant negative trends ( p -value < 0.05) were evident for multiple taxa (see Table 1 ). The strongest advancement was recorded for Urticaceae (-1.4 days/yr, R 2 = 0.33). Strong advancements were also observed for Fraxinus (− 0.9 days/yr, R 2 = 0.18) and Corylus (− 0.7 days/yr, R 2 = 0.20). Furthermore, Alnus (− 0.7 days/yr, R 2 = 0.18), Cupressaceae/Taxaceae (− 0.6 days/yr, R 2 = 0.21), and Pinus (-0.6 days/yr, R 2 = 0.19) showed steady advancements. In contrast to the clear patterns of advancement observed in Primorje and Ljubljana, the continental station in Maribor displayed weaker and more variable relationships. Only one taxon showed a statistically significant negative trend (advancement), namely Fagus, w hich exhibited a modest advancement (-0.4 days/yr, R 2 = 0.23). Most other taxa in Maribor displayed trends that were not statistically significant ( p > 0.05). Furthermore, Maribor was the only site where the trend for Artemisia was not one of clear advancement; it showed a negligible negative trend (-0.006 days/yr) that was highly insignificant ( p = 0.978). These findings demonstrate a consistent national-scale advancement in the start of the pollen season, especially for early-blooming arboreal taxa. Table 1 Statistically Significant Trends in Pollen Season Start Time ( T start) (DOY). The columns are defined as follows: Location indicates the monitoring station where the trend was observed (Ljubljana, Maribor, or Primorje). Pollen indicates the specific pollen taxon exhibiting a significant trend. Trend shows the linear change in SPT, expressed in days per year (negative values signify an earlier onset/phenological advancement). p -value is the Bonferroni-corrected p -value for the trend. Only results with a p -value < 0.05 are shown. STD is the standard deviation of the observed SPT values (in days). R 2 is the coefficient of determination. Min, avg, and max represent the minimum, average, and maximum Day of the Year (DOY) when the pollen season started across the entire observation period, respectively. Location Pollen taxon Trend (D/Y) p -value STD (DOY) R 2 Min (DOY) Avg (DOY) Max (DOY) Ljubljana Urticaceae -1.4 0.004 0.44 0.33 103 133 165 Ljubljana Platanus spp. -0.5 0.025 0.21 0.22 91 101 114 Ljubljana Carp . spp./ Ostr . spp. -0.6 0.027 0.26 0.21 73 90 105 Ljubljana Cupress./Taxa. -0.6 0.029 0.27 0.21 44 58 77 Ljubljana Corylus sp. -0.7 0.032 0.30 0.20 31 41 67 Ljubljana Pinus spp. -0.6 0.040 0.26 0.19 89 118 133 Ljubljana Quercus spp. -0.5 0.044 0.23 0.18 88 102 114 Ljubljana Alnus spp. -0.7 0.044 0.30 0.18 33 46 66 Ljubljana Fraxinus spp. -0.9 0.046 0.41 0.18 57 76 100 Ljubljana Rumex spp. -0.5 0.049 0.22 0.17 98 113 123 Maribor Fagus sp. -0.4 0.021 0.17 0.23 90 102 114 Primorje Platanus spp. -0.7 0.005 0.21 0.34 83 95 108 Primorje Carp. spp./ Ostr . spp. -0.8 0.019 0.31 0.24 57 87 104 3.1.2 Season End Spatial variability was also examined for the end of the pollen season ( T end ), defined as the day when the cumulative normalized pollen curve reached 97.5% of the APIn . Figure 4 presents boxplots of the end dates for all pollen taxa across the three monitoring locations—Ljubljana, Maribor, and Primorje—illustrating substantial regional differences in the termination of pollen season. The Primorje region exhibited a later end of the season for several taxa, resulting in later season endings compared to the continental sites. In contrast, Ljubljana and Maribor generally showed shorter and more variable season durations. For instance, taxa such as Poaceae, Urticaceae, and Quercus displayed consistently earlier season endings at continental sites, while Corylus , Alnus , and Cupressaceae/Taxaceae maintained later season endings near the coast. We again performed the pairwise statistical comparisons using the Mann–Whitney U test with Bonferroni correction confirmed multiple significant regional differences (Table 2 ). Notably, the end of the season occurred significantly earlier in continental sites for Betula , Quercus , Alnus , Poaceae, and Artemisia , whereas Urticaceae exhibited pronounced inter-regional shifts, with the Primorje region showing markedly delayed season termination ( p < 0.001 for most comparisons). Results related to temporal trends in T end for each pollen taxon and monitoring location are shown in (Table 2 ). Positive slopes indicate later endings. Negative slopes correspond to earlier season terminations. Across all sites, trends were generally weaker and less consistent compared with the start of the season, with several taxa showing opposing patterns among regions. Table 2 Statistically Significant Trends in Pollen Season End Time ( T end ) (DOY). The columns are defined as follows: Location indicates the monitoring station where the trend was observed (Ljubljana, Maribor, or Primorje). Pollen indicates the specific pollen taxon exhibiting a significant trend. Trend shows the linear change in T end , expressed in days per year (negative values signify an earlier season termination/shortening, and positive values signify a later season termination/extension). The p -value is the Bonferroni-corrected p -value for the trend. Only results with a p -value < 0.05 are shown. STD is the standard deviation of the observed T end values (in days). R 2 is the coefficient of determination. Min, avg, and max represent the minimum, average, and maximum Day of the Year (DOY) when the pollen season ended across the entire observation period, respectively. Location Pollen taxon Trend (D/Y) p -value STD (DOY) R 2 Min (DOY) Avg (DOY) Max (DOY) Ljubljana Cupress./Taxa. -2.95 0.022 1.19 0.23 111 150 317 Ljubljana Alnus spp. -2.70 0.017 1.04 0.24 64 108 166 Ljubljana Carp . spp./ Ostr . spp. -0.75 0.002 0.21 0.36 110 127 144 Ljubljana Corylus sp. -0.70 0.034 0.31 0.20 56 78 95 Ljubljana Platanus spp. -0.69 0.017 0.27 0.24 106 123 140 Ljubljana Poaceae 0.55 0.028 0.23 0.21 208 230 241 Maribor Cupress./Taxa. -1.14 0.038 0.52 0.19 113 137 187 Maribor Olea sp. -0.84 0.013 0.30 0.33 126 160 174 Maribor Carp . spp./ Ostr . spp. -0.49 0.034 0.22 0.20 106 124 140 Maribor Poaceae 0.83 0.001 0.20 0.44 219 236 248 Maribor Artemisia spp. 1.64 0.002 0.46 0.38 235 265 302 Primorje Corylus sp. -0.68 0.029 0.29 0.22 58 78 94 Primorje Olea sp. -0.55 0.025 0.23 0.23 155 167 180 Primorje Poaceae 0.75 0.004 0.23 0.34 246 261 289 In Ljubljana, the analysis of T end shows a complex response with multiple statistically significant trends. Several early-blooming arboreal taxa showed significant negative trends, indicating an earlier end to the pollen season. Cupressaceae/Taxaceae showed the strongest shortening trend at -2.95 days/yr ( R 2 = 0.23). Alnus had a shortening trend of -2.70 days/yr ( R 2 = 0.24). Carpinus/Ostrya had a shortening trend of -0.75 days/yr ( R 2 = 0.36), representing the most significant trend observed for T end . Platanus showed a strong shortening trend at -0.69 days/yr ( R 2 = 0.24). Lastly, Corylus showed a shortening trend of -0.70 days/yr ( R 2 = 0.20). Only Poaceae showed a statistically significant positive slope, indicating a later end to the season. It showed an advancement of + 0.55 days/yr ( R 2 = 0.21). The analysis of the season end in Primorje reveals a mixed pattern where only a few taxa exhibit statistically significant shifts toward either earlier or later dates. While most taxa showed a general trend toward an earlier end to the season, Poaceae was the notable exception as it displayed the only statistically significant positive slope. This indicates a later end to the season for grasses with the strongest overall trend of + 0.75 days/yr ( R 2 = 0.34). Conversely, two arboreal taxa showed significant negative slopes which indicate an earlier conclusion to their pollination periods. Specifically, Corylus shifted earlier by -0.68 days/yr ( R 2 = 0.22) while Olea exhibited a significant trend of -0.55 days/yr ( R 2 = 0.23). Most other taxa in Primorje displayed negative trends that were not statistically significant ( p > 0.05). Noteworthy, but non-significant, trends included the earlier end of Cupressaceae/Taxaceae (-1.44 days/yr) and Pinus (-0.52 days/yr). In Maribor, the analysis of T end revealed several statistically significant trends, with most taxa showing a later end of the season, while some arboreal taxa exhibited a significantly earlier end. Two taxa showed a statistically significant positive slope, indicating a strong later end to the season, Artemisia (+ 1.64 days/yr, R 2 = 0.38) and Poaceae (+ 0.83 day/yr, R 2 = 0.44). Two arboreal taxa showed statistically significant negative slopes, indicating an earlier end to the season. More precisely, Cupressaceae/Taxaceae showed the strongest trend at -1.14 days/yr ( R 2 = 0.19), and Carpinus/Ostrya a trend of -0.49 days/yr ( R 2 = 0.20). Most other taxa in Maribor displayed trends that were not statistically significant ( p > 0.05). Noteworthy, but non-significant, trends included an earlier end of Platanus (-0.37 days/yr) and the later end of Rumex (+ 0.60 days/yr). 3.1.3 Season duration The duration of the main pollen season measured in days (Δ T ), defined as the interval between 2.5% ( T start ) and 97.5% ( T end ) of the annual cumulative pollen curve, exhibited pronounced interspecific and regional variability (Fig. 5 ). At the regional scale, Primorje consistently displayed longer pollen seasons, reflecting the moderating influence of the coastal climate, where higher autumn temperatures and extended growing periods can delay phenological termination. In contrast, Maribor and Ljubljana exhibited shorter pollen seasons, a pattern characteristic of continental sites with stronger thermal gradients and earlier onset of autumn dormancy. Temporal analyses of Δ T (season duration) trends indicated that, for most taxa, changes in the start date ( T start ) exerted a stronger influence on season duration than shifts in the end date ( T end ). However, trends were highly asymmetric and location specific. Contrary to general expectations, several early-flowering arboreal taxa in the continental sites showed a significant shortening of their season, driven primarily by an accelerated earlier end ( T end ) despite earlier onsets. The spatial and temporal variations in pollen season dynamics are further quantified through the trend analysis of specific phenological parameters. Table 3 summarizes the annual rates of change for the start, end, and duration of the pollen season for those taxa exhibiting the most prominent shifts, specifically where the trend in season duration reached a significant level of p < 0.1. The analysis of the trends presented in Table 3 underscores the complexity of these phenological shifts, revealing that the observed shortening of the season in continental sites is often more rapid than the trends toward extension. This pattern was most pronounced, though not reaching the standard threshold for statistical significance ( p < 0.05), for Cupressaceae/Taxaceae in Ljubljana with a decrease of − 2.31 days/yr and Alnus in the same location at − 2.05 days/yr. Within the arboreal species, the only statistically significant shortening of the pollen season was identified for Olea in Primorje, which showed a decline of − 0.90 days/yr with a p -value of 0.017. In contrast, several late-spring and late-summer herbaceous taxa exhibited significant season extensions, driven primarily by a later cessation of flowering. The most substantial increases were recorded for Artemisia in Maribor (+ 1.64 days/yr), followed by Urticaceae (Ljubljana: +1.46 days/yr; Maribor: +0.37 days/yr) and Poaceae (Maribor: +1.15 days/yr; Ljubljana: +0.83 days/yr). Table 3 Statistical parameters of temporal trends for the start ( T start ), end ( T end ), and duration (Δ T ) of the pollen season for taxa exhibiting significant shifts in season length ( p < 0.1).This table presents the annual trend values, expressed in days per year, and their corresponding statistical significance ( p -values) for three phenological parameters across the monitoring sites of Ljubljana, Maribor, and Primorje. The list includes specific arboreal taxa, such as Alnus spp., Cupressaceae/Taxaceae, Olea sp., and Pinus spp., as well as herbaceous taxa, including Poaceae, Urticaceae, and Artemisia spp., for which the trend in season duration reached a significance level of p < 0.1. T start T end ΔT Location Pollen taxa Trend p -value Trend p -value Trend p -value Maribor Poaceae -0.31 0.141 0.83 0.001 1.15 0.000 Maribor Artemisia spp. -0.01 0.978 1.64 0.002 1.64 0.001 Ljubljana Urticaceae -1.41 0.004 0.05 0.774 1.46 0.002 Ljubljana Poaceae -0.28 0.221 0.55 0.028 0.83 0.011 Primorje Olea sp. 0.35 0.169 -0.55 0.025 -0.90 0.017 Maribor Urticaceae -0.29 0.051 0.08 0.493 0.37 0.026 Ljubljana Alnus spp. -0.65 0.044 -2.70 0.017 -2.05 0.067 Ljubljana Pinus spp. -0.58 0.040 0.07 0.858 0.64 0.085 Ljubljana Cupressaceae -0.64 0.029 -2.95 0.022 -2.31 0.086 This suggests an overall prolongation of the summer allergen exposure period. These patterns highlight the asymmetric response of pollen-season dynamics to climatic forcing, where the onset advances faster than the end retreats, leading to a complex restructuring of the seasonal timeline. Overall, the combined evidence suggests that Slovenian pollen seasons are gradually advancing (earlier start) and are either undergoing compression for early arboreal taxa or lengthening for late herbaceous taxa. 3.2 Trends in Annual Pollen Integral ( APIn ) The annual pollen integral ( APIn ) serves as a critical measure of the overall severity of the season. As shown in Fig. 2 , we computed for every measuring site and pollen taxon the APIn . In addition, we analyzed the long-term trends in APIn using linear regression, with the results summarized in Table 4 . The majority of pollen taxa exhibit positive trends, indicating a rising total pollen load over time, the trend analysis highlights regional differences. The strongest, statistically significant national increases are seen in herbaceous species. Urticaceae showed highly significant trends across all sites. The strongest rise occurred in Maribor (+ 200.2 pollen grains/(m³ yr − 1 ), p < 0.001, R 2 = 0.50) and Primorje (+ 182.4 pollen grains/(m³ yr − 1 ), p < 0.001, R 2 = 0.55). Table 4 Long-term Trends in annual pollen integral ( APIn ). The columns are defined as follows: Pollen taxa indicate the specific pollen taxon analyzed. Trend APIn shows the linear change in APIn , expressed in pollen grains day m − 3 year − 1 (positive values signify an increasing total pollen load). The p -value is the p -value for the linear regression trend. Only results with a p -value < 0.05 (marked with an asterisk * in the table) are considered statistically significant. R 2 is the coefficient of determination, indicating the proportion of the variance in APIn explained by the linear time trend. Ljubljana Maribor Primorje Pollen taxa Trend APIn p -value R 2 Trend APIn p -value R 2 Trend APIn p -value R 2 Alnus spp. 130.6 0.026(*) 0.21 / / / 52.5 0.022(*) 0.24 Ambrosia sp. -0.6 0.945 0.00 13.4 0.138 0.10 25.0 0.019(*) 0.24 Artemisia spp. -10.2 0.000(*) 0.48 -14.3 0.000(*) 0.51 -1.4 0.181 0.08 Betula spp. 109.3 0.097 0.13 27.7 0.731 0.01 4.5 0.417 0.03 Carp. spp./ Ostr. spp. 108.0 0.476 0.02 68.5 0.489 0.02 230.5 0.255 0.06 Castanea sp. -18.1 0.478 0.02 -30.7 0.331 0.04 -12.1 0.227 0.07 Corylus sp. 48.1 0.036(*) 0.19 / / / 34.0 0.008(*) 0.30 Cupress./Taxa. 239.4 0.000(*) 0.48 95.0 0.000(*) 0.50 294.6 0.170 0.09 Fagus sp. 34.4 0.500 0.02 57.8 0.426 0.03 8.8 0.523 0.02 Fraxinus spp. 23.5 0.567 0.02 9.2 0.751 0.00 133.5 0.387 0.04 Olea sp. 1.6 0.564 0.02 0.3 0.756 0.01 54.8 0.058 0.17 Pinus spp. 27.5 0.290 0.05 72.3 0.196 0.08 75.9 0.220 0.07 Plantago spp. 21.7 0.001(*) 0.39 29.5 0.000(*) 0.54 17.4 0.002(*) 0.37 Platanus spp. 72.9 0.023(*) 0.22 11.7 0.219 0.07 -21.7 0.003(*) 0.38 Poaceae 65.7 0.006(*) 0.31 97.9 0.000(*) 0.53 54.1 0.036(*) 0.19 Quercus spp. 37.3 0.298 0.05 96.1 0.078 0.14 116.7 0.021(*) 0.24 Rumex spp. -4.7 0.000(*) 0.45 -2.4 0.038(*) 0.19 -0.4 0.624 0.01 Urticaceae 114.9 0.000(*) 0.46 200.2 0.000(*) 0.50 182.4 0.000(*) 0.55 Poaceae also shows a strong, highly significant increase at all sites. The highest magnitude was in Maribor (+ 97.9 pollen grains/(m³ yr − 1 ), p < 0.001, R 2 = 0.53), followed by Ljubljana (+ 65.7 pollen grains/(m³ yr − 1 ), p = 0.006, R 2 = 0.31) and Primorje (+ 54.1 pollen grains/(m³ yr − 1 ), p < 0.036, R 2 = 0.19). This indicates that the continental interior, particularly Maribor, is experiencing a more rapidly increasing exposure to grass allergens. Primorje shows the largest absolute increases for multiple early blooming arboreal taxa, namely Cupressaceae/Taxaceae (+ 294.6 pollen grains/(m³ yr − 1 )), Carpinus/Ostrya (+ 230.5 pollen grains/(m³ yr − 1 )), and Fraxinus (+ 133.5 pollen grains/(m³ yr − 1 )). Ljubljana shows a high and statistically significant increase in Cupressaceae/Taxaceae (+ 239.4 pollen grains/(m³ yr − 1 ), p < 0.001, R 2 = 0.48) as well as the highest significant trend for Alnus (+ 130.6 pollen grains/(m³ yr − 1 ), R 2 = 0.21) indicating a rapidly rising pollen density for these early spring taxa in the capital. While most pollen taxa show increasing APIn , a few exhibit negative or low trends, some of which are statistically significant. Artemisia showed a statistically significant negative trend at Ljubljana (-10.2 pollen grains/(m³ yr − 1 ), R 2 = 0.48) and Maribor (-14.3 pollen grains/(m³ yr − 1 ), R 2 = 0.51). Platanus showed a statistically significant decrease only in Primorje (-21.7 pollen grains/(m³ yr − 1 ), R 2 = 0.38). Castanea, Rumex, Fagus , and Fraxinus generally show non-significant low or negative trends at most sites. 3.3 Inter-regional Synchrony of Pollen Seasons To examine the spatial coherence of airborne pollen variability across Slovenia, we calculated the Pearson correlation coefficient ( r ) between complete time series of pollen concentrations measured at the three monitoring sites — Ljubljana, Maribor, and Primorje. For each pollen taxon, correlations were computed pairwise between annual time series of daily mean concentrations to determine the synchrony of temporal fluctuations in pollen levels rather than similarity in long-term trends. The results revealed a distinct geographical structure of synchrony. The two continental sites, Ljubljana and Maribor, showed the highest correlations ( r ≈ 0.6–0.8), indicating that interannual and intraseasonal variations in pollen concentrations occur almost simultaneously under shared continental climatic forcing. Correlations involving the coastal Primorje station were consistently weaker ( r ≈ 0.4–0.6), reflecting partial decoupling caused by maritime moderation, longer vegetation periods, and specific coastal vegetation composition. Among individual taxa, Poaceae and Urticaceae displayed the strongest coherence across sites, with r > 0.7 between Ljubljana and Maribor, confirming their uniform response to regional-scale meteorological drivers. Tree taxa such as Quercus and Pinus also showed high synchrony across continental Slovenia, whereas in the coastal region Olea , Platanus and Ambrosia exhibited low coherence ( r < 0.4) between coastal and inland stations, reflecting their localized distribution and microclimatic dependence. As an illustrative example, Urticaceae demonstrates this behavior (Fig. 6 ). Inter-regional correlations of daily pollen concentrations were consistently stronger between Ljubljana and Maribor than between either continental site and Primorje for most taxa (Table 5 ), with notable exceptions including Fagus and Castanea that showed comparably high correlations across all three regions. This general pattern underscores the close coupling of continental sites and their more moderate connection with the coastal zone. Table 5 Inter-regional Pearson correlation coefficients ( r ) based on complete time series of daily pollen concentrations. Pollen taxa Ljubljana – Maribor Ljubljana – Primorje Maribor – Primorje Ambrosia sp. 0.78 0.66 0.59 Pinus spp. 0.62 0.42 0.28 Betula spp. 0.72 0.58 0.41 Fagus sp. 0.69 0.7 0.47 Cupress./Taxa. 0.67 0.6 0.44 Carp. spp./ Ostr. spp. 0.77 0.72 0.64 Quercus spp. 0.75 0.58 0.53 Alnus spp. 0.54 0.5 0.43 Fraxinus spp. 0.67 0.48 0.24 Rumex spp. 0.62 0.43 0.41 Urticaceae 0.78 0.65 0.56 Corylus sp. 0.68 0.67 0.43 Olea sp. 0.68 0.32 0.24 Artemisia spp. 0.79 0.48 0.46 Platanus spp. 0.61 0.29 0.33 Castanea sp. 0.7 0.68 0.56 Poaceae 0.82 0.77 0.73 Plantago spp. 0.72 0.62 0.57 average( r ) 0.70 0.56 0.46 In summary, the inter-regional correlations ( r ) represent the degree of day-to-day synchrony in pollen concentrations across Slovenia. High correlations between Ljubljana and Maribor confirm strong continental coherence, whereas the weaker association with Primorje demonstrates the buffering influence of the coastal Mediterranean climate. This pattern indicates that Slovenia's daily pollen dynamics are largely synchronized at the national scale, but are regionally modulated by climatic gradients, vegetation structure, and phenological timing. 4. Discussion This study provides the first comprehensive analysis of long-term pollen season dynamics in Slovenia, revealing significant temporal shifts that vary by taxon, phenological timing, and geographic region. Over the 23-year period (2002–2024), we observed a general advancement in season onset across most taxa, particularly pronounced for early-blooming arboreal species, alongside complex and often opposing trends in season termination. While early-flowering trees showed earlier season endings—resulting in compressed but intensified pollen periods—late-season herbaceous taxa exhibited delayed termination and substantial increases in annual pollen load. These asymmetric responses indicate that climate-driven phenological shifts are restructuring the allergenic landscape in ways that extend beyond simple season prolongation (Anderegg et al. 2021 ; Ziska et al. 2019 ). Notably, the coastal Mediterranean site (Primorje) consistently exhibited earlier onset, later termination, and longer overall seasons compared to continental stations, underscoring the role of regional climatic gradients in modulating pollen dynamics across relatively small spatial scales (Cristofolini et al. 2020 ; Rojo et al. 2021 ). The observed shifts in pollen dynamics and intensity reflect multiple interrelated drivers. Climate change, characterized by elevated atmospheric CO₂ and rising temperatures, represents the primary force reshaping phenological patterns across taxa (D’Amato et al. 2020 ; Schramm et al. 2021 ; Zhang and Steiner 2022 ; Ziska and Beggs 2012 ). However, local factors—including urbanization, urban heat islands, ornamental tree plantings, and the introduction of invasive allergenic species—modulate these climate signals and contribute to the regional variability documented in our results (Damialis et al. 2019 ; Schramm et al. 2021 ). While these factors were not quantitatively assessed in the present study, they provide a mechanistic framework for interpreting our findings and are discussed below where relevant. 4.1 Pollen Season Onset Across Slovenia, a general tendency toward earlier pollen season onset was observed for most taxa, as previously reported by numerous European studies (Adams-Groom et al. 2022 ; Cristofolini et al. 2020 ; Glick et al. 2021 ; Hoebeke et al. 2018 ; Rojo et al. 2021 ). In Ljubljana, significant negative trends were evident for most studied arboreal taxa, with the exception of Betula, Castanea , and Fagus , while among herbaceous plants, the most pronounced negative trends were observed for the family Urticaceae and the genus Rumex . A stronger effect on the onset of pollen seasons in Ljubljana may be attributed to the influence of the urban environment, as plants in urban areas generally tend to show earlier flowering than those in rural surroundings (Jochner and Menzel 2015 ). In Maribor and Primorje, weaker negative trends were also observed. The negative trend in the onset of early-flowering taxa could be associated with the observed warming trend in mean daily winter temperatures (December–February) over the study period, with a smaller increase along the coast than in inland areas (“ARSO” 2026). It is worth noting that the negative trends toward earlier onset may be limited by primary factors, namely photoperiod and thermoperiod conditions, which influence the growth and development of plant species before blossoming occurs (Laaidi et al. 2001). 4.2 Pollen Season Termination Seasonal end dates exhibited more complex patterns. Pollen seasons of early-flowering arboreal taxa (e.g., Cupressaceae/Taxaceae, Corylus, and Carpinus/Ostrya ) tended to end earlier, while in Primorje an earlier termination was also recorded for Olea . In contrast, herbaceous taxa generally exhibited later end dates, particularly the Poaceae family, which showed consistently later end dates across all monitoring stations. These findings are consistent with previous studies (Lind et al. 2016 ; Rojo et al. 2021 ) reporting similar differences between arboreal and herbaceous pollen-season end-date trends. 4.3 Pollen Season Duration Based on our analysis, trends in season duration also indicate differences between herbaceous and arboreal species. Herbaceous plants exhibited more uniform trends, reflecting a lengthening of pollen seasons, whereas arboreal species showed more heterogeneous and largely non-significant changes in season duration. An exception was Olea in the Primorje region, which exhibited a shortened pollen season. Although a general tendency toward earlier pollen season onset was observed, this shift did not result in extended pollen seasons for arboreal species, a finding that has also been reported in previous studies (Lind et al. 2016 ; Gehrig and Clot 2021 ). Likewise, Makra et al. and Rojo et al. reported no significant changes in overall season length, except for Poaceae and Urticaceae, which exhibited an increase in pollen season duration (Makra et al. 2011 ; Rojo et al. 2021 ). The lengthening of the pollen season in herbaceous species has also been documented in other European studies (Bogawski et al. 2014 ; Lind et al. 2016 ). Our results are consistent with these findings, with the trend toward prolonged seasons in herbaceous taxa being most pronounced for Poaceae and Urticaceae at continental sites, whereas increases were less pronounced in the coastal region. A similar tendency toward season prolongation was also observed for Artemisia . Changes in the timing of pollen seasons are associated with climatic drivers, particularly temperature variability, with trends dependent on species-specific sensitivity to temperature (Dahl et al. 2013 ; Gehrig and Clot 2021 ; Makra et al. 2011 ; Menzel et al. 2020 ; Ziska et al. 2019 ). 4.4 Annual Pollen Integral Trends Across the 23-year observation period, the majority of taxa exhibited positive APIn trends, indicating an overall increase in airborne pollen load throughout Slovenia. This pattern was most pronounced in coastal Primorje, where warmer temperatures and extended vegetation periods favor prolonged pollination and higher emission totals—a relationship that climate change is projected to amplify globally. Specifically, pollen emission seasons are expected to shift earlier in spring and later in summer, extending the overall duration of pollen presence in the air by 10–40 days (Zhang and Steiner 2022 ). This shift is particularly evident in regions experiencing pronounced warming, where grass pollen seasons have lengthened by approximately 7 days per decade (Nguyen et al. 2025 ). Additionally, increased atmospheric CO2 levels are expected to enhance pollen production, potentially doubling emissions by the end of the century (Zhang and Steiner 2022 ). APIn trends exhibited distinct patterns between herbaceous and arboreal taxa. While herbaceous taxa showed relatively consistent directional trends across study sites, arboreal taxa exhibited more variable regional responses. Notably, our observed increases in APIn for certain herbaceous taxa contrast with the general declining trends broadly documented in the literature. Our results indicate a significant and widespread increase in APIn for Poaceae, Urticaceae, and Plantago across all three monitoring sites. Similar exceptions to the general trend for Poaceae—a family comprising multiple species (Gonzalez Minero et al. 1998 )—have also been documented in a recent systematic review (Mousavi et al. 2024 ) and in a regional study from Northern Italy (Tagliaferro et al. 2024 ). In line with the increasing trend observed for Urticaceae, similar upward trends have been reported in a multi-site Swiss analysis (Glick et al. 2021 ) and in long-term single-site studies from Basel (Gehrig and Clot 2021 ), and Szeged (Makra et al. 2011 ). Comparable patterns have also been described for Plantago (Cristofolini et al. 2020 ). In contrast to Poaceae and Urticaceae, other herbaceous taxa—namely Artemisia and Rumex —showed declining APIn trends. The most pronounced negative trend was observed for Artemisia and appears to be widespread across Europe (de Weger et al. 2021 ; Gehrig and Clot 2021 ; Hoebeke et al. 2018 ; Ziello et al. 2012 ). Trends for Ambrosia at the continental scale are highly heterogeneous and strongly region-specific, with certain areas such as the Pannonian Plain representing well-known hotspots of pollen production (Sikoparija et al. 2017 ). In Ljubljana, our data indicates a negative but non-significant trend, which may reflect effective local management strategies, whereas a non-significant positive trend in Maribor may be influenced by its proximity to the Pannonian Plain. For arboreal taxa, significant increases in APIn were observed for Alnus and Corylus (Ljubljana, Primorje), Cupressaceae/Taxaceae (Ljubljana, Maribor), Platanus (Ljubljana), and Quercus (Primorje), consistent with trends reported across Europe (Cristofolini et al. 2020 ; de Weger et al. 2021 ; Gehrig and Clot 2021 ; Hoebeke et al. 2018 ; Lind et al. 2016 ; Rojo et al. 2021 ; Tagliaferro et al. 2024 ; Ziello et al. 2012 ). These patterns may reflect changes in the quantitative pollen production of the taxa, shifts in their local abundance, or their closer proximity to the pollen traps (Lind et al. 2016 ). The increasing trends in Cupressaceae/Taxaceae and Platanus pollen may additionally be linked to their widespread use as ornamentals in urban parks and gardens (Ciani et al. 2021 ; Lara et al. 2020 ). Although Betula pollen generally shows an increasing trend driven by higher temperatures (Adams-Groom et al. 2022 ; Rojo et al. 2021 ), non-significant trends have been reported in some studies (Gehrig and Clot 2021 ; Hoebeke et al. 2018 ), including at all our monitoring sites. Of note, large but non-significant increasing trends were observed in Primorje for Carpinus/Ostrya , Cupressaceae/Taxaceae, and Fraxinus —taxa that are widespread in this region. Olea , a major aeroallergen in the Mediterranean region (Bonofiglio et al. 2013 ; Penedos et al. 2024 ), showed a moderate non-significant positive pollen trend, likely reflecting the influence of agricultural plantations, which serve as the main source of airborne pollen in the area and whose extent may affect local pollen levels. 4.5 Regional Patterns and Inter-site Synchrony Inter-regional comparisons revealed distinct phenological and compositional differences between coastal and continental monitoring sites. The coastal Mediterranean site (Primorje) exhibited significantly earlier pollen season onset compared to both continental stations, with the strongest effects observed for Pinus , Cupressaceae/Taxaceae, Urticaceae, and Poaceae. This advance is likely attributable to the milder coastal climate. Furthermore, for Cupressaceae/Taxaceae, the earlier season in Primorje is partly driven by Cupressus arizonica , often planted as an ornamental, which blooms before Cupressus sempervirens (Brus 2004 ; Brus 2005 ). At the continental sites, most pollen from the Cupressaceae/Taxaceae taxon likely originates from the Taxaceae family. Conversely, in Primorje the pollen season ended later for several taxa, most notably for Betula, Quercus, Alnus , Poaceae, Artemisia , and especially Urticacea e . The results for Betula and Alnus may be biased by the percentage-based season definition, as the annual pollen quantity of both taxa at the coastal site is notably lower than on the continent. The later termination of Quercus in Primorje could be related to the late flowering of Quercus ilex , a typical Mediterranean species (Brus 2004 ; Brus 2005 ). Herbaceous species at the coastal site also ended their pollen season later, possibly due to differences in local flora composition, although precise mechanisms remain unclear. Analysis of inter-site temporal correlations indicated that the two continental sites, Ljubljana and Maribor, exhibited the highest synchrony in pollen dynamics, while correlations with the coastal Primorje station were consistently weaker. The largest regional differences were observed in the timing of season onset and termination, whereas trends in season intensity showed no major regional differentiation. In addition to climatic influences, these differences should also be attributed to variations in local vegetation composition and pollen species assemblages. In contrast, a study from Italy (Cristofolini et al. 2020 ) did not detect clear patterns in pollen-season trends in relation to biogeographic regions. 4.6 Study Limitations This study provides the first comprehensive assessment of long-term pollen trends in Slovenia, but several limitations warrant consideration. First, the analysis is based on data from three monitoring stations, all located in densely populated urban areas. While these sites capture exposure patterns relevant to the majority of the Slovenian population, a denser aerobiological network that includes rural, suburban, and mid-elevation sites would provide greater spatial resolution and better characterize localized patterns across Slovenia's diverse phytogeographic regions. Second, although we identified clear temporal trends, the current analysis did not quantitatively integrate meteorological drivers (e.g., temperature, precipitation, humidity) that mechanistically underlie these phenological shifts. Third, taxonomic resolution is inherently constrained by morphological identification limits—several of our taxa represent families or genera rather than individual species (e.g., Cupressaceae/Taxaceae, Poaceae), potentially masking species-specific responses within these groups. Finally, systematic data gaps necessitated the exclusion of winter months (January, November, December) from the Maribor dataset, which may have affected trend detection for very early- and late-season taxa at this site, particularly Corylus and Alnus . 4.7 Public Health Implications The observed trends in pollen season timing and intensity have important implications for allergenic exposure patterns and public health. A Slovenian clinical study on sensitization to inhalant allergens in patients with allergic airway diseases revealed that the majority of the studied population was sensitized to Betula , Poaceae, Olea , and Artemisia pollen (Zidarn et al. 2013), making temporal and quantitative shifts in these taxa particularly relevant for clinical management. Our findings indicate taxon-specific changes with varying public health consequences. For Betula , while trends are not statistically significant, there is a consistent tendency toward earlier season onset and increased annual pollen load across continental sites, suggesting a potential shift in the timing of peak exposure for birch-sensitized individuals. Allergy sufferers sensitized to Betula pollen may additionally be affected by observed increases in APIn of the Betulaceae genera Corylus and Alnus . More notably, Poaceae exhibited significant increases in annual pollen load and substantially longer exposure periods at all monitoring sites, indicating intensified and prolonged allergenic exposure for grass-sensitized patients, the largest sensitized group in many European populations. In the coastal region of Primorje, rising trends were observed for Olea and Fraxinus pollen, which contain highly homologous allergens (Asam et al. 2015 ), potentially compounding cross-sensitization risk, and complicating allergen-specific immunotherapy strategies. Conversely, despite a trend toward later season termination for Artemisia , the significant decline in annual pollen load at all monitoring sites suggests reduced overall population exposure to this late-summer allergen, which may translate to decreased symptom burden for mugwort-sensitized individuals. These diverse patterns—intensification in some taxa, weakening in others, and temporal restructuring across all domains—mainly reflect the complexity of climate-driven changes in allergen exposure and underscore the need for dynamic, taxon-specific public health responses. 4.8 Future Research Directions The findings presented here open several promising avenues for future research. First, the robust, quantified trends documented in this study provide an ideal foundation for developing mechanistic predictive models that explicitly incorporate meteorological variables (temperature, precipitation, growing degree days) to forecast pollen season onset, duration, and intensity with greater accuracy. Such models would be invaluable for clinical preparedness and public health interventions. Second, integrating these aerobiological findings with air quality measurements (particulate matter, nitrogen dioxide, ozone) and clinical data from Slovenian health institutions—including allergy diagnoses, emergency department visits, and medication usage—represents a critical next step to directly quantify the public health impact of changing pollen seasons and to assess the synergistic effects of pollen exposure and air pollution on allergic disease burden. This multi-dimensional approach would enable us to evaluate whether the documented environmental trends are translating into measurable changes in allergy prevalence and symptom severity. Third, expanding the monitoring network to include additional sites across Slovenia's distinct biogeographic regions would enhance spatial resolution and enable more nuanced regional comparisons. Fourth, insights into the spectrum and dynamics of airborne allergens at the investigated monitoring sites provide a valuable basis for the selection and placement of automatic monitoring stations, which would enable public access to near–real-time pollen exposure data. Finally, investigating the physiological, genetic, and plastic responses of local plant populations could provide mechanistic insights into the drivers of the observed phenological shifts and improve our ability to predict future trajectories under continued climate change. 5. Conclusion In conclusion, this 23-year analysis reveals that Slovenian pollen seasons are undergoing significant restructuring, characterized by earlier onset for most taxa, divergent termination patterns between early-flowering arboreal and late-season herbaceous species, and substantial increases in allergenic pollen load—particularly for Poaceae and Urticaceae. The pronounced regional differentiation between coastal mediterranean and continental sites underscores the importance of local climatic and ecological factors in modulating climate-driven phenological shifts. These findings provide critical baseline data for public health planning and establish a foundation for integrating aerobiological monitoring with clinical surveillance to better protect allergic populations in a changing climate. Declarations Funding This work was supported by the Slovenian Research and Innovation Agency (ARIS) through research project V1-2566, research program P1-0055, and infrastructure program I0-0029. Author information Authors and Affiliations Faculty of Natural Sciences and Mathematics, University of Maribor, Koroška cesta 160, SI-2000 Maribor, Slovenia and Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, 2000, Maribor, Slovenia Rene Markovič Faculty of Electrical Engineering and Computer Science, University of Maribor, Koroška cesta 46, 2000, Maribor, Slovenia Vladimir Grubelnik National Laboratory of Health, Environment and Food, Prvomajska ulica 1, SI-2000 Maribor, Slovenia Anja Simčič National Laboratory of Health, Environment and Food, Prvomajska ulica 1, SI-2000 Maribor, Slovenia Andreja Kofol Seliger National Laboratory of Health, Environment and Food, Prvomajska ulica 1, SI-2000 Maribor, Slovenia Urška Razboršek Faculty of Natural Sciences and Mathematics, University of Maribor, Koroška cesta 160, SI-2000 Maribor, Slovenia and Faculty of Education, University of Maribor, Koroška cesta 160, 2000, Maribor, Slovenia and Faculty of Medicine, University of Maribor, Taborska ulica 8, 2000, Maribor, Slovenia Marko Marhl National Laboratory of Health, Environment and Food, Prvomajska ulica 1, SI-2000 Maribor, Slovenia Uroš Lešnik Contributions All authors contributed to the study conception and design. Material preparation and data collection were performed by Anja Simčič, Andreja Kofol Seliger, Urška Razboršek, and Uroš Lešnik. Data analysis was conducted by Rene Markovič. The initial draft of the manuscript was written by Rene Markovič, and all authors revised and commented on subsequent versions. All authors read and approved of the final manuscript. Corresponding author Correspondence to Rene Markovič Faculty of Natural Sciences and Mathematics, University of Maribor Koroška cesta 160, SI-2000 Maribor, Slovenia Email: [email protected] Phone: +386 2 220 73 91 Ethics declarations Conflict of interest The authors declare no conflict of interests. References Adams-Groom, B., Selby, K., Derrett, S., Frisk, C. A., Pashley, C. H., Satchwell, J., et al. (2022). Pollen season trends as markers of climate change impact: Betula, Quercus and Poaceae. Science of The Total Environment , 831 , 154882. https://doi.org/10.1016/j.scitotenv.2022.154882 Anderegg, W. R. L., Abatzoglou, J. T., Anderegg, L. D. L., Bielory, L., Kinney, P. L., & Ziska, L. (2021). Anthropogenic climate change is worsening North American pollen seasons. Proceedings of the National Academy of Sciences , 118 (7), e2013284118. https://doi.org/10.1073/pnas.2013284118 ARSO. (2026, January 16). https://meteo.arso.gov.si/met/en/climate/maps/ Asam, C., Hofer, H., Wolf, M., Aglas, L., & Wallner, M. (2015) Tree pollen allergens-an update from a molecular perspective. Allergy , 70(10), 1201-1211. https://doi.org/10.1111/all.12696 Bastl, K., Kmenta, M., & Berger, U. E. (2018). Defining Pollen Seasons: Background and Recommendations. Current Allergy and Asthma Reports , 18 (12), 73. https://doi.org/10.1007/s11882-018-0829-z Beggs, P. J. (2021). Climate change, aeroallergens, and the aeroexposome. Environmental Research Letters , 16 (3), 035006. https://doi.org/10.1088/1748-9326/abda6f Berezhanskiy, P. V., Mahmoudizeh, A., & Fakhri, Y. (2025). Pollen exposure and allergy risk: a systematic review and meta-analysis. International Journal of Environmental Health Research , 1–15. https://doi.org/10.1080/09603123.2025.2533354 Besancenot, J.-P., Sindt, C., & Thibaudon, M. (2019). Pollen et changement climatique. Bouleau et graminées en France métropolitaine. Revue Française d’Allergologie , 59 (8), 563–575. https://doi.org/10.1016/j.reval.2019.09.006 Bogawski, P., Grewling, Ł., Nowak, M., Smith, M., & Jackowiak, B. (2014). Trends in atmospheric concentrations of weed pollen in the context of recent climate warming in Poznań (Western Poland). International Journal of Biometeorology , 58 (8), 1759–1768. https://doi.org/10.1007/s00484-013-0781-5 Bonofiglio, T., Orlandi, F., Ruga, L., Romano, B., & Fornaciari, M. (2013). Climate change impact on the olive pollen season in Mediterranean areas of Italy: air quality in late spring from an allergenic point of view. Environmental Monitoring and Assessment , 185, 877-890. https://doi.org/10.1007/s10661-012-2598-9 Boullayali, A., Hassoun, M., & Bouziane, H. (2025). Variation of pollen season trends under mediterranean climate: a systematic review. Aerobiologia , 41 (2), 469–488. https://doi.org/10.1007/s10453-025-09862-0 Brus, R. (2004). Drevesne vrste na slovenskem . Mladinska knjiga. Brus, R. (2005). Dendrologija za gozdarje. Oddelek za gozdarstvo in obnovljive vire, Biotehniška fakulteta. Ciani, F., Dell’Olmo, L., Foggi, B., & Mariotti Lippi, M. (2021). The effect of urban green areas on pollen concentrations at ground level: A study in the city of Florence (Italy). Urban Forestry & Urban Greening , 60, 127045. https://doi.org/10.1016/j.ufug.2021.127045 Clot, B. (2003). Trends in airborne pollen: An overview of 21 years of data in Neuchâtel (Switzerland). Aerobiologia , 19 (3–4), 227–234. https://doi.org/10.1023/B:AERO.0000006572.53105.17 Cristofolini, F., Anelli, P., Billi,B. M., Bocchi, C., Borney, M. F., Bucher, E., Cassoni, F., Coli, S., De Gironimo, V., Gottardini, E., Martinet, N., Miraglia, A., Para, C., Rossi, M., Tassan-Mazzocco, F., Travaglini, A., Verardo, P., & Marchesi, S. (2020). Temporal trends in airborne pollen seasonality: evidence from the Italian POLLnet network data. Aerobiologia , 36, 63-70. https://doi.org/10.1007/s10453-019-09609-8 Dahl, Å., Galán, C., Hajkova, L., Pauling, A., Sikoparija, B., Smith, M., & Vokou, D. (2013). The Onset, Course and Intensity of the Pollen Season. In M. Sofiev & K.-C. Bergmann (Eds.), Allergenic Pollen (pp. 29–70). Dordrecht: Springer Netherlands. https://doi.org/10.1007/978-94-007-4881-1_3 D’Amato, G., Chong-Neto, H. J., Monge Ortega, O. P., Vitale, C., Ansotegui, I., Rosario, N., Haahtela, T., Galan, C., Pawankar, R., Murrieta-Aguttes, M., Cecchi, L., Bergmann, C., Ridolo, E., Ramon, G., Gonzalez Diaz, S., D’Amato, M., & Annesi-Maesano, I. (2020). The effects of climate change on respiratory allergy and asthma induced by pollen and mold allergens. Allergy , 75(9), 2219–2228. https://doi.org/10.1111/all.14476 D’Amato, G., Vitale, C., Lanza, M., Molino, A., & D’Amato, M. (2016). Climate change, air pollution, and allergic respiratory diseases: an update. Current Opinion in Allergy & Clinical Immunology , 16 (5), 434–440. https://doi.org/10.1097/ACI.0000000000000301 Damialis, A., Traidl-Hoffmann, C., & Treudler, R. (2019). Climate Change and Pollen Allergies. In M. R. Marselle, J. Stadler, H. Korn, K. N. Irvine, & A. Bonn (Eds.), Biodiversity and Health in the Face of Climate Change (pp. 47–66). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-02318-8_3 de Weger, L. A., Bruffaerts, N., Koenders, M. M. J. F., Verstraeten, W. W., Delcloo, A. W., Hentges, P., & Hentges, F. (2021). Long-Term Pollen Monitoring in the Benelux: Evaluation of Allergenic Pollen Levels and Temporal Variations of Pollen Seasons. Frontiers in Allergy , 2 , 676176. https://doi.org/10.3389/falgy.2021.676176 Eeftens, M., & Gehrig, R. (2019). Airborne pollen: trends in season onset, duration and intensity and the importance for environmental epidemiology. Environmental Epidemiology , 3 (Supplement 1), 107–107. https://doi.org/10.1097/01.EE9.0000606884.39242.88 Galán, C., Alcázar, P., Oteros, J., García-Mozo, H., Aira, M. J., Belmonte, J., et al. (2016). Airborne pollen trends in the Iberian Peninsula. Science of The Total Environment , 550 , 53–59. https://doi.org/10.1016/j.scitotenv.2016.01.069 Galán, C., Smith, M., Thibaudon, M., Frenguelli, G., Oteros, J., Gehrig, R., et al. (2014). Pollen monitoring: minimum requirements and reproducibility of analysis. Aerobiologia , 30 (4), 385–395. https://doi.org/10.1007/s10453-014-9335-5 Gehrig, R., & Clot, B. (2021). 50 Years of Pollen Monitoring in Basel (Switzerland) Demonstrate the Influence of Climate Change on Airborne Pollen. Frontiers in Allergy , 2 , 677159. https://doi.org/10.3389/falgy.2021.677159 Glick, S., Gehrig, R., & Eeftens, M. (2021). Multi-decade changes in pollen season onset, duration, and intensity: A concern for public health? Science of The Total Environment , 781 , 146382. https://doi.org/10.1016/j.scitotenv.2021.146382 Guilbert, A., Cox, B., Bruffaerts, N., Hoebeke, L., Packeu, A., Hendrickx, M., De Cremer, K., Bladt, S., Brasseur, O., & Van Nieuwenhuyse, A. (2018). Relationships between aeroallergen levels and hospital admissions for asthma in the Brussels‑Capital Region: A daily time series analysis. Environmental Health , 17, Article 35. https://doi.org/10.1186/s12940-018-0378-x Hirst, J. M. (1952). AN AUTOMATIC VOLUMETRIC SPORE TRAP. Annals of Applied Biology , 39 (2), 257–265. https://doi.org/10.1111/j.1744-7348.1952.tb00904.x Hoebeke, L., Bruffaerts, N., Verstraeten, C., Delcloo, A., De Smedt, T., Packeu, A., Detandt M., & Hendrickx, M. (2018). Thirty-four years of pollen monitoring: an evaluation of the temporal variation of pollen seasons in Belgium. Aerobiologia , 34, 139–155. https://doi.org/10.1007/s10453-017-9503-5 J ochner, S., & Menzel, A. (2015). Urban phenological studies – Past, present, future. Environmental Pollution , 203, 250–261. https://doi.org/10.1016/j.envpol.2015.01.003 Jochner-Oette, S., Menzel, A., Gehrig, R., & Clot, B. (2019). Decrease or increase? Temporal changes in pollen concentrations assessed by Bayesian statistics. Aerobiologia , 35 (1), 153–163. https://doi.org/10.1007/s10453-018-9547-1 Laaidi, M. (2001). Forecasting the start of the pollen season of Poaceae: Evaluation of some methods based on meteorological factors. International Journal of Biometeorology , 45(1), 1–7. https://doi.org/10.1007/s004840000079 Lara, B., Rojo, J., Fernández-González, F., González-García-Saavedra, A., Serrano-Bravo, M. D., & Pérez-Badia, R. (2020). Impact of Plane Tree Abundance on Temporal and Spatial Variations in Pollen Concentration. Forests , 11(8), 817. https://doi.org/10.3390/f11080817 Lind, T., Ekebom, A., Alm Kübler, K., Östensson, P., Bellander, T., & Lõhmus, M. (2016). Pollen Season Trends (1973-2013) in Stockholm Area, Sweden. PLOS ONE , 11 (11), e0166887. https://doi.org/10.1371/journal.pone.0166887 Makra, L., Matyasovszky, I., & Deák, Á. J. (2011). Trends in the characteristics of allergenic pollen circulation in central Europe based on the example of Szeged, Hungary. Atmospheric Environment , 45 (33), 6010–6018. https://doi.org/10.1016/j.atmosenv.2011.07.051 Menzel, A., Yuan, Y., Matiu, M., Sparks, T., Scheifinger, H., Gehrig, R., & Estrella, N. (2020). Climate change fingerprints in recent European plant phenology. Global Change Biology , 26, 2599–2612. https://doi.org/10.1111/gcb.15000 Minero, F. J. G., Candau, P., Tomás, C., & Morales, J. (1998). Airborne grass (Poaceae) pollen in southern Spain: Results of a 10-year study (1987–1996). Allergy , 53(3), 266–274. https://doi.org/10.1111/j.1398-9995.1998.tb03886.x Mousavi, F., Oteros, J., Shahali, Y. & Carinanos, P. (2024). Impacts of climate change on allergenic pollen production: A systematic review and meta-analysis. Agricultural and Forest Meteorology , 349,109948. https://doi.org/10.1016/j.agrformet.2024.109948 Nguyen, H., Huete, A., Beggs, P., Davies, J., & Newbigins, E. (2025, September 16). Climate warming and drying drive shifts in grass pollen phenology at a Sentinel Southern Hemisphere site. In Review. https://doi.org/10.21203/rs.3.rs-7606687/v1 Ogrin, D. (1996). Podnebni tipi v Sloveniji. Geografski vestnik , 68 , 39–56. Pawankar, R., Canonica, G. W., Holgate, S., & Lockey, R. (2013). WAO White Book on Allergy: Update 2013. Penedos, C., Salamanca, G., Tavares, B., Fonseca, J., Carreiro-Martins, P., Rodrigues-Alves, R., Moral de Gregorio, Á., Valero, A., & Branco Ferreira, M. (2024). Aerobiology of Olive Pollen (Olea europaea L. ) in the Atmosphere of the Iberian Peninsula. Atmosphere , 15(9), 1087. https://doi.org/10.3390/atmos15091087 Rojo, J., Oteros, J., Picornell, A., Ruëff, F., Werchan, B., Werchan, M., et al. (2020). Land-Use and Height of Pollen Sampling Affect Pollen Exposure in Munich, Germany. Atmosphere , 11 (2), 145. https://doi.org/10.3390/atmos11020145 Rojo, J., Picornell, A., Oteros, J., Werchan, M., Werchan, B., Bergmann, K. C., Smith, M., Weichenmeier, I., Schmidt-Weber, C. B. & Buters, J. (2021). Consequences of climate change on airborne pollen in Bavaria, Central Europe. Regional Environmental Change , 21(9). https://doi.org/10.1007/s10113-020-01729-z Rojo, J., Rivero, R., Romero-Morte, J., Fernández-González, F., & Pérez-Badia, R. (2017). Modeling pollen time series using seasonal-trend decomposition procedure based on LOESS smoothing. International Journal of Biometeorology , 61 (2), 335–348. https://doi.org/10.1007/s00484-016-1215-y Sapkota, A., Murtugudde, R., Curriero, F. C., Upperman, C. R., Ziska, L., & Jiang, C. (2019). Associations between alteration in plant phenology and hay fever prevalence among US adults: Implication for changing climate. PLOS ONE , 14 (3), e0212010. https://doi.org/10.1371/journal.pone.0212010 Schramm, P. J., Brown, C. L., Saha, S., Conlon, K. C., Manangan, A. P., Bell, J. E., & Hess, J. J. (2021). A systematic review of the effects of temperature and precipitation on pollen concentrations and season timing, and implications for human health. International Journal of Biometeorology , 65 (10), 1615–1628. https://doi.org/10.1007/s00484-021-02128-7 Sikoparija, B., Skjøth, C. A., Celenk, S., Testoni, C., Abramidze, T., Alm Kübler, K., Belmonte, J., Berger, U., Bonini, M., Charalampopoulos, A., Damialis, A., Clot, B., Dahl, Å., de Weger, L. A., Gehrig, R., Hendrickx, M., Hoebeke, L., Ianovici, N., Kofol Seliger, A., … Smith, M. (2017). Spatial and temporal variations in airborne Ambrosia pollen in Europe. Aerobiologia , 33(2), 181–189. https://doi.org/10.1007/s10453-016-9463-1 Sofiev, M., Bousquet, J., Linkosalo, T., Ranta, H., Rantio-Lehtimaki, A., Siljamo, P., et al. (2009). Pollen, Allergies and Adaptation. In K. L. Ebi, I. Burton, & G. R. McGregor (Eds.), Biometeorology for Adaptation to Climate Variability and Change (Vol. 1, pp. 75–106). Dordrecht: Springer Netherlands. https://doi.org/10.1007/978-1-4020-8921-3_5 Tagliaferro, S., Maechetti, P., Dall' Ara, B., Domenichini, F., Lazzarin, S., Nicolis, M., Selle, D., Silibello, C., & Marcon, A. (2024). Temporal trends of seasonal pollen indexes in a region of Northern Italy (2001-2022). Atmospheric Environment , 338, 1352-2310. https://doi.org/10.1016/j.atmosenv.2024.120826 Taylor, S. J., & Letham, B. (2017, September 27). Forecasting at scale. PeerJ Preprints. https://doi.org/10.7287/peerj.preprints.3190v2 The pandas development team. (2026, January 14). pandas-dev/pandas: Pandas. Zenodo. https://doi.org/10.5281/ZENODO.3509134 Zhang, Y., & Steiner, A. L. (2022). Projected climate-driven changes in pollen emission season length and magnitude over the continental United States. Nature Communications , 13 (1), 1234. https://doi.org/10.1038/s41467-022-28764-0 Zidarn, M. (2013). Sensitization to inhalant allergens in patients with allergic airway disease in Slovenia. Zdravniški vestn ik, 82(6), 378–385. http://www.dlib.si/?URN=URN:NBN:SI:DOC-6CTCE3PZ Ziello, C., Sparks, T.H., Estrella, N., Belmonte, J., Bergmann, K.C., Bucher, E., Brighetti, M.A., Damialis, A., Detandt, M., Galán, C., Gehrig, R., Grewling, L., Gutiérrez Bustillo, A.M., Hallsdóttir, M., Kockhans-Bieda, M.C., De Linares, C., Myszkowsk,a D., Pàldy, A., Sánchez, A., … Menzel, A. (2012). Changes to airborne pollen counts across Europe. PLoS One , 7(4), e34076. https://doi.org/10.1371/journal.pone.0034076 Ziska, L. H., & Beggs, P. J. (2012). Anthropogenic climate change and allergen exposure: The role of plant biology. Journal of Allergy and Clinical Immunology , 129 (1), 27–32. https://doi.org/10.1016/j.jaci.2011.10.032 Ziska, L. H., Makra, L., Harry, S. K., Bruffaerts, N., Hendrickx, M., Coates, F., et al. (2019). Temperature-related changes in airborne allergenic pollen abundance and seasonality across the northern hemisphere: a retrospective data analysis. The Lancet Planetary Health , 3 (3), e124–e131. https://doi.org/10.1016/S2542-5196(19)30015-4 Zupančič, M., & Vreš, B. (2019). Phythogeographic analysis of Slovenia / Fitogeografska oznaka Slovenije. Folia biologica et geologica , 59 (2), 159–211. https://doi.org/10.3986/fbg0048 Additional Declarations No competing interests reported. Supplementary Files SupportingMaterial.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 07 Apr, 2026 Reviews received at journal 07 Apr, 2026 Reviews received at journal 22 Mar, 2026 Reviewers agreed at journal 12 Mar, 2026 Reviewers agreed at journal 10 Mar, 2026 Reviewers invited by journal 03 Feb, 2026 Editor assigned by journal 03 Feb, 2026 Submission checks completed at journal 30 Jan, 2026 First submitted to journal 29 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8731030","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":584960240,"identity":"fa85ca00-116d-46bb-80af-3975f134be4e","order_by":0,"name":"Rene Markovic","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYDADCSA+wFABYjI2kKLlDKlaGBjbiFBp3t578AFjjl2+ZHuP4YGf8w5HM/Afxm+LzJlzyQaM25ItZ/OcMTjYu+1wboNEIn4tEhI5ZhKM25gN5CTSEg4zgrUQ8IuE/BvzH4zb6qFa5gC18B8kZAuPGQPQcANpieQDhxkbgFoYCDmMJ8dYInHbcQPJnsMHDvYcS89tI+gX9jOGHz5uqzaQON7Y/OFHjXVuP//xB3i1gEECMoeNsPpRMApGwSgYBYQAAH0MRQkbM/8QAAAAAElFTkSuQmCC","orcid":"","institution":"University of Maribor, Faculty of Natural Sciences and Mathematics","correspondingAuthor":true,"prefix":"","firstName":"Rene","middleName":"","lastName":"Markovic","suffix":""},{"id":584960241,"identity":"fc679157-ede0-407e-92ea-2f947f22a76f","order_by":1,"name":"Vladimir Grubelnik","email":"","orcid":"","institution":"University of Maribor, Faculty of Electrical Engineering and Computer Science","correspondingAuthor":false,"prefix":"","firstName":"Vladimir","middleName":"","lastName":"Grubelnik","suffix":""},{"id":584960242,"identity":"12387aa7-9b00-4a05-97cb-f492355ca9d8","order_by":2,"name":"Anja Simčič","email":"","orcid":"","institution":"National Laboratory of Health, Environment and Food","correspondingAuthor":false,"prefix":"","firstName":"Anja","middleName":"","lastName":"Simčič","suffix":""},{"id":584960243,"identity":"2f6609b7-9402-4147-89f0-81db1f8a0950","order_by":3,"name":"Andreja Kofol Seliger","email":"","orcid":"","institution":"National Laboratory of Health, Environment and Food","correspondingAuthor":false,"prefix":"","firstName":"Andreja","middleName":"Kofol","lastName":"Seliger","suffix":""},{"id":584960244,"identity":"a2a01df1-fe0a-47c6-a814-2dee95eb6dab","order_by":4,"name":"Urška Razboršek","email":"","orcid":"","institution":"National Laboratory of Health, Environment and Food","correspondingAuthor":false,"prefix":"","firstName":"Urška","middleName":"","lastName":"Razboršek","suffix":""},{"id":584960245,"identity":"a0f0ce12-e610-4103-8ce0-c52abc6346cc","order_by":5,"name":"Marko Marhl","email":"","orcid":"","institution":"University of Maribor, Faculty of Natural Sciences and Mathematics","correspondingAuthor":false,"prefix":"","firstName":"Marko","middleName":"","lastName":"Marhl","suffix":""},{"id":584960246,"identity":"a839bee7-c410-434d-8faf-a67023ba31fd","order_by":6,"name":"Uroš Lešnik","email":"","orcid":"","institution":"National Laboratory of Health, Environment and Food","correspondingAuthor":false,"prefix":"","firstName":"Uroš","middleName":"","lastName":"Lešnik","suffix":""}],"badges":[],"createdAt":"2026-01-29 11:39:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8731030/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8731030/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101987994,"identity":"c54aa757-a48f-49f8-b37d-cc664b06dcac","added_by":"auto","created_at":"2026-02-05 18:46:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3652876,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of the study area in Europe and Slovenia. Red dots indicate the locations of the three main pollen monitoring sites: Maribor and Ljubljana (Continental climate) and Primorje (Mediterranean climate).\u003c/p\u003e","description":"","filename":"Figure01.png","url":"https://assets-eu.researchsquare.com/files/rs-8731030/v1/de6e859c486fc8edd815468c.png"},{"id":101988000,"identity":"92992d4a-9867-47dc-8821-4565d0b6b87b","added_by":"auto","created_at":"2026-02-05 18:46:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":511018,"visible":true,"origin":"","legend":"\u003cp\u003eExample of the pollen season analysis pipeline applied to Cupressaceae/Taxaceae in Ljubljana. The top row presents the seasonal dynamics and data normalization: (A) daily pollen concentrations showing average values (black) and 7-day smoothing (red dashed line); (B) cumulative annual pollen totals; and (C) normalized cumulative sums used for phenological thresholding. The bottom row illustrates inter-annual trends over 23 years for (D) season start (2.5% threshold), (E) season end (97.5% threshold), and (F) the annual pollen integral (\u003cem\u003eAPIn\u003c/em\u003e). Linear regressions and coefficients of determination (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e) highlight shift toward an earlier and more intense pollen season.\u003c/p\u003e","description":"","filename":"Figure02.png","url":"https://assets-eu.researchsquare.com/files/rs-8731030/v1/6b9d22e7f7f2950eb04fadba.png"},{"id":101988001,"identity":"38b16998-6d82-48d3-ba4f-b2a16919c876","added_by":"auto","created_at":"2026-02-05 18:46:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":130444,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the Start Time (\u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003estart\u003c/em\u003e\u003c/sub\u003e, Day of Year) for the Main Pollen Season (MPS) across different pollen taxa and three monitoring locations (Ljubljana, Maribor, and Primorje). The start time (\u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003estart\u003c/em\u003e\u003c/sub\u003e) is defined as the day the cumulative annual pollen sum reaches 2.5% of the annual pollen integral (\u003cem\u003eAPIn\u003c/em\u003e) of the corresponding year. This self-scaling approach ensures the validity of the comparison across years with variable pollen loads. Boxplots illustrate the inter-annual variability (median, range, and quartiles) of \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003estart\u003c/em\u003e\u003c/sub\u003e for each taxon and location over the study period. Asterix symbols (*) between bars indicate significant differences; if significant differences for a pollen type are found, the corresponding pollen type label is colored red.\u003c/p\u003e","description":"","filename":"Figure03.png","url":"https://assets-eu.researchsquare.com/files/rs-8731030/v1/d24e939edbf02d26ef604cb0.png"},{"id":101987993,"identity":"cfef7500-a0af-4958-9a58-0a6b13b86cb0","added_by":"auto","created_at":"2026-02-05 18:46:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":139344,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the Pollen Season End Time (\u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003eend\u003c/em\u003e\u003c/sub\u003e, Day of Year) across different pollen taxa and three monitoring locations (Ljubljana, Maribor, and Primorje). The end time (\u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003eend\u003c/em\u003e\u003c/sub\u003e) is defined as the day the cumulative annual pollen sum reached 97.5% of the annual pollen integral (\u003cem\u003eAPIn\u003c/em\u003e) of the corresponding year. This self-scaling approach ensures the validity of the comparison across years with variable pollen loads. Boxplots illustrate the inter-annual variability (median, range, and quartiles) of \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003eend\u003c/em\u003e\u003c/sub\u003e for each pollen type and location over the study period. Asterix symbols (*) between bars indicate significant differences; if significant differences for a pollen type are found, the corresponding pollen type label is colored red.\u003c/p\u003e","description":"","filename":"Figure04.png","url":"https://assets-eu.researchsquare.com/files/rs-8731030/v1/d689b2d6f52a99b46a7682f2.png"},{"id":101988003,"identity":"e999320b-be96-451b-93b4-df8b8c966a87","added_by":"auto","created_at":"2026-02-05 18:46:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":149196,"visible":true,"origin":"","legend":"\u003cp\u003eInter-site Comparison of Pollen Season Length (Duration in Days) by Taxon. This boxplot illustrates the distribution of the total pollen season length (duration in days) for various pollen taxa across the three monitoring sites: Ljubljana (Continental), Maribor (Continental), and Primorje (Mediterranean). The boxes represent the interquartile range (IQR), the line inside is the median, and the whiskers show the range of the data. Asterix symbols (*) between bars indicate significant differences; if significant differences for a pollen type are found, the corresponding pollen type label is colored red.\u003c/p\u003e","description":"","filename":"Figure05.png","url":"https://assets-eu.researchsquare.com/files/rs-8731030/v1/4887c252215eadab5ff7e29f.png"},{"id":101987995,"identity":"18d8745a-d99f-42eb-b058-038ff608defa","added_by":"auto","created_at":"2026-02-05 18:46:00","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":167423,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal evolution of Urticaceae pollen concentrations (A) and inter-regional correlation matrix (B) based on complete time series. Correlations quantify synchrony of pollen variability between sites rather than trend similarity.\u003c/p\u003e","description":"","filename":"Figure06.png","url":"https://assets-eu.researchsquare.com/files/rs-8731030/v1/d6c47f721dbaeec4e6b49499.png"},{"id":101988005,"identity":"6130a8ff-94ae-4e68-a80d-8f774975061f","added_by":"auto","created_at":"2026-02-05 18:46:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7378125,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8731030/v1/ef47b439-9309-4a50-bd4e-ce9cc63c9a64.pdf"},{"id":101987998,"identity":"32dc0b43-90ef-40db-8316-ec2719b6c569","added_by":"auto","created_at":"2026-02-05 18:46:01","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":6761510,"visible":true,"origin":"","legend":"","description":"","filename":"SupportingMaterial.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8731030/v1/954ba041bf6e69f78c2b06b6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Long-term changes in the timing and intensity of the pollen season in Slovenia (2002 – 2024)","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe World Allergy Organization (WAO) identifies allergies as a major global public health concern (Pawankar et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Pollen was one of the first allergens identified and is considered one of the most important triggers of allergic asthma and rhinoconjunctivitis (Sofiev et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Consequently, airborne pollen is monitored worldwide to characterize pollen seasons and to support allergy prevention and treatment (Damialis et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAirborne pollen is recognized as a sensitive indicator of climate change (Clot \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), a driver that contributes to the increasing incidence of pollen allergies. Climate change can influence the timing, duration, and intensity of pollen seasons, with effects varying by region and plant species (Anderegg et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Beggs \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; D\u0026rsquo;Amato et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Damialis et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Rojo et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNumerous studies have reported a general tendency toward an earlier start of pollen seasons for a wide range of taxa across Europe, with the most pronounced advances observed in early-season tree taxa such as \u003cem\u003eCorylus\u003c/em\u003e, \u003cem\u003eAlnus\u003c/em\u003e, \u003cem\u003eQuercus\u003c/em\u003e, and members of the Cupressaceae family. Earlier season starts have also been documented for herbaceous taxa, particularly the Poaceae and Urticaceae families (Adams-Groom et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Cristofolini et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Glick et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hoebeke et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Rojo et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, these responses show considerable spatial variability, as trends in the timing of pollen season onset differ among regions and are not statistically significant at all monitoring sites. Furthermore, long-term studies focusing on seasonal breakpoints indicate that trends for several species in Switzerland have been less pronounced or absent over the past two decades (Eeftens and Gehrig \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Comparable results have been reported for \u003cem\u003eBetula\u003c/em\u003e pollen in France, Belgium, and Switzerland, where the timing of season onset shifted after the late 1990s and early 2000s, suggesting a trend toward a later start (Besancenot et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hoebeke et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Jochner-Oette et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePredominantly negative trends in pollen season end dates have been reported, with variations associated with the taxa examined and the definitions of the pollen season applied (Gehrig and Clot \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Earlier pollen season endings are reported mainly for arboreal taxa, whereas later end dates are more frequently observed for herbaceous species (Bogawski et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Lind et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Rojo et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Alterations in pollen season start and end dates can impact the season\u0026rsquo;s overall duration. Several studies indicate that the pollen season has lengthened over time for various taxa (Anderegg et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Tagliaferro et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ziska et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Other studies, however, report generally stable season lengths with no significant trends, with late-flowering herbaceous taxa, such as Poaceae and Urticaceae, often emerging as exceptions and exhibiting a tendency toward season extension (Lind et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Makra et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Rojo et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite regional variability, increases in the seasonal pollen integral (\u003cem\u003eSPIn\u003c/em\u003e) or the annual pollen integral (\u003cem\u003eAPIn\u003c/em\u003e) were consistently observed across the Northern Hemisphere (Ziska et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In their review article, Ziello et al. reported that the \u003cem\u003eAPIn\u003c/em\u003e value has been increasing across Europe, particularly for arboreal taxa, whereas many herbaceous species tend to show a decreasing trend (Ziello et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Similar outcomes have also been reported in several later studies, with most research indicating increasing pollen trends among arboreal taxa of the Betulaceae, Fagaceae, Cupressaceae, and Oleaceae families, in contrast to declining or stable trends observed for herbaceous taxa such as \u003cem\u003eArtemisia\u003c/em\u003e, Urticaceae, and Poaceae (de Weger et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Gal\u0026aacute;n et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Rojo et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlterations in pollen season characteristics are driven by climate change and are linked to adverse health outcomes, as pollen exposure is a recognized trigger of symptoms in sensitized individuals (Beggs \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Schramm et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Consistent with these observations, Sapkota et al. demonstrated that shifts in the timing of pollen season onset\u0026mdash;whether earlier or later\u0026mdash;are associated with increased hay fever prevalence (Sapkota et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Moreover, increased pollen concentrations are also associated with negative health impacts, as they elevate the risk of allergies (Berezhanskiy et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and exacerbate asthma symptoms (Guilbert et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRojo et al. and Hoebeke et al. provide further insight into the mechanistic factors influencing pollen seasonality, demonstrating how statistical modelling and long-term monitoring can enhance our understanding of these shifts (Hoebeke et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Rojo et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The results are strongly influenced by the specific years analyzed (Boullayali et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and the criteria used to define the pollen season (Bastl et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Gehrig and Clot \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Glick et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The selection of the most suitable method depends on the main goal of the study (Gal\u0026aacute;n et al., 2017).\u003c/p\u003e \u003cp\u003eIn this study, we aimed to identify changes in the timing and intensity of the pollen season for 18 clinically relevant taxa in Slovenia over the last twenty-three years (2002\u0026ndash;2024). Using long-term daily pollen concentration data from three monitoring stations, we characterized regional seasonal patterns and assessed the degree of interregional synchrony.\u003c/p\u003e \u003cp\u003eSlovenia represents a particularly informative study area, as it lies at the intersection of Mediterranean, Alpine, Dinaric, and Pannonian climatic and phytogeographical regions. Despite its small spatial extent, the country exhibits pronounced environmental heterogeneity, making it a natural laboratory for investigating climate-driven phenological responses across contrasting bioclimatic settings. In this context, our analysis focuses on the transition between the coastal Mediterranean region (Primorje) and the continental interior, represented by monitoring stations in Ljubljana and Maribor. This spatial configuration enables a direct comparison of pollen season dynamics across climatically distinct yet geographically proximate regions, allowing us to disentangle region-specific responses from broader, shared trends such as the general advancement of pollen season onset.\u003c/p\u003e \u003cp\u003eTo our knowledge, this is the first comprehensive long-term study of pollen season dynamics in Slovenia that explicitly addresses both regional differentiation and interregional coherence within a climatically transitional zone. By providing insights into asymmetric and taxa-specific phenological shifts across this contact region, the study contributes to a more nuanced understanding of how climate change affects aeroallergen exposure at fine spatial scales.\u003c/p\u003e \u003cp\u003eThe article is organized as follows: first, we describe the study region, monitoring sites, and analytical methods used to define pollen season characteristics and trends. This is followed by a presentation of the results and their discussion in the context of regional climate variability and previous European studies. Finally, we outline perspectives for future research, including the planned integration of aerobiological data with air quality measurements and health records from regional healthcare institutions. Such linkage is essential for assessing the clinical and societal impacts of changing pollen exposure, given the well-established association between airborne pollen, allergic disease burden, and public health relevance.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study area, climate, and phytogeographic position\u003c/h2\u003e \u003cp\u003eSlovenia is situated within the temperate zone at the convergence of four major European geographic regions: the Mediterranean to the southwest, the Alps to the northwest, the Dinaric Alps to the south, and the Pannonian Plain to the northeast. Due to this unique geographical positioning, the country exhibits a transitional climate that can be broadly classified into moderate continental, moderate Mediterranean, and moderate mountain climate types (Ogrin \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1996\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe climatic conditions of the specific study areas are further differentiated by distinct environmental profiles. Primorje is characterized by a moderate Mediterranean climate, with a mean annual temperature of 14.1\u0026deg;C and annual precipitation of 968 mm. Ljubljana exhibits the moderate continental climate typical of western and southern Slovenia, with a mean temperature of 11.8\u0026deg;C and 1403 mm of precipitation. Maribor is defined by the moderate continental climate of eastern Slovenia, recording a mean temperature of 11.1\u0026deg;C and 956 mm of annual precipitation (\u0026ldquo;ARSO\u0026rdquo; 2026). The spatial distribution of these climatic zones and the locations of the sampling sites are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAccording to the phytogeographical classification, Slovenia comprises five floral provinces, all of which belong to the Euro-Siberian\u0026ndash;North American Region. Regarding the specific study sites, Primorje is situated within the Illyrian\u0026ndash;Adriatic floral province, Ljubljana within the Illyrian floral province, and Maribor within the Pre-Noricum\u0026ndash;Slovenian floral province (Zupančič and Vreš \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Aerobiological Data Acquisition and Preprocessing\u003c/h2\u003e \u003cp\u003eDaily airborne pollen concentration data were obtained from three permanent monitoring stations (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) located in Ljubljana (48.1482\u0026deg; N, 16.0385\u0026deg; E), Maribor (46.5646\u0026deg; N, 15.6460\u0026deg; E), and Primorje (45.5333\u0026deg; N, 13.6500\u0026deg; E), each equipped with a volumetric pollen sampler of the Hirst type (Hirst \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1952\u003c/span\u003e). The dataset spans the period 2002\u0026ndash;2024 and was provided by the National Laboratory of Health, Environment and Food (NLZOH), which operates the national aerobiological monitoring network following the European Aerobiology Society (EAS) methodological guidelines requirements (Gal\u0026aacute;n et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor this study, 18 pollen taxa were selected based on their allergic relevance and abundance: \u003cem\u003eAlnus\u003c/em\u003e spp. (alder), \u003cem\u003eAmbrosia\u003c/em\u003e sp. (ragweed), \u003cem\u003eArtemisia\u003c/em\u003e spp. (mugwort), \u003cem\u003eBetula\u003c/em\u003e spp. (birch), \u003cem\u003eCastanea\u003c/em\u003e sp. (sweet chestnut), \u003cem\u003eCorylus\u003c/em\u003e sp. (hazel), Cupressaceae/Taxaceae (cypress/yew), \u003cem\u003eFagus\u003c/em\u003e sp. (beech), \u003cem\u003eFraxinus\u003c/em\u003e spp. (ash), \u003cem\u003eCarpinus\u003c/em\u003e spp.\u003cem\u003e/Ostrya\u003c/em\u003e spp. (hornbeam/hop-hornbeam), \u003cem\u003eOlea\u003c/em\u003e sp. (olive), \u003cem\u003ePinus\u003c/em\u003e spp. (pine), \u003cem\u003ePlantago\u003c/em\u003e spp. (plantain), \u003cem\u003ePlatanus\u003c/em\u003e spp. (plane tree), Poaceae \u003cem\u003e(\u003c/em\u003egrasses), \u003cem\u003eQuercus\u003c/em\u003e spp. (oak), \u003cem\u003eRumex\u003c/em\u003e spp. \u003cem\u003e(\u003c/em\u003edock\u003cem\u003e)\u003c/em\u003e, and Urticaceae (nettle). It should be noted that a pollen taxon may comprise one species or an entire family, depending on the taxonomic level where morphological identification is possible (Dahl et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eOlea\u003c/em\u003e pollen detected in the Maribor and Ljubljana monitoring sites was primarily attributed to long-range transport from coastal regions and was therefore included among the analyzed taxa despite the limited local presence of olive trees at the continental monitoring sites.\u003c/p\u003e \u003cp\u003ePollen concentrations are expressed as grains per cubic meter of air (pollen grains/m\u0026sup3;) and recorded on a daily basis throughout each year (ordinal days 1\u0026ndash;365/366).\u003c/p\u003e \u003cp\u003eTo harmonize the temporal dimension, a fully automated Python preprocessing pipeline was developed using the Pandas library (The pandas development team \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). In this step, ordinal day indices were systematically converted into ISO-compliant datetime objects. All records were merged into a single long-format (tidy) table comprising the following standardized fields: Date, Year, Location, Pollen taxa, and Value (pollen concentration).\u003c/p\u003e \u003cp\u003eNon-numeric placeholders and textual markers (e.g., \u0026ldquo;x\u0026rdquo; or empty cells) were automatically converted to NaN values, ensuring proper handling of missing data in subsequent analyses. This preprocessing workflow guarantees full reproducibility and inter-regional comparability of the data, serving as a standardized foundation for the subsequent steps of temporal interpolation, normalization, and trend detection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data Completeness and Time Series Imputation\u003c/h2\u003e \u003cp\u003eBefore statistical analyses were conducted, a detailed assessment of data completeness was performed for each unique combination of pollen taxon, year, and monitoring site. In the supplemental materials we provide detailed results showing how the completeness of the measurements over time (annually and monthly) and across different pollen taxa was assessed. A detailed assessment of monthly data completeness for the Maribor monitoring site revealed a pattern of persistent data gaps during the shoulder seasons. The annual data completeness by pollen taxon and year for the various sites is displayed in Figures S2a (Ljubljana), S6a (Maribor), and S10a (Primorje), while Figures S2b, S6b, and S10b present the corresponding monthly completeness over the same period.\u003c/p\u003e \u003cp\u003eA detailed assessment of monthly data completeness for the Maribor monitoring site revealed a pattern of persistent data gaps during the shoulder seasons (Figure S6b). Specifically, we observed that the data for the months of January, November, and December were either completely missing or severely incomplete (indicated by low fractions ranging from 0.0 to 0.5) in the years prior to 2015 (Figure S6b). To ensure that our results were not biased or statistically unreliable due to this chronic lack of data during these specific periods, we decided to exclude all measurements corresponding to the months of January, November, and December for all analysis related to the Maribor region.\u003c/p\u003e \u003cp\u003eConsequently, the analysis of the Maribor dataset was restricted to the core measuring period spanning from February to October. Because the typical pollination periods for \u003cem\u003eCorylus\u003c/em\u003e and \u003cem\u003eAlnus\u003c/em\u003e occur during these excluded months, these taxa were entirely excluded from the study for the Maribor region (Figure S7).\u003c/p\u003e \u003cp\u003eTo preserve the temporal integrity and biological seasonality of each series, data gaps were filled using the Prophet forecasting model (Taylor and Letham \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Prophet is a robust additive model designed for time series with pronounced seasonal components, making it particularly suitable for aerobiological data characterized by strong annual periodicity. The model was trained independently for each pollen taxon and year, thereby maintaining the seasonal dynamics profile of each taxon and avoiding the introduction of inter-annual bias.\u003c/p\u003e \u003cp\u003eThe imputation workflow followed three key steps. in step one, each daily time series was first sorted chronologically and converted into Prophet\u0026rsquo;s required input format (ds, y). In step 2, Prophet was configured with yearly seasonality enabled while daily and weekly periodicities were omitted, as short-term fluctuations are biologically irrelevant to pollen release dynamics. In step 3, missing values were predicted by the model and post-processed to ensure non-negativity\u0026mdash;all negative estimates were replaced with zero, consistent with the physical interpretation of pollen concentration.\u003c/p\u003e \u003cp\u003eWhen the available data for a given year were insufficient for stable model fitting (less than five valid points), a fallback mean-imputation procedure was applied, assigning the average of existing values to all missing entries. This hybrid strategy ensures that all annual records are continuous and comparable without artificially amplifying inter-seasonal variability.\u003c/p\u003e \u003cp\u003eThe imputation approach balances statistical rigor and biological plausibility, maintaining both the amplitude and phase characteristics of the original pollen signal. The resulting gap-free datasets form the basis for the subsequent normalization and delineation of pollen seasons (Section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e2.4\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Determination of Pollen Season\u003c/h2\u003e \u003cp\u003eTo objectively determine the onset and cessation of pollen seasons across taxa, years, and monitoring locations, we implemented a normalized cumulative-sum approach. For each unique combination of pollen taxon, year, and site, daily concentrations were first normalized by the annual pollen integral (\u003cem\u003eAPIn\u003c/em\u003e), the annual sum of all daily pollen concentrations. This normalization ensured that inter-annual variations in total pollen load did not bias the identification of pollen season descriptors.\u003c/p\u003e \u003cp\u003eFollowing normalization, the cumulative proportion of the \u003cem\u003eAPIn\u003c/em\u003e was calculated for each day of the season. The start, \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003estart\u003c/em\u003e\u003c/sub\u003e, and end, \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003eend\u003c/em\u003e\u003c/sub\u003e, of the pollen season were then defined as the calendar days when the cumulative normalized curve reached 2.5% and 97.5% of the annual total, respectively. These percentile thresholds delineate the main pollen season (MPS) while excluding sporadic early or late pollen grains that may originate from long-range transport, secondary flowering, or resuspension of deposited pollen in the lower atmosphere. This approach is particularly recommended for ecological comparisons involving a large number of pollen taxa, including those with low \u003cem\u003eAPIn\u003c/em\u003e (Gehrig and Clot \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Rojo et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis method offers several advantages over fixed-date or concentration-threshold approaches. By referencing each season to its own \u003cem\u003eAPIn\u003c/em\u003e, the approach is self-scaling, allowing valid comparison between years of high and low pollen productivity. It also preserves the seasonal integrity of each taxon by focusing on the relative timing of emission rather than absolute concentration magnitude. Furthermore, by applying the same percentile boundaries across all taxa and regions, the method supports standardized inter-regional and inter-annual comparisons, which are essential for multi-site trend analysis.\u003c/p\u003e \u003cp\u003eThe selection of 95% method, effectively characterizing the biologically relevant core of the season (Bastl et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; de Weger et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Gehrig and Clot \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Rojo et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ziska et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). By adopting this normalized cumulative approach, pollen season shifts can be interpreted independently of inter-annual fluctuations in \u003cem\u003eAPIn\u003c/em\u003e, thereby isolating genuine climatic or ecological effects from variations in emission intensity. The implementation of the pollen season analysis pipeline is demonstrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, using Cupressaceae/Taxaceae data from the Ljubljana monitoring site as a representative example.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe implementation of the analysis pipeline for all other taxa across the three regions is provided in the supplementary material (Figures S4a\u0026ndash;r, S8a\u0026ndash;r, and S12a\u0026ndash;r).\u003c/p\u003e \u003cp\u003eThe bottom row in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the corresponding inter-annual trends in \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003estart\u003c/em\u003e\u003c/sub\u003e, end \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003eend\u003c/em\u003e\u003c/sub\u003e, and total annual pollen integral \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:APIn\\)\u003c/span\u003e\u003c/span\u003e. Linear regressions and coefficients of determination quantify the direction and strength of the temporal trends, demonstrating how the season\u0026rsquo;s onset, termination, and intensity evolve over the 23-year period.\u003c/p\u003e \u003cp\u003eThe normalized cumulative-sum method, combined with regression-based trend analysis, provides a rigorous and reproducible framework for detecting climate-driven pollen season shifts. The importance of explicitly stating the chosen limits is underscored by research showing that different methods can significantly influence aerobiological results, thus affecting the comparability of studies (Bastl et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Gehrig and Clot \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Glick et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Adherence to a standardized Hirst method is critical for ensuring data is reproducible and comparable across different monitoring stations, a principle outlined in the minimum requirements for pollen monitoring. Furthermore, for robust trend analysis, the use of a standardized baseline is essential for isolating seasonal shifts from annual variations in pollen production (Gal\u0026aacute;n et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Inter-regional Correlation Analysis\u003c/h2\u003e \u003cp\u003eTo assess the spatial coherence of pollen dynamics in Slovenia, pairwise Pearson correlation coefficients (\u003cem\u003er\u003c/em\u003e) were computed between the complete annual time series of daily pollen concentrations for the three monitoring sites (Ljubljana, Maribor, Primorje). All results are presented in supplementary material (see Figures S13a\u0026ndash;S13r for the full suite of inter-regional correlation matrices and time series comparison for each taxon). This analysis quantified inter-regional synchrony of temporal fluctuations rather than long-term trends. Correlations were calculated for each taxon independently, using non-detrended time series to preserve the biological structure of year-to-year variability.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Pollen Season Characteristics\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 Season Start\u003c/h2\u003e \u003cp\u003eTo assess spatial differences and characteristics in the temporal aspects of pollen season across Slovenia, the start of the season, \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003estart\u003c/em\u003e\u003c/sub\u003e was compared among the three monitoring sites\u0026mdash;Ljubljana, Maribor, and Primorje\u0026mdash;for all pollen taxa. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the distribution of \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003estart\u003c/em\u003e\u003c/sub\u003e values using boxplots that illustrate interannual variability and median onset dates for each taxon and region. The visual comparison reveals clear regional clustering patterns, particularly between the continental (Ljubljana, Maribor) and coastal (Primorje) stations.\u003c/p\u003e \u003cp\u003eComparison among monitoring sites revealed that the Primorje region exhibited an earlier onset of the pollen season for most arboreal taxa (e.g., \u003cem\u003ePinus\u003c/em\u003e, Cupressaceae/Taxaceae, \u003cem\u003eCorylus\u003c/em\u003e). The boxplot distribution further indicates lower variability in Primorje. To identify regional differences, a pairwise Mann\u0026ndash;Whitney U test was applied to the distribution of the computed start times for each pollen taxon. Bonferroni-corrected \u003cem\u003ep\u003c/em\u003e-values confirmed statistically significant regional differences (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) for multiple taxa. The strongest effects were observed for \u003cem\u003ePinus\u003c/em\u003e, Cupressaceae/Taxaceae, Urticaceae, Poaceae and \u003cem\u003eOlea\u003c/em\u003e. In all cases, the coastal site exhibited significantly earlier pollen season onset compared to both continental stations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn continuation, we investigated temporal trends in the onset of the pollen season (\u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003estart\u003c/em\u003e\u003c/sub\u003e) for each pollen taxon and monitoring location. The trends of the regression lines (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) represent the annual rate of change in season onset (days per year), with negative values indicating an earlier start overtime. Corresponding coefficients of determination (\u003cem\u003eR\u003c/em\u003e\u0026sup2;) quantify the strength of the trend.\u003c/p\u003e \u003cp\u003eAcross Slovenia, a general tendency toward earlier pollen season onset was observed for most taxa. Based on the analysis (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), statistically significant advancements (negative trends) were confirmed for two key arboreal taxa in Primorje. \u003cem\u003ePlatanus\u003c/em\u003e showed a strong advancement of -0.7 days/yr (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.34). \u003cem\u003eCarpinus/Ostrya\u003c/em\u003e also exhibited an advancement of -0.8 days/yr (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.24). Although not statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), other arboreal taxa consistently showed a strong trend towards season advancement, with the most pronounced non-significant trends observed for \u003cem\u003eFraxinus\u003c/em\u003e (-1.27 days/yr, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.15) and \u003cem\u003ePinus\u003c/em\u003e (-1.01 days/yr, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.06).\u003c/p\u003e \u003cp\u003eIn Ljubljana, significant negative trends (\u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were evident for multiple taxa (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The strongest advancement was recorded for Urticaceae (-1.4 days/yr, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.33). Strong advancements were also observed for \u003cem\u003eFraxinus\u003c/em\u003e (\u0026minus;\u0026thinsp;0.9 days/yr, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.18) and \u003cem\u003eCorylus\u003c/em\u003e (\u0026minus;\u0026thinsp;0.7 days/yr, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.20). Furthermore, \u003cem\u003eAlnus\u003c/em\u003e (\u0026minus;\u0026thinsp;0.7 days/yr, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.18), Cupressaceae/Taxaceae (\u0026minus;\u0026thinsp;0.6 days/yr, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.21), and \u003cem\u003ePinus\u003c/em\u003e (-0.6 days/yr, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.19) showed steady advancements.\u003c/p\u003e \u003cp\u003eIn contrast to the clear patterns of advancement observed in Primorje and Ljubljana, the continental station in Maribor displayed weaker and more variable relationships. Only one taxon showed a statistically significant negative trend (advancement), namely \u003cem\u003eFagus, w\u003c/em\u003ehich exhibited a modest advancement (-0.4 days/yr, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.23). Most other taxa in Maribor displayed trends that were not statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Furthermore, Maribor was the only site where the trend for \u003cem\u003eArtemisia\u003c/em\u003e was not one of clear advancement; it showed a negligible negative trend (-0.006 days/yr) that was highly insignificant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.978). These findings demonstrate a consistent national-scale advancement in the start of the pollen season, especially for early-blooming arboreal taxa.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistically Significant Trends in Pollen Season Start Time (\u003cem\u003eT\u003c/em\u003estart) (DOY). The columns are defined as follows: Location indicates the monitoring station where the trend was observed (Ljubljana, Maribor, or Primorje). Pollen indicates the specific pollen taxon exhibiting a significant trend. Trend shows the linear change in SPT, expressed in days per year (negative values signify an earlier onset/phenological advancement). \u003cem\u003ep\u003c/em\u003e-value is the Bonferroni-corrected \u003cem\u003ep\u003c/em\u003e-value for the trend. Only results with a \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 are shown. STD is the standard deviation of the observed SPT values (in days). \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e is the coefficient of determination. Min, avg, and max represent the minimum, average, and maximum Day of the Year (DOY) when the pollen season started across the entire observation period, respectively.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePollen\u003c/p\u003e \u003cp\u003etaxon\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTrend\u003c/p\u003e \u003cp\u003e(D/Y)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSTD\u003c/p\u003e \u003cp\u003e(DOY)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMin (DOY)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAvg\u003c/p\u003e \u003cp\u003e(DOY)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003cp\u003e(DOY)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLjubljana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrticaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLjubljana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePlatanus\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLjubljana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCarp\u003c/em\u003e. spp./\u003cem\u003eOstr\u003c/em\u003e. spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLjubljana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCupress./Taxa.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLjubljana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCorylus\u003c/em\u003e sp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLjubljana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePinus\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLjubljana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eQuercus\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLjubljana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAlnus\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLjubljana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFraxinus\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLjubljana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eRumex\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaribor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFagus\u003c/em\u003e sp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e114\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimorje\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePlatanus\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimorje\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCarp.\u003c/em\u003e spp./\u003cem\u003eOstr\u003c/em\u003e. spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 Season End\u003c/h2\u003e \u003cp\u003eSpatial variability was also examined for the end of the pollen season (\u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003eend\u003c/em\u003e\u003c/sub\u003e), defined as the day when the cumulative normalized pollen curve reached 97.5% of the \u003cem\u003eAPIn\u003c/em\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents boxplots of the end dates for all pollen taxa across the three monitoring locations\u0026mdash;Ljubljana, Maribor, and Primorje\u0026mdash;illustrating substantial regional differences in the termination of pollen season. The Primorje region exhibited a later end of the season for several taxa, resulting in later season endings compared to the continental sites. In contrast, Ljubljana and Maribor generally showed shorter and more variable season durations. For instance, taxa such as Poaceae, Urticaceae, and \u003cem\u003eQuercus\u003c/em\u003e displayed consistently earlier season endings at continental sites, while \u003cem\u003eCorylus\u003c/em\u003e, \u003cem\u003eAlnus\u003c/em\u003e, and Cupressaceae/Taxaceae maintained later season endings near the coast. We again performed the pairwise statistical comparisons using the Mann\u0026ndash;Whitney U test with Bonferroni correction confirmed multiple significant regional differences (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Notably, the end of the season occurred significantly earlier in continental sites for \u003cem\u003eBetula\u003c/em\u003e, \u003cem\u003eQuercus\u003c/em\u003e, \u003cem\u003eAlnus\u003c/em\u003e, Poaceae, and \u003cem\u003eArtemisia\u003c/em\u003e, whereas Urticaceae exhibited pronounced inter-regional shifts, with the Primorje region showing markedly delayed season termination (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for most comparisons).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eResults related to temporal trends in \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003eend\u003c/em\u003e\u003c/sub\u003e for each pollen taxon and monitoring location are shown in (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Positive slopes indicate later endings. Negative slopes correspond to earlier season terminations. Across all sites, trends were generally weaker and less consistent compared with the start of the season, with several taxa showing opposing patterns among regions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistically Significant Trends in Pollen Season End Time (\u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003eend\u003c/em\u003e\u003c/sub\u003e) (DOY). The columns are defined as follows: Location indicates the monitoring station where the trend was observed (Ljubljana, Maribor, or Primorje). Pollen indicates the specific pollen taxon exhibiting a significant trend. Trend shows the linear change in \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003eend\u003c/em\u003e\u003c/sub\u003e, expressed in days per year (negative values signify an earlier season termination/shortening, and positive values signify a later season termination/extension). The \u003cem\u003ep\u003c/em\u003e-value is the Bonferroni-corrected \u003cem\u003ep\u003c/em\u003e-value for the trend. Only results with a \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 are shown. STD is the standard deviation of the observed \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003eend\u003c/em\u003e\u003c/sub\u003e values (in days). \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e is the coefficient of determination. Min, avg, and max represent the minimum, average, and maximum Day of the Year (DOY) when the pollen season ended across the entire observation period, respectively.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePollen\u003c/p\u003e \u003cp\u003etaxon\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTrend\u003c/p\u003e \u003cp\u003e(D/Y)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSTD\u003c/p\u003e \u003cp\u003e(DOY)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003cp\u003e(DOY)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAvg\u003c/p\u003e \u003cp\u003e(DOY)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003cp\u003e(DOY)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLjubljana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCupress./Taxa.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e317\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLjubljana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAlnus\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e166\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLjubljana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCarp\u003c/em\u003e. spp./\u003cem\u003eOstr\u003c/em\u003e. spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLjubljana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCorylus\u003c/em\u003e sp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLjubljana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePlatanus\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLjubljana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e241\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaribor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCupress./Taxa.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e187\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaribor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eOlea\u003c/em\u003e sp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaribor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCarp\u003c/em\u003e. spp./\u003cem\u003eOstr\u003c/em\u003e. spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaribor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e248\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaribor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eArtemisia\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e302\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimorje\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCorylus\u003c/em\u003e sp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimorje\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eOlea\u003c/em\u003e sp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimorje\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e289\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn Ljubljana, the analysis of \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003eend\u003c/em\u003e\u003c/sub\u003e shows a complex response with multiple statistically significant trends. Several early-blooming arboreal taxa showed significant negative trends, indicating an earlier end to the pollen season. Cupressaceae/Taxaceae showed the strongest shortening trend at -2.95 days/yr (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.23). \u003cem\u003eAlnus\u003c/em\u003e had a shortening trend of -2.70 days/yr (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.24). \u003cem\u003eCarpinus/Ostrya\u003c/em\u003e had a shortening trend of -0.75 days/yr (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.36), representing the most significant trend observed for \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003eend\u003c/em\u003e\u003c/sub\u003e. \u003cem\u003ePlatanus\u003c/em\u003e showed a strong shortening trend at -0.69 days/yr (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.24). Lastly, \u003cem\u003eCorylus\u003c/em\u003e showed a shortening trend of -0.70 days/yr (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.20). Only Poaceae showed a statistically significant positive slope, indicating a later end to the season. It showed an advancement of +\u0026thinsp;0.55 days/yr (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.21).\u003c/p\u003e \u003cp\u003eThe analysis of the season end in Primorje reveals a mixed pattern where only a few taxa exhibit statistically significant shifts toward either earlier or later dates. While most taxa showed a general trend toward an earlier end to the season, Poaceae was the notable exception as it displayed the only statistically significant positive slope. This indicates a later end to the season for grasses with the strongest overall trend of +\u0026thinsp;0.75 days/yr (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.34). Conversely, two arboreal taxa showed significant negative slopes which indicate an earlier conclusion to their pollination periods. Specifically, \u003cem\u003eCorylus\u003c/em\u003e shifted earlier by -0.68 days/yr (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.22) while \u003cem\u003eOlea\u003c/em\u003e exhibited a significant trend of -0.55 days/yr (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.23). Most other taxa in Primorje displayed negative trends that were not statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Noteworthy, but non-significant, trends included the earlier end of Cupressaceae/Taxaceae (-1.44 days/yr) and \u003cem\u003ePinus\u003c/em\u003e (-0.52 days/yr).\u003c/p\u003e \u003cp\u003eIn Maribor, the analysis of \u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003eend\u003c/em\u003e\u003c/sub\u003e revealed several statistically significant trends, with most taxa showing a later end of the season, while some arboreal taxa exhibited a significantly earlier end. Two taxa showed a statistically significant positive slope, indicating a strong later end to the season, \u003cem\u003eArtemisia\u003c/em\u003e (+\u0026thinsp;1.64 days/yr, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.38) and Poaceae (+\u0026thinsp;0.83 day/yr, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.44). Two arboreal taxa showed statistically significant negative slopes, indicating an earlier end to the season. More precisely, Cupressaceae/Taxaceae showed the strongest trend at -1.14 days/yr (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.19), and \u003cem\u003eCarpinus/Ostrya\u003c/em\u003e a trend of -0.49 days/yr (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.20). Most other taxa in Maribor displayed trends that were not statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Noteworthy, but non-significant, trends included an earlier end of \u003cem\u003ePlatanus\u003c/em\u003e (-0.37 days/yr) and the later end of \u003cem\u003eRumex\u003c/em\u003e (+\u0026thinsp;0.60 days/yr).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.1.3 Season duration\u003c/h2\u003e \u003cp\u003eThe duration of the main pollen season measured in days (Δ\u003cem\u003eT\u003c/em\u003e), defined as the interval between 2.5% (\u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003estart\u003c/em\u003e\u003c/sub\u003e) and 97.5% (\u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003eend\u003c/em\u003e\u003c/sub\u003e) of the annual cumulative pollen curve, exhibited pronounced interspecific and regional variability (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAt the regional scale, Primorje consistently displayed longer pollen seasons, reflecting the moderating influence of the coastal climate, where higher autumn temperatures and extended growing periods can delay phenological termination. In contrast, Maribor and Ljubljana exhibited shorter pollen seasons, a pattern characteristic of continental sites with stronger thermal gradients and earlier onset of autumn dormancy. Temporal analyses of Δ\u003cem\u003eT\u003c/em\u003e (season duration) trends indicated that, for most taxa, changes in the start date (\u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003estart\u003c/em\u003e\u003c/sub\u003e) exerted a stronger influence on season duration than shifts in the end date (\u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003eend\u003c/em\u003e\u003c/sub\u003e). However, trends were highly asymmetric and location specific. Contrary to general expectations, several early-flowering arboreal taxa in the continental sites showed a significant shortening of their season, driven primarily by an accelerated earlier end (\u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003eend\u003c/em\u003e\u003c/sub\u003e) despite earlier onsets. The spatial and temporal variations in pollen season dynamics are further quantified through the trend analysis of specific phenological parameters. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes the annual rates of change for the start, end, and duration of the pollen season for those taxa exhibiting the most prominent shifts, specifically where the trend in season duration reached a significant level of \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1.\u003c/p\u003e \u003cp\u003eThe analysis of the trends presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e underscores the complexity of these phenological shifts, revealing that the observed shortening of the season in continental sites is often more rapid than the trends toward extension. This pattern was most pronounced, though not reaching the standard threshold for statistical significance (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), for Cupressaceae/Taxaceae in Ljubljana with a decrease of \u0026minus;\u0026thinsp;2.31 days/yr and \u003cem\u003eAlnus\u003c/em\u003e in the same location at \u0026minus;\u0026thinsp;2.05 days/yr. Within the arboreal species, the only statistically significant shortening of the pollen season was identified for \u003cem\u003eOlea\u003c/em\u003e in Primorje, which showed a decline of \u0026minus;\u0026thinsp;0.90 days/yr with a \u003cem\u003ep\u003c/em\u003e-value of 0.017. In contrast, several late-spring and late-summer herbaceous taxa exhibited significant season extensions, driven primarily by a later cessation of flowering. The most substantial increases were recorded for \u003cem\u003eArtemisia\u003c/em\u003e in Maribor (+\u0026thinsp;1.64 days/yr), followed by Urticaceae (Ljubljana: +1.46 days/yr; Maribor: +0.37 days/yr) and \u003cem\u003ePoaceae\u003c/em\u003e (Maribor: +1.15 days/yr; Ljubljana: +0.83 days/yr).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistical parameters of temporal trends for the start (\u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003estart\u003c/em\u003e\u003c/sub\u003e), end (\u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003eend\u003c/em\u003e\u003c/sub\u003e), and duration (Δ\u003cem\u003eT\u003c/em\u003e) of the pollen season for taxa exhibiting significant shifts in season length (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1).This table presents the annual trend values, expressed in days per year, and their corresponding statistical significance (\u003cem\u003ep\u003c/em\u003e-values) for three phenological parameters across the monitoring sites of Ljubljana, Maribor, and Primorje. The list includes specific arboreal taxa, such as \u003cem\u003eAlnus\u003c/em\u003e spp., Cupressaceae/Taxaceae, \u003cem\u003eOlea\u003c/em\u003e sp., and \u003cem\u003ePinus\u003c/em\u003e spp., as well as herbaceous taxa, including Poaceae, Urticaceae, and \u003cem\u003eArtemisia\u003c/em\u003e spp., for which the trend in season duration reached a significance level of \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cem\u003eT\u003c/em\u003e\u003csub\u003estart\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e\u003cem\u003eT\u003c/em\u003e\u003csub\u003e\u003cem\u003eend\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e\u003cem\u003eΔT\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePollen taxa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTrend\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTrend\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTrend\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaribor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaribor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eArtemisia\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLjubljana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrticaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLjubljana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimorje\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eOlea\u003c/em\u003e sp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaribor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrticaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLjubljana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAlnus\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLjubljana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePinus\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLjubljana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCupressaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThis suggests an overall prolongation of the summer allergen exposure period. These patterns highlight the asymmetric response of pollen-season dynamics to climatic forcing, where the onset advances faster than the end retreats, leading to a complex restructuring of the seasonal timeline. Overall, the combined evidence suggests that Slovenian pollen seasons are gradually advancing (earlier start) and are either undergoing compression for early arboreal taxa or lengthening for late herbaceous taxa.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Trends in Annual Pollen Integral (\u003cem\u003eAPIn\u003c/em\u003e)\u003c/h2\u003e \u003cp\u003eThe annual pollen integral (\u003cem\u003eAPIn\u003c/em\u003e) serves as a critical measure of the overall severity of the season. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, we computed for every measuring site and pollen taxon the \u003cem\u003eAPIn\u003c/em\u003e. In addition, we analyzed the long-term trends in \u003cem\u003eAPIn\u003c/em\u003e using linear regression, with the results summarized in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The majority of pollen taxa exhibit positive trends, indicating a rising total pollen load over time, the trend analysis highlights regional differences. The strongest, statistically significant national increases are seen in herbaceous species. Urticaceae showed highly significant trends across all sites. The strongest rise occurred in Maribor (+\u0026thinsp;200.2 pollen grains/(m\u0026sup3; yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.50) and Primorje (+\u0026thinsp;182.4 pollen grains/(m\u0026sup3; yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.55).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLong-term Trends in annual pollen integral (\u003cem\u003eAPIn\u003c/em\u003e). The columns are defined as follows: Pollen taxa indicate the specific pollen taxon analyzed. Trend \u003cem\u003eAPIn\u003c/em\u003e shows the linear change in \u003cem\u003eAPIn\u003c/em\u003e, expressed in pollen grains day m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e year\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (positive values signify an increasing total pollen load). The \u003cem\u003ep\u003c/em\u003e-value is the \u003cem\u003ep\u003c/em\u003e-value for the linear regression trend. Only results with a \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (marked with an asterisk * in the table) are considered statistically significant. \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e is the coefficient of determination, indicating the proportion of the variance in \u003cem\u003eAPIn\u003c/em\u003e explained by the linear time trend.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eLjubljana\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eMaribor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003ePrimorje\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePollen taxa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrend \u003cem\u003eAPIn\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTrend \u003cem\u003eAPIn\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTrend \u003cem\u003eAPIn\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAlnus\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e130.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.026(*)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e52.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.022(*)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAmbrosia\u003c/em\u003e sp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e25.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.019(*)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eArtemisia\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000(*)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-14.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000(*)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBetula\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e109.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCarp.\u003c/em\u003e spp./\u003cem\u003eOstr.\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e108.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e68.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e230.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCastanea\u003c/em\u003e sp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-18.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-30.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-12.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCorylus\u003c/em\u003e sp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.036(*)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e34.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.008(*)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCupress./Taxa.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e239.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000(*)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000(*)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e294.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFagus\u003c/em\u003e sp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.426\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFraxinus\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e133.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eOlea\u003c/em\u003e sp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e54.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePinus\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e72.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e75.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePlantago\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001(*)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000(*)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e17.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.002(*)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePlatanus\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.023(*)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-21.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.003(*)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e65.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006(*)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e97.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000(*)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e54.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.036(*)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eQuercus\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e96.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e116.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.021(*)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRumex\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000(*)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.038(*)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.624\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrticaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e114.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000(*)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e200.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000(*)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e182.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.000(*)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ePoaceae also shows a strong, highly significant increase at all sites. The highest magnitude was in Maribor (+\u0026thinsp;97.9 pollen grains/(m\u0026sup3; yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.53), followed by Ljubljana (+\u0026thinsp;65.7 pollen grains/(m\u0026sup3; yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.31) and Primorje (+\u0026thinsp;54.1 pollen grains/(m\u0026sup3; yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.036, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.19). This indicates that the continental interior, particularly Maribor, is experiencing a more rapidly increasing exposure to grass allergens. Primorje shows the largest absolute increases for multiple early blooming arboreal taxa, namely Cupressaceae/Taxaceae (+\u0026thinsp;294.6 pollen grains/(m\u0026sup3; yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)), \u003cem\u003eCarpinus/Ostrya\u003c/em\u003e (+\u0026thinsp;230.5 pollen grains/(m\u0026sup3; yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)), and \u003cem\u003eFraxinus\u003c/em\u003e (+\u0026thinsp;133.5 pollen grains/(m\u0026sup3; yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)).\u003c/p\u003e \u003cp\u003eLjubljana shows a high and statistically significant increase in Cupressaceae/Taxaceae (+\u0026thinsp;239.4 pollen grains/(m\u0026sup3; yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.48) as well as the highest significant trend for \u003cem\u003eAlnus\u003c/em\u003e (+\u0026thinsp;130.6 pollen grains/(m\u0026sup3; yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.21) indicating a rapidly rising pollen density for these early spring taxa in the capital. While most pollen taxa show increasing \u003cem\u003eAPIn\u003c/em\u003e, a few exhibit negative or low trends, some of which are statistically significant. \u003cem\u003eArtemisia\u003c/em\u003e showed a statistically significant negative trend at Ljubljana (-10.2 pollen grains/(m\u0026sup3; yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.48) and Maribor (-14.3 pollen grains/(m\u0026sup3; yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.51). \u003cem\u003ePlatanus\u003c/em\u003e showed a statistically significant decrease only in Primorje (-21.7 pollen grains/(m\u0026sup3; yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.38). \u003cem\u003eCastanea, Rumex, Fagus\u003c/em\u003e, and \u003cem\u003eFraxinus\u003c/em\u003e generally show non-significant low or negative trends at most sites.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Inter-regional Synchrony of Pollen Seasons\u003c/h2\u003e \u003cp\u003eTo examine the spatial coherence of airborne pollen variability across Slovenia, we calculated the Pearson correlation coefficient (\u003cem\u003er\u003c/em\u003e) between complete time series of pollen concentrations measured at the three monitoring sites \u0026mdash; Ljubljana, Maribor, and Primorje. For each pollen taxon, correlations were computed pairwise between annual time series of daily mean concentrations to determine the synchrony of temporal fluctuations in pollen levels rather than similarity in long-term trends. The results revealed a distinct geographical structure of synchrony.\u003c/p\u003e \u003cp\u003eThe two continental sites, Ljubljana and Maribor, showed the highest correlations (\u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026asymp;\u0026thinsp;0.6\u0026ndash;0.8), indicating that interannual and intraseasonal variations in pollen concentrations occur almost simultaneously under shared continental climatic forcing.\u003c/p\u003e \u003cp\u003eCorrelations involving the coastal Primorje station were consistently weaker (\u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026asymp;\u0026thinsp;0.4\u0026ndash;0.6), reflecting partial decoupling caused by maritime moderation, longer vegetation periods, and specific coastal vegetation composition.\u003c/p\u003e \u003cp\u003eAmong individual taxa, Poaceae and Urticaceae displayed the strongest coherence across sites, with \u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.7 between Ljubljana and Maribor, confirming their uniform response to regional-scale meteorological drivers. Tree taxa such as \u003cem\u003eQuercus\u003c/em\u003e and \u003cem\u003ePinus\u003c/em\u003e also showed high synchrony across continental Slovenia, whereas in the coastal region \u003cem\u003eOlea\u003c/em\u003e, \u003cem\u003ePlatanus and Ambrosia\u003c/em\u003e exhibited low coherence (\u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.4) between coastal and inland stations, reflecting their localized distribution and microclimatic dependence. As an illustrative example, Urticaceae demonstrates this behavior (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eInter-regional correlations of daily pollen concentrations were consistently stronger between Ljubljana and Maribor than between either continental site and Primorje for most taxa (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), with notable exceptions including \u003cem\u003eFagus\u003c/em\u003e and \u003cem\u003eCastanea\u003c/em\u003e that showed comparably high correlations across all three regions. This general pattern underscores the close coupling of continental sites and their more moderate connection with the coastal zone.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInter-regional Pearson correlation coefficients (\u003cem\u003er\u003c/em\u003e) based on complete time series of daily pollen concentrations.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePollen taxa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLjubljana \u0026ndash; Maribor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLjubljana \u0026ndash; Primorje\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMaribor \u0026ndash; Primorje\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAmbrosia\u003c/em\u003e sp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePinus\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBetula\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFagus\u003c/em\u003e sp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCupress./Taxa.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCarp.\u003c/em\u003e spp./\u003cem\u003eOstr.\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eQuercus\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAlnus\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFraxinus\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRumex\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrticaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCorylus\u003c/em\u003e sp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eOlea\u003c/em\u003e sp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eArtemisia\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePlatanus\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCastanea\u003c/em\u003e sp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePlantago\u003c/em\u003e spp.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eaverage(\u003c/b\u003e\u003cb\u003er\u003c/b\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.70\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.56\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.46\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn summary, the inter-regional correlations (\u003cem\u003er\u003c/em\u003e) represent the degree of day-to-day synchrony in pollen concentrations across Slovenia. High correlations between Ljubljana and Maribor confirm strong continental coherence, whereas the weaker association with Primorje demonstrates the buffering influence of the coastal Mediterranean climate. This pattern indicates that Slovenia's daily pollen dynamics are largely synchronized at the national scale, but are regionally modulated by climatic gradients, vegetation structure, and phenological timing.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study provides the first comprehensive analysis of long-term pollen season dynamics in Slovenia, revealing significant temporal shifts that vary by taxon, phenological timing, and geographic region. Over the 23-year period (2002\u0026ndash;2024), we observed a general advancement in season onset across most taxa, particularly pronounced for early-blooming arboreal species, alongside complex and often opposing trends in season termination. While early-flowering trees showed earlier season endings\u0026mdash;resulting in compressed but intensified pollen periods\u0026mdash;late-season herbaceous taxa exhibited delayed termination and substantial increases in annual pollen load. These asymmetric responses indicate that climate-driven phenological shifts are restructuring the allergenic landscape in ways that extend beyond simple season prolongation (Anderegg et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ziska et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Notably, the coastal Mediterranean site (Primorje) consistently exhibited earlier onset, later termination, and longer overall seasons compared to continental stations, underscoring the role of regional climatic gradients in modulating pollen dynamics across relatively small spatial scales (Cristofolini et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rojo et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe observed shifts in pollen dynamics and intensity reflect multiple interrelated drivers. Climate change, characterized by elevated atmospheric CO₂ and rising temperatures, represents the primary force reshaping phenological patterns across taxa (D\u0026rsquo;Amato et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Schramm et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhang and Steiner \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ziska and Beggs \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). However, local factors\u0026mdash;including urbanization, urban heat islands, ornamental tree plantings, and the introduction of invasive allergenic species\u0026mdash;modulate these climate signals and contribute to the regional variability documented in our results (Damialis et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Schramm et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). While these factors were not quantitatively assessed in the present study, they provide a mechanistic framework for interpreting our findings and are discussed below where relevant.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Pollen Season Onset\u003c/h2\u003e \u003cp\u003eAcross Slovenia, a general tendency toward earlier pollen season onset was observed for most taxa, as previously reported by numerous European studies (Adams-Groom et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Cristofolini et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Glick et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hoebeke et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Rojo et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In Ljubljana, significant negative trends were evident for most studied arboreal taxa, with the exception of \u003cem\u003eBetula, Castanea\u003c/em\u003e, and \u003cem\u003eFagus\u003c/em\u003e, while among herbaceous plants, the most pronounced negative trends were observed for the family Urticaceae and the genus \u003cem\u003eRumex\u003c/em\u003e. A stronger effect on the onset of pollen seasons in Ljubljana may be attributed to the influence of the urban environment, as plants in urban areas generally tend to show earlier flowering than those in rural surroundings (Jochner and Menzel \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In Maribor and Primorje, weaker negative trends were also observed. The negative trend in the onset of early-flowering taxa could be associated with the observed warming trend in mean daily winter temperatures (December\u0026ndash;February) over the study period, with a smaller increase along the coast than in inland areas (\u0026ldquo;ARSO\u0026rdquo; 2026). It is worth noting that the negative trends toward earlier onset may be limited by primary factors, namely photoperiod and thermoperiod conditions, which influence the growth and development of plant species before blossoming occurs (Laaidi et al. 2001).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Pollen Season Termination\u003c/h2\u003e \u003cp\u003eSeasonal end dates exhibited more complex patterns. Pollen seasons of early-flowering arboreal taxa (e.g., Cupressaceae/Taxaceae, \u003cem\u003eCorylus, and Carpinus/Ostrya\u003c/em\u003e) tended to end earlier, while in Primorje an earlier termination was also recorded for \u003cem\u003eOlea\u003c/em\u003e. In contrast, herbaceous taxa generally exhibited later end dates, particularly the Poaceae family, which showed consistently later end dates across all monitoring stations. These findings are consistent with previous studies (Lind et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Rojo et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) reporting similar differences between arboreal and herbaceous pollen-season end-date trends.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Pollen Season Duration\u003c/h2\u003e \u003cp\u003eBased on our analysis, trends in season duration also indicate differences between herbaceous and arboreal species. Herbaceous plants exhibited more uniform trends, reflecting a lengthening of pollen seasons, whereas arboreal species showed more heterogeneous and largely non-significant changes in season duration. An exception was \u003cem\u003eOlea\u003c/em\u003e in the Primorje region, which exhibited a shortened pollen season. Although a general tendency toward earlier pollen season onset was observed, this shift did not result in extended pollen seasons for arboreal species, a finding that has also been reported in previous studies (Lind et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Gehrig and Clot \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLikewise, Makra et al. and Rojo et al. reported no significant changes in overall season length, except for Poaceae and Urticaceae, which exhibited an increase in pollen season duration (Makra et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Rojo et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The lengthening of the pollen season in herbaceous species has also been documented in other European studies (Bogawski et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Lind et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Our results are consistent with these findings, with the trend toward prolonged seasons in herbaceous taxa being most pronounced for Poaceae and Urticaceae at continental sites, whereas increases were less pronounced in the coastal region. A similar tendency toward season prolongation was also observed for \u003cem\u003eArtemisia\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eChanges in the timing of pollen seasons are associated with climatic drivers, particularly temperature variability, with trends dependent on species-specific sensitivity to temperature (Dahl et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Gehrig and Clot \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Makra et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Menzel et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ziska et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Annual Pollen Integral Trends\u003c/h2\u003e \u003cp\u003eAcross the 23-year observation period, the majority of taxa exhibited positive \u003cem\u003eAPIn\u003c/em\u003e trends, indicating an overall increase in airborne pollen load throughout Slovenia. This pattern was most pronounced in coastal Primorje, where warmer temperatures and extended vegetation periods favor prolonged pollination and higher emission totals\u0026mdash;a relationship that climate change is projected to amplify globally. Specifically, pollen emission seasons are expected to shift earlier in spring and later in summer, extending the overall duration of pollen presence in the air by 10\u0026ndash;40 days (Zhang and Steiner \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This shift is particularly evident in regions experiencing pronounced warming, where grass pollen seasons have lengthened by approximately 7 days per decade (Nguyen et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Additionally, increased atmospheric CO2 levels are expected to enhance pollen production, potentially doubling emissions by the end of the century (Zhang and Steiner \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eAPIn\u003c/em\u003e trends exhibited distinct patterns between herbaceous and arboreal taxa. While herbaceous taxa showed relatively consistent directional trends across study sites, arboreal taxa exhibited more variable regional responses. Notably, our observed increases in \u003cem\u003eAPIn\u003c/em\u003e for certain herbaceous taxa contrast with the general declining trends broadly documented in the literature. Our results indicate a significant and widespread increase in \u003cem\u003eAPIn\u003c/em\u003e for Poaceae, Urticaceae, and \u003cem\u003ePlantago\u003c/em\u003e across all three monitoring sites. Similar exceptions to the general trend for Poaceae\u0026mdash;a family comprising multiple species (Gonzalez Minero et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1998\u003c/span\u003e)\u0026mdash;have also been documented in a recent systematic review (Mousavi et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and in a regional study from Northern Italy (Tagliaferro et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In line with the increasing trend observed for Urticaceae, similar upward trends have been reported in a multi-site Swiss analysis (Glick et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and in long-term single-site studies from Basel (Gehrig and Clot \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and Szeged (Makra et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Comparable patterns have also been described for \u003cem\u003ePlantago\u003c/em\u003e (Cristofolini et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn contrast to Poaceae and Urticaceae, other herbaceous taxa\u0026mdash;namely \u003cem\u003eArtemisia\u003c/em\u003e and \u003cem\u003eRumex\u003c/em\u003e\u0026mdash;showed declining \u003cem\u003eAPIn\u003c/em\u003e trends. The most pronounced negative trend was observed for \u003cem\u003eArtemisia\u003c/em\u003e and appears to be widespread across Europe (de Weger et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Gehrig and Clot \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hoebeke et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ziello et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Trends for \u003cem\u003eAmbrosia\u003c/em\u003e at the continental scale are highly heterogeneous and strongly region-specific, with certain areas such as the Pannonian Plain representing well-known hotspots of pollen production (Sikoparija et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In Ljubljana, our data indicates a negative but non-significant trend, which may reflect effective local management strategies, whereas a non-significant positive trend in Maribor may be influenced by its proximity to the Pannonian Plain.\u003c/p\u003e \u003cp\u003eFor arboreal taxa, significant increases in \u003cem\u003eAPIn\u003c/em\u003e were observed for \u003cem\u003eAlnus\u003c/em\u003e and \u003cem\u003eCorylus\u003c/em\u003e (Ljubljana, Primorje), Cupressaceae/Taxaceae (Ljubljana, Maribor), \u003cem\u003ePlatanus\u003c/em\u003e (Ljubljana), and \u003cem\u003eQuercus\u003c/em\u003e (Primorje), consistent with trends reported across Europe (Cristofolini et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; de Weger et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Gehrig and Clot \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hoebeke et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Lind et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Rojo et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Tagliaferro et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ziello et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). These patterns may reflect changes in the quantitative pollen production of the taxa, shifts in their local abundance, or their closer proximity to the pollen traps (Lind et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The increasing trends in Cupressaceae/Taxaceae and \u003cem\u003ePlatanus\u003c/em\u003e pollen may additionally be linked to their widespread use as ornamentals in urban parks and gardens (Ciani et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Lara et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Although \u003cem\u003eBetula\u003c/em\u003e pollen generally shows an increasing trend driven by higher temperatures (Adams-Groom et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Rojo et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), non-significant trends have been reported in some studies (Gehrig and Clot \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hoebeke et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), including at all our monitoring sites. Of note, large but non-significant increasing trends were observed in Primorje for \u003cem\u003eCarpinus/Ostrya\u003c/em\u003e, Cupressaceae/Taxaceae, and \u003cem\u003eFraxinus\u003c/em\u003e\u0026mdash;taxa that are widespread in this region. \u003cem\u003eOlea\u003c/em\u003e, a major aeroallergen in the Mediterranean region (Bonofiglio et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Penedos et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), showed a moderate non-significant positive pollen trend, likely reflecting the influence of agricultural plantations, which serve as the main source of airborne pollen in the area and whose extent may affect local pollen levels.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Regional Patterns and Inter-site Synchrony\u003c/h2\u003e \u003cp\u003eInter-regional comparisons revealed distinct phenological and compositional differences between coastal and continental monitoring sites. The coastal Mediterranean site (Primorje) exhibited significantly earlier pollen season onset compared to both continental stations, with the strongest effects observed for \u003cem\u003ePinus\u003c/em\u003e, Cupressaceae/Taxaceae, Urticaceae, and Poaceae. This advance is likely attributable to the milder coastal climate. Furthermore, for Cupressaceae/Taxaceae, the earlier season in Primorje is partly driven by \u003cem\u003eCupressus arizonica\u003c/em\u003e, often planted as an ornamental, which blooms before \u003cem\u003eCupressus sempervirens\u003c/em\u003e (Brus \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Brus \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). At the continental sites, most pollen from the Cupressaceae/Taxaceae taxon likely originates from the Taxaceae family.\u003c/p\u003e \u003cp\u003eConversely, in Primorje the pollen season ended later for several taxa, most notably for \u003cem\u003eBetula, Quercus, Alnus\u003c/em\u003e, Poaceae, \u003cem\u003eArtemisia\u003c/em\u003e, and especially Urticacea\u003cem\u003ee\u003c/em\u003e. The results for \u003cem\u003eBetula\u003c/em\u003e and \u003cem\u003eAlnus\u003c/em\u003e may be biased by the percentage-based season definition, as the annual pollen quantity of both taxa at the coastal site is notably lower than on the continent. The later termination of \u003cem\u003eQuercus\u003c/em\u003e in Primorje could be related to the late flowering of \u003cem\u003eQuercus ilex\u003c/em\u003e, a typical Mediterranean species (Brus \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Brus \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Herbaceous species at the coastal site also ended their pollen season later, possibly due to differences in local flora composition, although precise mechanisms remain unclear.\u003c/p\u003e \u003cp\u003eAnalysis of inter-site temporal correlations indicated that the two continental sites, Ljubljana and Maribor, exhibited the highest synchrony in pollen dynamics, while correlations with the coastal Primorje station were consistently weaker. The largest regional differences were observed in the timing of season onset and termination, whereas trends in season intensity showed no major regional differentiation. In addition to climatic influences, these differences should also be attributed to variations in local vegetation composition and pollen species assemblages. In contrast, a study from Italy (Cristofolini et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) did not detect clear patterns in pollen-season trends in relation to biogeographic regions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Study Limitations\u003c/h2\u003e \u003cp\u003eThis study provides the first comprehensive assessment of long-term pollen trends in Slovenia, but several limitations warrant consideration. First, the analysis is based on data from three monitoring stations, all located in densely populated urban areas. While these sites capture exposure patterns relevant to the majority of the Slovenian population, a denser aerobiological network that includes rural, suburban, and mid-elevation sites would provide greater spatial resolution and better characterize localized patterns across Slovenia's diverse phytogeographic regions. Second, although we identified clear temporal trends, the current analysis did not quantitatively integrate meteorological drivers (e.g., temperature, precipitation, humidity) that mechanistically underlie these phenological shifts. Third, taxonomic resolution is inherently constrained by morphological identification limits\u0026mdash;several of our taxa represent families or genera rather than individual species (e.g., Cupressaceae/Taxaceae, Poaceae), potentially masking species-specific responses within these groups. Finally, systematic data gaps necessitated the exclusion of winter months (January, November, December) from the Maribor dataset, which may have affected trend detection for very early- and late-season taxa at this site, particularly \u003cem\u003eCorylus\u003c/em\u003e and \u003cem\u003eAlnus\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.7 Public Health Implications\u003c/h2\u003e \u003cp\u003eThe observed trends in pollen season timing and intensity have important implications for allergenic exposure patterns and public health. A Slovenian clinical study on sensitization to inhalant allergens in patients with allergic airway diseases revealed that the majority of the studied population was sensitized to \u003cem\u003eBetula\u003c/em\u003e, Poaceae, \u003cem\u003eOlea\u003c/em\u003e, and \u003cem\u003eArtemisia\u003c/em\u003e pollen (Zidarn et al. 2013), making temporal and quantitative shifts in these taxa particularly relevant for clinical management.\u003c/p\u003e \u003cp\u003eOur findings indicate taxon-specific changes with varying public health consequences. For \u003cem\u003eBetula\u003c/em\u003e, while trends are not statistically significant, there is a consistent tendency toward earlier season onset and increased annual pollen load across continental sites, suggesting a potential shift in the timing of peak exposure for birch-sensitized individuals. Allergy sufferers sensitized to \u003cem\u003eBetula\u003c/em\u003e pollen may additionally be affected by observed increases in APIn of the Betulaceae genera \u003cem\u003eCorylus\u003c/em\u003e and \u003cem\u003eAlnus\u003c/em\u003e. More notably, Poaceae exhibited significant increases in annual pollen load and substantially longer exposure periods at all monitoring sites, indicating intensified and prolonged allergenic exposure for grass-sensitized patients, the largest sensitized group in many European populations. In the coastal region of Primorje, rising trends were observed for \u003cem\u003eOlea\u003c/em\u003e and \u003cem\u003eFraxinus\u003c/em\u003e pollen, which contain highly homologous allergens (Asam et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), potentially compounding cross-sensitization risk, and complicating allergen-specific immunotherapy strategies. Conversely, despite a trend toward later season termination for \u003cem\u003eArtemisia\u003c/em\u003e, the significant decline in annual pollen load at all monitoring sites suggests reduced overall population exposure to this late-summer allergen, which may translate to decreased symptom burden for mugwort-sensitized individuals.\u003c/p\u003e \u003cp\u003eThese diverse patterns\u0026mdash;intensification in some taxa, weakening in others, and temporal restructuring across all domains\u0026mdash;mainly reflect the complexity of climate-driven changes in allergen exposure and underscore the need for dynamic, taxon-specific public health responses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.8 Future Research Directions\u003c/h2\u003e \u003cp\u003eThe findings presented here open several promising avenues for future research. First, the robust, quantified trends documented in this study provide an ideal foundation for developing mechanistic predictive models that explicitly incorporate meteorological variables (temperature, precipitation, growing degree days) to forecast pollen season onset, duration, and intensity with greater accuracy. Such models would be invaluable for clinical preparedness and public health interventions. Second, integrating these aerobiological findings with air quality measurements (particulate matter, nitrogen dioxide, ozone) and clinical data from Slovenian health institutions\u0026mdash;including allergy diagnoses, emergency department visits, and medication usage\u0026mdash;represents a critical next step to directly quantify the public health impact of changing pollen seasons and to assess the synergistic effects of pollen exposure and air pollution on allergic disease burden. This multi-dimensional approach would enable us to evaluate whether the documented environmental trends are translating into measurable changes in allergy prevalence and symptom severity. Third, expanding the monitoring network to include additional sites across Slovenia's distinct biogeographic regions would enhance spatial resolution and enable more nuanced regional comparisons. Fourth, insights into the spectrum and dynamics of airborne allergens at the investigated monitoring sites provide a valuable basis for the selection and placement of automatic monitoring stations, which would enable public access to near\u0026ndash;real-time pollen exposure data. Finally, investigating the physiological, genetic, and plastic responses of local plant populations could provide mechanistic insights into the drivers of the observed phenological shifts and improve our ability to predict future trajectories under continued climate change.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, this 23-year analysis reveals that Slovenian pollen seasons are undergoing significant restructuring, characterized by earlier onset for most taxa, divergent termination patterns between early-flowering arboreal and late-season herbaceous species, and substantial increases in allergenic pollen load\u0026mdash;particularly for Poaceae and Urticaceae. The pronounced regional differentiation between coastal mediterranean and continental sites underscores the importance of local climatic and ecological factors in modulating climate-driven phenological shifts. These findings provide critical baseline data for public health planning and establish a foundation for integrating aerobiological monitoring with clinical surveillance to better protect allergic populations in a changing climate.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Slovenian Research and Innovation Agency (ARIS) through research project V1-2566, research program P1-0055, and infrastructure program I0-0029.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors and Affiliations\u003c/p\u003e\n\u003cp\u003eFaculty of Natural Sciences and Mathematics, University of Maribor, Koro\u0026scaron;ka cesta 160, SI-2000 Maribor, Slovenia and Faculty of Electrical Engineering and Computer Science, University of Maribor, Koro\u0026scaron;ka cesta 46, 2000, Maribor, Slovenia\u003c/p\u003e\n\u003cp\u003eRene Markovič\u003c/p\u003e\n\u003cp\u003eFaculty of Electrical Engineering and Computer Science, University of Maribor, Koro\u0026scaron;ka cesta 46, 2000, Maribor, Slovenia\u003c/p\u003e\n\u003cp\u003eVladimir Grubelnik\u003c/p\u003e\n\u003cp\u003eNational Laboratory of Health, Environment and Food, Prvomajska ulica 1, SI-2000 Maribor, Slovenia\u003c/p\u003e\n\u003cp\u003eAnja Simčič\u003c/p\u003e\n\u003cp\u003eNational Laboratory of Health, Environment and Food, Prvomajska ulica 1, SI-2000 Maribor, Slovenia\u003c/p\u003e\n\u003cp\u003eAndreja Kofol Seliger\u003c/p\u003e\n\u003cp\u003eNational Laboratory of Health, Environment and Food, Prvomajska ulica 1, SI-2000 Maribor, Slovenia\u003c/p\u003e\n\u003cp\u003eUr\u0026scaron;ka Razbor\u0026scaron;ek\u003c/p\u003e\n\u003cp\u003eFaculty of Natural Sciences and Mathematics, University of Maribor, Koro\u0026scaron;ka cesta 160, SI-2000 Maribor, Slovenia and Faculty of Education, University of Maribor, Koro\u0026scaron;ka cesta 160, 2000, Maribor, Slovenia and Faculty of Medicine, University of Maribor, Taborska ulica 8, 2000, Maribor, Slovenia\u003c/p\u003e\n\u003cp\u003eMarko Marhl\u003c/p\u003e\n\u003cp\u003eNational Laboratory of Health, Environment and Food, Prvomajska ulica 1, SI-2000 Maribor, Slovenia\u003c/p\u003e\n\u003cp\u003eUro\u0026scaron; Le\u0026scaron;nik\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation and data collection were performed by Anja Simčič, Andreja Kofol Seliger, Ur\u0026scaron;ka Razbor\u0026scaron;ek, and Uro\u0026scaron; Le\u0026scaron;nik. Data analysis was conducted by Rene Markovič. The initial draft of the manuscript was written by Rene Markovič, and all authors revised and commented on subsequent versions. All authors read and approved of the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Rene Markovič Faculty of Natural Sciences and Mathematics, University of Maribor Koro\u0026scaron;ka cesta 160, SI-2000 Maribor, Slovenia Email: [email protected] Phone: +386 2 220 73 91\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdams-Groom, B., Selby, K., Derrett, S., Frisk, C. A., Pashley, C. H., Satchwell, J., et al. (2022). Pollen season trends as markers of climate change impact: Betula, Quercus and Poaceae. \u003cem\u003eScience of The Total Environment\u003c/em\u003e, \u003cem\u003e831\u003c/em\u003e, 154882. https://doi.org/10.1016/j.scitotenv.2022.154882\u003c/li\u003e\n\u003cli\u003eAnderegg, W. R. L., Abatzoglou, J. T., Anderegg, L. D. L., Bielory, L., Kinney, P. L., \u0026amp; Ziska, L. (2021). Anthropogenic climate change is worsening North American pollen seasons. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e, \u003cem\u003e118\u003c/em\u003e(7), e2013284118. https://doi.org/10.1073/pnas.2013284118\u003c/li\u003e\n\u003cli\u003eARSO. (2026, January 16). https://meteo.arso.gov.si/met/en/climate/maps/\u003c/li\u003e\n\u003cli\u003eAsam, C., Hofer, H., Wolf, M., Aglas, L., \u0026amp; Wallner, M. (2015) Tree pollen allergens-an update from a molecular perspective. \u003cem\u003eAllergy\u003c/em\u003e, 70(10), 1201-1211. https://doi.org/10.1111/all.12696\u003c/li\u003e\n\u003cli\u003eBastl, K., Kmenta, M., \u0026amp; Berger, U. E. (2018). Defining Pollen Seasons: Background and Recommendations. \u003cem\u003eCurrent Allergy and Asthma Reports\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(12), 73. https://doi.org/10.1007/s11882-018-0829-z\u003c/li\u003e\n\u003cli\u003eBeggs, P. J. (2021). Climate change, aeroallergens, and the aeroexposome. \u003cem\u003eEnvironmental Research Letters\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(3), 035006. https://doi.org/10.1088/1748-9326/abda6f\u003c/li\u003e\n\u003cli\u003eBerezhanskiy, P. V., Mahmoudizeh, A., \u0026amp; Fakhri, Y. (2025). Pollen exposure and allergy risk: a systematic review and meta-analysis. \u003cem\u003eInternational Journal of Environmental Health Research\u003c/em\u003e, 1\u0026ndash;15. https://doi.org/10.1080/09603123.2025.2533354\u003c/li\u003e\n\u003cli\u003eBesancenot, J.-P., Sindt, C., \u0026amp; Thibaudon, M. (2019). Pollen et changement climatique. Bouleau et gramin\u0026eacute;es en France m\u0026eacute;tropolitaine. \u003cem\u003eRevue Fran\u0026ccedil;aise d\u0026rsquo;Allergologie\u003c/em\u003e, \u003cem\u003e59\u003c/em\u003e(8), 563\u0026ndash;575. https://doi.org/10.1016/j.reval.2019.09.006\u003c/li\u003e\n\u003cli\u003eBogawski, P., Grewling, Ł., Nowak, M., Smith, M., \u0026amp; Jackowiak, B. (2014). Trends in atmospheric concentrations of weed pollen in the context of recent climate warming in Poznań (Western Poland). \u003cem\u003eInternational Journal of Biometeorology\u003c/em\u003e, \u003cem\u003e58\u003c/em\u003e(8), 1759\u0026ndash;1768. https://doi.org/10.1007/s00484-013-0781-5\u003c/li\u003e\n\u003cli\u003eBonofiglio, T., Orlandi, F., Ruga, L., Romano, B., \u0026amp; Fornaciari, M. (2013). Climate change impact on the olive pollen season in Mediterranean areas of Italy: air quality in late spring from an allergenic point of view. \u003cem\u003eEnvironmental Monitoring and Assessment\u003c/em\u003e, 185, 877-890. https://doi.org/10.1007/s10661-012-2598-9\u003c/li\u003e\n\u003cli\u003eBoullayali, A., Hassoun, M., \u0026amp; Bouziane, H. (2025). Variation of pollen season trends under mediterranean climate: a systematic review. \u003cem\u003eAerobiologia\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e(2), 469\u0026ndash;488. https://doi.org/10.1007/s10453-025-09862-0\u003c/li\u003e\n\u003cli\u003eBrus, R. (2004). \u003cem\u003eDrevesne vrste na slovenskem\u003c/em\u003e. Mladinska knjiga.\u003c/li\u003e\n\u003cli\u003eBrus, R. (2005). \u003cem\u003eDendrologija za gozdarje. \u003c/em\u003eOddelek za gozdarstvo in obnovljive vire, Biotehni\u0026scaron;ka fakulteta.\u003c/li\u003e\n\u003cli\u003eCiani, F., Dell\u0026rsquo;Olmo, L., Foggi, B., \u0026amp; Mariotti Lippi, M. (2021). The effect of urban green areas on pollen concentrations at ground level: A study in the city of Florence (Italy). \u003cem\u003eUrban Forestry \u0026amp; Urban Greening\u003c/em\u003e, 60, 127045. https://doi.org/10.1016/j.ufug.2021.127045\u003c/li\u003e\n\u003cli\u003eClot, B. (2003). Trends in airborne pollen: An overview of 21 years of data in Neuch\u0026acirc;tel (Switzerland). \u003cem\u003eAerobiologia\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(3\u0026ndash;4), 227\u0026ndash;234. https://doi.org/10.1023/B:AERO.0000006572.53105.17\u003c/li\u003e\n\u003cli\u003eCristofolini, F., Anelli, P., Billi,B. M., Bocchi, C., Borney, M. F., Bucher, E., Cassoni, F., Coli, S., De Gironimo, V., Gottardini, E., Martinet, N., Miraglia, A., Para, C., Rossi, M., Tassan-Mazzocco, F., Travaglini, A., Verardo, P., \u0026amp; Marchesi, S. (2020). Temporal trends in airborne pollen seasonality: evidence from the Italian POLLnet network data. \u003cem\u003eAerobiologia\u003c/em\u003e, 36, 63-70. https://doi.org/10.1007/s10453-019-09609-8\u003c/li\u003e\n\u003cli\u003eDahl, \u0026Aring;., Gal\u0026aacute;n, C., Hajkova, L., Pauling, A., Sikoparija, B., Smith, M., \u0026amp; Vokou, D. (2013). The Onset, Course and Intensity of the Pollen Season. In M. Sofiev \u0026amp; K.-C. Bergmann (Eds.), \u003cem\u003eAllergenic Pollen\u003c/em\u003e (pp. 29\u0026ndash;70). Dordrecht: Springer Netherlands. https://doi.org/10.1007/978-94-007-4881-1_3\u003c/li\u003e\n\u003cli\u003eD\u0026rsquo;Amato, G., Chong-Neto, H. J., Monge Ortega, O. P., Vitale, C., Ansotegui, I., Rosario, N., Haahtela, T., Galan, C., Pawankar, R., Murrieta-Aguttes, M., Cecchi, L., Bergmann, C., Ridolo, E., Ramon, G., Gonzalez Diaz, S., D\u0026rsquo;Amato, M., \u0026amp; Annesi-Maesano, I. (2020). The effects of climate change on respiratory allergy and asthma induced by pollen and mold allergens. \u003cem\u003eAllergy\u003c/em\u003e, 75(9), 2219\u0026ndash;2228. https://doi.org/10.1111/all.14476\u003c/li\u003e\n\u003cli\u003eD\u0026rsquo;Amato, G., Vitale, C., Lanza, M., Molino, A., \u0026amp; D\u0026rsquo;Amato, M. (2016). Climate change, air pollution, and allergic respiratory diseases: an update. \u003cem\u003eCurrent Opinion in Allergy \u0026amp; Clinical Immunology\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(5), 434\u0026ndash;440. https://doi.org/10.1097/ACI.0000000000000301\u003c/li\u003e\n\u003cli\u003eDamialis, A., Traidl-Hoffmann, C., \u0026amp; Treudler, R. (2019). Climate Change and Pollen Allergies. In M. R. Marselle, J. Stadler, H. Korn, K. N. Irvine, \u0026amp; A. Bonn (Eds.), \u003cem\u003eBiodiversity and Health in the Face of Climate Change\u003c/em\u003e (pp. 47\u0026ndash;66). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-02318-8_3\u003c/li\u003e\n\u003cli\u003ede Weger, L. A., Bruffaerts, N., Koenders, M. M. J. F., Verstraeten, W. W., Delcloo, A. W., Hentges, P., \u0026amp; Hentges, F. (2021). Long-Term Pollen Monitoring in the Benelux: Evaluation of Allergenic Pollen Levels and Temporal Variations of Pollen Seasons. \u003cem\u003eFrontiers in Allergy\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e, 676176. https://doi.org/10.3389/falgy.2021.676176\u003c/li\u003e\n\u003cli\u003eEeftens, M., \u0026amp; Gehrig, R. (2019). Airborne pollen: trends in season onset, duration and intensity and the importance for environmental epidemiology. \u003cem\u003eEnvironmental Epidemiology\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(Supplement 1), 107\u0026ndash;107. https://doi.org/10.1097/01.EE9.0000606884.39242.88\u003c/li\u003e\n\u003cli\u003eGal\u0026aacute;n, C., Alc\u0026aacute;zar, P., Oteros, J., Garc\u0026iacute;a-Mozo, H., Aira, M. J., Belmonte, J., et al. (2016). Airborne pollen trends in the Iberian Peninsula. \u003cem\u003eScience of The Total Environment\u003c/em\u003e, \u003cem\u003e550\u003c/em\u003e, 53\u0026ndash;59. https://doi.org/10.1016/j.scitotenv.2016.01.069\u003c/li\u003e\n\u003cli\u003eGal\u0026aacute;n, C., Smith, M., Thibaudon, M., Frenguelli, G., Oteros, J., Gehrig, R., et al. (2014). Pollen monitoring: minimum requirements and reproducibility of analysis. \u003cem\u003eAerobiologia\u003c/em\u003e, \u003cem\u003e30\u003c/em\u003e(4), 385\u0026ndash;395. https://doi.org/10.1007/s10453-014-9335-5\u003c/li\u003e\n\u003cli\u003eGehrig, R., \u0026amp; Clot, B. (2021). 50 Years of Pollen Monitoring in Basel (Switzerland) Demonstrate the Influence of Climate Change on Airborne Pollen. \u003cem\u003eFrontiers in Allergy\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e, 677159. https://doi.org/10.3389/falgy.2021.677159\u003c/li\u003e\n\u003cli\u003eGlick, S., Gehrig, R., \u0026amp; Eeftens, M. (2021). Multi-decade changes in pollen season onset, duration, and intensity: A concern for public health? \u003cem\u003eScience of The Total Environment\u003c/em\u003e, \u003cem\u003e781\u003c/em\u003e, 146382. https://doi.org/10.1016/j.scitotenv.2021.146382\u003c/li\u003e\n\u003cli\u003eGuilbert, A., Cox, B., Bruffaerts, N., Hoebeke, L., Packeu, A., Hendrickx, M., De Cremer, K., Bladt, S., Brasseur, O., \u0026amp; Van Nieuwenhuyse, A. (2018). Relationships between aeroallergen levels and hospital admissions for asthma in the Brussels‑Capital Region: A daily time series analysis. \u003cem\u003eEnvironmental Health\u003c/em\u003e, 17, Article 35. https://doi.org/10.1186/s12940-018-0378-x\u003c/li\u003e\n\u003cli\u003eHirst, J. M. (1952). AN AUTOMATIC VOLUMETRIC SPORE TRAP. \u003cem\u003eAnnals of Applied Biology\u003c/em\u003e, \u003cem\u003e39\u003c/em\u003e(2), 257\u0026ndash;265. https://doi.org/10.1111/j.1744-7348.1952.tb00904.x\u003c/li\u003e\n\u003cli\u003eHoebeke, L., Bruffaerts, N., Verstraeten, C., Delcloo, A., De Smedt, T., Packeu, A., Detandt M., \u0026amp; Hendrickx, M. (2018). Thirty-four years of pollen monitoring: an evaluation of the temporal variation of pollen seasons in Belgium. \u003cem\u003eAerobiologia\u003c/em\u003e, 34, 139\u0026ndash;155. https://doi.org/10.1007/s10453-017-9503-5\u003c/li\u003e\n\u003cli\u003e\u003cu\u003eJ\u003c/u\u003eochner, S., \u0026amp; Menzel, A. (2015). Urban phenological studies \u0026ndash; Past, present, future. \u003cem\u003eEnvironmental Pollution\u003c/em\u003e, 203, 250\u0026ndash;261. https://doi.org/10.1016/j.envpol.2015.01.003\u003c/li\u003e\n\u003cli\u003eJochner-Oette, S., Menzel, A., Gehrig, R., \u0026amp; Clot, B. (2019). Decrease or increase? Temporal changes in pollen concentrations assessed by Bayesian statistics. \u003cem\u003eAerobiologia\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e(1), 153\u0026ndash;163. https://doi.org/10.1007/s10453-018-9547-1\u003c/li\u003e\n\u003cli\u003eLaaidi, M. (2001). Forecasting the start of the pollen season of Poaceae: Evaluation of some methods based on meteorological factors. \u003cem\u003eInternational Journal of Biometeorology\u003c/em\u003e, 45(1), 1\u0026ndash;7. https://doi.org/10.1007/s004840000079\u003c/li\u003e\n\u003cli\u003eLara, B., Rojo, J., Fern\u0026aacute;ndez-Gonz\u0026aacute;lez, F., Gonz\u0026aacute;lez-Garc\u0026iacute;a-Saavedra, A., Serrano-Bravo, M. D., \u0026amp; P\u0026eacute;rez-Badia, R. (2020). Impact of Plane Tree Abundance on Temporal and Spatial Variations in Pollen Concentration. \u003cem\u003eForests\u003c/em\u003e, 11(8), 817. https://doi.org/10.3390/f11080817\u003c/li\u003e\n\u003cli\u003eLind, T., Ekebom, A., Alm K\u0026uuml;bler, K., \u0026Ouml;stensson, P., Bellander, T., \u0026amp; L\u0026otilde;hmus, M. (2016). Pollen Season Trends (1973-2013) in Stockholm Area, Sweden. \u003cem\u003ePLOS ONE\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(11), e0166887. https://doi.org/10.1371/journal.pone.0166887\u003c/li\u003e\n\u003cli\u003eMakra, L., Matyasovszky, I., \u0026amp; De\u0026aacute;k, \u0026Aacute;. J. (2011). Trends in the characteristics of allergenic pollen circulation in central Europe based on the example of Szeged, Hungary. \u003cem\u003eAtmospheric Environment\u003c/em\u003e, \u003cem\u003e45\u003c/em\u003e(33), 6010\u0026ndash;6018. https://doi.org/10.1016/j.atmosenv.2011.07.051\u003c/li\u003e\n\u003cli\u003eMenzel, A., Yuan, Y., Matiu, M., Sparks, T., Scheifinger, H., Gehrig, R., \u0026amp; Estrella, N. (2020). Climate change fingerprints in recent European plant phenology. \u003cem\u003eGlobal Change Biology\u003c/em\u003e, 26, 2599\u0026ndash;2612. https://doi.org/10.1111/gcb.15000\u003c/li\u003e\n\u003cli\u003eMinero, F. J. G., Candau, P., Tom\u0026aacute;s, C., \u0026amp; Morales, J. (1998). Airborne grass (Poaceae) pollen in southern Spain: Results of a 10-year study (1987\u0026ndash;1996). \u003cem\u003eAllergy\u003c/em\u003e, 53(3), 266\u0026ndash;274. https://doi.org/10.1111/j.1398-9995.1998.tb03886.x\u003c/li\u003e\n\u003cli\u003eMousavi, F., Oteros, J., Shahali, Y. \u0026amp; Carinanos, P. (2024). Impacts of climate change on allergenic pollen production: A systematic review and meta-analysis. \u003cem\u003eAgricultural and Forest Meteorology\u003c/em\u003e, 349,109948. https://doi.org/10.1016/j.agrformet.2024.109948\u003c/li\u003e\n\u003cli\u003eNguyen, H., Huete, A., Beggs, P., Davies, J., \u0026amp; Newbigins, E. (2025, September 16). Climate warming and drying drive shifts in grass pollen phenology at a Sentinel Southern Hemisphere site. In Review. https://doi.org/10.21203/rs.3.rs-7606687/v1\u003c/li\u003e\n\u003cli\u003eOgrin, D. (1996). Podnebni tipi v Sloveniji. \u003cem\u003eGeografski vestnik\u003c/em\u003e, \u003cem\u003e68\u003c/em\u003e, 39\u0026ndash;56.\u003c/li\u003e\n\u003cli\u003ePawankar, R., Canonica, G. W., Holgate, S., \u0026amp; Lockey, R. (2013). WAO White Book on Allergy: Update 2013.\u003c/li\u003e\n\u003cli\u003ePenedos, C., Salamanca, G., Tavares, B., Fonseca, J., Carreiro-Martins, P., Rodrigues-Alves, R., Moral de Gregorio, \u0026Aacute;., Valero, A., \u0026amp; Branco Ferreira, M. (2024). Aerobiology of Olive Pollen \u003cem\u003e(Olea europaea L.\u003c/em\u003e) in the Atmosphere of the Iberian Peninsula. \u003cem\u003eAtmosphere\u003c/em\u003e, 15(9), 1087. https://doi.org/10.3390/atmos15091087\u003c/li\u003e\n\u003cli\u003eRojo, J., Oteros, J., Picornell, A., Ru\u0026euml;ff, F., Werchan, B., Werchan, M., et al. (2020). Land-Use and Height of Pollen Sampling Affect Pollen Exposure in Munich, Germany. \u003cem\u003eAtmosphere\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(2), 145. https://doi.org/10.3390/atmos11020145\u003c/li\u003e\n\u003cli\u003eRojo, J., Picornell, A., Oteros, J., Werchan, M., Werchan, B., Bergmann, K. C., Smith, M., Weichenmeier, I., Schmidt-Weber, C. B. \u0026amp; Buters, J. (2021). Consequences of climate change on airborne pollen in Bavaria, Central Europe. \u003cem\u003eRegional Environmental Change\u003c/em\u003e, 21(9). https://doi.org/10.1007/s10113-020-01729-z\u003c/li\u003e\n\u003cli\u003eRojo, J., Rivero, R., Romero-Morte, J., Fern\u0026aacute;ndez-Gonz\u0026aacute;lez, F., \u0026amp; P\u0026eacute;rez-Badia, R. (2017). Modeling pollen time series using seasonal-trend decomposition procedure based on LOESS smoothing. \u003cem\u003eInternational Journal of Biometeorology\u003c/em\u003e, \u003cem\u003e61\u003c/em\u003e(2), 335\u0026ndash;348. https://doi.org/10.1007/s00484-016-1215-y\u003c/li\u003e\n\u003cli\u003eSapkota, A., Murtugudde, R., Curriero, F. C., Upperman, C. R., Ziska, L., \u0026amp; Jiang, C. (2019). Associations between alteration in plant phenology and hay fever prevalence among US adults: Implication for changing climate. \u003cem\u003ePLOS ONE\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(3), e0212010. https://doi.org/10.1371/journal.pone.0212010\u003c/li\u003e\n\u003cli\u003eSchramm, P. J., Brown, C. L., Saha, S., Conlon, K. C., Manangan, A. P., Bell, J. E., \u0026amp; Hess, J. J. (2021). A systematic review of the effects of temperature and precipitation on pollen concentrations and season timing, and implications for human health. \u003cem\u003eInternational Journal of Biometeorology\u003c/em\u003e, \u003cem\u003e65\u003c/em\u003e(10), 1615\u0026ndash;1628. https://doi.org/10.1007/s00484-021-02128-7\u003c/li\u003e\n\u003cli\u003eSikoparija, B., Skj\u0026oslash;th, C. A., Celenk, S., Testoni, C., Abramidze, T., Alm K\u0026uuml;bler, K., Belmonte, J., Berger, U., Bonini, M., Charalampopoulos, A., Damialis, A., Clot, B., Dahl, \u0026Aring;., de Weger, L. A., Gehrig, R., Hendrickx, M., Hoebeke, L., Ianovici, N., Kofol Seliger, A., \u0026hellip; Smith, M. (2017). Spatial and temporal variations in airborne Ambrosia pollen in Europe. \u003cem\u003eAerobiologia\u003c/em\u003e, 33(2), 181\u0026ndash;189. https://doi.org/10.1007/s10453-016-9463-1\u003c/li\u003e\n\u003cli\u003eSofiev, M., Bousquet, J., Linkosalo, T., Ranta, H., Rantio-Lehtimaki, A., Siljamo, P., et al. (2009). Pollen, Allergies and Adaptation. In K. L. Ebi, I. Burton, \u0026amp; G. R. McGregor (Eds.), \u003cem\u003eBiometeorology for Adaptation to Climate Variability and Change\u003c/em\u003e (Vol. 1, pp. 75\u0026ndash;106). Dordrecht: Springer Netherlands. https://doi.org/10.1007/978-1-4020-8921-3_5\u003c/li\u003e\n\u003cli\u003eTagliaferro, S., Maechetti, P., Dall\u0026apos; Ara, B., Domenichini, F., Lazzarin, S., Nicolis, M., Selle, D., Silibello, C., \u0026amp; Marcon, A. (2024). Temporal trends of seasonal pollen indexes in a region of Northern Italy (2001-2022). \u003cem\u003eAtmospheric Environment\u003c/em\u003e, 338, 1352-2310. https://doi.org/10.1016/j.atmosenv.2024.120826\u003c/li\u003e\n\u003cli\u003eTaylor, S. J., \u0026amp; Letham, B. (2017, September 27). Forecasting at scale. PeerJ Preprints. https://doi.org/10.7287/peerj.preprints.3190v2\u003c/li\u003e\n\u003cli\u003eThe pandas development team. (2026, January 14). pandas-dev/pandas: Pandas. Zenodo. https://doi.org/10.5281/ZENODO.3509134\u003c/li\u003e\n\u003cli\u003eZhang, Y., \u0026amp; Steiner, A. L. (2022). Projected climate-driven changes in pollen emission season length and magnitude over the continental United States. \u003cem\u003eNature Communications\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(1), 1234. https://doi.org/10.1038/s41467-022-28764-0\u003c/li\u003e\n\u003cli\u003eZidarn, M. (2013). Sensitization to inhalant allergens in patients with allergic airway disease in Slovenia. \u003cem\u003eZdravni\u0026scaron;ki vestn\u003c/em\u003eik, 82(6), 378\u0026ndash;385. http://www.dlib.si/?URN=URN:NBN:SI:DOC-6CTCE3PZ\u003c/li\u003e\n\u003cli\u003eZiello, C., Sparks, T.H., Estrella, N., Belmonte, J., Bergmann, K.C., Bucher, E., Brighetti, M.A., Damialis, A., Detandt, M., Gal\u0026aacute;n, C., Gehrig, R., Grewling, L., Guti\u0026eacute;rrez Bustillo, A.M., Hallsd\u0026oacute;ttir, M., Kockhans-Bieda, M.C., De Linares, C., Myszkowsk,a D., P\u0026agrave;ldy, A., S\u0026aacute;nchez, A., \u0026hellip; Menzel, A. (2012). Changes to airborne pollen counts across Europe. \u003cem\u003ePLoS One\u003c/em\u003e, 7(4), e34076. https://doi.org/10.1371/journal.pone.0034076\u003c/li\u003e\n\u003cli\u003eZiska, L. H., \u0026amp; Beggs, P. J. (2012). Anthropogenic climate change and allergen exposure: The role of plant biology. \u003cem\u003eJournal of Allergy and Clinical Immunology\u003c/em\u003e, \u003cem\u003e129\u003c/em\u003e(1), 27\u0026ndash;32. https://doi.org/10.1016/j.jaci.2011.10.032\u003c/li\u003e\n\u003cli\u003eZiska, L. H., Makra, L., Harry, S. K., Bruffaerts, N., Hendrickx, M., Coates, F., et al. (2019). Temperature-related changes in airborne allergenic pollen abundance and seasonality across the northern hemisphere: a retrospective data analysis. \u003cem\u003eThe Lancet Planetary Health\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(3), e124\u0026ndash;e131. https://doi.org/10.1016/S2542-5196(19)30015-4\u003c/li\u003e\n\u003cli\u003eZupančič, M., \u0026amp; Vre\u0026scaron;, B. (2019). Phythogeographic analysis of Slovenia / Fitogeografska oznaka Slovenije. \u003cem\u003eFolia biologica et geologica\u003c/em\u003e, \u003cem\u003e59\u003c/em\u003e(2), 159\u0026ndash;211. https://doi.org/10.3986/fbg0048\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"aerobiologia","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aero","sideBox":"Learn more about [Aerobiologia](http://link.springer.com/journal/10453)","snPcode":"10453","submissionUrl":"https://submission.nature.com/new-submission/10453/3","title":"Aerobiologia","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Airborne pollen, Phenological trends, Pollen season, Slovenia, Mediterranean climate, Continental climate, Allergenic exposure","lastPublishedDoi":"10.21203/rs.3.rs-8731030/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8731030/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eClimate change is reshaping pollen season dynamics across Europe, with significant implications for allergic populations. This study presents the first comprehensive long-term analysis of pollen season trends in Slovenia, a region characterized by high phytogeographic diversity at the intersection of Mediterranean, Alpine, Dinaric, and Pannonian climatic influences. Daily airborne pollen concentration data from three monitoring stations were analyzed for 18 allergenic taxa over 23 years (2002-2024). Pollen season timing was determined using a normalized cumulative-sum approach, and linear regression quantified temporal trends in season onset, termination, duration, and total annual pollen load. Results reveal general advancement in season onset across most taxa, particularly pronounced for early-blooming arboreal species in the Mediterranean region. Season termination exhibited asymmetric patterns: earlier endings for spring-flowering trees and delayed termination for herbaceous taxa, especially Poaceae. Notably, early blooming trees showed compressed seasons due to faster advancement of season end relative to onset, potentially leading to more intense allergen exposure despite shorter duration. Significant increases in annual pollen load were detected for Poaceae, Urticaceae, and \u003cem\u003ePlantago\u003c/em\u003e, while \u003cem\u003eArtemisia\u003c/em\u003eshowed widespread decline. The coastal Mediterranean site exhibited significantly earlier onset, later termination, and longer seasons compared to continental stations, which showed strong inter-site synchrony. These findings demonstrate asymmetric phenological responses to climate forcing across small spatial scales, with important implications for region-specific public health strategies.\u003c/p\u003e","manuscriptTitle":"Long-term changes in the timing and intensity of the pollen season in Slovenia (2002 – 2024)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-05 18:45:55","doi":"10.21203/rs.3.rs-8731030/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-07T08:44:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-07T05:46:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-22T17:53:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"53372549983532192910032003384521771981","date":"2026-03-12T09:03:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"250709473690445499021505435768197912631","date":"2026-03-10T08:57:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-03T10:14:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-03T09:45:54+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-31T01:45:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"Aerobiologia","date":"2026-01-29T10:56:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"aerobiologia","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"aero","sideBox":"Learn more about [Aerobiologia](http://link.springer.com/journal/10453)","snPcode":"10453","submissionUrl":"https://submission.nature.com/new-submission/10453/3","title":"Aerobiologia","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"23fe7af8-3466-4a2d-85aa-27b1a73baf9f","owner":[],"postedDate":"February 5th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-25T15:53:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-05 18:45:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8731030","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8731030","identity":"rs-8731030","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}