Source Apportionment of PM10 and PM2.5 at a Traffic Site in Constantine, Algeria: Combining Back-Trajectory Analysis and Correlation Studies

Source Apportionment of PM10 and PM2.5 at a Traffic Site in Constantine, Algeria: Combining Back-Trajectory Analysis and Correlation Studies | 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 Source Apportionment of PM10 and PM2.5 at a Traffic Site in Constantine, Algeria: Combining Back-Trajectory Analysis and Correlation Studies HOCINE ALI KHODJA, Amina Kemmouche, Ahmed Terrouche, Lamri Naidja, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9418719/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study investigated PM10 and PM2.5 concentrations, major components, trace elements, and source identification at a traffic site near Zouaghi in Constantine, Algeria, from February 2021 to November 2021. The mean PM10 and PM2.5 concentrations were 150 ± 103 µg/m 3 and 55 ± 42 µg/m 3 , respectively, exceeding WHO air quality guidelines (2021). Coarse particles predominated, reflecting soil dust resuspension, and trace elements were concentrated in the PM10-2.5 fraction. Four distinct sources were identified using cluster back-trajectory analysis: the Sahara, Mediterranean Sea, northwest, and Atlantic Ocean. PM10 and 17 metal elements (Sb, Ba, Al, Ca, Mg, Fe, K, Na, Cu, Pb, V, Zn, Ti, Mn, Ni, Mo, and Sr) were enriched in air masses from the Sahara, Mediterranean Sea, and northwest, suggesting contributions from natural and anthropogenic sources. Correlation analysis revealed that well-correlated pollutants were transported by air masses from the same back-trajectory groups, indicating a common origin. Enrichment factors helped distinguish between natural and anthropogenic sources. The consistency between correlation analysis and back-trajectory clustering reinforced the effectiveness of these methods for source apportionment of PM10-related metallic elements. This study validated the efficacy of correlation analysis and back-trajectory clustering as tools for identifying PM sources in Constantine, providing a cost-effective approach for understanding PM origins and composition. These findings enabled the proposal of targeted interventions, such as traffic flow improvements and industrial emission controls, to mitigate pollution levels and inform public health officials in developing strategies to protect population health. Atmospheric Sciences Environmental Chemistry PM10 PM2.5 Back-trajectory analysis Correlation studies Trace elements Source apportionment Enrichment factors Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction This study focuses on particulate matter (PM) pollution in Constantine, Algeria, to address the critical data gap in the region. Despite the known adverse effects of PM on health, comprehensive data on PM sources and concentrations are lacking in Constantine, Algeria, hindering effective air quality management. This study encompasses several key aspects of air quality monitoring and analysis with significant implications for public health and environmental policy. Air quality monitoring is essential for characterizing health effects and developing pollution abatement strategies (Gulia et al., 2019 ). This provides crucial data on pollutant levels and sources, enabling the development of targeted air quality management plans. Furthermore, it facilitates the identification of pollution sources in urban areas and the assessment of transportation-induced pollution impacts (Bakirci, 2024 ). This monitoring also enables the evaluation of air quality attainment and the assessment of health and economic benefits resulting from improvements (Qiu et al., 2015 ). The context of air quality monitoring in Algeria, characterized by significant challenges such as limited national-level data, technical failures, and inconsistent monitoring, sets the stage for the importance of our study (Kerchich and Kerbachi, 2012 ). For instance, in Annaba, local air quality monitoring stations have not been used for years because of technical issues. Similarly, in Algiers, network data are unreliable, and the "Sama Safia" air quality index has not been communicated since 2009. Constantine, the focus of this study, has limited available research, highlighting the need for a more comprehensive investigation. Air quality monitoring in Algeria presents a complex landscape of challenges and gaps in data collection and dissemination. The absence of reliable national-level data, coupled with technical failures and inconsistent monitoring practices, has created a significant void in understanding the true state of air quality across the country. The focus on Constantine in this study is particularly significant given the limited available research on air quality in this region. This gap in knowledge highlights the need for more comprehensive investigations and emphasizes the importance of establishing robust and consistent monitoring systems across Algeria. By addressing these challenges and conducting in-depth studies, researchers can provide valuable insights into air pollution levels, sources, and impacts, which are essential for developing effective air quality management strategies and policies. Furthermore, such research can serve as a foundation for raising public awareness of air quality issues and promoting actions to improve environmental conditions and public health in Constantine and other cities across Algeria. Previous studies on PM pollution in Constantine have predominantly utilized sophisticated statistical tools for source apportionment, including factor analysis, PCA, and PMF (Terrouche et al., 2016 ; Bencharif-Madani et al., 2019 ; Naidja et al., 2022). Although effective in identifying and quantifying source contributions to PM levels, these methods require extensive datasets and complex computational efforts (Lee et al., 2016 ). Building upon these previous efforts, our study takes a different approach to address the specific challenges and data limitations in Constantine. Our study adopted a streamlined yet effective approach employing correlation analysis combined with back-trajectory methods to provide a comprehensive understanding of PM sources. This choice is motivated by the need to develop a methodological framework that is not only effective in source identification but also adaptable to regions with limited air quality data infrastructure, such as Constantine. We posit that correlation analysis, when combined with back-trajectory analysis, provides a viable alternative for understanding the origins of PM and trace elements. By employing this method, we tracked the movement of air masses and linked them to both local and regional pollution sources. This approach provides valuable insights into the dynamics of PM pollution while potentially reducing the computational and data requirements, as only 20 samples were collected for PM10 and PM2.5. Notably, this study is the first to present the results of analyzing several metal elements in both PM2.5 and PM10. Examining multiple metals in these particulate matter sizes offers a more comprehensive evaluation of air quality, pollution origins, and possible health implications. The identification of both local and distant sources of particulate matter (PM) and trace elements is crucial for understanding air pollution dynamics and implementing effective mitigation strategies. Correlation analysis and back-trajectory clustering are two coherent and reliable methodologies that are often employed in source apportionment studies. The chosen methods provide a balance between analytical depth and practicality, allowing for meaningful insights without the need for extensive computational resources or large datasets. These methods allow researchers to differentiate between local emissions and long-range transport sources, thereby providing a comprehensive understanding of pollution sources. 2. Material and methods 2.1. Sampling location The monitoring study was conducted at a traffic site situated on the University of Constantine Earth Sciences Campus "Zouaghi Slimane" (Fig. 1 ) from February 21, 2021, to November 01, 2021, with biweekly data collection. National Road 79 (NR79) experiences heavy traffic, with approximately 20,000 vehicles passing daily. This high traffic volume poses a significant challenge to commuters and local authorities. The constant flow of vehicles leads to frequent congestion, especially during peak hours, resulting in increased travel times and reduced mobility for residents and travellers alike. Such heavy traffic also contributes to elevated levels of air and noise pollution in the surrounding areas, potentially impacting the health and well-being of nearby communities. Two low-volume samplers were used to monitor PM10 and PM2.5 simultaneously. Twenty filters were used for each PM size. The study site is located in the immediate vicinity of a heavy-traffic road on the southern outskirts of the city. This road connects the city of Constantine to nearby southern cities, such as Ain-Mlila, Batna, and Biskra. 2.2. Filter mineralization To prepare the samples for analysis, we treated one-quarter of each air quartz 10”x8” filter with specific acids in a controlled environment. This process, known as filter mineralization, helps extract trace metals from the particles collected on the filters. One-quarter of each filter was digested with 1 mL HNO 3 and 2 mL HF in a closed PFA flask at 90°C for at least 8 h. After cooling, the containers were opened, and 1 mL of HClO 4 was added. The acids were completely evaporated by placing the PFA containers on a hot plate (Stuart SD 500) at 240°C. The remaining dry residue was dissolved with 2.5 mL of HNO 3 , diluted with distilled water (MilliQ) to 25 mL, to obtain 5% solutions of HNO 3 , which were centrifuged for 20 min at 3000 rpm. This method was described by Kemmouche et al. ( 2017 ). 2.3. Trace metals analysis The solutions obtained were then analyzed by ICP-AES (IRIS Advantage Solutions TJA THERMO) for the elements (Sb, Ba, Al, Ca, Mg, K, Na, Fe) and by ICP-MS (X Series II THERMO) for the elements (Cu, Pb, V, Zn, Ti, Mn, Ni, Mo, Sr). 2.4. Spearman correlation coefficients and back-trajectory analysis Spearman’s correlation coefficient, a method that does not assume a normal distribution of data, measures the strength and direction of the relationship between two variables. Unlike Pearson's correlation, Spearman's correlation does not assume that both datasets are normally distributed and is used to identify the strength and direction of a monotonic relationship between paired data. In our study, Spearman correlation coefficients were calculated to explore the associations between the concentrations of different metallic elements in PM and meteorological parameters, providing insights into potential common sources and transport mechanisms of these pollutants. Back-trajectory analysis and clustering are techniques widely used in atmospheric science to investigate the origin and transport pathways of air masses and their potential impact on air quality at receptor sites. Back-trajectory analysis involves calculating the path that an air parcel takes before reaching a specific location (receptor site) at a given time. This is typically done using models such as hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) (Cui et al., 2021 ; Byčenkienė et al., 2014 ), which use meteorological data to trace the movement of air parcels backward in time, usually for several days. These trajectories provide information about potential source regions and transport pathways of air pollutants (Warner, 2018 ). Daily 72-h back-trajectories arriving at 12:00 GMT at the sampling site were used. Clustering was then applied to group similar trajectories, thereby reducing the complexity of the dataset and identifying dominant airflow patterns (Ding et al., 2024 ). Through this methodological framework, we not only address specific air quality challenges in Constantine but also provide a potential model for regions facing comparable environmental and data constraints. 2.5. Enrichment factors To determine the sources (natural or human-made) of trace elements in atmospheric particulate matter (PM) and assess the level of anthropogenic impact, researchers have used enrichment factors (EFs). These EFs are calculated for trace elements in atmospheric PM by comparing them with local soil composition or the Earth’s crust. The EF value of element X can be determined using the following equation: (Xi)/(Al) sample /(Xi)(Al) crust whereXi" represents the element being examined. The subscripts "sample" and "crust" indicate the medium to which the concentration pertains. EFs are typically calculated by comparing trace element concentrations in PM to local soil composition or the Earth's crust (Chakraborty et al., 2023 ; Moskovchenko et al., 2021 ). Common reference elements used include Al, Sc, Ti, Li, Zr, and sometimes Fe and Mn, which are abundant in the Earth's crust. In the studies presented, Al was frequently used as the baseline element, with the Earth's crust composition representing the regional geochemistry (McLennan, 2001 ; Samayamanthula et al., 2020 ). Although various classifications of EF values of atmospheric PM exist in the literature, elements can be categorized based on their enrichment levels as follows: no enrichment (EF < 1), minor enrichment (EF < 10), moderate enrichment (10 < EF 100) (Bouziane et al., 2025 ). 3. Results and discussion 3.1 PM concentrations and ratios 3.1.1. PM and metallic elements concentrations overview This study investigated PM 10 and PM 2.5 concentrations, major components, trace elements, and source identification at a traffic site near Zouaghi in Constantine, Algeria, from February 2021 to November 2021. The mean PM 10 and PM 2.5 concentrations were 150 ± 103 µg/m 3 and 55 ± 42 µg/m 3 , respectively, exceeding the World Health Organization air quality guidelines (Tables 1 and 2 ). Coarse particles predominated, reflecting the resuspension of soil dust (Table 3 ). Trace elements were concentrated in the PM10–2.5 fraction. Four distinct distant sources were identified using cluster back-trajectory analysis. PM 10 and 17 metal elements (Sb, Ba, Al, Ca, Mg, Fe, K, Na, Cu, Pb, V, Zn, Ti, Mn, Ni, Mo, and Sr) were enriched in air masses originating from the Sahara, Mediterranean Sea, the Atlantic Oceana and Spain, suggesting contributions from natural and anthropogenic sources. Correlation analysis revealed that well-correlated pollutants were transported by air masses from the same back-trajectory groups, indicating a common origin. Enrichment factors helped distinguish between natural (sea and soil) and anthropogenic sources. To illustrate the variability and trends in PM concentrations, Fig. 2 presents a detailed breakdown of daily PM 10 , PM 2.5 , and PM 10−2.5 levels. Tables 1 – 3 present the statistical results of PM 10 , PM 2.5 , and metallic element concentrations in PM 10 and PM 2.5 , respectively. The selection of these metals for analysis was based on their potential to indicate specific pollution sources, aligning with the study's aim of differentiating between natural and anthropogenic contributions to PM levels. PM 10 and PM 2.5 concentrations ranged between 31 and 287 µg/m 3 and between 3 and 153 µg/m 3 , respectively. The results obtained show that the daily PM 10 and PM 2.5 levels varied widely (Fig. 2 ). On average, PM10 concentrations were triple those of PM2.5, indicating a significant predominance of coarse particles. Coarse particles reflect naturally occurring dust and soil dustgenerated by wind and traffic (Swet et al., 2020 ). Coarse particles reflect naturally occurring dust and soil dust generated by wind and traffic (Swet et al., 2020 ). PM 2.5 represents dust of anthropogenic origin, including particles resulting from the condensation of exhaust gases (Zheng et al., 2024 ; Bessagnet et al., 2022 ). Table 1 PM 10 and PM 2.5 concentrations measured at the study area level Number of samples PM 10 PM 2.5 20 20 Average concentration (µg/m 3 ) 150 55 Maximum concentration (µg/m 3 ) 287 153 Minimum concentration (µg/m 3 ) 31 3 Standard deviation (µg/m 3 ) 103 42 Table 2 Statistical data of the concentrations of metallic elements in PM 10 Element µg/m 3 ng/m 3 Al Ca Mg Fe K Na Sb Ba Cu Pb V Zn Ti Mn Ni Mo Sr Average 2,4 30,3 1,0 1,9 4,2 1,6 209,2 74,8 22,8 180,5 10,4 143,3 143,9 24,8 19,7 22,9 22,7 Max. 9,9 108,3 2,8 6,5 27,6 3,9 631,2 293,0 47,6 323,4 32,0 604,2 588,7 71,9 278,5 58,5 77,2 Min. 0,0 6,2 0,0 0,0 0,0 0,0 6,7 0,0 0,0 65,2 1,5 0,0 0,0 4,1 0,0 0,0 0,0 Sd dev. 2,4 23,1 0,8 1,9 7,6 1,3 142,6 84,9 16,2 82,4 7,9 142,5 163,0 20,5 61,5 18,8 21,2 Table 3 Statistical data of the concentrations of metallic elements in PM 2.5 Element µg/m 3 ng/m 3 Al Ca Mg Fe K Na Sb Ba Cu Pb V Zn Ti Mn Ni Mo Sr Average 1,0 12,8 0,4 0,9 0,9 0,4 40,1 21,0 6.01 71,6 2,6 50,1 39,5 9,6 5,8 3,6 7,3 Max. 5,4 32,5 1,5 3,9 2,9 2,2 188,7 127,6 47,6 186,4 7,4 157,5 299,6 47,6 72,0 25,7 29,6 Min. 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 0,0 37,6 0,0 0,0 0,0 0,0 0,0 0,0 0,0 Sd dev. 1,3 10,2 0,4 1,1 0,9 0,6 53,6 37,5 16,2 32,1 2,5 61,2 82,3 14,4 17,6 7,4 9,0 3.1.2. PM 2.5 /PM 10 ratios The relatively low PM2.5/PM10 particle ratio of 36.56% suggests that coarse particles (PM10) constitute a larger proportion of particulate matter in the air than fine particles (PM2.5). This implies that local sources of dust and larger particulates may be more significant contributors to air pollution in this area than fine particles from combustion or long-range transport. Natural sources, such as sea spray or windblown soil, could be important contributors to particulate matter composition. Similarly, in the United Arab Emirates, PM2.5/PM10 ratios ranged between 0.29 and 0.49 across industrial, urban, and suburban areas, which is characteristic of arid and semi-arid environments where natural processes such as sand particle uplift contribute significantly to air pollution (Abuelgasim and Farahat, 2020 ). This fraction contains resuspended dust from traffic, particles from braking and tire wear, mechanical parts, and road abrasion (Celo et al. 2021 ; Habre et al. 2021 ). Table 4 shows the PM 2.5 /PM 10 ratios ​​for the trace elements. Table 4 Trace elements ratios (PM 2.5 /PM 10 ) Element Sb Ba Al Ca Mg Fe K Na Cu Pb V Zn Ti Mn Ni Mo Sr Ratio 0.19 0.28 0.41 0.42 0.41 0.46 0.22 0.28 0.26 0.40 0.25 0.35 0.27 0.38 0.29 0.16 0.32 The trace element ratios (PM2.5/PM10) provided in Table 5 indicate that the coarse fraction (PM10-2.5) was predominant for all trace elements, with varying degrees of significance. This observation is consistent with the findings of several previous studies. In urban environments, road transport is a major source of PM that directly affects pedestrians (Jandacka and Durcanska, 2021 ). The coarse fraction (PM10-2.5) shows a significant relationship with elements such as Cu, Sb, Ba, Ca, Cr, Fe, Mg, and Al, which are primarily associated with non-exhaust emissions, such as road dust resuspension and tire/brake wear (Celo et al., 2021 ; Jandacka and Durcanska, 2021 ). This fraction was significant for Sb, Ba, K, Na, Cu, V, Ti, Ni, and Mo. The fraction contained in PM 2.5 is derived from exhaust gases or anthropogenic activities (heating, industry, and agriculture) (Li et al., 2023 ; Zheng et al., 2024 ; Rai et al., 2020 ). 3.1.3. Enrichment factors The values of the EF are listed in Table 5 . Table 5 Enrichment factors computed according to crustal abundances in PM10 and PM2.5 PM10 Ca Mg Fe K Na Sb Ba Cu Pb V Zn Ti Mn Ni Mo Sr 34 27 18 52 18 35642 5 31 362 3 69 1.2 1.4 15 520 2 PM2.5 36 3 2 3 1.3 16775 3 20 353 2 59 0.8 1.3 11 199 2 In PM10, the average EFs for metal elements ranged from 1.2 (for Ti) to 35642 (for Sb). In PM2.5, the average EFs for metal elements ranged from 0.8 (for Ti) to 16755 (for Sb). In PM10, the samples exhibited minor enrichment for Ti, Mn, V, Sr, and Ba; moderate enrichment for Cu, Zn, Ni, Ca, Mg, Fe, K, and Na; and significant enrichment for Pb, Mo, and Sb. In PM2.5, the samples exhibited no enrichment for Ti, minor enrichment for V, Mn, Sr, Ba, Mg, Fe, K and Na, moderate enrichment for Cu, Zn, Ni and Ca, and significant enrichment for Pb, Mo and Sb. The EFs for metallic elements in both PM10 and PM2.5 fractions displayed a wide range, indicating a mixture of crustal and anthropogenic sources. Ti consistently shows very low EFs in both PM10 and PM2.5 (1.2 and 0.8, respectively), affirming its crustal origin. It is often used as a reference element in such studies owing to its geochemical stability and minimal anthropogenic emissions. Ca, Mg, Fe, K, and Na are primarily of crustal origin but may include an anthropogenic component, especially in coarser particles. Moderate enrichment (EF ~ 10–100) for elements such as Cu, Zn, Ni, and Ca suggests a blend of natural and anthropogenic sources, such as traffic-related abrasion (brake and tire wear), industrial emissions, and resuspended road dust. Significant enrichment (EF ˃ 100) for Pb, Mo, and Sb in both PM10 and PM2.5 strongly indicates anthropogenic sources, such as vehicular exhaust, industrial combustion, smelting, and brake wear. The extremely high EF for Sb in both size fractions (up to 35,642) clearly identifies it as a tracer of traffic emissions (e.g., brake pads) or specific industrial activities. The notable enrichment of toxic metals, such as Pb and Sb, in PM2.5 is particularly concerning because of their enhanced ability to penetrate the respiratory tract and exert harmful health effects. 3.2. Influence of meteorological parameters on PM levels The meteorological data obtained, illustrating the impact of wind direction on pollutant levels, underscores the significance of regional climate factors in our analysis of PM source apportionment. Figure 3 shows a wind rose for the study period. Meteorological data were obtained from a local airport weather station. The prevailing winds were northwest, with a frequency of 13.12%, and west, with a frequency of 11.04%. These winds were relatively weak because their velocity was in the range of 0.5–5.7 m/s. The range of calm winds corresponded to a speed of less than 0.5 m/s; these winds represented 34.4% of the time. Pollution roses (Fig. 4 ) indicate that higher PM 10 and PM 2.5 concentrations were associated with northern and southwestern winds, that is, from near-field sources such as heavy road traffic of approximately 20,000 vehicles/day. 3.3. Source identification through back trajectory analysis Back-trajectory analysis involves tracing the path that air masses have travelled to reach the study site using advanced modeling based on meteorological data. Figure 5 illustrates the 3-day average back-trajectories modelled by HYSPLIT. Four average back-trajectories representing 20 modelled paths of plots arriving at the sampling site were identified. Each sampling day was the subject of a back-trajectory arriving at the study site at 12h00. The predominant modelled pathway crosses the western side of the Mediterranean Sea and eastern Spain (45%). The second most representative pathway originated in the Algerian Sahara (25%). The third trajectory originated in the north and crossed a large portion of the Mediterranean Sea (20%). The fourth and last modelled pathways arose from the Atlantic (10%). Back-trajectories were split according to their origin (desert, sea, and sea/continent) and the distance travelled for PM10 (Table 6 ) and PM2.5 (Table 7 ). Tables 6 and 7 summarize the findings on PM concentrations and associated metallic elements categorized by their source trajectories. The latter were identified as follows: • Group 1 and 2 from the northwest • Group 3 from the Mediterranean Sea • Group 4 from the Sahara With regard to PM 10 , Group 1 contributed to Na, Cu, Pb and Mo; Group 2 to K only; Group 3 to Ca, Fe, K, Pb, Zn, Ti, Mn, Ni, Sr; and Group 4 to PM, Al, Ca, Mg, Fe, Na, Sb, Ba, Cu, V, Zn, Ti, Mn, Mo and Sr. As for PM 2.5 , Group 1 of back-trajectories was associated with Ca, K, Na, Sb, Cu, Pb, Zn, Ni, and Sr; Group 2 with Sb and Ni; Group 3 with Ca, Fe, K, and Mn; and Group 4 with PM, Al, Ca, Mg, Fe, K, Na, Ba, V, Ti, Mn, Mo and Sr. Table 6 Average concentrations of PM10 and metallic elements in each group Groupe µg/m 3 ng/m 3 PM 10 Al Ca Mg Fe K Na Sb Ba Cu Pb V Zn Ti Mn Ni Mo Sr 1 (Sea-continent) 142 2 24 1 1 1 2 188 53 25 203 10 123 81 19 5 26 14 2 (Sea-continent-ocean) 107 1 24 1 1 11 1 206 22 5 167 6 52 59 11 0 11 15 3 (Sea) 93 2 40 1 2 8 1 176 53 19 196 9 227 165 35 75 20 29 4 (Desert) 239 4 32 2 3 4 2 276 153 29 134 14 149 274 33 9 24 36 Measured average concentration (a.c.) 150 2.4 30.3 1.04 1.9 4 1.6 209 75 23 181 10.4 143 144 25 20 23 23 Enriched metal elements are in bold Table 7 Average concentrations of PM2.5 and metallic elements in each group Groupe µg/m 3 ng/m 3 PM 2.5 Al Ca Mg Fe K Na Sb Ba Cu Pb V Zn Ti Mn Ni Mo Sr 1 (Sea-continent) 39 1 13 0 1 1 1 58 20 10 84 64 38 7 12 4 9 2 (Sea-continent-ocean) 14 0 8 0 0 0 0 59 0 0 47 1 0 0 6 11 2 3 (Sea) 43 1 14 0 1 1 0 16 12 0 64 1 14 11 0 2 4 4 (Desert) 109 2 14 1 1 1 1 19 38 5 67 4 50 77 15 1 3 10 Measured average concentration (a.c.) 55 1 12.8 0.43 0.86 0.93 0.44 40 21 6 72 2,6 50 39 10 6 4 7 Enriched metal elements are in bold 3.3.1. PM10 source identification Air masses belonging to Group 1 from the northwest, which accounted for 45% of the air masses reaching the site, crossed a small part of southeastern Spain, the Mediterranean Sea, and northern Algeria. They carried Pb, Cu, and Mo in PM 10 with percentages relative to the average concentration (a.c.) and concentrations of (112% and 203 ng/m 3 ), (109% and 25 ng/m 3 ), and (113% and 26 ng/m 3 ), respectively. Substantial emissions of Pb, Cu, and Mo were predominantly attributed to the wear of vehicle tires and dust from brake linings, elements associated with high traffic density near the roundabout situated to the west of the sampling site (Larsen et al., 2011 ). Cu may also originate from metallurgical, chemical, and electrical cable industries in Béjaïa, 155 km northwest of the study site. PM10-associated lead (Pb) is present in vehicular emissions, as leaded gasoline was still utilized in Algeria in 2021. The use of leaded gasoline was discontinued in October 2021. Air masses originating from the northwest and crossing the Atlantic Ocean, Portugal, Spain, and the Mediterranean Sea (Group 2) represented 10% of all air masses arriving at the study site. These air masses were uniquely overloaded with K (159% a. c., 11 ng/m 3 ), likely originating from areas with intensive agricultural activities northwest of Constantine (Zhai et al., 2023 ). Air masses originating from the north, designated as Group 3, constituted 20% of the air masses reaching the study site and were enriched in various metal elements within PM10. These air masses traverse the industrial region of Didouche Mourad, which is situated 13 km north of the sampling location (Ali-Khodja et al., 2007 ; Bouziane et al., 2025 ). This was the case for, in order of relative abundance, Ni (375% a. c.), Zn (159% a. c.), Mn (140% a. c.), Sr (126% a. c.), Ti (115% a. c.), K (190% a. c.), Ca (132% a. c.), Pb (108% a. c.), and Fe (105% a. c.). Zn and Ti may be related to urban road dust (Pey et al., 2013 ). Zn is likely to have a predominantly anthropogenic origin. Zn is likely to have a predominantly anthropogenic origin and is often associated with industrial activities, traffic emissions, and combustion processes (Bimenyimana et al. 2023 ; Rajput et al. 2016 ). In contrast, Ti, along with Mn, is primarily associated with crustal or natural sources (Calzolai et al., 2015 ). For Ni and Fe, the sources were likely a combination of industrial emissions and natural sources. Millán-Martínez et al. ( 2021 ) mentioned that toxic trace elements derived from industrial emissions showed higher concentrations during North African dust events. Additionally, Zhao et al. ( 2021 ) identified Ni as being associated with automotive emissions, which could contribute to its presence in air masses. Furthermore, Mostafa et al. ( 2023 ) identified Pb originating from vehicle exhaust in urban environments (Mostafa et al., 2023 ). Pb was emitted in exhaust gases because of its use in fuels in Algeria during the study period. It was completely banned in October 2021. PM10 originated mainly from southern Algeria, as its concentrations in Group 4 were 159% a.c. (average concentration). Desert air masses (group 4) which accounted for 25% of back-trajectories were enriched in trace metal elements in PM10 such as Al (167% a. c.), Mg (192% a. c. and 2 µg m − 3 ), Fe (158% a. c. and 3 µg m − 3 ), Ca (106% a. c. and 32 µg m − 3 ), Ba (204% a. c.), Ti (190% a. c.), Sr (157% a. c.), and to a lesser degree V (135% a. c.), Mn (132% a. c.), Sb (132% a. c.), Cu (126% a. c.), Mo (104% a. c.) and Zn (104% a. c.). The average enrichment of trace metals in Saharan PM10 was in the following order: Ba > Mg > Ti > Al > Fe > Sr > V > Sb/ Mn > Cu> Ca > Zn/Mo. Elements such as Al, Mg, Fe, Ti, and Ca are often associated with mineral dust, which can be transported over long distances (Sharma et al., 2021 ). As shown in Table 5 , Ba, Ti, Sr, and V were slightly enriched, indicating their predominant crustal origin. Mg, Fe, Cu, Ca, and Zn have mixed natural and anthropogenic origins. Sb and Mo had strictly anthropogenic origin enrichment factors of 35642 and 520, respectively. Martín-Cruz and Gómez-Losada ( 2023 ) identified non-exhaust vehicle emissions as a source of PM10, which could include brake and tire wear particles that may contain calcium compounds. Roadside dust can contain particulates enriched with Mg, Fe, Cu, and Zn owing to the degradation of mechanical vehicular parts, tire wear, and combustion processes (Cowan et al., 2020 ). Their presence in PM10 may also suggest a significant contribution from natural sources, such as wind-blown dust, soil resuspension, and crustal weathering (Millán-Martínez et al., 2021 ). Southerly air masses did not significantly contribute to the transport of trace elements Pb and Ni. The PM 10 -related Pb concentrations were more affected by Groups 1 (Sea–continent) and 3 (Sea) of the back trajectories, and Ni by Group 3 (Sea), as they were higher than the average measured levels throughout the sampling period. 3.3.2. PM2.5 source identification Group 1 air masses carried in PM 2.5 trace elements, such as Na (227% a.c., 1 µg m − 3 ), Ni (200% a.c., 12 ng m − 3 ), Cu (167% a.c., 10 ng m − 3 ), Sb (145% a.c., 58 ng m − 3 ), Sr (129% a.c., 9 ng m − 3 ), Zn (128% a.c., 64 ng m − 3 ), Pb (117% a.c., 84 ng m − 3 ), Fe (116% a.c., 1 µg m − 3 ), Ca (109% a.c., 13 µg m − 3 ), K (108% a.c., 1 µg m − 3 ). Brake and tire wear, as well as road dust resuspension, are identified as significant sources of Cu, Pb, Zn, and Ni in both PM10 and PM2.5 (Celo et al., 2021 ). Sb is frequently utilized in brake pads as an indicator of brake wear emissions (Habre et al., 2021 ; Jandacka and Durcanska, 2021 ; Jiang et al., 2021 ). Sr is typically associated with the resuspension of soil dust, which has been identified as a major contributor to PM2.5 in numerous studies (Habre et al., 2021 ; Jandacka and Durcanska, 2021 ). Additionally, dust sources may also add Sr to PM2.5 (An et al., 2021 ). Na, Ca, Fe, and K were moderately enriched. In urban settings, Na and Ca are often derived from road dust (Hama et al., 2021 ). According to O’Day et al. ( 2022 ), Fe in PM2.5 likely originates from human activities, possibly as a result of abrasion. Similar to PM10, K in PM2.5 is frequently linked to combustion sources, such as vehicle emissions (Hama et al., 2021 ). Air masses in Groups 1 and 2 exhibited nearly identical concentrations of Ni (12 and 11 ng m − 3 , respectively) and Sb (58 and 59 ng m − 3 , respectively) in PM2.5. The presence of Ni in PM2.5 is predominantly associated with fossil fuel combustion and industrial emissions (Maciejczyk et al., 2021 ; Potter et al., 2021 ). The high loadings of both Ni (183%) and Sb (148%) in Group 2 air masses from the northwest, travelling through various regions, suggest a combination of long-range transport and local emissions (Yu et al., 2020 ). Air masses from the Mediterranean Sea (Group 3) were slightly loaded with Ca, Fe, K, and Mn in PM2.5, with above-average concentrations of 9%, 16%, 8%, and 10%, respectively. These elements may be of mixed natural and industrial origin (Sharma and Mandal, 2023 ; Tefera et al., 2021 ; Habre et al., 2021 ). Urban road dust and emissions from cement and metal processing located in the vicinity of Didouche Mourad (13 km north of the sampling site) are potential sources. PM2.5 originated mainly from southern Algeria, as their concentrations in group 4 represented 198% a.c. (average concentration). Desert air masses (Group 4) were highly enriched in trace elements in PM2.5, including Mg (233%), Na (227%), Al (200%, 2 µg m − 3 ), Ti (197%), Ba (181%), V (154%), Mn (150%), Sr (143%), and slightly enriched in Fe (116%), Ca (109%) and K (108%). Ti, Ba, V, Mn, and Sr were slightly enriched, whereas Mg, Na, Fe, Ca, and K were moderately enriched. The emission sources of these metal elements in PM2.5 can be attributed to both natural and anthropogenic sources, with a significant contribution from crustal or soil dust. Crustal or soil dust is likely the primary source of many of these elements, particularly Ti, Ba, Mn, Sr, and Ca, which are commonly found in the Earth's crust. The high enrichment of these elements in desert air masses suggests that wind-blown dust from arid regions is a major contributor (Nuchdang et al., 2023 ; Sharma et al., 2021 ). Moreover, industrial emissions and vehicular sources, particularly non-exhaust emissions from brake and tire wear, can be significant contributors of Ba, Mn, and other metals (Celo et al., 2021 ; Jandacka and Durcanska, 2021 ). Traffic-related sources may also play a significant role (Celo et al., 2021 ; Habre et al., 2021 ). Vanadium (V), with an enrichment factor of 3, suggests a mix of natural and anthropogenic sources. It is likely associated with fossil fuel combustion, particularly oil combustion, and industrial emissions (An et al., 2021 ; Barrera et al., 2023 ). The relatively low enrichment factor also indicates a significant contribution from crustal sources. Potential sources of Mn and Na include road dust and industrial processes, whereas major sources of Fe include crustal/soil/road dust, industrial emissions, and vehicular emissions (Barrera et al., 2023 ; Hama et al., 2021 ; Sharma and Mandal, 2023 ). Particulate matter generated by dust storms in Riyadh, Saudi Arabia, showed elevated levels of metals and elements, including K (Farahani et al., 2022 ). Mg may be derived from traffic-related emissions and resuspended road dust (Habre et al., 2021 ; Hama et al., 2021 ). 3.4. Correlation analysis insights 3.4.1. P10-related elements The identification of distinct background sources using cluster back-trajectory analysis directly reinforces the efficacy of combining this method with correlation analysis for nuanced source apportionment. The correlation matrices for PM10-bound metal element concentrations are presented in Table 8 . Group 1-related elements: Weak correlations between Na, Cu, and Pb suggest distinct origins. These elements share similar transport pathways but may be contributed by different contamination sources. Pb serves as an indicator of emissions from vehicle fuels. Cu contamination is predominantly from anthropogenic rather than natural sources, as its enrichment factor was 31. This is probably due to brake and tire wear. Cu emissions are continuous and mechanical (braking, traffic) (Figliuzzi et al., 2020 ), whereas Pb may come from sporadic or legacy sources. Furthermore, Pb concentrations may be influenced by soil resuspension, whereas Cu may show stronger patterns with rush hour traffic. Table 8 Correlations between ICP-AES-determined PM10-bound metal element concentrations Sb Sb Ba Al Ca Mg Fe K Na Cu Pb V Zn Ti Mn Ni Mo Sr - 0.39 0.20 0.13 0.21 0.38 0.26 -0.05 -0.18 -0.44 0.04 0.04 0.34 0.30 0.14 0.11 0.24 Ba 0.39 - 0.48 0.39 0.61 0.67 0.22 0.22 0.53 -0.17 0.42 0.30 0.78 0.34 0.30 0.02 0.58 Al 0.20 0.48 - 0.49 0.91 0.79 0.30 0.37 0.21 -0.08 0.20 0.24 0.86 0.74 0.22 0.38 0.76 Ca 0.13 0.39 0.49 - 0.65 0.70 0.77 0.19 0.19 0.03 -0.07 0.68 0.63 0.55 0.79 0.12 0.87 Mg 0.21 0.61 0.91 0.65 - 0.83 0.47 0.49 0.37 0.07 0.17 0.43 0.91 0.75 0.37 0.33 0.89 Fe 0.38 0.67 0.79 0.70 0.83 0.58 0.49 0.32 -0.31 0.15 0.56 0.92 0.71 0.56 0.20 0.84 K 0.26 0.22 0.30 0,77 0.47 0.58 0.28 -0.13 -0.15 -0.02 0.70 0.48 0.50 0.71 0.05 0.67 Na -0.05 0.22 0.37 0,19 0.49 0.49 0.28 0.32 0.01 0.12 0.42 0.40 0.26 0.00 0.13 0.34 Cu -0.18 0.53 0.21 0,19 0.37 0.32 -0.13 0.32 0.22 0.32 0.24 0.35 0.07 0.05 0.20 0.31 Pb -0.44 .-.,17 -0.08 0,03 0.07 -0.31 -0,15 0.01 0.22 -0.13 -0.09 -0.23 -0.35 -0.23 -0.15 -0.07 V 0.04 .0,4. 0.20 -0,07 0.17 0.15 -0,02 0.12 0.32 -0.13 0.14 0.19 -0.03 -0.14 0.38 0.07 Zn 0.04 0,3. 0.24 0,68 0.43 0.56 0,70 0.42 0.24 -0.09 0.14 0.45 0.40 0.78 -0.05 0.57 Ti 0.34 0.78 0.86 0,63 0.91 0.92 0,48 0.40 0.35 -0.23 0.19 0.45 0.73 0.49 0.15 0.86 Mn 0.30 0.34 0.74 0,55 0.75 0.71 0,50 0.26 0.07 -0.35 -0.03 0.40 0.73 0.55 0.52 0.75 Ni 0.14 0.30 0.22 0,79 0.37 0.56 0,71 0.00 0.05 -0.23 -0.14 0.78 0.49 0.55 -0.08 0.63 Mo 0.11 0.02 0.38 0,12 0.33 0.20 0,05 0.13 0.20 -0.15 0.38 -0.05 0.15 0.52 -0.08 0.26 Sr 0.24 0.58 0.76 0,87 0.89 0.84 0,67 0.34 0.31 -0.07 0.07 0.57 0.86 0.75 0.63 0.26 Group 2-related elements: It is important to note that while K is often associated with soil/crustal sources, its high EF (52) in this case suggests anthropogenic influence. This could be due to agricultural practices in the regions along the back trajectory (An et al., 2021 ; Oujidi et al., 2023 ). Group 3-related elements: Correlation coefficients for Ca, Fe, K, Zn, Ti, Mn, Ni, and Sr range from 0.4 to 0.86. The correlated elements reflect a blend of crustal dust, industrial emissions, and urban/agricultural activities along the northern transport pathways to the central Mediterranean. Elements such as Ti and Mn, which exhibit enrichment factors close to unity (EF ≈ 1), are indicative of a predominantly crustal origin (Fan et al., 2021 ). These elements are typical markers of mineral dust and are commonly associated with the resuspension of local soils or long-range transport of Saharan dust. Their strong correlations with other elements in the group further support the hypothesis that mineral dust is a significant component of particulate matter transported along these trajectories. Although Pb was associated with these elements in Group 3 (Sea), it was not correlated with any of them. Pb can have more localized sources, whereas other elements are influenced by long-range transport due to emission regulations. Stricter control of Pb emissions in many countries has reduced the long-range transport potential of Pb. Pb sources may also be more concentrated in urban centers than in other elements. In contrast, elements such as Ca (EF = 34), Fe (EF = 18), K (EF = 52), Zn (EF = 69), and Ni (EF = 15) displayed markedly elevated EF values, reflecting substantial anthropogenic or industrial contributions. The high EF of Ca is consistent with emissions from cement manufacturing in the industrial area of Didouche Mourad, which is located eight miles north of the sampling site. Both natural and anthropogenic sources contribute to Fe in PM10, including industrial activities and transportation-related emissions. Zheng et al. ( 2024 ) mentioned iron and steel production as a typical anthropogenic source of PM emissions. Transportation-related sources also play a significant role in Fe emissions. Wang et al. ( 2022 ) highlights the presence of Fe as a marker for brake wear near highways. The correlation between Fe, Ti, and Mn arises from their shared crustal origin, whereas the elevated EF of Fe reflects localized anthropogenic contributions superimposed on natural dust (Alghamdi, 2016 ). K, with an EF of 52, is strongly indicative of biomass burning and the application of K-rich fertilizers, both of which are common in agricultural regions. A high EF for Zn indicates sources such as traffic-related emissions (notably from tires and brake wear). Nickel enrichment is likely attributable to emissions from maritime shipping (particularly heavy-fuel oil combustion) and industrial metallurgy, both of which are significant along the Mediterranean transport corridors. Sr, with a modest EF of 2, likely reflects a combination of natural geological sources (such as celestite) and minor anthropogenic contributions, possibly from phosphate fertilizers or other industrial processes. The observed co-occurrence and correlation of these elements can be explained by the mixing of diverse sources along air mass trajectories. As air masses traverse urban, industrial, and agricultural regions, crustal dust (Ti and Mn) is entrained and mixed with emissions from cement manufacturing (Ca), dust resuspension (Fe and Ni), metallurgy/traffic (Ni), industry/traffic (Zn), and biomass burning/agriculture (K and Sr). The resulting PM reflects both the mineralogical signature of crustal materials and the superimposed influence of anthropogenic activities. Group 4-related elements: As shown in Table 8 , the PM10-related elements Al, Mg, Fe, Ti, Ca, Mn, and Sr exhibit varying degrees of correlation with one another, with coefficients ranging from 0.55 to 0.91. These well-correlated species were likely uplifted simultaneously during regional dust events from arid Saharan sources, confirming the dominance of crustal inputs under Group 4. Dust originates from weathered rocks, soils, and sediments in the Sahara Desert and has a significant impact on distant regions (Bayon et al., 2024 ). The coexistence of strong inter-element correlations and elevated EFs (Mg: 27, Fe: 18, Ca: 34) in PM10 can be explained by anthropogenic enhancement of natural dust during long-range transport or local pollution sources. Saharan dust events transport mineral particles that may contain anthropogenically altered crustal material. Agricultural practices (fertilizer use and plowing) increase the Mg/Ca content in soils. Local resuspension of polluted dust loaded with residues of Fe and Mg from brake and tire wear retains crustal correlations but carries anthropogenic signatures. Several studies support this dual-source explanation (Samayamanthula et al., 2020 ; Cheng et al., 2023 ; Sharma et al., 2021 ). Ba’s moderate correlation with Al/Mg/Fe/Ti/Sr (CC = 0.48–0.78) suggests partial co-transport with Saharan dust, but its lower correlation with Ca (CC = 0.39) indicates divergent sources or enrichment mechanisms. Ba occurs naturally in barite (BaSO₃) and witherite (BaCO₃) within mineral dust (Kresse et al., 2007 ). Its moderate correlation with crustal elements (Al, Mg, Fe, Ti, and Sr) reflects co-transport during dust events. However, an EF of 5 suggests minor anthropogenic enhancement over natural crustal levels. Ba is used in the production of paints and ceramics in the industrial region of Didouche Mourad (Tokonami et al., 2024 ). Emissions from these industries are mixed with resuspended crustal dust. Zn demonstrated weak to moderate correlations with Mg, Fe, Ti, Ni, Sr and Ca (CC = 0.43–0.78). Zn was only partially associated with Saharan dust, with its primary component derived from Group 3 backtrajectories. Na was partly associated with Saharan dust and partly with marine sources. Na was only moderately correlated with Mg, Ti and Fe (0.40–0.49). Sb, Cu, and V showed no notable correlation with any element. Traffic emissions are a significant source of Sb in the atmosphere (Jiang et al., 2021 ). A negative correlation between Sb and Cu excludes a common origin. It is likely that Cu was transported by Saharan dust, which traversed the cable industry located in Biskra, approximately 185 km southwest of the Zouaghi campus. The + 35% V concentration of the Sahara-oriented cluster arises from anthropogenic V onto dust particles during transport, whereas the lack of metal correlations underscores the isolation of V in oil combustion emissions. Saharan air often passes over oil-rich regions (Das et al., 2023 )d is probably emitted by the crude oil processing facility located in the Ouargla Province of Algeria, within the Hassi Messaoud district. This facility is situated approximately 500 km south of the sampling site. Such a source emits V-dominated aerosols without significant co-emissions of other metals. This isolates V in PM profiles. 3.4.2. PM2.5-related elements The correlation matrices for PM2.5-bound metal concentrations are shown in Table 9 . In Group 1-related elements, the strong correlations between Ca, K, and Sr (r = 0.71–0.80) suggest that soil is a critical natural source. The high EF of Ca suggests significant anthropogenic enhancement, likely due to dust resuspension from roads and disturbed soils (Gad et al., 2024; Wang et al., 2023 ). Ca, K, and Sr potentially originate from the use of fertilizers and agricultural amendments (Yin et al., 2022 ). Elements Zn, Cu, and Sr are quite well correlated (r = 0.61–0.72). Zn is a hallmark of tire wear (zinc oxide in rubber) and brake linings (Jandacka and Durcanska, 2021 ; Celo et al., 2021 ). Strong correlations with Cu (r = 0.72 in PM₂.₅) underscore shared urban emission pathways. A moderate correlation with Sr (r = 0.61) suggests partial adsorption onto crustal dust during transport, although the EF of Zn confirms minimal natural contributions. Cu is a key component of brake pads, particularly in heavy-duty vehicles. The correlation with Sr (r = 0.65) indicates mixing with crustal particles, although the low correlation of Cu with purely crustal elements (e.g., Al and Fe) confirms its urban provenance. The differentiation and specific enrichment factors of Ni, Na, Pb, and Sb suggest varied sources. The Mediterranean Sea is a major route for international shipping, which can contribute to Ni emissions through the combustion of heavy fuel oils in ship engines (Toscano, 2023 ). Given that its enrichment factor is close to that of sea salt, Na is primarily associated with marine aerosols (Toscano, 2023 ). Sb is used in brake linings and can be emitted from vehicular wear and tear (Woo et al., 2020 ). Vehicular traffic is a major source of Pb emissions. As leaded gasoline was still in use during the study period, Pb emissions occurred in addition to the wear and tear of vehicle components, such as brakes and tires. Road dust, which accumulates heavy metals deposited over time, further contributes to atmospheric Pb levels when resuspended by traffic and other disturbances (La Colla et al., 2021 ; Aguilera et al., 2021 ; Mostafa et al., 2023 ). In Group 2 (sea–continent–ocean), both Ni and Sb can originate from vehicular emissions. The wear and tear of brake pads often contribute significantly to Sb emissions, whereas Ni is emitted from both fuel combustion and vehicle exhaust systems (Khardi, 2024 ; Dziubak and Ślęzak, 2024 ). Elements in Group 3 (Sea-continent) Ca, Fe, K and Mn are quite well correlated (r = 0.61–0.78). Cement manufacturing and metal processing in the industrial area of Didouche Mourad, north of Constantine, are notable sources of Ca, Fe, Mn (Farahani et al., 2022 )d (Rivera Sasso et al., 2024 ). Elements within Group 4 (desert), Al, Ba, Fe, Ca, Mg, and K, exhibited significant correlations with each other, with correlation coefficients ranging from 0.55 to 0.91. These elements are predominantly found in mineral dust, including desert dust (Ramírez-Romero et al., 2021 ). Furthermore, they demonstrate strong correlations with Ti in both PM10 (0.48–0.92) and PM2.5 (0.70–0.92). The strong correlations observed among Al, Ba, Fe, Ca, Mg, and K in PM2.5 align with their status as major constituents of mineral dust, which is primarily composed of Al, Si, Ca, Fe, K, Mg, and Ti, among other typical crustal species (Ramírez-Romero et al., 2021 ). Mineral dust is influenced by local and regional dust resuspension processes as well as long-range transport episodes of desert dust outbreaks. The data presented in these tables also indicate a lack of correlation between Sb and other elements because Sb is primarily emitted through anthropogenic activities, particularly brake abrasion (Megido et al., 2016 ; Amato et al., 2011 ). High levels of Cu and Fe are emitted during brake wear because they are essential constituents of brake linings, pads, and discs (Charron et al., 2019 ). There was also no correlation between any of the elements and meteorological parameters. Table 9 Correlations between ICP-AES-determined PM2.5-bound metal element concentrations Sb Sb Ba Al Ca Mg Fe K Na Cu Pb V Zn Ti Mn Ni Mo Sr 1.00 -0.34 -0.14 -0,21 -0.15 -0.40 0.01 0.19 -0.19 0.14 -0.07 0.04 -0.24 -0.21 -0.08 0.40 -0.21 Ba -0.34 0.83 0,60 0.77 0.87 0.55 0.29 0.60 0.03 0.23 0.67 0.92 0.44 -0.06 -0.29 0.73 Al -0.14 0.83 0,68 0.91 0.86 0.70 0.50 0.52 -0.10 0.38 0.55 0.94 0.70 -0.12 -0.09 0.80 Ca -0.21 0.60 0.68 0.84 0.78 0.71 0.18 0.40 0.06 0.27 0.33 0.64 0.66 -0.07 -0.04 0.78 Mg -0.15 0.77 0.91 0,84 0.83 0.82 0.51 0.51 -0.06 0.34 0.52 0.87 0.73 -0.12 -0.07 0.88 Fe -0.40 0.87 0.86 0,78 0.83 0.61 0.31 0.57 0.01 0.28 0.54 0.88 0.65 -0.09 -0.21 0.74 K 001 0.55 0070 0,71 0.82 0.61 0.67 0.38 0.01 0.58 0.42 0.70 0.76 -0.10 0.07 0.80 Na 0.19 0.29 0.50 0,18 0.51 0.31 0.67 0.17 -0.08 0.63 0.34 0.45 0.41 -0.21 0.04 0.36 Cu -0.19 0.60 0.52 0,40 0.51 0.57 0.38 0.17 0.11 0.35 0.72 0.67 0.40 0.59 -0.09 0.65 Pb 0.14 0.03 -0.10 0,06 -0.06 0.01 0.01 -0.08 0.11 0.00 0.06 -0.02 -0.14 0.17 -0.33 0.01 V -0.07 0.23 0.38 0,27 0.34 0.28 0.58 0.63 0.35 0.00 0.39 0.28 0.31 0.23 -0.07 0.43 Zn 0.04 0.67 0.55 0,33 0.52 0.54 0.42 0.34 0.72 0.06 0.39 0.61 0.26 0.42 -0.26 0.61 Ti -0.24 0.92 0.94 0,64 0.87 0.88 0.70 0.45 0.67 -0.02 0.28 0.61 0.67 -0.06 -0.15 0.81 Mn -0.21 0.44 0,70 0,66 0.73 0.65 0.76 0.41 0.40 -0.14 0.31 0.26 0.67 0.05 0.13 0.69 Ni -0.08 -0.06 -0.12 -0,07 -0.12 -0.09 -0.10 -0.21 0.59 0.17 0.23 0.42 -0.06 0.05 0.06 0.24 Mo 0.40 -0.29 -0.09 -0,04 -0.07 -0.21 0.07 0.04 -0.09 -0.33 -0.07 -0.26 -0.15 0.13 0.06 -0.06 Sr -0.21 0.73 0.80 0,78 0.88 0.74 0.80 0.36 0.65 0.01 0.43 0.61 0.81 0.69 0.24 -0.06 Conclusion During the study period, the mean concentrations of PM10 and PM2.5 were 150 and 55 µg/m 3 , respectively. These levels significantly exceeded the World Health Organization's annual guideline values of 25 and 10 µg/m 3 for PM10 and PM2.5, respectively . The coarse particles, which were predominant, primarily consisted of soil dust. The coarse fraction PM10-2.5 contained high concentrations of metallic elements, particularly Sb, Ba, K, Na, Cu, V, Ti, Ni, and Mo. Distant source identification was accomplished through cluster back trajectories, resulting in four distinct groups. For each group, average concentrations of PM10 and 17 metal elements (Sb, Ba, Al, Ca, Mg, Fe, K, Na, Cu, Pb, V, Zn, Ti, Mn, Ni, Mo, and Sr) were calculated. This analysis enabled the estimation of excess pollutant concentrations within each trajectory group. Potential natural and anthropogenic sources in these directions were subsequently identified. Correlation coefficients between metal element concentrations and PM10, PM2.5, PM10-2.5, and meteorological parameters were determined, with coefficients above 0.5 considered significant. Pollutants with strong correlations were transported by air masses from the same back-trajectory group, indicating a common origin. The consistency between the back-trajectory clusters and correlation coefficients reinforces the effectiveness of correlation analysis in source apportionment studies of PM10-related metallic elements. This analysis serves as a valuable complementary tool to trajectory analysis for identifying the sources of PM and trace elements. Additionally, the enrichment factors distinguished between natural (sea or soil) and anthropogenic sources of the studied pollutants. The consistency of the results across these methods increased confidence in the identified sources of PM and its elemental components. The combination of back-trajectory analysis and correlation techniques proved to be a coherent and compatible methodology in this study. These approaches effectively complement each other, providing a comprehensive understanding of the air pollution sources and transport patterns in Constantine, Algeria. The synergy between these methods provides a comprehensive and nuanced perspective that is often underutilized in air quality studies. Our research validates the efficacy of correlation analysis and back-trajectory clustering as tools for source apportionment, directly addressing the study's aim of identifying PM sources in Constantine. This approach provides a cost-effective and relatively straightforward method for understanding the origins and composition of PM. By establishing strong correlations between specific elements and PM10 concentrations, researchers can infer common sources or formation processes. 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Environ Monit Assess 193(12). https://doi.org/10.1007/s10661-021-09584-z Zheng L, Wu D, Chen X, Li Y, Cheng A, Yi J, Li Q (2024) Chemical Profiles of Particulate Matter Emitted from Anthropogenic Sources in Selected Regions of China. Sci Data 11(1). https://doi.org/10.1038/s41597-024-04058-6 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted 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. 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18:10:27","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9418719/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9418719/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107098140,"identity":"fa7ffe5f-e210-4f5f-9ac5-d2ea0b7248d8","added_by":"auto","created_at":"2026-04-16 17:56:32","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":100165,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of the sampling site\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9418719/v1/c6f0d47d389569b6d9eec359.jpg"},{"id":107480858,"identity":"b51548c9-05af-4fce-82cb-4e7970bab3aa","added_by":"auto","created_at":"2026-04-22 02:13:59","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":191315,"visible":true,"origin":"","legend":"\u003cp\u003eThe temporal variability of the average daily PM\u003csub\u003e10\u003c/sub\u003e, PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10-2.5\u003c/sub\u003e concentrations\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9418719/v1/e18d251b60578a3d09449eaf.jpg"},{"id":107481007,"identity":"230c861c-c797-48a3-83e6-e93a7f298578","added_by":"auto","created_at":"2026-04-22 02:15:05","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":52733,"visible":true,"origin":"","legend":"\u003cp\u003eWind rose for the sampling period (05/04/2012 - 08/01/2013)\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9418719/v1/77a25ebbe8e015d25f83526a.jpg"},{"id":107098142,"identity":"2af4e790-ade4-4a8f-ad61-266c5318c241","added_by":"auto","created_at":"2026-04-16 17:56:32","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":21925,"visible":true,"origin":"","legend":"\u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e\u003csub\u003e\u003cem\u003e \u003c/em\u003e\u003c/sub\u003epollution rose\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9418719/v1/5ce7cf1d1d41dfa6cad1adaf.jpg"},{"id":107705072,"identity":"9e5cb51e-e0ff-43ea-ae3d-c0aac51803bb","added_by":"auto","created_at":"2026-04-24 09:07:32","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":35980,"visible":true,"origin":"","legend":"\u003cp\u003eMean 3-day back-trajectories for the main trajectory clusters at Constantine\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9418719/v1/a3f9c1c26c2282a609fd152d.jpg"},{"id":107708762,"identity":"9e680a65-0968-42bc-b0bb-ae2335e15d58","added_by":"auto","created_at":"2026-04-24 09:31:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1459961,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9418719/v1/be91249c-beec-4467-9420-38dc52c048b9.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eSource Apportionment of PM10 and PM2.5 at a Traffic Site in Constantine, Algeria: Combining Back-Trajectory Analysis and Correlation Studies\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThis study focuses on particulate matter (PM) pollution in Constantine, Algeria, to address the critical data gap in the region. Despite the known adverse effects of PM on health, comprehensive data on PM sources and concentrations are lacking in Constantine, Algeria, hindering effective air quality management. This study encompasses several key aspects of air quality monitoring and analysis with significant implications for public health and environmental policy. Air quality monitoring is essential for characterizing health effects and developing pollution abatement strategies (Gulia et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This provides crucial data on pollutant levels and sources, enabling the development of targeted air quality management plans. Furthermore, it facilitates the identification of pollution sources in urban areas and the assessment of transportation-induced pollution impacts (Bakirci, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This monitoring also enables the evaluation of air quality attainment and the assessment of health and economic benefits resulting from improvements (Qiu et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe context of air quality monitoring in Algeria, characterized by significant challenges such as limited national-level data, technical failures, and inconsistent monitoring, sets the stage for the importance of our study (Kerchich and Kerbachi, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). For instance, in Annaba, local air quality monitoring stations have not been used for years because of technical issues. Similarly, in Algiers, network data are unreliable, and the \"Sama Safia\" air quality index has not been communicated since 2009. Constantine, the focus of this study, has limited available research, highlighting the need for a more comprehensive investigation. Air quality monitoring in Algeria presents a complex landscape of challenges and gaps in data collection and dissemination. The absence of reliable national-level data, coupled with technical failures and inconsistent monitoring practices, has created a significant void in understanding the true state of air quality across the country.\u003c/p\u003e \u003cp\u003eThe focus on Constantine in this study is particularly significant given the limited available research on air quality in this region. This gap in knowledge highlights the need for more comprehensive investigations and emphasizes the importance of establishing robust and consistent monitoring systems across Algeria. By addressing these challenges and conducting in-depth studies, researchers can provide valuable insights into air pollution levels, sources, and impacts, which are essential for developing effective air quality management strategies and policies. Furthermore, such research can serve as a foundation for raising public awareness of air quality issues and promoting actions to improve environmental conditions and public health in Constantine and other cities across Algeria.\u003c/p\u003e \u003cp\u003ePrevious studies on PM pollution in Constantine have predominantly utilized sophisticated statistical tools for source apportionment, including factor analysis, PCA, and PMF (Terrouche et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Bencharif-Madani et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Naidja et al., 2022). Although effective in identifying and quantifying source contributions to PM levels, these methods require extensive datasets and complex computational efforts (Lee et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Building upon these previous efforts, our study takes a different approach to address the specific challenges and data limitations in Constantine. Our study adopted a streamlined yet effective approach employing correlation analysis combined with back-trajectory methods to provide a comprehensive understanding of PM sources. This choice is motivated by the need to develop a methodological framework that is not only effective in source identification but also adaptable to regions with limited air quality data infrastructure, such as Constantine. We posit that correlation analysis, when combined with back-trajectory analysis, provides a viable alternative for understanding the origins of PM and trace elements. By employing this method, we tracked the movement of air masses and linked them to both local and regional pollution sources. This approach provides valuable insights into the dynamics of PM pollution while potentially reducing the computational and data requirements, as only 20 samples were collected for PM10 and PM2.5. Notably, this study is the first to present the results of analyzing several metal elements in both PM2.5 and PM10. Examining multiple metals in these particulate matter sizes offers a more comprehensive evaluation of air quality, pollution origins, and possible health implications.\u003c/p\u003e \u003cp\u003eThe identification of both local and distant sources of particulate matter (PM) and trace elements is crucial for understanding air pollution dynamics and implementing effective mitigation strategies. Correlation analysis and back-trajectory clustering are two coherent and reliable methodologies that are often employed in source apportionment studies. The chosen methods provide a balance between analytical depth and practicality, allowing for meaningful insights without the need for extensive computational resources or large datasets. These methods allow researchers to differentiate between local emissions and long-range transport sources, thereby providing a comprehensive understanding of pollution sources.\u003c/p\u003e"},{"header":"2. Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Sampling location\u003c/h2\u003e \u003cp\u003eThe monitoring study was conducted at a traffic site situated on the University of Constantine Earth Sciences Campus \"Zouaghi Slimane\" (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) from February 21, 2021, to November 01, 2021, with biweekly data collection. National Road 79 (NR79) experiences heavy traffic, with approximately 20,000 vehicles passing daily. This high traffic volume poses a significant challenge to commuters and local authorities. The constant flow of vehicles leads to frequent congestion, especially during peak hours, resulting in increased travel times and reduced mobility for residents and travellers alike. Such heavy traffic also contributes to elevated levels of air and noise pollution in the surrounding areas, potentially impacting the health and well-being of nearby communities. Two low-volume samplers were used to monitor PM10 and PM2.5 simultaneously. Twenty filters were used for each PM size. The study site is located in the immediate vicinity of a heavy-traffic road on the southern outskirts of the city. This road connects the city of Constantine to nearby southern cities, such as Ain-Mlila, Batna, and Biskra.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Filter mineralization\u003c/h2\u003e \u003cp\u003eTo prepare the samples for analysis, we treated one-quarter of each air quartz 10\u0026rdquo;x8\u0026rdquo; filter with specific acids in a controlled environment. This process, known as filter mineralization, helps extract trace metals from the particles collected on the filters. One-quarter of each filter was digested with 1 mL HNO\u003csub\u003e3\u003c/sub\u003e and 2 mL HF in a closed PFA flask at 90\u0026deg;C for at least 8 h. After cooling, the containers were opened, and 1 mL of HClO\u003csub\u003e4\u003c/sub\u003e was added. The acids were completely evaporated by placing the PFA containers on a hot plate (Stuart SD 500) at 240\u0026deg;C. The remaining dry residue was dissolved with 2.5 mL of HNO\u003csub\u003e3\u003c/sub\u003e, diluted with distilled water (MilliQ) to 25 mL, to obtain 5% solutions of HNO\u003csub\u003e3\u003c/sub\u003e, which were centrifuged for 20 min at 3000 rpm. This method was described by Kemmouche et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Trace metals analysis\u003c/h2\u003e \u003cp\u003eThe solutions obtained were then analyzed by ICP-AES (IRIS Advantage Solutions TJA THERMO) for the elements (Sb, Ba, Al, Ca, Mg, K, Na, Fe) and by ICP-MS (X Series II THERMO) for the elements (Cu, Pb, V, Zn, Ti, Mn, Ni, Mo, Sr).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Spearman correlation coefficients and back-trajectory analysis\u003c/h2\u003e \u003cp\u003eSpearman\u0026rsquo;s correlation coefficient, a method that does not assume a normal distribution of data, measures the strength and direction of the relationship between two variables. Unlike Pearson's correlation, Spearman's correlation does not assume that both datasets are normally distributed and is used to identify the strength and direction of a monotonic relationship between paired data. In our study, Spearman correlation coefficients were calculated to explore the associations between the concentrations of different metallic elements in PM and meteorological parameters, providing insights into potential common sources and transport mechanisms of these pollutants.\u003c/p\u003e \u003cp\u003eBack-trajectory analysis and clustering are techniques widely used in atmospheric science to investigate the origin and transport pathways of air masses and their potential impact on air quality at receptor sites. Back-trajectory analysis involves calculating the path that an air parcel takes before reaching a specific location (receptor site) at a given time. This is typically done using models such as hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) (Cui et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Byčenkienė et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), which use meteorological data to trace the movement of air parcels backward in time, usually for several days. These trajectories provide information about potential source regions and transport pathways of air pollutants (Warner, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Daily 72-h back-trajectories arriving at 12:00 GMT at the sampling site were used. Clustering was then applied to group similar trajectories, thereby reducing the complexity of the dataset and identifying dominant airflow patterns (Ding et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Through this methodological framework, we not only address specific air quality challenges in Constantine but also provide a potential model for regions facing comparable environmental and data constraints.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Enrichment factors\u003c/h2\u003e \u003cp\u003eTo determine the sources (natural or human-made) of trace elements in atmospheric particulate matter (PM) and assess the level of anthropogenic impact, researchers have used enrichment factors (EFs). These EFs are calculated for trace elements in atmospheric PM by comparing them with local soil composition or the Earth\u0026rsquo;s crust. The EF value of element X can be determined using the following equation:\u003c/p\u003e \u003cp\u003e(Xi)/(Al)\u003csub\u003esample\u003c/sub\u003e/(Xi)(Al)\u003csub\u003ecrust\u003c/sub\u003e\u003c/p\u003e \u003cp\u003ewhereXi\" represents the element being examined. The subscripts \"sample\" and \"crust\" indicate the medium to which the concentration pertains. EFs are typically calculated by comparing trace element concentrations in PM to local soil composition or the Earth's crust (Chakraborty et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Moskovchenko et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Common reference elements used include Al, Sc, Ti, Li, Zr, and sometimes Fe and Mn, which are abundant in the Earth's crust. In the studies presented, Al was frequently used as the baseline element, with the Earth's crust composition representing the regional geochemistry (McLennan, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Samayamanthula et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Although various classifications of EF values of atmospheric PM exist in the literature, elements can be categorized based on their enrichment levels as follows: no enrichment (EF\u0026thinsp;\u003cem\u003e\u0026lt;\u003c/em\u003e\u0026thinsp;1), minor enrichment (EF\u0026thinsp;\u003cem\u003e\u0026lt;\u003c/em\u003e\u0026thinsp;10), moderate enrichment (10\u0026thinsp;\u003cem\u003e\u0026lt;\u003c/em\u003e\u0026thinsp;EF\u0026thinsp;\u003cem\u003e\u0026lt;\u003c/em\u003e\u0026thinsp;100), and significant enrichment (EF\u0026thinsp;\u003cem\u003e\u0026gt;\u003c/em\u003e\u0026thinsp;100) (Bouziane et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 PM concentrations and ratios\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1. PM and metallic elements concentrations overview\u003c/h2\u003e \u003cp\u003eThis study investigated PM\u003csub\u003e10\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e concentrations, major components, trace elements, and source identification at a traffic site near Zouaghi in Constantine, Algeria, from February 2021 to November 2021. The mean PM\u003csub\u003e10\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e concentrations were 150\u0026thinsp;\u0026plusmn;\u0026thinsp;103 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e and 55\u0026thinsp;\u0026plusmn;\u0026thinsp;42 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e, respectively, exceeding the World Health Organization air quality guidelines (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Coarse particles predominated, reflecting the resuspension of soil dust (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Trace elements were concentrated in the PM10\u0026ndash;2.5 fraction. Four distinct distant sources were identified using cluster back-trajectory analysis. PM\u003csub\u003e10\u003c/sub\u003e and 17 metal elements (Sb, Ba, Al, Ca, Mg, Fe, K, Na, Cu, Pb, V, Zn, Ti, Mn, Ni, Mo, and Sr) were enriched in air masses originating from the Sahara, Mediterranean Sea, the Atlantic Oceana and Spain, suggesting contributions from natural and anthropogenic sources. Correlation analysis revealed that well-correlated pollutants were transported by air masses from the same back-trajectory groups, indicating a common origin. Enrichment factors helped distinguish between natural (sea and soil) and anthropogenic sources.\u003c/p\u003e \u003cp\u003eTo illustrate the variability and trends in PM concentrations, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents a detailed breakdown of daily PM\u003csub\u003e10\u003c/sub\u003e, PM\u003csub\u003e2.5\u003c/sub\u003e, and PM\u003csub\u003e10\u0026minus;2.5\u003c/sub\u003e levels. Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e present the statistical results of PM\u003csub\u003e10\u003c/sub\u003e, PM\u003csub\u003e2.5\u003c/sub\u003e, and metallic element concentrations in PM\u003csub\u003e10\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e, respectively. The selection of these metals for analysis was based on their potential to indicate specific pollution sources, aligning with the study's aim of differentiating between natural and anthropogenic contributions to PM levels.\u003c/p\u003e \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e concentrations ranged between 31 and 287 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e and between 3 and 153 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e, respectively. The results obtained show that the daily PM\u003csub\u003e10\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e levels varied widely (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). On average, PM10 concentrations were triple those of PM2.5, indicating a significant predominance of coarse particles. Coarse particles reflect naturally occurring dust and soil dustgenerated by wind and traffic (Swet et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Coarse particles reflect naturally occurring dust and soil dust generated by wind and traffic (Swet et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). PM\u003csub\u003e2.5\u003c/sub\u003e represents dust of anthropogenic origin, including particles resulting from the condensation of exhaust gases (Zheng et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Bessagnet et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\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\u003ePM\u003csub\u003e10\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e concentrations measured at the study area level\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNumber of samples\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage concentration (\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum concentration (\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e153\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimum concentration (\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandard deviation (\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42\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\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\u003eStatistical data of the concentrations of metallic elements in PM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"18\"\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=\"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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eElement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003e\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"11\" nameend=\"c18\" namest=\"c8\"\u003e \u003cp\u003eng/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMg\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eK\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eBa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCu\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eZn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eTi\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003eMn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e \u003cp\u003eNi\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c17\"\u003e \u003cp\u003eMo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c18\"\u003e \u003cp\u003eSr\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e209,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e74,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e22,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e180,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e10,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e143,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e143,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e24,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e19,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e22,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e22,7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMax.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e108,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e27,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e631,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e293,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e47,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e323,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e32,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e604,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e588,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e71,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e278,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e58,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e77,2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMin.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e65,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e4,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSd dev.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e142,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e84,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e16,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e82,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e7,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e142,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e163,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e20,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e61,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e18,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e21,2\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\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 data of the concentrations of metallic elements in PM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"18\"\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=\"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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eElement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003e\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"11\" nameend=\"c18\" namest=\"c8\"\u003e \u003cp\u003eng/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMg\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eK\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eBa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCu\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eZn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eTi\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003eMn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e \u003cp\u003eNi\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c17\"\u003e \u003cp\u003eMo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c18\"\u003e \u003cp\u003eSr\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e40,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e21,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e71,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e50,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e39,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e9,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e5,8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e3,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e7,3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMax.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e188,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e127,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e47,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e186,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e7,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e157,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e299,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e47,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e72,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e25,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e29,6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMin.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e37,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0,0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSd dev.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e53,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e37,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e16,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e32,1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2,5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e61,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e82,3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e14,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e17,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e7,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e9,0\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\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2. PM\u003csub\u003e2.5\u003c/sub\u003e/PM\u003csub\u003e10\u003c/sub\u003e ratios\u003c/h2\u003e \u003cp\u003eThe relatively low PM2.5/PM10 particle ratio of 36.56% suggests that coarse particles (PM10) constitute a larger proportion of particulate matter in the air than fine particles (PM2.5). This implies that local sources of dust and larger particulates may be more significant contributors to air pollution in this area than fine particles from combustion or long-range transport. Natural sources, such as sea spray or windblown soil, could be important contributors to particulate matter composition. Similarly, in the United Arab Emirates, PM2.5/PM10 ratios ranged between 0.29 and 0.49 across industrial, urban, and suburban areas, which is characteristic of arid and semi-arid environments where natural processes such as sand particle uplift contribute significantly to air pollution (Abuelgasim and Farahat, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This fraction contains resuspended dust from traffic, particles from braking and tire wear, mechanical parts, and road abrasion (Celo et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Habre et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the PM\u003csub\u003e2.5\u003c/sub\u003e/PM\u003csub\u003e10\u003c/sub\u003e ratios ​​for the trace elements.\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\u003eTrace elements ratios (PM\u003csub\u003e2.5\u003c/sub\u003e/PM\u003csub\u003e10\u003c/sub\u003e)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"18\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMg\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eK\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCu\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eZn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eTi\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003eMn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e \u003cp\u003eNi\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c17\"\u003e \u003cp\u003eMo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c18\"\u003e \u003cp\u003eSr\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRatio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e \u003cp\u003e0.32\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\u003eThe trace element ratios (PM2.5/PM10) provided in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e indicate that the coarse fraction (PM10-2.5) was predominant for all trace elements, with varying degrees of significance. This observation is consistent with the findings of several previous studies. In urban environments, road transport is a major source of PM that directly affects pedestrians (Jandacka and Durcanska, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The coarse fraction (PM10-2.5) shows a significant relationship with elements such as Cu, Sb, Ba, Ca, Cr, Fe, Mg, and Al, which are primarily associated with non-exhaust emissions, such as road dust resuspension and tire/brake wear (Celo et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Jandacka and Durcanska, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This fraction was significant for Sb, Ba, K, Na, Cu, V, Ti, Ni, and Mo.\u003c/p\u003e \u003cp\u003eThe fraction contained in PM\u003csub\u003e2.5\u003c/sub\u003e is derived from exhaust gases or anthropogenic activities (heating, industry, and agriculture) (Li et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zheng et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Rai et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.1.3. Enrichment factors\u003c/h2\u003e \u003cp\u003eThe values of the EF are listed in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\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\u003eEnrichment factors computed according to crustal abundances in PM10 and PM2.5\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"17\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePM10\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMg\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eK\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCu\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eZn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eTi\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eMn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003eNi\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e \u003cp\u003eMo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c17\"\u003e \u003cp\u003eSr\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e35642\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e362\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e69\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e \u003cp\u003e520\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c17\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePM2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e2\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 PM10, the average EFs for metal elements ranged from 1.2 (for Ti) to 35642 (for Sb). In PM2.5, the average EFs for metal elements ranged from 0.8 (for Ti) to 16755 (for Sb). In PM10, the samples exhibited minor enrichment for Ti, Mn, V, Sr, and Ba; moderate enrichment for Cu, Zn, Ni, Ca, Mg, Fe, K, and Na; and significant enrichment for Pb, Mo, and Sb. In PM2.5, the samples exhibited no enrichment for Ti, minor enrichment for V, Mn, Sr, Ba, Mg, Fe, K and Na, moderate enrichment for Cu, Zn, Ni and Ca, and significant enrichment for Pb, Mo and Sb. The EFs for metallic elements in both PM10 and PM2.5 fractions displayed a wide range, indicating a mixture of crustal and anthropogenic sources.\u003c/p\u003e \u003cp\u003eTi consistently shows very low EFs in both PM10 and PM2.5 (1.2 and 0.8, respectively), affirming its crustal origin. It is often used as a reference element in such studies owing to its geochemical stability and minimal anthropogenic emissions. Ca, Mg, Fe, K, and Na are primarily of crustal origin but may include an anthropogenic component, especially in coarser particles. Moderate enrichment (EF\u0026thinsp;~\u0026thinsp;10\u0026ndash;100) for elements such as Cu, Zn, Ni, and Ca suggests a blend of natural and anthropogenic sources, such as traffic-related abrasion (brake and tire wear), industrial emissions, and resuspended road dust. Significant enrichment (EF ˃ 100) for Pb, Mo, and Sb in both PM10 and PM2.5 strongly indicates anthropogenic sources, such as vehicular exhaust, industrial combustion, smelting, and brake wear. The extremely high EF for Sb in both size fractions (up to 35,642) clearly identifies it as a tracer of traffic emissions (e.g., brake pads) or specific industrial activities. The notable enrichment of toxic metals, such as Pb and Sb, in PM2.5 is particularly concerning because of their enhanced ability to penetrate the respiratory tract and exert harmful health effects.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Influence of meteorological parameters on PM levels\u003c/h2\u003e \u003cp\u003eThe meteorological data obtained, illustrating the impact of wind direction on pollutant levels, underscores the significance of regional climate factors in our analysis of PM source apportionment. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows a wind rose for the study period. Meteorological data were obtained from a local airport weather station. The prevailing winds were northwest, with a frequency of 13.12%, and west, with a frequency of 11.04%. These winds were relatively weak because their velocity was in the range of 0.5\u0026ndash;5.7 m/s. The range of calm winds corresponded to a speed of less than 0.5 m/s; these winds represented 34.4% of the time. Pollution roses (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) indicate that higher PM\u003csub\u003e10\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e concentrations were associated with northern and southwestern winds, that is, from near-field sources such as heavy road traffic of approximately 20,000 vehicles/day.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Source identification through back trajectory analysis\u003c/h2\u003e \u003cp\u003eBack-trajectory analysis involves tracing the path that air masses have travelled to reach the study site using advanced modeling based on meteorological data. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e illustrates the 3-day average back-trajectories modelled by HYSPLIT. Four average back-trajectories representing 20 modelled paths of plots arriving at the sampling site were identified. Each sampling day was the subject of a back-trajectory arriving at the study site at 12h00. The predominant modelled pathway crosses the western side of the Mediterranean Sea and eastern Spain (45%). The second most representative pathway originated in the Algerian Sahara (25%). The third trajectory originated in the north and crossed a large portion of the Mediterranean Sea (20%). The fourth and last modelled pathways arose from the Atlantic (10%).\u003c/p\u003e \u003cp\u003eBack-trajectories were split according to their origin (desert, sea, and sea/continent) and the distance travelled for PM10 (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) and PM2.5 (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Tables\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and \u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e summarize the findings on PM concentrations and associated metallic elements categorized by their source trajectories. The latter were identified as follows:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e\u0026bull; Group 1 and 2 from the northwest\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e\u0026bull; Group 3 from the Mediterranean Sea\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e\u0026bull; Group 4 from the Sahara\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eWith regard to PM\u003csub\u003e10\u003c/sub\u003e, Group 1 contributed to Na, Cu, Pb and Mo; Group 2 to K only; Group 3 to Ca, Fe, K, Pb, Zn, Ti, Mn, Ni, Sr; and Group 4 to PM, Al, Ca, Mg, Fe, Na, Sb, Ba, Cu, V, Zn, Ti, Mn, Mo and Sr.\u003c/p\u003e \u003cp\u003eAs for PM\u003csub\u003e2.5\u003c/sub\u003e, Group 1 of back-trajectories was associated with Ca, K, Na, Sb, Cu, Pb, Zn, Ni, and Sr; Group 2 with Sb and Ni; Group 3 with Ca, Fe, K, and Mn; and Group 4 with PM, Al, Ca, Mg, Fe, K, Na, Ba, V, Ti, Mn, Mo and Sr.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAverage concentrations of PM10 and metallic elements in each group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"19\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGroupe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003e\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"11\" nameend=\"c19\" namest=\"c9\"\u003e \u003cp\u003eng/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMg\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eK\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eBa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eCu\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eZn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003eTi\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e \u003cp\u003eMn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c17\"\u003e \u003cp\u003eNi\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c18\"\u003e \u003cp\u003eMo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c19\"\u003e \u003cp\u003eSr\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 (Sea-continent)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e25\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e203\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e\u003cb\u003e26\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 (Sea-continent-ocean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 (Sea)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e40\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e196\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e227\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e165\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e35\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e\u003cb\u003e75\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e\u003cb\u003e29\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4 (Desert)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e239\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e32\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e276\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e153\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e29\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e274\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e33\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e\u003cb\u003e24\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e\u003cb\u003e36\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasured average concentration (a.c.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"19\"\u003eEnriched metal elements are in bold\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAverage concentrations of PM2.5 and metallic elements in each group\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"19\"\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=\"left\" 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=\"left\" 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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGroupe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003e\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"11\" nameend=\"c19\" namest=\"c9\"\u003e \u003cp\u003eng/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMg\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eK\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eBa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eCu\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003ePb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eZn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003eTi\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e \u003cp\u003eMn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c17\"\u003e \u003cp\u003eNi\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c18\"\u003e \u003cp\u003eMo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c19\"\u003e \u003cp\u003eSr\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1 (Sea-continent)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e58\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e84\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e64\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e\u003cb\u003e12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e\u003cb\u003e9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2 (Sea-continent-ocean)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e59\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e\u003cb\u003e11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 (Sea)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4 (Desert)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e109\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e38\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e77\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeasured average concentration (a.c.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e2,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c19\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"19\"\u003eEnriched metal elements are in bold\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1. PM10 source identification\u003c/h2\u003e \u003cp\u003eAir masses belonging to Group 1 from the northwest, which accounted for 45% of the air masses reaching the site, crossed a small part of southeastern Spain, the Mediterranean Sea, and northern Algeria. They carried Pb, Cu, and Mo in PM\u003csub\u003e10\u003c/sub\u003e with percentages relative to the average concentration (a.c.) and concentrations of (112% and 203 ng/m\u003csup\u003e3\u003c/sup\u003e), (109% and 25 ng/m\u003csup\u003e3\u003c/sup\u003e), and (113% and 26 ng/m\u003csup\u003e3\u003c/sup\u003e), respectively. Substantial emissions of Pb, Cu, and Mo were predominantly attributed to the wear of vehicle tires and dust from brake linings, elements associated with high traffic density near the roundabout situated to the west of the sampling site (Larsen et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Cu may also originate from metallurgical, chemical, and electrical cable industries in B\u0026eacute;ja\u0026iuml;a, 155 km northwest of the study site. PM10-associated lead (Pb) is present in vehicular emissions, as leaded gasoline was still utilized in Algeria in 2021. The use of leaded gasoline was discontinued in October 2021.\u003c/p\u003e \u003cp\u003eAir masses originating from the northwest and crossing the Atlantic Ocean, Portugal, Spain, and the Mediterranean Sea (Group 2) represented 10% of all air masses arriving at the study site. These air masses were uniquely overloaded with K (159% a. c., 11 ng/m\u003csup\u003e3\u003c/sup\u003e), likely originating from areas with intensive agricultural activities northwest of Constantine (Zhai et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAir masses originating from the north, designated as Group 3, constituted 20% of the air masses reaching the study site and were enriched in various metal elements within PM10. These air masses traverse the industrial region of Didouche Mourad, which is situated 13 km north of the sampling location (Ali-Khodja et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Bouziane et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This was the case for, in order of relative abundance, Ni (375% a. c.), Zn (159% a. c.), Mn (140% a. c.), Sr (126% a. c.), Ti (115% a. c.), K (190% a. c.), Ca (132% a. c.), Pb (108% a. c.), and Fe (105% a. c.). Zn and Ti may be related to urban road dust (Pey et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Zn is likely to have a predominantly anthropogenic origin. Zn is likely to have a predominantly anthropogenic origin and is often associated with industrial activities, traffic emissions, and combustion processes (Bimenyimana et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Rajput et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In contrast, Ti, along with Mn, is primarily associated with crustal or natural sources (Calzolai et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). For Ni and Fe, the sources were likely a combination of industrial emissions and natural sources. Mill\u0026aacute;n-Mart\u0026iacute;nez et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) mentioned that toxic trace elements derived from industrial emissions showed higher concentrations during North African dust events. Additionally, Zhao et al. (\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) identified Ni as being associated with automotive emissions, which could contribute to its presence in air masses. Furthermore, Mostafa et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) identified Pb originating from vehicle exhaust in urban environments (Mostafa et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Pb was emitted in exhaust gases because of its use in fuels in Algeria during the study period. It was completely banned in October 2021.\u003c/p\u003e \u003cp\u003ePM10 originated mainly from southern Algeria, as its concentrations in Group 4 were 159% a.c. (average concentration). Desert air masses (group 4) which accounted for 25% of back-trajectories were enriched in trace metal elements in PM10 such as Al (167% a. c.), Mg (192% a. c. and 2 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), Fe (158% a. c. and 3 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), Ca (106% a. c. and 32 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), Ba (204% a. c.), Ti (190% a. c.), Sr (157% a. c.), and to a lesser degree V (135% a. c.), Mn (132% a. c.), Sb (132% a. c.), Cu (126% a. c.), Mo (104% a. c.) and Zn (104% a. c.). The average enrichment of trace metals in Saharan PM10 was in the following order: Ba\u0026thinsp;\u0026gt;\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eMg\u003c/span\u003e\u0026gt;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eTi\u003c/span\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAl\u003c/span\u003e\u0026gt;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eFe\u003c/span\u003e\u0026thinsp;\u0026gt;\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSr\u003c/span\u003e\u0026thinsp;\u0026gt;\u0026thinsp;V\u0026thinsp;\u0026gt;\u0026thinsp;Sb/\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eMn\u003c/span\u003e\u0026thinsp;\u0026gt;\u0026thinsp;Cu\u0026gt;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCa\u003c/span\u003e\u0026thinsp;\u0026gt;\u0026thinsp;Zn/Mo. Elements such as Al, Mg, Fe, Ti, and Ca are often associated with mineral dust, which can be transported over long distances (Sharma et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Ba, Ti, Sr, and V were slightly enriched, indicating their predominant crustal origin. Mg, Fe, Cu, Ca, and Zn have mixed natural and anthropogenic origins. Sb and Mo had strictly anthropogenic origin enrichment factors of 35642 and 520, respectively. Mart\u0026iacute;n-Cruz and G\u0026oacute;mez-Losada (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) identified non-exhaust vehicle emissions as a source of PM10, which could include brake and tire wear particles that may contain calcium compounds. Roadside dust can contain particulates enriched with Mg, Fe, Cu, and Zn owing to the degradation of mechanical vehicular parts, tire wear, and combustion processes (Cowan et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Their presence in PM10 may also suggest a significant contribution from natural sources, such as wind-blown dust, soil resuspension, and crustal weathering (Mill\u0026aacute;n-Mart\u0026iacute;nez et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSoutherly air masses did not significantly contribute to the transport of trace elements Pb and Ni. The PM\u003csub\u003e10\u003c/sub\u003e-related Pb concentrations were more affected by Groups 1 (Sea\u0026ndash;continent) and 3 (Sea) of the back trajectories, and Ni by Group 3 (Sea), as they were higher than the average measured levels throughout the sampling period.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2. PM2.5 source identification\u003c/h2\u003e \u003cp\u003eGroup 1 air masses carried in PM\u003csub\u003e2.5\u003c/sub\u003e trace elements, such as Na (227% a.c., 1 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), Ni (200% a.c., 12 ng m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), Cu (167% a.c., 10 ng m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), Sb (145% a.c., 58 ng m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), Sr (129% a.c., 9 ng m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), Zn (128% a.c., 64 ng m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), Pb (117% a.c., 84 ng m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), Fe (116% a.c., 1 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), Ca (109% a.c., 13 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), K (108% a.c., 1 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e). Brake and tire wear, as well as road dust resuspension, are identified as significant sources of Cu, Pb, Zn, and Ni in both PM10 and PM2.5 (Celo et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Sb is frequently utilized in brake pads as an indicator of brake wear emissions (Habre et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Jandacka and Durcanska, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Jiang et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Sr is typically associated with the resuspension of soil dust, which has been identified as a major contributor to PM2.5 in numerous studies (Habre et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Jandacka and Durcanska, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, dust sources may also add Sr to PM2.5 (An et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Na, Ca, Fe, and K were moderately enriched. In urban settings, Na and Ca are often derived from road dust (Hama et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). According to O\u0026rsquo;Day et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), Fe in PM2.5 likely originates from human activities, possibly as a result of abrasion. Similar to PM10, K in PM2.5 is frequently linked to combustion sources, such as vehicle emissions (Hama et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAir masses in Groups 1 and 2 exhibited nearly identical concentrations of Ni (12 and 11 ng m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, respectively) and Sb (58 and 59 ng m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, respectively) in PM2.5. The presence of Ni in PM2.5 is predominantly associated with fossil fuel combustion and industrial emissions (Maciejczyk et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Potter et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe high loadings of both Ni (183%) and Sb (148%) in Group 2 air masses from the northwest, travelling through various regions, suggest a combination of long-range transport and local emissions (Yu et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAir masses from the Mediterranean Sea (Group 3) were slightly loaded with Ca, Fe, K, and Mn in PM2.5, with above-average concentrations of 9%, 16%, 8%, and 10%, respectively. These elements may be of mixed natural and industrial origin (Sharma and Mandal, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Tefera et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Habre et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Urban road dust and emissions from cement and metal processing located in the vicinity of Didouche Mourad (13 km north of the sampling site) are potential sources.\u003c/p\u003e \u003cp\u003ePM2.5 originated mainly from southern Algeria, as their concentrations in group 4 represented 198% a.c. (average concentration). Desert air masses (Group 4) were highly enriched in trace elements in PM2.5, including Mg (233%), Na (227%), Al (200%, 2 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), Ti (197%), Ba (181%), V (154%), Mn (150%), Sr (143%), and slightly enriched in Fe (116%), Ca (109%) and K (108%). Ti, Ba, V, Mn, and Sr were slightly enriched, whereas Mg, Na, Fe, Ca, and K were moderately enriched. The emission sources of these metal elements in PM2.5 can be attributed to both natural and anthropogenic sources, with a significant contribution from crustal or soil dust. Crustal or soil dust is likely the primary source of many of these elements, particularly Ti, Ba, Mn, Sr, and Ca, which are commonly found in the Earth's crust. The high enrichment of these elements in desert air masses suggests that wind-blown dust from arid regions is a major contributor (Nuchdang et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sharma et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moreover, industrial emissions and vehicular sources, particularly non-exhaust emissions from brake and tire wear, can be significant contributors of Ba, Mn, and other metals (Celo et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Jandacka and Durcanska, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Traffic-related sources may also play a significant role (Celo et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Habre et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Vanadium (V), with an enrichment factor of 3, suggests a mix of natural and anthropogenic sources. It is likely associated with fossil fuel combustion, particularly oil combustion, and industrial emissions (An et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Barrera et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The relatively low enrichment factor also indicates a significant contribution from crustal sources. Potential sources of Mn and Na include road dust and industrial processes, whereas major sources of Fe include crustal/soil/road dust, industrial emissions, and vehicular emissions (Barrera et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Hama et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Sharma and Mandal, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Particulate matter generated by dust storms in Riyadh, Saudi Arabia, showed elevated levels of metals and elements, including K (Farahani et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Mg may be derived from traffic-related emissions and resuspended road dust (Habre et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hama et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Correlation analysis insights\u003c/h2\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1. P10-related elements\u003c/h2\u003e \u003cp\u003eThe identification of distinct background sources using cluster back-trajectory analysis directly reinforces the efficacy of combining this method with correlation analysis for nuanced source apportionment. The correlation matrices for PM10-bound metal element concentrations are presented in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eGroup 1-related elements: Weak correlations between Na, Cu, and Pb suggest distinct origins. These elements share similar transport pathways but may be contributed by different contamination sources. Pb serves as an indicator of emissions from vehicle fuels. Cu contamination is predominantly from anthropogenic rather than natural sources, as its enrichment factor was 31. This is probably due to brake and tire wear. Cu emissions are continuous and mechanical (braking, traffic) (Figliuzzi et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), whereas Pb may come from sporadic or legacy sources. Furthermore, Pb concentrations may be influenced by soil resuspension, whereas Cu may show stronger patterns with rush hour traffic.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelations between ICP-AES-determined PM10-bound metal element concentrations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"18\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eSb\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMg\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eK\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCu\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eZn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eTi\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003eMn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e \u003cp\u003eNi\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c17\"\u003e \u003cp\u003eMo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c18\"\u003e \u003cp\u003eSr\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.18\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.44\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c18\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBa\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.61\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.67\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e0.78\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e\u003cb\u003e0.58\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAl\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.91\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.79\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e 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\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.49\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\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.70\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.77\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e0.68\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e0.63\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e0.55\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e0.79\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e\u003cb\u003e0.87\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMg\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.61\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.91\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.65\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.83\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e0.91\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e0.75\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e\u003cb\u003e0.89\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFe\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.67\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.79\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.70\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.83\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.58\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e0.56\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e0.92\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e0.71\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e0.56\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e\u003cb\u003e0.84\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eK\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,77\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.58\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e0.70\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e0.50\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e0.71\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e\u003cb\u003e0.67\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNa\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCu\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.53\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePb\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.-.,17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0,15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e-0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e-0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e-0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.0,4.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0,07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0,02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eZn\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,68\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.56\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0,70\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e0.78\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e\u003cb\u003e0.57\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTi\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.78\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.86\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,63\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.91\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.92\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e0.73\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e\u003cb\u003e0.86\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMn\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.74\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,55\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.75\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.71\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0,50\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e0.73\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e0.55\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e\u003cb\u003e0.52\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e\u003cb\u003e0.75\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNi\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,79\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.56\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0,71\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e0.78\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e0.55\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e\u003cb\u003e0.63\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0,05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSr\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.58\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.76\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,87\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.89\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.84\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0,67\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e0.86\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e0.75\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e\u003cb\u003e0.63\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eGroup 2-related elements: It is important to note that while K is often associated with soil/crustal sources, its high EF (52) in this case suggests anthropogenic influence. This could be due to agricultural practices in the regions along the back trajectory (An et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Oujidi et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGroup 3-related elements: Correlation coefficients for Ca, Fe, K, Zn, Ti, Mn, Ni, and Sr range from 0.4 to 0.86. The correlated elements reflect a blend of crustal dust, industrial emissions, and urban/agricultural activities along the northern transport pathways to the central Mediterranean. Elements such as Ti and Mn, which exhibit enrichment factors close to unity (EF\u0026thinsp;\u0026asymp;\u0026thinsp;1), are indicative of a predominantly crustal origin (Fan et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These elements are typical markers of mineral dust and are commonly associated with the resuspension of local soils or long-range transport of Saharan dust. Their strong correlations with other elements in the group further support the hypothesis that mineral dust is a significant component of particulate matter transported along these trajectories. Although Pb was associated with these elements in Group 3 (Sea), it was not correlated with any of them. Pb can have more localized sources, whereas other elements are influenced by long-range transport due to emission regulations. Stricter control of Pb emissions in many countries has reduced the long-range transport potential of Pb. Pb sources may also be more concentrated in urban centers than in other elements.\u003c/p\u003e \u003cp\u003eIn contrast, elements such as Ca (EF\u0026thinsp;=\u0026thinsp;34), Fe (EF\u0026thinsp;=\u0026thinsp;18), K (EF\u0026thinsp;=\u0026thinsp;52), Zn (EF\u0026thinsp;=\u0026thinsp;69), and Ni (EF\u0026thinsp;=\u0026thinsp;15) displayed markedly elevated EF values, reflecting substantial anthropogenic or industrial contributions. The high EF of Ca is consistent with emissions from cement manufacturing in the industrial area of Didouche Mourad, which is located eight miles north of the sampling site.\u003c/p\u003e \u003cp\u003eBoth natural and anthropogenic sources contribute to Fe in PM10, including industrial activities and transportation-related emissions. Zheng et al. (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) mentioned iron and steel production as a typical anthropogenic source of PM emissions. Transportation-related sources also play a significant role in Fe emissions. Wang et al. (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) highlights the presence of Fe as a marker for brake wear near highways. The correlation between Fe, Ti, and Mn arises from their shared crustal origin, whereas the elevated EF of Fe reflects localized anthropogenic contributions superimposed on natural dust (Alghamdi, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eK, with an EF of 52, is strongly indicative of biomass burning and the application of K-rich fertilizers, both of which are common in agricultural regions. A high EF for Zn indicates sources such as traffic-related emissions (notably from tires and brake wear). Nickel enrichment is likely attributable to emissions from maritime shipping (particularly heavy-fuel oil combustion) and industrial metallurgy, both of which are significant along the Mediterranean transport corridors. Sr, with a modest EF of 2, likely reflects a combination of natural geological sources (such as celestite) and minor anthropogenic contributions, possibly from phosphate fertilizers or other industrial processes.\u003c/p\u003e \u003cp\u003eThe observed co-occurrence and correlation of these elements can be explained by the mixing of diverse sources along air mass trajectories. As air masses traverse urban, industrial, and agricultural regions, crustal dust (Ti and Mn) is entrained and mixed with emissions from cement manufacturing (Ca), dust resuspension (Fe and Ni), metallurgy/traffic (Ni), industry/traffic (Zn), and biomass burning/agriculture (K and Sr). The resulting PM reflects both the mineralogical signature of crustal materials and the superimposed influence of anthropogenic activities.\u003c/p\u003e \u003cp\u003eGroup 4-related elements: As shown in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, the PM10-related elements Al, Mg, Fe, Ti, Ca, Mn, and Sr exhibit varying degrees of correlation with one another, with coefficients ranging from 0.55 to 0.91. These well-correlated species were likely uplifted simultaneously during regional dust events from arid Saharan sources, confirming the dominance of crustal inputs under Group 4. Dust originates from weathered rocks, soils, and sediments in the Sahara Desert and has a significant impact on distant regions (Bayon et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe coexistence of strong inter-element correlations and elevated EFs (Mg: 27, Fe: 18, Ca: 34) in PM10 can be explained by anthropogenic enhancement of natural dust during long-range transport or local pollution sources. Saharan dust events transport mineral particles that may contain anthropogenically altered crustal material. Agricultural practices (fertilizer use and plowing) increase the Mg/Ca content in soils. Local resuspension of polluted dust loaded with residues of Fe and Mg from brake and tire wear retains crustal correlations but carries anthropogenic signatures. Several studies support this dual-source explanation (Samayamanthula et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Cheng et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sharma et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBa\u0026rsquo;s moderate correlation with Al/Mg/Fe/Ti/Sr (CC\u0026thinsp;=\u0026thinsp;0.48\u0026ndash;0.78) suggests partial co-transport with Saharan dust, but its lower correlation with Ca (CC\u0026thinsp;=\u0026thinsp;0.39) indicates divergent sources or enrichment mechanisms. Ba occurs naturally in barite (BaSO₃) and witherite (BaCO₃) within mineral dust (Kresse et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Its moderate correlation with crustal elements (Al, Mg, Fe, Ti, and Sr) reflects co-transport during dust events. However, an EF of 5 suggests minor anthropogenic enhancement over natural crustal levels. Ba is used in the production of paints and ceramics in the industrial region of Didouche Mourad (Tokonami et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Emissions from these industries are mixed with resuspended crustal dust.\u003c/p\u003e \u003cp\u003eZn demonstrated weak to moderate correlations with Mg, Fe, Ti, Ni, Sr and Ca (CC\u0026thinsp;=\u0026thinsp;0.43\u0026ndash;0.78). Zn was only partially associated with Saharan dust, with its primary component derived from Group 3 backtrajectories. Na was partly associated with Saharan dust and partly with marine sources. Na was only moderately correlated with Mg, Ti and Fe (0.40\u0026ndash;0.49). Sb, Cu, and V showed no notable correlation with any element. Traffic emissions are a significant source of Sb in the atmosphere (Jiang et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A negative correlation between Sb and Cu excludes a common origin. It is likely that Cu was transported by Saharan dust, which traversed the cable industry located in Biskra, approximately 185 km southwest of the Zouaghi campus. The +\u0026thinsp;35% V concentration of the Sahara-oriented cluster arises from anthropogenic V onto dust particles during transport, whereas the lack of metal correlations underscores the isolation of V in oil combustion emissions. Saharan air often passes over oil-rich regions (Das et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)d is probably emitted by the crude oil processing facility located in the Ouargla Province of Algeria, within the Hassi Messaoud district. This facility is situated approximately 500 km south of the sampling site. Such a source emits V-dominated aerosols without significant co-emissions of other metals. This isolates V in PM profiles.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.4.2. PM2.5-related elements\u003c/h2\u003e \u003cp\u003eThe correlation matrices for PM2.5-bound metal concentrations are shown in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e. In Group 1-related elements, the strong correlations between Ca, K, and Sr (r\u0026thinsp;=\u0026thinsp;0.71\u0026ndash;0.80) suggest that soil is a critical natural source. The high EF of Ca suggests significant anthropogenic enhancement, likely due to dust resuspension from roads and disturbed soils (Gad et al., 2024; Wang et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Ca, K, and Sr potentially originate from the use of fertilizers and agricultural amendments (Yin et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eElements Zn, Cu, and Sr are quite well correlated (r\u0026thinsp;=\u0026thinsp;0.61\u0026ndash;0.72). Zn is a hallmark of tire wear (zinc oxide in rubber) and brake linings (Jandacka and Durcanska, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Celo et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Strong correlations with Cu (r\u0026thinsp;=\u0026thinsp;0.72 in PM₂.₅) underscore shared urban emission pathways. A moderate correlation with Sr (r\u0026thinsp;=\u0026thinsp;0.61) suggests partial adsorption onto crustal dust during transport, although the EF of Zn confirms minimal natural contributions. Cu is a key component of brake pads, particularly in heavy-duty vehicles. The correlation with Sr (r\u0026thinsp;=\u0026thinsp;0.65) indicates mixing with crustal particles, although the low correlation of Cu with purely crustal elements (e.g., Al and Fe) confirms its urban provenance.\u003c/p\u003e \u003cp\u003eThe differentiation and specific enrichment factors of Ni, Na, Pb, and Sb suggest varied sources. The Mediterranean Sea is a major route for international shipping, which can contribute to Ni emissions through the combustion of heavy fuel oils in ship engines (Toscano, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Given that its enrichment factor is close to that of sea salt, Na is primarily associated with marine aerosols (Toscano, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Sb is used in brake linings and can be emitted from vehicular wear and tear (Woo et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Vehicular traffic is a major source of Pb emissions. As leaded gasoline was still in use during the study period, Pb emissions occurred in addition to the wear and tear of vehicle components, such as brakes and tires. Road dust, which accumulates heavy metals deposited over time, further contributes to atmospheric Pb levels when resuspended by traffic and other disturbances (La Colla et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Aguilera et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mostafa et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Group 2 (sea\u0026ndash;continent\u0026ndash;ocean), both Ni and Sb can originate from vehicular emissions. The wear and tear of brake pads often contribute significantly to Sb emissions, whereas Ni is emitted from both fuel combustion and vehicle exhaust systems (Khardi, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Dziubak and Ślęzak, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eElements in Group 3 (Sea-continent) Ca, Fe, K and Mn are quite well correlated (r\u0026thinsp;=\u0026thinsp;0.61\u0026ndash;0.78). Cement manufacturing and metal processing in the industrial area of Didouche Mourad, north of Constantine, are notable sources of Ca, Fe, Mn (Farahani et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)d (Rivera Sasso et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eElements within Group 4 (desert), Al, Ba, Fe, Ca, Mg, and K, exhibited significant correlations with each other, with correlation coefficients ranging from 0.55 to 0.91. These elements are predominantly found in mineral dust, including desert dust (Ram\u0026iacute;rez-Romero et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, they demonstrate strong correlations with Ti in both PM10 (0.48\u0026ndash;0.92) and PM2.5 (0.70\u0026ndash;0.92). The strong correlations observed among Al, Ba, Fe, Ca, Mg, and K in PM2.5 align with their status as major constituents of mineral dust, which is primarily composed of Al, Si, Ca, Fe, K, Mg, and Ti, among other typical crustal species (Ram\u0026iacute;rez-Romero et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Mineral dust is influenced by local and regional dust resuspension processes as well as long-range transport episodes of desert dust outbreaks. The data presented in these tables also indicate a lack of correlation between Sb and other elements because Sb is primarily emitted through anthropogenic activities, particularly brake abrasion (Megido et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Amato et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). High levels of Cu and Fe are emitted during brake wear because they are essential constituents of brake linings, pads, and discs (Charron et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). There was also no correlation between any of the elements and meteorological parameters.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelations between ICP-AES-determined PM2.5-bound metal element concentrations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"18\"\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=\"left\" 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=\"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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eSb\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMg\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eK\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCu\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePb\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eZn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eTi\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003eMn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e \u003cp\u003eNi\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c17\"\u003e \u003cp\u003eMo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c18\"\u003e \u003cp\u003eSr\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.34\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0,21\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.40\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.19\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003e-0.24\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c16\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c18\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBa\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.83\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,60\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.77\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.87\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.55\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.60\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e0.67\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e0.92\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e-0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e\u003cb\u003e0.73\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAl\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.83\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,68\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.91\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.86\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.70\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.50\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.52\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e0.55\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e0.94\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e0.70\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e\u003cb\u003e0.80\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCa\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.60\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.68\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.84\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.78\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.71\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e0.64\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e0.66\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e\u003cb\u003e0.78\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMg\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.77\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.91\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,84\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.83\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.82\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.51\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.51\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e0.52\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e0.87\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e0.73\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFe\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.87\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.86\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,78\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.83\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.61\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.57\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e0.54\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e0.88\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e0.65\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e\u003cb\u003e0.74\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eK\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.55\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0070\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,71\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.82\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.61\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.67\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e0.58\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e0.70\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e0.76\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e\u003cb\u003e0.80\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNa\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.50\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.51\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.67\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e0.63\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e0.34\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCu\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.60\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.52\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.51\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.57\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e0.72\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePb\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e-0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,27\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\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.58\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.63\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eZn\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.67\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.55\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.52\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.54\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.72\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e0.61\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e-0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e\u003cb\u003e0.61\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTi\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.92\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.94\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,64\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.87\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.88\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.70\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.67\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e0.67\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e\u003cb\u003e0.81\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMn\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0,70\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,66\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.73\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.65\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.76\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e0.67\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e\u003cb\u003e0.69\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNi\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0,07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.59\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.40\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=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0,04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e-0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e-0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e-0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c18\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSr\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.73\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.80\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,78\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.88\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.74\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.80\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0.65\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e0.61\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e \u003cp\u003e\u003cb\u003e0.81\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e \u003cp\u003e\u003cb\u003e0.69\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c16\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c17\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c18\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eDuring the study period, the mean concentrations of PM10 and PM2.5 were 150 and 55 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e, respectively. These levels significantly exceeded the World Health Organization's annual guideline values of 25 and 10 \u0026micro;g/m\u003csup\u003e3 for PM10 and PM2.5, respectively\u003c/sup\u003e. The coarse particles, which were predominant, primarily consisted of soil dust. The coarse fraction PM10-2.5 contained high concentrations of metallic elements, particularly Sb, Ba, K, Na, Cu, V, Ti, Ni, and Mo.\u003c/p\u003e \u003cp\u003eDistant source identification was accomplished through cluster back trajectories, resulting in four distinct groups. For each group, average concentrations of PM10 and 17 metal elements (Sb, Ba, Al, Ca, Mg, Fe, K, Na, Cu, Pb, V, Zn, Ti, Mn, Ni, Mo, and Sr) were calculated. This analysis enabled the estimation of excess pollutant concentrations within each trajectory group. Potential natural and anthropogenic sources in these directions were subsequently identified.\u003c/p\u003e \u003cp\u003eCorrelation coefficients between metal element concentrations and PM10, PM2.5, PM10-2.5, and meteorological parameters were determined, with coefficients above 0.5 considered significant. Pollutants with strong correlations were transported by air masses from the same back-trajectory group, indicating a common origin. The consistency between the back-trajectory clusters and correlation coefficients reinforces the effectiveness of correlation analysis in source apportionment studies of PM10-related metallic elements. This analysis serves as a valuable complementary tool to trajectory analysis for identifying the sources of PM and trace elements.\u003c/p\u003e \u003cp\u003eAdditionally, the enrichment factors distinguished between natural (sea or soil) and anthropogenic sources of the studied pollutants. The consistency of the results across these methods increased confidence in the identified sources of PM and its elemental components.\u003c/p\u003e \u003cp\u003eThe combination of back-trajectory analysis and correlation techniques proved to be a coherent and compatible methodology in this study. These approaches effectively complement each other, providing a comprehensive understanding of the air pollution sources and transport patterns in Constantine, Algeria. The synergy between these methods provides a comprehensive and nuanced perspective that is often underutilized in air quality studies.\u003c/p\u003e \u003cp\u003eOur research validates the efficacy of correlation analysis and back-trajectory clustering as tools for source apportionment, directly addressing the study's aim of identifying PM sources in Constantine. This approach provides a cost-effective and relatively straightforward method for understanding the origins and composition of PM. By establishing strong correlations between specific elements and PM10 concentrations, researchers can infer common sources or formation processes. This information is vital for developing targeted pollution control strategies and enhancing our understanding of atmospheric chemistry and transport mechanisms. The agreement between the correlation analysis and back-trajectory clustering further emphasizes the value of integrating multiple analytical techniques in comprehensive air quality assessments.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbuelgasim A, Farahat A (2020) Investigations on PM10, PM2.5, and Their Ratio over the Emirate of Abu Dhabi, United Arab Emirates. 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Sci Data 11(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41597-024-04058-6\u003c/span\u003e\u003cspan address=\"10.1038/s41597-024-04058-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University Frères mentouri-Constantine 1","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"PM10, PM2.5, Back-trajectory analysis, Correlation studies, Trace elements, Source apportionment, Enrichment factors","lastPublishedDoi":"10.21203/rs.3.rs-9418719/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9418719/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigated PM10 and PM2.5 concentrations, major components, trace elements, and source identification at a traffic site near Zouaghi in Constantine, Algeria, from February 2021 to November 2021. The mean PM10 and PM2.5 concentrations were 150\u0026thinsp;\u0026plusmn;\u0026thinsp;103 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e and 55\u0026thinsp;\u0026plusmn;\u0026thinsp;42 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e, respectively, exceeding WHO air quality guidelines (2021). Coarse particles predominated, reflecting soil dust resuspension, and trace elements were concentrated in the PM10-2.5 fraction. Four distinct sources were identified using cluster back-trajectory analysis: the Sahara, Mediterranean Sea, northwest, and Atlantic Ocean. PM10 and 17 metal elements (Sb, Ba, Al, Ca, Mg, Fe, K, Na, Cu, Pb, V, Zn, Ti, Mn, Ni, Mo, and Sr) were enriched in air masses from the Sahara, Mediterranean Sea, and northwest, suggesting contributions from natural and anthropogenic sources. Correlation analysis revealed that well-correlated pollutants were transported by air masses from the same back-trajectory groups, indicating a common origin. Enrichment factors helped distinguish between natural and anthropogenic sources. The consistency between correlation analysis and back-trajectory clustering reinforced the effectiveness of these methods for source apportionment of PM10-related metallic elements. This study validated the efficacy of correlation analysis and back-trajectory clustering as tools for identifying PM sources in Constantine, providing a cost-effective approach for understanding PM origins and composition. These findings enabled the proposal of targeted interventions, such as traffic flow improvements and industrial emission controls, to mitigate pollution levels and inform public health officials in developing strategies to protect population health.\u003c/p\u003e","manuscriptTitle":"Source Apportionment of PM10 and PM2.5 at a Traffic Site in Constantine, Algeria: Combining Back-Trajectory Analysis and Correlation Studies","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-16 17:56:28","doi":"10.21203/rs.3.rs-9418719/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"09ca5297-b667-4a8c-92ec-869af2127fbe","owner":[],"postedDate":"April 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":66321205,"name":"Atmospheric Sciences"},{"id":66321206,"name":"Environmental Chemistry"}],"tags":[],"updatedAt":"2026-04-16T17:56:28+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-16 17:56:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9418719","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9418719","identity":"rs-9418719","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}