Digital Epidemiology of Allergic Conjunctivitis and Rhinitis in Saudi Arabia: Association with Air Pollutions and Meteorological Factors

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AlHarthi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8767450/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 Background climate changes and air pollutions are potential drivers for allergic conjunctivitis and rhinitis. In arid region such as Saudi Arabia, limited studies have explored the influence of environmental factors on health diseases. Purpose this study aims to investigate seasonality of allergic conjunctivitis and rhinitis, and to explore association with air pollutants and climate changes in Saudi Arabia using Google Trends (GTs). Methods Quasi-Poisson Generalized Additive Models (GAMs) combined with Distributed Lag Non-linear Model (DLNM) were used to explore the effect of air pollutions and climate changes on population-level interest on allergic conjunctivitis and rhinitis. Results Both allergic diseases demonstrated seasonal pattern, with peak in autumn and spring. Relative risks (RRs) for ocular allergy given increase in PM2.5, PM10, NH₃, CO, and BC concentration were at lag 0 (RR = 1.114, 95% CI: 1.020–1.217), (RR = 1.079, 95% CI: 1.007–1.155), and (RR = 1.286, 95% CI: 1.081–1.530), (RR = 1.011, 95% CI: 1.003–1.019), and (RR = 1.506, 95% CI: 1.118–2.028), respectively. Ocular allergy may be more heighten to ambient environmental irritants. In contrast, allergic rhinitis search interest was governed primarily by seasonality. Conclusion this study highlight association between both allergic conjunctivitis and rhinitis with air pollution and climate. This can offer evidence for regional air quality policy decisions and implementing preventive measures to reduce the risk of allergies burden. Epidemiology Ophthalmology Otorhinolaryngology Google Trends Seasonality Digital epidemiology Air pollution Allergic Conjunctivitis Allergic Rhinitis Figures Figure 1 Figure 2 Figure 3 Introduction Allergic conjunctivitis and allergic rhinitis are frequently associated diseases that have common pathophysiological features 1 . It is multifactorial disease characterized by specific ocular and nasal symptoms. These allergies are common worldwide and affect 10–40% of the population 2 , 3 . The prevalence of allergic disorder has been increasing over past decades partly attributed to emissions from industrial activities and substantial changes in environment 4 , and It has negative impact on individual’s quality of life 5 . Allergic rhinitis is subclassified traditionally into seasonal or perennial 6 . While allergic conjunctivitis is subclassified according to Ocular Allergy group of the European Academy of Allergy and Clinical Immunology (EAACI) into ocular allergy or ocular nonallergic hypersensitivity, with first type include seasonal allergic, perennial allergic, atopic keratoconjunctivitis, and vernal conjunctivitis 2 . Allergic diseases are commonly triggered by allergens. Airhorn allergens such as pollen and fungal air spore are considered important cause of allergic diseases. In Saudi Arabia, there are two pollen season, one in autumn and second in spring with weeds as the most common type 7 . In Kuwait, fungal bioaerosols exhibit seasonal pattern with two consistent similar fungal composition during period March–May and November–December 8 . However, counting allergens/ pollen content in the air need sophisticated methods. Meteorological and air pollution data may reflect interest findings. These factors can alter the immunogenicity and abundance of aeroallergens 9 . Limited studies in Saudi Arabia have explored the effect of air pollution on health. Recently, Cardiopulmonary Morbidity risk was significantly increased with exposure to Black Carbon (BC) and the Secondary Aerosol (p-SO 4 2− and p-NO 3 − ) 10 . Our study aims to explore seasonality and association between allergic conjunctivitis and rhinitis search trends with climatic variables and air quality indicators in Saudi Arabia. By using Google Trends (GTs) 11 , which is an open-access tool that reflects public search behaviour, offers a real-time proxy for population-level interest and potential disease burden. Materials and Methods Google Trends, air pollution and Meteorological Factors Data We extracted monthly aggregated normalized relative search volume data (RSV) (scale 0-100) from Google Trends as CSV file in September 2025 for the search term in Arabic language “حساسية العين” for eye allergy (allergic conjunctivitis), and “حساسية الانف” for allergic rhinitis and its unified topic. The location is set in Saudi Arabia for the period from January 2011 to December 2024, for web searches and without category selection. Google Trends does categorize and aggregate related searches, variations in spelling and related terms into unified topic. Therefore, the data we used for the selected term likely to capture wide range of queries. For historical environmental factors data, we collected monthly average temperature (°C), relative humidity (%), and accumulated precipitation at national level for Saudi Arabia. Data were retrieved from ERA5 0.25-degree dataset in World Bank Group website ( https://climateknowledgeportal.worldbank.org/ ). Air pollutants data were retrieved from EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, a collaboration between the European Commission, Joint Research Centre (JRC), the International Energy Agency (IEA) version 2024 European Commission, JRC (Datasets) 12 . Statistical Analysis All data management and statistical analyses were performed using R Software (Version 4.5.2). The packages mgcv (for Generalized Additive Models) and dlnm (for Distributed Lag Non-linear Models) were the primary analytical tools. A p-value of 0.05 was considered statistically significant. Initial data quality checks were conducted to identify missing values and outliers. Descriptive statistics (mean, median, standard deviation, minimum, and maximum) were calculated for the monthly Google Trends Search Volume Indices for Allergic Rhinitis (AR) and Allergic Conjunctivitis (AC), as well as for all meteorological and air pollution variables. Normality of the data distributions was assessed using the Shapiro-Wilk test. To detect potential collinearity between environmental predictors, a Spearman correlation matrix was constructed. Given that Google Trends data are non-negative count-like indices and exhibited over-dispersion in the preliminary analysis, a Quasi-Poisson distribution was selected as the link function, consistent with standard time-series epidemiological methods 13 . Generalized Additive Models (GAM) were employed to analyze the non-linear associations. GAM extends generalized linear models by utilizing non-parametric smoothing functions to control for confounding factors and simulate the complex non-linear correlation between environmental exposures and health endpoints. The general form of the GAM is defined as follows: Environmental exposures often exhibit delayed effects (lag effects). To capture both the linear exposure-response and the temporal lag-response simultaneously, we integrated the Distributed Lag Non-linear Model (DLNM) framework into the GAM. As described by Gasparrini (2011) and applied by Yang et al. 13,14 , the DLNM constructs a "cross-basis" function (cb) defined by two dimensions: the predictor-response dimension and the lag-response dimension. Our core statistical model controlled for long-term time trends (to account for increasing internet usage or awareness over the decade) and cyclic seasonality. The specific equation used to estimate the Relative Risk (RR) of search volume associated with environmental factors. Ethical Consideration Google trends dataset is open public-source. Climatic data from the World Bank Group and air pollution from European Commission are under Creative Commons Attribution 4.0 International (CC BY 4.0) Therefore, No institutional Review Board (IRB) approval was needed. Results Data Description Figures 1 and 2 show the monthly Google Trends search volume indices, meteorological variables, and air pollution indicators over study period. Meteorological variables reflected arid characteristic of dry and hot climate in Saudi Arabia. Average monthly temperatures showed a wide seasonal range (mean = 26.56°C, SD = 7.21°C), Relative humidity remained low overall (mean = 30.81%), while precipitation was minimal and highly skewed (mean = 4.72 mm). Normality and Stationarity Assessment Shapiro-Wilk test detailed shown in Supplementary Table S1, the null hypothesis of normality was rejected for the majority of variables (p < 0.05), except for carbon monoxide (CO) and organic carbon (OC) did not show statistically significant departures from normality. Stationarity of the time-series variables was assessed using the Augmented Dickey–Fuller (ADF) test, with results summarized in Supplementary Table S2. Google Trends series were identified as non-stationary, including AR topic (p = 0.065), AR term (p = 0.054), and AC topic (p = 0.465). In contrast, AC term was stationary (p = 0.011). Among environmental factors, average temperature, relative humidity, precipitation, CO, NOx, and SO₂ were stationary (p ≤ 0.05), consistent with their predominantly seasonal and cyclic patterns. Conversely, particulate matter (PM₁₀ and PM₂.₅), black carbon (BC), ammonia (NH₃), non-methane volatile organic compounds (NMVOC), and organic carbon (OC) were non-stationary (p > 0.05). These non-stationary behaviors likely reflect changes in emission sources, urbanization, or industrial activity. Correlation Analysis The complete correlation matrix is provided in Supplementary Table S3. Strong positive correlations were observed between topic-based and term-based search metrics for the same condition (e.g., AR topic vs. AR term: r = 0.877, p < 0.001). In addition, moderate to strong correlations were identified between allergic rhinitis and allergic conjunctivitis search indices (e.g., AR term vs. AC term: r = 0.653, p < 0.001), suggesting shared temporal drivers and concurrent public interest patterns for upper airway and ocular allergic conditions in Saudi Arabia. Meteorological variables exhibited a strong inverse correlation between average temperature and relative humidity (r = − 0.891, p < 0.001). Meteorology was also strongly linked to air pollution levels; for example, higher temperatures were associated with increased concentrations of carbon monoxide (r = 0.463, p < 0.001) and nitrogen oxides (r = 0.249, p = 0.003), while relative humidity showed inverse associations with several gaseous pollutants. Air pollution indicators demonstrated pronounced clustering. Very strong correlations were observed among particulate and carbonaceous pollutants, including PM₁₀, PM₂.₅, black carbon (BC), organic carbon (OC), and NOx (e.g., PM₁₀ vs. PM₂.₅: r = 0.995; BC vs. PM₂.₅: r = 0.892; BC vs. NOx: r = 0.845; all p < 0.001). Trend and Seasonality Analysis The results of Generalized Additive models are summarized in Table 1 . Allergic rhinitis (topic), the smooth term for long-term trend was statistically significant (EDF = 8.44, F = 46.59, p < 0.001), indicating a complex, non-linear temporal changes of search interest over the study period. The seasonal component was statistically significant (EDF = 8.10, F = 14.90, p < 0.001). For allergic conjunctivitis (topic), the trend was statistically significant (EDF = 6.50, F = 37.38, p < 0.001). while the seasonal component was statistically significant (EDF = 4.65, F = 1.63, p = 0.003) (Fig. 3). Table 1 Results of Generalized Additive Models (GAM) Testing Trend and Seasonality Dependent Variable (SVI) Trend Effect Seasonality Effect Model Fit EDF F-Statistic P-Value EDF F-Statistic P-Value Deviance Expl. (%) Allergic Rhinitis (Topic) 8.444 46.59 < 0.001 *** 8.099 14.9 < 0.001 *** 83% Allergic Rhinitis (Term) 6.910 22.72 < 0.001 *** 8.101 13.01 < 0.001 *** 73% Allergic Conjunctivitis (Topic) 6.500 37.38 < 0.001 *** 4.652 1.63 0.003 ** 67% Allergic Conjunctivitis (Term) 4.637 41.77 < 0.001 *** 4.771 1.08 0.037 * 64% Note: *** p < 0.001, ** p < 0.01, * p < 0.05. Seasonal Trend Decomposition (STL) of allergic disease search series are shown in (Figures S1 to S4). Allergic rhinitis displays a seasonal structure with dominant peak occurs in October; A secondary, broader elevation is observed during (March-May). Allergic conjunctivitis follows similar seasonal pattern. Associations with Meteorological Variables The result of single environmental factors variable of Generalized Additive Models (GAMs) presented in Table 2 . For allergic conjunctivitis search terms; mean temperature had statistically significant non-linear association (EDF = 2.24, F = 4.86, p = 0.005). This indicates search activity increased during specific temperature ranges. RH was significantly associated with conjunctivitis search term (F = 4.63, p = 0.033). The non-linear nature suggests that both low and high humidity extremes may exacerbate ocular irritation. Precipitation demonstrated statistically significant association was only for conjunctivitis topic (EDF = 2.29, F = 2.73, p = 0.033). In contrast, temperature, relative humidity and Precipitation failed to observe statistically significant association with allergic rhinitis. Table 2 Univariate Screening of Environmental Predictors for Allergic Disease Search Volumes GAM models adjusted for Trend and Cyclic Seasonality Predictor AR term AR topic AC term AC topic EDF F value P value EDF F value P value EDF F value P value EDF F value P value RH 1.00 1.39 0.240 1.00 0.10 0.748 1.00 4.63 0.033* 2.76 5.78 0.001* Precipitation 1.00 3.36 0.069 1.00 0.78 0.378 1.00 1.23 0.270 2.29 2.73 0.033* Average Temp 2.52 2.77 0.087 2.02 1.40 0.278 2.24 4.86 0.005* 2.44 7.86 0.000* BC 1.00 0.15 0.702 1.00 0.01 0.910 1.00 8.89 0.003* 1.00 3.99 0.048* CO 1.88 1.41 0.221 1.74 1.15 0.294 1.00 8.97 0.003* 1.00 9.02 0.003* NH3 1.00 0.00 0.967 1.62 1.48 0.275 1.00 2.78 0.098 1.00 7.59 0.007* NMVOC 1.93 2.05 0.112 2.05 1.35 0.223 1.98 2.96 0.039* 1.09 0.07 0.827 NOx 1.00 0.31 0.576 1.00 0.01 0.941 1.43 4.51 0.017 1.00 2.02 0.158 OC 2.44 4.43 0.008* 2.37 3.93 0.011* 1.27 10.29 0.001* 1.32 11.40 0.001* PM10 1.00 0.03 0.863 1.06 0.03 0.960 1.00 6.41 0.012* 1.00 4.77 0.031* PM2.5 1.00 0.00 0.958 1.03 0.10 0.823 1.00 6.93 0.009* 1.00 5.46 0.021* SO2 1.81 1.10 0.382 2.23 2.89 0.055 1.00 3.38 0.068 1.00 1.20 0.275 * Significant association at p < 0.05. Associations with Air Pollution Variables Associations with Allergic Rhinitis (AR) Organic carbon (OC) was significantly associated with allergic rhinitis searches. OC demonstrated significant non-linear effects for AR search terms (EDF = 2.44, F = 4.43, p = 0.008). Other pollutants (PM10, PM2.5, NOx, SO₂, CO, and BC) were not statistically significant after adjustment for temporal structure (all p > 0.20). Associations with Allergic Conjunctivitis (AC) Both PM2.5 and PM10 were significant associated with conjunctivitis terms (F = 6.93, p = 0.009), and (F = 6.41, p = 0.012), respectively. CO was strongly associated with conjunctivitis term (p = 0.003), while BC reached significance for conjunctivitis terms (p = 0.003). OC emerged as the single strongest pollutant predictor, with highly significant associations for conjunctivitis terms (F = 10.29, p = 0.001). Nitrogen oxides and NMVOCs showed weaker but still notable effects, achieving statistical significance for conjunctivitis terms (NOx: p = 0.017; NMVOC: p = 0.039) but not for topic-based searches. Delayed Effects and Lag Structure Analysis The result of all lag-response estimates of key factors on allergic disease search volume are reported in Table S4. Lagged Environmental Effects on Allergic Rhinitis (AR) DLNM analysis revealed minimal evidence of either immediate or delayed responsiveness to environmental exposures once seasonal and long-term confounding were rigorously controlled. For nearly all predictors and lag periods (Lag 0–Lag 6), the estimated confidence intervals encompassed unity, providing little support in the context of rhinitis-related search behavior. Contrary to expectations for respiratory outcomes, neither PM10 nor PM2.5 demonstrated significant effects at any lag. For AR search terms, the immediate effect (Lag 0) for PM10 was RR = 0.979 (95% CI: 0.898–1.068), and for PM2.5 was RR = 0.979 (95% CI: 0.873–1.098), with similarly null findings persisting through Lag 3 and beyond. Gaseous Pollutants and Other Constituents: nitrogen oxides (NOx), sulfur dioxide (SO₂), and carbon monoxide (CO), failed to exhibit significant effect. Ammonia (NH₃) showed relative risks for AR topics at Lag 0 and Lag 1 (RR ≈ 1.10), but wide confidence intervals (e.g., 0.947–1.279) rendered these associations statistically non-significant. The impact of Organic carbon (OC) was not significant. Lagged Environmental Effects on Allergic Conjunctivitis (AC) Both PM10 and PM2.5 had significant immediate effects. One-unit increase in PM2.5 concentration was associated with an 11.4% increase in search volume at Lag 0 (RR = 1.114, 95% CI: 1.020–1.217). PM10 had similar pattern, with a significant Lag 0 effect (RR = 1.079, 95% CI: 1.007–1.155). NH₃ exposure at Lag 0 resulted in a 28.6% increase in search volume (RR = 1.286, 95% CI: 1.081–1.530). Carbon Monoxide (CO) showed significant correlation at Lag 0 (RR = 1.011, 95% CI: 1.003–1.019). the effect of Black Carbon (BC) immediate effect (RR = 1.506, 95% CI: 1.118–2.028) with delayed effect risks for conjunctivitis at Lag 5–6. Discussion During the study period, The DLNM findings reveal differences in allergic disease search behavior. Allergic conjunctivitis search activity had immediate responding to short-term spikes in particulate and chemical pollution, particularly at Lag 0. This pattern suggests rapid symptom perception and prompt health-information seeking behavior. In contrast, allergic rhinitis demonstrates temporal rigidity, with search interest governed primarily by seasonality. Compared to previous studies, In meta-analysis, NO 2 showed greatest risk impact with conjunctivitis, followed by O 3 15 . In multi-city paediatric case crossover, found higher risk of conjunctivitis linked to CO, NO 2 , SO 2 , and O 3 16 . Gui et al reported that RRs of outpatient visits for conjunctivitis increase in all air pollution PM 2.5 , PM 10 , NO 2 , CO, and O 3 except for SO 2 17 . However, allergic rhinitis has been shown variation in reported significant associating with PM 2.5 , PM 10 , SO 2 , CO, and NO 2 13 . Variation on impact of air pollution exist across studies given different geographical location. The predominance of immediate effects observed for conjunctivitis in our study aligns with preclinical and clinical evidence demonstrating oxidative stress as well as allergic response of the conjunctiva following particulate Matter exposure 18 . In recent study, concentrations of PM 2.5 chemical constituents, including NO 3 − , SO 4 2− , NH 4 + , organic matter, and black carbon (BC) were associated with elevated risk for allergic conjunctivitis and rhinitis 19 . Black carbon can also act as carrier of various constituents due to its porosity 20 . The delayed effect with BC observed can also be explained by evidence reported by Jung et al., who found personal exposure to black carbon has been associated with epigenetic (DNA methylation) changes in allergic asthma gene 21 . Given ammonia’s high water solubility and rapid reaction with tear film to form ammonium hydroxide, this strong same-month signal is biologically plausible and suggests that NH₃ acts as a specific and immediate chemical trigger for ocular irritation in the Saudi environment. The reason for dichotomy in allergic disease is that the ocular surface likely has direct exposure to ambient air and rapid inflammatory responses. Unlike the nose which is primary function is filtration given tightly adherent epithelial cells and mucociliary clearance that is forming protective barrier against external agents 22 . Previous studies on impact of meteorological factors revealed no consensus results. A large time-series study on conjunctivitis in China reported extremely low relative humidity, and elevated mean temperature were associated with risk of conjunctivitis, but the association is varied by age, gender, and season 23 . Hong et al. reported that temperature was associated with allergic conjunctivitis, but relative humidity was statistically marginal 24 . Similarly, association between allergic rhinitis and meteorological factors have contradicted results. In recent meta-analysis reported positive association between temperature and risk of allergic rhinitis while relative humidity has protective effect 3 . Our study had some limitation as analysis is based on Google Trends data, which reflect health information seeking behaviour rather than clinically diagnosed allergic diseases nor the severity or chronicity, nor the demographic differences. Search activity can be influenced by media and public awareness. Environmental data were retrieved from national level aggregated datasets which may not reflect personal exposure. Multiple allergens such as pollen, fungal spore- air pollution, and volatile chemicals are other causes for allergies which did not account for in our study. Lack of validation based on the region due to limited studies conducted on air pollution and allergies. The data from Google Trends raise concern for Selection bias for those who may not have literacy and access to internet., underrepresented children who are more susceptible to air pollution and allergic disease. Conclusion Our study is the first conducted in the region, we provide new evidence in the field. This study investigates seasonal characteristics of ocular and nasal allergy and the association with ambient air pollution exposure in Saudi Arabia. Our study can enhance our understanding of common allergic diseases. 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Sci Rep 6:23858. 10.1038/srep23858 Additional Declarations The authors declare no competing interests. Supplementary Files Supplementaryfile.docx 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8767450","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":584515864,"identity":"0e32f087-3ceb-4e92-9906-42a74c589d87","order_by":0,"name":"Abdulaziz S. AlHarthi","email":"data:image/png;base64,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","orcid":"","institution":"Ophthalmology Division, Department of Surgical Specialities, College of Medicine, Majmaah University, Majmaah, Saudi Arabia. P.O. Box 66, Majmaah 11952, Saudi Arabia.","correspondingAuthor":true,"prefix":"","firstName":"Abdulaziz","middleName":"S.","lastName":"AlHarthi","suffix":""}],"badges":[],"createdAt":"2026-02-02 16:40:07","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-8767450/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8767450/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101758331,"identity":"2c938681-a96f-4bd2-b2ff-fd85db7f35f8","added_by":"auto","created_at":"2026-02-03 11:06:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":504569,"visible":true,"origin":"","legend":"\u003cp\u003epresents the monthly time series of Google Trends search volume indices for AR and AC (topic- and term-based), overlaid with smoothed trend curves. Across all four outcomes, marked non-random temporal behavior is evident. Search volumes display recurrent oscillations superimposed on a gradual upward trajectory, indicating the coexistence of both seasonal and long-term components.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8767450/v1/31d0f0cc9a7c3cab134b1621.png"},{"id":101758324,"identity":"e6447234-c8b8-4895-8d33-80b132b45c6e","added_by":"auto","created_at":"2026-02-03 11:06:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":683694,"visible":true,"origin":"","legend":"\u003cp\u003eillustrates the temporal evolution of meteorological variables and air pollutants over the same period. Clear annual cycles are apparent for temperature and relative humidity, while precipitation remains sparse and episodic. Several pollutants exhibit long-term shifts or step-like changes in level, underscoring the necessity of detrending in subsequent models. The visual alignment between seasonal meteorological cycles and peaks in allergic disease search interest provides initial qualitative support for H1 and motivates formal decomposition and modeling.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8767450/v1/c93e229a525765e4f631b1f3.png"},{"id":101758246,"identity":"fcfe957f-0061-4e54-9030-04303f99a97f","added_by":"auto","created_at":"2026-02-03 11:06:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":371192,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8767450/v1/9052b1657d29ca35b00a548f.png"},{"id":101759618,"identity":"d20cfaa5-04bc-40bc-abd1-59e18d0392ce","added_by":"auto","created_at":"2026-02-03 11:12:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2407793,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8767450/v1/bc98a481-2f80-45ff-b9ee-44926646367f.pdf"},{"id":101758338,"identity":"2c00063a-bc0d-46f8-8b3a-e3f2b8433ee9","added_by":"auto","created_at":"2026-02-03 11:06:52","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":5644294,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile.docx","url":"https://assets-eu.researchsquare.com/files/rs-8767450/v1/a08a598baac6946730bf12c3.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eDigital Epidemiology of Allergic Conjunctivitis and Rhinitis in Saudi Arabia: Association with Air Pollutions and Meteorological Factors\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAllergic conjunctivitis and allergic rhinitis are frequently associated diseases that have common pathophysiological features\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. It is multifactorial disease characterized by specific ocular and nasal symptoms. These allergies are common worldwide and affect 10\u0026ndash;40% of the population\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. The prevalence of allergic disorder has been increasing over past decades partly attributed to emissions from industrial activities and substantial changes in environment\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, and It has negative impact on individual\u0026rsquo;s quality of life\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Allergic rhinitis is subclassified traditionally into seasonal or perennial\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. While allergic conjunctivitis is subclassified according to Ocular Allergy group of the European Academy of Allergy and Clinical Immunology (EAACI) into ocular allergy or ocular nonallergic hypersensitivity, with first type include seasonal allergic, perennial allergic, atopic keratoconjunctivitis, and vernal conjunctivitis\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Allergic diseases are commonly triggered by allergens. Airhorn allergens such as pollen and fungal air spore are considered important cause of allergic diseases. In Saudi Arabia, there are two pollen season, one in autumn and second in spring with weeds as the most common type\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. In Kuwait, fungal bioaerosols exhibit seasonal pattern with two consistent similar fungal composition during period March\u0026ndash;May and November\u0026ndash;December\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. However, counting allergens/ pollen content in the air need sophisticated methods. Meteorological and air pollution data may reflect interest findings. These factors can alter the immunogenicity and abundance of aeroallergens\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eLimited studies in Saudi Arabia have explored the effect of air pollution on health. Recently, Cardiopulmonary Morbidity risk was significantly increased with exposure to Black Carbon (BC) and the Secondary Aerosol (p-SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e and p-NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e)\u003csup\u003e10\u003c/sup\u003e. Our study aims to explore seasonality and association between allergic conjunctivitis and rhinitis search trends with climatic variables and air quality indicators in Saudi Arabia. By using Google Trends (GTs)\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, which is an open-access tool that reflects public search behaviour, offers a real-time proxy for population-level interest and potential disease burden.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eGoogle Trends, air pollution and Meteorological Factors Data\u003c/h2\u003e \u003cp\u003eWe extracted monthly aggregated normalized relative search volume data (RSV) (scale 0-100) from Google Trends as CSV file in September 2025 for the search term in Arabic language \u0026ldquo;حساسية العين\u0026rdquo; for eye allergy (allergic conjunctivitis), and \u0026ldquo;حساسية الانف\u0026rdquo; for allergic rhinitis and its unified topic. The location is set in Saudi Arabia for the period from January 2011 to December 2024, for web searches and without category selection. Google Trends does categorize and aggregate related searches, variations in spelling and related terms into unified topic. Therefore, the data we used for the selected term likely to capture wide range of queries.\u003c/p\u003e \u003cp\u003eFor historical environmental factors data, we collected monthly average temperature (\u0026deg;C), relative humidity (%), and accumulated precipitation at national level for Saudi Arabia. Data were retrieved from ERA5 0.25-degree dataset in World Bank Group website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://climateknowledgeportal.worldbank.org/\u003c/span\u003e\u003cspan address=\"https://climateknowledgeportal.worldbank.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Air pollutants data were retrieved from EDGAR (Emissions Database for Global Atmospheric Research) Community GHG Database, a collaboration between the European Commission, Joint Research Centre (JRC), the International Energy Agency (IEA) version 2024 European Commission, JRC (Datasets)\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll data management and statistical analyses were performed using R Software (Version 4.5.2). The packages mgcv (for Generalized Additive Models) and dlnm (for Distributed Lag Non-linear Models) were the primary analytical tools. A p-value of 0.05 was considered statistically significant. Initial data quality checks were conducted to identify missing values and outliers. Descriptive statistics (mean, median, standard deviation, minimum, and maximum) were calculated for the monthly Google Trends Search Volume Indices for Allergic Rhinitis (AR) and Allergic Conjunctivitis (AC), as well as for all meteorological and air pollution variables. Normality of the data distributions was assessed using the Shapiro-Wilk test. To detect potential collinearity between environmental predictors, a Spearman correlation matrix was constructed.\u003c/p\u003e \u003cp\u003eGiven that Google Trends data are non-negative count-like indices and exhibited over-dispersion in the preliminary analysis, a Quasi-Poisson distribution was selected as the link function, consistent with standard time-series epidemiological methods\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGeneralized Additive Models (GAM) were employed to analyze the non-linear associations. GAM extends generalized linear models by utilizing non-parametric smoothing functions to control for confounding factors and simulate the complex non-linear correlation between environmental exposures and health endpoints. The general form of the GAM is defined as follows:\u003c/p\u003e \u003cp\u003eEnvironmental exposures often exhibit delayed effects (lag effects). To capture both the linear exposure-response and the temporal lag-response simultaneously, we integrated the Distributed Lag Non-linear Model (DLNM) framework into the GAM. As described by Gasparrini (2011) and applied by Yang et al.\u003csup\u003e13,14\u003c/sup\u003e, the DLNM constructs a \"cross-basis\" function (cb) defined by two dimensions: the predictor-response dimension and the lag-response dimension. Our core statistical model controlled for long-term time trends (to account for increasing internet usage or awareness over the decade) and cyclic seasonality. The specific equation used to estimate the Relative Risk (RR) of search volume associated with environmental factors.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthical Consideration\u003c/h3\u003e\n\u003cp\u003eGoogle trends dataset is open public-source. Climatic data from the World Bank Group and air pollution from European Commission are under Creative Commons Attribution 4.0 International (CC BY 4.0) Therefore, No institutional Review Board (IRB) approval was needed.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eData Description\u003c/h2\u003e \u003cp\u003eFigures \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e show the monthly Google Trends search volume indices, meteorological variables, and air pollution indicators over study period. Meteorological variables reflected arid characteristic of dry and hot climate in Saudi Arabia. Average monthly temperatures showed a wide seasonal range (mean\u0026thinsp;=\u0026thinsp;26.56\u0026deg;C, SD\u0026thinsp;=\u0026thinsp;7.21\u0026deg;C), Relative humidity remained low overall (mean\u0026thinsp;=\u0026thinsp;30.81%), while precipitation was minimal and highly skewed (mean\u0026thinsp;=\u0026thinsp;4.72 mm).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eNormality and Stationarity Assessment\u003c/h2\u003e \u003cp\u003eShapiro-Wilk test detailed shown in Supplementary Table S1, the null hypothesis of normality was rejected for the majority of variables (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), except for carbon monoxide (CO) and organic carbon (OC) did not show statistically significant departures from normality.\u003c/p\u003e \u003cp\u003eStationarity of the time-series variables was assessed using the Augmented Dickey\u0026ndash;Fuller (ADF) test, with results summarized in Supplementary Table S2. Google Trends series were identified as non-stationary, including AR topic (p\u0026thinsp;=\u0026thinsp;0.065), AR term (p\u0026thinsp;=\u0026thinsp;0.054), and AC topic (p\u0026thinsp;=\u0026thinsp;0.465). In contrast, AC term was stationary (p\u0026thinsp;=\u0026thinsp;0.011). Among environmental factors, average temperature, relative humidity, precipitation, CO, NOx, and SO₂ were stationary (p\u0026thinsp;\u0026le;\u0026thinsp;0.05), consistent with their predominantly seasonal and cyclic patterns. Conversely, particulate matter (PM₁₀ and PM₂.₅), black carbon (BC), ammonia (NH₃), non-methane volatile organic compounds (NMVOC), and organic carbon (OC) were non-stationary (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). These non-stationary behaviors likely reflect changes in emission sources, urbanization, or industrial activity.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCorrelation Analysis\u003c/h3\u003e\n\u003cp\u003eThe complete correlation matrix is provided in Supplementary Table S3. Strong positive correlations were observed between topic-based and term-based search metrics for the same condition (e.g., AR topic vs. AR term: r\u0026thinsp;=\u0026thinsp;0.877, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In addition, moderate to strong correlations were identified between allergic rhinitis and allergic conjunctivitis search indices (e.g., AR term vs. AC term: r\u0026thinsp;=\u0026thinsp;0.653, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting shared temporal drivers and concurrent public interest patterns for upper airway and ocular allergic conditions in Saudi Arabia.\u003c/p\u003e \u003cp\u003eMeteorological variables exhibited a strong inverse correlation between average temperature and relative humidity (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.891, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Meteorology was also strongly linked to air pollution levels; for example, higher temperatures were associated with increased concentrations of carbon monoxide (r\u0026thinsp;=\u0026thinsp;0.463, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and nitrogen oxides (r\u0026thinsp;=\u0026thinsp;0.249, p\u0026thinsp;=\u0026thinsp;0.003), while relative humidity showed inverse associations with several gaseous pollutants. Air pollution indicators demonstrated pronounced clustering. Very strong correlations were observed among particulate and carbonaceous pollutants, including PM₁₀, PM₂.₅, black carbon (BC), organic carbon (OC), and NOx (e.g., PM₁₀ vs. PM₂.₅: r\u0026thinsp;=\u0026thinsp;0.995; BC vs. PM₂.₅: r\u0026thinsp;=\u0026thinsp;0.892; BC vs. NOx: r\u0026thinsp;=\u0026thinsp;0.845; all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\n\u003ch3\u003eTrend and Seasonality Analysis\u003c/h3\u003e\n\u003cp\u003eThe results of Generalized Additive models are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Allergic rhinitis (topic), the smooth term for long-term trend was statistically significant (EDF\u0026thinsp;=\u0026thinsp;8.44, F\u0026thinsp;=\u0026thinsp;46.59, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating a complex, non-linear temporal changes of search interest over the study period. The seasonal component was statistically significant (EDF\u0026thinsp;=\u0026thinsp;8.10, F\u0026thinsp;=\u0026thinsp;14.90, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). For allergic conjunctivitis (topic), the trend was statistically significant (EDF\u0026thinsp;=\u0026thinsp;6.50, F\u0026thinsp;=\u0026thinsp;37.38, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). while the seasonal component was statistically significant (EDF\u0026thinsp;=\u0026thinsp;4.65, F\u0026thinsp;=\u0026thinsp;1.63, p\u0026thinsp;=\u0026thinsp;0.003) (Fig.\u0026nbsp;3).\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\u003eResults of Generalized Additive Models (GAM) Testing Trend and Seasonality\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDependent Variable (SVI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eTrend Effect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eSeasonality\u0026nbsp;Effect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eModel Fit\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEDF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF-Statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEDF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF-Statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDeviance Expl. (%)\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\u003eAllergic Rhinitis (Topic)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e83%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAllergic Rhinitis (Term)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e73%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAllergic Conjunctivitis (Topic)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.003 **\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e67%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAllergic Conjunctivitis (Term)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.037 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e64%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSeasonal Trend Decomposition (STL) of allergic disease search series are shown in (Figures S1 to S4). Allergic rhinitis displays a seasonal structure with dominant peak occurs in October; A secondary, broader elevation is observed during (March-May). Allergic conjunctivitis follows similar seasonal pattern.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAssociations with Meteorological Variables\u003c/h2\u003e \u003cp\u003eThe result of single environmental factors variable of Generalized Additive Models (GAMs) presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. For allergic conjunctivitis search terms; mean temperature had statistically significant non-linear association (EDF\u0026thinsp;=\u0026thinsp;2.24, F\u0026thinsp;=\u0026thinsp;4.86, p\u0026thinsp;=\u0026thinsp;0.005). This indicates search activity increased during specific temperature ranges. RH was significantly associated with conjunctivitis search term (F\u0026thinsp;=\u0026thinsp;4.63, p\u0026thinsp;=\u0026thinsp;0.033). The non-linear nature suggests that both low and high humidity extremes may exacerbate ocular irritation. Precipitation demonstrated statistically significant association was only for conjunctivitis topic (EDF\u0026thinsp;=\u0026thinsp;2.29, F\u0026thinsp;=\u0026thinsp;2.73, p\u0026thinsp;=\u0026thinsp;0.033). In contrast, temperature, relative humidity and Precipitation failed to observe statistically significant association with allergic rhinitis.\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\u003eUnivariate Screening of Environmental Predictors for Allergic Disease Search Volumes GAM models adjusted for Trend and Cyclic Seasonality\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eAR term\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eAR topic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eAC term\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c13\" namest=\"c11\"\u003e \u003cp\u003eAC topic\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEDF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEDF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eF value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eEDF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eF value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eEDF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eF value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eP value\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\u003eRH\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.033*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e5.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrecipitation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.033*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAverage Temp\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.005*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e7.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.000*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.003*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e3.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.048*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCO\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.003*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e9.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.003*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNH3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e7.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.007*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNMVOC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.039*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.09\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\u003e0.827\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNOx\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.576\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.008*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.011*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e10.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e11.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePM10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.012*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e4.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.031*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePM2.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.009*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e5.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.021*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSO2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e \u003cp\u003e0.275\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"13\"\u003e* Significant association at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAssociations with Air Pollution Variables\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003eAssociations with Allergic Rhinitis (AR)\u003c/h2\u003e \u003cp\u003eOrganic carbon (OC) was significantly associated with allergic rhinitis searches. OC demonstrated significant non-linear effects for AR search terms (EDF\u0026thinsp;=\u0026thinsp;2.44, F\u0026thinsp;=\u0026thinsp;4.43, p\u0026thinsp;=\u0026thinsp;0.008). Other pollutants (PM10, PM2.5, NOx, SO₂, CO, and BC) were not statistically significant after adjustment for temporal structure (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.20).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eAssociations with Allergic Conjunctivitis (AC)\u003c/h2\u003e \u003cp\u003eBoth PM2.5 and PM10 were significant associated with conjunctivitis terms (F\u0026thinsp;=\u0026thinsp;6.93, p\u0026thinsp;=\u0026thinsp;0.009), and (F\u0026thinsp;=\u0026thinsp;6.41, p\u0026thinsp;=\u0026thinsp;0.012), respectively. CO was strongly associated with conjunctivitis term (p\u0026thinsp;=\u0026thinsp;0.003), while BC reached significance for conjunctivitis terms (p\u0026thinsp;=\u0026thinsp;0.003). OC emerged as the single strongest pollutant predictor, with highly significant associations for conjunctivitis terms (F\u0026thinsp;=\u0026thinsp;10.29, p\u0026thinsp;=\u0026thinsp;0.001). Nitrogen oxides and NMVOCs showed weaker but still notable effects, achieving statistical significance for conjunctivitis terms (NOx: p\u0026thinsp;=\u0026thinsp;0.017; NMVOC: p\u0026thinsp;=\u0026thinsp;0.039) but not for topic-based searches.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eDelayed Effects and Lag Structure Analysis\u003c/h2\u003e \u003cp\u003eThe result of all lag-response estimates of key factors on allergic disease search volume are reported in Table S4.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eLagged Environmental Effects on Allergic Rhinitis (AR)\u003c/h2\u003e \u003cp\u003eDLNM analysis revealed minimal evidence of either immediate or delayed responsiveness to environmental exposures once seasonal and long-term confounding were rigorously controlled. For nearly all predictors and lag periods (Lag 0\u0026ndash;Lag 6), the estimated confidence intervals encompassed unity, providing little support in the context of rhinitis-related search behavior.\u003c/p\u003e \u003cp\u003eContrary to expectations for respiratory outcomes, neither PM10 nor PM2.5 demonstrated significant effects at any lag. For AR search terms, the immediate effect (Lag 0) for PM10 was RR\u0026thinsp;=\u0026thinsp;0.979 (95% CI: 0.898\u0026ndash;1.068), and for PM2.5 was RR\u0026thinsp;=\u0026thinsp;0.979 (95% CI: 0.873\u0026ndash;1.098), with similarly null findings persisting through Lag 3 and beyond.\u003c/p\u003e \u003cp\u003eGaseous Pollutants and Other Constituents: nitrogen oxides (NOx), sulfur dioxide (SO₂), and carbon monoxide (CO), failed to exhibit significant effect. Ammonia (NH₃) showed relative risks for AR topics at Lag 0 and Lag 1 (RR\u0026thinsp;\u0026asymp;\u0026thinsp;1.10), but wide confidence intervals (e.g., 0.947\u0026ndash;1.279) rendered these associations statistically non-significant. The impact of Organic carbon (OC) was not significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eLagged Environmental Effects on Allergic Conjunctivitis (AC)\u003c/h2\u003e \u003cp\u003eBoth PM10 and PM2.5 had significant immediate effects. One-unit increase in PM2.5 concentration was associated with an 11.4% increase in search volume at Lag 0 (RR\u0026thinsp;=\u0026thinsp;1.114, 95% CI: 1.020\u0026ndash;1.217). PM10 had similar pattern, with a significant Lag 0 effect (RR\u0026thinsp;=\u0026thinsp;1.079, 95% CI: 1.007\u0026ndash;1.155). NH₃ exposure at Lag 0 resulted in a 28.6% increase in search volume (RR\u0026thinsp;=\u0026thinsp;1.286, 95% CI: 1.081\u0026ndash;1.530). Carbon Monoxide (CO) showed significant correlation at Lag 0 (RR\u0026thinsp;=\u0026thinsp;1.011, 95% CI: 1.003\u0026ndash;1.019). the effect of Black Carbon (BC) immediate effect (RR\u0026thinsp;=\u0026thinsp;1.506, 95% CI: 1.118\u0026ndash;2.028) with delayed effect risks for conjunctivitis at Lag 5\u0026ndash;6.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eDuring the study period, The DLNM findings reveal differences in allergic disease search behavior. Allergic conjunctivitis search activity had immediate responding to short-term spikes in particulate and chemical pollution, particularly at Lag 0. This pattern suggests rapid symptom perception and prompt health-information seeking behavior. In contrast, allergic rhinitis demonstrates temporal rigidity, with search interest governed primarily by seasonality.\u003c/p\u003e \u003cp\u003eCompared to previous studies, In meta-analysis, NO\u003csub\u003e2\u003c/sub\u003e showed greatest risk impact with conjunctivitis, followed by O\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e15\u003c/sup\u003e. In multi-city paediatric case crossover, found higher risk of conjunctivitis linked to CO, NO\u003csub\u003e2\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e, and O\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e16\u003c/sup\u003e. Gui et al reported that RRs of outpatient visits for conjunctivitis increase in all air pollution PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, CO, and O\u003csub\u003e3\u003c/sub\u003e except for SO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e17\u003c/sup\u003e. However, allergic rhinitis has been shown variation in reported significant associating with PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e, CO, and NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e13\u003c/sup\u003e. Variation on impact of air pollution exist across studies given different geographical location.\u003c/p\u003e \u003cp\u003eThe predominance of immediate effects observed for conjunctivitis in our study aligns with preclinical and clinical evidence demonstrating oxidative stress as well as allergic response of the conjunctiva following particulate Matter exposure\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. In recent study, concentrations of PM\u003csub\u003e2.5\u003c/sub\u003e chemical constituents, including NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, SO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e2\u0026minus;\u003c/sup\u003e, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, organic matter, and black carbon (BC) were associated with elevated risk for allergic conjunctivitis and rhinitis\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Black carbon can also act as carrier of various constituents due to its porosity\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. The delayed effect with BC observed can also be explained by evidence reported by Jung et al., who found personal exposure to black carbon has been associated with epigenetic (DNA methylation) changes in allergic asthma gene\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Given ammonia\u0026rsquo;s high water solubility and rapid reaction with tear film to form ammonium hydroxide, this strong same-month signal is biologically plausible and suggests that NH₃ acts as a specific and immediate chemical trigger for ocular irritation in the Saudi environment.\u003c/p\u003e \u003cp\u003eThe reason for dichotomy in allergic disease is that the ocular surface likely has direct exposure to ambient air and rapid inflammatory responses. Unlike the nose which is primary function is filtration given tightly adherent epithelial cells and mucociliary clearance that is forming protective barrier against external agents\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePrevious studies on impact of meteorological factors revealed no consensus results. A large time-series study on conjunctivitis in China reported extremely low relative humidity, and elevated mean temperature were associated with risk of conjunctivitis, but the association is varied by age, gender, and season\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Hong \u003cem\u003eet al.\u003c/em\u003e reported that temperature was associated with allergic conjunctivitis, but relative humidity was statistically marginal\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Similarly, association between allergic rhinitis and meteorological factors have contradicted results. In recent meta-analysis reported positive association between temperature and risk of allergic rhinitis while relative humidity has protective effect\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur study had some limitation as analysis is based on Google Trends data, which reflect health information seeking behaviour rather than clinically diagnosed allergic diseases nor the severity or chronicity, nor the demographic differences. Search activity can be influenced by media and public awareness. Environmental data were retrieved from national level aggregated datasets which may not reflect personal exposure. Multiple allergens such as pollen, fungal spore- air pollution, and volatile chemicals are other causes for allergies which did not account for in our study. Lack of validation based on the region due to limited studies conducted on air pollution and allergies. The data from Google Trends raise concern for Selection bias for those who may not have literacy and access to internet., underrepresented children who are more susceptible to air pollution and allergic disease.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study is the first conducted in the region, we provide new evidence in the field. This study investigates seasonal characteristics of ocular and nasal allergy and the association with ambient air pollution exposure in Saudi Arabia. Our study can enhance our understanding of common allergic diseases. Highlighting the importance of implementation of preventive measures for better management of allergic conjunctivitis and rhinitis. Clearly, further studies are needed to fully assess the impact of air pollution on health.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eIordache A, Boruga M, Mușat O, Jipa DA, Tătaru CP, Mușat GC (2022) Relationship between allergic rhinitis and allergic conjunctivitis (allergic rhinoconjunctivitis) - review. Romanian J Ophthalmol 66(1):8\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.22336/rjo.2022.3\u003c/span\u003e\u003cspan address=\"10.22336/rjo.2022.3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVillegas BV, Benitez-Del-Castillo JM (2021) Current Knowledge in Allergic Conjunctivitis. 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Sci Rep 6:23858. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/srep23858\u003c/span\u003e\u003cspan address=\"10.1038/srep23858\" 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":"Majmaah University","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":"Google Trends, Seasonality, Digital epidemiology, Air pollution, Allergic Conjunctivitis, Allergic Rhinitis","lastPublishedDoi":"10.21203/rs.3.rs-8767450/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8767450/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eclimate changes and air pollutions are potential drivers for allergic conjunctivitis and rhinitis. In arid region such as Saudi Arabia, limited studies have explored the influence of environmental factors on health diseases.\u003c/p\u003e\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003ethis study aims to investigate seasonality of allergic conjunctivitis and rhinitis, and to explore association with air pollutants and climate changes in Saudi Arabia using Google Trends (GTs).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eQuasi-Poisson Generalized Additive Models (GAMs) combined with Distributed Lag Non-linear Model (DLNM) were used to explore the effect of air pollutions and climate changes on population-level interest on allergic conjunctivitis and rhinitis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eBoth allergic diseases demonstrated seasonal pattern, with peak in autumn and spring. Relative risks (RRs) for ocular allergy given increase in PM2.5, PM10, NH₃, CO, and BC concentration were at lag 0 (RR\u0026thinsp;=\u0026thinsp;1.114, 95% CI: 1.020\u0026ndash;1.217), (RR\u0026thinsp;=\u0026thinsp;1.079, 95% CI: 1.007\u0026ndash;1.155), and (RR\u0026thinsp;=\u0026thinsp;1.286, 95% CI: 1.081\u0026ndash;1.530), (RR\u0026thinsp;=\u0026thinsp;1.011, 95% CI: 1.003\u0026ndash;1.019), and (RR\u0026thinsp;=\u0026thinsp;1.506, 95% CI: 1.118\u0026ndash;2.028), respectively. Ocular allergy may be more heighten to ambient environmental irritants. In contrast, allergic rhinitis search interest was governed primarily by seasonality.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003ethis study highlight association between both allergic conjunctivitis and rhinitis with air pollution and climate. This can offer evidence for regional air quality policy decisions and implementing preventive measures to reduce the risk of allergies burden.\u003c/p\u003e","manuscriptTitle":"Digital Epidemiology of Allergic Conjunctivitis and Rhinitis in Saudi Arabia: Association with Air Pollutions and Meteorological Factors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-03 10:45:01","doi":"10.21203/rs.3.rs-8767450/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":"3510698e-7a23-4350-9459-ba4be0fcb6ed","owner":[],"postedDate":"February 3rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":62181815,"name":"Epidemiology"},{"id":62181816,"name":"Ophthalmology"},{"id":62181817,"name":"Otorhinolaryngology"}],"tags":[],"updatedAt":"2026-02-03T10:45:01+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-03 10:45:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8767450","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8767450","identity":"rs-8767450","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}