Short-Term Exposure to Ambient Air Pollution and Onset of Severe Mental Disorders: A Case-Crossover Study in Northwestern China

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Methods We conducted a time-stratified case-crossover study of 49,707 individuals diagnosed with SMDs in Gansu Province, China, from 2013 to 2020. Individual-level exposures to particulate matter ≤ 2.5 µm in aerodynamic diameter (PM 2.5 ), particulate matter ≤ 10 µm in aerodynamic diameter (PM 10 ), sulfur dioxide (SO 2 ), nitrogen dioxide (NO 2 ), carbon monoxide (CO), and ozone (O 3 ) were estimated using high-resolution spatiotemporal data from the China High Air Pollutants dataset. We employed conditional logistic regression models to estimate associations between pollutant exposures and SMDs onset, controlling for temperature and humidity. Stratified analyses were performed to identify potentially vulnerable subpopulations. Results Each interquartile range increase in exposure to PM 2.5 (23.1 µg/m³), CO (0.51 mg/m³), NO 2 (11.9 µg/m³), PM 10 (57.5 µg/m³), and SO 2 (19.1 µg/m³) was associated with increased odds of SMDs onset: 4.37% (95% CI: 2.28%-6.50%), 16.54% (95% CI: 12.26%-20.98%), 9.58% (95% CI: 6.30%-12.95%), 2.58% (95% CI: 1.34%-3.84%), and 30.65% (95% CI: 24.37%-37.25%), respectively. Exposure-response relationships displayed positive trends for all significant pollutants. Effect estimates were generally stronger among females, elderly individuals (≥ 65 years), and during warm seasons (May-October). Associations remained robust in two-pollutant models and various sensitivity analyses. Conclusions Short-term exposure to multiple air pollutants is positively associated with SMDs onset, with differential vulnerability across population subgroups. These results suggest that air pollution may represent an important modifiable environmental risk factor for SMDs, particularly in regions with elevated pollution levels. Mental disorders Ambient air pollution Case-crossover Short-term exposure Figures Figure 1 Figure 2 Introduction Severe mental disorders (SMDs), such as schizophrenia, bipolar disorder, and major depression, are characterized by significant alterations in cognition, emotion regulation, and behavior that substantially impair daily functioning. 1 Mental disorders constitute a major global public health challenge, ranking among the top ten leading causes of disease burden worldwide in 2021, accounting for approximately 1.1 billion prevalence counts (14.4% of the total) and 155.4 million disability-adjusted life-years (5.4% of the total). 2 China bears a disproportionate share of this burden, contributing approximately 15.9% of global cases and 14.9% of global disability, representing the second-largest number of cases worldwide. 3 Given the considerable socioeconomic impact and limited healthcare resources, severe mental disorders have become a priority focus in China's public health initiatives. 4 , 5 Growing evidence identifies ambient air pollution as a significant environmental risk factor for mental health outcomes. 6 – 8 Epidemiological studies have demonstrated positive associations between air pollution exposure and various mental disorders, including schizophrenia 9 , depression 10 , 11 , and anxiety 12 , 13 . These associations are supported by toxicological studies suggesting that air pollutants may induce neuroinflammation, oxidative stress, and hypothalamic-pituitary-adrenal axis dysregulation, potentially affecting neurodevelopment and precipitating or exacerbating psychiatric symptoms. 14 While substantial evidence supports the association between long-term air pollution exposure and mental disorders, research examining short-term exposure effects remains limited and inconsistent. Existing short-term studies face several methodological challenges, including exposure misclassification due to reliance on sparse monitoring stations, inadequate control for time-varying confounders, and insufficient statistical power from limited sample sizes. Moreover, few studies have investigated these associations in regions with particularly severe air pollution, such as northwestern China, where geographical conditions and industrial development contribute to frequent pollution episodes and dust storms. Additionally, while studies have examined specific mental disorders, comprehensive research on severe mental disorders as a collective category is notably lacking, particularly in the Chinese population. 15 To fill these research gaps, we investigate the associations between short-term exposure to six major air pollutants and the onset of severe mental disorders using high-resolution exposure data and a case-crossover design in Gansu Province, China, from 2013 to 2020. Methods Study population Using the Mental Health System of the Gansu Provincial Centre for Disease Control and Prevention, we included individuals who were diagnosed with SMDs in Gansu province, China, between January 1, 2013, and December 30, 2020. This comprehensive surveillance system covers the entire population of Gansu province during the study period. Gansu province is located in northwestern China, spanning approximately 425,800 km² with an east-west extension of 1480 km and a north-south span of 1132 km. In 2020, the population was approximately 25 million (representing about 1.8% of China's total population), with an estimated population density of 59 people/km 2 . 16 For each case, we extracted demographic information including sex, age at onset, race, residential address, and date of onset. Outcomes The primary outcome was the onset of severe mental disorders as defined according to the International Statistical Classification of Diseases and Related Health Problems Tenth Revision (ICD-10), covering "schizophrenia, schizotypal, and delusional disorders", "paranoid psychosis", "bipolar disorder, psychotic disorder due to epilepsy", and "mental retardation with mental disorders" (ICD-10 codes: F00-F99). 17 For subgroup analyses, we focused on three major diagnostic categories: "schizophrenia, schizotypal, and delusional disorders" (ICD-10 codes: F20-F29), "mood disorders" (ICD-10 codes: F30-F39), and "other mental disorders", as the first two categories represented 77.9% of all identified SMDs cases in our study population. Date of onset for each case was precisely documented through two complementary channels: (1) mandatory reporting by community healthcare centers, which are required by national regulations to identify and register all SMDs cases within their jurisdictions; and (2) hospital-based diagnosis reporting, where all medical institutions are required to report newly diagnosed SMDs cases to the central Mental Health System. This dual-source reporting system ensures comprehensive case ascertainment throughout the province. Study design We employed a time-stratified case-crossover design to evaluate the associations between air pollution and SMDs onset. This design is particularly advantageous for studying acute effects of environmental exposures as each case serves as their own control, effectively controlling for time-invariant individual confounders such as genetic factors, socioeconomic status, and lifestyle characteristics. 18 The case day was defined as the date of SMDs onset. Control days were selected from the same year, month, and day of the week as the case day, resulting in 3–4 control days matched to each case day. This time-stratified approach controls for long-term trends, seasonality, and day-of-week effects. By comparing exposure levels between case and control days, we estimated the relative risk associated with short-term air pollution exposure. Exposure Assessment We assessed exposure to six major air pollutants: particulate matter ≤ 2.5 µm in aerodynamic diameter (PM 2.5 ), particulate matter ≤ 10 µm in aerodynamic diameter (PM 10 ), sulfur dioxide (SO 2 ), nitrogen dioxide (NO 2 ), carbon monoxide (CO), and ozone (O 3 ). Daily average pollutant concentrations were obtained from the China High Air Pollutants (CHAP) dataset, which provides high-resolution spatial data: 1 km resolution for PM 2.5 , PM 10 , and O 3 , and 10 km resolution for SO 2 , NO 2 , and CO. 19 – 24 The CHAP dataset integrates comprehensive, high-resolution, and high-quality ground-level air pollution data from diverse sources (for example, ground observations, satellite remote sensing, model simulations, and atmospheric reanalysis), processed using artificial intelligence 25 . The cross-validation coefficient of determination (CV-R 2 ) ranging from 0.84 to 0.93 for PM 2.5 , PM 10 , O 3 , NO 2 , SO 2 , and CO, respectively. 19 – 24 Covariates Daily meteorological data, including temperature and relative humidity were extracted from the fifth generation of European ReAnalysis (ERA5)-Land dataset, which had a spatial resolution of around 10km × 10km. 26 These variables were linked to each case's residential location and incorporated into all statistical models to control for potential confounding effects. Due to the case-crossover design, individual-level variables such as sex, age, race, genetics, and lifestyle factors were inherently controlled for, as these characteristics remain constant between case and control days. Statistical Analysis The relationships between air pollutants and meteorological conditions were measured using the Spearman correlation coefficients. Conditional logistic regression models were used to explore the associations of air pollutants with SMDs onset cross various exposure windows. Results are presented as percentage changes in odds ratios (ORs) of SMDs onset per interquartile range (IQR) increase in each pollutant ([OR-1]×100%) with corresponding 95% confidence intervals (CIs). To identify the most relevant exposure windows, we examined single-day lag effects (lag 0–7 days) and moving averages over multiple days (lag 01–07 days). For each pollutant, the lag period demonstrating the strongest association was selected for primary analyses. All models included natural cubic spline functions of daily average temperature (degrees of freedom [ df ] = 6) and relative humidity ( df = 3) to adjust for meteorological conditions. 27 To explore potential non-linear exposure-response relationships, we applied a 3 df natural cubic spline transformation to each air pollutant in the main lag period, with nonlinearity evaluated using likelihood ratio tests comparing linear and spline models. We conducted subgroup analyses stratified by sex (male, female), age (<65 or ≥ 65 years), season (warm: May to October and cold: November to April), and disease subtype (“schizophrenia, schizotypal, and delusional disorders”, “mood disorders”, and “other mental disorders”) to identify potentially susceptible populations and examine possible effect modifications. The difference across each stratification variable was calculated using 2-sample z tests, with the point estimates of both stratifications (β = ln odds ratio) and the standard errors (SEs). 28 $$\:z=\:\frac{{\beta\:}_{1}+{\beta\:}_{2}}{\sqrt{{SE}_{1}^{2}+{SE}_{2}^{2}}}$$ Sensitivity analyses were conducted to test the robustness of our results, including: 1) two-pollutant models controlling for co-pollutants; 2) restricting the analyses to cases diagnosed with “schizophrenia, schizotypal, and delusional disorders” (ICD-10 code: F20-F29); 3) restricting the analyses to Han race; 4) adjusting for temperature using a df of 3. All analyses were performed using R version 4.4.2. All P values were reported as two-sided values, and a P value less than 0.05 was regarded as statistically significant. Results Descriptive statistics During the 8-year study period (2013–2020), 49,707 SMDs cases were identified in Gansu Province, which included 30,983 cases (62.3%) of schizophrenia, schizotypal, and delusional disorders, 7,760 cases (15.6%) of mood disorders, and 10,964 cases (22.1%) of other mental disorders (Table 1 ). Of the cases, 50.4% were male and 91.7% were of the Han race. The mean age was 40.7 years, with 91.8% of individuals aged under 65 years. Case distribution was slightly higher during the warm season (May-October, 50.9%) compared to the cool season (November-April, 49.1%). The geographical distribution of cases is shown in Figure S1 , with higher case densities represented by darker clusters. Our case-crossover design yielded 198,828 control days matched to the 49,707 case days. Table 1 Baseline characteristics of study population. Baseline Characteristic Value Total Number, n 49,707 Case days, n 49,707 Control days, n 198,828 Sex, n (%) Male 25,054 (50.4) Female 24,653 (49.6) Age Mean (SD), yr 40.7 (17.0) Median (IQR), yr 42.2 (25.3) Age, n (%) < 65 yr 45,643 (91.8) ≥ 65 yr 4,064 (8.2) Race, n (%) Han 45,576 (91.7) Other 4,131 (8.3) Diagnostic subgroups, n (%) Schizophrenia, schizotypal, and delusional disorders 30,983 (62.3) Mood disorders 7,760 (15.6) Other mental disorders 10,964 (22.1) Season * , n (%) Cold season 24,386 (49.1) Warm season 25,321 (50.9) Abbreviations: SD, standard deviation; IQR, interquartile range. Notes * : Cold season, October to March; Warm season, April to September. Table 2 presents the distribution of air pollutants and meteorological conditions during the study period. Mean concentrations were 37.9 µg/m 3 for PM 2.5 , 92.2 µg/m 3 for PM 10 , 23.1 µg/m 3 for SO 2 , 25.1 µg/m 3 for NO 2 , 1.0 mg/m 3 for CO, and 94.7 µg/m 3 for O 3 . The correlation analysis (Table S1 ) revealed moderate to strong positive correlations among PM 2.5 , CO, NO 2 , PM 10 , and SO 2 . In contrast, O 3 exposure was negatively associated with other pollutants. Temperature was positively associated with O 3 and negatively associated with air pollutants, while relative humidity was negatively associated with all air pollutants. Table 2 Distribution of air pollutants and meteorological conditions during case and control days in Gansu Province, China, 2013–2020 Variable Mean SD Min P 25 Median P 75 Max Air pollutant PM 2.5 , µg/m 3 37.9 20.0 2.5 24.3 33.9 47.4 568.1 CO, mg/m 3 1.0 0.4 0.03 0.6 0.9 1.1 11.3 NO 2 , µg/m 3 25.1 12.0 1.5 17.7 22.5 29.6 208.8 O 3 , µg/m 3 94.7 25.4 5.9 74.4 93.5 112.7 619.6 PM 10 , µg/m 3 92.2 68.4 7.9 54.2 79.1 111.7 2,508.7 SO 2 , µg/m 3 23.1 18.5 1.1 10.5 16.1 29.6 610.2 Meteorological condition Relative humidity, % 55.2 21.1 3.3 38.7 55.7 72.0 103.5 Temperature, ℃ 7.7 10.3 -29.5 -0.8 8.7 16.0 38.1 Abbreviations: SD, standard deviation; P 25 , the 25th percentile; Median, the 50th percentile; P 75 , the 75th percentile; PM 2.5 , particulate matter with an aerodynamic diameter ≤ 2.5 mm; CO, carbon monoxide; NO 2 , nitrogen dioxide; O 3 , ozone; PM 10 , particulate matter with an aerodynamic diameter ≤ 10 mm; SO 2 , sulfur dioxide. Associations of air pollutants with SMDs Figure 1 shows the lag-specific associations between air pollutants and SMDs onset. Significant positive associations were observed for PM 2.5 (lag0, lag1, lag7, lag01-lag05, lag07), CO (lag0-lag5, lag7, lag01-lag07), NO 2 (lag0-lag5, lag01-lag07), PM 10 (lag0, lag1, lag01-lag03) and SO 2 (lag0-lag7, lag01-lag07), with the strongest effects generally observed at cumulative exposure windows. For subsequent analyses, we selected the exposure windows demonstrating the strongest associations: lag01 for PM 2.5 , lag05 for CO, lag05 for NO 2 , lag01 for PM 10, and lag07 for SO 2 . Each IQR increase in pollutant concentration was associated with significantly increased odds of SMDs onset: 4.37% (95% CI: 2.28%-6.50%) for PM 2.5 , 16.54% (95% CI: 12.26%-20.98%) for CO, 9.58% (95% CI: 6.30%-12.95%) for NO 2 , 2.58% (95% CI: 1.34%-3.84%) for PM 10 , and 30.65% (95% CI: 24.37%-37.25%) for SO 2 . The concentration-response curves for each pollutant and SMDs onset are presented in Fig. 2 . We observed monotonically increasing relationships between SMDs risk and concentrations of CO, PM 10 , and SO 2 across their respective distribution ranges. For PM 2.5 , the risk increased monotonically at concentrations above 29.4 µg/m³, while for NO 2 , a similar positive trend was observed at concentrations below 56.1 µg/m³. Likelihood ratio tests confirmed significant non-linearity in the exposure-response relationships for PM 2.5 and NO 2 ( P < 0.05), while relationships for CO, PM 10 , and SO 2 did not significantly deviate from linearity. Stratified analyses Table 3 presents the results of stratified analyses by sex, age, season, and disease subtype. When stratified by disease subtype, we observed consistent positive associations across the three diagnostic categories (schizophrenia spectrum disorders, mood disorders, and other mental disorders) for all pollutants except O 3 . We identified significant effect modification by sex for PM 10 , with females showing greater susceptibility compared to males ( P for difference = 0.002). Age significantly modified the effects of PM 2.5 , NO 2 , and SO 2 , with stronger associations observed among elderly individuals (≥ 65 years) compared to younger individuals. Each IQR increase in PM 2.5 , NO 2 , and SO 2 was associated with 0.48% (95% CI: 0.17%-0.79%), 1.75% (95% CI: 0.84%-2.68%), and 2.57% (95% CI: 1.55%-3.59%) higher odds of SMDs onset in the elderly population, respectively. The effect estimates for these pollutants were substantially lower in the younger population (P for difference < 0.05 for all three comparisons). Seasonal stratification revealed significantly stronger associations during the warm season compared to the cold season for PM 2.5 , CO, and NO 2 . In the warm season, IQR increases in these pollutants were associated with 0.26% (95% CI: 0.13%-0.38%), 102.49% (95% CI: 72.78%-137.30%), and 1.97% (95% CI: 1.30%-2.65%) higher odds of SMDs onset, respectively. The corresponding estimates for the cold season were significantly lower (P for difference < 0.05 for all three comparisons). Table 3 Association of each IQR increase of exposures to PM 2.5 (lag01), CO (lag05), NO 2 (lag05), PM 10 (lag01), and SO 2 (lag07) with onset of severe mental disorders stratified by sex, age, season, and disease subtype. Variable Percent change (95% CI) PM 2.5 CO NO 2 PM 10 SO 2 Sex Male 0.12 (-0.003,0.25) 41.00 (26.97,56.57) 0.93 (0.53,1.34) 0.01 (-0.02,0.04) * 1.59 (1.22,1.96) Female 0.25 (0.13,0.37) 29.62 (16.87,43.75) 0.77 (0.37,1.16) 0.08 (0.05,0.11) * 1.22 (0.85,1.58) Age < 65 0.16 (0.06,0.25) * 33.14 (23.28,43.77) 0.75 (0.45,1.05) * 0.05 (0.03,0.07) 1.31 (1.05,1.58) * ≥ 65 0.48 (0.17,0.79) * 58.33 (22.51,104.62) 1.75 (0.84,2.68) * -0.02 (-0.10,0.06) 2.57 (1.55,3.59) * Season Warm 0.26 (0.13,0.38) * 102.49 (72.78,137.30) * 1.97 (1.30,2.65) * 0.04 (0.01,0.07) 1.36 (0.76,1.97) Cold 0.05 (-0.08,0.18) * 29.99 (19.43,41.49) * 0.74 (0.43,1.05) * 0.04 (0.01,0.06) 1.75 (1.45,2.04) Disease subtype Schizophrenia, schizotypal, and delusional disorders 0.19 (0.08,0.30) 38.5 (26.48,51.66) 0.85 (0.51,1.20) 0.03 (0.003,0.06) 1.37 (1.05,1.69) Mood disorders 0.23 (0.01,0.46) 52.55 (24.72,86.6) 1.15 (0.37,1.94) 0.09 (0.04,0.14) 1.75 (1.07,2.43) Other mental disorders 0.14 (-0.05,0.33) 14.27 (-2.82,34.37) 0.60 (-0.002,1.21) 0.05 (0.003,0.09) 1.18 (0.60,1.76) Abbreviations: PM 2.5 , particulate matter with an aerodynamic diameter ≤ 2.5 mm; CO, carbon monoxide; NO 2 , nitrogen dioxide; O 3 , ozone; PM 10 , particulate matter with an aerodynamic diameter ≤ 10 mm; SO 2 , sulfur dioxide. Statistically significant associations are shown in bold. Notes * : differences across strata are statistically significant. Sensitivity analyses Two-pollutant models were conducted to test the robustness of our findings (Table 4 ). The associations between each pollutant and SMDs onset remained statistically significant after adjustment for co-pollutants, with the exception of NO 2 , which became non-significant when adjusted for other pollutants. To prevent multicollinearity issues, we did not include PM 2.5 and PM 10 in the same model. Additional sensitivity analyses restricting the study population to Han ethnicity or to cases diagnosed with schizophrenia spectrum disorders yielded results consistent with our primary findings (Figures S2-S3). Similarly, using an alternative approach to control for temperature ( df = 3 instead of df = 6) did not materially change the observed associations (Figure S4), further supporting the robustness of our findings. Table 4 Percent changes in ORs and 95% confidence intervals for Onset of severe mental disorders Associated with Each IQR Increase of Exposures to PM 2.5 (lag01), NO 2 (lag 05), PM 10 (lag01), and SO 2 (lag07) Estimated by Single- and Two-pollutant Models. Models Percent change (95% CI) PM 2.5 CO NO 2 PM 10 SO 2 Single 4.37 (2.28,6.50) 16.54 (12.26,20.98) 9.58 (6.30,12.95) 2.58 (1.34,3.84) 30.65 (24.37,37.25) Adjusted for PM 2.5 — 15.93 (11.45,20.59) 9.26 (5.87,12.75) — 29.39 (22.82,36.33) Adjusted for CO 3.22 (1.08,5.40) — 3.13 (-0.60,7.01) 2.50 (1.26,3.76) 27.05 (20.36,34.11) Adjusted for NO 2 3.68 (1.57,5.84) 14.05 (8.98,19.35) — 2.63 (1.39,3.89) 31.18 (24.21,38.54) Adjusted for PM 10 10.44 (6.51,14.5) 16.42 (12.14,20.86) 9.53 (6.25,12.91) — 30.48 (24.20,37.08) Adjusted for SO 2 3.39 (1.30,5.54) 8.73 (4.33,13.31) 2.71 (-0.69,6.22) 2.50 (1.25,3.76) — Adjusted for O 3 4.32 (2.24,6.46) 15.77 (11.50,20.19) 9.43 (6.16,12.81) 2.54 (1.30,3.80) 28.91 (22.70,35.44) Abbreviations: PM 2.5 , particulate matter with an aerodynamic diameter ≤ 2.5 mm; CO, carbon monoxide; NO 2 , nitrogen dioxide; O 3 , ozone; PM 10 , particulate matter with an aerodynamic diameter ≤ 10 mm; SO 2 , sulfur dioxide. Discussion In this large case-crossover study involving 49,707 individuals with SMDs in northwestern China, we found positive associations between short-term exposure to multiple air pollutants and SMDs onset. Specifically, each IQR increase in PM 2.5 , CO, NO 2 , PM 10 , and SO 2 was associated with increased odds of SMDs onset ranging from 2.58–30.65%, with exposure-response relationships exhibiting positive trends. Our analyses further revealed potentially vulnerable subgroups, with females demonstrating greater susceptibility to PM 10 , elderly individuals showing stronger associations with PM 2.5 , NO 2 , and SO 2 , and warm-season exposures exhibiting more pronounced effects for several pollutants. This study represents the first comprehensive investigation of short-term air pollution effects on severe mental disorders in northwestern China, employing high-resolution exposure data, a large population-based sample, and rigorous methodology to address critical knowledge gaps in environmental psychiatry. Our findings extend the current understanding of air pollution's relationship with mental health in several important ways. While previous studies have examined associations between air pollution and specific psychiatric conditions or healthcare utilization, our research uniquely addresses SMDs as a broader category with significant public health implications. Several multi-city studies in China reported positive associations between air pollutants and hospital admissions for mental disorders, but with substantially different exposure assessment methodology and geographical context. 29 – 31 Similarly, research in the United States found positive associations between PM 2.5 and NO 2 exposure and psychiatric hospital admissions, though with different effect magnitudes and population characteristics. 32 Notably, our observation of strong SO 2 associations (30.65% increased odds per IQR) represents a novel finding compared to previous work, potentially reflecting the specific industrial pollution profile of northwestern China. The divergence between our findings and those reported in some European studies, which found limited associations except for O 3 , likely reflects regional differences in pollution characteristics, healthcare systems, and population susceptibilities—underscoring the importance of context-specific environmental health research. 33 While the precise mechanisms linking short-term air pollution exposure to SMDs onset remain incompletely understood, several biologically plausible pathways may explain our observed associations. Neuroimaging studies have revealed that air pollution exposure is associated with altered white matter integrity and blood-brain barrier dysfunction. 34 , 35 Air pollutants may also activate the hypothalamic-pituitary-adrenal axis, potentially disrupting stress hormone regulation and neurotransmitter systems crucial for mental health. 36 – 38 These neurobiological changes could potentially lower the threshold for psychiatric symptom manifestation in susceptible individuals, particularly during acute pollution episodes. However, we acknowledge that our epidemiological findings cannot establish these mechanistic pathways, which require further investigation through experimental and clinical studies. The observed vulnerability patterns in our study offer important insights for targeted public health interventions. The enhanced susceptibility among females to particulate matter aligns with emerging evidence of sex-based differences in air pollution responses, potentially attributable to physiological differences in immune response, hormonal factors, or differential exposure patterns. 39 – 41 The stronger associations observed among elderly individuals for multiple pollutants suggest age-related vulnerability mechanisms, possibly related to decreased physiological reserve, compromised blood-brain barrier integrity, or reduced antioxidant capacity with advancing age. 42 , 43 Seasonal variations in effect estimates, with stronger associations during warmer months, may reflect seasonal differences in activity patterns, ventilation behaviors, or photochemical pollutant formation. 44 These heterogeneous effects highlight the importance of identifying vulnerable subpopulations for targeted protection measures, particularly as climate change may exacerbate both air pollution and temperature extremes in coming decades. Our study has several strengths that enhance the reliability and significance of the findings. First, the time-stratified case-crossover design effectively controls for individual-level confounders and time trends, providing robust estimates of acute exposure effects. Second, our use of high-resolution air pollution data (1–10 km) based on multiple data sources represents a substantial improvement over previous studies relying on sparse monitoring networks, significantly reducing exposure misclassification. Third, the large sample size drawn from comprehensive provincial surveillance data provides sufficient statistical power for detecting subtle associations and conducting meaningful subgroup analyses. Fourth, the study setting in Gansu Province—characterized by severe air pollution episodes and unique geographical conditions—offers valuable insights into pollution-health relationships in a previously understudied high-exposure setting. 15 Finally, our extensive sensitivity analyses demonstrate the robustness of our findings across alternative model specifications and subpopulations. Several limitations should be considered when interpreting our results. First, despite using high-resolution spatial data, we could not account for individual time-activity patterns or indoor air pollution exposure, potentially introducing some exposure misclassification. 45 However, the case-crossover design, which compares exposures within individuals across time rather than between individuals, likely minimizes the impact of this limitation. Second, while we controlled for temperature and humidity, other time-varying factors such as viral epidemics, social events, or healthcare access fluctuations might introduce residual confounding. Third, the strong correlations among pollutants present challenges in disentangling the independent effects of individual pollutants, although our two-pollutant models suggest persistent associations after mutual adjustment. Fourth, while we observed consistent associations across diagnostic subgroups, differences in case ascertainment or diagnostic practices across the region could influence our findings. Finally, caution is warranted in generalizing these results to other populations with different pollution profiles, genetic backgrounds, or healthcare systems. In conclusion, this study provides robust evidence of associations between short-term exposure to multiple air pollutants and the SMDs onset in northwestern China. Our findings suggest that air pollution may represent an important modifiable environmental risk factor for mental health, with particularly pronounced effects among vulnerable subpopulations including females, the elderly, and during warmer seasons. These results underscore the potential public health benefits of improved air quality for reducing the burden of severe mental disorders, especially in regions with elevated pollution levels. Declarations Ethics approval and consent to participate The study protocol was approved by the Institutional Review Board (IRB) of School of Public Health, Lanzhou University (IRB 25010202) and conducted in accordance with the tenets of the Declaration of Helsinki. This study was approved by the Ethical Committee of School of Public Health, Lanzhou University with a waiver of informed consent. Consent for publication Not applicable. Availability of data and materials The datasets generated and/or analysed during this study are not publicly available, but are available from the corresponding author on reasonable request. Competing interests The authors declare no competing interests. Funding This study was supported by the STI 2030–Major Projects (2022ZD0209102), and the Natural Science Foundation of Gansu Province, China (21JR7RA654). Acknowledgements The authors acknowledge the contribution and collaboration of all those who participated in this study. Author contributions YH and GY contributed equally to the paper as joint first authors. XL ( [email protected] ) and WP ( [email protected] ) are joint corresponding authors. XL, WP, YH, and GY designed the research. YH and GY performed statistical analyses. YH, GY, and XL drafted the manuscript. All authors interpreted data, critically reviewed and revised the manuscript, and have read and approved the final version. XL is the guarantor of this work and, as such, has full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. References Goldfarb M, De Hert M, Detraux J, et al. Severe Mental Illness and Cardiovascular Disease. Journal of the American College of Cardiology . 2022;80(9):918-933. doi:10.1016/j.jacc.2022.06.017 Ferrari AJ, Santomauro DF, Aali A, et al. Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021. The Lancet . 2024;403(10440):2133-2161. doi:10.1016/S0140-6736(24)00757-8 Institute for Health Metrics and Evaluation. GBD Results，2021. Institute for Health Metrics and Evaluation. 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Atmospheric Chemistry and Physics . 2020;20(6):3273-3289. doi:10.5194/acp-20-3273-2020 Ma Y, Zhang Y, Wang W, et al. Estimation of health risk and economic loss attributable to PM2.5 and O3 pollution in Jilin Province, China. Sci Rep . 2023;13(1):17717. doi:10.1038/s41598-023-45062-x Zou J, Lu N, Jiang H, et al. Performance of air temperature from ERA5-Land reanalysis in coastal urban agglomeration of Southeast China. Science of The Total Environment . 2022;828:154459. doi:10.1016/j.scitotenv.2022.154459 Liu Y, Pan J, Zhang H, et al. Short-Term Exposure to Ambient Air Pollution and Asthma Mortality. Am J Respir Crit Care Med . 2019;200(1):24-32. doi:10.1164/rccm.201810-1823OC Di Q, Dai L, Wang Y, et al. Association of Short-term Exposure to Air Pollution With Mortality in Older Adults. JAMA . 2017;318(24):2446. doi:10.1001/jama.2017.17923 Bai L, Jiang Y, Wang K, et al. Ambient Air Pollution and Hospitalizations for Schizophrenia in China. JAMA Netw Open . 2024;7(10):e2436915. doi:10.1001/jamanetworkopen.2024.36915 Ma Y, Wang W, Li Z, et al. Short-term exposure to ambient air pollution and risk of daily hospital admissions for anxiety in China: A multicity study. J Hazard Mater . 2022;424:127535. doi:10.1016/j.jhazmat.2021.127535 Gu X, Guo T, Si Y, et al. Association Between Ambient Air Pollution and Daily Hospital Admissions for Depression in 75 Chinese Cities. Am J Psychiatry . 2020;177(8):735-743. doi:10.1176/appi.ajp.2020.19070748 Qiu X, Danesh-Yazdi M, Wei Y, et al. Associations of short-term exposure to air pollution and increased ambient temperature with psychiatric hospital admissions in older adults in the USA: a case–crossover study. The Lancet Planetary Health . 2022;6(4):e331-e341. doi:10.1016/S2542-5196(22)00017-1 Bernardini F, Attademo L, Trezzi R, et al. Air pollutants and daily number of admissions to psychiatric emergency services: evidence for detrimental mental health effects of ozone. Epidemiol Psychiatr Sci . 2020;29:e66. doi:10.1017/S2045796019000623 Calderón-Garcidueñas L, Reynoso-Robles R, Vargas- Martínez J, et al. Prefrontal white matter pathology in air pollution exposed Mexico City young urbanites and their potential impact on neurovascular unit dysfunction and the development of Alzheimer’s disease. Environmental Research . 2016;146:404-417. doi:10.1016/j.envres.2015.12.031 Calderón-Garcidueñas L, Reed W, Maronpot RR, et al. Brain Inflammation and Alzheimer’s-Like Pathology in Individuals Exposed to Severe Air Pollution. Toxicol Pathol . 2004;32(6):650-658. doi:10.1080/01926230490520232 Thomson EM. Neurobehavioral and metabolic impacts of inhaled pollutants: A role for the hypothalamic-pituitary-adrenal axis? Endocrine Disruptors . 2013;1(1):e27489. doi:10.4161/endo.27489 Thomson EM, Vladisavljevic D, Mohottalage S, Kumarathasan P, Vincent R. Mapping Acute Systemic Effects of Inhaled Particulate Matter and Ozone: Multiorgan Gene Expression and Glucocorticoid Activity. Toxicological Sciences . 2013;135(1):169-181. doi:10.1093/toxsci/kft137 Chrousos GP, Kino T. Glucocorticoid Signaling in the Cell: Expanding Clinical Implications to Complex Human Behavioral and Somatic Disorders. Annals of the New York Academy of Sciences . 2009;1179(1):153-166. doi:10.1111/j.1749-6632.2009.04988.x Gao Q, Xu Q, Guo X, Fan H, Zhu H. Particulate matter air pollution associated with hospital admissions for mental disorders: A time-series study in Beijing, China. Eur psychiatr . 2017;44:68-75. doi:10.1016/j.eurpsy.2017.02.492 Liang Z, Xu C, Cao Y, et al. The association between short-term ambient air pollution and daily outpatient visits for schizophrenia: A hospital-based study. Environmental Pollution . 2019;244:102-108. doi:10.1016/j.envpol.2018.09.142 Chen H, Chen L, Hao G. Sex difference in the association between solid fuel use and cognitive function in rural China. Environ Res . 2021;195:110820. doi:10.1016/j.envres.2021.110820 Blanner Kristiansen C, Kjær JN, Hjorth P, Andersen K, Prina AM. Prevalence of common mental disorders in widowhood: A systematic review and meta-analysis. Journal of Affective Disorders . 2019;245:1016-1023. doi:10.1016/j.jad.2018.11.088 Shumake KL, Sacks JD, Lee JS, Johns DO. Susceptibility of older adults to health effects induced by ambient air pollutants regulated by the European Union and the United States. Aging Clin Exp Res . 2013;25(1):3-8. doi:10.1007/s40520-013-0001-5 Lee S, Lee W, Kim D, et al. Short-term PM2.5 exposure and emergency hospital admissions for mental disease. Environmental Research . 2019;171:313-320. doi:10.1016/j.envres.2019.01.036 Luo Y, Zhong Y, Pang L, Zhao Y, Liang R, Zheng X. The effects of indoor air pollution from solid fuel use on cognitive function among middle-aged and older population in China. Sci Total Environ . 2021;754:142460. doi:10.1016/j.scitotenv.2020.142460 Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Published Journal Publication published 22 Dec, 2025 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Revision requested 06 Oct, 2025 Reviews received at journal 02 Oct, 2025 Reviews received at journal 29 Sep, 2025 Reviewers agreed at journal 08 Sep, 2025 Reviewers agreed at journal 08 Sep, 2025 Reviewers agreed at journal 08 Sep, 2025 Reviewers invited by journal 08 Sep, 2025 Editor invited by journal 04 Sep, 2025 Editor assigned by journal 02 Sep, 2025 Submission checks completed at journal 02 Sep, 2025 First submitted to journal 27 Aug, 2025 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. 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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-7468223","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":511844981,"identity":"422f74bc-2f2d-4183-aba7-ae88a686bdb4","order_by":0,"name":"Yajin Han","email":"","orcid":"","institution":"Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Yajin","middleName":"","lastName":"Han","suffix":""},{"id":511844982,"identity":"9a1eef1d-c7d5-45af-a1c6-1d199d635e43","order_by":1,"name":"Guangrui Yang","email":"","orcid":"","institution":"Shanghai Jiao Tong University School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Guangrui","middleName":"","lastName":"Yang","suffix":""},{"id":511844983,"identity":"0fc2b91b-4993-4243-9cff-7908223ec1f0","order_by":2,"name":"Jinshi Wang","email":"","orcid":"","institution":"Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Jinshi","middleName":"","lastName":"Wang","suffix":""},{"id":511844987,"identity":"b0b8f0f1-7087-4419-bebd-fa5c1842efdc","order_by":3,"name":"Weimin Pan","email":"","orcid":"","institution":"Institute of Chronic Noncommunicable Disease Control and Prevention, Gansu Provincial Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Weimin","middleName":"","lastName":"Pan","suffix":""},{"id":511844988,"identity":"28a486da-a67e-4614-8dd3-d8cec288af73","order_by":4,"name":"Xiaofeng Luo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIie3RsQqCQBzH8TsEb5Fu/Tv0DidCBPowdwjnIs0NEoKgW829RY8gHNxke6Mtzk3RJFkE0aJGS8N9hx/8h8/0R8hk+sNmj+EIUUqetzVO7Bdx9/lXpM89qMkEIg1NGoKvSAtoHYiMHKsRIiPGtYSFQhJQHYvMWfERkngNt9WmJxpwoUQGDhsjrOKdAj/HBeBuGvEaUShglmUDzqYQp42Y2EoAZVtLrmO/cJJhQkmk3ds1BFqW59MlDeY7Ug+Tzzh6f8pkMplMP3QHW3A6C0OsPqUAAAAASUVORK5CYII=","orcid":"","institution":"Lanzhou University","correspondingAuthor":true,"prefix":"","firstName":"Xiaofeng","middleName":"","lastName":"Luo","suffix":""}],"badges":[],"createdAt":"2025-08-27 06:08:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7468223/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7468223/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-025-25906-z","type":"published","date":"2025-12-22T15:58:08+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":91370873,"identity":"f9d40c18-2032-41db-b52e-6ef85c7a14df","added_by":"auto","created_at":"2025-09-15 18:47:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":255184,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage changes in odds ratios and 95% confidence intervals for onset of severe mental disorders associated with each interquartile range increase of exposures to PM\u003csub\u003e2.5 \u003c/sub\u003e(interquartile range，23.1 μg/m\u003csup\u003e3\u003c/sup\u003e), PM\u003csub\u003e10 \u003c/sub\u003e(57.5 μg/m\u003csup\u003e3\u003c/sup\u003e), SO\u003csub\u003e2\u003c/sub\u003e (19.1 μg/m\u003csup\u003e3\u003c/sup\u003e), NO\u003csub\u003e2\u003c/sub\u003e (11.9 μg/m\u003csup\u003e3\u003c/sup\u003e), CO (0.51 mg/m\u003csup\u003e3\u003c/sup\u003e), and O\u003csub\u003e3 \u003c/sub\u003e(38.3 μg/m\u003csup\u003e3\u003c/sup\u003e) with different lag periods.\u003c/p\u003e\n\u003cp\u003eAbbreviations: PM\u003csub\u003e2.5\u003c/sub\u003e, particulate matter with an aerodynamic diameter ≤2.5 mm; CO, carbon monoxide; NO\u003csub\u003e2\u003c/sub\u003e, nitrogen dioxide; O\u003csub\u003e3\u003c/sub\u003e, ozone; PM\u003csub\u003e10\u003c/sub\u003e, particulate matter with an aerodynamic diameter ≤10 mm; SO\u003csub\u003e2\u003c/sub\u003e, sulfur dioxide\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7468223/v1/19cc90e2a2f2ab3a9b7c4ed8.png"},{"id":91372134,"identity":"ed3bdde4-4091-4ab4-ba10-3b5c0b8c52c5","added_by":"auto","created_at":"2025-09-15 18:55:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":224991,"visible":true,"origin":"","legend":"\u003cp\u003eExposure–response curves between exposures to PM\u003csub\u003e2.5\u003c/sub\u003e (lag01), CO (lag05), NO\u003csub\u003e2\u003c/sub\u003e (lag05), O\u003csub\u003e3\u003c/sub\u003e (lag3), PM\u003csub\u003e10\u003c/sub\u003e (lag01), SO\u003csub\u003e2\u003c/sub\u003e (lag07), and onset of severe mental disorders.\u003c/p\u003e\n\u003cp\u003eAbbreviations: PM\u003csub\u003e2.5\u003c/sub\u003e, particulate matter with an aerodynamic diameter ≤2.5 mm; CO, carbon monoxide; NO\u003csub\u003e2\u003c/sub\u003e, nitrogen dioxide; O\u003csub\u003e3\u003c/sub\u003e, ozone; PM\u003csub\u003e10\u003c/sub\u003e, particulate matter with an aerodynamic diameter ≤10 mm; SO\u003csub\u003e2\u003c/sub\u003e, sulfur dioxide.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7468223/v1/c894d41de34c03a467207335.png"},{"id":99172347,"identity":"c1cbc85a-4ca1-44bc-bd06-369b4af4441a","added_by":"auto","created_at":"2025-12-29 16:08:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1672658,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7468223/v1/82ec9bc5-3bc9-4292-bc1f-9d97ceb4af8a.pdf"},{"id":91372607,"identity":"ada02688-3455-464b-abca-e1360ec5c738","added_by":"auto","created_at":"2025-09-15 19:03:28","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2051716,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7468223/v1/c8709a29b2a9f8415d13d5c2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Short-Term Exposure to Ambient Air Pollution and Onset of Severe Mental Disorders: A Case-Crossover Study in Northwestern China","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSevere mental disorders (SMDs), such as schizophrenia, bipolar disorder, and major depression, are characterized by significant alterations in cognition, emotion regulation, and behavior that substantially impair daily functioning.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Mental disorders constitute a major global public health challenge, ranking among the top ten leading causes of disease burden worldwide in 2021, accounting for approximately 1.1\u0026nbsp;billion prevalence counts (14.4% of the total) and 155.4\u0026nbsp;million disability-adjusted life-years (5.4% of the total).\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e China bears a disproportionate share of this burden, contributing approximately 15.9% of global cases and 14.9% of global disability, representing the second-largest number of cases worldwide.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Given the considerable socioeconomic impact and limited healthcare resources, severe mental disorders have become a priority focus in China's public health initiatives.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eGrowing evidence identifies ambient air pollution as a significant environmental risk factor for mental health outcomes.\u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e Epidemiological studies have demonstrated positive associations between air pollution exposure and various mental disorders, including schizophrenia\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, depression\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, and anxiety\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. These associations are supported by toxicological studies suggesting that air pollutants may induce neuroinflammation, oxidative stress, and hypothalamic-pituitary-adrenal axis dysregulation, potentially affecting neurodevelopment and precipitating or exacerbating psychiatric symptoms.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eWhile substantial evidence supports the association between long-term air pollution exposure and mental disorders, research examining short-term exposure effects remains limited and inconsistent. Existing short-term studies face several methodological challenges, including exposure misclassification due to reliance on sparse monitoring stations, inadequate control for time-varying confounders, and insufficient statistical power from limited sample sizes. Moreover, few studies have investigated these associations in regions with particularly severe air pollution, such as northwestern China, where geographical conditions and industrial development contribute to frequent pollution episodes and dust storms. Additionally, while studies have examined specific mental disorders, comprehensive research on severe mental disorders as a collective category is notably lacking, particularly in the Chinese population.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eTo fill these research gaps, we investigate the associations between short-term exposure to six major air pollutants and the onset of severe mental disorders using high-resolution exposure data and a case-crossover design in Gansu Province, China, from 2013 to 2020.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy population\u003c/h2\u003e\u003cp\u003eUsing the Mental Health System of the Gansu Provincial Centre for Disease Control and Prevention, we included individuals who were diagnosed with SMDs in Gansu province, China, between January 1, 2013, and December 30, 2020. This comprehensive surveillance system covers the entire population of Gansu province during the study period.\u003c/p\u003e\u003cp\u003eGansu province is located in northwestern China, spanning approximately 425,800 km\u0026sup2; with an east-west extension of 1480 km and a north-south span of 1132 km. In 2020, the population was approximately 25\u0026nbsp;million (representing about 1.8% of China's total population), with an estimated population density of 59 people/km\u003csup\u003e2\u003c/sup\u003e.\u003csup\u003e16\u003c/sup\u003e For each case, we extracted demographic information including sex, age at onset, race, residential address, and date of onset.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eOutcomes\u003c/h3\u003e\n\u003cp\u003eThe primary outcome was the onset of severe mental disorders as defined according to the International Statistical Classification of Diseases and Related Health Problems Tenth Revision (ICD-10), covering \"schizophrenia, schizotypal, and delusional disorders\", \"paranoid psychosis\", \"bipolar disorder, psychotic disorder due to epilepsy\", and \"mental retardation with mental disorders\" (ICD-10 codes: F00-F99).\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e For subgroup analyses, we focused on three major diagnostic categories: \"schizophrenia, schizotypal, and delusional disorders\" (ICD-10 codes: F20-F29), \"mood disorders\" (ICD-10 codes: F30-F39), and \"other mental disorders\", as the first two categories represented 77.9% of all identified SMDs cases in our study population.\u003c/p\u003e\u003cp\u003eDate of onset for each case was precisely documented through two complementary channels: (1) mandatory reporting by community healthcare centers, which are required by national regulations to identify and register all SMDs cases within their jurisdictions; and (2) hospital-based diagnosis reporting, where all medical institutions are required to report newly diagnosed SMDs cases to the central Mental Health System. This dual-source reporting system ensures comprehensive case ascertainment throughout the province.\u003c/p\u003e\n\u003ch3\u003eStudy design\u003c/h3\u003e\n\u003cp\u003eWe employed a time-stratified case-crossover design to evaluate the associations between air pollution and SMDs onset. This design is particularly advantageous for studying acute effects of environmental exposures as each case serves as their own control, effectively controlling for time-invariant individual confounders such as genetic factors, socioeconomic status, and lifestyle characteristics.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e The case day was defined as the date of SMDs onset. Control days were selected from the same year, month, and day of the week as the case day, resulting in 3\u0026ndash;4 control days matched to each case day. This time-stratified approach controls for long-term trends, seasonality, and day-of-week effects. By comparing exposure levels between case and control days, we estimated the relative risk associated with short-term air pollution exposure.\u003c/p\u003e\n\u003ch3\u003eExposure Assessment\u003c/h3\u003e\n\u003cp\u003eWe assessed exposure to six major air pollutants: particulate matter\u0026thinsp;\u0026le;\u0026thinsp;2.5 \u0026micro;m in aerodynamic diameter (PM\u003csub\u003e2.5\u003c/sub\u003e), particulate matter\u0026thinsp;\u0026le;\u0026thinsp;10 \u0026micro;m in aerodynamic diameter (PM\u003csub\u003e10\u003c/sub\u003e), sulfur dioxide (SO\u003csub\u003e2\u003c/sub\u003e), nitrogen dioxide (NO\u003csub\u003e2\u003c/sub\u003e), carbon monoxide (CO), and ozone (O\u003csub\u003e3\u003c/sub\u003e). Daily average pollutant concentrations were obtained from the China High Air Pollutants (CHAP) dataset, which provides high-resolution spatial data: 1 km resolution for PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, and O\u003csub\u003e3\u003c/sub\u003e, and 10 km resolution for SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and CO.\u003csup\u003e\u003cspan additionalcitationids=\"CR20 CR21 CR22 CR23\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e The CHAP dataset integrates comprehensive, high-resolution, and high-quality ground-level air pollution data from diverse sources (for example, ground observations, satellite remote sensing, model simulations, and atmospheric reanalysis), processed using artificial intelligence\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. The cross-validation coefficient of determination (CV-R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e) ranging from 0.84 to 0.93 for PM\u003csub\u003e2.5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, O\u003csub\u003e3\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e, and CO, respectively.\u003csup\u003e\u003cspan additionalcitationids=\"CR20 CR21 CR22 CR23\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eDaily meteorological data, including temperature and relative humidity were extracted from the fifth generation of European ReAnalysis (ERA5)-Land dataset, which had a spatial resolution of around 10km \u0026times; 10km.\u003csup\u003e26\u003c/sup\u003e These variables were linked to each case's residential location and incorporated into all statistical models to control for potential confounding effects. Due to the case-crossover design, individual-level variables such as sex, age, race, genetics, and lifestyle factors were inherently controlled for, as these characteristics remain constant between case and control days.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eThe relationships between air pollutants and meteorological conditions were measured using the Spearman correlation coefficients. Conditional logistic regression models were used to explore the associations of air pollutants with SMDs onset cross various exposure windows. Results are presented as percentage changes in odds ratios (ORs) of SMDs onset per interquartile range (IQR) increase in each pollutant ([OR-1]\u0026times;100%) with corresponding 95% confidence intervals (CIs).\u003c/p\u003e\u003cp\u003eTo identify the most relevant exposure windows, we examined single-day lag effects (lag 0\u0026ndash;7 days) and moving averages over multiple days (lag 01\u0026ndash;07 days). For each pollutant, the lag period demonstrating the strongest association was selected for primary analyses. All models included natural cubic spline functions of daily average temperature (degrees of freedom [\u003cem\u003edf\u003c/em\u003e ]\u0026thinsp;=\u0026thinsp;6) and relative humidity (\u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3) to adjust for meteorological conditions.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e To explore potential non-linear exposure-response relationships, we applied a 3 \u003cem\u003edf\u003c/em\u003e natural cubic spline transformation to each air pollutant in the main lag period, with nonlinearity evaluated using likelihood ratio tests comparing linear and spline models.\u003c/p\u003e\u003cp\u003eWe conducted subgroup analyses stratified by sex (male, female), age (\u0026lt;65 or \u0026ge;\u0026thinsp;65 years), season (warm: May to October and cold: November to April), and disease subtype (\u0026ldquo;schizophrenia, schizotypal, and delusional disorders\u0026rdquo;, \u0026ldquo;mood disorders\u0026rdquo;, and \u0026ldquo;other mental disorders\u0026rdquo;) to identify potentially susceptible populations and examine possible effect modifications. The difference across each stratification variable was calculated using 2-sample z tests, with the point estimates of both stratifications (β\u0026thinsp;=\u0026thinsp;ln odds ratio) and the standard errors (SEs).\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:z=\\:\\frac{{\\beta\\:}_{1}+{\\beta\\:}_{2}}{\\sqrt{{SE}_{1}^{2}+{SE}_{2}^{2}}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSensitivity analyses were conducted to test the robustness of our results, including: 1) two-pollutant models controlling for co-pollutants; 2) restricting the analyses to cases diagnosed with \u0026ldquo;schizophrenia, schizotypal, and delusional disorders\u0026rdquo; (ICD-10 code: F20-F29); 3) restricting the analyses to Han race; 4) adjusting for temperature using a \u003cem\u003edf\u003c/em\u003e of 3.\u003c/p\u003e\u003cp\u003eAll analyses were performed using R version 4.4.2. All \u003cem\u003eP\u003c/em\u003e values were reported as two-sided values, and a \u003cem\u003eP\u003c/em\u003e value less than 0.05 was regarded as statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eDescriptive statistics\u003c/h2\u003e\u003cp\u003eDuring the 8-year study period (2013\u0026ndash;2020), 49,707 SMDs cases were identified in Gansu Province, which included 30,983 cases (62.3%) of schizophrenia, schizotypal, and delusional disorders, 7,760 cases (15.6%) of mood disorders, and 10,964 cases (22.1%) of other mental disorders (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Of the cases, 50.4% were male and 91.7% were of the Han race. The mean age was 40.7 years, with 91.8% of individuals aged under 65 years. Case distribution was slightly higher during the warm season (May-October, 50.9%) compared to the cool season (November-April, 49.1%). The geographical distribution of cases is shown in Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, with higher case densities represented by darker clusters. Our case-crossover design yielded 198,828 control days matched to the 49,707 case days.\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\u003eBaseline characteristics of study population.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBaseline Characteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eValue\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Number, n\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49,707\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCase days, n\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49,707\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl days, n\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e198,828\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25,054 (50.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24,653 (49.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean (SD), yr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40.7 (17.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian (IQR), yr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42.2 (25.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;65 yr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45,643 (91.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;65 yr\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4,064 (8.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRace, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45,576 (91.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4,131 (8.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiagnostic subgroups, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSchizophrenia, schizotypal, and delusional disorders\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30,983 (62.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMood disorders\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7,760 (15.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther mental disorders\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10,964 (22.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeason\u003csup\u003e*\u003c/sup\u003e, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCold season\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24,386 (49.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWarm season\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25,321 (50.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003eAbbreviations: SD, standard deviation; IQR, interquartile range.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003eNotes\u003csup\u003e*\u003c/sup\u003e: Cold season, October to March; Warm season, April to September.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the distribution of air pollutants and meteorological conditions during the study period. Mean concentrations were 37.9 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e for PM\u003csub\u003e2.5\u003c/sub\u003e, 92.2 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e for PM\u003csub\u003e10\u003c/sub\u003e, 23.1 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e for SO\u003csub\u003e2\u003c/sub\u003e, 25.1 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e for NO\u003csub\u003e2\u003c/sub\u003e, 1.0 mg/m\u003csup\u003e3\u003c/sup\u003e for CO, and 94.7 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e for O\u003csub\u003e3\u003c/sub\u003e. The correlation analysis (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) revealed moderate to strong positive correlations among PM\u003csub\u003e2.5\u003c/sub\u003e, CO, NO\u003csub\u003e2\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, and SO\u003csub\u003e2\u003c/sub\u003e. In contrast, O\u003csub\u003e3\u003c/sub\u003e exposure was negatively associated with other pollutants. Temperature was positively associated with O\u003csub\u003e3\u003c/sub\u003e and negatively associated with air pollutants, while relative humidity was negatively associated with all air pollutants.\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\u003eDistribution of air pollutants and meteorological conditions during case and control days in Gansu Province, China, 2013\u0026ndash;2020\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=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u003csub\u003e25\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMedian\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eP\u003csub\u003e75\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAir pollutant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e, \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e20.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e24.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e33.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e47.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e568.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCO, mg/m\u003csup\u003e3\u003c/sup\u003e\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\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.6\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\u003e1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e11.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e, \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.0\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\u003e17.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e22.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e29.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e208.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eO\u003csub\u003e3\u003c/sub\u003e, \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e94.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e25.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e74.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e93.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e112.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e619.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e, \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e92.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e68.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e54.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e79.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e111.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2,508.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSO\u003csub\u003e2\u003c/sub\u003e, \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e16.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e29.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e610.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMeteorological condition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRelative humidity, %\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e55.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e21.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e38.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e55.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e72.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e103.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTemperature, ℃\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e7.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-29.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e16.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e38.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eAbbreviations: SD, standard deviation; P\u003csub\u003e25\u003c/sub\u003e, the 25th percentile; Median, the 50th percentile; P\u003csub\u003e75\u003c/sub\u003e, the 75th percentile; PM\u003csub\u003e2.5\u003c/sub\u003e, particulate matter with an aerodynamic diameter\u0026thinsp;\u0026le;\u0026thinsp;2.5 mm; CO, carbon monoxide; NO\u003csub\u003e2\u003c/sub\u003e, nitrogen dioxide; O\u003csub\u003e3\u003c/sub\u003e, ozone; PM\u003csub\u003e10\u003c/sub\u003e, particulate matter with an aerodynamic diameter\u0026thinsp;\u0026le;\u0026thinsp;10 mm; SO\u003csub\u003e2\u003c/sub\u003e, sulfur dioxide.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eAssociations of air pollutants with SMDs\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the lag-specific associations between air pollutants and SMDs onset. Significant positive associations were observed for PM\u003csub\u003e2.5\u003c/sub\u003e (lag0, lag1, lag7, lag01-lag05, lag07), CO (lag0-lag5, lag7, lag01-lag07), NO\u003csub\u003e2\u003c/sub\u003e (lag0-lag5, lag01-lag07), PM\u003csub\u003e10\u003c/sub\u003e (lag0, lag1, lag01-lag03) and SO\u003csub\u003e2\u003c/sub\u003e (lag0-lag7, lag01-lag07), with the strongest effects generally observed at cumulative exposure windows. For subsequent analyses, we selected the exposure windows demonstrating the strongest associations: lag01 for PM\u003csub\u003e2.5\u003c/sub\u003e, lag05 for CO, lag05 for NO\u003csub\u003e2\u003c/sub\u003e, lag01 for PM\u003csub\u003e10,\u003c/sub\u003e and lag07 for SO\u003csub\u003e2\u003c/sub\u003e. Each IQR increase in pollutant concentration was associated with significantly increased odds of SMDs onset: 4.37% (95% CI: 2.28%-6.50%) for PM\u003csub\u003e2.5\u003c/sub\u003e, 16.54% (95% CI: 12.26%-20.98%) for CO, 9.58% (95% CI: 6.30%-12.95%) for NO\u003csub\u003e2\u003c/sub\u003e, 2.58% (95% CI: 1.34%-3.84%) for PM\u003csub\u003e10\u003c/sub\u003e, and 30.65% (95% CI: 24.37%-37.25%) for SO\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e\u003cp\u003eThe concentration-response curves for each pollutant and SMDs onset are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e2\u003c/span\u003e. We observed monotonically increasing relationships between SMDs risk and concentrations of CO, PM\u003csub\u003e10\u003c/sub\u003e, and SO\u003csub\u003e2\u003c/sub\u003e across their respective distribution ranges. For PM\u003csub\u003e2.5\u003c/sub\u003e, the risk increased monotonically at concentrations above 29.4 \u0026micro;g/m\u0026sup3;, while for NO\u003csub\u003e2\u003c/sub\u003e, a similar positive trend was observed at concentrations below 56.1 \u0026micro;g/m\u0026sup3;. Likelihood ratio tests confirmed significant non-linearity in the exposure-response relationships for PM\u003csub\u003e2.5\u003c/sub\u003e and NO\u003csub\u003e2\u003c/sub\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while relationships for CO, PM\u003csub\u003e10\u003c/sub\u003e, and SO\u003csub\u003e2\u003c/sub\u003e did not significantly deviate from linearity.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eStratified analyses\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the results of stratified analyses by sex, age, season, and disease subtype. When stratified by disease subtype, we observed consistent positive associations across the three diagnostic categories (schizophrenia spectrum disorders, mood disorders, and other mental disorders) for all pollutants except O\u003csub\u003e3\u003c/sub\u003e. We identified significant effect modification by sex for PM\u003csub\u003e10\u003c/sub\u003e, with females showing greater susceptibility compared to males (\u003cem\u003eP\u003c/em\u003e for difference\u0026thinsp;=\u0026thinsp;0.002). Age significantly modified the effects of PM\u003csub\u003e2.5\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and SO\u003csub\u003e2\u003c/sub\u003e, with stronger associations observed among elderly individuals (\u0026ge;\u0026thinsp;65 years) compared to younger individuals. Each IQR increase in PM\u003csub\u003e2.5\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and SO\u003csub\u003e2\u003c/sub\u003e was associated with 0.48% (95% CI: 0.17%-0.79%), 1.75% (95% CI: 0.84%-2.68%), and 2.57% (95% CI: 1.55%-3.59%) higher odds of SMDs onset in the elderly population, respectively. The effect estimates for these pollutants were substantially lower in the younger population (P for difference\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all three comparisons). Seasonal stratification revealed significantly stronger associations during the warm season compared to the cold season for PM\u003csub\u003e2.5\u003c/sub\u003e, CO, and NO\u003csub\u003e2\u003c/sub\u003e. In the warm season, IQR increases in these pollutants were associated with 0.26% (95% CI: 0.13%-0.38%), 102.49% (95% CI: 72.78%-137.30%), and 1.97% (95% CI: 1.30%-2.65%) higher odds of SMDs onset, respectively. The corresponding estimates for the cold season were significantly lower (P for difference\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all three comparisons).\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\u003eAssociation of each IQR increase of exposures to PM\u003csub\u003e2.5\u003c/sub\u003e (lag01), CO (lag05), NO\u003csub\u003e2\u003c/sub\u003e (lag05), PM\u003csub\u003e10\u003c/sub\u003e (lag01), and SO\u003csub\u003e2\u003c/sub\u003e (lag07) with onset of severe mental disorders stratified by sex, age, season, and disease subtype.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003ePercent change (95% CI)\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\u003eCO\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.12 (-0.003,0.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e41.00 (26.97,56.57)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.93 (0.53,1.34)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.01 (-0.02,0.04) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.59 (1.22,1.96)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.25 (0.13,0.37)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e29.62 (16.87,43.75)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.77 (0.37,1.16)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.08 (0.05,0.11)\u003c/b\u003e \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.22 (0.85,1.58)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.16 (0.06,0.25)\u003c/b\u003e \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e33.14 (23.28,43.77)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.75 (0.45,1.05)\u003c/b\u003e \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.05 (0.03,0.07)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.31 (1.05,1.58)\u003c/b\u003e \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.48 (0.17,0.79)\u003c/b\u003e \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e58.33 (22.51,104.62)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.75 (0.84,2.68)\u003c/b\u003e \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.02 (-0.10,0.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e2.57 (1.55,3.59)\u003c/b\u003e \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeason\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWarm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.26 (0.13,0.38)\u003c/b\u003e \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e102.49 (72.78,137.30)\u003c/b\u003e \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.97 (1.30,2.65)\u003c/b\u003e \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.04 (0.01,0.07)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.36 (0.76,1.97)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCold\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.05 (-0.08,0.18) \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e29.99 (19.43,41.49)\u003c/b\u003e \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.74 (0.43,1.05)\u003c/b\u003e \u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.04 (0.01,0.06)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.75 (1.45,2.04)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDisease subtype\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSchizophrenia, schizotypal, and delusional disorders\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.19 (0.08,0.30)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e38.5 (26.48,51.66)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.85 (0.51,1.20)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.03 (0.003,0.06)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.37 (1.05,1.69)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMood disorders\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.23 (0.01,0.46)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e52.55 (24.72,86.6)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e1.15 (0.37,1.94)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.09 (0.04,0.14)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.75 (1.07,2.43)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther mental disorders\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.14 (-0.05,0.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.27 (-2.82,34.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.60 (-0.002,1.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.05 (0.003,0.09)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e1.18 (0.60,1.76)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: PM\u003csub\u003e2.5\u003c/sub\u003e, particulate matter with an aerodynamic diameter\u0026thinsp;\u0026le;\u0026thinsp;2.5 mm; CO, carbon monoxide; NO\u003csub\u003e2\u003c/sub\u003e, nitrogen dioxide; O\u003csub\u003e3\u003c/sub\u003e, ozone; PM\u003csub\u003e10\u003c/sub\u003e, particulate matter with an aerodynamic diameter\u0026thinsp;\u0026le;\u0026thinsp;10 mm; SO\u003csub\u003e2\u003c/sub\u003e, sulfur dioxide.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eStatistically significant associations are shown in bold.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNotes\u003csup\u003e*\u003c/sup\u003e: differences across strata are statistically significant.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eSensitivity analyses\u003c/h2\u003e\u003cp\u003eTwo-pollutant models were conducted to test the robustness of our findings (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The associations between each pollutant and SMDs onset remained statistically significant after adjustment for co-pollutants, with the exception of NO\u003csub\u003e2\u003c/sub\u003e, which became non-significant when adjusted for other pollutants. To prevent multicollinearity issues, we did not include PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e in the same model. Additional sensitivity analyses restricting the study population to Han ethnicity or to cases diagnosed with schizophrenia spectrum disorders yielded results consistent with our primary findings (Figures S2-S3). Similarly, using an alternative approach to control for temperature (\u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3 instead of \u003cem\u003edf\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6) did not materially change the observed associations (Figure S4), further supporting the robustness of our findings.\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\u003ePercent changes in ORs and 95% confidence intervals for Onset of severe mental disorders Associated with Each IQR Increase of Exposures to PM\u003csub\u003e2.5\u003c/sub\u003e (lag01), NO\u003csub\u003e2\u003c/sub\u003e (lag 05), PM\u003csub\u003e10\u003c/sub\u003e (lag01), and SO\u003csub\u003e2\u003c/sub\u003e (lag07) Estimated by Single- and Two-pollutant Models.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eModels\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e\u003cp\u003ePercent change (95% CI)\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\u003eCO\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSingle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.37 (2.28,6.50)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.54 (12.26,20.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.58 (6.30,12.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.58 (1.34,3.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e30.65 (24.37,37.25)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdjusted for PM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.93 (11.45,20.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.26 (5.87,12.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e29.39 (22.82,36.33)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdjusted for CO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.22 (1.08,5.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.13 (-0.60,7.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.50 (1.26,3.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e27.05 (20.36,34.11)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdjusted for NO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.68 (1.57,5.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.05 (8.98,19.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.63 (1.39,3.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e31.18 (24.21,38.54)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdjusted for PM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10.44 (6.51,14.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.42 (12.14,20.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.53 (6.25,12.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e30.48 (24.20,37.08)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdjusted for SO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.39 (1.30,5.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.73 (4.33,13.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.71 (-0.69,6.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.50 (1.25,3.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026mdash;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdjusted for O\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.32 (2.24,6.46)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.77 (11.50,20.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.43 (6.16,12.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.54 (1.30,3.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e28.91 (22.70,35.44)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: PM\u003csub\u003e2.5\u003c/sub\u003e, particulate matter with an aerodynamic diameter\u0026thinsp;\u0026le;\u0026thinsp;2.5 mm; CO, carbon monoxide; NO\u003csub\u003e2\u003c/sub\u003e, nitrogen dioxide; O\u003csub\u003e3\u003c/sub\u003e, ozone; PM\u003csub\u003e10\u003c/sub\u003e, particulate matter with an aerodynamic diameter\u0026thinsp;\u0026le;\u0026thinsp;10 mm; SO\u003csub\u003e2\u003c/sub\u003e, sulfur dioxide.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this large case-crossover study involving 49,707 individuals with SMDs in northwestern China, we found positive associations between short-term exposure to multiple air pollutants and SMDs onset. Specifically, each IQR increase in PM\u003csub\u003e2.5\u003c/sub\u003e, CO, NO\u003csub\u003e2\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, and SO\u003csub\u003e2\u003c/sub\u003e was associated with increased odds of SMDs onset ranging from 2.58\u0026ndash;30.65%, with exposure-response relationships exhibiting positive trends. Our analyses further revealed potentially vulnerable subgroups, with females demonstrating greater susceptibility to PM\u003csub\u003e10\u003c/sub\u003e, elderly individuals showing stronger associations with PM\u003csub\u003e2.5\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and SO\u003csub\u003e2\u003c/sub\u003e, and warm-season exposures exhibiting more pronounced effects for several pollutants.\u003c/p\u003e\u003cp\u003eThis study represents the first comprehensive investigation of short-term air pollution effects on severe mental disorders in northwestern China, employing high-resolution exposure data, a large population-based sample, and rigorous methodology to address critical knowledge gaps in environmental psychiatry. Our findings extend the current understanding of air pollution's relationship with mental health in several important ways. While previous studies have examined associations between air pollution and specific psychiatric conditions or healthcare utilization, our research uniquely addresses SMDs as a broader category with significant public health implications. Several multi-city studies in China reported positive associations between air pollutants and hospital admissions for mental disorders, but with substantially different exposure assessment methodology and geographical context.\u003csup\u003e\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e Similarly, research in the United States found positive associations between PM\u003csub\u003e2.5\u003c/sub\u003e and NO\u003csub\u003e2\u003c/sub\u003e exposure and psychiatric hospital admissions, though with different effect magnitudes and population characteristics.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e Notably, our observation of strong SO\u003csub\u003e2\u003c/sub\u003e associations (30.65% increased odds per IQR) represents a novel finding compared to previous work, potentially reflecting the specific industrial pollution profile of northwestern China. The divergence between our findings and those reported in some European studies, which found limited associations except for O\u003csub\u003e3\u003c/sub\u003e, likely reflects regional differences in pollution characteristics, healthcare systems, and population susceptibilities\u0026mdash;underscoring the importance of context-specific environmental health research.\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eWhile the precise mechanisms linking short-term air pollution exposure to SMDs onset remain incompletely understood, several biologically plausible pathways may explain our observed associations. Neuroimaging studies have revealed that air pollution exposure is associated with altered white matter integrity and blood-brain barrier dysfunction.\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e Air pollutants may also activate the hypothalamic-pituitary-adrenal axis, potentially disrupting stress hormone regulation and neurotransmitter systems crucial for mental health.\u003csup\u003e\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e These neurobiological changes could potentially lower the threshold for psychiatric symptom manifestation in susceptible individuals, particularly during acute pollution episodes. However, we acknowledge that our epidemiological findings cannot establish these mechanistic pathways, which require further investigation through experimental and clinical studies.\u003c/p\u003e\u003cp\u003eThe observed vulnerability patterns in our study offer important insights for targeted public health interventions. The enhanced susceptibility among females to particulate matter aligns with emerging evidence of sex-based differences in air pollution responses, potentially attributable to physiological differences in immune response, hormonal factors, or differential exposure patterns.\u003csup\u003e\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e The stronger associations observed among elderly individuals for multiple pollutants suggest age-related vulnerability mechanisms, possibly related to decreased physiological reserve, compromised blood-brain barrier integrity, or reduced antioxidant capacity with advancing age.\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e Seasonal variations in effect estimates, with stronger associations during warmer months, may reflect seasonal differences in activity patterns, ventilation behaviors, or photochemical pollutant formation.\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e These heterogeneous effects highlight the importance of identifying vulnerable subpopulations for targeted protection measures, particularly as climate change may exacerbate both air pollution and temperature extremes in coming decades.\u003c/p\u003e\u003cp\u003eOur study has several strengths that enhance the reliability and significance of the findings. First, the time-stratified case-crossover design effectively controls for individual-level confounders and time trends, providing robust estimates of acute exposure effects. Second, our use of high-resolution air pollution data (1\u0026ndash;10 km) based on multiple data sources represents a substantial improvement over previous studies relying on sparse monitoring networks, significantly reducing exposure misclassification. Third, the large sample size drawn from comprehensive provincial surveillance data provides sufficient statistical power for detecting subtle associations and conducting meaningful subgroup analyses. Fourth, the study setting in Gansu Province\u0026mdash;characterized by severe air pollution episodes and unique geographical conditions\u0026mdash;offers valuable insights into pollution-health relationships in a previously understudied high-exposure setting.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e Finally, our extensive sensitivity analyses demonstrate the robustness of our findings across alternative model specifications and subpopulations.\u003c/p\u003e\u003cp\u003eSeveral limitations should be considered when interpreting our results. First, despite using high-resolution spatial data, we could not account for individual time-activity patterns or indoor air pollution exposure, potentially introducing some exposure misclassification.\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e However, the case-crossover design, which compares exposures within individuals across time rather than between individuals, likely minimizes the impact of this limitation. Second, while we controlled for temperature and humidity, other time-varying factors such as viral epidemics, social events, or healthcare access fluctuations might introduce residual confounding. Third, the strong correlations among pollutants present challenges in disentangling the independent effects of individual pollutants, although our two-pollutant models suggest persistent associations after mutual adjustment. Fourth, while we observed consistent associations across diagnostic subgroups, differences in case ascertainment or diagnostic practices across the region could influence our findings. Finally, caution is warranted in generalizing these results to other populations with different pollution profiles, genetic backgrounds, or healthcare systems.\u003c/p\u003e\u003cp\u003eIn conclusion, this study provides robust evidence of associations between short-term exposure to multiple air pollutants and the SMDs onset in northwestern China. Our findings suggest that air pollution may represent an important modifiable environmental risk factor for mental health, with particularly pronounced effects among vulnerable subpopulations including females, the elderly, and during warmer seasons. These results underscore the potential public health benefits of improved air quality for reducing the burden of severe mental disorders, especially in regions with elevated pollution levels.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the Institutional Review Board (IRB) of School of Public Health, Lanzhou University (IRB 25010202) and conducted in accordance with the tenets of the Declaration of Helsinki. This study was approved by the Ethical Committee of School of Public Health, Lanzhou University with a waiver of informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during this study are not publicly available, but are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the STI 2030\u0026ndash;Major Projects (2022ZD0209102), and the Natural Science Foundation of Gansu Province, China (21JR7RA654).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge the contribution and collaboration of all those who participated in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYH and GY contributed equally to the paper as joint first authors. XL ([email protected]) and WP ([email protected]) are joint corresponding authors. XL, WP, YH, and GY designed the research. YH and GY performed statistical analyses. YH, GY, and XL drafted the manuscript. All authors interpreted data, critically reviewed and revised the manuscript, and have read and approved the final version. XL is the guarantor of this work and, as such, has full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGoldfarb M, De Hert M, Detraux J, et al. Severe Mental Illness and Cardiovascular Disease. \u003cem\u003eJournal of the American College of Cardiology\u003c/em\u003e. 2022;80(9):918-933. doi:10.1016/j.jacc.2022.06.017\u003c/li\u003e\n\u003cli\u003eFerrari AJ, Santomauro DF, Aali A, et al. 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The effects of indoor air pollution from solid fuel use on cognitive function among middle-aged and older population in China. \u003cem\u003eSci Total Environ\u003c/em\u003e. 2021;754:142460. doi:10.1016/j.scitotenv.2020.142460\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Mental disorders, Ambient air pollution, Case-crossover, Short-term exposure","lastPublishedDoi":"10.21203/rs.3.rs-7468223/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7468223/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe associations between short-term exposure to air pollution and severe mental disorders (SMDs) remain poorly understood, particularly in China and among potentially vulnerable subpopulations.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe conducted a time-stratified case-crossover study of 49,707 individuals diagnosed with SMDs in Gansu Province, China, from 2013 to 2020. Individual-level exposures to particulate matter\u0026thinsp;\u0026le;\u0026thinsp;2.5 \u0026micro;m in aerodynamic diameter (PM\u003csub\u003e2.5\u003c/sub\u003e), particulate matter\u0026thinsp;\u0026le;\u0026thinsp;10 \u0026micro;m in aerodynamic diameter (PM\u003csub\u003e10\u003c/sub\u003e), sulfur dioxide (SO\u003csub\u003e2\u003c/sub\u003e), nitrogen dioxide (NO\u003csub\u003e2\u003c/sub\u003e), carbon monoxide (CO), and ozone (O\u003csub\u003e3\u003c/sub\u003e) were estimated using high-resolution spatiotemporal data from the China High Air Pollutants dataset. We employed conditional logistic regression models to estimate associations between pollutant exposures and SMDs onset, controlling for temperature and humidity. Stratified analyses were performed to identify potentially vulnerable subpopulations.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003eEach interquartile range increase in exposure to PM\u003csub\u003e2.5\u003c/sub\u003e (23.1 \u0026micro;g/m\u0026sup3;), CO (0.51 mg/m\u0026sup3;), NO\u003csub\u003e2\u003c/sub\u003e (11.9 \u0026micro;g/m\u0026sup3;), PM\u003csub\u003e10\u003c/sub\u003e (57.5 \u0026micro;g/m\u0026sup3;), and SO\u003csub\u003e2\u003c/sub\u003e (19.1 \u0026micro;g/m\u0026sup3;) was associated with increased odds of SMDs onset: 4.37% (95% CI: 2.28%-6.50%), 16.54% (95% CI: 12.26%-20.98%), 9.58% (95% CI: 6.30%-12.95%), 2.58% (95% CI: 1.34%-3.84%), and 30.65% (95% CI: 24.37%-37.25%), respectively. Exposure-response relationships displayed positive trends for all significant pollutants. Effect estimates were generally stronger among females, elderly individuals (\u0026ge;\u0026thinsp;65 years), and during warm seasons (May-October). Associations remained robust in two-pollutant models and various sensitivity analyses.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e\u003cp\u003eShort-term exposure to multiple air pollutants is positively associated with SMDs onset, with differential vulnerability across population subgroups. These results suggest that air pollution may represent an important modifiable environmental risk factor for SMDs, particularly in regions with elevated pollution levels.\u003c/p\u003e","manuscriptTitle":"Short-Term Exposure to Ambient Air Pollution and Onset of Severe Mental Disorders: A Case-Crossover Study in Northwestern China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-15 18:47:23","doi":"10.21203/rs.3.rs-7468223/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-06T09:15:28+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-02T15:50:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-29T09:03:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"199220990830859957992464385638917759177","date":"2025-09-08T10:48:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"266373508466733558795191735840539746633","date":"2025-09-08T05:59:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"36541566259765457671896621202529538535","date":"2025-09-08T05:43:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-08T05:40:54+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-04T07:56:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-02T10:59:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-02T10:58:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-08-27T06:00:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"efa02063-f513-45e0-8b29-fd3011881629","owner":[],"postedDate":"September 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-29T16:02:30+00:00","versionOfRecord":{"articleIdentity":"rs-7468223","link":"https://doi.org/10.1186/s12889-025-25906-z","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2025-12-22 15:58:08","publishedOnDateReadable":"December 22nd, 2025"},"versionCreatedAt":"2025-09-15 18:47:23","video":"","vorDoi":"10.1186/s12889-025-25906-z","vorDoiUrl":"https://doi.org/10.1186/s12889-025-25906-z","workflowStages":[]},"version":"v1","identity":"rs-7468223","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7468223","identity":"rs-7468223","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}