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Wildfires have become more frequent and intense due to climate change and outdoor wildfire fine particulate matter (PM 2.5 ) concentrations differ from relatively smoothly varying total PM 2.5 . Thus, we introduced a conceptual model for computing long-term wildfire PM 2.5 and assessed disproportionate exposures among marginalized communities. We used monitoring data and statistical techniques to characterize annual wildfire PM 2.5 exposure based on intermittent and extreme daily wildfire PM 2.5 concentrations in California census tracts (2006 to 2020). Metrics included: 1) weeks with wildfire PM 2.5 < 5 μg/m 3 ; 2) days with non-zero wildfire PM 2.5 ; 3) mean wildfire PM 2.5 during peak exposure week; 4) smoke waves (≥2 consecutive days with <15 μg/m 3 wildfire PM 2.5 ); and 5) mean annual wildfire PM 2.5 concentration. We classified tracts by their racial/ethnic composition and CalEnviroScreen (CES) score, an environmental and social vulnerability composite measure. We examined associations of CES and racial/ethnic composition with the wildfire PM 2.5 metrics using mixed-effects models. Averaged 2006 to 2020, we detected little difference in exposure by CES score or racial/ethnic composition, except for non-Hispanic American Indian and Alaska Native populations, where a 1-SD increase was associated with higher exposure for 4/5 metrics. CES or racial/ethnic × year interaction term models revealed exposure disparities in some years. Compared to their California-wide representation, the exposed populations of non-Hispanic American Indian and Alaska Native (1.68×, 95% CI: 1.01 to 2.81), white (1.13×, 95% CI: 0.99 to 1.32), and multiracial (1.06×, 95% CI: 0.97 to 1.23) people were over-represented from 2006 to 2020. In conclusion, during our study period in California, we detected disproportionate long-term wildfire PM 2.5 exposure for several racial/ethnic groups., Competing Interests: Competing interests statement:The authors declare no competing interest.

Background: Little is known about the impact of environmental exposure change on metabolic biomarkers associated with cancer risk. Furthermore, this limited epidemiological evidence on metabolic biomarkers focused on residential exposure, without considering the activity space which can be done by modelling dynamic exposures. In this longitudinal study, we aimed to investigate the impact of environmental exposures change on metabolic biomarkers using GPS-GIS based measurements., Methods: Among two weight loss interventions, the Reach for Health and the MENU studies, which included ∼460 women at risk of breast cancer or breast cancer survivors residing in Southern California, three metabolic biomarkers (insulin resistance, fasting glucose, and C-reactive protein) were assessed. Dynamic GPS-GIS based exposure to green spaces, recreation, walkability, NO 2 , and PM2.5 were calculated at baseline and 6 months follow-up using time-weighted spatial averaging. Generalized estimating equations models were used to examine the relationship between changes in environmental exposures and biomarker levels over time., Results: Overall, six-month environmental exposure change was not associated with metabolic biomarkers change. Stratified analyses by level of environmental exposures at baseline revealed that reduced NO2 and PM2.5 exposure was associated with reduced fasting glucose concentration among women living in a healthier environment at baseline (β -0.010, 95%CI -0.025, 0.005; β -0.019, 95%CI -0.034, -0.003, respectively). Women living in poor environmental conditions at baseline and exposed to greener environments had decreased C-reactive protein concentrations (β -1.001, 95%CI -1.888, -0.131)., Conclusions: The impact of environmental exposure changes on metabolic biomarkers over time may be modified by baseline exposure conditions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Inc.)

Wildfire smoke has been associated with adverse respiratory outcomes, but the impacts of wildfire on other health outcomes and sensitive subpopulations are not fully understood. We examined associations between smoke events and emergency department visits (EDVs) for respiratory, cardiovascular, diabetes, and mental health outcomes in California during the wildfire season June-December 2016-2019. Daily, zip code tabulation area-level wildfire-specific fine particulate matter (PM 2.5 ) concentrations were aggregated to air basins. A "smoke event" was defined as an air basin-day with a wildfire-specific PM 2.5 concentration at or above the 98th percentile across all air basin-days (threshold = 13.5 μg/m 3 ). We conducted a two-stage time-series analysis using quasi-Poisson regression considering lag effects and random effects meta-analysis. We also conducted analyses stratified by race/ethnicity, age, and sex to assess potential effect modification. Smoke events were associated with an increased risk of EDVs for all respiratory diseases at lag 1 [14.4%, 95% confidence interval (CI): (6.8, 22.5)], asthma at lag 0 [57.1% (44.5, 70.8)], and chronic lower respiratory disease at lag 0 [12.7% (6.2, 19.6)]. We also found positive associations with EDVs for all cardiovascular diseases at lag 10. Mixed results were observed for mental health outcomes. Stratified results revealed potential disparities by race/ethnicity. Short-term exposure to smoke events was associated with increased respiratory and schizophrenia EDVs. Cardiovascular impacts may be delayed compared to respiratory outcomes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Immortal time bias is a well-recognized bias in clinical epidemiology but is rarely discussed in environmental epidemiology. Under the target trial framework, this bias is formally conceptualized as a misalignment between the start of study follow-up (time 0) and treatment assignment. This misalignment can occur when attained duration of follow-up is encoded into treatment assignment using minimums, maximums, or averages. The bias can be exacerbated in the presence of time trends commonly found in environmental exposures. Using lung cancer cases from the California Cancer Registry (2000-2010) linked with estimated concentrations of particulate matter less than or equal to 2.5 μm in aerodynamic diameter (PM2.5), we replicated previous studies that averaged PM2.5 exposure over follow-up in a time-to-event model. We compared this approach with one that ensures alignment between time 0 and treatment assignment, a discrete-time approach. In the former approach, the estimated overall hazard ratio for a 5-μg/m3 increase in PM2.5 was 1.38 (95% confidence interval: 1.36, 1.40). Under the discrete-time approach, the estimated pooled odds ratio was 0.99 (95% confidence interval: 0.98, 1.00). We conclude that the strong estimated effect in the former approach was likely driven by immortal time bias, due to misalignment at time 0. Our findings highlight the importance of appropriately conceptualizing a time-varying environmental exposure under the target trial framework to avoid introducing preventable systematic errors., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Importance: Women are especially vulnerable to mental health matters post partum because of biological, emotional, and social changes during this period. However, epidemiologic evidence of an association between air pollution exposure and postpartum depression (PPD) is limited., Objective: To examine the associations between antepartum and postpartum maternal air pollution exposure and PPD., Design, Setting, and Participants: This retrospective cohort study used data from Kaiser Permanente Southern California (KPSC) electronic health records and included women who had singleton live births at KPSC facilities between January 1, 2008, and December 31, 2016. Data were analyzed between January 1 and May 10, 2023., Exposures: Ambient air pollution exposures were assessed based on maternal residential addresses using monthly averages of particulate matter less than or equal to 2.5 μm (PM2.5), particulate matter less than or equal to 10 μm (PM10), nitrogen dioxide (NO2), and ozone (O3) from spatial interpolation of monitoring station measurements. Constituents of PM2.5 (sulfate, nitrate, ammonium, organic matter, and black carbon) were obtained from fine-resolution geoscience-derived models based on satellite, ground-based monitor, and chemical transport modeling data., Main Outcomes and Measures: Participants with an Edinburgh Postnatal Depression Scale score of 10 or higher during the 6 months after giving birth were referred to a clinical interview for further assessment and diagnosis. Ascertainment of PPD was defined using a combination of diagnostic codes and prescription medications., Results: The study included 340 679 participants (mean [SD] age, 30.05 [5.81] years), with 25 674 having PPD (7.54%). Increased risks for PPD were observed to be associated with per-IQR increases in antepartum and postpartum exposures to O3 (adjusted odds ratio [AOR], 1.09; 95% CI, 1.06-1.12), PM10 (AOR, 1.02; 95% CI, 1.00-1.04), and PM2.5 (AOR, 1.02; 95% CI, 1. 00-1.03) but not with NO2; PPD risks were mainly associated with PM2.5 organic matter and black carbon. Overall, a higher risk of PPD was associated with O3 during the entire pregnancy and postpartum periods and with PM exposure during the late pregnancy and postpartum periods., Conclusions and Relevance: The study findings suggest that long-term exposure to antepartum and postpartum air pollution was associated with higher PPD risks. Identifying the modifiable environmental risk factors and developing interventions are important public health issues to improve maternal mental health and alleviate the disease burden of PPD.

Background: People using electricity-dependent durable medical equipment (DME) may be vulnerable to health effects from wildfire smoke, residence near wildfires, or residence in evacuation zones. To our knowledge, no studies have examined their healthcare utilization during wildfires., Methods: We obtained 2016-2020 counts of residential Zip Code Tabulation Area (ZCTA) level outpatient, emergency department (ED), and inpatient visits made by DME-using Kaiser Permanente Southern California members 45+. We linked counts to daily ZCTA-level wildfire particulate matter (PM) 2.5 and wildfire boundary and evacuation data from the 2018 Woolsey and 2019 Getty wildfires. We estimated the association of lagged (up to 7 days) wildfire PM 2.5 and residence near a fire or in an evacuation zone and healthcare visit frequency with negative binomial and difference-in-differences models., Results: Among 236,732 DME users, 10 µg/m 3 increases in wildfire PM 2.5 concentration were associated with the reduced rate (RR = 0.96; 95% confidence interval [CI] = 0.94, 0.99) of all-cause outpatient visits 1 day after exposure and increased rate on 4 of 5 subsequent days (RR range 1.03-1.12). Woolsey Fire proximity (<20 km) was associated with reduced all-cause outpatient visits, whereas evacuation and proximity were associated with increased inpatient cardiorespiratory visits (proximity RR = 1.45; 95% CI = 0.99, 2.12, evacuation RR = 1.72; 95% CI = 1.00, 2.96). Neither Getty Fire proximity nor evacuation was associated with healthcare visit frequency., Conclusions: Our results support the hypothesis that wildfire smoke or proximity interrupts DME users' routine outpatient care, via sheltering in place. However, wildfire exposures were also associated with increased urgent healthcare utilization in this vulnerable group., Competing Interests: The authors report no conflicts of interest., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

Gestational diabetes mellitus (GDM) is a major pregnancy complication affecting approximately 14.0% of pregnancies around the world. Air pollution exposure, particularly exposure to PM 2.5 , has become a major environmental issue affecting health, especially for vulnerable pregnant women. Associations between PM 2.5 exposure and adverse birth outcomes are generally assumed to be the same throughout a large geographical area. However, the effects of air pollution on health can very spatially in subpopulations. Such spatially varying effects are likely due to a wide range of contextual neighborhood and individual factors that are spatially correlated, including SES, demographics, exposure to housing characteristics and due to different composition of particulate matter from different emission sources. This combination of elevated environmental hazards in conjunction with socioeconomic-based disparities forms what has been described as a "double jeopardy" for marginalized sub-populations. In this manuscript our analysis combines both an examination of spatially varying effects of a) unit-changes in exposure and examines effects of b) changes from current exposure levels down to a fixed compliance level, where compliance levels correspond to the Air Quality Standards (AQS) set by the U.S. Environmental Protection Agency (EPA) and World Health Organization (WHO) air quality guideline values. Results suggest that exposure reduction policies should target certain "hotspot" areas where size and effects of potential reductions will reap the greatest rewards in terms of health benefits, such as areas of southeast Los Angeles County which experiences high levels of PM 2.5 exposures and consist of individuals who may be particularly vulnerable to the effects of air pollution on the risk of GDM., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background: There is minimal evidence of relationships between maternal air pollution exposure and spontaneous premature rupture of membranes (SPROM), a critical obstetrical problem that can significantly increase maternal and fetal mortality and morbidity. No prior study has explored the PROM risk related to specific components of particulate matter with aerodynamic diameters of ≤ 2.5 µm (PM 2.5 ). We examined associations between maternal exposure to nitrogen dioxide (NO 2 ), ozone (O 3 ), PM 2.5 , PM 10 , and PM 2.5 constituents and SPROM., Methods: A large retrospective cohort study was conducted and included 427,870 singleton live births from Kaiser Permanente Southern California during 2008-2018. Monthly averages of NO 2 , O 3 (8-h daily maximum), PM 2.5 , and PM 10 were measured using empirical Bayesian kriging based on measurements from monitoring stations. Data on PM 2.5 sulfate, nitrate, ammonium, organic matter, and black carbon were obtained from a fine-resolution model. A discrete time approach with pooled logistic regressions was used to estimate associations throughout the pregnancy and based on trimesters and gestational months. The quantile-based g-computation models were fitted to examine the effects of 1) the air pollution mixture of four pollutants of interest and 2) the mixture of PM 2.5 components., Results: There were 37,857 SPROM cases (8.8%) in our study population. We observed relationships between SPROM and maternal exposure to NO 2 , O 3 , and PM 2.5 . PM 2.5 sulfate, nitrate, ammonium, and organic matter were associated with higher SPROM risks in the single-pollutant model. Mixture analyses demonstrated that the overall effects of the air pollution mixture and PM 2.5 mixture in this study were mainly driven by O 3 and PM 2.5 nitrate, respectively. Underweight mothers had a significantly higher risk of SPROM associated with NO 2 ., Conclusion: Our findings add to the literature on associations between air pollution exposure and SPROM. This is the first study reporting the impact of PM 2.5 constituents on SPROM., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Heat and tropospheric ozone have acute impacts on rates of premature death. Warm temperatures affect the photochemical processes in ozone formation, suggesting ozone as a mediator of the acute health effect of heat on mortality. We assembled a summertime daily time-series data set of 15 French urban areas during 2000-2015 to decompose the acute total effect of heat waves on mortality into natural direct and indirect effects using a regression-based product method under the potential outcomes framework. For each area, we estimated the effect of heat waves on mortality using a quasi-Poisson model with adjustment for covariates such as lagged nitrogen dioxide concentration, and we modeled ozone with a linear regression of heat waves and the same set of covariates. We pooled estimates across areas using random-effects models. We also provide R software code (R Foundation for Statistical Computing, Vienna, Austria) with which to reproduce or replicate our analysis. Most areas demonstrated evidence of mediation by ozone, with the pooled relative risks for natural indirect effects being 1.03 (95% confidence interval (CI): 1.02, 1.05), 1.03 (95% CI: 1.01, 1.04), and 1.04 (95% CI: 1.00, 1.07) for nonaccidental, cardiovascular, and respiratory mortality, respectively. We found evidence of a mediation effect by ozone in the association between heat waves and mortality in France which varied by geographic location and cause of mortality., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: This study capitalized on coal and oil facility retirements to quantify their potential effects on fine particulate matter (PM 2.5 ) concentrations and cardiorespiratory hospitalizations in affected areas using a generalized synthetic control method., Methods: We identified 11 coal and oil facilities in California that retired between 2006 and 2013. We classified zip code tabulation areas (ZCTA) as exposed or unexposed to a facility retirement using emissions information, distance, and a dispersion model. We calculated weekly ZCTA-specific PM 2.5 concentrations based on previously estimated daily time-series PM 2.5 concentrations from an ensemble model, and weekly cardiorespiratory hospitalization rates based on hospitalization data collected by the California Department of Health Care Access and Information. We estimated the average differences in weekly average PM 2.5 concentrations and cardiorespiratory hospitalization rates in four weeks after each facility retirement between the exposed ZCTAs and the synthetic control using all unexposed ZCTAs (i.e., the average treatment effect among the treated [ATT]) and pooled ATTs using meta-analysis. We conducted sensitivity analyses to consider different classification schemes to distinguish exposed from unexposed ZCTAs, including aggregating outcomes with different time intervals and including a subset of facilities with reported retirement date confirmed via emission record., Results: The pooled ATTs were 0.02 μg/m 3 (95% confidence interval (CI): -0.25 to 0.29 μg/m 3 ) and 0.34 per 10,000 person-weeks (95%CI: -0.08 to 0.75 per 10,000 person-weeks) following the facility closure for weekly PM 2.5 and cardiorespiratory hospitalization rates, respectively. Our inferences remained the same after conducting sensitivity analyses., Conclusions: We demonstrated a novel approach to study the potential benefits associated with industrial facility retirements. The declining contribution of industrial emissions to ambient air pollution in California may explain our null findings. We encourage future research to replicate this work in regions with different industrial activities., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Chen Chen reports a relationship with Health Canada that includes: funding grants and travel reimbursement. Sindana D. Ilango reports a relationship with National Institute of Environmental Health Sciences that includes: funding grants. Joan A. Casey reports a relationship with National Institute of Environmental Health Sciences that includes: funding grants. Lucas R. F. Henneman reports a relationship with United States Environmental Protection Agency that includes: funding grants., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Purpose: Understanding the relationship between an environmental determinant and a given health outcome is key to inform public health policies. The short-term mortality and morbidity responses to outdoor air pollutants are traditionally assessed as a log-linear relationship, but few studies suggest a possible deviation from linearity. This paper investigates the shape of the relationship between ozone, NO 2 and fine particulate matter (PM 10 and PM 2.5 ), mortality and hospital admissions in 18 French cities between 2000 and 2017., Method: A multi-centric time series design, using quasi-Poisson generalized additive models, was used. Four approaches were compared to model concentration-response curves (log-linear, piecewise-linear with a priori defined breakpoints, piecewise-linear with no a priori breakpoint and cubic spline)., Results: All the models indicated evidence of supra-linearity between PM 10 , PM 2.5 , NO 2 , mortality and hospital admissions. For instance, with a log-linear model, a 10 µg/m 3 increase in PM 2.5 was associated with a 0.4% [95% CI 0.2; 0.7] increase in non-accidental mortality. When using a piecewise model with a priori set breakpoint at 10 µg/m 3 , the mortality increase was 3.8% [4.4; 6.3] below 10 µg/m 3 , and 0.3% [0; 0.6] above. Non-significant impacts of ozone were found for concentrations below 90 µg/m 3 to 120 µg/m 3 , with some variability in the identified threshold across the heath indicator studied., Conclusion: The supra-linearity of the relationship between PM 10 , PM 2.5 , NO 2 , mortality and hospital admissions supports the need to further reduce air pollution concentrations well below regulatory values., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: Numerous epidemiological studies have documented the adverse health impact of long-term exposure to fine particulate matter [particulate matter ≤ 2.5 μ m in aerodynamic diameter ( PM 2.5 )] on mortality even at relatively low levels. However, methodological challenges remain to consider potential regulatory intervention's complexity and provide actionable evidence on the predicted benefits of interventions. We propose the parametric g-computation as an alternative analytical approach to such challenges., Method: We applied the parametric g-computation to estimate the cumulative risks of nonaccidental death under different hypothetical intervention strategies targeting long-term exposure to PM 2.5 in the Canadian Community Health Survey cohort from 2005 to 2015. On both relative and absolute scales, we explored the benefits of hypothetical intervention strategies compared with the natural course that a ) set the simulated exposure value at each follow-up year to a threshold value if exposure was above the threshold ( 8.8   μ g / m 3 , 7.04   μ g / m 3 , 5   μ g / m 3 , and 4   μ g / m 3 ), and b ) reduced the simulated exposure value by a percentage (5% and 10%) at each follow-up year. We used the 3-y average PM 2.5 concentration with 1-y lag at the postal code of respondents' annual mailing addresses as their long-term exposure to PM 2.5 . We considered baseline and time-varying confounders, including demographics, behavior characteristics, income level, and neighborhood socioeconomic status. We also included the R syntax for reproducibility and replication., Results: All hypothetical intervention strategies explored led to lower 11-y cumulative mortality risks than the estimated value under the natural course without intervention, with the smallest reduction of 0.20 per 1,000 participants (95% CI: 0.06, 0.34) under the threshold of 8.8   μ g / m 3 , and the largest reduction of 3.40 per 1,000 participants (95% CI: - 0.23 , 7.03) under the relative reduction of 10% per interval. The reductions in cumulative risk, or numbers of deaths that would have been prevented if the intervention was employed instead of maintaining the status quo, increased over time but flattened toward the end of the follow-up period. Estimates among those ≥ 65 years of age were greater with a similar pattern. Our estimates were robust to different model specifications., Discussion: We found evidence that any intervention further reducing the long-term exposure to PM 2.5 would reduce the cumulative mortality risk, with greater benefits in the older population, even in a population already exposed to low levels of ambient PM 2.5 . The parametric g-computation used in this study provides flexibilities in simulating real-world interventions, accommodates time-varying exposure and confounders, and estimates adjusted survival curves with clearer interpretation and more information than a single hazard ratio, making it a valuable analytical alternative in air pollution epidemiological research. https://doi.org/10.1289/EHP11095.

Importance: Previous studies have focused on exposure to fine particulate matter 2.5 μm or less in diameter (PM2.5) and on birth outcome risks; however, few studies have evaluated the health consequences of PM2.5 exposure on infants during their first year of life and whether prematurity could exacerbate such risks., Objective: To assess the association of PM2.5 exposure with emergency department (ED) visits during the first year of life and determine whether preterm birth status modifies the association., Design, Setting, and Participants: This individual-level cohort study used data from the Study of Outcomes in Mothers and Infants cohort, which includes all live-born, singleton deliveries in California. Data from infants' health records through their first birthday were included. Participants included 2 175 180 infants born between 2014 and 2018, and complete data were included for an analytic sample of 1 983 700 (91.2%). Analysis was conducted from October 2021 to September 2022., Exposures: Weekly PM2.5 exposure at the residential ZIP code at birth was estimated from an ensemble model combining multiple machine learning algorithms and several potentially associated variables., Main Outcomes and Measures: Main outcomes included the first all-cause ED visit and the first infection- and respiratory-related visits separately. Hypotheses were generated after data collection and prior to analysis. Pooled logistic regression models with a discrete time approach assessed PM2.5 exposure and time to ED visits during each week of the first year of life and across the entire year. Preterm birth status, sex, and payment type for delivery were assessed as effect modifiers., Results: Of the 1 983 700 infants, 979 038 (49.4%) were female, 966 349 (48.7%) were Hispanic, and 142 081 (7.2%) were preterm. Across the first year of life, the odds of an ED visit for any cause were greater among both preterm (AOR, 1.056; 95% CI, 1.048-1.064) and full-term (AOR, 1.051; 95% CI, 1.049-1.053) infants for each 5-μg/m3 increase in exposure to PM2.5. Elevated odds were also observed for infection-related ED visit (preterm: AOR, 1.035; 95% CI, 1.001-1.069; full-term: AOR, 1.053; 95% CI, 1.044-1.062) and first respiratory-related ED visit (preterm: AOR, 1.080; 95% CI, 1.067-1.093; full-term: AOR,1.065; 95% CI, 1.061-1.069). For both preterm and full-term infants, ages 18 to 23 weeks were associated with the greatest odds of all-cause ED visits (AORs ranged from 1.034; 95% CI, 0.976-1.094 to 1.077; 95% CI, 1.022-1.135)., Conclusions and Relevance: Increasing PM2.5 exposure was associated with an increased ED visit risk for both preterm and full-term infants during the first year of life, which may have implications for interventions aimed at minimizing air pollution.

Background: Exposure to fine particulate matter (PM 2.5 ) is associated with adverse health outcomes but communities are not randomly exposed to PM 2.5 . Previous cross-sectional environmental injustice analyses in Canada found disproportionately higher exposure to PM 2.5 in low-income populations, visible minorities and immigrants. Beyond static surveillance, it is also important to evaluate how changes in PM 2.5 exposure over time may differentially impact disadvantaged communities. We examine whether communities with different sociodemographic characteristics benefited equitably from the overall decreases in ambient concentrations of PM 2.5 from 2001 to 2016 in Canada., Methods: We derived census tract level estimates of average annual PM 2.5 using validated satellite-based estimations of annual average PM 2.5 concentration surfaces. We investigated how the spatial distribution of PM 2.5 has evolved over 15 years (2001-2016) by comparing absolute values and rank percentiles of census tract level annual average PM 2.5 concentrations in 2001 and 2016. Using decennial census data and multivariable linear regression, we determined if sociodemographic characteristics are associated with changes in exposure to PM 2.5 , accounting for geographic boundary changes between census periods., Results: Overall, ambient PM 2.5 concentrations decreased from 2001 (median of 9.1 μg/m 3 ) to 2016 (median of 6.4 μg/m 3 ), with varying provincial patterns. Across communities, ranked census tract specific PM 2.5 in 2001 and in 2016 are highly correlated (Spearman's rho = 0.75). We found that, on average and accounting for provincial differences and baseline PM 2.5 , communities with greater density of aboriginal population, lower education, higher shelter-cost-to-income ratio, unemployment or lower income experienced smaller absolute decreases in PM 2.5 from 2001 to 2016., Conclusions: Identifying sociodemographic groups that benefit least from decreasing exposure to PM 2.5 highlights the need to consider environmental injustice when designing or revising air pollution policies., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Hong Chen reports financial support was provided by Government of Canada., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

Objective: To evaluate the association of short-term exposure to overall fine particulate matter of <2.5 μm (PM 2.5 ) and wildfire-specific PM 2.5 with emergency department (ED) visits for headache., Background: Studies have reported associations between PM 2.5 exposure and headache risk. As climate change drives longer and more intense wildfire seasons, wildfire PM 2.5 may contribute to more frequent headaches., Methods: Our study included adult Californian members (aged ≥18 years) of a large de-identified commercial and Medicare Advantage claims database from 2006 to 2020. We identified ED visits for primary headache disorders (subtypes: tension-type headache, migraine headache, cluster headache, and "other" primary headache). Claims included member age, sex, and residential zip code. We linked daily overall and wildfire-specific PM 2.5 to residential zip code and conducted a time-stratified case-crossover analysis considering 7-day average PM 2.5 concentrations, first for primary headache disorders combined, and then by headache subtype., Results: Among 9898 unique individuals we identified 13,623 ED encounters for primary headache disorders. Migraine was the most frequently diagnosed headache (N = 5534/13,623 [47.6%]) followed by "other" primary headache (N = 6489/13,623 [40.6%]). For all primary headache ED diagnoses, we observed an association of 7-day average wildfire PM 2.5 (odds ratio [OR] 1.17, 95% confidence interval [CI] 0.95-1.44 per 10 μg/m 3 increase) and by subtype we observed increased odds of ED visits associated with 7-day average wildfire PM 2.5 for tension-type headache (OR 1.42, 95% CI 0.91-2.22), "other" primary headache (OR 1.40, 95% CI 0.96-2.05), and cluster headache (OR 1.29, 95% CI 0.71-2.35), although these findings were not statistically significant under traditional null hypothesis testing. Overall PM 2.5 was associated with tension-type headache (OR 1.29, 95% CI 1.03-1.62), but not migraine, cluster, or "other" primary headaches., Conclusions: Although imprecise, these results suggest short-term wildfire PM 2.5 exposure may be associated with ED visits for headache. Patients, healthcare providers, and systems may need to respond to increased headache-related healthcare needs in the wake of wildfires and on poor air quality days., (© 2023 American Headache Society.)

Though fine particulate matter (PM 2.5 ) has decreased in the United States (U.S.) in the past two decades, the increasing frequency, duration, and severity of wildfires significantly (though episodically) impairs air quality in wildfire-prone regions and beyond. Increasing PM 2.5 concentrations derived from wildfire smoke and associated impacts on public health require dedicated epidemiological studies. Main sources of PM 2.5 data are provided by government-operated monitors sparsely located across U.S., leaving several regions and potentially vulnerable populations unmonitored. Current approaches to estimate PM 2.5 concentrations in unmonitored areas often rely on big data, such as satellite-derived aerosol properties and meteorological variables, apply computationally-intensive deterministic modeling, and do not distinguish wildfire-specific PM 2.5 from other sources of emissions such as traffic and industrial sources. Furthermore, modelling wildfire-specific PM 2.5 presents a challenge since measurements of the smoke contribution to PM 2.5 pollution are not available. Here, we aim to use statistical methods to isolate wildfire-specific PM 2.5 from other sources of emissions. Our study presents an ensemble model that optimally combines multiple machine learning algorithms (including gradient boosting machine, random forest and deep learning), and a large set of explanatory variables to, first, estimate daily PM 2.5 concentrations at the ZIP code level, a relevant spatiotemporal resolution for epidemiological studies. Subsequently, we propose a novel implementation of an imputation approach to estimate the wildfire-specific PM 2.5 concentrations that could be applied geographical regions in the US or worldwide. Our ensemble model achieved comparable results to previous machine learning studies for PM 2.5 prediction while avoiding processing larger, computationally intensive datasets. Our study is the first to apply a suite of statistical models using readily available datasets to provide daily wildfire-specific PM 2.5 at a fine spatial scale for a 15-year period, thus providing a relevant spatiotemporal resolution and timely contribution for epidemiological studies., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Introduction: No evidence exists about the impact of air pollution reduction on incidence of dementia. The aim of this study was to quantify how air quality improvement leads to dementia-incidence benefits., Methods: In the French Three-City cohort (12 years of follow-up), we used parametric g-computation to quantify the expected number of prevented dementia cases under different hypothetical interventions with particulate matter measuring <2.5 μm (PM 2.5 ) reductions., Results: Among 7051 participants, 789 participants developed dementia. The median PM 2.5 reduction between 1990 and 2000 was 12.2 (μg/m 3 ). Such a reduction reduced the risk of all-cause dementia (hazard ratio [HR], 0.85; 95% confidence interval [CI], 0.76 to 0.95). If all study participants were enjoying a hypothetical reduction of more than 13.10 μg/m 3 (median reduction observed in the city of Montpellier), the rate difference was -0.37 (95% CI, -0.57 to -0.17) and the rate ratio was 0.67 (95% CI, 0.50 to 0.84)., Discussion: These findings highlight the possible substantial benefits of reducing air pollution in the prevention of dementia., (© 2022 the Alzheimer's Association.)

Background Long-term exposure to outdoor fine particulate matter (PM 2.5 ) is the leading environmental risk factor for premature mortality worldwide. Characterizing important pathways through which PM 2.5 increases individuals' mortality risk can clarify the PM 2.5 -mortality relationship and identify possible points of interventions. Recent evidence has linked PM 2.5 to the onset of diabetes and cardiovascular disease, but to what extent these associations contribute to the effect of PM 2.5 on mortality remains poorly understood. Methods and Results We conducted a population-based cohort study to investigate how the effect of PM 2.5 on nonaccidental mortality is mediated by its impacts on incident diabetes, acute myocardial infarction, and stroke. Our study population comprised ≈200 000 individuals aged 20 to 90 years who participated in population-based health surveys in Ontario, Canada, from 1996 to 2014. Follow-up extended until December 2017. Using causal mediation analyses with Aalen additive hazards models, we decomposed the total effect of PM 2.5 on mortality into a direct effect and several path-specific indirect effects mediated by diabetes, each cardiovascular event, or both combined. A series of sensitivity analyses were also conducted. After adjusting for various individual- and neighborhood-level covariates, we estimated that for every 1000 adults, each 10 μg/m 3 increase in PM 2.5 was associated with ≈2 incident cases of diabetes, ≈1 major cardiovascular event (acute myocardial infarction and stroke combined), and ≈2 deaths annually. Among PM 2.5 -related deaths, 31.7% (95% CI, 17.2%-53.2%) were attributable to diabetes and major cardiovascular events in relation to PM 2.5 . Specifically, 4.5% were explained by PM 2.5 -induced diabetes, 22.8% by PM 2.5 -induced major cardiovascular events, and 4.5% through their interaction. Conclusions This study suggests that a significant portion of the estimated effect of long-term exposure to PM 2.5 on deaths can be attributed to its effect on diabetes and cardiovascular diseases, highlighting the significance of PM 2.5 on deteriorating cardiovascular health. Our findings should raise awareness among professionals that improving metabolic and cardiovascular health may reduce mortality burden in areas with higher exposure to air pollution.

Introduction: Exposure to air pollution disproportionately affects racial/ethnic minorities that could contribute to health inequalities including metabolic disorders. However, most existing studies used a static assessment of air pollution exposure (mostly using the residential address) and do not account for activity space when modelling exposure to air pollution. The aim of this study is to understand how exposure to air pollution impacts metabolic disorders biomarkers, how this effect differs according to ethnicity, and for the first time compare these findings with two methods of exposure assessment: dynamic and static measures., Methods: Among the Community of Mine study, a cross-sectional study conducted in San Diego County, insulin resistance, diabetes, hypertension, obesity, dyslipidemia, and metabolic syndrome (MetS) were assessed. Exposure to air pollution (PM 2.5 , NO 2 , traffic) was calculated using static measures around the home, and dynamic measures of mobility derived from Global Positioning Systems (GPS) traces using kernel density estimators to account for exposure variability across space and time. Associations of air pollution with metabolic disorders were quantified using generalized estimating equation models to account for the clustered nature of the data., Results: Among 552 participants (mean age 58.7 years, 42% Hispanic/Latino), Hispanics/Latinos had a higher exposure to PM 2.5 compared to non-Hispanics using static measures. In contrast, Hispanics/Latinos had less exposure to PM 2.5 using dynamic measures. For all participants, higher dynamic exposure to PM 2.5 and NO 2 was associated with increased insulin resistance and cholesterol levels, and increased risk of obesity, dyslipidemia and MetS (RR 1.17, 95% CI: 1.07-1.28; RR 1.21, 95% CI: 1.12-1.30, respectively). The association between dynamic PM 2.5 exposure and MetS differed by Hispanic/Latino ethnicity., Conclusion: These results highlight the importance of considering people's daily mobility in assessing the impact of air pollution on health., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: The tremendous global health burden related to COVID-19 means that identifying determinants of COVID-19 severity is important for prevention and intervention. We aimed to explore long-term exposure to ambient air pollution as a potential contributor to COVID-19 severity, given its known impact on the respiratory system., Methods: We used a cohort of all people with confirmed SARS-CoV-2 infection, aged 20 years and older and not residing in a long-term care facility in Ontario, Canada, during 2020. We evaluated the association between long-term exposure to fine particulate matter (PM 2.5 ), nitrogen dioxide (NO 2 ) and ground-level ozone (O 3 ), and risk of COVID-19-related hospital admission, intensive care unit (ICU) admission and death. We ascertained individuals' long-term exposures to each air pollutant based on their residence from 2015 to 2019. We used logistic regression and adjusted for confounders and selection bias using various individual and contextual covariates obtained through data linkage., Results: Among the 151 105 people with confirmed SARS-CoV-2 infection in Ontario in 2020, we observed 8630 hospital admissions, 1912 ICU admissions and 2137 deaths related to COVID-19. For each interquartile range increase in exposure to PM 2.5 (1.70 μg/m 3 ), we estimated odds ratios of 1.06 (95% confidence interval [CI] 1.01-1.12), 1.09 (95% CI 0.98-1.21) and 1.00 (95% CI 0.90-1.11) for hospital admission, ICU admission and death, respectively. Estimates were smaller for NO 2 . We also estimated odds ratios of 1.15 (95% CI 1.06-1.23), 1.30 (95% CI 1.12-1.50) and 1.18 (95% CI 1.02-1.36) per interquartile range increase of 5.14 ppb in O 3 for hospital admission, ICU admission and death, respectively., Interpretation: Chronic exposure to air pollution may contribute to severe outcomes after SARS-CoV-2 infection, particularly exposure to O 3 ., Competing Interests: Competing interests: Jeff Kwong is supported by a Clinician Scientist Award from the Department of Family and Community Medicine, University of Toronto. Chen Chen reports receiving salary support by the funding of this project, from Health Canada. Megan Kirby-Mcgregor reports receiving doctoral student funding from Health Canada, paid through McGill University. Jay Kaufman reports receiving payments from Health Canada (paid to institution) for student salaries and other research expenses associated with this work. Hong Chen reports receiving support for the present manuscript from Health Canada. No other competing interests were declared., (© 2022 CMA Impact Inc. or its licensors.)

A growing literature in economics and epidemiology has exploited changes in wind patterns as a source of exogenous variation to better measure the acute health effects of air pollution. Since the distribution of wind components is not randomly distributed over time and related to other weather parameters, multivariate regression models are used to adjust for these confounding factors. However, this type of analysis relies on its ability to correctly adjust for all confounding factors and extrapolate to units without empirical counterfactuals. As an alternative to current practices and to gauge the extent of these issues, we propose to implement a causal inference pipeline to embed this type of observational study within an hypothetical randomized experiment. We illustrate this approach using daily data from Paris, France, over the 2008-2018 period. Using the Neyman-Rubin potential outcomes framework, we first define the treatment of interest as the effect of North-East winds on particulate matter concentrations compared to the effects of other wind directions. We then implement a matching algorithm to approximate a pairwise randomized experiment. It adjusts nonparametrically for observed confounders while avoiding model extrapolation by discarding treated days without similar control days. We find that the effective sample size for which treated and control units are comparable is surprisingly small. It is however reassuring that results on the matched sample are consistent with a standard regression analysis of the initial data. We finally carry out a quantitative bias analysis to check whether our results could be altered by an unmeasured confounder: estimated effects seem robust to a relatively large hidden bias. Our causal inference pipeline is a principled approach to improve the design of air pollution studies based on wind patterns., (© 2022. The Author(s).)

Mineral dust is one of the largest natural constituents of coarse particulate matter (PM 10 ). Most of these dust emissions originate from northern Africa, and several hundred tera-grams of dust are emitted annually from this region. Previous evidence has linked dust PM 10 to adverse respiratory outcomes in children. However, most of these studies have been from high-income countries (HICs) or examined dust from other regions of the world, mainly Asia. Evidence from low-to-middle-income countries (LMICs) in Africa is scarce. Respiratory infections are one of the leading causes of under-five mortality across the globe. However, there is a poignant disparity in studies examining these outcomes in children in the region where most dust is emitted. This study linked remotely sensed satellite data to a nationally representative survey to examine acute exposure to dust in children living in Benin using a time-stratified case-crossover analysis. We identified acute effects of exposure to dust and increased risk of cough in children under five. The effect of increased risk is strongest within two weeks of exposure and dissipates by four weeks. Children living in rural areas and households with lower income had a greater risk of adverse respiratory outcomes when exposed to dust. We could elucidate the specific period and conditions of increased risk for respiratory problems in children living in Benin.

Introduction: MobiliSense explores effects of air pollution and noise related to personal transport habits on respiratory and cardiovascular health. Its objectives are to quantify the contribution of personal transport/mobility to air pollution and noise exposures of individuals; to compare exposures in different transport modes; and to investigate whether total and transport-related personal exposures are associated with short-term and longer-term changes in respiratory and cardiovascular health., Methods and Analysis: MobiliSense uses sensors of location, behaviour, environmental nuisances and health in 290 census-sampled participants followed-up after 1/2 years with an identical sensor-based strategy. It addresses knowledge gaps by: (1) assessing transport behaviour over 6 days with GPS receivers and GPS-based mobility surveys; (2) considering personal exposures to both air pollution and noise and improving their characterisation (inhaled doses, noise frequency components, etc); (3) measuring respiratory and cardiovascular outcomes (smartphone-assessed respiratory symptoms, lung function with spirometry, resting blood pressure, ambulatory brachial/central blood pressure, arterial stiffness and heart rate variability) and (4) investigating short-term and longer-term (over 1-2 years) effects of transport., Ethics and Dissemination: The sampling and data collection protocol was approved by the National Council for Statistical Information, the French Data Protection Authority and the Ethical Committee of Inserm. Our final aim is to determine, for communicating with policy-makers, how scenarios of changes in personal transport behaviour affect individual exposure and health., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Epidemiological findings are inconsistent regarding the associations between air pollution exposure during pregnancy and gestational diabetes mellitus (GDM). Several limitations exist in previous studies, including potential outcome and exposure misclassification, unassessed confounding, and lack of simultaneous consideration of air pollution mixtures and particulate matter (PM) constituents., Objectives: To assess the association between GDM and maternal residential exposure to air pollution, and the joint effect of the mixture of air pollutants and PM constituents., Methods: Detailed clinical data were obtained for 395,927 pregnancies in southern California (2008-2018) from Kaiser Permanente Southern California (KPSC) electronic health records. GDM diagnosis was based on KPSC laboratory tests. Monthly average concentrations of fine particulate matter < 2.5 μm (PM 2.5 ), <10 μm (PM 10 ), nitrogen dioxide (NO 2 ), and ozone (O 3 ) were estimated using kriging interpolation of Environmental Protection Agency's routine monitoring station data, while PM 2.5 constituents (i.e., sulfate, nitrate, ammonium, organic matter and black carbon) were estimated using a fine-resolution geoscience-derived model. A multilevel logistic regression was used to fit single-pollutant models; quantile g-computation approach was applied to estimate the joint effect of air pollution and PM component mixtures. Main analyses adjusted for maternal age, race/ethnicity, education, median family household income, pre-pregnancy BMI, smoking during pregnancy, insurance type, season of conception and year of delivery., Results: The incidence of GDM was 10.9% in the study population. In single-pollutant models, we observed an increased odds for GDM associated with exposures to PM 2.5 , PM 10 , NO 2 and PM 2.5 constituents. The association was strongest for NO 2 [adjusted odds ratio (OR) per interquartile range: 1.176, 95% confidence interval (CI): 1.147-1.205)]. In multi-pollutant models, increased ORs for GDM in association with one quartile increase in air pollution mixtures were found for both kriging-based regional air pollutants (NO 2 , PM 2.5 , and PM 10 , OR = 1.095, 95% CI: 1.082-1.108) and PM 2.5 constituents (i.e., sulfate, nitrate, ammonium, organic matter and black carbon, OR = 1.258, 95% CI: 1.206-1.314); NO 2 (78%) and black carbon (48%) contributed the most to the overall mixture effects among all krigged air pollutants and all PM 2.5 constituents, respectively. The risk of GDM associated with air pollution exposure were significantly higher among Hispanic mothers, and overweight/obese mothers., Conclusion: This study found that exposure to a mixture of ambient PM 2.5 , PM 10 , NO 2 , and PM 2.5 chemical constituents was associated with an increased risk of GDM. NO 2 and black carbon PM 2.5 contributed most to GDM risk., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Daily exposure to air pollution has been shown to increase cardiovascular and respiratory mortality. While increases in short-term exposure to air pollutants at any daily concentrations has been shown to be associated to adverse health outcomes, days with extreme levels, also known as air pollution peaks based on specific thresholds, have been used to implement air quality alerts in various cities across the globe., Objectives: We aimed at evaluating the potential effects of the Air Quality Alerts (AQA) system on different causes of premature mortality in Paris, France., Methods: Air quality alerts (AQA) based on particulate matter (PM 10 ) levels and related interventions were implemented in the region of Paris in 2008 and were revised to be more stringent in 2011. In this study, we applied a difference-in-differences (DID) approach coupled with propensity-score matching (PSM) to daily mortality data for the period 2000 to 2015 to evaluate the effects of the Paris AQA program on different causes of premature mortality for the entire population and for adults > 75 years old., Results: Overall, results did not show evidence of a reduction in mortality of the PM 10 AQA program when first implemented in 2008 with initial thresholds (80 µg/m 3 ); DID estimates were slightly above 1 for cardiovascular and respiratory mortality. However, when evaluating the drastic reduction in revised thresholds in 2011 (50 µg/m 3 ) to trigger interventions, we identified a reduction in cardiovascular (DID = 0.84, 95% CI: 0.755 to 0.930) mortality, but no change in respiratory mortality was detected (DID = 0.97, 95% CI: 0.796, 1.191)., Discussion: Our study suggests that AQA may not have health benefits for the population when thresholds are set at high daily PM 10 levels. Given that such policies are implemented in many other metropolitan areas across the globe, evaluating the effectiveness of AQA is important to provide public authorities and researchers a rationale for defining specific thresholds and extending the scope of these policies to lower air pollution levels., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Previous observational studies have shown that exposure to ambient temperature and air pollution were associated with the incidence of gestational diabetes mellitus (GDM). However, the susceptible time window of non-optimal temperature on GDM is still unknown, and the interaction with air pollution has not been examined. We conducted a prospective cohort study in Guangzhou, China to investigate the windows of susceptibility of temperature extremes and variability on the risk of GDM and to explore any interaction effect with air pollution. Daily maximum (T max ), minimum temperature (T min ) and diurnal temperature range (DTR) were obtained from Guangdong Meteorological Service. Distributed lag non-linear models with a logistic regression were applied to assess the effect of temperature extremes and DTR in different weeks of gestation on GDM. To examine the interaction effect, relative excess risk due to interaction index, attributable proportion and synergy index were calculated. There were 5,165 pregnant women enrolled, of which 604 were diagnosed with GDM (11.7%). Compared with a reference temperature (50th percentile of T max ), we found that extreme high temperature (99th percentile of T max ) exposure during 21st and 22nd gestational weeks was associated with an increased risk of GDM. Extreme low temperature (1st percentile of T max ) exposure during 14th to 17th weeks increased the risk of GDM. We observed that per 1 °C increment of DTR during 21st to 24th weeks was associated with an elevated GDM risk. No interaction effect of temperature extremes or variability with air pollution on GDM were observed. Our results suggested that non-optimal temperature is an independent risk factor of GDM. The time window of susceptibility for extreme temperatures and DTR exposure on the risk of GDM generally occurred in second trimester of pregnancy. In the context of climate change, our study has important implications for reproductive health and justifies more research in different climate zones., (Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Extreme heat and ozone are co-occurring exposures that independently and synergistically increase the risk of respiratory disease. To our knowledge, no joint warning systems consider both risks; understanding their interactive effect can warrant use of comprehensive warning systems to reduce their burden. We examined heterogeneity in joint effects (on the additive scale) between heat and ozone at small geographical scales. A within-community matched design with a Bayesian hierarchical model was applied to study this association at the zip code level. Spatially varying relative risks due to interaction (RERI) were quantified to consider joint effects. Determinants of the spatial variability of effects were assessed using a random effects metaregression to consider the role of demographic/neighborhood characteristics that are known effect modifiers. A total of 817,354 unscheduled respiratory hospitalizations occurred in California from 2004 to 2013 in the May to September period. RERIs revealed no additive interaction when considering overall joint effects. However, when considering the zip code level, certain areas observed strong joint effects. A lower median income, higher percentage of unemployed residents, and exposure to other air pollutants within a zip code drove stronger joint effects; a higher percentage of commuters who walk/bicycle, a marker for neighborhood wealth, showed decreased effects. Results indicate the importance of going beyond average measures to consider spatial variation in the health burden of these exposures and predictors of joint effects. This information can be used to inform early warning systems that consider both heat and ozone to protect populations from these deleterious effects in identified areas., Competing Interests: The authors declare no competing interest.

Background: Emerging epidemiological evidence suggests a relationship between exposure to air pollution and dementia. However, most of the existing studies relied on health administrative databases for the diagnosis of dementia. In a large French population-based cohort (the 3C Study), we assessed the effects of particulate matter ≤2.5 µm (PM2.5), nitrogen dioxide (NO 2 ) and black carbon (BC) on the risk of dementia diagnosed with reliable tools., Methods: Participants aged ≥65 years were recruited between 1999 and 2001 and followed for 12 years. At baseline and every 2 years, dementia was suspected on the basis of the neuropsychological and neurological examination and confirmed by an independent committee of clinicians. Exposure to NO 2 , BC and PM2.5 at the participants' residential address was estimated using land use regression models. For each pollutant and year of follow-up, the 10-year moving average of past exposure was estimated. Multilevel spatial random-effects Cox proportional hazards models were used in which exposure was included as a time-varying variable. Analyses were adjusted for individual (age, sex, education, APOE4 genotype, health behaviours) and contextual (neighbourhood deprivation index) confounders., Results: At baseline, the median age of the 7066 participants was 73.4 years, and 62% were women. The median follow-up duration was 10.0 years during which 791 participants developed dementia (n = 541 Alzheimer's disease (AD) and n = 155 vascular/mixed dementia (VaD)). The 10-year moving average of PM2.5 concentrations ranged from 14.6 to 31.3 µg/m 3 . PM2.5 concentration was positively associated with dementia risk: HR = 1.20, 95% CI (1.08-1.32) for all-cause dementia, 1.20 (1.09-1.32) for AD, and 1.33 (1.05-1.68) for VaD per 5 µg/m 3 PM2.5 increase. No association was detected between NO 2 or BC exposure and dementia risk., Conclusion: In this large cohort of older adults, long-term PM2.5 exposure was associated with increased dementia incidence. Reducing PM2.5 emissions might lessen the burden of dementia in aging populations., (Copyright © 2021 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Ambient air pollution exposure is associated with exacerbating respiratory illnesses. Race/ethnicity (R/E) have been shown to influence an individual's vulnerability to environmental health risks such as fine particles (PM 2.5). This study aims to assess the R/E disparities in vulnerability to air pollution with regards to respiratory hospital admissions in San Diego County, California where most days fall below National Ambient Air Quality Standards (NAAQS) for daily PM 2.5 concentrations. Daily PM 2.5 levels were estimated at the zip code level using a spatial interpolation using inverse-distance weighting from monitor networks. The association between daily PM 2.5 levels and respiratory hospital admissions in San Diego County over a 15-year period from 1999 to 2013 was assessed with a time-series analysis using a multi-level Poisson regression model. Cochran Q tests were used to assess the effect modification of race/ethnicity on this association. Daily fine particle levels varied greatly from 1 μg/m 3 to 75.86 μg/m 3 (SD = 6.08 μg/m 3 ) with the majority of days falling below 24-hour NAAQS for PM 2.5 of 35 μg/m 3 . For every 10 μg/m 3 increase in PM 2.5 levels, Black and White individuals had higher rates (8.6% and 6.2%, respectively) of hospitalization for respiratory admissions than observed in the county as a whole (4.1%). Increases in PM 2.5 levels drive an overall increase in respiratory hospital admissions with a disparate burden of health effects by R/E group. These findings suggest an opportunity to design interventions that address the unequal burden of air pollution among vulnerable communities in San Diego County that exist even below NAAQS for daily PM 2.5 concentrations.

Background: Ozone pollution keeps deteriorating in the context of climate change. Maternal ozone exposure may be associated with low birth weight (LBW), but the results are still inconsistent. The identification of the critical exposure windows, a specific period of particular susceptibility during pregnancy, remains unresolved. We aimed to evaluate whether ozone exposure was associated with term LBW and further identify the susceptible exposure windows., Methods: A retrospective cohort study was conducted in Guangzhou, a megacity in the most populous and economically developed city clusters in China. We included 444,096 singleton live births between January 2015 and July 2017. From 11 fixed stations, we collected daily 1-h maximum and 8-h maximum moving average ozone level (O 3 -1 h and O 3 -8 h) and calculated exposures for each participant based on their district of residence during pregnancy. We used traditional Logistic regression to estimate the trimester-specific association between ozone exposure and term LBW, and further estimated monthly- and weekly association by distributed lag models (DLMs) with Logistic regression. Odds ratios (ORs) and 95% confidence intervals (CIs) of term LBW were calculated for an interquartile range (IQR) increase in ozone exposure. Stratified analyses and heterogeneity tests were conducted by maternal age and infant sex., Results: The incidence of term LBW was 1.9%. During the study period, the mean O 3 -1 h and O 3 -8 h levels were 112.6 µg/m 3 and 84.5 µg/m 3 , respectively. Increased O 3 -1 h (IQR: 90 µg/m 3 ) and O 3 -8 h (73 µg/m 3 ) exposure during the second trimester were associated with increased risk of term LBW. At a monthly level, the term LBW risk was associated with O 3 -1 h exposure during the 4th-6th month and O 3 -8 h exposure during the 6th month. By estimating the weekly-specific association, we observed that critical exposure windows were the 15th- 26th gestational weeks for O 3 -1 h, and the 20th-26th weeks for O 3 -8 h, respectively. Estimated ORs and 95% CIs ranged from 1.012 (1.000, 1.024) to 1.023 (1.007, 1.039). When examined by subgroups, the effects were present among women ≥ 35 years or < 25 years old and those with female babies., Conclusions: This study provides compelling evidence that exposure to O 3 was associated with increased risk of term LBW, and gestational weeks 15th- 26th was found to be particularly susceptible. These findings provide a research basis for further mechanism examination, public health interventions, and targeted environmental policy-making., (Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The coronavirus disease 2019 (COVID-19) pandemic revealed and exacerbated existing social and economic health disparities, and actionable epidemiologic evidence is needed to identify potential vulnerability factors to help inform targeted responses. In this commentary, methodological challenges and opportunities regarding the links between air pollution and COVID-19 are discussed with a focus on 2 factors: 1) the role of differential exposure to air pollution across populations as an explanation for spatiotemporal variability of the epidemic spread and resultant mortality; and 2) the indirect impacts of interventions to control COVID-19 person-to-person spread treated as natural experiments on air pollution and population health. I first discuss the potential mechanisms between exposure to air pollution and COVID-19 and the opportunity to clearly formulate causal questions of interest through the target trial framework. Then, I discuss challenges regarding the use of quasiexperimental designs that capitalize on the differential timing of COVID-19 policies including the selection of control groups and potential violations of the common shock assumption. Finally, I discuss environmental justice implications of this many-headed beast of a crisis., (© The Author(s) 2020. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Recent studies have reported inconsistent associations between maternal residential green space and preterm birth (PTB, born < 37 completed gestational weeks). In addition, windows of susceptibility during pregnancy have not been explored and potential interactions of green space with air pollution exposures during pregnancy are still unclear., Objectives: To evaluate the relationships between green space and PTB, identify windows of susceptibility, and explore potential interactions between green space and air pollution., Methods: Birth certificate records for all births in California (2001-2008) were obtained. The Normalized Difference Vegetation Index (NDVI) was used to characterized green space exposure. Gestational age was treated as a time-to-event outcome; Cox proportional hazard models were applied to estimate the association between green space exposure and PTB, moderately PTB (MPTB, gestational age < 35 weeks), and very PTB (VPTB, gestational age < 30 weeks), after controlling for maternal age, race/ethnicity, education, and median household income. Month-specific green space exposure was used to identify potential windows of susceptibility. Potential interactions between green space and air pollution [fine particulate matter < 2.5 µm (PM 2.5 ), nitrogen dioxide (NO 2 ), and ozone (O 3 )] were examined on both additive and multiplicative scales., Results: In total, 3,753,799 eligible births were identified, including 341,123 (9.09%) PTBs, 124,631 (3.32%) MPTBs, and 22,313 (0.59%) VPTBs. A reduced risk of PTB was associated with increases in residential NDVI exposure in 250 m, 500 m, 1000 m, and 2000 m buffers. In the 2000 m buffer, the association was strongest for VPTB [adjusted hazard ratio (HR) per interquartile range increase in NDVI: 0.959, 95% confidence interval (CI): 0.942-0.976)], followed by MPTB (HR = 0.970, 95% CI: 0.962-0.978) and overall PTB (HR = 0.972, 95% CI: 0.966-0.978). For PTB, green space during the 3rd - 5th gestational months had stronger associations than those in the other time periods, especially during the 4th gestational month (NDVI 2000 m: HR = 0.970, 95% CI: 0.965-0.975). We identified consistent positive additive and multiplicative interactions between decreasing green space and higher air pollution., Conclusion: This large study found that maternal exposure to residential green space was associated with decreased risk of PTB, MPTB, and VPTB, especially in the second trimester. There is a synergistic effect between low green space and high air pollution levels on PTB, indicating that increasing exposure to green space may be more beneficial for women with higher air pollution exposures during pregnancy., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Rationale: There is significant evidence of increased healthcare utilization from cardiopulmonary causes in adults from exposure to wildfire smoke, but evidence in pediatric age groups is limited. Objectives: To quantify and examine the healthcare utilization effects of the December 2017 Lilac Fire in San Diego County among pediatric patients at the Rady Children's Hospital (RCH) emergency department and urgent care (UC) clinics. Methods: Using data from 2011 to 2017, including data on daily particulate matter <2.5 μm (PM 2.5 ) in an inverse-distance interpolation model and RCH electronic medical records, we retrospectively analyzed pediatric respiratory visits at the RCH emergency department and UC clinics during the Santa Ana wind (SAW)-driven Lilac Fire from December 7 to 16, 2017. An interrupted time series study design was applied as our primary analysis to compare the observed pediatric respiratory visits from December 7 to 16, 2017 to what would have occurred in a counterfactual situation, namely, if the Lilac Fire had not occurred. A complementary descriptive spatial analysis was also used to evaluate the geographic distribution of respiratory visits in relationship to satellite imaging of the Lilac Fire and the associated wind pattern. Results: The Lilac Fire was associated with 16.0 (95% confidence interval [CI], 11.2-20.9) excess respiratory visits per day at the RCH emergency department across all pediatric age groups. Children aged 0 to 5 years had the highest absolute excess respiratory visits per day with 7.3 (95% CI, 3.0-11.7), whereas those aged 6 to 12 years had the highest relative increase in visits, with 3.4 (95% CI, 2.3-4.6). RCH UC clinics had similar results. The top five ZIP codes in San Diego County with the highest standard deviations of age-adjusted respiratory visits were all located generally downwind of the fire perimeter, as expected for the SAW pattern. Conclusions: We have demonstrated an increase in pediatric respiratory visits during the SAW-driven Lilac Fire in San Diego County in a patterned geographic distribution that is attributable to an increase in PM 2.5 exposure. Younger children were particularly affected. Climate change is expected to result in more frequent and extensive wildfires in the region and will require greater preparedness and adaptation efforts to protect vulnerable populations, such as young children.

Background: Evidence suggests a link between air pollution and dementia. Cardiovascular disease (CVD) may be a potential determinant of dementia. This motivated us to quantify the contribution of CVD to the association between air pollution and dementia., Methods: A cohort of Canadian-born residents of Ontario, who participated in the 1996-2003 Canadian Community Health Surveys, was followed through 2013 or until dementia diagnosis. Exposure to nitrogen dioxide (NO2) and fine particulate matter (PM2.5) was estimated with a 3-year average and 5-year lag before dementia diagnosis. Incident CVD was evaluated as a mediator. We used multi-level Cox proportional and Aalen additive hazard regression models, adjusting for individual- and neighbourhood-level risk factors to estimate associations with NO2 and PM2.5. We estimated the total, direct and indirect effects of air pollution on dementia through cardiovascular disease., Results: This study included 34 391 older adults. At baseline, the mean age of this cohort was 59 years. The risk of dementia was moderately higher among those more exposed to NO2 (hazard ratio (HR) 1.10, 95% confidence interval (CI) 0.99-1.19; and 100 additional cases per 100 000 [standard error (SE) <100x10-5]) and PM2.5 [(HR 1.29, 95% CI 0.99-1.64; 200 additional cases per 100 000] [SE 100x10-5]) after adjusting for covariates; however, these estimates are imprecise. A greater proportion of the relationship between PM2.5 and dementia was mediated through CVD than NO2 for both scales., Conclusions: These results suggest some of the association between air pollution and dementia is mediated through CVD, indicating that improving cardiovascular health may prevent dementia in areas with higher exposure to air pollution., (© The Author(s) 2019; all rights reserved. Published by Oxford University Press on behalf of the International Epidemiological Association.)

Exposure to ambient fine particulate matter (particulate matter ≤2.5 μm in aerodynamic diameter (PM2.5)) during pregnancy is associated with preterm birth (PTB), a leading cause of infant morbidity and mortality. Results from studies attempting to identify etiologically relevant exposure periods of vulnerability have been inconsistent, possibly because of failure to consider the time-to-event nature of the outcome and lagged exposure effects of PM2.5. In this study, we aimed to identify critical exposure windows for weekly PM2.5 exposure and PTB in California using California birth cohort data from 2005-2010. Associations were assessed using distributed-lag Cox proportional hazards models. We assessed effect-measure modification by race/ethnicity by calculating the weekly relative excess risk due to interaction. For a 10-μg/m3 increase in PM2.5 exposure over the entire period of gestation, PTB risk increased by 11% (hazard ratio = 1.11, 95% confidence interval: 1.09, 1.14). Gestational weeks 17-24 and 36 were associated with increased vulnerability to PM2.5 exposure. We find that non-Hispanic black mothers may be more susceptible to effects of PM2.5 exposure than non-Hispanic white mothers, particularly at the end of pregnancy. These findings extend our knowledge about the existence of specific exposure periods during pregnancy that have the greatest impact on preterm birth., (© The Author(s) 2019. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Maternal exposure to ambient air pollution has been associated with preterm birth (PTB), however, entire pregnancy or trimester-specific associations were generally reported, which may not sufficiently identify windows of susceptibility. Using birth registry data from Guangzhou, a megacity of southern China (population ~14.5 million), including 469,975 singleton live births between January 2015 and July 2017, we assessed the association between weekly air pollution exposure and PTB in a retrospective cohort study. Daily average concentrations of PM 2.5 , PM 10 , NO 2 , SO 2 , and O 3 from 11 monitoring stations were used to estimate district-specific exposures for each participant based on their district residency during pregnancy. Distributed lag models (DLMs) incorporating Cox proportional hazard models were applied to estimate the association between weekly maternal exposure to air pollutant and PTB risk (as a time-to-event outcome), after controlling for temperature, seasonality, and individual-level covariates. We also considered moderate PTB (32-36 gestational weeks) and very PTB (28-31 gestational weeks) as outcomes of interest. Hazard ratios (HRs) and 95% confidential intervals (95% CIs) were calculated for an interquartile range (IQR) increase in air pollutants during the study period. An IQR increase in PM 2.5 exposure during the 20th to 28th gestational weeks (27.0 μg/m 3 ) was significantly associated with PTB risk, with the strongest effect in the 25th week (HR = 1.034, 95% CI:1.010-1.059). The significant exposure windows were the 19th-28th weeks for PM 10 , the 18th-31st weeks for NO 2 , and the 23rd-31st weeks for O 3 , respectively. The strongest associations were observed in the 25th week for PM 10 (IQR = 37.0 μg/m 3 ; HR = 1.048, 95% CI:1.034-1.062), the 26th week for NO 2 (IQR = 29.0 μg/m 3 ; HR = 1.060, 95% CI:1.028-1.094), and in the 28th week for O 3 (IQR = 90.0 μg/m 3 ; HR = 1.063, 95% CI:1.046-1.081). Similar patterns were observed for moderate PTB (32-36 gestational weeks) and very PTB (28-31 gestational weeks) for PM 2.5 , PM 10 , NO 2 exposure, but the effects were greater for very PTB. We did not observe any association between pregnancy SO 2 exposure and the risk of PTB. Our results suggest that middle to late pregnancy is the most susceptible air pollution exposure window for air pollution and PTB among women in Guangzhou, China., (Copyright © 2018 Elsevier Ltd. All rights reserved.)

Studies assessing socio-economic disparities in air pollution exposure and susceptibility are usually based on a single air pollution model. A time stratified case-crossover study was designed to assess the impact of the type of model on differential exposure and on the differential susceptibility in the relationship between ozone exposure and daily mortality by socio-economic strata (SES) in Montreal. Non-accidental deaths along with deaths from cardiovascular and respiratory causes on the island of Montreal for the period 1991-2002 were included as cases. Daily ozone concentration estimates at partictaipants' residence were obtained from the five following air pollution models: Average value (AV), Nearest station model (NS), Inverse-distance weighting interpolation (IDW), Land-use regression model with back-extrapolation (LUR-BE) and Bayesian maximum entropy model combined with a land-use regression (BME-LUR). The prevalence of a low household income (< 20,000/year) was used as socio-economic variable, divided into two categories as a proxy for deprivation. Multivariable conditional logistic regressions were used considering 3-day average concentrations. Multiplicative and additive interactions (using Relative Excess Risk due to Interaction) as well as Cochran's tests were calculated and results were compared across the different air pollution models. Heterogeneity of susceptibility and exposure according to socio-economic status (SES) were found. Ratio of exposure across SES groups means ranged from 0.75 [0.74-0.76] to 1.01 [1.00-1.02], respectively for the LUR-BE and the BME-LUR models. Ratio of mortality odds ratios ranged from 1.01 [0.96-1.05] to 1.02 [0.97-1.08], respectively for the IDW and LUR-BE models. Cochran's test of heterogeneity between the air pollution models showed important heterogeneity regarding the differential exposure by SES, but the air pollution model was not found to influence heterogeneity regarding the differential susceptibility. The study showed air pollution models can influence the assessment of disparities in exposure according to SES in Montreal but not that of disparities in susceptibility., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Background: While a great number of papers have been published on the short-term effects of air pollution on mortality, few have tried to assess whether this association varies according to the neighbourhood socioeconomic level and long-term ambient air concentrations measured at the place of residence. We explored the effect modification of 1) socioeconomic status, 2) long-term NO2 ambient air concentrations, and 3) both combined, on the association between short-term exposure to NO2 and all-cause mortality in Paris (France)., Methods: A time-stratified case-crossover analysis was performed to evaluate the effect of short-term NO2 variations on mortality, based on 79,107 deaths having occurred among subjects aged over 35 years, from 2004 to 2009, in the city of Paris. Simple and double interactions were statistically tested in order to analyse effect modification by neighbourhood characteristics on the association between mortality and short-term NO2 exposure. The data was estimated at the census block scale (n=866)., Results: The mean of the NO2 concentrations during the five days prior to deaths were associated with an increased risk of all-cause mortality: overall Excess Risk (ER) was 0.94% (95%CI=[0.08;1.80]. A higher risk was revealed for subjects living in the most deprived census blocks in comparison with higher socioeconomic level areas (ER=3.14% (95%CI=[1.41-4.90], p<0.001). Among these deprived census blocks, excess risk was even higher where long-term average NO2 concentrations were above 55.8 μg/m3 (the top tercile of distribution): ER=4.84% (95%CI=[1.56;8.24], p for interaction=0.02)., Conclusion: Our results show that people living in census blocks characterized by low socioeconomic status are more vulnerable to air pollution episodes. There is also an indication that people living in these disadvantaged census blocks might experience even higher risk following short-term air pollution episodes, when they are also chronically exposed to higher NO2 levels.

BACKGROUND: Epidemiological findings regarding the association of particulate matter ≤2.5 μm (PM2.5) exposure with hypertensive disorders in pregnancy (HDP) are inconsistent; evidence for HDP risk related to PM2.5 components, mixture effects, and windows of susceptibility is limited. We aimed to investigate the relationships between HDP and exposure to PM2.5 during pregnancy. METHODS AND FINDINGS: A large retrospective cohort study was conducted among mothers with singleton pregnancies in Kaiser Permanente Southern California from 2008 to 2017. HDP were defined by International Classification of Diseases-9/10 (ICD-9/10) diagnostic codes and were classified into 2 subcategories based on the severity of HDP: gestational hypertension (GH) and preeclampsia and eclampsia (PE-E). Monthly averages of PM2.5 total mass and its constituents (i.e., sulfate, nitrate, ammonium, organic matter, and black carbon) were estimated using outputs from a fine-resolution geoscience-derived model. Multilevel Cox proportional hazard models were used to fit single-pollutant models; quantile g-computation approach was applied to estimate the joint effect of PM2.5 constituents. The distributed lag model was applied to estimate the association between monthly PM2.5 exposure and HDP risk. This study included 386,361 participants (30.3 ± 6.1 years) with 4.8% (17,977/373,905) GH and 5.0% (19,381/386,361) PE-E cases, respectively. In single-pollutant models, we observed increased relative risks for PE-E associated with exposures to PM2.5 total mass [adjusted hazard ratio (HR) per interquartile range: 1.07, 95% confidence interval (CI) [1.04, 1.10] p < 0.001], black carbon [HR = 1.12 (95% CI [1.08, 1.16] p < 0.001)] and organic matter [HR = 1.06 (95% CI [1.03, 1.09] p < 0.001)], but not for GH. The population attributable fraction for PE-E corresponding to the standards of the US Environmental Protection Agency (9 μg/m3) was 6.37%. In multi-pollutant models, the PM2.5 mixture was associated with an increased relative risk of PE-E ([HR = 1.05 (95% CI [1.03, 1.07] p < 0.001)], simultaneous increase in PM2.5 constituents of interest by a quartile) and PM2.5 black carbon gave the greatest contribution of the overall mixture effects (71%) among all individual constituents. The susceptible window is the late first trimester and second trimester. Furthermore, the risks of PE-E associated with PM2.5 exposure were significantly higher among Hispanic and African American mothers and mothers who live in low- to middle-income neighborhoods (p < 0.05 for Cochrans Q test). Study limitations include potential exposure misclassification solely based on residential outdoor air pollution, misclassification of disease status defined by ICD codes, the date of diagnosis not reflecting the actual time of onset, and lack of information on potential covariates and unmeasured factors for HDP. CONCLUSIONS: Our findings add to the literature on associations between air pollution exposure and HDP. To our knowledge, this is the first study reporting that specific air pollution components, mixture effects, and susceptible windows of PM2.5 may affect GH and PE-E differently.

BACKGROUND: This study capitalized on coal and oil facility retirements to quantify their potential effects on fine particulate matter (PM2.5) concentrations and cardiorespiratory hospitalizations in affected areas using a generalized synthetic control method. METHODS: We identified 11 coal and oil facilities in California that retired between 2006 and 2013. We classified zip code tabulation areas (ZCTA) as exposed or unexposed to a facility retirement using emissions information, distance, and a dispersion model. We calculated weekly ZCTA-specific PM2.5 concentrations based on previously estimated daily time-series PM2.5 concentrations from an ensemble model, and weekly cardiorespiratory hospitalization rates based on hospitalization data collected by the California Department of Health Care Access and Information. We estimated the average differences in weekly average PM2.5 concentrations and cardiorespiratory hospitalization rates in four weeks after each facility retirement between the exposed ZCTAs and the synthetic control using all unexposed ZCTAs (i.e., the average treatment effect among the treated [ATT]) and pooled ATTs using meta-analysis. We conducted sensitivity analyses to consider different classification schemes to distinguish exposed from unexposed ZCTAs, including aggregating outcomes with different time intervals and including a subset of facilities with reported retirement date confirmed via emission record. RESULTS: The pooled ATTs were 0.02 μg/m3 (95% confidence interval (CI): -0.25 to 0.29 μg/m3) and 0.34 per 10,000 person-weeks (95%CI: -0.08 to 0.75 per 10,000 person-weeks) following the facility closure for weekly PM2.5 and cardiorespiratory hospitalization rates, respectively. Our inferences remained the same after conducting sensitivity analyses. CONCLUSIONS: We demonstrated a novel approach to study the potential benefits associated with industrial facility retirements. The declining contribution of industrial emissions to ambient air pollution in California may explain our null findings. We encourage future research to replicate this work in regions with different industrial activities.

Though fine particulate matter (PM2.5) has decreased in the United States (U.S.) in the past two decades, the increasing frequency, duration, and severity of wildfires significantly (though episodically) impairs air quality in wildfire-prone regions and beyond. Increasing PM2.5 concentrations derived from wildfire smoke and associated impacts on public health require dedicated epidemiological studies. Main sources of PM2.5 data are provided by government-operated monitors sparsely located across U.S., leaving several regions and potentially vulnerable populations unmonitored. Current approaches to estimate PM2.5 concentrations in unmonitored areas often rely on big data, such as satellite-derived aerosol properties and meteorological variables, apply computationally-intensive deterministic modeling, and do not distinguish wildfire-specific PM2.5 from other sources of emissions such as traffic and industrial sources. Furthermore, modelling wildfire-specific PM2.5 presents a challenge since measurements of the smoke contribution to PM2.5 pollution are not available. Here, we aim to use statistical methods to isolate wildfire-specific PM2.5 from other sources of emissions. Our study presents an ensemble model that optimally combines multiple machine learning algorithms (including gradient boosting machine, random forest and deep learning), and a large set of explanatory variables to, first, estimate daily PM2.5 concentrations at the ZIP code level, a relevant spatiotemporal resolution for epidemiological studies. Subsequently, we propose a novel implementation of an imputation approach to estimate the wildfire-specific PM2.5 concentrations that could be applied geographical regions in the US or worldwide. Our ensemble model achieved comparable results to previous machine learning studies for PM2.5 prediction while avoiding processing larger, computationally intensive datasets. Our study is the first to apply a suite of statistical models using readily available datasets to provide daily wildfire-specific PM2.5 at a fine spatial scale for a 15-year period, thus providing a relevant spatiotemporal resolution and timely contribution for epidemiological studies.

IntroductionExposure to air pollution disproportionately affects racial/ethnic minorities that could contribute to health inequalities including metabolic disorders. However, most existing studies used a static assessment of air pollution exposure (mostly using the residential address) and do not account for activity space when modelling exposure to air pollution. The aim of this study is to understand how exposure to air pollution impacts metabolic disorders biomarkers, how this effect differs according to ethnicity, and for the first time compare these findings with two methods of exposure assessment: dynamic and static measures.MethodsAmong the Community of Mine study, a cross-sectional study conducted in San Diego County, insulin resistance, diabetes, hypertension, obesity, dyslipidemia, and metabolic syndrome (MetS) were assessed. Exposure to air pollution (PM2.5, NO2, traffic) was calculated using static measures around the home, and dynamic measures of mobility derived from Global Positioning Systems (GPS) traces using kernel density estimators to account for exposure variability across space and time. Associations of air pollution with metabolic disorders were quantified using generalized estimating equation models to account for the clustered nature of the data.ResultsAmong 552 participants (mean age 58.7 years, 42% Hispanic/Latino), Hispanics/Latinos had a higher exposure to PM2.5 compared to non-Hispanics using static measures. In contrast, Hispanics/Latinos had less exposure to PM2.5 using dynamic measures. For all participants, higher dynamic exposure to PM2.5 and NO2 was associated with increased insulin resistance and cholesterol levels, and increased risk of obesity, dyslipidemia and MetS (RR 1.17, 95% CI: 1.07-1.28; RR 1.21, 95% CI: 1.12-1.30, respectively). The association between dynamic PM2.5 exposure and MetS differed by Hispanic/Latino ethnicity.ConclusionThese results highlight the importance of considering people's daily mobility in assessing the impact of air pollution on health.

BackgroundThe tremendous global health burden related to COVID-19 means that identifying determinants of COVID-19 severity is important for prevention and intervention. We aimed to explore long-term exposure to ambient air pollution as a potential contributor to COVID-19 severity, given its known impact on the respiratory system.MethodsWe used a cohort of all people with confirmed SARS-CoV-2 infection, aged 20 years and older and not residing in a long-term care facility in Ontario, Canada, during 2020. We evaluated the association between long-term exposure to fine particulate matter (PM2.5), nitrogen dioxide (NO2) and ground-level ozone (O3), and risk of COVID-19-related hospital admission, intensive care unit (ICU) admission and death. We ascertained individuals' long-term exposures to each air pollutant based on their residence from 2015 to 2019. We used logistic regression and adjusted for confounders and selection bias using various individual and contextual covariates obtained through data linkage.ResultsAmong the 151 105 people with confirmed SARS-CoV-2 infection in Ontario in 2020, we observed 8630 hospital admissions, 1912 ICU admissions and 2137 deaths related to COVID-19. For each interquartile range increase in exposure to PM2.5 (1.70 μg/m3), we estimated odds ratios of 1.06 (95% confidence interval [CI] 1.01-1.12), 1.09 (95% CI 0.98-1.21) and 1.00 (95% CI 0.90-1.11) for hospital admission, ICU admission and death, respectively. Estimates were smaller for NO2. We also estimated odds ratios of 1.15 (95% CI 1.06-1.23), 1.30 (95% CI 1.12-1.50) and 1.18 (95% CI 1.02-1.36) per interquartile range increase of 5.14 ppb in O3 for hospital admission, ICU admission and death, respectively.InterpretationChronic exposure to air pollution may contribute to severe outcomes after SARS-CoV-2 infection, particularly exposure to O3.

BackgroundEpidemiological findings are inconsistent regarding the associations between air pollution exposure during pregnancy and gestational diabetes mellitus (GDM). Several limitations exist in previous studies, including potential outcome and exposure misclassification, unassessed confounding, and lack of simultaneous consideration of air pollution mixtures and particulate matter (PM) constituents.ObjectivesTo assess the association between GDM and maternal residential exposure to air pollution, and the joint effect of the mixture of air pollutants and PM constituents.MethodsDetailed clinical data were obtained for 395,927 pregnancies in southern California (2008-2018) from Kaiser Permanente Southern California (KPSC) electronic health records. GDM diagnosis was based on KPSC laboratory tests. Monthly average concentrations of fine particulate matter

Limited previous work has identified a relationship between exposure to ambient air pollution and aggressive somatic lung tumor mutations. More work is needed to confirm this relationship, especially using spatially resolved air pollution. We aimed to quantify the association between different air pollution metrics and aggressive tumor biology. Among patients treated at City of Hope Comprehensive Cancer Center in Duarte, CA (2013-2018), three non-small cell lung cancer somatic tumor mutations, TP53, KRAS, and KRAS G12C/V, were documented. PM2.5 exposure was assessed using state-of-the art ensemble models five and ten years before lung cancer diagnosis. We also explored the role of NO2 using inverse-distance-weighting approaches. We fitted logistic regression models to estimate odds ratio (OR) and their 95% confidence intervals (CIs). Among 435 participants (median age: 67, female: 51%), an IQR increase in NO2 exposure (3.5 μg/m3) five years before cancer diagnosis was associated with an increased risk in TP53 mutation (OR, 95% CI: 1.30, 0.99-1.71). We found an association between highly-exposed participants to PM2.5 (>12 μg/m3) five and ten years before cancer diagnosis and TP53 mutation (OR, 95% CI: 1.61, 0.95-2.73; 1.57, 0.93-2.64, respectively). Future studies are needed to confirm this association and better understand how air pollution impacts somatic profiles and the molecular mechanisms through which they operate.

Wildfires are becoming more frequent and destructive in a changing climate. Fine particulate matter, PM2.5, in wildfire smoke adversely impacts human health. Recent toxicological studies suggest that wildfire particulate matter may be more toxic than equal doses of ambient PM2.5. Air quality regulations however assume that the toxicity of PM2.5 does not vary across different sources of emission. Assessing whether PM2.5 from wildfires is more or less harmful than PM2.5 from other sources is a pressing public health concern. Here, we isolate the wildfire-specific PM2.5 using a series of statistical approaches and exposure definitions. We found increases in respiratory hospitalizations ranging from 1.3 to up to 10% with a 10 μg m-3 increase in wildfire-specific PM2.5, compared to 0.67 to 1.3% associated with non-wildfire PM2.5. Our conclusions point to the need for air quality policies to consider the variability in PM2.5 impacts on human health according to the sources of emission.

BACKGROUND: Recent studies have reported inconsistent associations between maternal residential green space and preterm birth (PTB, born

BackgroundRecent studies have reported inconsistent associations between maternal residential green space and preterm birth (PTB, born

BackgroundHeat and air pollution are both associated with increases in mortality. However, the interactive effect of temperature and air pollution on mortality remains unsettled. Similarly, the relationship between air pollution, air temperature, and social deprivation has never been explored.MethodsWe used daily mortality data from 2004 to 2009, daily mean temperature variables and relative humidity, for Paris, France. Estimates of chronic exposure to air pollution and social deprivation at a small spatial scale were calculated and split into three strata. We developed a stratified Poisson regression models to assess daily temperature and mortality associations, and tested the heterogeneity of the regression coefficients of the different strata. Deaths due to ambient temperature were calculated from attributable fractions and mortality rates were estimated.ResultsWe found that chronic air pollution exposure and social deprivation are effect modifiers of the association between daily temperature and mortality. We found a potential interactive effect between social deprivation and chronic exposure with regards to air pollution in the mortality-temperature relationship.ConclusionOur results may have implications in considering chronically polluted areas as vulnerable in heat action plans and in the long-term measures to reduce the burden of heat stress especially in the context of climate change.