Do Machine Learning Methods Improve Prediction of Ambient Air Pollutants with High Spatial Contrast? A Systematic Review.

BACKGROUND & OBJECTIVE: The use of machine learning for air pollution modelling is rapidly increasing. We conducted a systematic review of studies comparing statistical and machine learning models predicting the spatiotemporal variation of ambient nitrogen dioxide (NO2), ultrafine particles (UFPs) and black carbon (BC) to determine whether and in which scenarios machine learning generates more accurate predictions. METHODS: Web of Science and Scopus were searched up to June 13, 2024. All records were screened by two independent reviewers. Differences in the coefficient of determination (R2) and Root Mean Square Error (RMSE) between best statistical and machine learning methods were compared across categories of methodological elements. RESULTS: A total of 38 studies with 46 model comparisons (30 for NO2, 8 for UFPs and 8 for BC) were included. Linear non-regularized methods and Random Forest were most frequently used. Machine learning outperformed statistical models in 34 comparisons. Mean differences (95% confidence intervals) in R2 and RMSE between best machine learning and statistical models were 0.12 (0.08, 0.17) and 20% (11%, 29%) respectively. Tree-based methods performed best in 12 of 17 multi-model comparisons. Nonlinear or regularization regression methods were used in only 12 comparisons and provided similar performance to machine learning methods. CONCLUSION: This systematic review suggests that machine learning methods, especially tree-based methods, may be superior to linear non-regularized methods for predicting ambient concentrations of NO2, UFPs and BC. Additional comparison studies using nonlinear, regularized and a wider array of machine learning methods are needed to confirm their relative performance. Future air pollution studies would also benefit from more explicit and standardized reporting of methodologies and results.

Fine Particulate Matter Components and Risk of Rheumatoid Arthritis: A Large General Canadian Open Cohort Study.

OBJECTIVE: Exposure to fine particulate matter (PM2.5) has been linked to many diseases. However, it remains unclear which PM2.5 chemical components for these diseases, including rheumatoid arthritis (RA), are more harmful. This study aimed to assess potential associations between PM2.5 components and RA and quantify the individual effects of each chemical component on RA risk. METHODS: An open cohort of 11,696,930 Canadian adults was assembled using Ontario administrative health data from January 2007 onward. Individuals were followed until RA onset, death, emigration from Ontario, or the end of the study (December 2019). Incident RA cases were defined by physician billing and hospitalization discharge diagnostic codes. The average levels of PM2.5 components (ammonium, black carbon, mineral dust, nitrate, organic matter, sea salt, and sulfate) for 5 years before cohort entry were assigned to participants based on residential postal codes. A quantile g-computation and Cox proportional hazard models for time to RA onset were developed for the mixture of PM2.5 components and environmental overall PM2.5, respectively. RESULTS: We identified 67,676 new RA cases across 130,934,256 person-years. The adjusted hazard ratios for the time to RA onset were 1.027 and 1.023 (95% confidence intervals 1.021-1.033 and 1.017-1.029) per every decile increase in exposures to all seven components and per 1 µg/m3 increase in the overall PM2.5, respectively. Ammonium contributed the most to RA onset in the seven components. CONCLUSION: Exposure to PM2.5 components was modestly associated with RA risk. Public health efforts focusing on specific components (eg, ammonium) may be a more efficient way to reduce RA burden.

Improving residential wood burning emission inventories with the integration of readily available data sources.

Residential wood burning (RWB) is the largest anthropogenic source of PM2.5 in many North American and European cities in the winter. The current lack of information on the locations, types, and intensity of use of wood burning appliances limits the ability to accurately assess the exposure of the population to RWB emissions. In this study, we generated a high spatial resolution emission inventory for RWB in the province of Quebec, Canada using a novel data driven approach. The method first combines real estate and socioeconomic census data through machine learning models to estimate ownership rates of fireplaces and wood stoves. These ownership rates are then combined with household survey data (on wood consumption and types of appliances), emission factors and building registry data to generate the emission inventory at a 1Km2 resolution. Our proposed approach was able to capture spatial patterns in RWB appliance ownership and intensity of use, which may be overlooked by using simple urban vs rural or population based spatial proxies. The machine learning models explained 80.3 % and 63 % of the variability in wood stove and fireplace ownership rates with each appliance type exhibiting different spatial trends. Wood stoves were common in rural areas and among lower income households, whereas fireplaces were more common in urban areas. Additionally, we observed large spatial and regional variability in emissions per residence due to differences in wood consumption, appliance ownership rates, and appliance mixes (e.g. conventional vs certified). Our results suggest that using simple spatial proxies based on population, urbanization levels or residence type are not enough to explain the spatial distribution of RWB emissions as they might overlook other factors such as socioeconomic factors or regional heating preferences. Finally, our spatially distributed emissions were strongly correlated (r = 0.86) with the increase in PM2.5 concentrations during peak-RWB hours on winter weekends at 42 reference stations across the province of Quebec.

High-resolution spatial and spatiotemporal modelling of air pollution using fixed site and mobile monitoring in a Canadian city.

The development of high-resolution spatial and spatiotemporal models of air pollutants is essential for exposure science and epidemiological applications. While fixed-site sampling has conventionally provided input data for statistical predictive models, the evolving mobile monitoring method offers improved spatial resolution, ideal for measuring pollutants with high spatial variability such as ultrafine particles (UFP). The Quebec Air Pollution Exposure and Epidemiology (QAPEE) study measured and modelled the spatial and spatiotemporal distributions of understudied pollutants, such as UFPs, black carbon (BC), and brown carbon (BrC), along with fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3) in Quebec City, Canada. We conducted a combined fixed-site (NO2 and O3) and mobile monitoring (PM2.5, BC, BrC, and UFPs) campaign over 10-months. Mobile monitoring routes were monitored on a weekly basis between 8am-10am and designed using location/allocation modelling. Seasonal fixed-site sampling campaigns captured continuous 24-h measurements over two-week periods. Generalized Additive Models (GAMs), which combined data on pollution concentrations with spatial, temporal, and spatiotemporal predictor variables were used to model and predict concentration surfaces. Annual models for PM2.5, NO2, O3 as well as seven of the smallest size fractions in the UFP range, had high out of sample predictive accuracy (range r2: 0.54-0.86). Varying spatial patterns were observed across UFP size ranges measured as Particle Number Counts (PNC). The monthly spatiotemporal models for PM2.5 (r2 = 0.49), BC (r2 = 0.27), BrC (r2 = 0.29), and PNC (r2 = 0.49) had moderate or moderate-low out of sample predictive accuracy. We conducted a sensitivity analysis and found that the minimum number of 'n visits' (mobile monitoring sessions) required to model annually representative air pollution concentrations was between 24 and 32 visits dependent on the pollutant. This study provides a single source of exposure models for a comprehensive set of air pollutants in Quebec City, Canada. These exposure models will feed into epidemiological research on the health impacts of ambient UFPs and other pollutants.

Residential surrounding greenness and the incidence of childhood asthma: Findings from a population-based cohort in Ontario, Canada.

Several epidemiological studies have investigated the possible role that living in areas with greater amounts of greenspace has on the incidence of childhood asthma. These findings have been inconsistent, and few studies explored the relevance of timing of exposure. We investigated the role of residential surrounding greenness on the risk of incident asthma using a population-based retrospective cohort study. We included 982,131 singleton births in Ontario, Canada between 2006 and 2013. Two measures of greenness, the Normalized Difference Vegetation Index (NDVI) and the Green View Index (GVI), were assigned to the residential histories of these infants from pregnancy through to 12 years of age. Longitudinally-based diagnoses of asthma were determined by using provincial administrative health data. The extended Cox hazards model was used to characterize associations between greenness measures and asthma (up to age 12 years) while adjusting for several risk factors. In a fully adjusted model, that included a term for traffic-related air pollution (NO2), we found no association between an interquartile range increase (0.08) of the NDVI during childhood and asthma incidence (HR = 0.99; 95 % CI = 0.99-1.01). In contrast, we found that an 0.08 increase in NDVI during childhood reduced the risk of asthma in children 7-12 years of age by 14 % (HR = 0.86, 95 % CI:0.79-0.95). Seasonal differences in the association between greenness and asthma were noted. Our findings suggest that residential proximity to greenness reduces the risk of asthma in children aged 7-12.

Predicting malaria risk considering vector control interventions under climate change scenarios.

Many studies have projected malaria risks with climate change scenarios by modelling one or two environmental variables and without the consideration of malaria control interventions. We aimed to predict the risk of malaria with climate change considering the influence of rainfall, humidity, temperatures, vegetation, and vector control interventions (indoor residual spraying (IRS) and long-lasting insecticidal nets (LLIN)). We used negative binomial models based on weekly malaria data from six facility-based surveillance sites in Uganda from 2010-2018, to estimate associations between malaria, environmental variables and interventions, accounting for the non-linearity of environmental variables. Associations were applied to future climate scenarios to predict malaria distribution using an ensemble of Regional Climate Models under two Representative Concentration Pathways (RCP4.5 and RCP8.5). Predictions including interaction effects between environmental variables and interventions were also explored. The results showed upward trends in the annual malaria cases by 25% to 30% by 2050s in the absence of intervention but there was great variability in the predictions (historical vs RCP 4.5 medians [Min-Max]: 16,785 [9,902-74,382] vs 21,289 [11,796-70,606]). The combination of IRS and LLIN, IRS alone, and LLIN alone would contribute to reducing the malaria burden by 76%, 63% and 35% respectively. Similar conclusions were drawn from the predictions of the models with and without interactions between environmental factors and interventions, suggesting that the interactions have no added value for the predictions. The results highlight the need for maintaining vector control interventions for malaria prevention and control in the context of climate change given the potential public health and economic implications of increasing malaria in Uganda.

Changes in industrial air pollution and the onset of childhood asthma in Quebec, Canada.

Ambient air pollution has been associated with asthma onset and exacerbation in children. Whether improvement in air quality due to reduced industrial emissions has resulted in improved health outcomes such as asthma in some localities has usually been assessed indirectly with studies on between-subject comparisons of air pollution from all sources and health outcomes. In this study we directly assessed, within small areas in the province of Quebec (Canada), the influence of changes in local industrial fine particulate matter (PM2.5), nitrogen dioxide (NO2), and sulfur dioxide (SO2) concentrations, on changes in annual asthma onset rates in children (≤12 years old) with a longitudinal ecological design. We identified the yearly number of new cases of childhood asthma in 1282 small areas (census tracts or local community service centers) for the years 2002, 2004, 2005, 2006, and 2015. Annual average concentrations of industrial air pollutants for each of the geographic areas, and three sectors (i.e., pulp and paper mills, petroleum refineries, and metal smelters) were estimated by the Polair3D chemical transport model. Fixed-effects negative binomial models adjusted for household income were used to assess associations; additional adjustments for environmental tobacco smoke, background pollutant concentrations, vegetation coverage, and sociodemographic characteristics were conducted in sensitivity analyses. The incidence rate ratios (IRR) for childhood asthma onset for the interquartile increase in total industrial PM2.5, NO2, and SO2 were 1.016 (95% confidence interval, CI: 1.006-1.026), 1.063 (1.045-1.090), and 1.048 (1.031-1.080), respectively. Positive associations were also found with pollutant concentrations from most individual sectors. Results suggest that changes in industrial pollutant concentrations influence childhood asthma onset rates in small localities.

Evidence Synthesis of Observational Studies in Environmental Health: Lessons Learned from a Systematic Review on Traffic-Related Air Pollution.

BACKGROUND: There is a long tradition in environmental health of using frameworks for evidence synthesis, such as those of the U.S. Environmental Protection Agency for its Integrated Science Assessments and the International Agency for Research on Cancer Monographs. The framework, Grading of Recommendations Assessment, Development, and Evaluation (GRADE), was developed for evidence synthesis in clinical medicine. The U.S. Office of Health Assessment and Translation (OHAT) elaborated an approach for evidence synthesis in environmental health building on GRADE. METHODS: We applied a modified OHAT approach and a broader "narrative" assessment to assess the level of confidence in a large systematic review on traffic-related air pollution and health outcomes. DISCUSSION: We discuss several challenges with the OHAT approach and its implementation and suggest improvements for synthesizing evidence from observational studies in environmental health. We consider the determination of confidence using a formal rating scheme of up- and downgrading of certain factors, the treatment of every factor as equally important, and the lower initial confidence rating of observational studies to be fundamental issues in the OHAT approach. We argue that some observational studies can offer high-confidence evidence in environmental health. We note that heterogeneity in magnitude of effect estimates should generally not weaken the confidence in the evidence, and consistency of associations across study designs, populations, and exposure assessment methods may strengthen confidence in the evidence. We mention that publication bias should be explored beyond statistical methods and is likely limited when large and collaborative studies comprise most of the evidence and when accrued over several decades. We propose to identify possible key biases, their most likely direction, and their potential impacts on the results. We think that the OHAT approach and other GRADE-type frameworks require substantial modification to align better with features of environmental health questions and the studies that address them. We emphasize that a broader, "narrative" evidence assessment based on the systematic review may complement a formal GRADE-type evaluation. https://doi.org/10.1289/EHP11532.

Differential Influence of Environmental Factors on Malaria Due to Vector Control Interventions in Uganda.

BACKGROUND: Few studies have explored how vector control interventions may modify associations between environmental factors and malaria. METHODS: We used weekly malaria cases reported from six public health facilities in Uganda. Environmental variables (temperature, rainfall, humidity, and vegetation) were extracted from remote sensing sources. The non-linearity of environmental variables was investigated, and negative binomial regression models were used to explore the influence of indoor residual spraying (IRS) and long-lasting insecticidal nets (LLINs) on associations between environmental factors and malaria incident cases for each site as well as pooled across the facilities, with or without considering the interaction between environmental variables and vector control interventions. RESULTS: An average of 73.3 weekly malaria cases per site (range: 0-597) occurred between 2010 and 2018. From the pooled model, malaria risk related to environmental variables was reduced by about 35% with LLINs and 63% with IRS. Significant interactions were observed between some environmental variables and vector control interventions. There was site-specific variability in the shape of the environment-malaria risk relationship and in the influence of interventions (6 to 72% reduction in cases with LLINs and 43 to 74% with IRS). CONCLUSION: The influence of vector control interventions on the malaria-environment relationship need to be considered at a local scale in order to efficiently guide control programs.

Long-Term Exposure to Traffic-Related Air Pollution and Diabetes: A Systematic Review and Meta-Analysis.

Objectives: We report results of a systematic review on the health effects of long-term traffic-related air pollution (TRAP) and diabetes in the adult population. Methods: An expert Panel appointed by the Health Effects Institute conducted this systematic review. We searched the PubMed and LUDOK databases for epidemiological studies from 1980 to July 2019. TRAP was defined based on a comprehensive protocol. Random-effects meta-analyses were performed. Confidence assessments were based on a modified Office for Health Assessment and Translation (OHAT) approach, complemented with a broader narrative synthesis. We extended our interpretation to include evidence published up to May 2022. Results: We considered 21 studies on diabetes. All meta-analytic estimates indicated higher diabetes risks with higher exposure. Exposure to NO2 was associated with higher diabetes prevalence (RR 1.09; 95% CI: 1.02; 1.17 per 10 µg/m3), but less pronounced for diabetes incidence (RR 1.04; 95% CI: 0.96; 1.13 per 10 µg/m3). The overall confidence in the evidence was rated moderate, strengthened by the addition of 5 recently published studies. Conclusion: There was moderate evidence for an association of long-term TRAP exposure with diabetes.

Associations between environmental noise and myocardial infarction and stroke: Investigating the potential mediating effects of hypertension.

BACKGROUND: We investigated whether hypertension may be a mediator in the pathway linking environmental noise exposure to incident MI and stroke. METHODS: Separately for MI and stroke, we built two population-based cohorts from linked health administrative data. Participants were residents of Montreal (Canada) between 2000 and 2014, aged 45 years and older who were free of hypertension and MI or stroke at time of entry. MI, stroke and hypertension were ascertained from validated case definitions. Residential long-term environmental noise exposure, expressed as the annual mean level acoustic equivalent 24 h (LAeq24h), was estimated from a land use regression model. We performed mediation analysis based on the potential outcomes framework. We used a Cox proportional hazards model for the exposure-outcome model and a logistic regression for the exposure-mediator model. In sensitivity analysis we applied a marginal structural approach to estimate the natural direct and indirect effects. RESULTS: Each cohort included approximately 900 000 individuals, with 26 647 incident cases of MI and 16 656 incident cases of stroke. 36% of incident MI and 40% of incident stokes had previously developed hypertension. The estimated total effect per interquartile range increase (from 55.0 to 60.5 dB A) in the annual mean LAeq24h was 1.073 (95% confidence interval (CI): 1.070-1.077) for both MI for stroke. We found no evidence of exposure-mediator interaction for both outcomes. The relationships between environmental noise and MI and stroke was not mediated by hypertension. CONCLUSIONS: This population-based cohort study suggests that the main route by which environmental noise exposure may cause MI or stroke is not through hypertension.

Systemic autoimmune rheumatic diseases and multiple industrial air pollutant emissions: A large general population Canadian cohort analysis.

BACKGROUND: Past investigations of air pollution and systemic autoimmune rheumatic diseases (SARDs) typically focused on individual (not mixed) and overall environmental emissions. We assessed mixtures of industrial emissions of fine particulate matter (PM2.5), nitrogen dioxide (NO2), and sulfur dioxide (SO2) and SARDs onset in Ontario, Canada. METHODS: We assembled an open cohort of over 12 million adults (without SARD diagnoses at cohort entry) based on provincial health data for 2007-2020 and followed them until SARD onset, death, emigration, or end of study (December 2020). SARDs were identified using physician billing and hospitalization diagnostic codes for systemic lupus, scleroderma, myositis, undifferentiated connective tissue disease, and Sjogren's. Rheumatoid arthritis and vasculitis were not included. Average PM2.5, NO2, and SO2 industrial emissions from 2002 to one year before SARDs onset or end of study were assigned using residential postal codes. A quantile g-computation model for time to SARD onset was developed for the industrial emission mixture, adjusting for sex, age, income, rurality index, chronic obstructive pulmonary disease (as a proxy for smoking), background (environmental overall) PM2.5, and calendar year. We conducted stratified analyses across age, sex, and rurality. RESULTS: We identified 43,931 new SARD diagnoses across 143,799,564 person-years. The adjusted hazard ratio for SARD onset for an increase in all emissions by one decile was 1.018 (95% confidence interval 1.013-1.022). Similar positive associations between SARDs and the mixed emissions were observed in most stratified analyses. Industrial PM2.5 contributed most to SARD risk. CONCLUSIONS: Industrial air pollution emissions were associated with SARDs risk.

Exposure to ultrafine particles and the incidence of asthma in children: A population-based cohort study in Montreal, Canada.

Asthma is the most prevalent chronic respiratory disease in children. The role of ultrafine particles (UFPs) in the development of the disease remains unclear. We used a population-based birth cohort to evaluate the association between prenatal and childhood exposure to low levels of ambient UFPs and childhood-onset asthma. Methods: The cohort included all children born and residing in Montreal, Canada, between 2000 and 2015. Children were followed for asthma onset from birth until <13 years of age. Spatially resolved annual mean concentrations of ambient UFPs were estimated from a land use regression model. We assigned prenatal exposure according to the residential postal code at birth. We also considered current exposure during childhood accounting for time-varying residence location. We estimated hazard ratios (HRs) using Cox proportional hazards models adjusted for age, sex, neighborhood material and social deprivation, calendar year, and coexposure to ambient nitrogen dioxide (NO2) and fine particles (PM2.5). Results: The cohort included 352,966 children, with 30,825 children developing asthma during follow-up. Mean prenatal and childhood UFP exposure were 24,706 particles/cm3 (interquartile range [IQR] = 3,785 particles/cm3) and 24,525 particles/cm3 (IQR = 3,427 particles/cm3), respectively. Both prenatal and childhood UFP exposure were not associated with childhood asthma onset in single pollutant models (HR per IQR increase of 0.99 [95% CI = 0.98, 1.00]). Estimates of association remained similar when adjusting for coexposure to ambient NO2 and PM2.5. Conclusion: In this population-based birth cohort, childhood asthma onset was not associated with prenatal or childhood exposure to low concentrations of UFPs.

The influence of urban trees and total vegetation on asthma development in children.

Objective: We aimed to assess whether the influence of urban vegetation on asthma development in children (<13 years) varies by type (e.g., total vegetation, tree type, and grass) and season. Methods: We used a cohort of all children born in Montreal, Canada, between 2000 and 2015. Children and cases were identified from linked medico-administrative databases. Exposure to residential vegetation was estimated using the Normalized Difference Vegetation Index (NDVI) for total vegetation and using the total area covered by deciduous and evergreen crowns for trees in 250 m buffers centered on residential postal codes. Seasonal variations in vegetation were modeled by setting values to zero on days outside of pollen and leaf-on seasons. Cox models with vegetation exposures, age as a time axis, and adjusted for sex, material deprivation, and health region were used to estimate hazard ratios (HR) for asthma development. Results: We followed 352,946 children for a total of 1,732,064 person-years and identified 30,816 incident cases of asthma. While annual vegetation (total and trees) measures did not appear to be associated with asthma development, models for pollen and leaf-on seasons yielded significant nonlinear associations. The risk of developing asthma was lower in children exposed to high levels (>33,300 m2) of deciduous crown area for the leaf-on season (HR = 0.69; 95% confidence interval [CI] = 0.67, 0.72) and increased for the pollen season (HR = 1.07; 95% CI =1.02, 1.12), compared with unexposed children. Similar results were found with the Normalized Difference Vegetation Index. Conclusion: The relationship between urban vegetation and childhood asthma development is nonlinear and influenced by vegetation characteristics, from protective during the leaf-on season to harmful during the pollen season.

Relationships between long-term residential exposure to total environmental noise and stroke incidence.

Background: Noise has been related to several cardiovascular diseases (CVDs) such as coronary heart disease and to their risk factors such as hypertension, but associations with stroke remain under-researched, even if CVD likely share similar pathophysiologic mechanisms. Aim: The objective of the study was to examine the association between long-term residential exposure to total environmental noise and stroke incidence in Montreal, Canada. Materials and Methods: We created an open cohort of adults aged ≥45years, free of stroke before entering the cohort for the years 2000 to 2014 with health administrative data. Residential total environmental noise levels were estimated with land use regression (LUR) models. Incident stroke was based on hospital admissions. Cox hazard models with age as the time axis and time-varying exposures were used to estimate associations, which were adjusted for material deprivation, year, nitrogen dioxide, stratified for sex, and indirectly adjusted for smoking. Results: There were 9,072,492 person-years of follow-up with 47% men; 26,741 developed stroke (21,402 ischemic; 4947 hemorrhagic; 392 had both). LUR total noise level acoustic equivalent for 24 hours (LAeq24h) ranged 44 to 79 dBA. The adjusted hazard ratio (HR) for stroke (all types), for a 10-dBA increase in LAeq24h, was 1.06 [95% confidence interval (CI): 1.03-1.09]. The LAeq24h was associated with ischemic (HR per 10 dBA: 1.08; 95% CI: 1.04-1.12) but not hemorrhagic stroke (HR per 10 dBA: 0.97; 95% CI: 0.90-1.04). Conclusion: The results suggest that total environmental noise is associated with incident stroke, which is consistent with studies on transportation noise and other CVD.

The influence of the environment and indoor residual spraying on malaria risk in a cohort of children in Uganda.

Studies have estimated the impact of the environment on malaria incidence although few have explored the differential impact due to malaria control interventions. Therefore, the objective of the study was to evaluate the effect of indoor residual spraying (IRS) on the relationship between malaria and environment (i.e. rainfall, temperatures, humidity, and vegetation) using data from a dynamic cohort of children from three sub-counties in Uganda. Environmental variables were extracted from remote sensing sources and averaged over different time periods. General linear mixed models were constructed for each sub-counties based on a log-binomial distribution. The influence of IRS was analysed by comparing marginal effects of environment in models adjusted and unadjusted for IRS. Great regional variability in the shape (linear and non-linear), direction, and magnitude of environmental associations with malaria risk were observed between sub-counties. IRS was significantly associated with malaria risk reduction (risk ratios vary from RR = 0.03, CI 95% [0.03-0.08] to RR = 0.35, CI95% [0.28-0.42]). Model adjustment for this intervention changed the magnitude and/or direction of environment-malaria associations, suggesting an interaction effect. This study evaluated the potential influence of IRS in the malaria-environment association and highlighted the necessity to control for interventions when they are performed to properly estimate the environmental influence on malaria. Local models are more informative to guide intervention program compared to national models.

Long-term exposure to fine particulate matter and ozone and the onset of systemic autoimmune rheumatic diseases: an open cohort study in Quebec, Canada.

OBJECTIVES: To estimate associations between fine particulate matter (PM2.5) and ozone and the onset of systemic autoimmune rheumatic diseases (SARDs). METHODS: An open cohort of over 6 million adults was constructed from provincial physician billing and hospitalization records between 2000 and 2013. We defined incident SARD cases (SLE, Sjogren's syndrome, scleroderma, polymyositis, dermatomyositis, polyarteritis nodosa and related conditions, polymyalgia rheumatic, other necrotizing vasculopathies, and undifferentiated connective tissue disease) based on at least two relevant billing diagnostic codes (within 2 years, with at least 1 billing from a rheumatologist), or at least one relevant hospitalization diagnostic code. Estimated PM2.5 and ozone concentrations (derived from remote sensing and/or chemical transport models) were assigned to subjects based on residential postal codes, updated throughout follow-up. Cox proportional hazards models with annual exposure levels were used to calculate hazard ratios (HRs) for SARDs incidence, adjusting for sex, age, urban-versus-rural residence, and socioeconomic status. RESULTS: The adjusted HR for SARDS related to one interquartile range increase in PM2.5 (3.97 µg/m3) was 1.12 (95% confidence interval 1.08-1.15), but there was no clear association with ozone. Indirectly controlling for smoking did not alter the findings. CONCLUSIONS: We found associations between SARDs incidence and PM2.5, but no relationships with ozone. Additional studies are needed to better understand interplays between the many constituents of air pollution and rheumatic diseases.

Assessment of the Oxidative Potential and Oxidative Burden from Occupational Exposures to Particulate Matter.

Oxidative potential (OP) is a toxicologically relevant metric that integrates features like mass concentration and chemical composition of particulate matter (PM). Although it has been extensively explored as a metric for the characterization of environmental particles, this is still an underexplored application in the occupational field. This study aimed to estimate the OP of particles in two occupational settings from a construction trades school. This characterization also includes the comparison between activities, sampling strategies, and size fractions. Particulate mass concentrations (PM4-Personal, PM4-Area, and PM2.5-Area) and number concentrations were measured during three weeks of welding and construction/bricklaying activities. The OP was assessed by the ascorbate assay (OPAA) using a synthetic respiratory tract lining fluid (RTLF), while the oxidative burden (OBAA) was determined by multiplying the OPAA values with PM concentrations. Median (25th-75th percentiles) of PM mass and number concentrations were 900 (672-1730) µg m-3 and 128 000 (78 000-169 000) particles cm-3 for welding, and 432 (345-530) µg m-3 and 2800 (1700-4400) particles cm-3 for construction. Welding particles, especially from the first week of activities, were also associated with higher redox activity (OPAA: 3.3 (2.3-4.6) ρmol min-1 µg-1; OBAA: 1750 (893-4560) ρmol min-1 m-3) compared to the construction site (OPAA: 1.4 (1.0-1.8) ρmol min-1 µg-1; OBAA: 486 (341-695) ρmol min-1 m-3). The OPAA was independent of the sampling strategy or size fraction. However, driven by the higher PM concentrations, the OBAA from personal samples was higher compared to area samples in the welding shop, suggesting an influence of the sampling strategy on PM concentrations and OBAA. These results demonstrate that important levels of OPAA can be found in occupational settings, especially during welding activities. Furthermore, the OBAA found in both workplaces largely exceeded the levels found in environmental studies. Therefore, measures of OP and OB could be further explored as metrics for exposure assessment to occupational PM, as well as for associations with cardiorespiratory outcomes in future occupational epidemiological studies.