Spatial and spatiotemporal modelling of intra-urban ultrafine particles: A comparison of linear, nonlinear, regularized, and machine learning methods.

BACKGROUND: Machine learning methods are proposed to improve the predictions of ambient air pollution, yet few studies have compared ultrafine particles (UFP) models across a broad range of statistical and machine learning approaches, and only one compared spatiotemporal models. Most reported marginal differences between methods. This limits our ability to draw conclusions about the best methods to model ambient UFPs. OBJECTIVE: To compare the performance and predictions of statistical and machine learning methods used to model spatial and spatiotemporal ambient UFPs. METHODS: Daily and annual models were developed from UFP measurements from a year-long mobile monitoring campaign in Quebec City, Canada, combined with 262 geospatial and six meteorological predictors. Various road segment lengths were considered (100/300/500 m) for UFP data aggregation. Four statistical methods included linear, non-linear, and regularized regressions, whereas eight machine learning regressions utilized tree-based, neural networks, support vector, and kernel ridge algorithms. Nested cross-validation was used for model training, hyperparameter tuning and performance evaluation. RESULTS: Mean annual UFP concentrations was 13,335 particles/cm3. Machine learning outperformed statistical methods in predicting UFPs. Tree-based methods performed best across temporal scales and segment lengths, with XGBoost producing the overall best performing models (annual R2 = 0.78-0.86, RMSE = 2163-2169 particles/cm3; daily R2 = 0.47-0.48, RMSE = 8651-11,422 particles/cm3). With 100 m segments, other annual models performed similarly well, but their prediction surfaces of annual mean UFP concentrations showed signs of overfitting. Spatial aggregation of monitoring data significantly impacted model performance. Longer segments yielded lower RMSE in all daily models and for annual statistical models, but not for annual machine learning models. CONCLUSIONS: The use of tree-based methods significantly improved spatiotemporal predictions of UFP concentrations, and to a lesser extent annual concentrations. Segment length and hyperparameter tuning had notable impacts on model performance and should be considered in future studies.

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.

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.

Burn Patients and Mental Health: A Matched Cohort Study.

OBJECTIVE: To determine the association between burns and hospitalization for mental health disorders up to three decades later. SUMMARY BACKGROUND DATA: Burns are associated with pain, disability, and scarring, but the long-term impact on mental health is unclear. METHODS: We analyzed a cohort of 23,726 burn patients aged ≥10 years who were matched to 223,626 controls from Quebec, Canada, between 1989 and 2022. The main exposure was admission for a burn. We followed patients during 3,642,206 person-years of follow-up to identify future hospitalizations for psychiatric disorders, substance use disorders, and suicide attempts. We estimated adjusted hazard ratios (HR) with 95% confidence intervals (CI) for the association between burns and subsequent mental health hospitalization using Cox proportional hazards regression. RESULTS: Burn patients had 1.76 times greater risk of mental health hospitalization over time (95% CI 1.72-1.81), compared with controls. Associations were present regardless of burn site, but were greatest for burns covering ≥50% of the body (HR 3.29, 95% CI 2.61-4.15), third degree burns (HR 2.04, 95% CI 1.94-2.14), and burns requiring skin grafts (HR 2.00, 95% CI 1.90-2.10). Compared with controls, burn patients had more than two times the risk of hospitalization for eating disorders (HR 3.14, 95% CI 2.50-3.95), psychoactive substance use disorders (HR 2.27, 95% CI 2.17-2.39), and suicide attempts (HR 2.42, 95% CI 2.23-2.62). Risks were particularly elevated within 5 years of the burn, but persisted throughout follow-up. CONCLUSIONS: Burns are associated with an increased risk of hospitalization for mental health disorders up to 30 years later.

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.

Association between First Trimester Exposure to Ambient PM2.5 and NO2 and Congenital Heart Defects: A Population-Based Cohort Study of 1,342,198 Live Births in Canada.

BACKGROUND: The extent to which ambient air pollution contributes to the pathogenesis of congenital heart defects remains uncertain. OBJECTIVE: We investigated whether first trimester exposure to ambient fine particulate matter (PM2.5) and nitrogen dioxide (NO2) was associated with the risk of critical and noncritical heart defects in a large population-based cohort of births. METHODS: We carried out a retrospective cohort study of children conceived between 2000 and 2016 in Quebec, Canada. Heart defects were identified via data from the Maintenance and Use of Data for the Study of Hospital Clientele registry. The main exposures were average concentration of PM2.5 and NO2 in a) the first trimester and b) the month of conception. Exposures were estimated at the residential postal code. Associations with critical and noncritical heart defects were assessed using logistic regression models, adjusted for maternal and infant characteristics. We considered single- and two-pollutant models and assessed modifying effects of maternal comorbidity, including preexisting hypertension, preeclampsia, anemia, and diabetes. RESULTS: The cohort comprised 1,342,198 newborns, including 12,715 with heart defects. Exposure in the first trimester and month of conception yielded similar results; both were associated with a greater risk of heart defects. Adjusted odds ratios (OR) for any heart defect per interquartile range increase were 1.02 (95% CI: 1.00, 1.05) for PM2.5 and 1.10 (95% CI: 1.07, 1.13) for NO2. Associations with atrial septal defects were 1.08 (95% CI: 1.03, 1.14) for PM2.5 and 1.19 (95% CI: 1.12, 1.25) for NO2. Corresponding ORs for ventricular septal defects and individual critical heart defects were not significant. PM2.5 (OR=1.11; 95% CI: 1.06, 1.17) and NO2 (OR=1.23; 95% CI: 1.17, 1.31) exposure were associated with a greater risk of heart defects in mothers with comorbidity. DISCUSSION: In this population-based cohort, prenatal exposure to ambient air pollution during the first trimester was associated with an increased risk of heart defects, particularly atrial septal defects. The association with heart defects was greater in mothers with comorbidity. https://doi.org/10.1289/EHP11120.

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.

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.

Environmental and Occupational Short-Term Exposure to Airborne Particles and FEV1 and FVC in Healthy Adults: A Systematic Review and Meta-Analysis.

BACKGROUND: No study has compared the respiratory effects of environmental and occupational particulate exposure in healthy adults. METHODS: We estimated, by a systematic review and meta-analysis, the associations between short term exposures to fine particles (PM2.5 and PM4) and certain parameters of lung function (FEV1 and FVC) in healthy adults. RESULTS: In total, 33 and 14 studies were included in the qualitative synthesis and meta-analyses, respectively. In environmental studies, a 10 µg/m3 increase in PM2.5 was associated with an FEV1 reduction of 7.63 mL (95% CI: -10.62 to -4.63 mL). In occupational studies, an increase of 10 µg/m3 in PM4 was associated with an FEV1 reduction of 0.87 mL (95% CI: -1.36 to -0.37 mL). Similar results were observed with FVC. CONCLUSIONS: Both occupational and environmental short-term exposures to fine particles are associated with reductions in FEV1 and FVC in healthy adults.

Association between Kawasaki Disease and Prenatal Exposure to Ambient and Industrial Air Pollution: A Population-Based Cohort Study.

BACKGROUND: Environmental factors may contribute to the development of Kawasaki disease in children, but prenatal environmental exposures are understudied. OBJECTIVE: We used a population-based cohort to investigate whether prenatal exposure to outdoor air pollution is associated with the incidence of Kawasaki disease in childhood. METHODS: We performed a longitudinal cohort study of all children born in Quebec, Canada, between 2006 and 2012. Children were followed for Kawasaki disease from birth until 31 March 2018. We assigned prenatal air pollutant exposure according to the residential postal code at birth. The main exposure was annual average concentration of ambient fine particulate matter [PM ≤2.5µm in aerodynamic diameter (PM2.5) and nitrogen dioxide (NO2) from satellite-based estimates and land-use regression models. As secondary exposures, we considered industrial PM2.5, NO2, and sulfur dioxide (SO2) emissions estimated from dispersion models. We estimated hazard ratios (HRs) using Cox proportional hazards models, adjusted for maternal age, parity, sex, multiple birth, maternal smoking during pregnancy, socioeconomic status, birth year, and rural residence. We considered single and multipollutant models. We performed several sensitivity analyses, including assessing modifying effects of maternal comorbidities (e.g., diabetes, preeclampsia). RESULTS: The cohort comprised 505,336 children, including 539 with Kawasaki disease. HRs for each interquartile range increase in ambient air pollution were 1.16 (95% CI: 0.96, 1.39) for PM2.5 and 1.12 (95% CI: 0.96, 1.31) for NO2. For industrial air pollution, HRs were 1.07 (95% CI: 1.01, 1.13) for SO2, 1.09 (95% CI: 0.99, 1.20) for NO2, and 1.01 (95% CI: 0.97, 1.05) for PM2.5. In multipollutant models, associations for ambient PM2.5 and NO2 (i.e., from all sources) were robust to adjustment for industrial pollution, and vice versa. DISCUSSION: In this population-based cohort study, both prenatal exposure to ambient and industrial air pollution were associated with the incidence of Kawasaki disease in childhood. Further studies are needed to consolidate the observed associations. https://doi.org/10.1289/EHP6920.

Air pollution from industries and asthma onset in childhood: A population-based birth cohort study using dispersion modeling.

BACKGROUND: Despite evidence that ambient air pollution may play a role in the development of asthma, little is known about the potential contribution of industrial emissions. OBJECTIVE: We used a population-based birth cohort to investigate the association between asthma onset in childhood and residential exposure to industrial emissions, estimated from atmospheric dispersion modeling. METHODS: The study population comprised all children born in the province of Quebec, Canada, 2002-2011. Asthma onset were ascertained from health administrative databases with validated algorithms. We used atmospheric dispersion modeling to develop time-varying annual mean concentration of ambient PM2.5, NO2 and SO2 at participants' residence from industries. For each pollutant, we assessed the association between industrial emissions exposure and childhood asthma onset using Cox proportional hazard model, adjusted for sex, material and social deprivation and calendar year. Sensitivity analysis included adjusting for long-term regional and traffic-related ambient PM2.5 and NO2, and assessing potential confounding by unmeasured secondhand smoke. RESULTS: The cohort included 722,667 children and 66,559 incident cases of asthma. For all pollutants, we found a non-linear association between childhood asthma onset and residential ambient air pollutant concentration from industries, with stronger effects at lower concentrations. A change from 25th to the 75th percentile in the mean annual ambient concentration of PM2.5 (0.13 µg/m3), NO2 (1.0 µg/m3) and SO2 (1.6 µg/m3) from industrial emissions was associated with a 19% (95% CI: 17-20%), 21% (95% CI: 19-23%) and 23% (95% CI: 21-24%) increase in the risk of asthma onset in children, respectively. For PM2.5 and NO2, associations were persisting after adjustments for long-term regional PM2.5 and traffic-related NO2 ambient concentration. CONCLUSION: Residential exposure to industrial emissions estimated from dispersion modeling was associated with asthma onset in childhood. Importantly, associations were stronger at lower concentrations and independent from those of other sources, thus adding up to the burden of regional and traffic-related air pollution.

Review of the effect of air pollution exposure from industrial point sources on asthma-related effects in childhood.

We reviewed epidemiologic studies of the association between exposure to air pollution from industries and asthma-related outcomes in childhood. We searched bibliographic databases and reference lists of relevant articles to identify studies examining the association between children's exposure to air pollution from industrial point-sources and asthma-related outcomes, including asthma, asthma-like symptoms, wheezing, and bronchiolitis. We extracted key characteristics of each study and when appropriate we performed a random-effects meta-analysis of results and quantified heterogeneity (I 2). Thirty-six studies were included in this review. Meta-analysis was generally not possible and limited to a few studies because of substantial variation across design characteristics and methodologies. In case-crossover studies using administrative health data, pooled odds ratio (OR) of hospitalization for asthma and bronchiolitis in children <5 years were 1.02 [95% confidence intervals (CI): 0.96, 1.08; I 2 = 56%] and 1.01 (95% CI: 0.97, 1.05; I 2 = 64%) per 10 ppb increase in the daily mean and hourly maximum concentration of sulfur dioxide (SO2), respectively. For PM2.5, pooled ORs were 1.02 (95% CI: 0.93, 1.10; I 2 = 56%) and 1.01 (95% CI: 0.98, 1.03 I 2 = 33%) per 10 µg/m3 increment in the daily mean and hourly maximum concentration. In cross-sectional studies using questionnaires, pooled ORs for the prevalence of asthma and wheezing in relation to residential proximity to industry were 1.98 (95% CI: 0.87, 3.09; I 2 =71%) and 1.33 (95% CI: 0.86, 1.79; I 2= 65%), respectively. In conclusion, this review showed substantial heterogeneity across study designs and methods. Meta-analysis results suggested no evidence of an association for short-term asthma-related effects and an indication for long-term effects, but heterogeneity between results and limitations in terms of design and exposure assessment preclude drawing definite conclusions. Further well-conducted studies making use of a longitudinal design and of refined exposure assessment methods are needed to improve risk estimates.

A population-based birth cohort study of the association between childhood-onset asthma and exposure to industrial air pollutant emissions.

BACKGROUND: Studies of the association between air pollution and asthma onset have mostly focused on urban and traffic-related air pollution. We investigated the associations between exposure to industrial emissions and childhood-onset asthma in a population-based birth cohort in Quebec, Canada, 2002-2011. METHODS: The cohort was built from administrative health databases. We developed separately for PM2.5 and SO2 different metrics representing children's time-varying residential exposure to industrial emissions: 1) yearly number of tons of air pollutant emitted by industries located within 2.5 km of the residence; 2) distance to the nearest "major emitter" (≥100 tons) of either PM2.5 and SO2 within 7.5 km of the residence, and; 3) tons of air pollutant emitted by the nearest "major emitter" within 7.5 km, weighted by the inverse of the distance and the percentage of time that the residence was downwind. To handle the large number of zeros (i.e., children unexposed) we decomposed the exposure variable into two covariates simultaneously included in the regression model: a binary indicator of exposure and a continuous exposure variable centered at the mean value among exposed children. We performed Cox models using age as the time axis, adjusted for gender, material and social deprivation and calendar year. We indirectly adjusted for unmeasured secondhand smoke. RESULTS: The cohort included 722,667 children and 66,559 incident cases of asthma. Across the different exposure metrics, mean percentage changes in the risk of asthma onset in children exposed to the mean relative to those unexposed ranged from 4.5% (95% CI: 2.8, 6.3%) to 10.6% (95% CI: 6.2, 15.2%) for PM2.5 and, from 1.1% (95% CI: -0.1, 3.3%) to 8.9% (95% CI: 7.1, 11.1%) for SO2. Indirect adjustment for secondhand smoke did not substantially affect the associations. In children exposed, the risk of asthma onset increased with the magnitude of the exposure for all metrics, except the distance to the nearest major emitter of SO2. CONCLUSIONS: In this population-based birth cohort, residential exposure to industrial air pollutant emissions was associated with childhood-onset asthma. Additional studies with improved models for estimating exposure to industrial point-sources are needed to further support the observed associations.

Does the air pollution model influence the evidence of socio-economic disparities in exposure and susceptibility?

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.

Associations between ambient air pollution and daily mortality in a cohort of congestive heart failure: Case-crossover and nested case-control analyses using a distributed lag nonlinear model.

BACKGROUND: Persons with congestive heart failure may be at higher risk of the acute effects related to daily fluctuations in ambient air pollution. To meet some of the limitations of previous studies using grouped-analysis, we developed a cohort study of persons with congestive heart failure to estimate whether daily non-accidental mortality were associated with spatially-resolved, daily exposures to ambient nitrogen dioxide (NO2) and ozone (O3), and whether these associations were modified according to a series of indicators potentially reflecting complications or worsening of health. METHODS: We constructed the cohort from the linkage of administrative health databases. Daily exposure was assigned from different methods we developed previously to predict spatially-resolved, time-dependent concentrations of ambient NO2 (all year) and O3 (warm season) at participants' residences. We performed two distinct types of analyses: a case-crossover that contrasts the same person at different times, and a nested case-control that contrasts different persons at similar times. We modelled the effects of air pollution and weather (case-crossover only) on mortality using distributed lag nonlinear models over lags 0 to 3 days. We developed from administrative health data a series of indicators that may reflect the underlying construct of "declining health", and used interactions between these indicators and the cross-basis function for air pollutant to assess potential effect modification. RESULTS: The magnitude of the cumulative as well as the lag-specific estimates of association differed in many instances according to the metric of exposure. Using the back-extrapolation method, which is our preferred exposure model, we found for the case-crossover design a cumulative mean percentage changes (MPC) in daily mortality per interquartile increment in NO2 (8.8 ppb) of 3.0% (95% CI: -0.4, 6.6%) and for O3 (16.5 ppb) 3.5% (95% CI: -4.5, 12.1). For O3 there was strong confounding by weather (unadjusted MPC = 7.1%; 95% CI: 1.7, 12.7%). For the nested case-control approach the cumulative MPC for NO2 in daily mortality was 2.9% (95% CI: -0.9, 6.9%) and for O3 7.3% (95% CI: 3.0, 11.9%). We found evidence of effect modification between daily mortality and cumulative NO2 and O3 according to the prescribed dose of furosemide in the nested case-control analysis, but not in the case-crossover analysis. CONCLUSIONS: Mortality in congestive heart failure was associated with exposure to daily ambient NO2 and O3 predicted from a back-extrapolation method using a land use regression model from dense sampling surveys. The methods used to assess exposure can have considerable influence on the estimated acute health effects of the two air pollutants.

Comparison of spatiotemporal prediction models of daily exposure of individuals to ambient nitrogen dioxide and ozone in Montreal, Canada.

BACKGROUND: In previous studies investigating the short-term health effects of ambient air pollution the exposure metric that is often used is the daily average across monitors, thus assuming that all individuals have the same daily exposure. Studies that incorporate space-time exposures of individuals are essential to further our understanding of the short-term health effects of ambient air pollution. OBJECTIVES: As part of a longitudinal cohort study of the acute effects of air pollution that incorporated subject-specific information and medical histories of subjects throughout the follow-up, the purpose of this study was to develop and compare different prediction models using data from fixed-site monitors and other monitoring campaigns to estimate daily, spatially-resolved concentrations of ozone (O3) and nitrogen dioxide (NO2) of participants' residences in Montreal, 1991-2002. METHODS: We used the following methods to predict spatially-resolved daily concentrations of O3 and NO2 for each geographic region in Montreal (defined by three-character postal code areas): (1) assigning concentrations from the nearest monitor; (2) spatial interpolation using inverse-distance weighting; (3) back-extrapolation from a land-use regression model from a dense monitoring survey, and; (4) a combination of a land-use and Bayesian maximum entropy model. We used a variety of indices of agreement to compare estimates of exposure assigned from the different methods, notably scatterplots of pairwise predictions, distribution of differences and computation of the absolute agreement intraclass correlation (ICC). For each pairwise prediction, we also produced maps of the ICCs by these regions indicating the spatial variability in the degree of agreement. RESULTS: We found some substantial differences in agreement across pairs of methods in daily mean predicted concentrations of O3 and NO2. On a given day and postal code area the difference in the concentration assigned could be as high as 131ppb for O3 and 108ppb for NO2. For both pollutants, better agreement was found between predictions from the nearest monitor and the inverse-distance weighting interpolation methods, with ICCs of 0.89 (95% confidence interval (CI): 0.89, 0.89) for O3 and 0.81 (95%CI: 0.80, 0.81) for NO2, respectively. For this pair of methods the maximum difference on a given day and postal code area was 36ppb for O3 and 74ppb for NO2. The back-extrapolation method showed a higher degree of disagreement with the nearest monitor approach, inverse-distance weighting interpolation, and the Bayesian maximum entropy model, which were strongly constrained by the sparse monitoring network. The maps showed that the patterns of agreement differed across the postal code areas and the variability depended on the pair of methods compared and the pollutants. For O3, but not NO2, postal areas showing greater disagreement were mostly located near the city centre and along highways, especially in maps involving the back-extrapolation method. CONCLUSIONS: In view of the substantial differences in daily concentrations of O3 and NO2 predicted by the different methods, we suggest that analyses of the health effects from air pollution should make use of multiple exposure assessment methods. Although we cannot make any recommendations as to which is the most valid method, models that make use of higher spatially resolved data, such as from dense exposure surveys or from high spatial resolution satellite data, likely provide the most valid estimates.

A structured review of panel studies used to investigate associations between ambient air pollution and heart rate variability.

INTRODUCTION: Dysfunction of the autonomic nervous system is one of the postulated pathways linking short-term exposure to air pollution to adverse cardiovascular outcomes. A hypothesis is that exposure to air pollution decreases heart rate variability, a recognized independent predictor of poorer cardiovascular prognosis. METHODS: We conducted a structured review of panel studies published between 1946 and July 2015 of the association between ambient air pollution and parameters of heart rate variability reflecting autonomic nervous function. We focused on exposure to mass concentrations of fine particles (PM2.5), nitrogen dioxide (NO2), and ozone (O3), and four commonly used indices of heart rate variability (HRV): standard deviation of all normal-to-normal intervals (SDNN); root mean square of successive differences in adjacent normal-to-normal intervals (RMSSD); high frequency power (HF); and low frequency power (LF). We searched bibliographic databases and references of identified articles and abstracted characteristics of their design and conduct, and synthesized the quantitative findings in graphic form according to health condition of the study population and the functional form of the HRV indices used in the regression analyses. RESULTS: A total of 33 panel studies were included: 31, 12, and 13 studies were used to investigate ambient exposure to PM2.5, NO2 and O3, respectively. We found substantial variation across studies in terms of design characteristics and statistical methodologies, and we identified some studies that may have had methodological and statistical issues. Because many panel studies were not comparable to each other, meta-analyses were not generally possible, although we were able to pool the results obtained amongst older adults who had cardiovascular disease for the 24-h average concentrations of PM2.5 prior to the heart rate variability measurements. In studies of PM2.5 among older adults with cardiovascular disease, logarithmic transformations of the HRV indices were used in ten studies. Negative associations across all HRV indices were found in 60-86% of these studies for periods of exposures ranging from 5-min to 5-days. The pooled percent changes for an increase of 10µg/m(3) in the 24-h prior to the measurements of HRV were: -2.11% for SDNN (95% confidence interval (95%CI): -4.00, -0.23%), -3.29% for RMSSD (95%CI: -6.32, -0.25%), -4.76% for LF (95%CI: -12.10, 2.58%), and -1.74% for HF (95%CI: -7.79, 4.31%). No transformations were used in seven studies of PM2.5 among older adults with cardiovascular disease, and we found for absolute differences pooled changes in the HRV indices, for an increase of 10µg/m(3), of -0.31ms for SDNN (95%CI: -1.02, 0.41ms) and -1.22ms for RMSSD (95%CI: -2.37; -0.07ms). For gaseous pollutants, negative associations over periods of exposure ranging from 5-min or to 5-days prior to the heart rate variability measurements were reported in 71-83% of studies of NO2 and 57-100% of studies of O3, depending of the indices of heart rate variability. However, many of these studies had statistical or methodological issues, and in the few studies without these issues the confidence intervals were relatively wide and mostly included the null. CONCLUSIONS AND DISCUSSION: We were not persuaded by the results that there was an association between PM2.5 and any of the four indices of heart rate variability. For NO2 and O3 the number of high-quality studies was insufficient to draw any definite conclusions. Further panel studies with improved design and methodologies are needed to help establish or refute an association between ambient exposure to air pollution and heart rate variability.

Methodological issues related to pooling results from panel studies of heart rate variability and its association with ambient air pollution.

Reviews of observational studies and subsequent meta-analyses are challenging to interpret because of potential methodological issues and biases inherent in studies. In reviewing panel studies of the association between heart rate variability and ambient air pollution we identified a number of methodological issues that make difficult interpreting and pooling findings from longitudinal studies, notably issues related to associations arising from different type of designs, differences in design characteristics, including study populations, measurements of heart rate variability (e.g., duration and condition of the electrocardiogram recordings), exposure assessment (e.g., types of monitoring), metrics of exposure used, and parameters estimated from regression models. We conclude that many panel studies of the association between heart rate variability and ambient air pollution may not be comparable to each other, and thus caution must be exercised to avoid misleading conclusions.