Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND

Understanding the health consequences associated with exposure to risk factors is necessary to inform public health policy and practice. To systematically quantify the contributions of risk factor exposures to specific health outcomes, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 aims to provide comprehensive estimates of exposure levels, relative health risks, and attributable burden of disease for 88 risk factors in 204 countries and territories and 811 subnational locations, from 1990 to 2021.

METHODS

The GBD 2021 risk factor analysis used data from 54 561 total distinct sources to produce epidemiological estimates for 88 risk factors and their associated health outcomes for a total of 631 risk-outcome pairs. Pairs were included on the basis of data-driven determination of a risk-outcome association. Age-sex-location-year-specific estimates were generated at global, regional, and national levels. Our approach followed the comparative risk assessment framework predicated on a causal web of hierarchically organised, potentially combinative, modifiable risks. Relative risks (RRs) of a given outcome occurring as a function of risk factor exposure were estimated separately for each risk-outcome pair, and summary exposure values (SEVs), representing risk-weighted exposure prevalence, and theoretical minimum risk exposure levels (TMRELs) were estimated for each risk factor. These estimates were used to calculate the population attributable fraction (PAF; ie, the proportional change in health risk that would occur if exposure to a risk factor were reduced to the TMREL). The product of PAFs and disease burden associated with a given outcome, measured in disability-adjusted life-years (DALYs), yielded measures of attributable burden (ie, the proportion of total disease burden attributable to a particular risk factor or combination of risk factors). Adjustments for mediation were applied to account for relationships involving risk factors that act indirectly on outcomes via intermediate risks. Attributable burden estimates were stratified by Socio-demographic Index (SDI) quintile and presented as counts, age-standardised rates, and rankings. To complement estimates of RR and attributable burden, newly developed burden of proof risk function (BPRF) methods were applied to yield supplementary, conservative interpretations of risk-outcome associations based on the consistency of underlying evidence, accounting for unexplained heterogeneity between input data from different studies. Estimates reported represent the mean value across 500 draws from the estimate's distribution, with 95% uncertainty intervals (UIs) calculated as the 2·5th and 97·5th percentile values across the draws.

FINDINGS

Among the specific risk factors analysed for this study, particulate matter air pollution was the leading contributor to the global disease burden in 2021, contributing 8·0% (95% UI 6·7-9·4) of total DALYs, followed by high systolic blood pressure (SBP; 7·8% [6·4-9·2]), smoking (5·7% [4·7-6·8]), low birthweight and short gestation (5·6% [4·8-6·3]), and high fasting plasma glucose (FPG; 5·4% [4·8-6·0]). For younger demographics (ie, those aged 0-4 years and 5-14 years), risks such as low birthweight and short gestation and unsafe water, sanitation, and handwashing (WaSH) were among the leading risk factors, while for older age groups, metabolic risks such as high SBP, high body-mass index (BMI), high FPG, and high LDL cholesterol had a greater impact. From 2000 to 2021, there was an observable shift in global health challenges, marked by a decline in the number of all-age DALYs broadly attributable to behavioural risks (decrease of 20·7% [13·9-27·7]) and environmental and occupational risks (decrease of 22·0% [15·5-28·8]), coupled with a 49·4% (42·3-56·9) increase in DALYs attributable to metabolic risks, all reflecting ageing populations and changing lifestyles on a global scale. Age-standardised global DALY rates attributable to high BMI and high FPG rose considerably (15·7% [9·9-21·7] for high BMI and 7·9% [3·3-12·9] for high FPG) over this period, with exposure to these risks increasing annually at rates of 1·8% (1·6-1·9) for high BMI and 1·3% (1·1-1·5) for high FPG. By contrast, the global risk-attributable burden and exposure to many other risk factors declined, notably for risks such as child growth failure and unsafe water source, with age-standardised attributable DALYs decreasing by 71·5% (64·4-78·8) for child growth failure and 66·3% (60·2-72·0) for unsafe water source. We separated risk factors into three groups according to trajectory over time: those with a decreasing attributable burden, due largely to declining risk exposure (eg, diet high in trans-fat and household air pollution) but also to proportionally smaller child and youth populations (eg, child and maternal malnutrition); those for which the burden increased moderately in spite of declining risk exposure, due largely to population ageing (eg, smoking); and those for which the burden increased considerably due to both increasing risk exposure and population ageing (eg, ambient particulate matter air pollution, high BMI, high FPG, and high SBP).

INTERPRETATION

Substantial progress has been made in reducing the global disease burden attributable to a range of risk factors, particularly those related to maternal and child health, WaSH, and household air pollution. Maintaining efforts to minimise the impact of these risk factors, especially in low SDI locations, is necessary to sustain progress. Successes in moderating the smoking-related burden by reducing risk exposure highlight the need to advance policies that reduce exposure to other leading risk factors such as ambient particulate matter air pollution and high SBP. Troubling increases in high FPG, high BMI, and other risk factors related to obesity and metabolic syndrome indicate an urgent need to identify and implement interventions.

FUNDING

Bill & Melinda Gates Foundation.

A Comparison of PM2.5 Exposure Estimates from Different Estimation Methods and their Associations with Cognitive Testing and Brain MRI Outcomes

BACKGROUND

Reported associations between particulate matter with aerodynamic diameter < 2.5μm (PM2.5) and cognitive outcomes remain mixed. Differences in exposure estimation method may contribute to this heterogeneity.

OBJECTIVES

To assess agreement between PM2.5 exposure concentrations across 11 exposure estimation methods and to compare resulting associations between PM2.5 and cognitive or MRI outcomes.

METHODS

We used Visit 5 (2011-2013) cognitive testing and brain MRI data from the Atherosclerosis Risk in Communities (ARIC) Study. We derived address-linked average 2000-2007 PM2.5 exposure concentrations in areas immediately surrounding the four ARIC recruitment sites (Forsyth County, NC; Jackson, MS; suburbs of Minneapolis, MN; Washington County, MD) using 11 estimation methods. We assessed agreement between method-specific PM2.5 concentrations using descriptive statistics and plots, overall and by site. We used adjusted linear regression to estimate associations of method-specific PM2.5 exposure estimates with cognitive scores (n=4,678) and MRI outcomes (n=1,518) stratified by study site and combined site-specific estimates using meta-analyses to derive overall estimates. We explored the potential impact of unmeasured confounding by spatially patterned factors.

RESULTS

Exposure estimates from most methods had high agreement across sites, but low agreement within sites. Within-site exposure variation was limited for some methods. Consistently null findings for the PM2.5-cognitive outcome associations regardless of method precluded empirical conclusions about the potential impact of method on study findings in contexts where positive associations are observed. Not accounting for study site led to consistent, adverse associations, regardless of exposure estimation method, suggesting the potential for substantial bias due to residual confounding by spatially patterned factors.

DISCUSSION

PM2.5 estimation methods agreed across sites but not within sites. Choice of estimation method may impact findings when participants are concentrated in small geographic areas. Understanding unmeasured confounding by factors that are spatially patterned may be particularly important in studies of air pollution and cognitive or brain health.

Enhancing Global Estimation of Fine Particulate Matter Concentrations by Including Geophysical a Priori Information in Deep Learning

Global fine particulate matter (PM2.5) assessment is impeded by a paucity of monitors. We improve estimation of the global distribution of PM2.5 concentrations by developing, optimizing, and applying a convolutional neural network with information from satellite-, simulation-, and monitor-based sources to predict the local bias in monthly geophysical a priori PM2.5 concentrations over 1998-2019. We develop a loss function that incorporates geophysical a priori estimates and apply it in model training to address the unrealistic results produced by mean-square-error loss functions in regions with few monitors. We introduce novel spatial cross-validation for air quality to examine the importance of considering spatial properties. We address the sharp decline in deep learning model performance in regions distant from monitors by incorporating the geophysical a priori PM2.5. The resultant monthly PM2.5 estimates are highly consistent with spatial cross-validation PM2.5 concentrations from monitors globally and regionally. We withheld 10% to 99% of monitors for testing to evaluate the sensitivity and robustness of model performance to the density of ground-based monitors. The model incorporating the geophysical a priori PM2.5 concentrations remains highly consistent with observations globally even under extreme conditions (e.g., 1% for training, R2 = 0.73), while the model without exhibits weaker performance (1% for training, R2 = 0.51).

Quantifying Prescribed-Fire Smoke Exposure Using Low-Cost Sensors and Satellites: Springtime Burning in Eastern Kansas

Prescribed fires (fires intentionally set for mitigation purposes) produce pollutants, which have negative effects on human and animal health. One of the pollutants produced from fires is fine particulate matter (PM2.5). The Flint Hills (FH) region of Kansas experiences extensive prescribed burning each spring (March-May). Smoke from prescribed fires is often understudied due to a lack of monitoring in the rural regions where prescribed burning occurs, as well as the short duration and small size of the fires. Our goal was to attribute PM2.5 concentrations to the prescribed burning in the FH. To determine PM2.5 increases from local burning, we used low-cost PM2.5 sensors (PurpleAir) and satellite observations. The FH were also affected by smoke transported from fires in other regions during 2022. We separated the transported smoke from smoke from fires in eastern Kansas. Based on data from the PurpleAir sensors, we found the 24-hr median PM2.5 to increase by 3.0-5.3 μg m-3 (based on different estimates) on days impacted by smoke from fires in the eastern Kansas region compared to days unimpacted by smoke. The FH region was the most impacted by smoke PM2.5 compared to other regions of Kansas, as observed in satellite products and in situ measurements. Additionally, our study found that hourly PM2.5 estimates from a satellite-derived product aligned with our ground-based measurements. Satellite-derived products are useful in rural areas like the FH, where monitors are scarce, providing important PM2.5 estimates.

Increasing Racial and Ethnic Disparities in Ambient Air Pollution-Attributable Morbidity and Mortality in the United States

BACKGROUND

Ambient nitrogen dioxide (NO 2 ) and fine particulate matter with aerodynamic diameter ≤ 2.5 μ m (PM 2.5 ) threaten public health in the US, and systemic racism has led to modern-day disparities in the distribution and associated health impacts of these pollutants.

OBJECTIVES

Many studies on environmental injustices related to ambient air pollution focus only on disparities in pollutant concentrations or provide only an assessment of pollution or health disparities at a snapshot in time. In this study, we compare injustices in NO 2 - and PM 2.5 -attributable health burdens, considering NO 2 -attributable health impacts across the entire US; document changing disparities in these health burdens over time (2010-2019); and evaluate how more stringent air quality standards would reduce disparities in health impacts associated with these pollutants.

METHODS

Through a health impact assessment, we quantified census tract-level variations in health outcomes attributable to NO 2 and PM 2.5 using health impact functions that combine demographic data from the US Census Bureau; two spatially resolved pollutant datasets, which fuse satellite data with physical and statistical models; and epidemiologically derived relative risk estimates and incidence rates from the Global Burden of Disease study.

RESULTS

Despite overall decreases in the public health damages associated with NO 2 and PM 2.5 , racial and ethnic relative disparities in NO 2 -attributable pediatric asthma and PM 2.5 -attributable premature mortality have widened in the US during the last decade. Racial relative disparities in PM 2.5 -attributable premature mortality and NO 2 -attributable pediatric asthma have increased by 16% and 19%, respectively, between 2010 and 2019. Similarly, ethnic relative disparities in PM 2.5 -attributable premature mortality have increased by 40% and NO 2 -attributable pediatric asthma by 10%.

DISCUSSION

Enacting and attaining more stringent air quality standards for both pollutants could preferentially benefit the most marginalized and minoritized communities by greatly reducing racial and ethnic relative disparities in pollution-attributable health burdens in the US. Our methods provide a semi-observational approach to track changes in disparities in air pollution and associated health burdens across the US. https://doi.org/10.1289/EHP11900.

Comparison of PM2.5 Air Pollution Exposures and Health Effects Associations Using 11 Different Modeling Approaches in the Women's Health Initiative Memory Study (WHIMS)

BACKGROUND

Many approaches to quantifying air pollution exposures have been developed. However, the impact of choice of approach on air pollution estimates and health-effects associations remains unclear.

OBJECTIVES

Our objective is to compare particulate matter with aerodynamic diameter ≤ 2.5 μ m (PM 2.5 ) concentrations and resulting health effects associations using multiple estimation approaches previously used in epidemiologic analyses.

METHODS

We assigned annual PM 2.5 exposure estimates from 1999 to 2004 derived from 11 different approaches to Women's Health Initiative Memory Study (WHIMS) participant addresses within the contiguous US. Approaches included geostatistical interpolation approaches, land-use regression or spatiotemporal models, satellite-derived approaches, air dispersion and chemical transport models, and hybrid models. We used descriptive statistics and plots to assess relative and absolute agreement among exposure estimates and examined the impact of approach on associations between PM 2.5 and death due to natural causes, cardiovascular disease (CVD) mortality, and incident CVD events, adjusting for individual-level covariates and climate-based region.

RESULTS

With a few exceptions, relative agreement of approach-specific PM 2.5 exposure estimates was high for PM 2.5 concentrations across the contiguous US. Agreement among approach-specific exposure estimates was stronger near PM 2.5 monitors, in certain regions of the country, and in 2004 vs. 1999. Collectively, our results suggest but do not quantify lower agreement at local spatial scales for PM 2.5 . There was no evidence of large differences in health effects associations with PM 2.5 among estimation approaches in analyses adjusted for climate region.

CONCLUSIONS

Different estimation approaches produced similar spatial patterns of PM 2.5 concentrations across the contiguous US and in areas with dense monitoring data, and PM 2.5 -health effects associations were similar among estimation approaches. PM 2.5 estimates and PM 2.5 -health effects associations may differ more in samples drawn from smaller areas or areas without substantial monitoring data, or in analyses with finer adjustment for participant location. Our results can inform decisions about PM 2.5 estimation approach in epidemiologic studies, as investigators balance concerns about bias, efficiency, and resource allocation. Future work is needed to understand whether these conclusions also apply in the context of other air pollutants of interest. https://doi.org/10.1289/EHP12995.

Fine particulate matter composition in American Indian vs. Non-American Indian communities

BACKGROUND

Fine particulate matter (PM2.5) exposure is a known risk factor for numerous adverse health outcomes, with varying estimates of component-specific effects. Populations with compromised health conditions such as diabetes can be more sensitive to the health impacts of air pollution exposure. Recent trends in PM2.5 in primarily American Indian- (AI-) populated areas examined in previous work declined more gradually compared to the declines observed in the rest of the US. To further investigate components contributing to these findings, we compared trends in concentrations of six PM2.5 components in AI- vs. non-AI-populated counties over time (2000-2017) in the contiguous US.

METHODS

We implemented component-specific linear mixed models to estimate differences in annual county-level concentrations of sulfate, nitrate, ammonium, organic matter, black carbon, and mineral dust from well-validated surface PM2.5 models in AI- vs. non-AI-populated counties, using a multi-criteria approach to classify counties as AI- or non-AI-populated. Models adjusted for population density and median household income. We included interaction terms with calendar year to estimate whether concentration differences in AI- vs. non-AI-populated counties varied over time.

RESULTS

Our final analysis included 3108 counties, with 199 (6.4%) classified as AI-populated. On average across the study period, adjusted concentrations of all six PM2.5 components in AI-populated counties were significantly lower than in non-AI-populated counties. However, component-specific levels in AI- vs. non-AI-populated counties varied over time: sulfate and ammonium levels were significantly lower in AI- vs. non-AI-populated counties before 2011 but higher after 2011 and nitrate levels were consistently lower in AI-populated counties.

CONCLUSIONS

This study indicates time trend differences of specific components by AI-populated county type. Notably, decreases in sulfate and ammonium may contribute to steeper declines in total PM2.5 in non-AI vs. AI-populated counties. These findings provide potential directives for additional monitoring and regulations of key emissions sources impacting tribal lands.

Reversal of trends in global fine particulate matter air pollution

Ambient fine particulate matter (PM2.5) is the world's leading environmental health risk factor. Quantification is needed of regional contributions to changes in global PM2.5 exposure. Here we interpret satellite-derived PM2.5 estimates over 1998-2019 and find a reversal of previous growth in global PM2.5 air pollution, which is quantitatively attributed to contributions from 13 regions. Global population-weighted (PW) PM2.5 exposure, related to both pollution levels and population size, increased from 1998 (28.3 μg/m3) to a peak in 2011 (38.9 μg/m3) and decreased steadily afterwards (34.7 μg/m3 in 2019). Post-2011 change was related to exposure reduction in China and slowed exposure growth in other regions (especially South Asia, the Middle East and Africa). The post-2011 exposure reduction contributes to stagnation of growth in global PM2.5-attributable mortality and increasing health benefits per µg/m3 marginal reduction in exposure, implying increasing urgency and benefits of PM2.5 mitigation with aging population and cleaner air.

Inequality in the Distribution of Air Pollution Attributable Mortality Within Canadian Cities

Recent studies have identified inequality in the distribution of air pollution attributable health impacts, but to our knowledge this has not been examined in Canadian cities. We evaluated the extent and sources of inequality in air pollution attributable mortality at the census tract (CT) level in seven of Canada's largest cities. We first regressed fine particulate matter (PM2.5) and nitrogen dioxide (NO2) attributable mortality against the neighborhood (CT) level prevalence of age 65 and older, low income, low educational attainment, and identification as an Indigenous (First Nations, Métis, Inuit) or Black person, accounting for spatial autocorrelation. We next examined the distribution of baseline mortality rates, PM2.5 and NO2 concentrations, and attributable mortality by neighborhood (CT) level prevalence of these characteristics, calculating the concentration index, Atkinson index, and Gini coefficient. Finally, we conducted a counterfactual analysis of the impact of reducing baseline mortality rates and air pollution concentrations on inequality in air pollution attributable mortality. Regression results indicated that CTs with a higher prevalence of low income and Indigenous identity had significantly higher air pollution attributable mortality. Concentration index, Atkinson index, and Gini coefficient values revealed different degrees of inequality among the cities. Counterfactual analysis indicated that inequality in air pollution attributable mortality tended to be driven more by baseline mortality inequalities than exposure inequalities. Reducing inequality in air pollution attributable mortality requires reducing disparities in both baseline mortality and air pollution exposure.

Prenatal air pollution, maternal immune activation, and autism spectrum disorder

BACKGROUND

Autism Spectrum Disorder (ASD) risk is highly heritable, with potential additional non-genetic factors, such as prenatal exposure to ambient particulate matter with aerodynamic diameter < 2.5 µm (PM2.5) and maternal immune activation (MIA) conditions. Because these exposures may share common biological effect pathways, we hypothesized that synergistic associations of prenatal air pollution and MIA-related conditions would increase ASD risk in children.

OBJECTIVES

This study examined interactions between MIA-related conditions and prenatal PM2.5 or major PM2.5 components on ASD risk.

METHODS

In a population-based pregnancy cohort of children born between 2001 and 2014 in Southern California, 318,751 mother-child pairs were followed through electronic medical records (EMR); 4,559 children were diagnosed with ASD before age 5. Four broad categories of MIA-related conditions were classified, including infection, hypertension, maternal asthma, and autoimmune conditions. Average exposures to PM2.5 and four PM2.5 components, black carbon (BC), organic matter (OM), nitrate (NO3-), and sulfate (SO42-), were estimated at maternal residential addresses during pregnancy. We estimated the ASD risk associated with MIA-related conditions, air pollution, and their interactions, using Cox regression models to adjust for covariates.

RESULTS

ASD risk was associated with MIA-related conditions [infection (hazard ratio 1.11; 95% confidence interval 1.05-1.18), hypertension (1.30; 1.19-1.42), maternal asthma (1.22; 1.08-1.38), autoimmune disease (1.19; 1.09-1.30)], with higher pregnancy PM2.5 [1.07; 1.03-1.12 per interquartile (3.73 μg/m3) increase] and with all four PM2.5 components. However, there were no interactions of each category of MIA-related conditions with PM2.5 or its components on either multiplicative or additive scales.

CONCLUSIONS

MIA-related conditions and pregnancy PM2.5 were independently associations with ASD risk. There were no statistically significant interactions of MIA conditions and prenatal PM2.5 exposure with ASD risk.

Source Contributions to Fine Particulate Matter and Attributable Mortality in India and the Surrounding Region

Fine particulate matter (PM_2.5) exposure is a leading mortality risk factor in India and the surrounding region of South Asia. This study evaluates the contribution of emission sectors and fuels to PM_2.5 mass for 29 states in India and 6 surrounding countries (Pakistan, Bangladesh, Nepal, Bhutan, Sri Lanka, and Myanmar) by combining source-specific emission estimates, stretched grid simulations from a chemical transport model, high resolution hybrid PM_2.5, and disease-specific mortality estimates. We find that 1.02 (95% Confidence Interval (CI): 0.78-1.26) million deaths in South Asia attributable to ambient PM_2.5 in 2019 were primarily from three leading sectors: residential combustion (28%), industry (15%), and power generation (12%). Solid biofuel is the leading combustible fuel contributing to the PM_2.5-attributable mortality (31%), followed by coal (17%), and oil and gas (14%). State-level analyses reveal higher residential combustion contributions (35%-39%) in states (Delhi, Uttar-Pradesh, Haryana) with high ambient PM_2.5 (>95 μg/m³). The combined mortality burden associated with residential combustion (ambient) and household air pollution (HAP) in India is 0.72 million (95% CI:0.54-0.89) (68% attributable to HAP, 32% attributable to residential combustion). Our results illustrate the potential to reduce PM_2.5 mass and improve population health by reducing emissions from traditional energy sources across multiple sectors in South Asia.

A cross-sectional analysis of ambient fine particulate matter (PM2.5) exposure and haemoglobin levels in children aged under 5 years living in 36 countries

Low haemoglobin (Hb) concentrations and anaemia in children have adverse effects on development and functioning, some of which may have consequences in later life. Exposure to ambient air pollution is reported to be associated with anaemia, but there is little evidence specific to low- and middle-income countries (LMICs), where childhood anaemia prevalence is greatest. We aimed to determine if long-term ambient fine particulate matter (≤2.5 μm in aerodynamic diameter [PM_2.5]) exposure was associated with Hb levels and the prevalence of anaemia in children aged <5 years living in 36 LMICs. We used Demographic and Health Survey data, collected between 2010 and 2019, which included blood Hb measurements. Satellite-derived estimates of annual average PM_2.5 was the main exposure variable, which was linked to children's area of residence. Anaemia was defined according to standard World Health Organization guidelines (Hb < 11 g/dL). The association of PM_2.5 with Hb levels and anaemia prevalence was examined using multivariable linear and logistic regression models, respectively. We examined whether the effects of ambient PM_2.5 were modified by a child's sex and age, household wealth index, and urban/rural place of residence. Models were adjusted for relevant covariates, including other outdoor pollutants and household cooking fuel. The study included 154,443 children, of which 89,904 (58.2%) were anaemic. The country-level prevalence of anaemia ranged from 15.8% to 87.9%. Mean PM_2.5 exposure was 33.0 (±21.6) μg/m³. The adjusted model showed that a 10 μg/m³ increase in annual PM_2.5 concentration was associated with greater odds of anaemia (OR = 1.098 95% CI: 1.087, 1.109). The same increase in PM_2.5 was associated with a decrease in average Hb levels of 0.075 g/dL (95% CI: 0.081, 0.068). There was evidence of effect modification by household wealth index and place of residence, with greater adverse effects in children from lower wealth quintiles and children in rural areas. Exposure to annual PM_2.5 was cross-sectionally associated with decreased blood Hb levels, and greater risk of anaemia, in children aged <5 years living in 36 LMICs.

National Cohort Study of Long-Term Exposure to PM2.5 Components and Mortality in Medicare American Older Adults

There is increasing evidence linking long-term fine particulate matter (PM2.5) exposure to negative health effects. However, the relative influence of each component of PM2.5 on health risk is poorly understood. In a cohort study in the contiguous United States between 2000 and 2017, we examined the effect of long-term exposure to PM2.5 main components and all-cause mortality in older adults who had to be at least 65 years old and enrolled in Medicare. We estimated the yearly mean concentrations of six key PM2.5 compounds, including black carbon (BC), organic matter (OM), soil dust (DUST), nitrate (NO3-), sulfate (SO42-), and ammonium (NH4+), using two independently sourced well-validated prediction models. We applied Cox proportional hazard models to evaluate the hazard ratios for mortality and penalized splines for assessing potential nonlinear concentration-response associations. Results suggested that increased exposure to PM2.5 mass and its six main constituents were significantly linked to elevated all-cause mortality. All components showed linear concentration-response relationships in the low exposure concentration ranges. Our research indicates that long-term exposure to PM2.5 mass and its essential compounds are strongly connected to increased mortality risk. Reductions of fossil fuel burning may yield significant air quality and public health benefit.

Advances in Simulating the Global Spatial Heterogeneity of Air Quality and Source Sector Contributions: Insights into the Global South

High-resolution simulations are essential to resolve fine-scale air pollution patterns due to localized emissions, nonlinear chemical feedbacks, and complex meteorology. However, high-resolution global simulations of air quality remain rare, especially of the Global South. Here, we exploit recent developments to the GEOS-Chem model in its high-performance implementation to conduct 1-year simulations in 2015 at cubed-sphere C360 (∼25 km) and C48 (∼200 km) resolutions. We investigate the resolution dependence of population exposure and sectoral contributions to surface fine particulate matter (PM2.5) and nitrogen dioxide (NO2), focusing on understudied regions. Our results indicate pronounced spatial heterogeneity at high resolution (C360) with large global population-weighted normalized root-mean-square difference (PW-NRMSD) across resolutions for primary (62-126%) and secondary (26-35%) PM2.5 species. Developing regions are more sensitive to spatial resolution resulting from sparse pollution hotspots, with PW-NRMSD for PM2.5 in the Global South (33%), 1.3 times higher than globally. The PW-NRMSD for PM2.5 for discrete southern cities (49%) is substantially higher than for more clustered northern cities (28%). We find that the relative order of sectoral contributions to population exposure depends on simulation resolution, with implications for location-specific air pollution control strategies.

Using Parametric g-Computation to Estimate the Effect of Long-Term Exposure to Air Pollution on Mortality Risk and Simulate the Benefits of Hypothetical Policies: The Canadian Community Health Survey Cohort (2005 to 2015)

BACKGROUND

Numerous epidemiological studies have documented the adverse health impact of long-term exposure to fine particulate matter [particulate matter ≤2.5μm in aerodynamic diameter (PM2.5)] 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 PM2.5 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 μg/m3, 7.04 μg/m3, 5 μg/m3, and 4 μg/m3), and b) reduced the simulated exposure value by a percentage (5% and 10%) at each follow-up year. We used the 3-y average PM2.5 concentration with 1-y lag at the postal code of respondents' annual mailing addresses as their long-term exposure to PM2.5. 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 μg/m3, 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 PM2.5 would reduce the cumulative mortality risk, with greater benefits in the older population, even in a population already exposed to low levels of ambient PM2.5. 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.

A cross-sectional analysis of long-term exposure to ambient air pollution and cognitive development in children aged 3-4 years living in 12 low- and middle-income countries

Exposure to ambient air pollution may affect cognitive functioning and development in children. Unfortunately, there is little evidence available for low- and middle-income countries (LMICs), where air pollution levels are highest. We analysed the association between exposure to ambient fine particulate matter (≤2.5 μm [PM_2.5]) and cognitive development indicators in a cross-sectional analysis of children (aged 3-4 years) in 12 LMICs. We linked Demographic and Health Survey data, conducted between 2011 and 2018, with global estimates of PM_2.5 mass concentrations to examine annual average exposure to PM_2.5 and cognitive development (literacy-numeracy and learning domains) in children. Cognitive development was assessed using the United Nations Children's Fund's early child development indicators administered to each child's mother. We used multivariable logistic regression models, adjusted for individual- and area-level covariates, and multi-pollutant models (including nitrogen dioxide and surface-level ozone). We assessed if sex and urban/rural status modified the association of PM_2.5 with the outcome. We included 57,647 children, of whom, 9613 (13.3%) had indicators of cognitive delay. In the adjusted model, a 5 μg/m³ increase in annual all composition PM_2.5 was associated with greater odds of cognitive delay (OR = 1.17; 95% CI: 1.13, 1.22). A 5 μg/m³ increase in anthropogenic PM_2.5 was also associated with greater odds of cognitive delay (OR = 1.05; 95% CI: 1.00, 1.10). These results were robust to several sensitivity analyses, including multi-pollutant models. Interaction terms showed that urban-dwelling children had greater odds of cognitive delay than rural-dwelling children, while there was no significant difference by sex. Our findings suggest that annual average exposure to PM_2.5 in young children was associated with adverse effects on cognitive development, which may have long-term consequences for educational attainment and health.

Effects of long-term ambient air pollution exposure on township-level pulmonary tuberculosis notification rates during 2005-2017 in Ningxia, China

Studies examining long-term effects of ambient air pollution exposure, measured as annual averages, on pulmonary tuberculosis (TB) incidence are scarce, particularly in endemic, rural settings. We performed a small-area study in Ningxia Hui Autonomous Region (NHAR), a high TB-burden area in rural China, using township-level (n = 358 non-overlapping townships) annual TB notification data (2005-2017). We aimed to determine if annual average concentrations of ambient air pollution (particulate matter <2·5 μm [PM_2·5], nitrogen dioxide [NO₂] ozone [O₃]) were associated with TB notification rates (as a proxy for incidence). Air pollution effects on TB notification rates at township-level were estimated as incidence rate ratios (IRR), fitted using a generalised estimating equation (GEE) adjusted for covariates (age, sex, occupation, education, ethnicity, remoteness [urban or rural], household crowding and solid fuel use). A total of 38,942 TB notifications were reported in NHAR between 2005 and 2017. The mean annual TB notification rate was 67 (standard deviation [SD]; 7) per 100,000 people. Median concentrations of PM_2·5, NO₂, and O₃ were 42 μg/m³ (interquartile range [IQR]; 38-48 μg/m³), 15 ppb (IQR; 12-16 ppb), and 56 ppb (IQR; 56-57 ppb), respectively. In single pollutant models, adjusted for covariates, an interquartile range (IQR) increase (10 μg/m³) in PM_2·5 was significantly associated with higher TB notification rates (IRR: 1∙35; 95% CI: 1·25-1·48). Comparable effects on notifications of TB were observed for increases in NO₂ exposure (IRR: 1·20 per IQR (4 ppb) increase; 95% CI: 1·08-1·31). Ground-level ozone was not associated with TB notification rate in any models. The observed effects were consistent over time, in multi-pollutant models, and appeared robust to additional adjustment for indicators of household crowding, solid fuel use and remoteness. More rigorous study designs are needed to understand if improving air quality has population-level benefits on TB disease incidence in endemic settings.

Associations of Autism Spectrum Disorder with PM2.5 Components: A Comparative Study Using Two Different Exposure Models

This retrospective cohort study examined associations of autism spectrum disorder (ASD) with prenatal exposure to major fine particulate matter (PM2.5) components estimated using two independent exposure models. The cohort included 318 750 mother-child pairs with singleton deliveries in Kaiser Permanente Southern California hospitals from 2001 to 2014 and followed until age five. ASD cases during follow-up (N = 4559) were identified by ICD codes. Prenatal exposures to PM2.5, elemental (EC) and black carbon (BC), organic matter (OM), nitrate (NO3-), and sulfate (SO42-) were constructed using (i) a source-oriented chemical transport model and (ii) a hybrid model. Exposures were assigned to each maternal address during the entire pregnancy, first, second, and third trimester. In single-pollutant models, ASD was associated with pregnancy-average PM2.5, EC/BC, OM, and SO42- exposures from both exposure models, after adjustment for covariates. The direction of effect estimates was consistent for EC/BC and OM and least consistent for NO3-. EC/BC, OM, and SO42- were generally robust to adjustment for other components and for PM2.5. EC/BC and OM effect estimates were generally larger and more consistent in the first and second trimester and SO42- in the third trimester. Future PM2.5 composition health effect studies might consider using multiple exposure models and a weight of evidence approach when interpreting effect estimates.

Disparities in Air Pollutants Across Racial, Ethnic, and Poverty Groups at US Public Schools

We investigate socioeconomic disparities in air quality at public schools in the contiguous US using high resolution estimates of fine particulate matter (PM_2.5) and nitrogen dioxide (NO₂) concentrations. We find that schools with higher proportions of people of color (POC) and students eligible for the federal free or reduced lunch program, a proxy for poverty level, are associated with higher pollutant concentrations. For example, we find that the median annual NO₂ concentration for White students, nationally, was 7.7 ppbv, compared to 9.2 ppbv for Black and African American students. Statewide and regional disparities in pollutant concentrations across racial, ethnic, and poverty groups are consistent with nationwide results, where elevated NO₂ concentrations were associated with schools with higher proportions of POC and higher levels of poverty. Similar, though smaller, differences were found in PM_2.5 across racial and ethnic groups in most states. Racial, ethnic, and economic segregation across the rural-urban divide is likely an important factor in pollution disparities at US public schools. We identify distinct regional patterns of disparities, highlighting differences between California, New York, and Florida. Finally, we highlight that disparities exist not only across urban and non-urban lines but also within urban environments.

Chronic Exposure to Fine Particulate Matter Increases Mortality Through Pathways of Metabolic and Cardiovascular Disease: Insights From a Large Mediation Analysis

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.

How low can you go? Air pollution affects mortality at very low levels

The World Health Organization (WHO) recently released new guidelines for outdoor fine particulate air pollution (PM_2.5) recommending an annual average concentration of 5 μg/m³. Yet, our understanding of the concentration-response relationship between outdoor PM_2.5 and mortality in this range of near-background concentrations remains incomplete. To address this uncertainty, we conducted a population-based cohort study of 7.1 million adults in one of the world's lowest exposure environments. Our findings reveal a supralinear concentration-response relationship between outdoor PM_2.5 and mortality at very low (<5 μg/m³) concentrations. Our updated global concentration-response function incorporating this new information suggests an additional 1.5 million deaths globally attributable to outdoor PM_2.5 annually compared to previous estimates. The global health benefits of meeting the new WHO guideline for outdoor PM_2.5 are greater than previously assumed and indicate a need for continued reductions in outdoor air pollution around the world.

Impacts of Sugarcane Fires on Air Quality and Public Health in South Florida

BACKGROUND

Preharvest burning of sugarcane is a common agricultural practice in Florida, which produces fine particulate matter [particulate matter (PM) with aerodynamic diameter ≤2.5μm (PM2.5)] that is associated with higher mortality.

OBJECTIVES

We estimated premature mortality associated with exposure to PM2.5 from sugarcane burning in people age 25 y and above for 20 counties in South Florida.

METHODS

We combined information from an atmospheric dispersion model, satellites, and surface measurements to quantify PM2.5 concentrations in South Florida and the fraction of PM2.5 from sugarcane fires. From these concentrations, estimated mortalities attributable to PM2.5 from sugarcane fires were calculated by census tract using health impact functions derived from literature for six causes of death linked to PM2.5. Confidence intervals (CI) are provided based on Monte Carlo simulations that propagate uncertainty in the emissions, dispersion model, health impact functions, and demographic data.

RESULTS

Sugarcane fires emitted an amount of primary PM2.5 similar to that of motor vehicles in Florida. PM2.5 from sugarcane fires is estimated to contribute to mortality rates within the Florida Sugarcane Growing Region (SGR) by 0.4 death per 100,000 people per year (95% CI: 0.3, 1.6 per 100,000). These estimates imply 2.5 deaths per year across South Florida were associated with PM2.5 from sugarcane fires (95% CI: 1.2, 6.1), with 0.16 in the SGR (95% CI: 0.09, 0.6) and 0.72 in Palm Beach County (95% CI: 0.17, 2.2).

DISCUSSION

PM2.5 from sugarcane fires was estimated to contribute to mortality risk across South Florida, particularly in the SGR. This is consistent with prior studies that documented impacts of sugarcane fire on air quality but did not quantify mortality. Additional health impacts of sugarcane fires, which were not quantified here, include exacerbating nonfatal health conditions such as asthma and cardiovascular problems. Harvesting sugarcane without field burning would likely reduce PM2.5 and health burdens in this region. https://doi.org/10.1289/EHP9957.

Association between long-term exposure to ambient air pollution and COVID-19 severity: a prospective cohort study

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 (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.

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/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.

Interpretation:

Chronic exposure to air pollution may contribute to severe outcomes after SARS-CoV-2 infection, particularly exposure to O₃.

Air Pollution in American Indian Versus Non-American Indian Communities, 2000-2018

Objectives. To compare fine particulate matter (PM2.5) concentrations in American Indian (AI)-populated with those in non-AI-populated counties over time (2000-2018) in the contiguous United States. Methods. We used a multicriteria approach to classify counties as AI- or non--AI-populated. We ran linear mixed effects models to estimate the difference in countywide annual PM2.5 concentrations from well-validated prediction models and monitoring sites (modeled and measured PM2.5, respectively) in AI- versus non-AI-populated counties. Results. On average, adjusted modeled PM2.5 concentrations in AI-populated counties were 0.38 micrograms per cubic meter (95% confidence interval [CI] = 0.23, 0.54) lower than in non-AI-populated counties. However, this difference was not constant over time: in 2000, modeled concentrations in AI-populated counties were 1.46 micrograms per cubic meter (95% CI = 1.25, 1.68) lower, and by 2018, they were 0.66 micrograms per cubic meter (95% CI = 0.45, 0.87) higher. Over the study period, adjusted modeled PM2.5 mean concentrations decreased by 2.13 micrograms per cubic meter in AI-populated counties versus 4.26 micrograms per cubic meter in non-AI-populated counties. Results were similar for measured PM2.5. Conclusions. This study highlights disparities in PM2.5 trends between AI- and non-AI-populated counties over time, underscoring the need to strengthen air pollution regulations and prevention implementation in tribal territories and areas where AI populations live. (Am J Public Health. 2022;112(4): 615-623. https://doi.org/10.2105/AJPH.2021.306650).

Associations of long-term exposure to fine particulate matter and its constituents with cardiovascular mortality: A prospective cohort study in China

BACKGROUND

Few studies have evaluated long-term cardiovascular effects of fine particulate matter (PM_2.5) and its constituents in countries with high air pollution levels. We aimed to investigate the associations of long-term exposure to PM_2.5 and constituents with cardiovascular mortality in China.

METHODS

We conducted a prospective cohort study of 90,672 adults ≥ 18 years from 2010 to 2017 in 161 districts/counties across China. The residential annual-average exposure to PM_2.5 and 6 main components from 2011 to 2017 were estimated by satellite-based and chemical transport models. Associations of PM_2.5 and constituents with cardiovascular mortality were analyzed by competing-risk Cox proportional hazards regression.

RESULTS

The average PM_2.5 exposure throughout the whole period was 46 ± 22 μg/m³. The hazard ratios of mortality (95% confidence intervals) per 10 μg/m³ increase in PM_2.5 concentrations were 1.02 (1.00, 1.05) for overall cardiovascular disease, 1.05 (1.01, 1.09) for ischemic heart disease, 1.03 (1.00, 1.06) for overall stroke, 0.99 (0.94, 1.04) for hemorrhagic stroke, and 1.11 (1.04, 1.19) for ischemic stroke. PM_2.5 constituents from fossil fuel combustion (i.e., black carbon, organic matter, nitrate, ammonium, and sulfate) showed larger hazard ratios than PM_2.5 total mass, while soil dust showed no risks.

CONCLUSIONS

This nationwide cohort study demonstrated associations of long-term exposure to PM_2.5 and its constituents with increased risks of cardiovascular mortality in the general population of China. Our study highlighted the importance of PM_2.5 constituents from fossil fuel combustion in the long-term cardiovascular effects of PM_2.5 in China.

Composition of fine particulate matter and risk of preterm birth: A nationwide birth cohort study in 336 Chinese cities

BACKGROUND

Potential hazards of fine particulate matter (PM_2.5) constituents on preterm birth (PTB) have rarely been explored in China.

OBJECTIVE

To quantify the associations of PM_2.5 constituents with PTB.

METHODS

This study was based on a nationwide cohort of 3,723,169 live singleton births delivered between January 2010 and December 2015 in China. We applied satellite-based estimates of 5 PM_2.5 constituents (organic carbon; black carbon; sulfate; ammonium; and nitrate). We used Cox proportional hazards regression models adjusted for individual covariates, temperature, humidity, and seasonality to evaluate the associations.

RESULTS

During the entire pregnancy, each interquartile range (29 μg/m³) increase in PM_2.5 concentrations was associated with a 7% increase in PTB risk [hazard ratio (HR): 1.07; 95% confidence interval (CI): 1.07-1.08). We observed the largest effect estimates on carbonaceous components (HR: 1.09; 95% CI: 1.08-1.10 for organic carbon and black carbon). Early pregnancy appeared to be the critical exposure window for most constituents. Women who were older, exposed to second-hand smoke, overweight or obese before pregnancy, conceived during winter, and living in northern China or rural areas were more susceptible.

CONCLUSIONS

Carbonaceous components of PM_2.5 were associated with higher PTB risk. Findings on characteristics of vulnerability underlined targeted protections on susceptible subgroups.

Ambient air pollution and the risk of acute myocardial infarction and stroke: A national cohort study

We used a large national cohort in Canada to assess the incidence of acute myocardial infarction (AMI) and stroke hospitalizations in association with long-term exposure to fine particulate matter (PM_2.5), nitrogen dioxide (NO₂), and ozone (O₃). The study population comprised 2.7 million respondents from the 2006 Canadian Census Health and Environment Cohort (CanCHEC), followed for incident hospitalizations of AMI or stroke between 2006 and 2016. We estimated 10-year moving average estimates of PM_2.5, NO₂, and O₃, annually. We used Cox proportional hazards models to examine the associations adjusting for various covariates. For AMI, each interquartile range (IQR) increase in exposure was found to be associated with a hazard ratio of 1.026 (95% CI: 1.007-1.046) for PM_2.5, 1.025 (95% CI: 1.001-1.050) for NO₂, and 1.062 (95% CI: 1.041-1.084) for O₃, respectively. Similarly, for stroke, an IQR increase in exposure was associated with a hazard ratio of 1.078 (95% CI: 1.052-1.105) for PM_2.5, 0.995 (95% CI: 0.965-1.030) for NO₂, and 1.055 (95% CI: 1.028-1.082) for O₃, respectively. We found consistent evidence of positive associations between long-term exposures to PM_2.5, and O₃, and to a lesser degree NO₂, with incident AMI and stroke hospitalizations.

Long-term effects of PM2.5 components on blood pressure and hypertension in Chinese children and adolescents

Growing evidence has linked fine particulate matter (PM_2.5) exposure to elevated blood pressure, but the effects of PM_2.5 components are unclear, particularly in children and adolescents. Based on a cross-sectional investigation in China, we analyzed the associations between long-term exposure to PM_2.5 and its major components with elevated blood pressure in children and adolescents. A representative sample (N = 37,610) of children and adolescents with age 7-18 years was collected in seven Chinese provinces. Exposures to PM_2.5 and five of its major components, including black carbon (BC), organic matter (OM), inorganic nitrate (NO₃^-), sulfate (SO₄^2-), and soil particles (SOIL), were estimated using satellite-based spatiotemporal models. The associations between long-term exposures to PM_2.5 and its components and diastolic blood pressure (DBP), systolic blood pressure (SBP), and hypertension were investigated using mixed-effects logistic and linear regression models. Within the populations, 11.5 % were classified as hypertension. After adjusting for a variety of covariates, per interquartile range (IQR) increment in PM_2.5 mass and BC levels were significantly associated with a higher hypertension prevalence with odds ratios (ORs) of 1.56 (95% confidence interval (CI): 1.08, 2.25) for PM_2.5 and 1.19 (95% CI: 1.04, 1.35) for BC. Long-term exposures to PM_2.5 and BC have also been associated with elevated SBP and DBP. Additionally, OM and NO₃^- were significantly associated with increased SBP, while SOIL was significantly associated with increased DBP. In the subgroup analysis, the associations between long-term exposures to BC and blood pressure vary significantly by urbanicity of residential area and diet habits. Our study suggests that long-term exposure to PM_2.5 mass and specific PM_2.5 components, especially for BC, are significantly associated with elevated blood pressure and a higher hypertension prevalence in Chinese children and adolescents.

Inequality in historical transboundary anthropogenic PM2.5 health impacts

Atmospheric transport of fine particulate matter (PM_2.5), the leading environmental risk factor for public health, is estimated to exert substantial transboundary effects at present. During the past several decades, human-produced pollutant emissions have undergone drastic and regionally distinctive changes, yet it remains unclear about the resulting global transboundary health impacts. Here we show that between 1950 and 2014, global anthropogenic PM_2.5 has led to 185.7 million premature deaths cumulatively, including about 14% from transboundary pollution. Among four country groups at different affluence levels, on a basis of per capita contribution to transboundary mortality, a richer region tends to exert severer cumulative health externality, with the poorest bearing the worst net externality after contrasting import and export of pollution mortality. The temporal changes in transboundary mortality and cross-regional inequality are substantial. Effort to reduce PM_2.5-related transboundary mortality should seek international collaborative strategies that account for historical responsibility and inequality.

Global urban temporal trends in fine particulate matter (PM2·5) and attributable health burdens: estimates from global datasets

BACKGROUND

With much of the world's population residing in urban areas, an understanding of air pollution exposures at the city level can inform mitigation approaches. Previous studies of global urban air pollution have not considered trends in air pollutant concentrations nor corresponding attributable mortality burdens. We aimed to estimate trends in fine particulate matter (PM_2·5) concentrations and associated mortality for cities globally.

METHODS

We use high-resolution annual average PM_2·5 concentrations, epidemiologically derived concentration response functions, and country-level baseline disease rates to estimate population-weighted PM_2·5 concentrations and attributable cause-specific mortality in 13 160 urban centres between the years 2000 and 2019.

FINDINGS

Although regional averages of urban PM_2·5 concentrations decreased between the years 2000 and 2019, we found considerable heterogeneity in trends of PM_2·5 concentrations between urban areas. Approximately 86% (2·5 billion inhabitants) of urban inhabitants lived in urban areas that exceeded WHO's 2005 guideline annual average PM_2·5 (10 μg/m³), resulting in an excess of 1·8 million (95% CI 1·34 million-2·3 million) deaths in 2019. Regional averages of PM_2·5-attributable deaths increased in all regions except for Europe and the Americas, driven by changes in population numbers, age structures, and disease rates. In some cities, PM_2·5-attributable mortality increased despite decreases in PM_2·5 concentrations, resulting from shifting age distributions and rates of non-communicable disease.

INTERPRETATION

Our study showed that, between the years 2000 and 2019, most of the world's urban population lived in areas with unhealthy levels of PM_2·5, leading to substantial contributions to non-communicable disease burdens. Our results highlight that avoiding the large public health burden from urban PM_2·5 will require strategies that reduce exposure through emissions mitigation, as well as strategies that reduce vulnerability to PM_2·5 by improving overall public health.

FUNDING

NASA, Wellcome Trust.

Maternal exposure to fine particulate matter and preterm birth and low birth weight in Africa

BACKGROUND

Fine particulate matter (PM_2.5) exposure has been reported to adversely affect birth outcomes, but the evidence is limited, particularly in low- and middle-income countries (LMICs). We assessed the associations between maternal PM_2.5 exposure and low birth weight (LBW) and preterm birth (PTB) in Africa.

METHODS

We used standard Demographic and Health Surveys (DHS) data (2005-2015) from 15 countries in Africa to conduct a cross-sectional study. The study population was composed of 131,594 births with detailed information on maternal and household variables. LBW was defined as a birth weight of < 2500 g after 37 weeks, and PTB was defined as live birth occurring before 37 weeks of gestation. Average exposure to PM_2.5 during pregnancy was estimated using satellite-based models. Multivariable logistic regression models were constructed, and analyses of data by region (Western, Eastern, Central, and Southern Africa) and data stratified by potential effect modifiers were conducted.

RESULTS

A total of 13,214 (10%) LBW and 4,377 (3.3%) PTB cases were identified. An interquartile range (IQR) (33.9 μg/m³) increase in PM_2.5 during pregnancy was associated with increased odds of LBW and PTB, with odds ratios (ORs) of 1.28 (95% CI: 1.23, 1.34) and 1.08 (95% CI: 1.01, 1.16), respectively. Region-specific analyses revealed significant associations between PM_2.5 and LBW in all regions, and significant associations between PM_2.5 and PTB in Western and Southern Africa. Subgroup analyses revealed that the association between PM_2.5 and LBW was present in all subgroups, and stronger associations were observed in female infants, while the association between PM_2.5 and PTB was larger in subgroups of older individuals living in urban areas.

CONCLUSION

This multicountry study in Africa demonstrated significant associations between maternal exposure to PM_2.5 and higher odds of LBW and PTB. Our findings may facilitate air quality control strategies that address adverse birth outcomes in LMICs.

Ambient air pollution and acute respiratory infection in children aged under 5 years living in 35 developing countries

BACKGROUND

Evidence from developed countries suggests that fine particulate matter (≤2.5 µm [PM_2.5]) contributes to childhood respiratory morbidity and mortality. However, few analyses have focused on resource-limited settings, where much of this burden occurs. We aimed to investigate the cross-sectional associations between annual average exposure to ambient PM_2.5 and acute respiratory infection (ARI) in children aged <5 years living in low- and middle-income countries (LMICs).

METHODS

We combined Demographic and Health Survey (DHS) data from 35 countries with gridded global estimates of annual PM_2.5 mass concentrations. We analysed the association between PM_2.5 and maternal-reported ARI in the two weeks preceding the survey among children aged <5 years living in 35 LMICs. We used multivariable logistic regression models that adjusted for child, maternal, household and cluster-level factors. We also fitted multi-pollutant models (adjusted for nitrogen dioxide [NO₂] and surface-level ozone [O₃]), among other sensitivity analyses. We assessed whether the associations between PM_2.5 and ARI were modified by sex, age and place of residence.

RESULTS

The analysis comprised 573,950 children, among whom the prevalence of ARI was 22,506 (3.92%). The mean (±SD) estimated annual concentration of PM_2.5 to which children were exposed was 48.2 (±31.0) µg/m³. The 5th and 95th percentiles of PM_2.5 were 9.8 µg/m³ and 110.9 µg/m³, respectively. A 10 µg/m³ increase in PM_2.5 was associated with greater odds of having an ARI (OR: 1.06; 95% CI: 1.05-1.07). The association between PM_2.5 and ARI was robust to adjustment for NO₂ and O₃. We observed evidence of effect modification by sex, age and place of residence, suggesting greater effects of PM_2.5 on ARI in boys, in younger children, and in children living in rural areas.

CONCLUSIONS

Annual average ambient PM_2.5, as an indicator for long-term exposure, was associated with greater odds of maternal-reported ARI in children aged <5 years living in 35 LMICs. Longitudinal studies in LMICs are required to corroborate our cross-sectional findings, to further elucidate the extent to which lowering PM_2.5 may have a role in the global challenge of reducing ARI-related morbidity and mortality in children.

Fine particles matter components and interstitial lung disease in rheumatoid arthritis

Exposure to ambient fine particulate matter (PM_2.5) is a risk factor for pulmonary and systemic autoimmune diseases, however evidence on which PM_2.5 chemical components are more harmful is still scant. Our goal is to investigate potential associations between PM_2.5 components and interstitial lung disease (ILD) onset in rheumatoid arthritis (RA).New-onset RA subjects identified from a United States health care insurance database (MarketScan) were followed for new onset of RA associated ILD (RA-ILD) from 2011 to 2018. Annual ambient PM_2.5 concentrations of its chemical components (i.e. sulfate, nitrate, ammonium, organic matter, black carbon, mineral dust, and sea salt) were estimated by combining satellite retrievals with chemical transport modelling and refined by geographically weighted regression. Exposures from 2006 up to one year before ILD onset or end of study were assigned to subjects based on their metropolitan division or core-based statistical area codes. A novel time-to-event quantile-based g(generalised)-computation approach was used to estimate potential associations between RA-ILD onset and the exposure mixture of all seven PM_2.5 chemical components adjusting for age, sex, and prior chronic obstructive pulmonary disease (as a proxy for smoking).We followed 280 516 new-onset RA patients and detected 2194 RA-ILD cases across 1 394 385 person-years. The adjusted hazard ratio for RA-ILD onset was 1.54 (95% confidence interval 1.47-1.63) per every decile increase in all seven exposures. Ammonium, mineral dust, and black carbon contributed more to ILD risk than the other PM_2.5 components.In conclusion, exposure to elements of PM_2.5, particularly ammonium, increases ILD risk in RA.

Monthly Global Estimates of Fine Particulate Matter and Their Uncertainty

Annual global satellite-based estimates of fine particulate matter (PM2.5) are widely relied upon for air-quality assessment. Here, we develop and apply a methodology for monthly estimates and uncertainties during the period 1998-2019, which combines satellite retrievals of aerosol optical depth, chemical transport modeling, and ground-based measurements to allow for the characterization of seasonal and episodic exposure, as well as aid air-quality management. Many densely populated regions have their highest PM2.5 concentrations in winter, exceeding summertime concentrations by factors of 1.5-3.0 over Eastern Europe, Western Europe, South Asia, and East Asia. In South Asia, in January, regional population-weighted monthly mean PM2.5 concentrations exceed 90 μg/m3, with local concentrations of approximately 200 μg/m3 for parts of the Indo-Gangetic Plain. In East Asia, monthly mean PM2.5 concentrations have decreased over the period 2010-2019 by 1.6-2.6 μg/m3/year, with decreases beginning 2-3 years earlier in summer than in winter. We find evidence that global-monitored locations tend to be in cleaner regions than global mean PM2.5 exposure, with large measurement gaps in the Global South. Uncertainty estimates exhibit regional consistency with observed differences between ground-based and satellite-derived PM2.5. The evaluation of uncertainty for agglomerated values indicates that hybrid PM2.5 estimates provide precise regional-scale representation, with residual uncertainty inversely proportional to the sample size.

Fine particulate matter constituents and infant mortality in Africa: A multicountry study

BACKGROUND

Few studies have investigated the association between exposure to fine particulate matter (PM_2.5) and infant mortality in developing countries, especially for the health effects of specific PM_2.5 constituents.

OBJECTIVE

We aimed to examine the association of long-term exposure to specific PM_2.5 constituents with infant mortality in 15 African countries from 2005 to 2015.

METHODS

Based on the Demographic and Health Surveys (DHS) dataset, we included birth history records from 15 countries in Africa and conducted a multicountry cross-sectional study to examine the associations between specific PM_2.5 constituents and infant mortality. We estimated annual residential exposure using satellite-derived PM_2.5 for mass and a chemical transport model (GEOS-Chem) for its six constituents, including organic matter (OM), black carbon (BC), sulfate (SO₄^2-), nitrate (NO₃^-), ammonium (NH₄⁺), and soil dust (DUST). Multivariable logistic regression analysis was employed by fitting single-constituent models, the constituent-PM_2.5 models, and the constituent-residual models. We also conducted stratified analyses by potential effect modifiers and examined the specific associations for each country.

RESULTS

We found positive and significant associations between PM_2.5 total mass and most of its constituents with infant mortality. In the single-constituent model, for an IQR increase in pollutant concentrations, the odds ratio (OR) of infant mortality was 1.03 (95 %CI; 1.01, 1.06) for PM_2.5 total mass, and was 1.04 (95 %CI: 1.02, 1.06), 1.04 (95 %CI: 1.02, 1.05), 1.02 (95 %CI: 1.00, 1.03), 1.04 (1.01, 1.06) for BC, OM, SO₄^2-, and DUST, respectively. The associations of BC, OM, and SO₄^2- remained significant in the other two models. We observed larger estimates in subgroups with older maternal age, living in urban areas, using unclean cooking energy, and with access to piped water. The associations varied among countries, and by different constituents.

CONCLUSIONS

The carbonaceous fractions and sulfate play a major important role among PM_2.5 constituents on infant mortality. Our findings have certain policy implications for implementing effective measures for targeted reduction in specific sources (fossil fuel combustion and biomass burning) of PM_2.5 constituents against the risk of infant mortality.

Changes in exposure to ambient fine particulate matter after relocating and long term survival in Canada: quasi-experimental study

OBJECTIVE

To investigate the association between changes in long term residential exposure to ambient fine particulate matter (PM_2.5) and premature mortality in Canada.

DESIGN

Population based quasi-experimental study.

SETTING

Canada.

PARTICIPANTS

663 100 respondents to the 1996, 2001, and 2006 Canadian censuses aged 25-89 years who had consistently lived in areas with either high or low PM_2.5 levels over five years preceding census day and moved during the ensuing five years.

INTERVENTIONS

Changes in long term exposure to PM_2.5 arising from residential mobility.

MAIN OUTCOME MEASURES

The primary outcome was deaths from natural causes. Secondary outcomes were deaths from any cardiometabolic cause, any respiratory cause, and any cancer cause. All outcomes were obtained from the national vital statistics database.

RESULTS

Using a propensity score matching technique with numerous personal, socioeconomic, health, and environment related covariates, each participant who moved to a different PM_2.5 area was matched with up to three participants who moved within the same PM_2.5 area. In the matched groups that moved from high to intermediate or low PM_2.5 areas, residential mobility was associated with a decline in annual PM_2.5 exposure from 10.6 μg/m³ to 7.4 and 5.0 μg/m³, respectively. Conversely, in the matched groups that moved from low to intermediate or high PM_2.5 areas, annual PM_2.5 increased from 4.6 μg/m³ to 6.7 and 9.2 μg/m³. Five years after moving, individuals who experienced a reduction in exposure to PM_2.5 from high to intermediate levels showed a 6.8% (95% confidence interval 1.7% to 11.7%) reduction in mortality (2510 deaths in 56 025 v 4925 deaths in 101 960). A greater decline in mortality occurred among those exposed to a larger reduction in PM_2.5. Increased mortality was found with exposure to PM_2.5 from low to high levels, and to a lesser degree from low to intermediate levels. Furthermore, the decreases in PM_2.5 exposure were most strongly associated with reductions in cardiometabolic deaths, whereas the increases in PM_2.5 exposure were mostly related to respiratory deaths. No strong evidence was found for the changes in PM_2.5 exposure with cancer related deaths.

CONCLUSIONS

In Canada, decreases in PM_2.5 were associated with lower mortality, whereas increases in PM_2.5 were associated with higher mortality. These results were observed at PM_2.5 levels considerably lower than many other countries, providing support for continuously improving air quality.

The prospective effects of long-term exposure to ambient PM2.5 and constituents on mortality in rural East China

Few cohort studies explored the associations of long-term exposure to ambient fine particulate matter with an aerodynamic diameter of 2.5 μm or less (PM2.5) and its chemical constituents with mortality risk in rural China. We conducted a 12-year prospective study of 28,793 adults in rural Deqing, China from 2006 to 2018. Annual mean PM2.5 and its constituents, including black carbon (BC), organic carbon (OC), ammonium (NH4+), nitrate (NO3-), sulfate (SO42-), and soil dust were measured at participants' addresses at enrollment from a satellite-based exposure predicting model. Cox proportional hazard model was used to estimate hazard ratios (HRs) and 95% confidence intervals (95%CIs) of long-term exposure to PM2.5 for mortality. A total of 1960 deaths were identified during the follow-up. We found PM2.5, BC, OC, NH4+, NO3-, and SO42- were significantly associated with an increased risk of non-accidental mortality. The HR for non-accidental mortality was 1.17 (95%CI: 1.07, 1.28) for each 10 μg/m3 increase in PM2.5. As for constituents, the strongest association was found for BC (HR = 1.21, 95%CI: 1.11, 1.33), followed by NO3-, NH4+, SO42-, and OC (HR = 1.14-1.17 per interquartile range). A non-linear relationship was found between PM2.5 and non-accidental mortality. Similar associations were found for cardio-cerebrovascular and cancer mortality. Associations were stronger among men and ever smokers. Conclusively, we found long-term exposure to ambient PM2.5 and its chemical constituents (especially BC and NO3-) increased mortality risk. Our results suggested the importance of adopting effective targeted emission control to improve air quality for health protection in rural East China.

Parkinson's disease aggravation in association with fine particle components in New York State

BACKGROUND

Long-term exposure to fine particulate matter (PM2.5) has been associated with neurodegenerative diseases, including disease aggravation in Parkinson's disease (PD), but associations with specific PM2.5 components have not been evaluated.

OBJECTIVE

To characterize the association between specific PM2.5 components and PD first hospitalization, a surrogate for disease aggravation.

METHODS

We obtained data on hospitalizations from the New York Department of Health Statewide Planning and Research Cooperative System (2000-2014) to calculate annual first PD hospitalization counts in New York State per county. We used well-validated prediction models at 1 km2 resolution to estimate county level population-weighted annual black carbon (BC), organic matter (OM), nitrate, sulfate, sea salt (SS), and soil particle concentrations. We then used a multi-pollutant mixed quasi-Poisson model with county-specific random intercepts to estimate rate ratios (RR) of one-year exposure to each PM2.5 component and PD disease aggravation. We evaluated potential nonlinear exposure-outcome relationships using penalized splines and accounted for potential confounders.

RESULTS

We observed a total of 197,545 PD first hospitalizations in NYS from 2000 to 2014. The annual average count per county was 212 first hospitalizations. The RR (95% confidence interval) for PD aggravation was 1.06 (1.03, 1.10) per one standard deviation (SD) increase in nitrate concentrations and 1.06 (1.04, 1.09) for the corresponding increase in OM concentrations. We also found a nonlinear inverse association between PD aggravation and BC at concentrations above the 96th percentile. We found a marginal association with SS and no association with sulfate or soil exposure.

CONCLUSION

In this study, we detected associations between the PM2.5 components OM and nitrate with PD disease aggravation. Our findings support that PM2.5 adverse effects on PD may vary by particle composition.

Ambient fine particulate matter air pollution and the risk of hospitalization among COVID-19 positive individuals: Cohort study

BACKGROUND

Ecologic analyses suggest that living in areas with higher levels of ambient fine particulate matter air pollution (PM2.5) is associated with higher risk of adverse COVID-19 outcomes. Studies accounting for individual-level health characteristics are lacking.

METHODS

We leveraged the breadth and depth of the US Department of Veterans Affairs national healthcare databases and built a national cohort of 169,102 COVID-19 positive United States Veterans, enrolled between March 2, 2020 and January 31, 2021, and followed them through February 15, 2021. Annual average 2018 PM2.5 exposure, at an approximately 1 km2 resolution, was linked with residential street address at the year prior to COVID-19 positive test. COVID-19 hospitalization was defined as first hospital admission between 7 days prior to, and 15 days after, the first COVID-19 positive date. Adjusted Poisson regression assessed the association of PM2.5 with risk of hospitalization.

RESULTS

There were 25,422 (15.0%) hospitalizations; 5,448 (11.9%), 5,056 (13.0%), 7,159 (16.1%), and 7,759 (19.4%) were in the lowest to highest PM2.5 quartile, respectively. In models adjusted for State, demographic and behavioral factors, contextual characteristics, and characteristics of the pandemic a one interquartile range increase in PM2.5 (1.9 µg/m3) was associated with a 10% (95% CI: 8%-12%) increase in risk of hospitalization. The association of PM2.5 and risk of hospitalization among COVID-19 individuals was present in each wave of the pandemic. Models of non-linear exposure-response suggested increased risk at PM2.5 concentrations below the national standard 12 µg/m3. Formal effect modification analyses suggested higher risk of hospitalization associated with PM2.5 in Black people compared to White people (p = 0.045), and in those living in socioeconomically disadvantaged neighborhoods (p < 0.001).

CONCLUSIONS

Exposure to higher levels of PM2.5 was associated with increased risk of hospitalization among COVID-19 infected individuals. The risk was evident at PM2.5 levels below the regulatory standards. The analysis identified those of Black race and those living in disadvantaged neighborhoods as population groups that may be more susceptible to the untoward effect of PM2.5 on risk of hospitalization in the setting of COVID-19.

Long-term exposure to air pollution and mortality in a prospective cohort: The Ontario Health Study

BACKGROUND

Air pollution has been associated with increased mortality. However, updated evidence from cohort studies with detailed information on various risk factors is needed, especially in regions with low air pollution levels. We investigated the associations between long-term exposure to air pollution and mortality in a prospective cohort.

METHODS

We studied 88,615 participants aged ≥30 years from an ongoing cohort study in Ontario, Canada from 2009 to 2017. Exposure to ambient fine particulate matter (PM_2.5) and nitrogen dioxide (NO₂) was estimated at participants' residence. Cox proportional hazard models were used to investigate the associations between air pollution and non-accidental, cardiovascular, and respiratory mortality, adjusted for a wide array of individual-level and contextual covariates. Potential effect modification by socio-demographic and behavioral factors was also examined in exploratory stratified analyses.

RESULTS

The fully adjusted hazard ratios (HRs) per 1 µg/m³ increment in PM_2.5 were 1.037 [95% confidence interval (CI): 1.018, 1.057]¸ 1.083 (95% CI: 1.040, 1.128) and 1.109 (95% CI: 1.035, 1.187) for non-accidental, cardiovascular, and respiratory mortality, respectively. Positive associations were also found for NO₂; the corresponding HRs per 1 ppb increment were 1.027 (95% CI: 1.021, 1.034), 1.032 (95% CI: 1.019, 1.046) and 1.044 (95% CI: 1.020, 1.068). We found suggestive evidence of stronger associations in physically active participants, smokers, and those with lower household income.

CONCLUSIONS

Long-term exposure to PM_2.5 and NO₂ was associated with increased risks for non-accidental, cardiovascular, and respiratory mortality, suggesting potential benefits of further improvement in air quality even in low-exposure environments.

Prenatal exposure to residential PM2.5 and its chemical constituents and weight in preschool children: A longitudinal study from Shanghai, China

BACKGROUND

Studies have reported that prenatal exposure to fine particulate matter (PM_2.5) might be associated with adverse birth outcomes in offspring. However, evidence with regard to the effects of prenatal exposure to PM_2.5 and, especially, its main chemical constituents on offspring's weight in childhood is limited and inconsistent.

OBJECTIVES

The present study aimed to examine associations of prenatal exposure to PM_2.5 total mass and its chemical constituents in each trimester with children's weight from birth to 6 years of age using data from Shanghai-Minhang Birth Cohort Study.

METHODS

A total of 1,084 mother-infant pairs were included with both PM_2.5 exposure data and at least one measurement of weight and height. Weight-for-Length (WLZ), BMI-for-Age (BMIz), and Weight-for-Age (WAZ) z-scores were generated according to the World Health Organization guidelines. Exposure to PM_2.5 total mass and its chemical constituents [organic carbon (OC), black carbon (BC), ammonium (NH₄⁺), nitrate (NO₃^-), sulfate (SO₄^2-), and soil dust (SOIL)] during pregnancy was estimated from a satellite based modelling framework. We used multiple informant model to estimate the associations of trimester-specific PM_2.5 total mass and its specific constituents concentrations with WLZ/BMIz and WAZ of offspring at birth and 1, 4, and 6 years of age.

RESULTS

In multiple informant model, we observed consistent patterns of associations between exposure to PM_2.5 total mass, OC, BC, NH₄⁺, NO₃^-, and SO₄^2- during the 2^nd and 3^rd trimesters and decreased WLZ/BMIz and WAZ at 1, 4, and 6 years of age in boys. We observed associations between prenatal exposure to PM_2.5 total mass, NH₄⁺, and NO₃^- during the 1^st and 2^nd trimesters and increased WLZ/BMIz and WAZ in girls at birth. However, there were null associations at 1 and 4 years of age and inverse associations at 6 years of age.

CONCLUSIONS

Prenatal exposure to PM_2.5 total mass and its main chemical constituents was associated with decreased weight in boys from 1 to 6 years of age, with increased weight at birth and decreased weight at 6 years of age in girls. Our findings suggest that prenatal exposure to PM_2.5 and its chemical constituents may have a lasting effect on offspring's weight in childhood.

Does exposure to air pollution increase the risk of acute care in young children with asthma? An Ontario, Canada study

Owing to their greater outdoor activity and ongoing lung development, children are particularly vulnerable to the harmful effects of exposure to fine particulate matter (PM_2.5)_. However, the effects of PM_2.5 components are poorly understood. This study aimed to use a longitudinal birth cohort of children with physician-diagnosed incident asthma to investigate the effect of PM_2.5 components at birth on morbidity measured by health services utilization. Of 1277 Toronto Child Health Evaluation Questionnaire (T-CHEQ) participants, the study population included 362 children diagnosed with asthma who were followed for a mean of 13 years from birth until March 31, 2016, or loss-to-follow-up. Concentrations of PM_2.5 and its components were assigned based on participants' postal codes at birth. Study outcomes included counts of asthma, asthma-related, and all-cause health services use. Poisson regression in single-, two-, and multi-pollutant models was used to estimate rate ratios (RR) per interquartile range (IQR) increase of exposures. Covariates were included in all models to further adjust for potential confounding. The adjusted RR for sulfate (SO₄) and all-cause hospitalizations was statistically significant with RR = 2.23 (95% confidence interval [CI]: 1.25-3.96) in a multi-pollutant model with nitrogen dioxide (NO₂) and ozone (O₃). In multi-pollutant models with oxidants, the adjusted RRs for SO₄ of all-cause hospitalizations and emergency department (ED) visits were also statistically significant with RR = 2.31 (95% CI: 1.32-4.03) and RR = 1.39 (95% CI: 1.02-1.90), respectively. While unadjusted single-pollutant RRs for asthma-specific and asthma-related health services use with the SO₄ component of PM_2.5 were above one, none were statistically significant. This study found significant associations with exposure to SO₄ in PM_2.5 and all-cause acute care, chiefly for hospitalizations, in children with asthma.

Air Pollution as a Risk Factor for Incident Chronic Obstructive Pulmonary Disease and Asthma. A 15-Year Population-based Cohort Study

Rationale: Current evidence on the relationship between long-term exposure to air pollution and new onset of chronic lung disease is inconclusive.Objectives: To examine associations of incident chronic obstructive pulmonary disease (COPD) and adult-onset asthma with past exposure to fine particulate matter ≤ 2.5 μm in diameter (PM_2.5), nitrogen dioxide (NO₂), ozone (O₃), and the redox-weighted average of NO₂ and O₃ (O_x) and characterize the concentration-response relationship.Methods: We conducted a population-based cohort study of all Ontarians, aged 35-85 years, from 2001 to 2015. A 3-year moving average of residential exposures to selected pollutants with a 1-year lag were estimated during follow-up. We used Cox proportional hazard models and Aalen additive-hazard models to quantify the pollution-disease associations and characterized the shape of these relationships using newly developed nonlinear risk models.Measurements and Main Results: Among 5.1 million adults, we identified 340,733 and 218,005 incident cases of COPD and asthma, respectively. We found positive associations of COPD with PM_2.5 per interquartile-range (IQR) increase of 3.4 μg/m³ (hazard ratio, 1.07; 95% confidence interval, 1.06-1.08), NO₂ per IQR increase of 13.9 ppb (1.04; 1.02-1.05), O₃ per IQR increase of 6.3 ppb (1.04; 1.03-1.04), and O_x per IQR increase of 4.4 ppb (1.03; 1.03-1.03). By contrast, we did not find strong evidence linking these pollutants to adult-onset asthma. In addition, we quantified that each IQR increase in pollution exposure yielded 3.0 (2.4-3.6), 3.2 (2.0-4.3), 1.9 (1.3-2.5), and 2.3 (1.7-2.9) excess cases of COPD per 100,000 adults for PM_2.5, NO₂, O₃, and O_x, respectively. Furthermore, most pollutant-COPD relationships exhibited supralinear shapes.Conclusions: Air pollution was associated with a higher incidence of COPD but was not associated with a higher incidence of adult-onset asthma.

Predicting Spatial Variations in Multiple Measures of Oxidative Burden for Outdoor Fine Particulate Air Pollution across Canada

Fine particulate air pollution (PM_2.5) is a leading contributor to the overall global burden of disease. Traditionally, outdoor PM_2.5 has been characterized using mass concentrations which treat all particles as equally harmful. Oxidative potential (OP) (per μg) and oxidative burden (OB) (per m³) are complementary metrics that estimate the ability of PM_2.5 to cause oxidative stress, which is an important mechanism in air pollution health effects. Here, we provide the first national estimates of spatial variations in multiple measures (glutathione, ascorbate, and dithiothreitol depletion) of annual median outdoor PM_2.5 OB across Canada. To do this, we combined a large database of ground-level OB measurements collected monthly prospectively across Canada for 2 years (2016-2018) with PM_2.5 components estimated using a chemical transport model (GEOS-Chem) and satellite aerosol observations. Our predicted ground-level OB values of all three methods were consistent with ground-level observations (cross-validation R² = 0.63-0.74). We found that forested regions and urban areas had the highest OB, predicted primarily by black carbon and organic carbon from wildfires and transportation sources. Importantly, the dominant components associated with OB were different than those contributing to PM_2.5 mass concentrations (secondary inorganic aerosol); thus, OB metrics may better indicate harmful components and sources on health than the bulk PM_2.5 mass, reinforcing that OB estimates can complement the existing PM_2.5 data in future national-level epidemiological studies.

Long-term exposure to iron and copper in fine particulate air pollution and their combined impact on reactive oxygen species concentration in lung fluid: a population-based cohort study of cardiovascular disease incidence and mortality in Toronto, Canada

BACKGROUND

Exposure to fine particulate (PM2.5) air pollution is associated with increased cardiovascular disease (CVD), but less is known about its specific components, such as metals originating from non-tailpipe emissions. We investigated the associations of long-term exposure to metal components [iron (Fe) and copper (Cu)] in PM2.5 with CVD incidence.

METHODS

We conducted a population-based cohort study in Toronto, Canada. Exposures to Fe and Cu in PM2.5 and their combined impact on the concentration of reactive oxygen species (ROS) in lung fluid were estimated using land use regression models. Incidence of acute myocardial infarction (AMI), congestive heart failure (CHF) and CVD death was ascertained using health administrative datasets. We used mixed-effects Cox regression models to examine the associations between the exposures and health outcomes. A series of sensitivity analyses were conducted, including indirect adjustment for individual-level cardiovascular risk factors (e.g. smoking), and adjustment for PM2.5 and nitrogen dioxide (NO2).

RESULTS

In single-pollutant models, we found positive associations between the three exposures and all three outcomes, with the strongest associations detected for the estimated ROS. The associations of AMI and CHF were sensitive to indirect adjustment, but remained robust for CVD death in all sensitivity analyses. In multi-pollutant models, the associations of the three exposures generally remained unaltered. Interestingly, adjustment for ROS did not substantially change the associations between PM2.5 and CVD, but attenuated the associations of NO2.

CONCLUSIONS

Long-term exposure to Fe and Cu in PM2.5 and their combined impact on ROS were consistently associated with increased CVD death.

Fine particulate matter concentration and composition and the incidence of childhood asthma

BACKGROUND

Several studies have found positive associations between outdoor fine particulate air pollution (≤2.5 μm, PM_2.5) and childhood asthma incidence. However, the impact of PM_2.5 composition on children's respiratory health remains uncertain.

OBJECTIVE

We examined whether joint exposure to PM_2.5 mass concentrations and its major chemical components was associated with childhood asthma development.

METHODS

We conducted a population-based cohort study by identifying 1,130,855 singleton live births occurring between 2006 and 2014 in the province of Ontario, Canada. Concentrations of PM_2.5 and its seven major chemical components were assigned to participants based on their postal codes using chemical transport models and remote sensing. The joint impact of outdoor PM_2.5 concentrations and its major components and childhood asthma incidence (up to age 6) were estimated using Cox proportional hazards models, allowing for potential nonlinearity.

RESULTS

We identified 167,080 children who developed asthma before age 6. In adjusted models, outdoor PM_2.5 mass concentrations during childhood were associated with increased incidence of childhood asthma (Hazard Ratio (HR) for each 1 μg/m³ increase = 1.026, 95% CI: 1.021-1.031). We found that the joint effects of PM_2.5 and its components on childhood asthma incidence may be 24% higher than the conventional approach. Specific components/source markers such as black carbon, ammonium, and nitrate appeared to play an important role.

CONCLUSIONS

Early life exposure to PM_2.5 and its chemical components is associated with an increased risk of asthma development in children. The heterogeneous nature of PM_2.5 should be considered in future health risk assessments.

Association of fine particulate matter air pollution and its constituents with lung function: The China Pulmonary Health study

The associations of long-term exposure to various constituents of fine particulate matter (≤2.5 μm in aerodynamic diameter, PM_2.5) air pollution with lung function were not clearly elucidated in developing countries. The aim was to evaluate the associations of long-term exposure to main constituents of PM_2.5 with lung function in China. This is a nationwide, cross-sectional analysis among 50,991 study participants from the China Pulmonary Health study. Multivariable linear regression models were used to obtain differences of forced expiratory volume in 1 s (FEV₁), forced vital capacity (FVC), FEV₁/FVC, peak expiratory flow (PEF), and forced expiratory flow at 25-75% of exhaled FVC (FEF_25-75%) associated with an interquartile range (IQR) change of PM_2.5 or its constituents. Residential annual PM_2.5 levels varied from 26 μg/m³ to 92 μg/m³ (average: 53 μg/m³). An IQR increase of PM_2.5 concentrations was associated with lower FEV₁ (19.82 mL, 95% CI: 11.30-28.33), FVC (17.45 mL, 95% CI: 7.16-27.74), PEF (86.64 mL/s, 95% CI: 59.77-113.52), and FEF_25-75% (31.93 mL/s, 95% CI: 16.64-47.22). Black carbon, organic matter, ammonium, sulfate, and nitrate were negatively associated with most lung function indicators, with organic matter and nitrate showing consistently larger magnitude of associations than PM_2.5 mass. This large-scale study provides first-hand epidemiological evidence that long-term exposure to ambient PM_2.5 and some constituents, especially organic matter and nitrate, were associated with lower large- and small- airway function.