A New Method to Jointly Estimate the Mortality Risk of Long-Term Exposure to Fine Particulate Matter and its Components

Black Carbon Air Pollution and Incident Mortality Among the Advance-Aged Adults in China: A Prospective Cohort Study

BACKGROUND

We aimed to assess associations between black carbon (BC) and nonaccidental mortality among advance-aged adults in China.

METHODS

We conducted a prospective cohort study in 22 provinces of Chinese Longitudinal Healthy Longevity Survey. We calculated concentrations of 3-year average BC, fine particulate matter (PM2.5), and other PM2.5 components (SO42-, NO3-, NH4+, and organic matter) at individual levels. We used Cox proportional hazards models to assess dose-response BC exposure on nonaccidental mortality, adjusted for total PM2.5, green space, temperature, humidity, and demographic covariates.

RESULTS

We studied 12 873 participants, with a median age of 88 years and 57.4% females. For a median follow-up of 4 years, we observed 7 426 mortality events. The mean 3-year average BC and total PM2.5 exposure concentrations were 3.49 and 66.97 μg/m3, respectively. An increase of 1 μg/m3 in BC was associated with a 39% increase in mortality risks (HR: 1.39, 95% CI: 1.36, 1.43), notably higher than the corresponding increase in mortality risks linked to total PM2.5 (HR: 1.003, 95% CI: 1.002, 1.004) in the adjusted model. The stratified analyses show that people living in rural areas, with lower social and leisure activity index, and lower physical activity, were at greater risk from BC exposure.

CONCLUSIONS

BC is a strong predictor of mortality, with a higher effect estimate compared with total PM2.5 and other PM2.5 components, particularly in rural populations. Although total PM2.5 has been a target indicator of clean air policy interventions, our results indicate that BC concentration should be routinely measured, reported, and studied to improve public health.

Does Nonsteroidal Anti-inflammatory Drug Use Modify All-Cause and Cause-Specific Mortality Associated with PM2.5 and Its Components? A Nationally Representative Cohort Study (2007-2017)

Several studies reported that nonsteroidal anti-inflammatory drug (NSAID) use could alleviate subclinical effects of short-term exposure to fine particulate matter (PM2.5). However, whether chronic NSAID use could mitigate the long-term effects of PM2.5 and its components on population mortality has been unknown. Based on a national representative survey of 47,086 adults (2007-2010) with follow-up information on the primary cause of death (until 2017), we investigated the long-term associations of PM2.5 and its major components, including black carbon (BC), ammonium (NH4 +), nitrate (NO3 -), organic matter (OM), and sulfate (SO4 2-), with all-cause and cause-specific mortality using the Cox proportional hazards model. We also evaluated the effect modification by NSAID use (including broad NSAIDs, aspirin, or ibuprofen) on the associations using interaction models. Long-term exposures to PM2.5 and its components were associated with increased risks of all-cause and cause-specific mortality, where BC, OM, and SO4 2- showed stronger associations. Ibuprofen use could mitigate the associations of PM2.5 and its components with mortality risks, while no significant modifying effects of aspirin were observed. For instance, along with per interquartile range increment in PM2.5 concentration (34.8 μg/m3), the hazard ratios (HRs) of all-cause mortality were 1.21 (95% CI: 1.19, 1.22) and 1.10 (95% CI: 1.01, 1.19) in nonibuprofen and ibuprofen use groups (P for interaction = 0.026), respectively. Cause-specific analyses indicated that ibuprofen use could mainly mitigate risks of cardiovascular disease (CVD) especially ischemic heart disease (IHD) mortality attributable to PM2.5 components. Stratified analyses found more apparent mitigating effects of ibuprofen use among participants without chronic diseases, participants ≤50 years, female participants, rural residents, and those with lower education levels. Our findings suggested potential implications in reducing population mortality caused by long-term exposures to PM2.5 and its components through personalized interventions.

A review of recent advances in metal-organic frameworks materials for zero-energy passive adsorption of chemical pollutants in indoor environments

Approximately 75-90 % of a person's lifetime is spent inside increasingly airtight buildings, where indoor pollutant levels typically exceed those outdoors. Poor indoor air quality can lead to allergies, respiratory diseases, and even cancer, and can also reduce the longevity of buildings. Passive adsorption materials play a crucial role in reducing indoor pollutants. This review highlights the latest advances in using Metal-organic Frameworks (MOFs) as passive adsorption materials for indoor pollutant capture and outlines the principles for developing high-performance adsorbents. It provides a comparative analysis of the development and performance of MOFs and composite adsorbent materials, highlighting their respective advantages and limitations in indoor pollutant adsorption technology. The article proposes strategies to address these challenges and offers a comprehensive review of current practical adsorption devices. Finally, aiming to advance commercialization of MOFs, the anticipated development of indoor pollutant adsorption technology is discussed in this paper.

Global associations between long-term exposure to PM2.5 constituents and health: A systematic review and meta-analysis of cohort studies

Existing studies on the most impactful component remain controversial, hindering the optimization of future air quality standards that concerns particle composition. We aimed to summarize the health risk associated with PM2.5 components and identify those components with the greatest health risk. We performed a meta-analysis to quantify the combined health effects of PM2.5 components, and used the meta-smoothing to produce the pooled concentration-response (C-R) curves. Out of 8954 initial articles, 80 cohort studies met the inclusion criteria, including a total of 198.08 million population. The pooled C-R curves demonstrated approximately J-shaped association between total mortality and exposure to BC, and NO3-, but U-shaped and inverted U-shaped relationship withSO42- and OC, respectively. In addition, this study found that exposure to various elements, including BC,SO42-NO3-, NH4+, Zn, Ni, and Si, were significantly associated with an increased risk of total mortality, with Ni presenting the largest estimate. And exposure to NO3-, Zn, and Si was positively associated with an increased risk of respiratory mortality, while exposure to BC, SO42-, and NO3- showed a positive association with risk of cardiovascular mortality. For health outcome of morbidity, BC was notably associated with a higher incidence of asthma, type 2 diabetes and stroke. Subgroup analysis revealed a higher susceptibility to PM2.5 components in Asia compared to Europe and North America, and females showed a higher vulnerability. Given the significant health effects of PM2.5 components, governments are advised to introduce them in regional monitoring and air quality control guidelines. ENVIRONMENTAL IMPLICATION: PM2.5 is a complex mixture of chemical components from various sources, and each component has unique physicochemical properties and uncertain toxicity, posing significant threat to public health. This study systematically reviewed cohort studies on the association between long-term exposure to 13 PM2.5 components and the risk of morbidity and mortality. And we applied the meta-smoothing approach to establish the pooled concentration-response associations between PM2.5 components and mortality globally. Our findings will provide strong support for PM2.5 components monitoring and the improvement of air quality-related regulations. This will aid in helping to enhance health intervention strategies and mitigating public exposure to detrimental particulate matter.

Epidemiological studies likely need to consider PM2.5 composition even if total outdoor PM2.5 mass concentration is the exposure of interest

Background

Outdoor fine particulate air pollution, <2.5 µm (PM2.5) mass concentrations can be constructed through many different combinations of chemical components that have varying levels of toxicity. This poses a challenge for studies interested in estimating the health effects of total outdoor PM2.5 (i.e., how much PM2.5 mass is present in the air regardless of composition) because we must consider possible confounders of the version of treatment-outcome relationships.

Methods

We evaluated the extent of possible bias in mortality hazard ratios for total outdoor PM2.5 by examining models with and without adjustment for sulfate and nitrate in PM2.5 as examples of potential confounders of version of treatment-outcome relationships. Our study included approximately 3 million Canadians and Cox proportional hazard models were used to estimate hazard ratios for total outdoor PM2.5 adjusting for sulfate and/or nitrate and other relevant covariates.

Results

Hazard ratios for total outdoor PM2.5 and nonaccidental, cardiovascular, and respiratory mortality were overestimated due to the confounding version of treatment-outcome relationships, and associations for lung cancer mortality were underestimated. Sulfate was most strongly associated with nonaccidental, cardiovascular, and respiratory mortality suggesting that regulations targeting this specific component of outdoor PM2.5 may have greater health benefits than interventions targeting total PM2.5.

Conclusions

Studies interested in estimating the health impacts of total outdoor PM2.5 (i.e., how much PM2.5 mass is present in the air) need to consider potential confounders of the version of treatment-outcome relationships. Otherwise, health risk estimates for total PM2.5 will reflect some unknown combination of how much PM2.5 mass is present in the air and the kind of PM2.5 mass that is present.

Long-term exposure to PM2.5 and its components is associated with elevated blood pressure and hypertension prevalence: Evidence from rural adults

INTRODUCTION

The toxicity of fine particulate matter (PM2.5) is determined by its components, while the evidence regarding associations of PM2.5 components with blood pressure (BP) is limited, especially in rural areas.

OBJECTIVES

This study aimed to explore the associations of PM2.5 and its chemical components with systolic BP (SBP), diastolic BP (DBP), pulse pressure (PP), mean artery pressure (MAP) levels and hypertension prevalence, and to identify key components in Chinese rural areas.

METHODS

39,211 adults from the Henan Rural Cohort were included during 2015-2017. Different periods of PM2.5 and chemical components were estimated by hybrid satellite model. The single-pollutant, component-PM2.5 model, component-residual model and component-proportion model were applied to explore the associations of pollutants with BP levels and hypertension prevalence. Exposure-response (E-R) relationships, stratified analyses and sensitivity analyses were used to explore these associations further.

RESULTS

12,826 (32.71%) were identified with hypertension. For each 1 μg/m3 increase of pollutants, the adjusted odds ratio (OR) for hypertension prevalence was 1.03 for PM2.5 mass, 1.40 for BC, 1.16 for NH4+, 1.08 for NO3-, 1.17 for OM, 1.12 for SO42- and 1.25 for SOIL in the single-pollutant model. BC and SOIL were statistically significant in the component-PM2.5 model, component-residual model and component-proportion model. Similarly, associations of these pollutants with elevated BP levels were also found in aforementioned four models. These pollutants produced a stronger association with SBP than DBP, PP and MAP. Most of associations were non-linear in E-R relationships. The groups of older, the men, with lower per capita monthly income, lower educational level and higher BMI were more vulnerable to these pollutants in stratified analyses. The results remained stable in sensitivity analyses.

CONCLUSION

Long-term exposure to PM2.5 and its components, especially BC and SOIL, was associated with elevated BP and hypertension prevalence in rural adults, and decreasing pollutants may provide additional benefits.

Long-term exposure to ambient PM2.5, particulate constituents and hospital admissions from non-respiratory infection

The association between PM2.5 and non-respiratory infections is unclear. Using data from Medicare beneficiaries and high-resolution datasets of PM2.5 and its constituents across 39,296 ZIP codes in the U.S between 2000 and 2016, we investigated the associations between annual PM2.5, PM2.5 constituents, source-specific PM2.5, and hospital admissions from non-respiratory infections. Each standard deviation (3.7-μg m-3) increase in PM2.5 was associated with a 10.8% (95%CI 10.8-11.2%) increase in rate of hospital admissions from non-respiratory infections. Sulfates (30.8%), Nickel (22.5%) and Copper (15.3%) contributed the largest weights in the observed associations. Each standard deviation increase in PM2.5 components sourced from oil combustion, coal burning, traffic, dirt, and regionally transported nitrates was associated with 14.5% (95%CI 7.6-21.8%), 18.2% (95%CI 7.2-30.2%), 20.6% (95%CI 5.6-37.9%), 8.9% (95%CI 0.3-18.4%) and 7.8% (95%CI 0.6-15.5%) increases in hospital admissions from non-respiratory infections. Our results suggested that non-respiratory infections are an under-appreciated health effect of PM2.5.

Long-Term Exposure to Low-Level PM2.5 and Mortality: Investigation of Heterogeneity by Harmonizing Analyses in Large Cohort Studies in Canada, United States, and Europe

BACKGROUND

Studies across the globe generally reported increased mortality risks associated with particulate matter with aerodynamic diameter ≤ 2.5 μ m (PM 2.5 ) exposure with large heterogeneity in the magnitude of reported associations and the shape of concentration-response functions (CRFs). We aimed to evaluate the impact of key study design factors (including confounders, applied exposure model, population age, and outcome definition) on PM 2.5 effect estimates by harmonizing analyses on three previously published large studies in Canada [Mortality-Air Pollution Associations in Low Exposure Environments (MAPLE), 1991-2016], the United States (Medicare, 2000-2016), and Europe [Effects of Low-Level Air Pollution: A Study in Europe (ELAPSE), 2000-2016] as much as possible.

METHODS

We harmonized the study populations to individuals 65 + years of age, applied the same satellite-derived PM 2.5 exposure estimates, and selected the same sets of potential confounders and the same outcome. We evaluated whether differences in previously published effect estimates across cohorts were reduced after harmonization among these factors. Additional analyses were conducted to assess the influence of key design features on estimated risks, including adjusted covariates and exposure assessment method. A combined CRF was assessed with meta-analysis based on the extended shape-constrained health impact function (eSCHIF).

RESULTS

More than 81 million participants were included, contributing 692 million person-years of follow-up. Hazard ratios and 95% confidence intervals (CIs) for all-cause mortality associated with a 5 - μ g / m 3 increase in PM 2.5 were 1.039 (1.032, 1.046) in MAPLE, 1.025 (1.021, 1.029) in Medicare, and 1.041 (1.014, 1.069) in ELAPSE. Applying a harmonized analytical approach marginally reduced difference in the observed associations across the three studies. Magnitude of the association was affected by the adjusted covariates, exposure assessment methodology, age of the population, and marginally by outcome definition. Shape of the CRFs differed across cohorts but generally showed associations down to the lowest observed PM 2.5 levels. A common CRF suggested a monotonically increased risk down to the lowest exposure level. https://doi.org/10.1289/EHP12141.

Long-term exposure to elemental components of fine particulate matter and all-natural and cause-specific mortality in a Danish nationwide administrative cohort study

BACKGROUND

Fine particulate matter (PM_2.5) is a well-recognized risk factor for premature death. However, evidence on which PM_2.5 components are most relevant is unclear.

METHODS

We evaluated the associations between mortality and long-term exposure to eight PM_2.5 elemental components [copper (Cu), iron (Fe), zinc (Zn), sulfur (S), nickel (Ni), vanadium (V), silicon (Si), and potassium (K)]. Studied outcomes included death from diabetes, chronic kidney disease (CKD), dementia, and psychiatric disorders as well as all-natural causes, cardiovascular disease (CVD), respiratory diseases (RD), and lung cancer. We followed all residents in Denmark (aged ≥30 years) from January 1, 2000 to December 31, 2017. We used European-wide land-use regression models at a 100 × 100 m scale to estimate the residential annual mean levels of exposure to PM_2.5 components. The models were developed with supervised linear regression (SLR) and random forest (RF). The associations were evaluated by Cox proportional hazard models adjusting for individual- and area-level socioeconomic factors and total PM_2.5 mass.

RESULTS

Of 3,081,244 individuals, we observed 803,373 death from natural causes during follow-up. We found significant positive associations between all-natural mortality with Si and K from both exposure modeling approaches (hazard ratios; 95% confidence intervals per interquartile range increase): SLR-Si (1.04; 1.03-1.05), RF-Si (1.01; 1.00-1.02), SLR-K (1.03; 1.02-1.04), and RF-K (1.06; 1.05-1.07). Strong associations of K and Si were detected with most causes of mortality except CKD and K, and diabetes and Si (the strongest associations for psychiatric disorders mortality). In addition, Fe was relevant for mortality from RD, lung cancer, CKD, and psychiatric disorders; Zn with mortality from CKD, RD, and lung cancer, and; Ni and V with lung cancer mortality.

CONCLUSIONS

We present novel results of the relevance of different PM_2.5 components for different causes of death, with K and Si seeming to be most consistently associated with mortality in Denmark.

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 satellite-driven model to estimate long-term particulate sulfate levels and attributable mortality burden in China

Ambient fine particulate matter (PM2.5) pollution is a major environmental and public health challenge in China. In the recent decade, the PM2.5 level has decreased mainly driven by reductions in particulate sulfate as a result of large-scale desulfurization efforts in coal-fired power plants and industrial facilities. Emerging evidence also points to the differential toxicity of particulate sulfate affecting human health. However, estimating the long-term spatiotemporal trend of sulfate is difficult because a ground monitoring network of PM2.5 constituents has not been established in China. Spaceborne sensors such as the Multi-angle Imaging SpectroRadiometer (MISR) instrument can provide complementary information on aerosol size and type. With the help of state-of-the-art machine learning techniques, we developed a sulfate prediction model under support from available ground measurements, MISR-retrieved aerosol microphysical properties, and atmospheric reanalysis data at a spatial resolution of 0.1°. Our sulfate model performed well with an out-of-bag cross-validationR2 of 0.68 at the daily level and 0.93 at the monthly level. We found that the national mean population-weighted sulfate concentration was relatively stable before the Air Pollution Prevention and Control Action Plan was enforced in 2013, ranging from 10.4 to 11.5 µg m-3. But the sulfate level dramatically decreased to 7.7 µg m-3 in 2018, with a change rate of -28.7 % from 2013 to 2018. Correspondingly, the annual mean total non-accidental and cardiopulmonary deaths attributed to sulfate decreased by 40.7 % and 42.3 %, respectively. The long-term, full-coverage sulfate level estimates will support future studies on evaluating air quality policies and understanding the adverse health effect of particulate sulfate.

Understanding Anthropogenic PM2.5 Concentrations and Their Drivers in China during 1998-2016

Air pollution poses serious challenges for human health and wellbeing. It also affects atmospheric visibility and contributes to climate change. As social and economic processes have increased, anthropogenic PM_2.5 pollution caused by intensive human activities has led to extremely severe air pollution. Spatiotemporal patterns and drivers of anthropogenic PM_2.5 concentrations have received increasing attention from the scientific community. Nonetheless, spatiotemporal patterns and drivers of anthropogenic PM_2.5 concentrations are still inadequately understood. Based on a time series of remotely sensed anthropogenic PM_2.5 concentrations, this study analyzed the spatiotemporal patterns of this crucial pollutant in China from 1998 to 2016 using Sen's slope estimator and the Mann-Kendall trend model. This, in combination with grey correlation analysis (GCA), was used to reveal the socioeconomic factors influencing anthropogenic PM_2.5 concentrations in eastern, central, and western China from 1998 to 2016. The results were as follows: (1) the average annual anthropogenic concentration of PM_2.5 in China increased quickly and reached its peak value in 2007, then remained stable in the following years; (2) only 63.30 to 55.09% of the land area reached the threshold value of 15 μg/m³ from 1998 to 2016; (3) regarding the polarization phenomenon of anthropogenic PM_2.5 concentrations existing in eastern and central China, the proportion of gradient 1 (≤15 μg/m³) gradually decreased and gradient 3 (≥35 μg/m³) gradually increased; and (4) the urbanization level (UR), population density (PD), and proportion of secondary industry to gross domestic product (SI) were the dominant socioeconomic factors affecting the formation of anthropogenic PM_2.5 concentrations in eastern, central, and western China, independently. The improvements in energy consumption per gross domestic product (EI) have a greater potential for mitigating anthropogenic PM_2.5 emissions in central and western China. These findings allow an interpretation of the spatial distribution of anthropogenic PM_2.5 concentrations and the mechanisms influencing anthropogenic PM_2.5 concentrations, which can help the Chinese government develop effective abatement strategies.

Individual PM2.5 component exposure model, elevated blood pressure and hypertension in middle-aged and older adults: A nationwide cohort study from 125 cities in China

Recently, elevated blood pressure (BP) and hypertension (HTN) have caused a huge burden of health loss. Previous studies used ambient air pollutants as a proxy for individual exposure, limiting the assessment of its multiple exposure to health effects. For the first time, this study constructed individual PM_2.5 component (SO₄^2-, NO₃^-, NH₄⁺, OM, and BC) exposure model DAG (Directed Acyclic Graph), DAG-oriented generalized linear model and random forest model, and explored the effects of single and multiple exposures to PM_2.5 components on BP at different stages by the generalized linear model (GLM) and Quantile g-Computation (QgC) model based on a large cohort study in China. We defined BP in four stages according to the 2017 ACC/AHA guidelines. After excluding the lack of key information, the cohort analyses ultimately included 9031 participants. Our results showed that the individual PM_2.5 component exposure model had good efficacy. Single or multiple exposure to PM_2.5 components had significant positive effects on normal BP to elevated BP and elevated BP to stage 1 HTN. In addition, males, the elderly and urban residents were more sensitive to PM_2.5 components. This study provided implications for environmental exposure assessment and control of particulate pollution in the future.

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.

Mortality-Air Pollution Associations in Low Exposure Environments (MAPLE): Phase 2

INTRODUCTION

Mortality is associated with long-term exposure to fine particulate matter (particulate matter ≤2.5 μm in aerodynamic diameter; PM2.5), although the magnitude and form of these associations remain poorly understood at lower concentrations. Knowledge gaps include the shape of concentration-response curves and the lowest levels of exposure at which increased risks are evident and the occurrence and extent of associations with specific causes of death. Here, we applied improved estimates of exposure to ambient PM2.5 to national population-based cohorts in Canada, including a stacked cohort of 7.1 million people who responded to census year 1991, 1996, or 2001. The characterization of the shape of the concentration-response relationship for nonaccidental mortality and several specific causes of death at low levels of exposure was the focus of the Mortality-Air Pollution Associations in Low Exposure Environments (MAPLE) Phase 1 report. In the Phase 1 report we reported that associations between outdoor PM2.5 concentrations and nonaccidental mortality were attenuated with the addition of ozone (O3) or a measure of gaseous pollutant oxidant capacity (Ox), which was estimated from O3 and nitrogen dioxide (NO2) concentrations. This was motivated by our interests in understanding both the effects air pollutant mixtures may have on mortality and also the role of O3 as a copollutant that shares common sources and precursor emissions with those of PM2.5. In this Phase 2 report, we further explore the sensitivity of these associations with O3 and Ox, evaluate sensitivity to other factors, such as regional variation, and present ambient PM2.5 concentration-response relationships for specific causes of death.

METHODS

PM2.5 concentrations were estimated at 1 km2 spatial resolution across North America using remote sensing of aerosol optical depth (AOD) combined with chemical transport model (GEOS-Chem) simulations of the AOD:surface PM2.5 mass concentration relationship, land use information, and ground monitoring. These estimates were informed and further refined with collocated measurements of PM2.5 and AOD, including targeted measurements in areas of low PM2.5 concentrations collected at five locations across Canada. Ground measurements of PM2.5 and total suspended particulate matter (TSP) mass concentrations from 1981 to 1999 were used to backcast remote-sensing-based estimates over that same time period, resulting in modeled annual surfaces from 1981 to 2016.

Annual exposures to PM2.5 were then estimated for subjects in several national population-based Canadian cohorts using residential histories derived from annual postal code entries in income tax files. These cohorts included three census-based cohorts: the 1991 Canadian Census Health and Environment Cohort (CanCHEC; 2.5 million respondents), the 1996 CanCHEC (3 million respondents), the 2001 CanCHEC (3 million respondents), and a Stacked CanCHEC where duplicate records of respondents were excluded (Stacked CanCHEC; 7.1 million respondents). The Canadian Community Health Survey (CCHS) mortality cohort (mCCHS), derived from several pooled cycles of the CCHS (540,900 respondents), included additional individual information about health behaviors. Follow-up periods were completed to the end of 2016 for all cohorts. Cox proportional hazard ratios (HRs) were estimated for nonaccidental and other major causes of death using a 10-year moving average exposure and 1-year lag. All models were stratified by age, sex, immigrant status, and where appropriate, census year or survey cycle. Models were further adjusted for income adequacy quintile, visible minority status, Indigenous identity, educational attainment, labor-force status, marital status, occupation, and ecological covariates of community size, airshed, urban form, and four dimensions of the Canadian Marginalization Index (Can-Marg; instability, deprivation, dependency, and ethnic concentration). The mCCHS analyses were also adjusted for individual-level measures of smoking, alcohol consumption, fruit and vegetable consumption, body mass index (BMI), and exercise behavior.

In addition to linear models, the shape of the concentration-response function was investigated using restricted cubic splines (RCS). The number of knots were selected by minimizing the Bayesian Information Criterion (BIC). Two additional models were used to examine the association between nonaccidental mortality and PM2.5. The first is the standard threshold model defined by a transformation of concentration equaling zero if the concentration was less than a specific threshold value and concentration minus the threshold value for concentrations above the threshold. The second additional model was an extension of the Shape Constrained Health Impact Function (SCHIF), the eSCHIF, which converts RCS predictions into functions potentially more suitable for use in health impact assessments. Given the RCS parameter estimates and their covariance matrix, 1,000 realizations of the RCS were simulated at concentrations from the minimum to the maximum concentration, by increments of 0.1 μg/m3. An eSCHIF was then fit to each of these RCS realizations. Thus, 1,000 eSCHIF predictions and uncertainty intervals were determined at each concentration within the total range.

Sensitivity analyses were conducted to examine associations between PM2.5 and mortality when in the presence of, or stratified by tertile of, O3 or Ox. Additionally, associations between PM2.5 and mortality were assessed for sensitivity to lower concentration thresholds, where person-years below a threshold value were assigned the mean exposure within that group. We also examined the sensitivity of the shape of the nonaccidental mortality-PM2.5 association to removal of person-years at or above 12 μg/m3 (the current U.S. National Ambient Air Quality Standard) and 10 μg/m3 (the current Canadian and former [2005] World Health Organization [WHO] guideline, and current WHO Interim Target-4). Finally, differences in the shapes of PM2.5-mortality associations were assessed across broad geographic regions (airsheds) within Canada.

RESULTS

The refined PM2.5 exposure estimates demonstrated improved performance relative to estimates applied previously and in the MAPLE Phase 1 report, with slightly reduced errors, including at lower ranges of concentrations (e.g., for PM2.5 <10 μg/m3).

Positive associations between outdoor PM2.5 concentrations and nonaccidental mortality were consistently observed in all cohorts. In the Stacked CanCHEC analyses (1.3 million deaths), each 10-μg/m3 increase in outdoor PM2.5 concentration corresponded to an HR of 1.084 (95% confidence interval [CI]: 1.073 to 1.096) for nonaccidental mortality. For an interquartile range (IQR) increase in PM2.5 mass concentration of 4.16 μg/m3 and for a mean annual nonaccidental death rate of 92.8 per 10,000 persons (over the 1991-2016 period for cohort participants ages 25-90), this HR corresponds to an additional 31.62 deaths per 100,000 people, which is equivalent to an additional 7,848 deaths per year in Canada, based on the 2016 population. In RCS models, mean HR predictions increased from the minimum concentration of 2.5 μg/m3 to 4.5 μg/m3, flattened from 4.5 μg/m3 to 8.0 μg/m3, then increased for concentrations above 8.0 μg/m3. The threshold model results reflected this pattern with -2 log-likelihood values being equal at 2.5 μg/m3 and 8.0 μg/m3. However, mean threshold model predictions monotonically increased over the concentration range with the lower 95% CI equal to one from 2.5 μg/m3 to 8.0 μg/m3. The RCS model was a superior predictor compared with any of the threshold models, including the linear model.

In the mCCHS cohort analyses inclusion of behavioral covariates did not substantially change the results for both linear and nonlinear models. We examined the sensitivity of the shape of the nonaccidental mortality-PM2.5 association to removal of person-years at or above the current U.S. and Canadian standards of 12 μg/m3 and 10 μg/m3, respectively. In the full cohort and in both restricted cohorts, a steep increase was observed from the minimum concentration of 2.5 μg/m3 to 5 μg/m3. For the full cohort and the <12 μg/m3 cohort the relationship flattened over the 5 to 9 μg/m3 range and then increased above 9 μg/m3. A similar increase was observed for the <10 μg/m3 cohort followed by a clear decline in the magnitude of predictions over the 5 to 9 μg/m3 range and an increase above 9 μg/m3. Together these results suggest that a positive association exists for concentrations >9 μg/m3 with indications of adverse effects on mortality at concentrations as low as 2.5 μg/m3.

Among the other causes of death examined, PM2.5 exposures were consistently associated with an increased hazard of mortality due to ischemic heart disease, respiratory disease, cardiovascular disease, and diabetes across all cohorts. Associations were observed in the Stacked CanCHEC but not in all other cohorts for cerebrovascular disease, pneumonia, and chronic obstructive pulmonary disease (COPD) mortality. No significant associations were observed between mortality and exposure to PM2.5 for heart failure, lung cancer, and kidney failure.

In sensitivity analyses, the addition of O3 and Ox attenuated associations between PM2.5 and mortality. When analyses were stratified by tertiles of copollutants, associations between PM2.5 and mortality were only observed in the highest tertile of O3 or Ox. Across broad regions of Canada, linear HR estimates and the shape of the eSCHIF varied substantially, possibly reflecting underlying differences in air pollutant mixtures not characterized by PM2.5 mass concentrations or the included gaseous pollutants. Sensitivity analyses to assess regional variation in population characteristics and access to healthcare indicated that the observed regional differences in concentration-mortality relationships, specifically the flattening of the concentration-mortality relationship over the 5 to 9 μg/m3 range, was not likely related to variation in the makeup of the cohort or its access to healthcare, lending support to the potential role of spatially varying air pollutant mixtures not sufficiently characterized by PM2.5 mass concentrations.

CONCLUSIONS

In several large, national Canadian cohorts, including a cohort of 7.1 million unique census respondents, associations were observed between exposure to PM2.5 with nonaccidental mortality and several specific causes of death. Associations with nonaccidental mortality were observed using the eSCHIF methodology at concentrations as low as 2.5 μg/m3, and there was no clear evidence in the observed data of a lower threshold, below which PM2.5 was not associated with nonaccidental mortality.

Mapping Mobility: Utilizing Local-Knowledge-Derived Activity Space to Estimate Exposure to Ambient Air Pollution among Individuals Experiencing Unsheltered Homelessness

Individuals experiencing homelessness represent a growing population in the United States. Air pollution exposure among individuals experiencing homelessness has not been quantified. Utilizing local knowledge mapping, we generated activity spaces for 62 individuals experiencing homelessness residing in a semi-rural county within the United States. Satellite derived measurements of fine particulate matter (PM2.5) were utilized to estimate annual exposure to air pollution experienced by our participants, as well as differences in the variation in estimated PM2.5 at the local scale compared with stationary monitor data and point location estimates for the same period. Spatial variation in exposure to PM2.5 was detected between participants at both the point and activity space level. Among all participants, annual median PM2.5 exposure was 16.22 μg/m³, exceeding the National Air Quality Standard. Local knowledge mapping represents a novel mechanism to capture mobility patterns and investigate exposure to air pollution within vulnerable populations. Reliance on stationary monitor data to estimate air pollution exposure may lead to exposure misclassification, particularly in rural and semirural regions where monitoring is limited.

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.

Exploring sources and health risks of metals in Beijing PM2.5: Insights from long-term online measurements

To gain a comprehensive understanding of sources, health risks, and regional transport of PM_2.5-bound metals in Beijing, one-year continuous measurement (K, Fe, Ca, Zn, Pb, Mn, Ba, Cu, As, Se, Cr, and Ni) was conducted from December 2016 to November 2017 and Positive Matrix Factorization analysis (PMF) was applied for source apportionment. It was found that the seasonal variation of sources could vary significantly among metals. Sources of Ca, Ba, As, Se, and Cr did not show much seasonal variations, with the contribution of its predominant source higher than 35% in each season. However, the major sources of K, Fe, Zn, Pb, Mn, Cu, and Ni exhibited obvious seasonal variations. In addition, the characteristics of metals in haze episodes were comprehensively investigated. Haze episodes in Beijing were characterized by higher metal concentrations and health risks, which were about 2- 6 times higher than non-haze periods. Moreover, the types of haze episode were different in winter and spring. Haze episodes in winter were mostly influenced by coal combustion, the contribution of which increased greatly and accounted for about 30% of PM_2.5. The metals such as K, Zn, Pb, As, and Se significantly increased, which were mainly transported from south of Beijing. During haze episodes in spring, dust was an important source, which contributed to higher concentrations of crustal metals that transported from northwest of Beijing. To quickly and effectively identify source regions of metals in Beijing during haze episodes, a new diagnostic ratio method using Ca as a reference was developed. The ratios of some anthropogenic metals to Ca significantly increased when air mass was mainly from south of Beijing during haze episodes while the ratios remained constantly low in non-haze periods, when local emissions dominated. This method could be useful for rapid identification and control of metal pollution in Beijing.

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.

Differential Mortality Risks Associated With PM2.5 Components: A Multi-Country, Multi-City Study

BACKGROUND

The association between fine particulate matter (PM2.5) and mortality widely differs between as well as within countries. Differences in PM2.5 composition can play a role in modifying the effect estimates, but there is little evidence about which components have higher impacts on mortality.

METHODS

We applied a 2-stage analysis on data collected from 210 locations in 16 countries. In the first stage, we estimated location-specific relative risks (RR) for mortality associated with daily total PM2.5 through time series regression analysis. We then pooled these estimates in a meta-regression model that included city-specific logratio-transformed proportions of seven PM2.5 components as well as meta-predictors derived from city-specific socio-economic and environmental indicators.

RESULTS

We found associations between RR and several PM2.5 components. Increasing the ammonium (NH4+) proportion from 1% to 22%, while keeping a relative average proportion of other components, increased the RR from 1.0063 (95% confidence interval [95% CI] = 1.0030, 1.0097) to 1.0102 (95% CI = 1.0070, 1.0135). Conversely, an increase in nitrate (NO3-) from 1% to 71% resulted in a reduced RR, from 1.0100 (95% CI = 1.0067, 1.0133) to 1.0037 (95% CI = 0.9998, 1.0077). Differences in composition explained a substantial part of the heterogeneity in PM2.5 risk.

CONCLUSIONS

These findings contribute to the identification of more hazardous emission sources. Further work is needed to understand the health impacts of PM2.5 components and sources given the overlapping sources and correlations among many components.

Long-term exposure to PM2.5 major components and mortality in the southeastern United States

BACKGROUND

Long-term exposure to fine particulate matter (PM_2.5) mass has been associated with adverse health effects. However, the health effects of PM_2.5 components have been less studied. There is a pressing need to better understand the relative contribution of components of PM_2.5, which can lay the scientific basis for designing effective policies and targeted interventions.

METHODS

We conducted a population-based cohort study, comprising all Medicare enrollees aged 65 or older in the southeastern United States from 2000 to 2016, to explore the associations between long-term exposure to PM_2.5 major components and all-cause mortality among the elderly. Based on well-validated prediction models, we estimated ZIP code-level annual mean concentrations for five major PM_2.5 components, including black carbon (BC), nitrate (NIT), organic matter (OM), sulfate (SO₄), and soil particles. Data were analyzed using Cox proportional hazards models, adjusting for potential confounders.

RESULTS

The cohort comprised 13,590,387 Medicare enrollees and a total of 107,191,652 person-years. In single-component models, all five major PM_2.5 components were significantly associated with elevated all-cause mortality. The hazard ratios (HR) per interquartile range (IQR) increase in exposure were 1.027 (95% CI: 1.025-1.030), 1.012 (95% CI: 1.010-1.013), 1.018 (95% CI: 1.017-1.020), 1.021 (95% CI: 1.017-1.024), and 1.004 (95% CI: 1.003-1.006) for BC, NIT, OM, SO₄, and soil particles, respectively. While the effect estimate of soil component was statistically significant, it is much smaller than those of combustion-related components.

CONCLUSION

Our study provides epidemiological evidence that long-term exposure to major PM_2.5 components is significantly associated with elevated mortality.

The influence of outdoor PM2.5 concentration at workplace on nonaccidental mortality estimates in a Canadian census-based cohort

Background

Associations between mortality and exposure to ambient air pollution are usually explored using concentrations of residential outdoor fine particulate matter (PM_2.5) to estimate individual exposure. Such studies all have an important limitation in that they do not capture data on individual mobility throughout the day to areas where concentrations may be substantially different, leading to possible exposure misclassification. We examine the possible role of outdoor PM_2.5 concentrations at work for a large population-based mortality cohort.

Methods

Using the 2001 Canadian Census Health and Environment Cohort (CanCHEC), we created a time-weighted average that incorporates employment hours worked in the past week and outdoor PM_2.5 concentration at work and home. We used a Cox proportional hazard model with a 15-year follow-up (2001 to 2016) to explore whether inclusion of workplace estimates had an impact on hazard ratios for mortality for this cohort.

Results

Hazard ratios relying on outdoor PM_2.5 concentration at home were not significantly different from those using a time-weighted estimate, for the full cohort, nor for those who commute to a regular workplace. When exploring cohort subgroups according to neighborhood type and commute distance, there was a notable but insignificant change in risk of nonaccidental death for those living in car-oriented neighborhoods, and with commutes greater than 10 km.

Conclusions

Risk analyses performed with large cohorts in low-pollution environments do not seem to be biased if relying solely on outdoor PM_2.5 concentrations at home to estimate exposure.

Long-term ozone exposure and mortality from neurological diseases in Canada

BACKGROUND

There is increasing interest in the health effects of air pollution. However, the relationships between ozone exposure and mortality attributable to neurological diseases remain unclear.

OBJECTIVES

To assess associations of long-term exposure to ozone with death from Parkinson's disease, dementia, stroke, and multiple sclerosis.

METHODS

Our analyses were based on the 2001 Canadian Census Health and Environment Cohort. Census participants were linked with vital statistics records through 2016, resulting in a cohort of 3.5 million adults/51,045,700 person-years, with 8,500/51,300/43,300/1,300 deaths from Parkinson's/dementia/stroke/multiple sclerosis, respectively. Ten-year average ozone concentrations estimated by chemical transport models and adjusted by ground measurements were assigned to subjects based on postal codes. Cox proportional hazards models were used to calculate hazard ratios (HRs) for deaths from the four neurological diseases, adjusting for eight common demographic and socioeconomic factors, seven environmental indexes, and six contextual covariates.

RESULTS

The fully adjusted HRs for Parkinson's, dementia, stroke, and multiple sclerosis mortalities related to one interquartile range increase in ozone (10.1 ppb), were 1.09 (95% confidence interval 1.04-1.14), 1.08 (1.06-1.10), 1.06 (1.04-1.09), and 1.35 (1.20-1.51), respectively. The covariates did not influence significance of the ozone-mortality associations, except airshed (i.e., broad region of Canada). During the period of 2001-2016, 5.66%/5.01%/ 3.77%/19.11% of deaths from Parkinson's/dementia/stroke/multiple sclerosis, respectively, were attributable to ozone exposure.

CONCLUSIONS

We found positive associations between ozone exposure and mortality due to Parkinson's, dementia, stroke, and multiple sclerosis.

Long-term exposure to black carbon and mortality: A 28-year follow-up of the GAZEL cohort

BACKGROUND

The current evidence on health effects of long-term exposure to outdoor airborne black carbon (BC) exposure remains scarce.

OBJECTIVES

To examine the association between long-term exposure to BC and mortality in a large population-based French cohort, with 28 years of follow-up.

METHODS

Data from the GAZEL cohort were collected between 1989 and 2017. Land use regression model with temporal extrapolation wa used to estimate yearly BC and PM_2.5 exposure at the residential addresses from 1989 until censoring for 19,906 participants. Time-varying Cox models with attained age as time-scale was used to estimate the associations between BC and all-cause and cardiovascular mortality, after adjusting for individual and area-level covariates. To handle confounding by PM_2.5, we used the residual of BC regressed on PM_2.5 as an alternate exposure variable. For all-cause mortality, we also examined effect modification by sex, smoking status, BMI and fruit/vegetable intake.

RESULTS

The median of 20-year moving average of BC exposure was 2.02 10^-5/m in study population. We found significant associations between BC exposure and all-cause mortality (n = 2357) using both 20-year moving average of BC and residual of BC, with corresponding hazard ratios (HR) of 1.14 (95 %CI: 1.07-1.22) and 1.17 (95 %CI: 1.10-1.24) for an inter-quartile range (IQR) increase (0.86 10^-5/m for BC and 0.57 10^-5/m for residual of BC). We found a similar association between BC and cardiovascular mortality (n = 277) with a HR of 1.15 (95 %CI: 0.95-1.38). The dose-response relationship between BC and all-cause mortality was monotonic but nonlinear with a steeper slope at high BC levels. In addition, the effect of BC was higher among never-smokers and among those having fruit/vegetables less than twice a week.

CONCLUSIONS

There was a positive association between long-term exposure to BC and increased mortality risk, reinforcing the emerging evidence that BC is a harmful component of PM_2.5.

Evaluation of Machine Learning Models for Estimating PM2.5 Concentrations across Malaysia

Southeast Asia (SEA) is a hotspot region for atmospheric pollution and haze conditions, due to extensive forest, agricultural and peat fires. This study aims to estimate the PM2.5 concentrations across Malaysia using machine-learning (ML) models like Random Forest (RF) and Support Vector Regression (SVR), based on satellite AOD (aerosol optical depth) observations, ground measured air pollutants (NO2, SO2, CO, O3) and meteorological parameters (air temperature, relative humidity, wind speed and direction). The estimated PM2.5 concentrations for a two-year period (2018–2019) are evaluated against measurements performed at 65 air-quality monitoring stations located at urban, industrial, suburban and rural sites. PM2.5 concentrations varied widely between the stations, with higher values (mean of 24.2 ± 21.6 µg m−3) at urban/industrial stations and lower (mean of 21.3 ± 18.4 µg m−3) at suburban/rural sites. Furthermore, pronounced seasonal variability in PM2.5 is recorded across Malaysia, with highest concentrations during the dry season (June–September). Seven models were developed for PM2.5 predictions, i.e., separately for urban/industrial and suburban/rural sites, for the four dominant seasons (dry, wet and two inter-monsoon), and an overall model, which displayed accuracies in the order of R2 = 0.46–0.76. The validation analysis reveals that the RF model (R2 = 0.53–0.76) exhibits slightly better performance than SVR, except for the overall model. This is the first study conducted in Malaysia for PM2.5 estimations at a national scale combining satellite aerosol retrievals with ground-based pollutants, meteorological factors and ML techniques. The satisfactory prediction of PM2.5 concentrations across Malaysia allows a continuous monitoring of the pollution levels at remote areas with absence of measurement networks.

Ambient Fine Particulate Matter Air Pollution and Risk of Weight Gain and Obesity in United States Veterans: An Observational Cohort Study

BACKGROUND

Experimental evidence and studies of children and adolescents suggest that ambient fine particulate matter [particulate matter ≤2.5μm in aerodynamic diameter (PM2.5)] air pollution may be obesogenic, but the relationship between PM2.5 and the risk of body weight gain and obesity in adults is uncertain.

OBJECTIVES

Our goal was to characterize the association between PM2.5 and the risks of weight gain and obesity.

METHODS

We followed 3,902,440 U.S. Veterans from 2010 to 2018 (median 8.1 y, interquartile range: 7.3-8.4) and assigned time-updated PM2.5 exposures by linking geocoded residential street addresses with satellite-based estimates of surface-level PM2.5 mass (at ∼1-km2 resolution). Associations with PM2.5 were estimated using Cox proportional hazards models for incident obesity [body mass index (BMI)≥30 kg/m2] and a 10-lb increase in weight relative to baseline and linear mixed models for associations with intra-individual changes in BMI and weight.

RESULTS

A 10-μg/m3 higher average annual PM2.5 concentration was associated with risk of incident obesity [n=2,325,769; hazard ratio (HR)=1.08 (95% CI: 1.06, 1.11)] and the risk of a 10-lb (4.54 kg) increase in weight [HR=1.07 (95% CI: 1.06, 1.08)] and with higher intra-individual changes in BMI [0.140 kg/m2 per year (95% CI: 0.139, 0.142)] and weight [0.968 lb/y (95% CI: 0.955, 0.981)]. Nonlinear exposure-response models indicated associations at PM2.5 concentrations below the national standard of 12 μg/m3. As expected, a negative exposure control (ambient air sodium) was not associated with obesity or weight gain. Associations were consistent in direction and magnitude across sensitivity analyses that included alternative outcomes and exposures assigned at different spatial resolutions.

DISCUSSION

PM2.5 air pollution was associated with the risk of obesity and weight gain in a large predominantly male cohort of U.S. Veterans. Discussions about health effects of PM2.5 should include its association with obesity, and deliberations about the epidemiology of obesity should consider its association with PM2.5. Investigation in other cohorts will deepen our understanding of the relationship between PM2.5 and weight gain and obesity. https://doi.org/10.1289/EHP7944.

A component-specific exposure-mortality model for ambient PM2.5 in China: findings from nationwide epidemiology based on outputs from a chemical transport model

Long-term exposure to ambient fine particles (PM_2.5) has been evidenced to be a leading contributor to premature mortality in China and many other countries. Previous studies assess the health risk using an exposure-response function, such as an exposure-mortality model (EMM) based on total concentration of PM_2.5. However, the risk assessment method can be problematic as it ignores the unequal toxicity between the different chemical components of PM_2.5. To derive a components-specific EMM (CS-EMM), we conducted a whole-population-based epidemiology study in China, using the Chinese Population Census data in 2000 and 2010. Concentrations of ambient PM_2.5 and its components were assessed by satellite-based concentrations of PM_2.5 and composition fractions simulated by a chemical transport model. We used a difference-in-difference approach to associate county-level changes of census-based total mortality with changes of PM_2.5 and its components between 2010 and 2000. The chemical components of PM_2.5 simulated by the model included sulfate (SO₄^2-), nitrate (NO₃^-), ammonium (NH₄⁺), organic carbon (OC), and black carbon (BC). We further compared CS-EMM with EMM based on a single pollutant of PM_2.5 (PM_2.5-EMM) or black carbon (BC-EMM), by evaluating their performance in a risk assessment. Using census-based total mortality and cross validation we evaluated the performance of the mortality prediction of an EMM, and found that the CS-EMM outperformed PM_2.5-EMM or BC-EMM. For instance, CS-EMM, PM_2.5-EMM, and BC-EMM all overestimated the average number of county-level deaths by 117, 142, and 149, respectively; hence CS-EMM overestimated by the lowest amount. Moreover, CS-EMM had the advantage of interpreting the toxicity of PM_2.5 mixture in its entirety. From 2000 to 2010, CS-EMM attributed a 205 496 increase in PM_2.5-associated mortality across China to the joint contribution of the growth of total concentration and the reduction of PM_2.5 toxicity. Among the components, BC contributed 6.4% of PM_2.5 concentration growth, but corresponded to a 46.7% increment in PM_2.5-associated deaths. This study developed a framework to establish and validate an exposure-response function based on PM_2.5 components, and illustrated its advantages in terms of risk prediction and result interpretation in China. Our approach can be utilized to evaluate how chemical composition modified the health impact of PM_2.5, and should help policy-makers target the toxic sources of air pollution.

Self-rated stress, distress, mental health, and health as modifiers of the association between long-term exposure to ambient pollutants and mortality

BACKGROUND

Individual and neighbourhood-scale socioeconomic characteristics modify associations between exposure to air pollution and mortality. The role of stress, which may integrate effects of social and environmental exposures on health, is unknown. We examined whether an individual's perspective on their own well-being, as assessed using self-rated measures of stress and health, modifies the pollutant-mortality relationship.

METHODS

The Canadian Community Health Survey (CCHS)-mortality cohort includes respondents from surveys administered between 2001 and 2012 linked to vital statistics and postal codes from 1981 until 2016. Annual fine particulate matter (PM_2.5), nitrogen dioxide (NO₂), and ozone (O₃) exposure estimates were attached to a sample of cohort members aged 30-89 years (n = 398,300 respondents/3,848,400 person-years). We examined whether self-rated stress, distress, mental health, and general health modified associations between long-term exposure to each pollutant (three-year moving average with one-year lag) and non-accidental mortality using Cox survival models, adjusted for individual- (i.e. socioeconomic and behavioural) and neighbourhood-scale covariates.

RESULTS

In fully-adjusted models, the relationship between exposure to pollutants and mortality was stronger among those with poor self-rated mental health, including a significant difference for NO₂ (hazard ratio (HR) = 1.15, 95% CI 1.06-1.25 per IQR) compared to those with very good/excellent mental health (HR = 1.05, 95% CI 1.01-1.08; Cochran's Q = 4.01; p < 0.05). Poor self-rated health was similarly associated with higher pollutant-associated HRs, but only in unadjusted models. Stress and distress did not modify pollutant-mortality associations.

CONCLUSIONS

Poor self-rated mental and general health were associated with increased mortality attributed to exposure to ambient pollutants.

A causal exposure response function with local adjustment for confounding: Estimating health effects of exposure to low levels of ambient fine particulate matter

In the last two decades, ambient levels of air pollution have declined substantially. At the same time, the Clean Air Act mandates that the National Ambient Air Quality Standards (NAAQS) must be routinely assessed to protect populations based on the latest science. Therefore, researchers should continue to address the following question: is exposure to levels of air pollution below the NAAQS harmful to human health? Furthermore, the contentious nature surrounding environmental regulations urges us to cast this question within a causal inference framework. Several parametric and semi-parametric regression approaches have been used to estimate the exposure-response (ER) curve between long-term exposure to ambient air pollution concentrations and health outcomes. However, most of the existing approaches are not formulated within a formal framework for causal inference, adjust for the same set of potential confounders across all levels of exposure, and do not account for model uncertainty regarding covariate selection and the shape of the ER. In this paper, we introduce a Bayesian framework for the estimation of a causal ER curve called LERCA (Local Exposure Response Confounding Adjustment), which a) allows for different confounders and different strength of confounding at the different exposure levels; and b) propagates model uncertainty regarding confounders' selection and the shape of the ER. Importantly, LERCA provides a principled way of assessing the observed covariates' confounding importance at different exposure levels, providing researchers with important information regarding the set of variables to measure and adjust for in regression models. Using simulation studies, we show that state of the art approaches perform poorly in estimating the ER curve in the presence of local confounding. LERCA is used to evaluate the relationship between long-term exposure to ambient PM2.5, a key regulated pollutant, and cardiovascular hospitalizations for 5,362 zip codes in the continental U.S. and located near a pollution monitoring site, while adjusting for a potentially varying set of confounders across the exposure range. Our data set includes rich health, weather, demographic, and pollution information for the years of 2011-2013. The estimated exposure-response curve is increasing indicating that higher ambient concentrations lead to higher cardiovascular hospitalization rates, and ambient PM2.5 was estimated to lead to an increase in cardiovascular hospitalization rates when focusing at the low exposure range. Our results indicate that there is no threshold for the effect of PM2.5 on cardiovascular hospitalizations.

Understanding the Joint Impacts of Fine Particulate Matter Concentration and Composition on the Incidence and Mortality of Cardiovascular Disease: A Component-Adjusted Approach

Past health impact assessments of ambient fine particulate matter (particles with an aerodynamic diameter ≤2.5 μm; PM_2.5) have generally considered mass concentration only, despite PM_2.5 being a heterogeneous mixture. Given constant changes in the concentration and the composition of atmospheric aerosol, uncertainty exists as to whether the current focus on PM_2.5 mass or individual components may fully characterize the health burden of PM_2.5. We proposed a component-adjusted method that jointly estimates the health impacts of PM_2.5 and its major components while allowing for a potential nonlinear PM_2.5-outcome relationship. Using this method, we quantified the effects of PM_2.5 on the risks of developing acute myocardial infarction (AMI) and dying from cardiovascular causes in comparison to three traditional approaches in the entire adult population across Ontario, Canada. We observed that PM_2.5 was positively associated with AMI incidence and cardiovascular mortality with all four methods. Compared to the traditional approaches, however, the new component-adjusted approach demonstrated a significant improvement in explaining the health impacts of PM_2.5, especially in the presence of a nonlinear PM_2.5-outcome relationship. Using the new approach, we found that the effects of PM_2.5 on AMI incidence and cardiovascular mortality may be 10% to 27% higher than what would be estimated from the conventional approaches examining PM_2.5 alone.

Fine particulate air pollution and human mortality: 25+ years of cohort studies

Much of the key epidemiological evidence that long-term exposure to fine particulate matter air pollution (PM_2.5) contributes to increased risk of mortality comes from survival studies of cohorts of individuals. Although the first two of these studies, published in the mid-1990s, were highly controversial, much has changed in the last 25 + years. The objectives of this paper are to succinctly compile and summarize the findings of these cohort studies using meta-analytic tools and to address several of the key controversies. Independent reanalysis and substantial extended analysis of the original cohort studies have been conducted and many additional studies using a wide variety of cohorts, including cohorts constructed from public data and leveraging natural experiments have been published. Meta-analytic estimates of the mean of the distribution of effects from cohort studies that are currently available, provide substantial evidence of adverse air pollution associations with all-cause, cardiopulmonary, and lung cancer mortality.

Premature mortality related to United States cross-state air pollution

Outdoor air pollution adversely affects human health and is estimated to be responsible for five to ten per cent of the total annual premature mortality in the contiguous United States^1-3. Combustion emissions from a variety of sources, such as power generation or road traffic, make a large contribution to harmful air pollutants such as ozone and fine particulate matter (PM_2.5)⁴. Efforts to mitigate air pollution have focused mainly on the relationship between local emission sources and local air quality². Air quality can also be affected by distant emission sources, however, including emissions from neighbouring federal states^5,6. This cross-state exchange of pollution poses additional regulatory challenges. Here we quantify the exchange of air pollution among the contiguous United States, and assess its impact on premature mortality that is linked to increased human exposure to PM_2.5 and ozone from seven emission sectors for 2005 to 2018. On average, we find that 41 to 53 per cent of air-quality-related premature mortality resulting from a state's emissions occurs outside that state. We also find variations in the cross-state contributions of different emission sectors and chemical species to premature mortality, and changes in these variations over time. Emissions from electric power generation have the greatest cross-state impacts as a fraction of their total impacts, whereas commercial/residential emissions have the smallest. However, reductions in emissions from electric power generation since 2005 have meant that, by 2018, cross-state premature mortality associated with the commercial/residential sector was twice that associated with power generation. In terms of the chemical species emitted, nitrogen oxides and sulfur dioxide emissions caused the most cross-state premature deaths in 2005, but by 2018 primary PM_2.5 emissions led to cross-state premature deaths equal to three times those associated with sulfur dioxide emissions. These reported shifts in emission sectors and emission species that contribute to premature mortality may help to guide improvements to air quality in the contiguous United States.

Mortality-Air Pollution Associations in Low-Exposure Environments (MAPLE): Phase 1

INTRODUCTION

Fine particulate matter (particulate matter ≤2.5 μm in aerodynamic diameter, or PM2.5) is associated with mortality, but the lower range of relevant concentrations is unknown. Novel satellite-derived estimates of outdoor PM2.5 concentrations were applied to several large population-based cohorts, and the shape of the relationship with nonaccidental mortality was characterized, with emphasis on the low concentrations (<12 μg/m3) observed throughout Canada.

METHODS

Annual satellite-derived estimates of outdoor PM2.5 concentrations were developed at 1-km2 spatial resolution across Canada for 2000-2016 and backcasted to 1981 using remote sensing, chemical transport models, and ground monitoring data. Targeted ground-based measurements were conducted to measure the relationship between columnar aerosol optical depth (AOD) and ground-level PM2.5. Both existing and targeted ground-based measurements were analyzed to develop improved exposure data sets for subsequent epidemiological analyses.

Residential histories derived from annual tax records were used to estimate PM2.5 exposures for subjects whose ages ranged from 25 to 90 years. About 8.5 million were from three Canadian Census Health and Environment Cohort (CanCHEC) analytic files and another 540,900 were Canadian Community Health Survey (CCHS) participants. Mortality was linked through the year 2016. Hazard ratios (HR) were estimated with Cox Proportional Hazard models using a 3-year moving average exposure with a 1-year lag, with the year of follow-up as the time axis. All models were stratified by 5-year age groups, sex, and immigrant status. Covariates were based on directed acyclical graphs (DAG), and included contextual variables (airshed, community size, neighborhood dependence, neighborhood deprivation, ethnic concentration, neighborhood instability, and urban form). A second model was examined including the DAG-based covariates as well as all subject-level risk factors (income, education, marital status, indigenous identity, employment status, occupational class, and visible minority status) available in each cohort. Additional subject-level behavioral covariates (fruit and vegetable consumption, leisure exercise frequency, alcohol consumption, smoking, and body mass index [BMI]) were included in the CCHS analysis.

Sensitivity analyses evaluated adjustment for covariates and gaseous copollutants (nitrogen dioxide [NO2] and ozone [O3]), as well as exposure time windows and spatial scales. Estimates were evaluated across strata of age, sex, and immigrant status. The shape of the PM2.5-mortality association was examined by first fitting restricted cubic splines (RCS) with a large number of knots and then fitting the shape-constrained health impact function (SCHIF) to the RCS predictions and their standard errors (SE). This method provides graphical results indicating the RCS predictions, as a nonparametric means of characterizing the concentration-response relationship in detail and the resulting mean SCHIF and accompanying uncertainty as a parametric summary.

Sensitivity analyses were conducted in the CCHS cohort to evaluate the potential influence of unmeasured covariates on air pollution risk estimates. Specifically, survival models with all available risk factors were fit and compared with models that omitted covariates not available in the CanCHEC cohorts. In addition, the PM2.5 risk estimate in the CanCHEC cohort was indirectly adjusted for multiple individual-level risk factors by estimating the association between PM2.5 and these covariates within the CCHS.

RESULTS

Satellite-derived PM2.5 estimates were low and highly correlated with ground monitors. HR estimates (per 10-μg/m3 increase in PM2.5) were similar for the 1991 (1.041, 95% confidence interval [CI]: 1.016-1.066) and 1996 (1.041, 1.024-1.059) CanCHEC cohorts with a larger estimate observed for the 2001 cohort (1.084, 1.060-1.108). The pooled cohort HR estimate was 1.053 (1.041-1.065). In the CCHS an analogous model indicated a HR of 1.13 (95% CI: 1.06-1.21), which was reduced slightly with the addition of behavioral covariates (1.11, 1.04-1.18). In each of the CanCHEC cohorts, the RCS increased rapidly over lower concentrations, slightly declining between the 25th and 75th percentiles and then increasing beyond the 75th percentile. The steepness of the increase in the RCS over lower concentrations diminished as the cohort start date increased. The SCHIFs displayed a supralinear association in each of the three CanCHEC cohorts and in the CCHS cohort.

In sensitivity analyses conducted with the 2001 CanCHEC, longer moving averages (1, 3, and 8 years) and smaller spatial scales (1 km2 vs. 10 km2) of exposure assignment resulted in larger associations between PM2.5 and mortality. In both the CCHS and CanCHEC analyses, the relationship between nonaccidental mortality and PM2.5 was attenuated when O3 or a weighted measure of oxidant gases was included in models. In the CCHS analysis, but not in CanCHEC, PM2.5 HRs were also attenuated by the inclusion of NO2. Application of the indirect adjustment and comparisons within the CCHS analysis suggests that missing data on behavioral risk factors for mortality had little impact on the magnitude of PM2.5-mortality associations. While immigrants displayed improved overall survival compared with those born in Canada, their sensitivity to PM2.5 was similar to or larger than that for nonimmigrants, with differences between immigrants and nonimmigrants decreasing in the more recent cohorts.

CONCLUSIONS

In several large population-based cohorts exposed to low levels of air pollution, consistent associations were observed between PM2.5 and nonaccidental mortality for concentrations as low as 5 μg/m3. This relationship was supralinear with no apparent threshold or sublinear association.

Low concentrations of fine particle air pollution and mortality in the Canadian Community Health Survey cohort

BACKGROUND

Approximately 2.9 million deaths are attributed to ambient fine particle air pollution around the world each year (PM2.5). In general, cohort studies of mortality and outdoor PM2.5 concentrations have limited information on individuals exposed to low levels of PM2.5 as well as covariates such as smoking behaviours, alcohol consumption, and diet which may confound relationships with mortality. This study provides an updated and extended analysis of the Canadian Community Health Survey-Mortality cohort: a population-based cohort with detailed PM2.5 exposure data and information on a number of important individual-level behavioural risk factors. We also used this rich dataset to provide insight into the shape of the concentration-response curve for mortality at low levels of PM2.5.

METHODS

Respondents to the Canadian Community Health Survey from 2000 to 2012 were linked by postal code history from 1981 to 2016 to high resolution PM2.5 exposure estimates, and mortality incidence to 2016. Cox proportional hazard models were used to estimate the relationship between non-accidental mortality and ambient PM2.5 concentrations (measured as a three-year average with a one-year lag) adjusted for socio-economic, behavioural, and time-varying contextual covariates.

RESULTS

In total, 50,700 deaths from non-accidental causes occurred in the cohort over the follow-up period. Annual average ambient PM2.5 concentrations were low (i.e. 5.9 μg/m3, s.d. 2.0) and each 10 μg/m3 increase in exposure was associated with an increase in non-accidental mortality (HR = 1.11; 95% CI 1.04-1.18). Adjustment for behavioural covariates did not materially change this relationship. We estimated a supra-linear concentration-response curve extending to concentrations below 2 μg/m3 using a shape constrained health impact function. Mortality risks associated with exposure to PM2.5 were increased for males, those under age 65, and non-immigrants. Hazard ratios for PM2.5 and mortality were attenuated when gaseous pollutants were included in models.

CONCLUSIONS

Outdoor PM2.5 concentrations were associated with non-accidental mortality and adjusting for individual-level behavioural covariates did not materially change this relationship. The concentration-response curve was supra-linear with increased mortality risks extending to low outdoor PM2.5 concentrations.

Characteristics of cohort studies of long-term exposure to PM2.5: a systematic review

This study systematically reviewed all the cohort studies investigating the relationship between long-term exposure to PM_2.5 and any health outcome until February 2018. We searched ISI Web of Knowledge, Pubmed, and Scopus databases for peer-reviewed journal research articles published in English. We only extracted the results of the single-pollutant main analysis of each study, excluding the effect modifications and sensitivity analyses. Out of the initial 9523 articles, 203 articles were ultimately included for analysis. Based on the different characteristics of studies such as study design, outcome, exposure assessment method, and statistical model, we calculated the number and relative frequency of analyses with statistically significant and insignificant results. Most of the studies were prospective (84.8%), assessed both genders (66.5%), and focused on a specific age range (86.8%). Most of the articles (78.1%) had used modeling techniques for exposure assessment of cohorts' participants. Among the total of 317 health outcomes, the most investigated outcomes include mortality due to cardiovascular disease (6.19%), all causes (5.48%), lung cancer (4.00%), ischemic heart disease (3.50%), and non-accidental causes (3.50%). The percentage of analyses with statistically significant results were higher among studies that used prospective design, mortality as the outcome, fixed stations as exposure assessment method, hazard ratio as risk measure, and no covariate adjustment. We can somehow conclude that the choice of right characteristics for cohort studies can make a difference in their results.

Examining the Shape of the Association between Low Levels of Fine Particulate Matter and Mortality across Three Cycles of the Canadian Census Health and Environment Cohort

BACKGROUND

Ambient fine particulate air pollution with aerodynamic diameter ≤ 2.5   μ m (PM 2.5 ) is an important contributor to the global burden of disease. Information on the shape of the concentration-response relationship at low concentrations is critical for estimating this burden, setting air quality standards, and in benefits assessments.

OBJECTIVES

We examined the concentration-response relationship between PM 2.5 and nonaccidental mortality in three Canadian Census Health and Environment Cohorts (CanCHECs) based on the 1991, 1996, and 2001 census cycles linked to mobility and mortality data.

METHODS

Census respondents were linked with death records through 2016, resulting in 8.5 million adults, 150 million years of follow-up, and 1.5 million deaths. Using annual mailing address, we assigned time-varying contextual variables and 3-y moving-average ambient PM 2.5 at a 1 × 1   km spatial resolution from 1988 to 2015. We ran Cox proportional hazards models for PM 2.5 adjusted for eight subject-level indicators of socioeconomic status, seven contextual covariates, ozone, nitrogen dioxide, and combined oxidative potential. We used three statistical methods to examine the shape of the concentration-response relationship between PM 2.5 and nonaccidental mortality.

RESULTS

The mean 3-y annual average estimate of PM 2.5 exposure ranged from 6.7 to 8.0   μ g / m 3 over the three cohorts. We estimated a hazard ratio (HR) of 1.053 [95% confidence interval (CI): 1.041, 1.065] per 10 - μ g / m 3 change in PM 2.5 after pooling the three cohort-specific hazard ratios, with some variation between cohorts (1.041 for the 1991 and 1996 cohorts and 1.084 for the 2001 cohort). We observed a supralinear association in all three cohorts. The lower bound of the 95% CIs exceeded unity for all concentrations in the 1991 cohort, for concentrations above 2   μ g / m 3 in the 1996 cohort, and above 5   μ g / m 3 in the 2001 cohort.

DISCUSSION

In a very large population-based cohort with up to 25 y of follow-up, PM 2.5 was associated with nonaccidental mortality at concentrations as low as 5   μ g / m 3 . https://doi.org/10.1289/EHP5204.

Particulate matter in the cultivation area may contaminate leafy vegetables with heavy metals above safe levels in Korea

Among air pollutants, particulate matter (PM) has been identified as a major cause of environmental pollutants due to the advancement of industrial development and the generation of smaller particles. Particulate matter, in particular, is defined only by the size of particles and thus is not enough to study its composition yet. However, edible crops grown in contaminated atmospheres can be contaminated with heavy metals contained in particulate matter in the atmosphere, which can seriously damage food safety. In this study, we investigated the influence of the accumulation of particulate matter on leafy vegetables cultivated at areas with different levels of PM in atmosphere. Four districts of Gyeongsangnam-do were chosen to conduct this experiment: outdoor spaces of three respectively located in industrial, near-highway, and rural areas were considered, and research plant growth chambers at Gyeongsang National University were used as the control. After 3 weeks of cultivation in those conditions, the results showed that Pb in milligrams per kilogram of fresh weight (FW) was 0.383 in Chrysanthemum coronarium and 0.427 in Spinacia oleracea that were grown near the highway, which exceeded the 0.3 mg kg^-1 FW standard set by the Republic of Korea, EU, and CODEX. However, when those vegetables were sufficiently washed with tap water, it was confirmed that the heavy metal content fell into the safety standard range.

Low Levels of Air Pollution and Health: Effect Estimates, Methodological Challenges, and Future Directions

PURPOSE OF REVIEW

Fine particle (PM2.5) levels have been decreasing in the USA over the past decades. Our goal was to assess the current literature to characterize the association between PM2.5 and adverse health at low exposure levels.

RECENT FINDINGS

We reviewed 26 papers that examined the association between short- and long-term exposure to PM2.5 and cardio-respiratory morbidity and mortality. There is evidence suggesting that these associations are stronger at lower levels. However, there are certain methodological and interpretational limitations specific to studies of low PM2.5 levels, and further methodological development is warranted. There is strong agreement across studies that air pollution effects on adverse health are still observable at low concentrations, even well below current US standards. These findings suggest that US standards need to be reevaluated, given that further improving air quality has the potential of benefiting public health.

Complex relationships between greenness, air pollution, and mortality in a population-based Canadian cohort

BACKGROUND

Epidemiological studies have consistently demonstrated that exposure to fine particulate matter (PM_2.5) is associated with increased risks of mortality. To a lesser extent, a series of studies suggest that living in greener areas is associated with reduced risks of mortality. Only a handful of studies have examined the interplay between PM_2.5, greenness, and mortality.

METHODS

We investigated the role of residential greenness in modifying associations between long-term exposures to PM_2.5 and non-accidental and cardiovascular mortality in a national cohort of non-immigrant Canadian adults (i.e., the 2001 Canadian Census Health and Environment Cohort). Specifically, we examined associations between satellite-derived estimates of PM_2.5 exposure and mortality across quintiles of greenness measured within 500 m of individual's place of residence during 11 years of follow-up. We adjusted our survival models for many personal and contextual measures of socioeconomic position, and residential mobility data allowed us to characterize annual changes in exposures.

RESULTS

Our cohort included approximately 2.4 million individuals at baseline, 194,270 of whom died from non-accidental causes during follow-up. Adjustment for greenness attenuated the association between PM_2.5 and mortality (e.g., hazard ratios (HRs) and 95% confidence intervals (CIs) per interquartile range increase in PM_2.5 in models for non-accidental mortality decreased from 1.065 (95% CI: 1.056-1.075) to 1.041 (95% CI: 1.031-1.050)). The strength of observed associations between PM_2.5 and mortality decreased as greenness increased. This pattern persisted in models restricted to urban residents, in models that considered the combined oxidant capacity of ozone and nitrogen dioxide, and within neighbourhoods characterised by high or low deprivation. We found no increased risk of mortality associated with PM_2.5 among those living in the greenest areas. For example, the HR for cardiovascular mortality among individuals in the least green areas was 1.17 (95% CI: 1.12-1.23) compared to 1.01 (95% CI: 0.97-1.06) among those in the greenest areas.

CONCLUSIONS

Studies that do not account for greenness may overstate the air pollution impacts on mortality. Residents in deprived neighbourhoods with high greenness benefitted by having more attenuated associations between PM_2.5 and mortality than those living in deprived areas with less greenness. The findings from this study extend our understanding of how living in greener areas may lead to improved health outcomes.

Short-term and long-term exposures to fine particulate matter constituents and health: A systematic review and meta-analysis

BACKGROUND

Fine particulate matter (Particulate matter with diameter ≤ 2.5 μm) is associated with multiple health outcomes, with varying effects across seasons and locations. It remains largely unknown that which components of PM_2.5 are most harmful to human health.

METHODS

We systematically searched all the relevent studies published before August 1, 2018, on the associations of fine particulate matter constituents with mortality and morbidity, using Web of Science, MEDLINE, PubMed and EMBASE. Studies were included if they explored the associations between short term or long term exposure of fine particulate matter constituents and natural, cardiovascular or respiratory health endpoints. The criteria for the risk of bias was adapted from OHAT and New Castle Ottawa. We applied a random-effects model to derive the risk estimates for each constituent. We performed main analyses restricted to studies which adjusted the PM_2.5 mass in their models.

RESULTS

Significant associations were observed between several PM_2.5 constituents and different health endpoints. Among them, black carbon and organic carbon were most robustly and consistently associated with all natural, cardiovascular mortality and morbidity. Other potential toxic constituents including nitrate, sulfate, Zinc, silicon, iron, nickel, vanadium, and potassium were associated with adverse cardiovascular health, while nitrate, sulfate and vanadium were relevant for adverse respiratory health outcomes.

CONCLUSIONS

Our analysis suggests that black carbon and organic carbon are important detrimental components of PM_2.5, while other constituents are probably hazardous to human health. However, more studies are needed to further confirm our results.

Regional Estimates of Chemical Composition of Fine Particulate Matter Using a Combined Geoscience-Statistical Method with Information from Satellites, Models, and Monitors

An accurate fine-resolution surface of the chemical composition of fine particulate matter (PM_2.5) would offer valuable information for epidemiological studies and health impact assessments. We develop geoscience-derived estimates of PM_2.5 composition from a chemical transport model (GEOS-Chem) and satellite observations of aerosol optical depth, and statistically fuse these estimates with ground-based observations using a geographically weighted regression over North America to produce a spatially complete representation of sulfate, nitrate, ammonium, black carbon, organic matter, mineral dust, and sea-salt over 2000-2016. Significant long-term agreement is found with cross-validation sites over North America (R² = 0.57-0.96), with the strongest agreement for sulfate (R² = 0.96), nitrate (R² = 0.90), and ammonium (R² = 0.86). We find that North American decreases in population-weighted fine particulate matter (PM_2.5) concentrations since 2000 have been most heavily influenced by regional changes in sulfate and organic matter. Regionally, the relative importance of several chemical components are found to change with PM_2.5 concentration, such as higher PM_2.5 concentrations having a larger proportion of nitrate and a smaller proportion of sulfate. This data set offers information for research into the health effects of PM_2.5 chemical components.

Diabetes Status and Susceptibility to the Effects of PM2.5 Exposure on Cardiovascular Mortality in a National Canadian Cohort

BACKGROUND

Diabetes is infrequently coded as the primary cause of death but may contribute to cardiovascular disease (CVD) mortality in response to fine particulate matter (PM2.5) exposure. We analyzed all contributing causes of death to examine susceptibility of diabetics to CVD mortality from long-term exposure.

METHODS

We linked a subset of the 2001 Canadian Census Health and Environment Cohort (CanCHEC) with 10 years of follow-up to all causes of death listed on death certificates. We used survival models to examine the association between CVD deaths (n = 123,500) and exposure to PM2.5 among deaths that co-occurred with diabetes (n = 20,600) on the death certificate. More detailed information on behavioral covariates and diabetes status at baseline available in the Canadian Community Health Survey (CCHS)-mortality cohort (n = 12,400 CVD deaths, with 2,800 diabetes deaths) complemented the CanCHEC analysis.

RESULTS

Among CanCHEC subjects, comention of diabetes on the death certificate increased the magnitude of association between CVD mortality and PM2.5 (HR = 1.51 [1.39-1.65] per 10 μg/m) versus all CVD deaths (HR = 1.25 [1.21-1.29]) or CVD deaths without diabetes (HR = 1.20 [1.16-1.25]). Among CCHS subjects, diabetics who used insulin or medication (included as proxies for severity) had higher HR estimates for CVD deaths from PM2.5 (HR = 1.51 [1.08-2.12]) relative to the CVD death estimate for all respondents (HR = 1.31 [1.16-1.47]).

CONCLUSIONS

Mention of diabetes on the death certificate resulted in higher magnitude associations between PM2.5 and CVD mortality, specifically among those who manage their diabetes with insulin or medication. Analyses restricted to the primary cause of death likely underestimate the role of diabetes in air pollution-related mortality. See video abstract at, http://links.lww.com/EDE/B408.

Evaluation of machine learning techniques with multiple remote sensing datasets in estimating monthly concentrations of ground-level PM2.5

Fine particulate matter (PM_2.5) has been recognized as a key air pollutant that can influence population health risk, especially during extreme cases such as wildfires. Previous studies have applied geospatial techniques such as land use regression to map the ground-level PM_2.5, while some recent studies have found that Aerosol Optical Depth (AOD) derived from satellite images and machine learning techniques may be two elements that can improve spatiotemporal prediction. However, there has been a lack of studies evaluating use of different machine learning techniques with AOD datasets for mapping PM_2.5, especially in areas with high spatiotemporal variability of PM_2.5. In this study, we compared the performance of eight predictive algorithms with the use of multiple remote sensing datasets, including satellite-derived AOD data, for the prediction of ground-level PM2.5 concentration. Based on the results, Cubist, random forest and eXtreme Gradient Boosting were the algorithms with better performance, while Cubist was the best (CV-RMSE = 2.64 μg/m3, CV-R² = 0.48). Variable importance analysis indicated that the predictors with the highest contributions in modelling were monthly AOD and elevation. In conclusion, appropriate selection of machine learning algorithms can improve ground-level PM2.5 estimation, especially for areas with nonlinear relationships between PM2.5 and predictors caused by complex terrain. Satellite-derived data such as AOD and land surface temperature (LST) can also be substitutes for traditional datasets retrieved from weather stations, especially for areas with sparse and uneven distribution of stations.

The concentration-response between long-term PM2.5 exposure and mortality; A meta-regression approach

BACKGROUND

Long-term exposure to ambient fine particulate matter (≤ 2.5 μg/m³ in aerodynamic diameter; PM_2.5) is significantly associated with increased risk of premature mortality. Our goal was to provide an updated meta-analysis of all-cause and cause-specific mortality associated with exposure to PM_2.5 and to better estimate the risk of death as a function of air pollution levels.

METHODS

We systematically searched all published cohort studies examining the association between long term exposure to PM_2.5 and mortality. We applied multivariate linear random effects meta-analysis with random effects for cohort, and study within cohort. Meta-regression techniques were used to test whether study population or analytic characteristics modify the PM_{2.5 -}mortality association and to estimate the shape of the concentration-response curve.

RESULTS

A total of 53 studies that provided 135 estimates of the quantitative association between the risk of mortality and exposure to PM_2.5 were included in the meta-analysis. There were 39 studies from North America, 8 from Europe, and 6 from Asia. Since 2015, 17 studies of long-term air pollution exposure have been published, covering, wider geographic areas with a wider range of mean exposures (e.g. <12 or > 20 μg/m³). A penalized spline showed the slope decreased at higher concentrations but appeared to level off. We found that the inverse transform of average PM_2.5 well approximated that spline and provided a parametric estimate that fit better than a linear or logarithmic term for average PM_2.5. In addition, we found that studies using space time exposure models or fixed monitors at Zip-code scale (as compared to land use regression method), or additionally controlling for area level socio-economic status, or with mean exposure less than 10 μg/m³ were associated with higher mortality effect estimates.

CONCLUSIONS

This meta-analysis provides strong evidence for the adverse effect of PM_2.5 on mortality, that studies with poorer exposure have lower effect size estimates, that more control for SES increases effect size estimates, and that significant effects are seen below 10 µg/m³. The concentration -response function produced here can be further applied in the global health risk assessment of air particulate matter.

Robust relationship between air quality and infant mortality in Africa

Poor air quality is thought to be an important mortality risk factor globally^1-3, but there is little direct evidence from the developing world on how mortality risk varies with changing exposure to ambient particulate matter. Current global estimates apply exposure-response relationships that have been derived mostly from wealthy, mid-latitude countries to spatial population data⁴, and these estimates remain unvalidated across large portions of the globe. Here we combine household survey-based information on the location and timing of nearly 1 million births across sub-Saharan Africa with satellite-based estimates⁵ of exposure to ambient respirable particulate matter with an aerodynamic diameter less than 2.5 μm (PM_2.5) to estimate the impact of air quality on mortality rates among infants in Africa. We find that a 10 μg m^-3 increase in PM_2.5 concentration is associated with a 9% (95% confidence interval, 4-14%) rise in infant mortality across the dataset. This effect has not declined over the last 15 years and does not diminish with higher levels of household wealth. Our estimates suggest that PM_2.5 concentrations above minimum exposure levels were responsible for 22% (95% confidence interval, 9-35%) of infant deaths in our 30 study countries and led to 449,000 (95% confidence interval, 194,000-709,000) additional deaths of infants in 2015, an estimate that is more than three times higher than existing estimates that attribute death of infants to poor air quality for these countries^2,6. Upward revision of disease-burden estimates in the studied countries in Africa alone would result in a doubling of current estimates of global deaths of infants that are associated with air pollution, and modest reductions in African PM_2.5 exposures are predicted to have health benefits to infants that are larger than most known health interventions.

Urban particulate matter triggers lung inflammation via the ROS-MAPK-NF-κB signaling pathway

Background

Particulate matter (PM) is a high risk factor for various respiratory diseases and triggers an inflammatory response in lung tissues. However, the molecular mechanism of the PM-induced inflammatory response is incompletely understood.

Methods

Human bronchial epithelial cells (HBECs) were treated with the urban PM 1649b for assessment of the inflammatory response. The intracellular level of reactive oxygen species (ROS) was measured by flow cytometry. PM-activated signaling pathways were addressed with specific inhibitors. In vivo, the C57 mice model of PM-induced acute lung inflammation was established with intratracheal instillation of PM for 2 consecutive days. The oxidant stress in lung tissues was assessed with dihydroethidium (DHE) staining, and malondialdehyde (MDA) activity and hydrogen peroxide (H₂O₂) assays. The histopathologic changes in lung tissues and number of inflammatory cells in bronchoalveolar lavage fluid (BALF) were examined. Expression of pro-inflammatory cytokines in BALF was measured by ELISA.

Results

PM increased the expression of interleukin (IL)-1β, IL-6, IL-8, matrix metalloproteinase (MMP)-9 and cyclooxygenase (COX)-2 in a dose-dependent manner. ROS generation and activation of MAPK (ERK, JNK, p38 MAPK) and NF-κB pathways were detected in PM-exposed HBECs. Pretreatment with N-acetylcysteine (NAC) led to the inflammatory response, ROS level and activation of the MAPK and NF-κB pathways to be attenuated. Blockade of ERK, JNK or p38 MAPK pathway with specific inhibitor prevented the release of pro-inflammatory cytokines and activation of the NF-κB pathway. Inhibition of the NF-κB pathway reduced the expression of pro-inflammatory cytokines. In vivo, PM exposure increased oxidant stress in lung tissues, infiltration of inflammatory cells around PM in lung tissues, the number of total cells and inflammatory cells in BALF, and the concentrations of IL-1β, IL-6, IL-8 and MMP-9 in BALF, all of which were reversed partially upon NAC treatment.

Conclusions

PM exposure enhanced the airway inflammatory response significantly through ROS-mediated activation of MAPK (ERK, JNK, p38 MAPK) and downstream NF-κB signaling pathways. Oxidative stress appeared to be the key regulator for PM-induced lung inflammation. These results suggested the molecular mechanism of lung inflammation caused by PM.

A spatio-temporal prediction model based on support vector machine regression: Ambient Black Carbon in three New England States

Fine ambient particulate matter has been widely associated with multiple health effects. Mitigation hinges on understanding which sources are contributing to its toxicity. Black Carbon (BC), an indicator of particles generated from traffic sources, has been associated with a number of health effects however due to its high spatial variability, its concentration is difficult to estimate. We previously fit a model estimating BC concentrations in the greater Boston area; however this model was built using limited monitoring data and could not capture the complex spatio-temporal patterns of ambient BC. In order to improve our predictive ability, we obtained more data for a total of 24,301 measurements from 368 monitors over a 12 year period in Massachusetts, Rhode Island and New Hampshire. We also used Nu-Support Vector Regression (nu-SVR) - a machine learning technique which incorporates nonlinear terms and higher order interactions, with appropriate regularization of parameter estimates. We then used a generalized additive model to refit the residuals from the nu-SVR and added the residual predictions to our earlier estimates. Both spatial and temporal predictors were included in the model which allowed us to capture the change in spatial patterns of BC over time. The 10 fold cross validated (CV) R² of the model was good in both cold (10-fold CV R² = 0.87) and warm seasons (CV R² = 0.79). We have successfully built a model that can be used to estimate short and long-term exposures to BC and will be useful for studies looking at various health outcomes in MA, RI and Southern NH.

Associations between fine particulate matter and mortality in the 2001 Canadian Census Health and Environment Cohort

BACKGROUND

Large cohort studies have been used to characterise the association between long-term exposure to fine particulate matter (PM_2.5) air pollution with non-accidental, and cause-specific mortality. However, there has been no consensus as to the shape of the association between concentration and response.

METHODS

To examine the shape of this association, we developed a new cohort based on respondents to the 2001 Canadian census long-form. We applied new annual PM_2.5 concentration estimates based on remote sensing and ground measurements for Canada at a 1km spatial scale from 1998 to 2011. We followed 2.4 million respondents who were non-immigrants aged 25-90 years and did not reside in an institution over a 10 year period for mortality. Exposures were assigned as a 3-year mean prior to the follow-up year. Income tax files were used to account for residential mobility among respondents using postal codes, with probabilistic imputation used for missing postal codes in the tax data. We used Cox survival models to determine hazard ratios (HRs) for cause-specific mortality. We also estimated Shape Constrained Health Impact Functions (a concentration-response function) for selected causes of death.

RESULTS

In models stratified by age, sex, airshed, and population centre size, and adjusted for individual and neighbourhood socioeconomic variables, HR estimates for non-accidental mortality were HR = 1.18 (95% CI: 1.15-1.21) per 10μg/m³ increase in concentration. We observed higher HRs for cardiovascular disease (HR=1.25; 95% CI: 1.19-1.31), cardio-metabolic disease (HR = 1.27; 95% CI: 1.21-1.33), ischemic heart disease (HR = 1.36; 95% CI: 1.28-1.44) and chronic obstructive pulmonary disease (COPD) mortality (HR = 1.24; 95% CI: 1.11-1.39) compared to HR for all non-accidental causes of death. For non-accidental, cardio-metabolic, ischemic heart disease, respiratory and COPD mortality, the shape of the concentration-response curve was supra-linear, with larger differences in relative risk for lower concentrations. For both pneumonia and lung cancer, there was some suggestion that the curves were sub-linear.

CONCLUSIONS

Associations between ambient concentrations of fine particulate matter and several causes of death were non-linear for each cause of death examined.