Historical Prediction Modeling Approach for Estimating Long-Term Concentrations of PM2.5 in Cohort Studies before the 1999 Implementation of Widespread Monitoring

An assessment of the mediating role of hypertension in the effect of long-term air pollution exposure on dementia

Background

Growing evidence links air pollution exposure to the risk of dementia. We hypothesized that hypertension may partially mediate this effect.

Methods

We previously documented an association between air pollution and dementia in the Ginkgo Evaluation of Memory Study, a randomized, placebo-controlled trial of 3069 adults ≥75 years across four US sites who were evaluated for dementia every 6 months from 2000-2008. We utilized a two-stage regression approach for causal mediation analysis to decompose the total effect of air pollution on dementia into its natural direct and indirect effect through prevalent hypertension. Exposure to air pollution in the 10 or 20 years before enrollment was assigned using estimates from fine-scale spatial-temporal models for PM2.5, PM10, and NO2. We used Poisson regression models for hypertension and Cox proportional hazard models for time-to-incident all-cause dementia, adjusting for a priori confounders.

Results

Participants were free of mild cognitive impairment at baseline (n = 2564 included in analyses); 69% had prevalent hypertension at baseline. During follow-up, 12% developed all-cause dementia (Alzheimer's disease [AD] = 212; vascular dementia with or without AD [VaD/AD mixed] = 97). We did not find an adverse effect of any air pollutant on hypertension. Hypertension was associated with VaD/AD mixed (HR, 1.92 [95% CI = 1.14, 3.24]) but not AD. We did not observe mediation through hypertension for the effect of any pollutant on dementia outcomes.

Conclusions

The lack of mediated effect may be due to other mechanistic pathways and the minimal effect of air pollution on hypertension in this cohort of older adults.

A comparison of PM2.5 exposure estimates from different estimation methods and their associations with cognitive testing and brain MRI outcomes

BACKGROUND

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

OBJECTIVES

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

METHODS

We used Visit 5 (2011-2013) cognitive testing and brain MRI data from the Atherosclerosis Risk in Communities (ARIC) Study. We derived address-linked average 2000-2007 PM2.5 exposure concentrations in areas immediately surrounding the four ARIC recruitment sites (Forsyth County, NC; Jackson, MS; suburbs of Minneapolis, MN; Washington County, MD) using 11 estimation methods. We assessed agreement between method-specific PM2.5 concentrations using descriptive statistics and plots, overall and by site. We used adjusted linear regression to estimate associations of method-specific PM2.5 exposure estimates with cognitive scores (n = 4678) and MRI outcomes (n = 1518) stratified by study site and combined site-specific estimates using meta-analyses to derive overall estimates. We explored the potential impact of unmeasured confounding by spatially patterned factors.

RESULTS

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

DISCUSSION

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

Ambient particulate matter air pollution exposure and ovarian cancer incidence in the USA: An ecological study

OBJECTIVE

To investigate associations between air particulate matter of ≤2.5 μm in diameter (PM2.5 ) and ovarian cancer.

DESIGN

County-level ecological study.

SETTING

Surveillance, epidemiology, and end results from a collection of state-level cancer registries across 744 counties. Data from the Environmental Protection Agency's network for PM2.5 monitoring was used to calculate trailing 5- and 10-year PM2.5 county-level values. County-level data on demographic characteristics were obtained from the American Community Survey.

POPULATION

A total of 98 751 patients with histologically confirmed ovarian cancer as a primary malignancy from 2000 to 2016.

METHODS

Generalised linear regression models were developed to estimate the association between PM2.5 and PM10 levels, over 5- and 10-year periods of exposure, and ovarian cancer risk, after accounting for county-level covariates.

MAIN OUTCOME MEASURES

Risk ratios for associations between ovarian cancer (both overall and specifically epithelial ovarian cancer) and PM2.5 levels.

RESULTS

For the 744 counties included, the average PM2.5 level from 1990 through 2018 was 11.75 μg/m3 (SD = 3.7) and the average PM10 level was 22.7 μg/m3 (SD = 5.7). After adjusting for county-level covariates, the overall annualised ovarian cancer incidence was significantly associated with increases in 5-year PM2.5 (RR = 1.11 per 10 units (μg/m3 ) increase, 95% CI 1.06-1.16). Similarly, when the analysis was limited to epithelial cell tumours and adjusted for county-level covariates there was a significant association with trailing 5-year PM2.5 exposure models (RR = 1.12 per 10 units increase, 95% CI 1.08-1.17). Likewise, 10-year PM2.5 exposure was associated with ovarian cancer overall and with epithelial ovarian cancer.

CONCLUSIONS

Higher county-level ambient PM2.5 levels are associated with 5- and 10-year incidences of ovarian cancer, as measurable in an ecological study.

Representativeness of the US EPA PM monitoring site locations to the US population: implications for air pollution prediction modeling

Air pollution prediction modeling establishes relationships between measurements and geographical and meteorological characteristics to infer concentrations at locations without measurements. Since air pollution monitors are limited in number, predictions may be generated for locations different than those used to train the model. The epidemiologic impacts of this potential mismatch hinge on whether the population resides in areas well-represented by monitoring sites. Here we quantify the fraction of the population with geographical characteristics not reflected by the 2000, 2010, and 2020 EPA PM2.5 and PM10 regulatory sites. We evaluated this measure nationwide, regionally, and by race. Nationally, the networks were very representative of the population experience; however, there was less overlap regionally and in regions stratified by race. This suggests that sub-national exposure modeling should carefully consider the representativeness of monitors for their populations. It also highlights that exposure models often borrow information from distal places to predict full population exposure.

Outdoor Ultrafine Particulate Matter and Risk of Lung Cancer in Southern California

Rationale: Particulate matter ⩽2.5 μm in aerodynamic diameter (PM2.5) is an established cause of lung cancer, but the association with ultrafine particulate matter (UFP; aerodynamic diameter < 0.1 μm) is unclear. Objectives: To investigate the association between UFP and lung cancer overall and by histologic subtype. Methods: The Los Angeles Ultrafines Study includes 45,012 participants aged ⩾50 years in southern California at enrollment (1995-1996) followed through 2017 for incident lung cancer (n = 1,770). We estimated historical residential ambient UFP number concentrations via land use regression and back extrapolation using PM2.5. In Cox proportional hazards models adjusted for smoking and other confounders, we estimated associations between 10-year lagged UFP (per 10,000 particles/cm3 and quartiles) and lung cancer overall and by major histologic subtype (adenocarcinoma, squamous cell carcinoma, and small cell carcinoma). We also evaluated relationships by smoking status, birth cohort, and historical duration at the residence. Measurements and Main Results: UFP was modestly associated with lung cancer risk overall (hazard ratio [HR], 1.03 [95% confidence interval (CI), 0.99-1.08]). For adenocarcinoma, we observed a positive trend among men; risk was increased in the highest exposure quartile versus the lowest (HR, 1.39 [95% CI, 1.05-1.85]; P for trend = 0.01) and was also increased in continuous models (HR per 10,000 particles/cm3, 1.09 [95% CI, 1.00-1.18]), but no increased risk was apparent among women (P for interaction = 0.03). Adenocarcinoma risk was elevated among men born between 1925 and 1930 (HR, 1.13 [95% CI, 1.02-1.26] per 10,000) but not for other birth cohorts, and was suggestive for men with ⩾10 years of residential duration (HR, 1.11 [95% CI, 0.98-1.26]). We found no consistent associations for women or other histologic subtypes. Conclusions: UFP exposure was modestly associated with lung cancer overall, with stronger associations observed for adenocarcinoma of the lung.

Ambient fine particulate matter and breast cancer incidence in a large prospective US cohort

BACKGROUND

Fine particulate matter (PM2.5) has been inconsistently associated with breast cancer incidence, however, few studies have considered historic exposure when levels were higher.

METHODS

Outdoor residential PM2.5 concentrations were estimated using a nationwide spatiotemporal model for women in the National Institutes of Health-AARP Diet and Health Study, a prospective cohort located in 6 states (California, Florida, Louisiana, New Jersey, North Carolina, and Pennsylvania) and 2 metropolitan areas (Atlanta, GA, and Detroit, MI) and enrolled in 1995-1996 (n = 196 905). Annual average PM2.5 concentrations were estimated for a 5-year historical period 10 years prior to enrollment (1980-1984). We used Cox regression to estimate adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between a 10 µg/m3 increase in PM2.5 and breast cancer incidence overall and by estrogen receptor status and catchment area.

RESULTS

With follow-up of participants through 2017, a total of 15 870 breast cancer cases were identified. A 10 ug/m3 increase in PM2.5 was statistically significantly associated with overall breast cancer incidence (HR = 1.08, 95% CI = 1.02 to 1.13). The association was evident for estrogen receptor-positive (HR = 1.10, 95% CI = 1.04 to 1.17) but not estrogen receptor-negative tumors (HR = 0.97, 95% CI = 0.84 to 1.13; Pheterogeneity = .3). Overall breast cancer hazard ratios were more than 1 across the catchment areas, ranging from a hazard ratio of 1.26 (95% CI = 0.96 to 1.64) for North Carolina to a hazard ratio of 1.04 (95% CI = 0.68 to 1.57) for Louisiana (Pheterogeneity = .9).

CONCLUSIONS

In this large US cohort with historical air pollutant exposure estimates, PM2.5 was associated with risk of estrogen receptor-positive breast cancer. State-specific estimates were imprecise but suggest that future work should consider region-specific associations and the potential contribution of PM2.5 chemical constituency in modifying the observed association.

Traffic-related air pollution and dementia incidence in the Adult Changes in Thought Study

BACKGROUND

While epidemiologic evidence links higher levels of exposure to fine particulate matter (PM2.5) to decreased cognitive function, fewer studies have investigated links with traffic-related air pollution (TRAP), and none have examined ultrafine particles (UFP, ≤100 nm) and late-life dementia incidence.

OBJECTIVE

To evaluate associations between TRAP exposures (UFP, black carbon [BC], and nitrogen dioxide [NO2]) and late-life dementia incidence.

METHODS

We ascertained dementia incidence in the Seattle-based Adult Changes in Thought (ACT) prospective cohort study (beginning in 1994) and assessed ten-year average TRAP exposures for each participant based on prediction models derived from an extensive mobile monitoring campaign. We applied Cox proportional hazards models to investigate TRAP exposure and dementia incidence using age as the time axis and further adjusting for sex, self-reported race, calendar year, education, socioeconomic status, PM2.5, and APOE genotype. We ran sensitivity analyses where we did not adjust for PM2.5 and other sensitivity and secondary analyses where we adjusted for multiple pollutants, applied alternative exposure models (including total and size-specific UFP), modified the adjustment covariates, used calendar year as the time axis, assessed different exposure periods, dementia subtypes, and others.

RESULTS

We identified 1,041 incident all-cause dementia cases in 4,283 participants over 37,102 person-years of follow-up. We did not find evidence of a greater hazard of late-life dementia incidence with elevated levels of long-term TRAP exposures. The estimated hazard ratio of all-cause dementia was 0.98 (95 % CI: 0.92-1.05) for every 2000 pt/cm3 increment in UFP, 0.95 (0.89-1.01) for every 100 ng/m3 increment in BC, and 0.96 (0.91-1.02) for every 2 ppb increment in NO2. These findings were consistent across sensitivity and secondary analyses.

DISCUSSION

We did not find evidence of a greater hazard of late-life dementia risk with elevated long-term TRAP exposures in this population-based prospective cohort study.

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

BACKGROUND

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Integrating Augmented In Situ Measurements and a Spatiotemporal Machine Learning Model To Back Extrapolate Historical Particulate Matter Pollution over the United Kingdom: 1980-2019

Historical PM2.5 data are essential for assessing the health effects of air pollution exposure across the life course or early life. However, a lack of high-quality data sources, such as satellite-based aerosol optical depth before 2000, has resulted in a gap in spatiotemporally resolved PM2.5 data for historical periods. Taking the United Kingdom as an example, we leveraged the light gradient boosting model to capture the spatiotemporal association between PM2.5 concentrations and multi-source geospatial predictors. Augmented PM2.5 from PM10 measurements expanded the spatiotemporal representativeness of the ground measurements. Observations before and after 2009 were used to train and test the models, respectively. Our model showed fair prediction accuracy from 2010 to 2019 [the ranges of coefficients of determination (R2) for the grid-based cross-validation are 0.71-0.85] and commendable back extrapolation performance from 1998 to 2009 (the ranges of R2 for the independent external testing are 0.32-0.65) at the daily level. The pollution episodes in the 1980s and pollution levels in the 1990s were also reproduced by our model. The 4-decade PM2.5 estimates demonstrated that most regions in England witnessed significant downward trends in PM2.5 pollution. The methods developed in this study are generalizable to other data-rich regions for historical air pollution exposure assessment.

Comparison of Particulate Air Pollution From Different Emission Sources and Incident Dementia in the US

Importance

Emerging evidence indicates that exposure to fine particulate matter (PM2.5) air pollution may increase dementia risk in older adults. Although this evidence suggests opportunities for intervention, little is known about the relative importance of PM2.5 from different emission sources.

Objective

To examine associations of long-term exposure of total and source-specific PM2.5 with incident dementia in older adults.

Design, Setting, and Participants

The Environmental Predictors of Cognitive Health and Aging study used biennial survey data from January 1, 1998, to December 31, 2016, for participants in the Health and Retirement Study, which is a nationally representative, population-based cohort study in the US. The present cohort study included all participants older than 50 years who were without dementia at baseline and had available exposure, outcome, and demographic data between 1998 and 2016 (N = 27 857). Analyses were performed from January 31 to May 1, 2022.

Exposures

The 10-year mean total PM2.5 and PM2.5 from 9 emission sources at participant residences for each month during follow-up using spatiotemporal and chemical transport models.

Main Outcomes and Measures

The main outcome was incident dementia as classified by a validated algorithm incorporating respondent-based cognitive testing and proxy respondent reports. Adjusted hazard ratios (HRs) were estimated for incident dementia per IQR of residential PM2.5 concentrations using time-varying, weighted Cox proportional hazards regression models with adjustment for the individual- and area-level risk factors.

Results

Among 27 857 participants (mean [SD] age, 61 [10] years; 15 747 [56.5%] female), 4105 (15%) developed dementia during a mean (SD) follow-up of 10.2 [5.6] years. Higher concentrations of total PM2.5 were associated with greater rates of incident dementia (HR, 1.08 per IQR; 95% CI, 1.01-1.17). In single pollutant models, PM2.5 from all sources, except dust, were associated with increased rates of dementia, with the strongest associations for agriculture, traffic, coal combustion, and wildfires. After control for PM2.5 from all other sources and copollutants, only PM2.5 from agriculture (HR, 1.13; 95% CI, 1.01-1.27) and wildfires (HR, 1.05; 95% CI, 1.02-1.08) were robustly associated with greater rates of dementia.

Conclusion and Relevance

In this cohort study, higher residential PM2.5 levels, especially from agriculture and wildfires, were associated with higher rates of incident dementia, providing further evidence supporting PM2.5 reduction as a population-based approach to promote healthy cognitive aging. These findings also indicate that intervening on key emission sources might have value, although more research is needed to confirm these findings.

An Examination of the Joint Effect of the Social Environment and Air Pollution on Dementia Among US Older Adults

Evidence suggests exposure to air pollution increases the risk of dementia. Cognitively stimulating activities and social interactions, made available through the social environment, may slow cognitive decline. We examined whether the social environment buffers the adverse effect of air pollution on dementia in a cohort of older adults.

Methods

This study draws from the Ginkgo Evaluation of Memory Study. Participants aged 75 years and older were enrolled between 2000 and 2002 and evaluated for dementia semi-annually through 2008. Long-term exposure to particulate matter and nitrogen dioxide was assigned from spatial and spatiotemporal models. Census tract-level measures of the social environment and individual measures of social activity were used as measures of the social environment. We generated Cox proportional hazard models with census tract as a random effect and adjusted for demographic and study visit characteristics. Relative excess risk due to interaction was estimated as a qualitative measure of additive interaction.

Results

This study included 2,564 individuals. We observed associations between increased risk of dementia and fine particulate matter (µg/m3), coarse particulate matter (µg/m3), and nitrogen dioxide (ppb); HRs per 5 unit increase were 1.55 (1.01, 2.18), 1.31 (1.07, 1.60), and 1.18 (1.02, 1.37), respectively. We found no evidence of additive interaction between air pollution and the neighborhood social environment.

Conclusions

We found no consistent evidence to suggest a synergistic effect between exposure to air pollution and measures of the social environment. Given the many qualities of the social environment that may reduce dementia pathology, further examination is encouraged.

Metabolomic Evaluation of Air Pollution-related Bone Damage and Potential Mediation

Ambient air pollution has been associated with bone damage. However, no studies have evaluated the metabolomic response to air pollutants and its potential influence on bone health in postmenopausal women. We analyzed data from WHI participants with plasma samples. Whole-body, total hip, femoral neck, and spine BMD at enrollment and follow-up (Y1, Y3, Y6). Daily particulate matter NO, NO2, PM10 and SO2 were averaged over 1-, 3-, and 5-year periods before metabolomic assessments. Statistical analyses included multivariable-adjusted linear mixed models, pathways analyses, and mediation modeling. NO, NO2, and SO2, but not PM10, were associated with taurine, inosine, and C38:4 phosphatidylethanolamine (PE), at all averaging periods. We found a partial mediation of C38:4 PE in the association between 1-year average NO and lumbar spine BMD (p-value: 0.032). This is the first study suggesting that a PE may partially mediate air pollution-related bone damage in postmenopausal women.

Air pollution and decreased bone mineral density among Women's Health Initiative participants

Background

Osteoporosis heavily affects postmenopausal women and is influenced by environmental exposures. Determining the impact of criteria air pollutants and their mixtures on bone mineral density (BMD) in postmenopausal women is an urgent priority.

Methods

We conducted a prospective observational study using data from the ethnically diverse Women's Health Initiative Study (WHI) (enrollment, September 1994-December 1998; data analysis, January 2020 to August 2022). We used log-normal, ordinary kriging to estimate daily mean concentrations of PM10, NO, NO2, and SO2 at participants' geocoded addresses (1-, 3-, and 5-year averages before BMD assessments). We measured whole-body, total hip, femoral neck, and lumbar spine BMD at enrollment and follow-up (Y1, Y3, Y6) via dual-energy X-ray absorptiometry. We estimated associations using multivariable linear and linear mixed-effects models and mixture effects using Bayesian kernel machine regression (BKMR) models.

Findings

In cross-sectional and longitudinal analyses, mean PM10, NO, NO2, and SO2 averaged over 1, 3, and 5 years before the visit were negatively associated with whole-body, total hip, femoral neck, and lumbar spine BMD. For example, lumbar spine BMD decreased 0.026 (95% CI: 0.016, 0.036) g/cm2/year per a 10% increase in 3-year mean NO2 concentration. BKMR suggested that nitrogen oxides exposure was inversely associated with whole-body and lumbar spine BMD.

Interpretation

In this cohort study, higher levels of air pollutants were associated with bone damage, particularly on lumbar spine, among postmenopausal women. These findings highlight nitrogen oxides exposure as a leading contributor to bone loss in postmenopausal women, expanding previous findings of air pollution-related bone damage.

Funding

US National Institutes of Health.

Air pollution and plasma amyloid beta in a cohort of older adults: Evidence from the Ginkgo Evaluation of Memory study

Air pollution has been linked to Alzheimer's disease and related dementias (ADRD), but the mechanisms connecting air pollution to ADRD have not been firmly established. Air pollution may cause oxidative stress and neuroinflammation and contribute to the deposition of amyloid beta (Aβ) in the brain. We examined the association between fine particulate matter<2.5 μm in diameter (PM2.5), particulate matter<10 μm in diameter (PM10), nitrogen dioxide (NO2), and plasma based measures of Aβ1-40, Aβ1-42 and Aβ1-42/Aβ1-40 using data from 3044 dementia-free participants of the Ginkgo Evaluation of Memory Study (GEMS). Air pollution exposures were estimated at residential addresses that incorporated address histories dating back to 1980, resulting in one-, five-, 10- and 20- year exposure averages. Aβ was measured at baseline (2000-2002) and then again at the end of the study (2007-2008) allowing for linear regression models to assess cross-sectional associations and linear random effects models to evaluate repeated measures. After adjustment for socio-demographic and behavioral covariates, we found small positive associations between each air pollutant and Aβ1-40 but no association with Aβ1-42 or the ratio measures in cross sectional analysis. In repeat measures analysis, we found larger positive associations between each air pollutant and all three outcomes. We observed a 4.43% (95% CI 3.26%, 5.60%) higher Aβ1-40 level, 9.73% (6.20%, 13.38%) higher Aβ1-42 and 1.57% (95% CI: 0.94%, 2.20%) higher Aβ1-42/Aβ1-40 ratio associated with a 2 µg/m3 higher 20-year average PM2.5. Associations with other air pollutants were similar. Our study contributes to the broader evidence base on air pollution and ADRD biomarkers by evaluating longer air pollution exposure averaging periods to better mimic disease progression and provides a modifiable target for ADRD prevention.

Air pollution and dementia in older adults in the Ginkgo Evaluation of Memory Study

INTRODUCTION

Growing evidence implicates air pollution as a risk factor for dementia, but prior work is limited by challenges in diagnostic accuracy and assessing exposures in the decades prior to disease development. We evaluated the impact of long-term fine particulate matter (PM2.5 ) exposures on incident dementia (all-cause, Alzheimer's disease [AD], and vascular dementia [VaD]) in older adults.

METHODS

A panel of neurologists adjudicated dementia cases based on extensive neuropsychological testing and magnetic resonance imaging. We applied validated fine-scale air pollutant models to reconstructed residential histories to assess exposures.

RESULTS

An interquartile range increase in 20-year PM2.5 was associated with a 20% higher risk of dementia (95% confidence interval [CI]: 5%, 37%) and an increased risk of mixed VaD/AD but not AD alone.

DISCUSSION

Our findings suggest that air pollutant exposures over decades contribute to dementia and that effects of current exposures may be experienced years into the future.

The impacts of reduction in ambient fine particulate (PM2.5) air pollution on life expectancy in Taiwan

Fine particulate matter, particles less than 2.5 um in diameter (PM_2.5), is an important environmental human health factor to consider. The long- and short-term influence of PM_2.5 on health has been extensively studied in relation to many health outcomes, although few investigations examined the consequences of chronic ambient PM_2.5 on life expectancy, which constitutes an important gauge of public human health status. Therefore, the aim of this study was to investigate the effects of reducing ambient PM_2.5 levels in Taiwan on life expectancy there from 2000 to 2020. Officially reported island-wide annually average concentrations of ambient PM_2.5, county-level life expectancies, and demographic and socioeconomic and proxy variable were collected for the prevalence of smoking from various national public agencies and organizations, since variables these might potentially confound life expectancy results. The relationship between changes in ambient PM_2.5 levels and life expectancy were determined using linear regression. Data demonstrated that counties with greater reductions in ambient PM_2.5 concentrations were associated with higher life expectancies. Adjusting for alterations in demographic and socioeconomic variables and proxy parameter, the prevalence of smoking data from a multiple regression model, it was found that a 0.3-year rise in life expectancy was noted for each 10 ug/m³ decrease in PM_2.5 in those counties. Our findings show that reducing ambient PM_2.5 levels play an important role for prolongation of life expectancy in Taiwan.

Impacts of long-term ambient particulate matter and gaseous pollutants on circulating biomarkers of inflammation in male and female health professionals

BACKGROUND

Systemic inflammation may serve as a biological mechanism linking air pollution to poor health but supporting evidence from studies of long-term pollutant exposure and inflammatory cytokines is inconsistent.

OBJECTIVE

We studied associations between multiple particulate matter (PM) and gaseous air pollutants and pro- and anti-inflammatory cytokines within two nationwide cohorts of men and women.

METHODS

Data were obtained from 16,151 women in the Nurses' Health Study and 7,930 men in the Health Professionals' Follow-up Study with at least one measure of circulating adiponectin, C-Reactive Protein (CRP), Interleukin-6 (IL-6) or soluble tumor necrosis-factor receptor-2 (sTNFR-2). Exposure to PM with aerodynamic diameter ≤2.5, 2.5-10, and ≤10 μm (PM2.5, PM2.5-10, PM10) and nitrogen dioxide (NO2) was estimated using spatio-temporal models and were linked to participants' addresses at the time of blood draw. Averages of the 1-, 3-, and 12-months prior to blood draw were examined. Associations between each biomarker and pollutant were estimated from linear regression models adjusted for individual and contextual covariates.

RESULTS

In adjusted models, we observed a 2.72% (95% CI: 0.43%, 5.95%), 3.11% (-0.12%, 6.45%), and 3.67% (0.19%, 7.26%) increase in CRP associated with a 10 μg/m3 increase in 1-, 3-, and 12- month averaged NO2 in women. Among men, there was a statistically significant 5.96% (95% CI: 0.07%, 12.20%), 6.99% (95% CI: 0.29%, 14.15%), and 8.33% (95% CI: 0.35%, 16.94%) increase in CRP associated with a 10 μg/m3 increase in 1-, 3-, and 12-month averaged PM2.5-10, respectively. Increasing PM2.5-10 was associated with increasing IL-6 and sTNFR-2 among men over shorter exposure durations. There were no associations with exposures to PM2.5 or PM10, or with adiponectin. Findings were robust to sensitivity analyses restricting to disease-free controls and non-movers.

CONCLUSIONS

Across multiple long-term pollutant exposures and inflammatory markers, associations were generally weak. Focusing on specific pollutant-inflammatory mechanisms may clarify pathways.

Within-City Variation in Ambient Carbon Monoxide Concentrations: Leveraging Low-Cost Monitors in a Spatiotemporal Modeling Framework

BACKGROUND

Based on human and animal experimental studies, exposure to ambient carbon monoxide (CO) may be associated with cardiovascular disease outcomes, but epidemiological evidence of this link is limited. The number and distribution of ground-level regulatory agency monitors are insufficient to characterize fine-scale variations in CO concentrations.

OBJECTIVES

To develop a daily, high-resolution ambient CO exposure prediction model at the city scale.

METHODS

We developed a CO prediction model in Baltimore, Maryland, based on a spatiotemporal statistical algorithm with regulatory agency monitoring data and measurements from calibrated low-cost gas monitors. We also evaluated the contribution of three novel parameters to model performance: high-resolution meteorological data, satellite remote sensing data, and copollutant (PM2.5, NO2, and NOx) concentrations.

RESULTS

The CO model had spatial cross-validation (CV) R2 and root-mean-square error (RMSE) of 0.70 and 0.02 parts per million (ppm), respectively; the model had temporal CV R2 and RMSE of 0.61 and 0.04 ppm, respectively. The predictions revealed spatially resolved CO hot spots associated with population, traffic, and other nonroad emission sources (e.g., railroads and airport), as well as sharp concentration decreases within short distances from primary roads.

DISCUSSION

The three novel parameters did not substantially improve model performance, suggesting that, on its own, our spatiotemporal modeling framework based on geographic features was reliable and robust. As low-cost air monitors become increasingly available, this approach to CO concentration modeling can be generalized to resource-restricted environments to facilitate comprehensive epidemiological research. https://doi.org/10.1289/EHP10889.

Evaluation of associations between estimates of particulate matter exposure and new onset type 2 diabetes in the REGARDS cohort

BACKGROUND

Studies of PM_2.5 and type 2 diabetes employ differing methods for exposure assignment, which could explain inconsistencies in this growing literature. We hypothesized associations between PM_2.5 and new onset type 2 diabetes would differ by PM_2.5 exposure data source, duration, and community type.

METHODS

We identified participants of the US-based REasons for Geographic and Racial Differences in Stroke (REGARDS) cohort who were free of diabetes at baseline (2003-2007); were geocoded at their residence; and had follow-up diabetes information. We assigned PM_2.5 exposure estimates to participants for periods of 1 year prior to baseline using three data sources, and 2 years prior to baseline for two of these data sources. We evaluated adjusted odds of new onset diabetes per 5 µg/m³ increases in PM_2.5 using generalized estimating equations with a binomial distribution and logit link, stratified by community type.

RESULTS

Among 11,208 participants, 1,409 (12.6%) had diabetes at follow-up. We observed no associations between PM_2.5 and diabetes in higher and lower density urban communities, but within suburban/small town and rural communities, increases of 5 µg/m³ PM_2.5 for 2 years (Downscaler model) were associated with diabetes (OR [95% CI] = 1.65 [1.09, 2.51], 1.56 [1.03, 2.36], respectively). Associations were consistent in direction and magnitude for all three PM_2.5 sources evaluated.

SIGNIFICANCE

1- and 2-year durations of PM_2.5 exposure estimates were associated with higher odds of incident diabetes in suburban/small town and rural communities, regardless of exposure data source. Associations within urban communities might be obfuscated by place-based confounding.

Characteristics of movers and predictors of residential mobility in the Atherosclerosis Risk in Communities (ARIC) cohort

Current efforts to characterize movers and identify predictors of moving have been limited. We used the ARIC cohort to characterize non-movers, short-distance movers, and long-distance movers, and employed best subset algorithms to identify important predictors of moving, including interactions between characteristics. Short- and long-distance movers were notably different from non-movers, and important predictors of moving differed based on the distance of the residential move. Importantly, systematic inclusion of interaction terms enhanced model fit and was substantively meaningful. This work has important implications for epidemiologic studies of contextual exposures and those treating residential mobility as an exposure.

Air quality improvement and cognitive decline in community-dwelling older women in the United States: A longitudinal cohort study

BACKGROUND

Late-life exposure to ambient air pollution is a modifiable risk factor for dementia, but epidemiological studies have shown inconsistent evidence for cognitive decline. Air quality (AQ) improvement has been associated with improved cardiopulmonary health and decreased mortality, but to the best of our knowledge, no studies have examined the association with cognitive function. We examined whether AQ improvement was associated with slower rate of cognitive decline in older women aged 74 to 92 years.

METHODS AND FINDINGS

We studied a cohort of 2,232 women residing in the 48 contiguous US states that were recruited from more than 40 study sites located in 24 states and Washington, DC from the Women's Health Initiative (WHI) Memory Study (WHIMS)-Epidemiology of Cognitive Health Outcomes (WHIMS-ECHO) study. They were predominantly non-Hispanic White women and were dementia free at baseline in 2008 to 2012. Measures of annual (2008 to 2018) cognitive function included the modified Telephone Interview for Cognitive Status (TICSm) and the telephone-based California Verbal Learning Test (CVLT). We used regionalized universal kriging models to estimate annual concentrations (1996 to 2012) of fine particulate matter (PM2.5) and nitrogen dioxide (NO2) at residential locations. Estimates were aggregated to the 3-year average immediately preceding (recent exposure) and 10 years prior to (remote exposure) WHIMS-ECHO enrollment. Individual-level improved AQ was calculated as the reduction from remote to recent exposures. Linear mixed effect models were used to examine the associations between improved AQ and the rates of cognitive declines in TICSm and CVLT trajectories, adjusting for sociodemographic (age; geographic region; race/ethnicity; education; income; and employment), lifestyle (physical activity; smoking; and alcohol), and clinical characteristics (prior hormone use; hormone therapy assignment; depression; cardiovascular disease (CVD); hypercholesterolemia; hypertension; diabetes; and body mass index [BMI]). For both PM2.5 and NO2, AQ improved significantly over the 10 years before WHIMS-ECHO enrollment. During a median of 6.2 (interquartile range [IQR] = 5.0) years of follow-up, declines in both general cognitive status (β = -0.42/year, 95% CI: -0.44, -0.40) and episodic memory (β = -0.59/year, 95% CI: -0.64, -0.54) were observed. Greater AQ improvement was associated with slower decline in TICSm (βPM2.5improvement = 0.026 per year for improved PM2.5 by each IQR = 1.79 μg/m3 reduction, 95% CI: 0.001, 0.05; βNO2improvement = 0.034 per year for improved NO2 by each IQR = 3.92 parts per billion [ppb] reduction, 95% CI: 0.01, 0.06) and CVLT (βPM2.5 improvement = 0.070 per year for improved PM2.5 by each IQR = 1.79 μg/m3 reduction, 95% CI: 0.02, 0.12; βNO2improvement = 0.060 per year for improved NO2 by each IQR = 3.97 ppb reduction, 95% CI: 0.005, 0.12) after adjusting for covariates. The respective associations with TICSm and CVLT were equivalent to the slower decline rate found with 0.9 to 1.2 and1.4 to 1.6 years of younger age and did not significantly differ by age, region, education, Apolipoprotein E (ApoE) e4 genotypes, or cardiovascular risk factors. The main limitations of this study include measurement error in exposure estimates, potential unmeasured confounding, and limited generalizability.

CONCLUSIONS

In this study, we found that greater improvement in long-term AQ in late life was associated with slower cognitive declines in older women. This novel observation strengthens the epidemiologic evidence of an association between air pollution and cognitive aging.

Association of improved air quality with lower dementia risk in older women

Late-life ambient air pollution is a risk factor for brain aging, but it remains unknown if improved air quality (AQ) lowers dementia risk. We studied a geographically diverse cohort of older women dementia free at baseline in 2008 to 2012 (n = 2,239, aged 74 to 92). Incident dementia was centrally adjudicated annually. Yearly mean concentrations of fine particulate matter (PM2.5) and nitrogen dioxide (NO2) were estimated using regionalized national universal kriging models and averaged over the 3-y period before baseline (recent exposure) and 10 y earlier (remote exposure). Reduction from remote to recent exposures was used as the indicator of improved AQ. Cox proportional hazard ratios (HRs) for dementia risk associated with AQ measures were estimated, adjusting for sociodemographic, lifestyle, and clinical characteristics. We identified 398 dementia cases during follow up (median = 6.1 y). PM2.5 and NO2 reduced significantly over the 10 y before baseline. Larger AQ improvement was associated with reduced dementia risks (HRPM2.5 0.80 per 1.78 μg/m3, 95% CI 0.71-0.91; HRNO2 0.80 per 3.91 parts per billion, 95% CI 0.71-0.90), equivalent to the lower risk observed in women 2.4 y younger at baseline. Higher PM2.5 at baseline was associated with higher dementia risk (HRPM2.5 1.16 per 2.90 μg/m3, 95% CI 0.98-1.38), but the lower dementia risk associated with improved AQ remained after further adjusting for recent exposure. The observed associations did not substantially differ by age, education, geographic region, Apolipoprotein E e4 genotypes, or cardiovascular risk factors. Long-term AQ improvement in late life was associated with lower dementia risk in older women.

Cohort Profile: The Environmental-Pollution-Induced Neurological EFfects (EPINEF) study, a multicenter cohort study of Korean adults

The general population is exposed to numerous environmental pollutants, and it remains unclear which pollutants affect the brain, accelerating brain aging and increasing the risk of dementia. The Environmental-Pollution-Induced Neurological Effects study is a multi-city prospective cohort study aiming to comprehensively investigate the effect of different environmental pollutants on brain structures, neuropsychological function, and the development of dementia in adults. The baseline data of 3,775 healthy elderly people were collected from August 2014 to March 2018. The eligibility criteria were age ≥50 years and no self-reported history of dementia, movement disorders, or stroke. The assessment included demographics and anthropometrics, laboratory test results, and individual levels of exposure to air pollution. A neuroimaging sub-cohort was also recruited with 1,022 participants during the same period, and brain magnetic resonance imaging and neuropsychological tests were conducted. The first follow-up environmental pollutant measurements will start in 2022 and the follow-up for the sub-cohort will be conducted every 3-4 years. We have found that subtle structural changes in the brain may be induced by exposure to airborne pollutants such as particulate matter 10 μm or less in diameter (PM₁₀), particulate matter 2.5 μm or less in diameter (PM_2.5) and Mn₁₀, manganese in PM₁₀; Mn_2.5, manganese in PM_2.5. PM₁₀, PM_2.5, and nitrogen dioxide in healthy adults. This study provides a basis for research involving large-scale, long-term neuroimaging assessments in community-based populations.

Reanalysis of the association between reduction in long-term PM2.5 concentrations and improved life expectancy

BACKGROUND

Much of the current evidence of associations between long-term PM_2.5 and health outcomes relies on national or regional analyses using exposures derived directly from regulatory monitoring data. These findings could be affected by limited spatial coverage of monitoring data, particularly for time periods before spatially extensive monitoring began in the late 1990s. For instance, Pope et al. (2009) showed that between 1980 and 2000 a 10 μg/m³ reduction in PM_2.5 was associated with an average 0.61 year (standard error (SE) = 0.20) longer life expectancy. That analysis used 1979-1983 averages of PM_2.5 across 51 U.S. Metropolitan Statistical Areas (MSAs) computed from about 130 monitoring sites. Our reanalysis re-examines this association using modeled PM_2.5 in order to assess population- or spatially-representative exposure. We hypothesized that modeled PM_2.5 with finer spatial resolution provides more accurate health effect estimates compared to limited monitoring data.

METHODS

We used the same data for life expectancy and confounders, as well as the same analysis models, and investigated the same 211 continental U.S. counties, as Pope et al. (2009). For modeled PM_2.5, we relied on a previously-developed point prediction model based on regulatory monitoring data for 1999-2015 and back-extrapolation to 1979. Using this model, we predicted annual average concentrations at centroids of all 72,271 census tracts and 12,501 25-km national grid cells covering the contiguous U.S., to represent population and space, respectively. We averaged these predictions to the county for the two time periods (1979-1983 and 1999-2000), whereas the original analysis used MSA averages given limited monitoring data. Finally, we estimated regression coefficients for PM_2.5 reduction on life expectancy improvement over the two periods, adjusting for area-level confounders.

RESULTS

A 10 μg/m³ decrease in modeled PM_2.5 based on census tract and national grid predictions was associated with 0.69 (standard error (SE) = 0.31) and 0.81 (0.29) -year increases in life expectancy. These estimates are higher than the estimate of Pope et al. (2009); they also have larger SEs likely because of smaller variability in exposure predictions, a standard property of regression. Two sets of effect estimates, however, had overlapping confidence intervals.

CONCLUSIONS

Our approach for estimating population- and spatially-representative PM_2.5 concentrations based on census tract and national grid predictions, respectively, provided generally consistent findings to the original findings using limited monitoring data. This finding lends additional support to the evidence that reduced fine particulate matter contributes to extended life expectancy.

Ambient Air Pollution and Long-Term Trajectories of Episodic Memory Decline among Older Women in the WHIMS-ECHO Cohort

BACKGROUND

Episodic memory decline varies by age and underlying neuropathology. Whether ambient air pollution contributes to the heterogeneity of episodic memory decline in older populations remains unclear.

OBJECTIVES

We estimated associations between air pollution exposures and episodic memory decline according to pollutant, exposure time window, age, and latent class subgroups defined by episodic memory trajectories.

METHODS

Participants were from the Women's Health Initiative Memory Study-Epidemiology of Cognitive Health Outcomes. Older women (n = 2,056 ; 74-92 years of age) completed annual (2008-2018) episodic memory assessments using the telephone-based California Verbal Learning Test (CVLT). We estimated 3-y average fine particulate matter [PM with an aerodynamic diameter of ≤ 2.5 μ m (PM 2.5 )] and nitrogen dioxide (NO 2 ) exposures at baseline and 10 y earlier (recent and remote exposures, respectively), using regionalized national universal kriging. Separate latent class mixed models were used to estimate associations between interquartile range increases in exposures and CVLT trajectories in women ≤ 80 and > 80  years of age , adjusting for covariates.

RESULTS

Two latent classes were identified for women ≤ 80  years of age (n = 828 ), "slow-decliners" {slope = - 0.12 / y [95% confidence interval (CI): - 0.23 , - 0.01 ] and "fast-decliners" [slope = - 1.79 / y (95% CI: - 2.08 , - 1.50 )]}. In the slow-decliner class, but not the fast-decliner class, PM 2.5 exposures were associated with a greater decline in CVLT scores over time, with a stronger association for recent vs. remote exposures [- 0.16 / y (95% CI: - 2.08 , - 0.03 ) per 2.88   μ g / m 3 and - 0.11 / y (95% CI: - 0.22 , 0.01) per 3.27   μ g / m 3 , respectively]. Among women ≥ 80  years of age (n = 1,128 ), the largest latent class comprised "steady-decliners" [slope = - 1.35 / y (95% CI: - 1.53 , - 1.17 )], whereas the second class, "cognitively resilient", had no decline in CVLT on average. PM 2.5 was not associated with episodic memory decline in either class. A 6.25 -ppb increase in recent NO 2 was associated with nonsignificant acceleration of episodic memory decline in the ≤ 80 -y-old fast-decliner class [- 0.21 / y (95% CI: - 0.45 , 0.04)], and in the > 80 -y-old cognitively resilient class [- 0.10 / y (95% CI: - 0.24 , 0.03)] and steady-decliner class [- 0.11 / y (95% CI: - 0.27 , 0.05)]. Associations with recent NO 2 exposure in women > 80  years of age were stronger and statistically significant when 267 women with incident probable dementia were excluded [e.g., - 0.12 / y (95% CI: - 0.22 , - 0.02 ) for the cognitively resilient class]. In contrast with changes in CVLT over time, there were no associations between exposures and CVLT scores during follow-up in any subgroup.

DISCUSSION

In a community-dwelling U.S. population of older women, associations between late-life exposure to ambient air pollution and episodic memory decline varied by age-related cognitive trajectories, exposure time windows, and pollutants. https://doi.org/10.1289/EHP7668.

Fine-Scale Air Pollution Models for Epidemiologic Research: Insights From Approaches Developed in the Multi-ethnic Study of Atherosclerosis and Air Pollution (MESA Air)

PURPOSE OF REVIEW

Epidemiological studies of short- and long-term health impacts of ambient air pollutants require accurate exposure estimates. We describe the evolution in exposure assessment and assignment in air pollution epidemiology, with a focus on spatiotemporal techniques first developed to meet the needs of the Multi-ethnic Study of Atherosclerosis and Air Pollution (MESA Air). Initially designed to capture the substantial variation in pollutant levels and potential health impacts that can occur over small spatial and temporal scales in metropolitan areas, these methods have now matured to permit fine-scale exposure characterization across the contiguous USA and can be used for understanding long- and short-term health effects of exposure across the lifespan. For context, we highlight how the MESA Air models compare to other available exposure models.

RECENT FINDINGS

Newer model-based exposure assessment techniques provide predictions of pollutant concentrations with fine spatial and temporal resolution. These validated models can predict concentrations of several pollutants, including particulate matter less than 2.5 μm in diameter (PM2.5), oxides of nitrogen, and ozone, at specific locations (such as at residential addresses) over short time intervals (such as 2 weeks) across the contiguous USA between 1980 and the present. Advances in statistical methods, incorporation of supplemental pollutant monitoring campaigns, improved geographic information systems, and integration of more complete satellite and chemical transport model outputs have contributed to the increasing validity and refined spatiotemporal spans of available models. Modern models for predicting levels of outdoor concentrations of air pollutants can explain a substantial amount of the spatiotemporal variation in observations and are being used to provide critical insights into effects of air pollutants on the prevalence, incidence, progression, and prognosis of diseases across the lifespan. Additional enhancements in model inputs and model design, such as incorporation of better traffic data, novel monitoring platforms, and deployment of machine learning techniques, will allow even further improvements in the performance of pollutant prediction models.

Estimating historical PM2.5 exposures for three decades (1987-2016) in Japan using measurements of associated air pollutants and land use regression

Accurate estimation of historical PM_2.5 exposures for epidemiological studies is challenging when extensive monitoring data are limited in duration. Here, we develop a national-scale PM_2.5 exposure model for Japan using measurements recorded between 2014 and 2016 to estimate monthly means for 1987 through 2016. Our objective is to obtain accurate PM_2.5 estimates for years prior to implementation of extensive PM_2.5 monitoring, using observations from a limited period. We utilize a neural network to convey the non-linear relationship between the target pollutant and predictors, while incorporating the associated air pollutants. We obtain high R² values of 0.76 and 0.73 through spatial and temporal cross validation, respectively. We evaluate estimation accuracy using an independent data set and achieve an R² of 0.75. Moreover, monthly variations for 2000-2013 are well reproduced with correlation coefficients of greater than 0.78, obtained through a comparison with observations. We estimate monthly means at 1 × 1 km resolution from 1987 through 2016. The estimates show decreases in the area and population weighted means beginning in the 1990s. We successfully estimate monthly mean PM_2.5 concentrations over three decades with outstanding predictive accuracy. Our findings illustrate that the presented approach achieves accurate long-term historical estimations using observations limited in duration.

PM2.5 air pollution and cause-specific cardiovascular disease mortality

BACKGROUND

Ambient air pollution is a modifiable risk factor for cardiovascular disease, yet uncertainty remains about the size of risks at lower levels of fine particulate matter (PM2.5) exposure which now occur in the USA and elsewhere.

METHODS

We investigated the relationship of ambient PM2.5 exposure with cause-specific cardiovascular disease mortality in 565 477 men and women, aged 50 to 71 years, from the National Institutes of Health-AARP Diet and Health Study. During 7.5 x 106 person-years of follow up, 41 286 cardiovascular disease deaths, including 23 328 ischaemic heart disease (IHD) and 5894 stroke deaths, were ascertained using the National Death Index. PM2.5 was estimated using a hybrid land use regression (LUR) geostatistical model. Multivariate Cox regression models were used to estimate relative risks (RRs) and 95% confidence intervals (CI).

RESULTS

Each increase of 10  μg/m3 PM2.5 (overall range, 2.9-28.0  μg/m3) was associated, in fully adjusted models, with a 16% increase in mortality from ischaemic heart disease [hazard ratio (HR) 1.16; 95% CI 1.09-1.22] and a 14% increase in mortality from stroke (HR 1.14; CI 1.02-1.27). Compared with PM2.5 exposure <8  μg/m3 (referent), risks for CVD were increased in relation to PM2.5 exposures in the range of 8-12  μg/m3 (CVD: HR 1.04; 95% CI 1.00-1.08), in the range 12-20  μg/m3 (CVD: HR 1.08; 95% CI 1.03-1.13) and in the range 20+ μg/m3 (CVD: HR 1.19; 95% CI 1.10-1.28). Results were robust to alternative approaches to PM2.5 exposure assessment and statistical analysis.

CONCLUSIONS

Long-term exposure to fine particulate air pollution is associated with ischaemic heart disease and stroke mortality, with excess risks occurring in the range of and below the present US long-term standard for ambient exposure to PM2.5 (12  µg/m3), indicating the need for continued improvements in air pollution abatement for CVD prevention.

Concentrations of criteria pollutants in the contiguous U.S., 1979 - 2015: Role of prediction model parsimony in integrated empirical geographic regression

National-scale empirical models for air pollution can include hundreds of geographic variables. The impact of model parsimony (i.e., how model performance differs for a large versus small number of covariates) has not been systematically explored. We aim to (1) build annual-average integrated empirical geographic (IEG) regression models for the contiguous U.S. for six criteria pollutants during 1979–2015; (2) explore systematically the impact on model performance of the number of variables selected for inclusion in a model; and (3) provide publicly available model predictions. We compute annual-average concentrations from regulatory monitoring data for PM₁₀, PM_2.5, NO₂, SO₂, CO, and ozone at all monitoring sites for 1979–2015. We also use ~350 geographic characteristics at each location including measures of traffic, land use, land cover, and satellite-based estimates of air pollution. We then develop IEG models, employing universal kriging and summary factors estimated by partial least squares (PLS) of geographic variables. For all pollutants and years, we compare three approaches for choosing variables to include in the PLS model: (1) no variables, (2) a limited number of variables selected from the full set by forward selection, and (3) all variables. We evaluate model performance using 10-fold cross-validation (CV) using conventional and spatially-clustered test data. Models using 3 to 30 variables selected from the full set generally have the best performance across all pollutants and years (median R² conventional [clustered] CV: 0.66 [0.47]) compared to models with no (0.37 [0]) or all variables (0.64 [0.27]). Concentration estimates for all Census Blocks reveal generally decreasing concentrations over several decades with local heterogeneity. Our findings suggest that national prediction models can be built by empirically selecting only a small number of important variables to provide robust concentration estimates. Model estimates are freely available online.

Mediterranean Diet and the Association Between Air Pollution and Cardiovascular Disease Mortality Risk

BACKGROUND

Recent experimental evidence suggests that nutritional supplementation can blunt adverse cardiopulmonary effects induced by acute air pollution exposure. However, whether usual individual dietary patterns can modify the association between long-term air pollution exposure and health outcomes has not been previously investigated. We assessed, in a large cohort with detailed diet information at the individual level, whether a Mediterranean diet modifies the association between long-term exposure to ambient air pollution and cardiovascular disease mortality risk.

METHODS

The National Institutes of Health-American Association for Retired Persons Diet and Health Study, a prospective cohort (N=548 845) across 6 states and 2 cities in the United States and with a follow-up period of 17 years (1995-2011), was linked to estimates of annual average exposures to fine particulate matter and nitrogen dioxide at the residential census-tract level. The alternative Mediterranean Diet Index, which uses a 9-point scale to assess conformity with a Mediterranean-style diet, was constructed for each participant from information in cohort baseline dietary questionnaires. We evaluated mortality risks for cardiovascular disease, ischemic heart disease, cerebrovascular disease, or cardiac arrest associated with long-term air pollution exposure. Effect modification of the associations between exposure and the mortality outcomes by alternative Mediterranean Diet Index was examined via interaction terms.

RESULTS

For fine particulate matter, we observed elevated and significant associations with cardiovascular disease (hazard ratio [HR] per 10 μg/m³, 1.13; 95% CI, 1.08-1.18), ischemic heart disease (HR, 1.16; 95% CI, 1.10-1.23), and cerebrovascular disease (HR, 1.15; 95% CI, 1.03-1.28). For nitrogen dioxide, we found significant associations with cardiovascular disease (HR per 10 ppb, 1.06; 95% CI, 1.04-1.08) and ischemic heart disease (HR, 1.08; 95% CI, 1.05-1.11). Analyses indicated that Mediterranean diet modified these relationships, as those with a higher alternative Mediterranean Diet Index score had significantly lower rates of cardiovascular disease mortality associated with long-term air pollution exposure ( P-interaction<0.05).

CONCLUSIONS

A Mediterranean diet reduced cardiovascular disease mortality risk related to long-term exposure to air pollutants in a large prospective US cohort. Increased consumption of foods rich in antioxidant compounds may aid in reducing the considerable disease burden associated with ambient air pollution.

Long-Term Exposure to Ozone and Cause-Specific Mortality Risk in the United States

Rationale: Many studies have linked short-term exposure to ozone (O3) with morbidity and mortality, but epidemiologic evidence of associations between long-term O3 exposure and mortality is more limited.Objectives: To investigate associations of long-term (annual or warm season average of daily 8-h maximum concentrations) O3 exposure with all-cause and cause-specific mortality in the NIH-AARP Diet and Health Study, a large prospective cohort of U.S. adults with 17 years of follow-up from 1995 to 2011.Methods: The cohort (n = 548,780) was linked to census tract-level estimates for O3. Associations between long-term O3 exposure (averaged values from 2002 to 2010) and multiple causes of death were evaluated using multivariate Cox proportional hazards models, adjusted for individual- and census tract-level covariates, and potentially confounding copollutants and temperature.Measurements and Main Results: Long-term annual average exposure to O3 was significantly associated with deaths caused by cardiovascular disease (per 10 ppb; hazard ratio [HR], 1.03; 95% confidence interval [CI], 1.01-1.06), ischemic heart disease (HR, 1.06; 95% CI, 1.02-1.09), respiratory disease (HR, 1.04; 95% CI, 1.00-1.09), and chronic obstructive pulmonary disease (HR, 1.09; 95% CI, 1.03-1.15) in single-pollutant models. The results were robust to alternative models and adjustment for copollutants (fine particulate matter and nitrogen dioxide), although some evidence of confounding by temperature was observed. Significantly elevated respiratory disease mortality risk associated with long-term O3 exposure was found among those living in locations with high temperature (Pinteraction < 0.05).Conclusions: This study found that long-term exposure to O3 is associated with increased risk for multiple causes of mortality, suggesting that establishment of annual and/or seasonal federal O3 standards is needed to more adequately protect public health from ambient O3 exposures.

A Deep Belief Network Combined with Modified Grey Wolf Optimization Algorithm for PM2.5 Concentration Prediction

Accurate PM2.5 concentration prediction is crucial for protecting public health and improving air quality. As a popular deep learning model, deep belief network (DBN) for PM2.5 concentration prediction has received increasing attention due to its effectiveness. However, the DBN structure parameters that have a significant impact on prediction accuracy and computation time are hard to be determined. To address this issue, a modified grey wolf optimization (MGWO) algorithm is proposed to optimize the DBN structure parameters containing number of hidden nodes, learning rate, and momentum coefficient. The methodology modifies the basic grey wolf optimization (GWO) algorithm using the nonlinear convergence and position update strategies, and then utilizes the training error of the DBN to calculate the fitness function of the MGWO algorithm. Through the multiple iterations, the optimal structure parameters are obtained, and a suitable predictor is finally generated. The proposed prediction model is validated on a real application case. Compared with the other prediction models, experimental results show that the proposed model has a simpler structure but higher prediction accuracy.

Relationship between fine particulate air pollution exposure and human adult life expectancy in Taiwan

Among the air pollutants, particulate matter with an aerodynamic diameter less than 2.5 um (PM_2.5) is of particular interest to environmental medicine as epidemiologic studies consistently reported that long-term exposure to PM_2.5 is associated with increased risk of premature death in adults. Life expectancy is a well-documented and important measure of overall public health policy. However, few investigators examined the relationship between PM_2.5 levels and adult life expectancy. In this Taiwan-wide study, county-level annual mean PM_2.5 concentrations data were collected concomitantly with potential confounding variables including demographic and socioeconomic status, as well as smoking prevalence. Subsequently, these PM_2.5 data were analyzed with respect to county-level adult life expectancy data for the period 2010 to 2017. Linear regression was used to determine the relationship between PM_2.5 and life expectancy in adults. Residents residing in the counties characterized as containing higher levels of PM_2.5 exhibited significantly reduced life expectancy after controlling for potential confounders. For each 10 ug/m³ increase in PM_2.5 there was an estimated mean decrease in life expectancy in adults of 0.3 years. The results of this study shed light on the relationship between fine particulate air pollution exposure and risk to human health in Taiwan.

Particulate matter air pollution and national and county life expectancy loss in the USA: A spatiotemporal analysis

BACKGROUND

Exposure to fine particulate matter pollution (PM2.5) is hazardous to health. Our aim was to directly estimate the health and longevity impacts of current PM2.5 concentrations and the benefits of reductions from 1999 to 2015, nationally and at county level, for the entire contemporary population of the contiguous United States.

METHODS AND FINDINGS

We used vital registration and population data with information on sex, age, cause of death, and county of residence. We used four Bayesian spatiotemporal models, with different adjustments for other determinants of mortality, to directly estimate mortality and life expectancy loss due to current PM2.5 pollution and the benefits of reductions since 1999, nationally and by county. The covariates included in the adjusted models were per capita income; percentage of population whose family income is below the poverty threshold, who are of Black or African American race, who have graduated from high school, who live in urban areas, and who are unemployed; cumulative smoking; and mean temperature and relative humidity. In the main model, which adjusted for these covariates and for unobserved county characteristics through the use of county-specific random intercepts, PM2.5 pollution in excess of the lowest observed concentration (2.8 μg/m3) was responsible for an estimated 15,612 deaths (95% credible interval 13,248-17,945) in females and 14,757 deaths (12,617-16,919) in males. These deaths would lower national life expectancy by an estimated 0.15 years (0.13-0.17) for women and 0.13 years (0.11-0.15) for men. The life expectancy loss due to PM2.5 was largest around Los Angeles and in some southern states such as Arkansas, Oklahoma, and Alabama. At any PM2.5 concentration, life expectancy loss was, on average, larger in counties with lower income and higher poverty rate than in wealthier counties. Reductions in PM2.5 since 1999 have lowered mortality in all but 14 counties where PM2.5 increased slightly. The main limitation of our study, similar to other observational studies, is that it is not guaranteed for the observed associations to be causal. We did not have annual county-level data on other important determinants of mortality, such as healthcare access and quality and diet, but these factors were adjusted for with use of county-specific random intercepts.

CONCLUSIONS

According to our estimates, recent reductions in particulate matter pollution in the USA have resulted in public health benefits. Nonetheless, we estimate that current concentrations are associated with mortality impacts and loss of life expectancy, with larger impacts in counties with lower income and higher poverty rate.

Estimated Long-Term (1981-2016) Concentrations of Ambient Fine Particulate Matter across North America from Chemical Transport Modeling, Satellite Remote Sensing, and Ground-Based Measurements

Accurate data concerning historical fine particulate matter (PM_2.5) concentrations are needed to assess long-term changes in exposure and associated health risks. We estimated historical PM_2.5 concentrations over North America from 1981 to 2016 for the first time by combining chemical transport modeling, satellite remote sensing, and ground-based measurements. We constrained and evaluated our estimates with direct ground-based PM_2.5 measurements when available and otherwise with historical estimates of PM_2.5 from PM₁₀ measurements or total suspended particle (TSP) measurements. The estimated PM_2.5 concentrations were generally consistent with direct ground-based PM_2.5 measurements over their duration from 1988 onward ( R² = 0.6 to 0.85) and to a lesser extent with PM_2.5 inferred from PM₁₀ measurements from 1985 to 1998 ( R² = 0.5 to 0.6). The collocated comparison of the trends of population-weighted annual average PM_2.5 from our estimates and ground-based measurements was highly consistent (RMSD = 0.66 μg m^-3). The population-weighted annual average PM_2.5 over North America decreased from 22 ± 6.4 μg m^-3 in 1981, to 12 ± 3.2 μg m^-3 in 1998, and to 7.9 ± 2.1 μg m^-3 in 2016, with an overall trend of -0.33 μg m^-3 yr^-1 (95% CI: -0.35, -0.31).

A System for Developing and Projecting PM2.5 Spatial Fields to Correspond to Just Meeting National Ambient Air Quality Standards

PM2.5 concentration fields that correspond to just meeting national ambient air quality standards (NAAQS) are useful for characterizing exposure in regulatory assessments. Computationally efficient methods that incorporate predictions from photochemical grid models (PGM) are needed to realistically project baseline concentration fields for these assessments. Thorough cross validation (CV) of hybrid spatial prediction models is also needed to better assess their predictive capability in sparsely monitored areas. In this study, a system for generating, evaluating, and projecting PM2.5 spatial fields to correspond with just meeting the PM2.5 NAAQS is developed and demonstrated. Results of ten-fold CV based on standard and spatial cluster withholding approaches indicate that performance of three spatial prediction models improves with decreasing distance to the nearest neighboring monitor, improved PGM performance, and increasing distance from sources of PM2.5 heterogeneity (e.g., complex terrain and fire). An air quality projection tool developed here is demonstrated to be effective for quickly projecting PM2.5 spatial fields to just meet NAAQS using realistic spatial response patterns based on air quality modeling. PM2.5 tends to be most responsive to primary PM2.5 emissions in urban areas, whereas response patterns are relatively smooth for NOx and SO2 emission changes. On average, PM2.5 is more responsive to changes in anthropogenic primary PM2.5 emissions than NOx and SO2 emissions in the contiguous U.S.

Satellite-Based Land-Use Regression for Continental-Scale Long-Term Ambient PM2.5 Exposure Assessment in Australia

Australia has relatively diverse sources and low concentrations of ambient fine particulate matter (<2.5 μm, PM_2.5). Few comparable regions are available to evaluate the utility of continental-scale land-use regression (LUR) models including global geophysical estimates of PM_2.5, derived by relating satellite-observed aerosol optical depth to ground-level PM_2.5 ("SAT-PM_2.5"). We aimed to determine the validity of such satellite-based LUR models for PM_2.5 in Australia. We used global SAT-PM_2.5 estimates (∼10 km grid) and local land-use predictors to develop four LUR models for year-2015 (two satellite-based, two nonsatellite-based). We evaluated model performance at 51 independent monitoring sites not used for model development. An LUR model that included the SAT-PM_2.5 predictor variable (and six others) explained the most spatial variability in PM_2.5 (adjusted R² = 0.63, RMSE (μg/m³ [%]): 0.96 [14%]). Performance decreased modestly when evaluated (evaluation R² = 0.52, RMSE: 1.15 [16%]). The evaluation R² of the SAT-PM_2.5 estimate alone was 0.26 (RMSE: 3.97 [56%]). SAT-PM_2.5 estimates improved LUR model performance, while local land-use predictors increased the utility of global SAT-PM_2.5 estimates, including enhanced characterization of within-city gradients. Our findings support the validity of continental-scale satellite-based LUR modeling for PM_2.5 exposure assessment in Australia.

The estimated change in the level and distribution of PM2.5-attributable health impacts in the United States: 2005-2014

BACKGROUND

Photochemical modeling can predict the level and distribution of pollutant concentrations over time, but is resource-intensive. Partly for this reason, there are few studies exploring the multi-year trajectory of the historical change in fine particle (PM_2.5) levels and associated health impacts in the U.S.

OBJECTIVES

We used a unique dataset of Community Multi-Scale Air Quality (CMAQ) model simulations performed for a subset of years over a decade-long period fused with observations to estimate the change in ambient levels of PM_2.5 across the contiguous U.S. We also quantified the change in PM_2.5-attributable health risks and characterized the level of risk inequality over this period.

METHODS

We estimated annual mean PM_2.5 concentrations in 2005, 2011 and 2014. Using log-linear and logistic concentration-response coefficients we estimated changes in the numbers of deaths, hospital admissions and other morbidity outcomes. Calculating the Gini coefficient and Atkinson Index, we characterized the extent to which PM_2.5 attributable risks were shared equally across the population or instead concentrated among certain subgroups.

RESULTS

In 2005 the estimated fraction of deaths due to PM_2.5 was 6.1%. This estimated value falls to 4.6% by 2014. Every portion of the contiguous U.S. experiences a decline in the risk of PM-related premature death over the 10-year period. As measured by the Gini coefficient and Atkinson index, the level of PM mortality risk is shared more equally in 2014 than in 2005 among all subgroups.

CONCLUSIONS

Between 2005 and 2014, the level of PM_2.5 concentrations fall, and the risk of premature death, declined and became more equitably distributed across the U.S.

Association between long-term exposure to ambient air pollution and diabetes mortality in the US

OBJECTIVE

Recent mechanistic and epidemiological evidence implicates air pollution as a potential risk factor for diabetes; however, mortality risks have not been evaluated in a large US cohort assessing exposures to multiple pollutants with detailed consideration of personal risk factors for diabetes.

RESEARCH DESIGN AND METHODS

We assessed the effects of long-term ambient air pollution exposures on diabetes mortality in the NIH-AARP Diet and Health Study, a cohort of approximately a half million subjects across the contiguous U.S. The cohort, with a follow-up period between 1995 and 2011, was linked to residential census tract estimates for annual mean concentration levels of PM_2.5, NO₂, and O₃. Associations between the air pollutants and the risk of diabetes mortality (N = 3598) were evaluated using multivariate Cox proportional hazards models adjusted for both individual-level and census-level contextual covariates.

RESULTS

Diabetes mortality was significantly associated with increasing levels of both PM_2.5 (HR = 1.19; 95% CI: 1.03-1.39 per 10 μg/m³) and NO₂ (HR = 1.09; 95% CI: 1.01-1.18 per 10 ppb). The strength of the relationship was robust to alternate exposure assessments and model specifications. We also observed significant effect modification, with elevated mortality risks observed among those with higher BMI and lower levels of fruit consumption.

CONCLUSIONS

We found that long-term exposure to PM_2.5 and NO₂, but not O₃, is related to increased risk of diabetes mortality in the U.S, with attenuation of adverse effects by lower BMI and higher fruit consumption, suggesting that air pollution is involved in the etiology and/or control of diabetes.

Early-Life Air Pollution Exposure, Neighborhood Poverty, and Childhood Asthma in the United States, 1990⁻2014

Ambient air pollution is a well-known risk factor of various asthma-related outcomes, however, past research has often focused on acute exacerbations rather than asthma development. This study draws on a population-based, multigenerational panel dataset from the United States to assess the association of childhood asthma risk with census block-level, annual-average air pollution exposure measured during the prenatal and early postnatal periods, as well as effect modification by neighborhood poverty. Findings suggest that early-life exposures to nitrogen dioxide (NO₂), a marker of traffic-related pollution, and fine particulate matter (PM_2.5), a mixture of industrial and other pollutants, are positively associated with subsequent childhood asthma diagnosis (OR = 1.25, 95% CI = 1.10–1.41 and OR = 1.25, 95% CI = 1.06–1.46, respectively, per interquartile range (IQR) increase in each pollutant (NO₂ IQR = 8.51 ppb and PM_2.5 IQR = 4.43 µ/m³)). These effects are modified by early-life neighborhood poverty exposure, with no or weaker effects in moderate- and low- (versus high-) poverty areas. This work underscores the importance of a holistic, developmental approach to elucidating the interplay of social and environmental contexts that may create conditions for racial-ethnic and socioeconomic disparities in childhood asthma risk.

Long-term nitrogen dioxide exposure assessment using back-extrapolation of satellite-based land-use regression models for Australia

Assessing historical exposure to air pollution in epidemiological studies is often problematic because of limited spatial and temporal measurement coverage. Several methods for modelling historical exposures have been described, including land-use regression (LUR). Satellite-based LUR is a recent technique that seeks to improve predictive ability and spatial coverage of traditional LUR models by using satellite observations of pollutants as inputs to LUR. Few studies have explored its validity for assessing historical exposures, reflecting the absence of historical observations from popular satellite platforms like Aura (launched mid-2004). We investigated whether contemporary satellite-based LUR models for Australia, developed longitudinally for 2006-2011, could capture nitrogen dioxide (NO₂) concentrations during 1990-2005 at 89 sites around the country. We assessed three methods to back-extrapolate year-2006 NO₂ predictions: (1) 'do nothing' (i.e., use the year-2006 estimates directly, for prior years); (2) change the independent variable 'year' in our LUR models to match the years of interest (i.e., assume a linear trend prior to year-2006, following national average patterns in 2006-2011), and; (3) adjust year-2006 predictions using selected historical measurements. We evaluated prediction error and bias, and the correlation and absolute agreement of measurements and predictions using R² and mean-square error R² (MSE-R²), respectively. We found that changing the year variable led to best performance; predictions captured between 41% (1991; MSE-R² = 31%) and 80% (2003; MSE-R² = 78%) of spatial variability in NO₂ in a given year, and 76% (MSE-R² = 72%) averaged over 1990-2005. We conclude that simple methods for back-extrapolating prior to year-2006 yield valid historical NO₂ estimates for Australia during 1990-2005. These results suggest that for the time scales considered here, satellite-based LUR has a potential role to play in long-term exposure assessment, even in the absence of historical predictor data.

Methods for Assessing Long-Term Exposures to Outdoor Air Pollutants

Purpose of Review

Epidemiological studies of health effects of long-term exposure to outdoor air pollution rely on different exposure assessment methods. This review discusses widely used methods with a special focus on new developments.

Recent Findings

New data and study designs have been applied, including satellite measurements of fine particles and nitrogen dioxide (NO₂). The methods to apply satellite data for epidemiological studies are improving rapidly and have already contributed significantly to national-, continental- and global-scale models. Spatiotemporal models have been developed allowing more detailed temporal resolution compared to spatial models. The development of hybrid models combining dispersion models, satellite observations, land use and surface monitoring has improved models substantially. Mobile monitoring designs to develop models for long-term UFP exposure have been conducted.

Summary

Methods to assess long-term exposure to outdoor air pollution have improved significantly over the past decade. Application of satellite data and mobile monitoring designs is promising new methods.

Ambient Air Pollution and Chronic Bronchitis in a Cohort of U.S. Women

BACKGROUND

Limited evidence links air pollution exposure to chronic cough and sputum production. Few reports have investigated the association between long-term exposure to air pollution and classically defined chronic bronchitis.

OBJECTIVES

Our objective was to estimate the association between long-term exposure to particulate matter (diameter <10 μm, PM₁₀; <2.5μm, PM_2.5), nitrogen dioxide (NO₂), and both incident and prevalent chronic bronchitis.

METHODS

We estimated annual average PM_2.5, PM₁₀, and NO₂ concentrations using a national land-use regression model with spatial smoothing at home addresses of participants in a prospective nationwide U.S. cohort study of sisters of women with breast cancer. Incident chronic bronchitis and prevalent chronic bronchitis, cough and phlegm, were assessed by questionnaires.

RESULTS

Among 47,357 individuals with complete data, 1,383 had prevalent chronic bronchitis at baseline, and 647 incident cases occurred over 5.7-y average follow-up. No associations with incident chronic bronchitis were observed. Prevalent chronic bronchitis was associated with PM₁₀ [adjusted odds ratio (aOR) per interquartile range (IQR) difference (5.8μg/m³)=1.07; 95% confidence interval (CI): 1.01, 1.13]. In never-smokers, PM_2.5 was associated with prevalent chronic bronchitis (aOR=1.18 per IQR difference; 95% CI: 1.04, 1.34), and _NO2 was associated with prevalent chronic bronchitis (aOR=1.10; 95% CI=1.01, 1.20), cough (aOR=1.10; 95% CI: 1.05, 1.16), and phlegm (aOR=1.07; 95% CI: 1.01, 1.14); interaction p-values (nonsmokers vs. smokers) <0.05.

CONCLUSIONS

PM₁₀ exposure was related to chronic bronchitis prevalence. Among never-smokers, PM_2.5 and NO₂ exposure was associated with chronic bronchitis and component symptoms. Results may have policy ramifications for PM₁₀ regulation by providing evidence for respiratory health effects related to long-term PM₁₀ exposure. https://doi.org/10.1289/EHP2199.

The Canadian Urban Environmental Health Research Consortium - a protocol for building a national environmental exposure data platform for integrated analyses of urban form and health

BACKGROUND

Multiple external environmental exposures related to residential location and urban form including, air pollutants, noise, greenness, and walkability have been linked to health impacts or benefits. The Canadian Urban Environmental Health Research Consortium (CANUE) was established to facilitate the linkage of extensive geospatial exposure data to existing Canadian cohorts and administrative health data holdings. We hypothesize that this linkage will enable investigators to test a variety of their own hypotheses related to the interdependent associations of built environment features with diverse health outcomes encompassed by the cohorts and administrative data.

METHODS

We developed a protocol for compiling measures of built environment features that quantify exposure; vary spatially on the urban and suburban scale; and can be modified through changes in policy or individual behaviour to benefit health. These measures fall into six domains: air quality, noise, greenness, weather/climate, and transportation and neighbourhood factors; and will be indexed to six-digit postal codes to facilitate merging with health databases. Initial efforts focus on existing data and include estimates of air pollutants, greenness, temperature extremes, and neighbourhood walkability and socioeconomic characteristics. Key gaps will be addressed for noise exposure, with a new national model being developed, and for transportation-related exposures, with detailed estimates of truck volumes and diesel emissions now underway in selected cities. Improvements to existing exposure estimates are planned, primarily by increasing temporal and/or spatial resolution given new satellite-based sensors and more detailed national air quality modelling. Novel metrics are also planned for walkability and food environments, green space access and function and life-long climate-related exposures based on local climate zones. Critical challenges exist, for example, the quantity and quality of input data to many of the models and metrics has changed over time, making it difficult to develop and validate historical exposures.

DISCUSSION

CANUE represents a unique effort to coordinate and leverage substantial research investments and will enable a more focused effort on filling gaps in exposure information, improving the range of exposures quantified, their precision and mechanistic relevance to health. Epidemiological studies may be better able to explore the common theme of urban form and health in an integrated manner, ultimately contributing new knowledge informing policies that enhance healthy urban living.

Air pollution and subclinical interstitial lung disease: the Multi-Ethnic Study of Atherosclerosis (MESA) air-lung study

We studied whether ambient air pollution is associated with interstitial lung abnormalities (ILAs) and high attenuation areas (HAAs), which are qualitative and quantitative measurements of subclinical interstitial lung disease (ILD) on computed tomography (CT).We performed analyses of community-based dwellers enrolled in the Multi-Ethnic Study of Atherosclerosis (MESA) study. We used cohort-specific spatio-temporal models to estimate ambient pollution (fine particulate matter (PM_2.5), nitrogen oxides (NO_x), nitrogen dioxide (NO₂) and ozone (O₃)) at each home. A total of 5495 participants underwent serial assessment of HAAs by cardiac CT; 2671 participants were assessed for ILAs using full lung CT at the 10-year follow-up. We used multivariable logistic regression and linear mixed models adjusted for age, sex, ethnicity, education, tobacco use, scanner technology and study site.The odds of ILAs increased 1.77-fold per 40 ppb increment in NO_x (95% CI 1.06 to 2.95, p = 0.03). There was an overall trend towards an association between higher exposure to NO_x and greater progression of HAAs (0.45% annual increase in HAAs per 40 ppb increment in NO_x; 95% CI -0.02 to 0.92, p = 0.06). Associations of ambient fine particulate matter (PM_2.5), NO_x and NO₂ concentrations with progression of HAAs varied by race/ethnicity (p = 0.002, 0.007, 0.04, respectively, for interaction) and were strongest among non-Hispanic white people.We conclude that ambient air pollution exposures were associated with subclinical ILD.

Estimated Changes in Life Expectancy and Adult Mortality Resulting from Declining PM2.5 Exposures in the Contiguous United States: 1980-2010

BACKGROUND

PM2.5 precursor emissions have declined over the course of several decades, following the implementation of local, state, and federal air quality policies. Estimating the corresponding change in population exposure and PM2.5-attributable risk of death prior to the year 2000 is made difficult by the lack of PM2.5 monitoring data.

OBJECTIVES

We used a new technique to estimate historical PM2.5 concentrations, and estimated the effects of changes in PM2.5 population exposures on mortality in adults (age ≥30y), and on life expectancy at birth, in the contiguous United States during 1980-2010.

METHODS

We estimated annual mean county-level PM2.5 concentrations in 1980, 1990, 2000, and 2010 using universal kriging incorporating geographic variables. County-level death rates and national life tables for each year were obtained from the U.S. Census and Centers for Disease Control and Prevention. We used log-linear and nonlinear concentration-response coefficients from previous studies to estimate changes in the numbers of deaths and in life years and life expectancy at birth, attributable to changes in PM2.5.

RESULTS

Between 1980 and 2010, population-weighted PM2.5 exposures fell by about half, and the estimated number of excess deaths declined by about a third. The States of California, Virginia, New Jersey, and Georgia had some of the largest estimated reductions in PM2.5-attributable deaths. Relative to a counterfactual population with exposures held constant at 1980 levels, we estimated that people born in 2050 would experience an ∼1-y increase in life expectancy at birth, and that there would be a cumulative gain of 4.4 million life years among adults ≥30y of age.

CONCLUSIONS

Our estimates suggest that declines in PM2.5 exposures between 1980 and 2010 have benefitted public health. https://doi.org/10.1289/EHP507.

Estimating PM2.5 Concentrations in the Conterminous United States Using the Random Forest Approach

To estimate PM_2.5 concentrations, many parametric regression models have been developed, while nonparametric machine learning algorithms are used less often and national-scale models are rare. In this paper, we develop a random forest model incorporating aerosol optical depth (AOD) data, meteorological fields, and land use variables to estimate daily 24 h averaged ground-level PM_2.5 concentrations over the conterminous United States in 2011. Random forests are an ensemble learning method that provides predictions with high accuracy and interpretability. Our results achieve an overall cross-validation (CV) R² value of 0.80. Mean prediction error (MPE) and root mean squared prediction error (RMSPE) for daily predictions are 1.78 and 2.83 μg/m³, respectively, indicating a good agreement between CV predictions and observations. The prediction accuracy of our model is similar to those reported in previous studies using neural networks or regression models on both national and regional scales. In addition, the incorporation of convolutional layers for land use terms and nearby PM_2.5 measurements increase CV R² by ∼0.02 and ∼0.06, respectively, indicating their significant contributions to prediction accuracy. A pair of different variable importance measures both indicate that the convolutional layer for nearby PM_2.5 measurements and AOD values are among the most-important predictor variables for the training process.

Advances in Understanding Air Pollution and CVD

The MESA Air (Multi-Ethnic Study of Atherosclerosis and Air Pollution) leveraged the platform of the MESA cohort into a prospective longitudinal study of relationships between air pollution and cardiovascular health. MESA Air researchers developed fine-scale, state-of-the-art air pollution exposure models for the MESA Air communities, creating individual exposure estimates for each participant. These models combine cohort-specific exposure monitoring, existing monitoring systems, and an extensive database of geographic and meteorological information. Together with extensive phenotyping in MESA-and adding participants and health measurements to the cohort-MESA Air investigated environmental exposures on a wide range of outcomes. Advances by the MESA Air team included not only a new approach to exposure modeling, but also biostatistical advances in addressing exposure measurement error and temporal confounding. The MESA Air study advanced our understanding of the impact of air pollutants on cardiovascular disease and provided a research platform for advances in environmental epidemiology.