Examining PM2.5 concentrations and exposure using multiple models

High-Resolution Geospatial Database: National Criteria-Air-Pollutant Concentrations in the Contiguous U.S., 2016-2020

Concentration estimates for ambient air pollution are used widely in fields such as environmental epidemiology, health impact assessment, urban planning, environmental equity and sustainability. This study builds on previous efforts by developing an updated high-resolution geospatial database of population-weighted annual-average concentrations for six criteria air pollutants (PM2.5, PM10, CO, NO2, SO2, O3) across the contiguous U.S. during a five-year period (2016-2020). We developed Land Use Regression (LUR) models within a partial-least-squares-universal kriging framework by incorporating several land use, geospatial and satellite-based predictor variables. The LUR models were validated using conventional and clustered cross-validation, with the former consistently showing superior performance in capturing the variability of air quality. Most models demonstrated reliable performance (e.g., mean squared error-based R 2 > 0.8, standardised root mean squared error < 0.1). We used the best modelling approach to develop estimates by Census Block, which were then population-weighted averaged at Census Block Group, Census Tract and County geographies. Our database provides valuable insights into the dynamics of air pollution, with utility for environmental risk assessment, public health, policy and urban planning.

Alignment of Air Pollution Exposure Inequality Metrics with Environmental Justice and Equity Goals in the United States

A growing literature within the field of air pollution exposure assessment addresses the issue of environmental justice. Leveraging the increasing availability of exposure datasets with broad spatial coverage and high spatial resolution, a number of works have assessed inequalities in exposure across racial/ethnic and other socioeconomic groupings. However, environmental justice research presents the additional need to evaluate exposure inequity-inequality that is systematic, unfair, and avoidable-which may be framed in several ways. We discuss these framings and describe inequality and inequity conclusions provided from several contrasting approaches drawn from recent work. We recommend that future work addressing environmental justice interventions include complementary "Exposure-driven" and "Socially weighted" metrics, taking an intersectional view of areas and social groups that are both disproportionately impacted by pollution and are impacted by additional health risks resulting from structural racism and consider implications for environmental justice beyond distributional equity.

Increased impacts of aircraft activities on PM2.5 concentration and human health in China

The rapid development of China's aviation industry has caused a rapid increase in airport PM2.5 emissions. This study uses the Global Exposure Mortality Model (GEMM) to evaluate the monthly deaths caused by aircraft activities at 164 airports in China from 2015 to 2023, based on the PM2.5 concentration of airport aircraft activities and the detection data of the China National Environmental Monitoring Center, including twenty age groups, six diseases, and gender. This paper presents three main conclusions. Firstly, aviation PM2.5 emissions significantly impact mortality, with notable variations by year and season. The highest cumulative deaths are recorded in 2023, particularly in the third quarter, which peaked at 8,305 deaths. Despite the comparatively modest total of 11,604 deaths in 2022, a mere 0.2965 μg/m3 increase in PM2.5 concentration would precipitate an additional 39,138 deaths, representing a 1.05-fold rise from 2015. Secondly, the 80-84 age bracket exhibited the highest death proportion (16.51 %-18.73 %), while the 5-9 and 10-14 age groups had the lowest (0 %-0.13 %). Males aged 80-84 are the most affected demographic, with each 1 μg/m3 increase in PM2.5 leading to an additional 87 male deaths monthly in 2023, primarily from stroke and ischemic heart disease. In contrast, females only experienced 67 additional deaths per month from the same concentration increase. Lastly, airports in the economically vibrant Beijing-Shanghai-Guangzhou-Shenzhen region showed the highest mortality rates due to PM2.5 emissions. Airports in eastern coastal areas are more severely impacted than those in central and western China, revealing a spatial clustering of high death tolls in developed regions.

Prescribed burn related increases of population exposure to PM2.5 and O3 pollution in the southeastern US over 2013-2020

Ambient air quality across the southeastern US has improved substantially in recent decades. However, emissions from prescribed burns remain high, which may pose a substantial health threat. We employed a multistage modeling framework to estimate year-round, long-term effects of prescribed burns on air quality and premature deaths. The framework integrates a chemical transport model with a data-fusion approach to estimate 24-h average PM2.5 and maximum daily 8-h averaged O3 (MDA8-O3) concentrations attributable to prescribed burns for the period 2013-2020. The Global Exposure Mortality Model and a log-linear exposure-response function were used to estimate the premature deaths ascribed to long-term prescribed burn PM2.5 and MDA8-O3 exposure in ten southeastern states. Our results indicate that prescribed burns contributed on annual average 0.59 ± 0.20 µg/m3 of PM2.5 (∼10 % of ambient PM2.5) over the ten southeastern states during the study period. On average around 15 % of the state-level ambient PM2.5 concentrations were contributed by prescribed burns in Alabama (0.90 ± 0.15 µg/m3), Florida (0.65 ± 0.19 µg/m3), Georgia (0.91 ± 0.19 µg/m3), Mississippi (0.65 ± 0.10 µg/m3) and South Carolina (0.65 ± 0.09 µg/m3). In the extensive burning season (January-April), daily average contributions to ambient PM2.5 increased up to 22 % in those states. A large part of Alabama and Georgia experiences ≥3.5 µg/m3 prescribed burn PM2.5 over 30 days/year. Additionally, prescribed burns are responsible for an average increase of 0.32 ± 0.12 ppb of MDA8-O3 (0.8 % of ambient MDA8-O3) over the ten southeastern states. The combined effect of prescribed burn PM2.5 exposure, population growth, and increase of baseline mortality over time resulted in a total of 20,416 (95 % confidence interval (CI): 16,562-24,174) excess non-accidental premature deaths in the ten southeastern states, with 25 % of these deaths in Georgia. Prescribed burn MDA8-O3 was responsible for an additional 1,332 (95 % CI: 858-1,803) premature deaths in the ten southeastern states. These findings indicate significant impacts from prescribed burns, suggesting potential benefits of enhanced forest management strategies.

Air Quality Sensor Experts Convene: Current Quality Assurance Considerations for Credible Data

Air sensors can provide valuable non-regulatory and supplemental data as they can be affordably deployed in large numbers and stationed in remote areas far away from regulatory air monitoring stations. Air sensors have inherent limitations that are critical to understand before collecting and interpreting the data. Many of these limitations are mechanistic in nature, which will require technological advances. However, there are documented quality assurance (QA) methods to promote data quality. These include laboratory and field evaluation to quantitatively assess performance, the application of corrections to improve precision and accuracy, and active management of the condition or state of health of deployed air quality sensors. This paper summarizes perspectives presented at the U.S. Environmental Protection Agency's 2023 Air Sensors Quality Assurance Workshop (https://www.epa.gov/air-sensor-toolbox/quality-assurance-air-sensors#QAworkshop) by stakeholders (e.g., manufacturers, researchers, air agencies) and identifies the most pressing needs. These include QA protocols, streamlined data processing, improved total volatile organic compound (TVOC) data interpretation, development of speciated VOC sensors, and increased documentation of hardware and data handling. Community members using air sensors need training and resources, timely data, accessible QA approaches, and shared responsibility with other stakeholders. In addition to identifying the vital next steps, this work provides a set of common QA and QC actions aimed at improving and homogenizing air sensor QA that will allow stakeholders with varying fields and levels of expertise to effectively leverage air sensor data to protect human health.

Local exposure misclassification in national models: relationships with urban infrastructure and demographics

BACKGROUND

National-scale linear regression-based modeling may mischaracterize localized patterns, including hyperlocal peaks and neighborhood- to regional-scale gradients. For studies focused on within-city differences, this mischaracterization poses a risk of exposure misclassification, affecting epidemiological and environmental justice conclusions.

OBJECTIVE

Characterize the difference between intraurban pollution patterns predicted by national-scale land use regression modeling and observation-based estimates within a localized domain and examine the relationship between that difference and urban infrastructure and demographics.

METHODS

We compare highly resolved (0.01 km2) observations of NO2 mixing ratio and ultrafine particle (UFP) count obtained via mobile monitoring with national model predictions in thirteen neighborhoods in the San Francisco Bay Area. Grid cell-level divergence between modeled and observed concentrations is termed "localized difference." We use a flexible machine learning modeling technique, Bayesian Additive Regression Trees, to investigate potentially nonlinear relationships between discrepancy between localized difference and known local emission sources as well as census block group racial/ethnic composition.

RESULTS

We find that observed local pollution extremes are not represented by land use regression predictions and that observed UFP count significantly exceeds regression predictions. Machine learning models show significant nonlinear relationships among localized differences between predictions and observations and the density of several types of pollution-related infrastructure (roadways, commercial and industrial operations). In addition, localized difference was greater in areas with higher population density and a lower share of white non-Hispanic residents, indicating that exposure misclassification by national models differs among subpopulations.

IMPACT

Comparing national-scale pollution predictions with hyperlocal observations in the San Francisco Bay Area, we find greater discrepancies near major roadways and food service locations and systematic underestimation of concentrations in neighborhoods with a lower share of non-Hispanic white residents. These findings carry implications for using national-scale models in intraurban epidemiological and environmental justice applications and establish the potential utility of supplementing large-scale estimates with publicly available urban infrastructure and pollution source information.

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.

Is model-estimated PM2.5 exposure equivalent to station-observed in mortality risk assessment? A literature review and meta-analysis

Model-estimated air pollution exposure assessments have been extensively employed in the evaluation of health risks associated with air pollution. However, few studies synthetically evaluate the reliability of model-estimated PM2.5 products in health risk assessment by comparing them with ground-based monitoring station air quality data. In response to this gap, we undertook a meticulously structured systematic review and meta-analysis. Our objective was to aggregate existing comparative studies to ascertain the disparity in mortality effect estimates derived from model-estimated ambient PM2.5 exposure versus those based on monitoring station-observed PM2.5 exposure. We conducted searches across multiple databases, namely PubMed, Scopus, and Web of Science, using predefined keywords. Ultimately, ten studies were included in the review. Of these, seven investigated long-term annual exposure, while the remaining three studies focused on short-term daily PM2.5 exposure. Despite variances in the estimated Exposure-Response (E-R) associations, most studies revealed positive associations between ambient PM2.5 exposure and all-cause and cardiovascular mortality, irrespective of the exposure being estimated through models or observed at monitoring stations. Our meta-analysis revealed that all-cause mortality risk associated with model-estimated PM2.5 exposure was in line with that derived from station-observed sources. The pooled Relative Risk (RR) was 1.083 (95% CI: 1.047, 1.119) for model-estimated exposure, and 1.089 (95% CI: 1.054, 1.125) for station-observed sources (p = 0.795). In conclusion, most model-estimated air pollution products have demonstrated consistency in estimating mortality risk compared to data from monitoring stations. However, only a limited number of studies have undertaken such comparative analyses, underscoring the necessity for more comprehensive investigations to validate the reliability of these model-estimated exposure in mortality risk assessment.

Ambient fine particulate matter and daily mortality: a comparative analysis of observed and estimated exposure in 347 cities

BACKGROUND

Model-estimated air pollution exposure products have been widely used in epidemiological studies to assess the health risks of particulate matter with diameters of ≤2.5 µm (PM2.5). However, few studies have assessed the disparities in health effects between model-estimated and station-observed PM2.5 exposures.

METHODS

We collected daily all-cause, respiratory and cardiovascular mortality data in 347 cities across 15 countries and regions worldwide based on the Multi-City Multi-Country collaborative research network. The station-observed PM2.5 data were obtained from official monitoring stations. The model-estimated global PM2.5 product was developed using a machine-learning approach. The associations between daily exposure to PM2.5 and mortality were evaluated using a two-stage analytical approach.

RESULTS

We included 15.8 million all-cause, 1.5 million respiratory and 4.5 million cardiovascular deaths from 2000 to 2018. Short-term exposure to PM2.5 was associated with a relative risk increase (RRI) of mortality from both station-observed and model-estimated exposures. Every 10-μg/m3 increase in the 2-day moving average PM2.5 was associated with overall RRIs of 0.67% (95% CI: 0.49 to 0.85), 0.68% (95% CI: -0.03 to 1.39) and 0.45% (95% CI: 0.08 to 0.82) for all-cause, respiratory, and cardiovascular mortality based on station-observed PM2.5 and RRIs of 0.87% (95% CI: 0.68 to 1.06), 0.81% (95% CI: 0.08 to 1.55) and 0.71% (95% CI: 0.32 to 1.09) based on model-estimated exposure, respectively.

CONCLUSIONS

Mortality risks associated with daily PM2.5 exposure were consistent for both station-observed and model-estimated exposures, suggesting the reliability and potential applicability of the global PM2.5 product in epidemiological studies.

24-hour average PM2.5 concentration caused by aircraft in Chinese airports from Jan. 2006 to Dec. 2023

Since 2006, the rapid development of China's aviation industry has been accompanied by a significant increase in one of its emissions, namely, PM2.5, which poses a substantial threat to human health. However, little data is describing the PM2.5 concentration caused by aircraft activities. This study addresses this gap by initially computing the monthly PM2.5 emissions of the landing-take-off (LTO) stage from Jan. 2006 to Dec. 2023 for 175 Chinese airports, employing the modified BFFM2-FOA-FPM method. Subsequently, the study uses the Gaussian diffusion model to measure the 24-hour average PM2.5 concentration resulting from flight activities at each airport. This study mainly draws the following conclusions: Between 2006 and 2023, the highest recorded PM2.5 concentration data at all airports was observed in 2018, reaching 5.7985 micrograms per cubic meter, while the lowest point was recorded in 2022, at 2.0574 micrograms per cubic meter. Moreover, airports with higher emissions are predominantly located in densely populated and economically vibrant regions such as Beijing, Shanghai, Guangzhou, Chengdu, and Shenzhen.

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.

PM2.5-Attributable Mortality Burden Variability in the Continental U.S

Epidemiologic studies have consistently observed associations between fine particulate matter (PM2.5) exposure and premature mortality. These studies use air quality concentration information from a combination of sources to estimate pollutant exposures and then assess how mortality varies as a result of differing exposures. Health impact assessments then typically use a single log-linear hazard ratio (HR) per health outcome to estimate counts of avoided human health effects resulting from air quality improvements. This paper estimates the total PM2.5-attributable premature mortality burden using a variety of methods for estimating exposures and quantifying PM2.5-attributable deaths in 2011 and 2028. We use: 1) several exposure models that apply a wide range of methods, and 2) a variety of HRs from the epidemiologic literature that relate long-term PM2.5 exposures to mortality among the U.S. population. We then further evaluate the variability of aggregated national premature mortality estimates to stratification by race and/or ethnicity or exposure level (e.g., below the current annual PM2.5 National Ambient Air Quality Standards). We find that unstratified annual adult mortality burden incidence estimates vary more (e.g., ~3-fold) by HR than by exposure model (e.g., <10%). In addition, future mortality burden estimates stratified by race/ethnicity are larger than the unstratified estimates of the entire population, and studies that stratify PM2.5-attributable mortality HRs by an exposure concentration threshold led to substantially higher estimates. These results are intended to provide transparency regarding the sensitivity of mortality estimates to upstream input choices.

Assessing uncertainty and heterogeneity in machine learning-based spatiotemporal ozone prediction in Beijing-Tianjin- Hebei region in China

Accurate prediction of spatiotemporal ozone concentration is of great significance to effectively establish advanced early warning systems and regulate air pollution control. However, the comprehensive assessment of uncertainty and heterogeneity in spatiotemporal ozone prediction remains unknown. Here, we systematically analyze the hourly and daily spatiotemporal predictive performances using convolutional long short term memory (ConvLSTM) and deep convolutional generative adversarial network (DCGAN) models over the Beijing-Tianjin-Hebei region in China from 2013 to 2018. In extensive scenarios, our results show that the machine learning-based (ML-based) models achieve better spatiotemporal ozone concentration prediction performance with multiple meteorological conditions. A further comparison to the air pollution model-Nested Air Quality Prediction Modelling System (NAQPMS) and monitoring observations, the ConvLSTM model demonstrates the practical feasibility of identifying high ozone concentration distribution and capturing spatiotemporal ozone variation patterns at a high spatial resolution (here 15 km × 15 km).

Burden of fine air pollution on mortality in the desert climate of Kuwait

BACKGROUND

Middle Eastern desert countries like Kuwait are known for intense dust storms and enormous petrochemical industries affecting ambient air pollution. However, local health authorities have not been able to assess the health impacts of air pollution due to limited monitoring networks and a lack of historical exposure data.

OBJECTIVE

To assess the burden of PM_2.5 on mortality in the understudied dusty environment of Kuwait.

METHODS

We analyzed the acute impact of fine particulate matter (PM_2.5) on daily mortality in Kuwait between 2001 and 2016. To do so, we used spatiotemporally resolved estimates of PM_2.5 in the region. Our analysis explored factors such as cause of death, sex, age, and nationality. We fitted quasi-Poisson time-series regression for lagged PM_2.5 adjusted for time trend, seasonality, day of the week, temperature, and relative humidity.

RESULTS

There was a total of 70,321 deaths during the study period of 16 years. The average urban PM_2.5 was estimated to be 46.2 ± 19.8 µg/m³. A 10 µg/m³ increase in a 3-day moving average of urban PM_2.5 was associated with 1.19% (95% CI: 0.59, 1.80%) increase in all-cause mortality. For a 10 µg/m³ reduction in annual PM_2.5 concentrations, a total of 52.3 (95% CI: 25.7, 79.1) deaths each year could be averted in Kuwait. That is, 28.6 (95% CI: 10.3, 47.0) Kuwaitis, 23.9 (95% CI: 6.4, 41.5) non-Kuwaitis, 9.4 (95% CI: 1.2, 17.8) children, and 20.9 (95% CI: 4.3, 37.6) elderly deaths each year.

IMPACT STATEMENT

The overwhelming prevalence of devastating dust storms and enormous petrochemical industries in the Gulf and the Middle East has intensified the urgency to address air pollution and its detrimental health effects. Alarmingly, the region's epidemiological research lags behind, hindered by a paucity of ground monitoring networks and historical exposure data. In response, we are harnessing the power of big data to generate predictive models of air pollution across time and space, providing crucial insights into the mortality burden associated with air pollution in this under-researched yet critically impacted area.

Health Impacts of Long-Term NO2 Exposure and Inequalities among the Chinese Population from 2013 to 2020

Nitrogen dioxide (NO₂) is associated with mortality and many other adverse health outcomes. In 2021, the World Health Organization established a new NO₂ air quality guideline (AQG) (annual average <10 μg/m³). However, the burden of diseases attributable to long-term NO₂ exposure above the AQG is unknown in China. Nitrogen oxide is a major air pollutant in populous cities, which are disproportionately impacted by NO₂; this represents a form of environmental inequality. We conducted a nationwide risk assessment of premature deaths attributable to long-term NO₂ exposure from 2013 to 2020 based on the exposure-response relationship, high-resolution annual NO₂ concentrations, and gridded population data (considering sex, age, and residence [urban vs rural]). We calculated health metrics including attributable deaths, years of life lost (YLL), and loss of life expectancy (LLE). Inequality in the distribution of attributable deaths and YLLs was evaluated by the Lorenz curve and Gini index. According to the health impact assessments, in 2013, long-term NO₂ exposure contributed to 315,847 (95% confidence interval [CI]: 306,709-319,269) premature deaths, 7.90 (7.68-7.99) million YLLs, and an LLE of 0.51 (0.50-0.52) years. The high-risk subgroup (top 20%) accounted for 85.7% of all NO₂-related deaths and 85.2% of YLLs, resulting in Gini index values of 0.81 and 0.67, respectively. From 2013 to 2020, the estimated health impact from NO₂ exposure was significantly reduced, but inequality displayed a slightly increasing trend. Our study revealed a considerable burden of NO₂-related deaths in China, which were disproportionally frequent in a small high-risk subgroup. Future clean air initiatives should focus not only on reducing the average level of NO₂ exposure but also minimizing inequality.

Nowcasting Applications of Geostationary Satellite Hourly Surface PM2.5 Data

The mass concentration of fine particulate matter (PM2.5; diameters less than 2.5 μm) estimated from geostationary satellite aerosol optical depth (AOD) data can supplement the network of ground monitors with high temporal (hourly) resolution. Estimates of PM2.5 over the United States (US) were derived from NOAA's operational geostationary satellites Advanced Baseline Imager (ABI) AOD data using a geographically weighted regression with hourly and daily temporal resolution. Validation versus ground observations shows a mean bias of -21.4% and -15.3% for hourly and daily PM2.5 estimates, respectively, for concentrations ranging from 0 to 1000 μg/m3. Because satellites only observe AOD in the daytime, the relation between observed daytime PM2.5 and daily mean PM2.5 was evaluated using ground measurements; PM2.5 estimated from ABI AODs were also examined to study this relationship. The ground measurements show that daytime mean PM2.5 has good correlation (r > 0.8) with daily mean PM2.5 in most areas of the US, but with pronounced differences in the western US due to temporal variations caused by wildfire smoke; the relation between the daytime and daily PM2.5 estimated from the ABI AODs has a similar pattern. While daily or daytime estimated PM2.5 provides exposure information in the context of the PM2.5 standard (> 35 μg/m3), the hourly estimates of PM2.5 used in Nowcasting show promise for alerts and warnings of harmful air quality. The geostationary satellite based PM2.5 estimates inform the public of harmful air quality ten times more than standard ground observations (1.8 vs. 0.17 million people per hour).

Communities of color are disproportionately exposed to long-term and short-term PM2.5 in metropolitan America

We conducted a novel investigation of neighborhood-level racial/ethnic exposure disparities employing measures aligned with long-term and short-term PM_2.5 air pollution benchmarks across metropolitan contexts of the contiguous United States, 2012-2016. We used multivariable generalized estimating equations (GEE) to quantify PM_2.5 exposure disparities based on the census tract composition of people of color (POC) and POC groups (Hispanic/Latina/x/o, Black, Asian). We examined eight census tract-level measures of longer-to-shorter term exposures derived from data on modeled daily ambient PM_2.5 concentrations. We found associations between increased POC composition and greater exposure to all PM_2.5 measures, with associations strengthening across measures of longer-to-shorter term exposures. In a GEE with a negative binomial distribution, a standard deviation increase in POC composition predicted a 0.6% increase (incidence rate ratio (IRR): 1.006, 95% confidence interval (CI): 1.005-1.008) in the number of days PM_2.5 concentrations were ≥5 μg/m³ (longest-term benchmark). In a GEE with an inverse Gaussian distribution, a standard deviation increase in POC composition predicted a 0.110 μg/m³ (1.0%) increase (B: 0.110, 95% CI: 0.076-0.143) in mean PM_2.5 concentration. In GEEs with a negative binomial distribution, the effect of a standard deviation increase in POC composition on exposure strengthened to 2.6% (IRR:1.026, 95% CI:1.017-1.035), 3.4% (IRR:1.034, 95% CI:1.022-1.047), 4.2% (IRR:1.042, 95% CI:1.025-1.058), 16.2% (IRR:1.162, 95% CI:1.117-1.210), 22.7% (IRR:1.227, 95% CI:1.137-1.325) and 28.3% (IRR:1.283, 95% CI:1.144-1.439) with respect to the number of days PM_2.5 concentrations were ≥10, 12, 15, 25, 35 and 55.5 μg/m³. POC group models indicated exposure disparities based on greater Hispanic/Latina/x/o, Asian, and Black composition. Evidence for stronger POC associations with shorter-term (higher concentration) PM_2.5 exceedances suggests that reducing PM_2.5 would attenuate racial/ethnic exposure disparities.

National Exposure Models for Source-Specific Primary Particulate Matter Concentrations Using Aerosol Mass Spectrometry Data

This paper investigates the feasibility of developing national empirical models to predict ambient concentrations of sparsely monitored air pollutants at high spatial resolution. We used a data set of cooking organic aerosol (COA) and hydrocarbon-like organic aerosol (HOA; traffic primary organic PM) measured using aerosol mass spectrometry across the continental United States. The monitoring locations were selected to span the national distribution of land-use and source-activity variables commonly used for land-use regression modeling (e.g., road length, restaurant count, etc.). The models explain about 60% of the spatial variability of the measured data (R2 0.63 for the COA model and 0.62 for the HOA model). Extensive cross-validation suggests that the models are robust with reasonable transferability. The models predict large urban-rural and intra-urban variability with hotspots in urban areas and along the road corridors. The predicted national concentration surfaces show reasonable spatial correlation with source-specific national chemical transport model (CTM) simulations (R2: 0.45 for COA, 0.4 for HOA). Our measured data, empirical models, and CTM predictions all show that COA concentrations are about two times higher than HOA. Since COA and HOA are important contributors to the intra-urban spatial variability of the total PM2.5, our results highlight the potential importance of controlling commercial cooking emissions for air quality management in the United States.

Inequality in exposure to air pollutants: A new perspective

In research and policy design we mainly use a 'population weighted average concentrations' perspective to study changes in air quality, to evaluate if past policies have been effective, or to assess the impact of future air quality plans. This angle is useful and informative, but sometimes masks other important patterns. In this paper we propose to add, to the existing population weighted average point of view, a new indicator that brings to the fore the 'inequalities' in exposure. This inequality indicator is based on the Gini coefficient, usually applied in Economics and here considered to evaluate if exposure to air pollutants is equally distributed among population. A case study for this new indicator is then proposed, to assess the evolution of exposure to air pollutants in Europe from 2000 to 2018, in terms of both average exposure and inequality levels. The results show that using only average exposure metrics can mask other interesting patterns, and confirm the benefits of including this alternative perspective into the analysis.

Air pollution exposure disparities in US public housing developments

Fine particulate matter 2.5 microns or less in diameter (PM_2.5) is widely recognized to be a major public health concern. While ethnic/racial minority and lower socioeconomic status individuals in the US experience higher PM_2.5 exposure, previous research on social disparities in PM_2.5 exposure has not examined residents of federally-assisted public housing developments (PHDs). Here we present the first national-scale analysis of the relationship between outdoor PM_2.5 exposure and PHD residency in the US, as well as exposure disparities within the population of households residing in PHDs. We integrated data on average annual PM_2.5 concentrations (2011–2015) with US Department of Housing and Urban Development data on PHDs (2015), and socio-demographic information from the 2011–2015 American Community Survey. Results from multivariable generalized estimating equations indicated that PHD locations, units, and residents are significantly overrepresented in neighborhoods with greater PM_2.5 exposure, after accounting for clustering, urbanization, and other socio-demographic factors. Additionally, significantly higher percentages of Black, Hispanic, disabled, and extremely low-income households reside in PHDs with greater PM_2.5 exposure. Findings represent an important starting point for future research and emphasize the urgent need to identify gaps in environmental, public health, and housing policies that contribute to disproportionate air pollution exposures among PHD residents.

Flexible Bayesian Ensemble Machine Learning Framework for Predicting Local Ozone Concentrations

3D-grid-based chemical transport models, such as the Community Multiscale Air Quality (CMAQ) modeling system, have been widely used for predicting concentrations of ambient air pollutants. However, typical horizontal resolutions of nationwide CMAQ simulations (12 × 12 km2) cannot capture local-scale gradients for accurately assessing human exposures and environmental justice disparities. In this study, a Bayesian ensemble machine learning (BEML) framework, which integrates 13 learning algorithms, was developed for downscaling CMAQ estimates of ozone daily maximum 8 h averages to the census tract level, across the contiguous US, and was demonstrated for 2011. Three-stage hyperparameter tuning and targeted validations were designed to ensure the ensemble model's ability to interpolate, extrapolate, and capture concentration peaks. The Shapley value metric from coalitional game theory was applied to interpret the drivers of subgrid gradients. The flexibility (transferability) of the 2011-trained BEML model was further tested by evaluating its ability to estimate fine-scale concentrations for other years (2012-2017) without retraining. To demonstrate the feasibility of using the BEML approach to strictly "data-limited" situations, the model was applied to downscale CMAQ outputs for a future-year scenario-based simulation that considers effects of variations in meteorology associated with climate change.

Assessing the health estimation capacity of air pollution exposure prediction models

BACKGROUND

The era of big data has enabled sophisticated models to predict air pollution concentrations over space and time. Historically these models have been evaluated using overall metrics that measure how close predictions are to monitoring data. However, overall methods are not designed to distinguish error at timescales most relevant for epidemiologic studies, such as day-to-day errors that impact studies of short-term health associations.

METHODS

We introduce frequency band model performance, which quantifies health estimation capacity of air quality prediction models for time series studies of air pollution and health. Frequency band model performance uses a discrete Fourier transform to evaluate prediction models at timescales of interest. We simulated fine particulate matter (PM_2.5), with errors at timescales varying from acute to seasonal, and health time series data. To compare evaluation approaches, we use correlations and root mean squared error (RMSE). Additionally, we assess health estimation capacity through bias and RMSE in estimated health associations. We apply frequency band model performance to PM_2.5 predictions at 17 monitors in 8 US cities.

RESULTS

In simulations, frequency band model performance rates predictions better (lower RMSE, higher correlation) when there is no error at a particular timescale (e.g., acute) and worse when error is added to that timescale, compared to overall approaches. Further, frequency band model performance is more strongly associated (R² = 0.95) with health association bias compared to overall approaches (R² = 0.57). For PM_2.5 predictions in Salt Lake City, UT, frequency band model performance better identifies acute error that may impact estimated short-term health associations.

CONCLUSIONS

For epidemiologic studies, frequency band model performance provides an improvement over existing approaches because it evaluates models at the timescale of interest and is more strongly associated with bias in estimated health associations. Evaluating prediction models at timescales relevant for health studies is critical to determining whether model error will impact estimated health associations.

Air pollution exposure disparities across US population and income groups

Air pollution contributes to the global burden of disease, with ambient exposure to fine particulate matter of diameters smaller than 2.5 μm (PM2.5) being identified as the fifth-ranking risk factor for mortality globally1. Racial/ethnic minorities and lower-income groups in the USA are at a higher risk of death from exposure to PM2.5 than are other population/income groups2-5. Moreover, disparities in exposure to air pollution among population and income groups are known to exist6-17. Here we develop a data platform that links demographic data (from the US Census Bureau and American Community Survey) and PM2.5 data18 across the USA. We analyse the data at the tabulation area level of US zip codes (N is approximately 32,000) between 2000 and 2016. We show that areas with higher-than-average white and Native American populations have been consistently exposed to average PM2.5 levels that are lower than areas with higher-than-average Black, Asian and Hispanic or Latino populations. Moreover, areas with low-income populations have been consistently exposed to higher average PM2.5 levels than areas with high-income groups for the years 2004-2016. Furthermore, disparities in exposure relative to safety standards set by the US Environmental Protection Agency19 and the World Health Organization20 have been increasing over time. Our findings suggest that more-targeted PM2.5 reductions are necessary to provide all people with a similar degree of protection from environmental hazards. Our study is observational and cannot provide insight into the drivers of the identified disparities.

Short-term PM2.5 and cardiovascular admissions in NY State: assessing sensitivity to exposure model choice

BACKGROUND

Air pollution health studies have been increasingly using prediction models for exposure assessment even in areas without monitoring stations. To date, most studies have assumed that a single exposure model is correct, but estimated effects may be sensitive to the choice of exposure model.

METHODS

We obtained county-level daily cardiovascular (CVD) admissions from the New York (NY) Statewide Planning and Resources Cooperative System (SPARCS) and four sets of fine particulate matter (PM_2.5) spatio-temporal predictions (2002-2012). We employed overdispersed Poisson models to investigate the relationship between daily PM_2.5 and CVD, adjusting for potential confounders, separately for each state-wide PM_2.5 dataset.

RESULTS

For all PM_2.5 datasets, we observed positive associations between PM_2.5 and CVD. Across the modeled exposure estimates, effect estimates ranged from 0.23% (95%CI: -0.06, 0.53%) to 0.88% (95%CI: 0.68, 1.08%) per 10 µg/m³ increase in daily PM_2.5. We observed the highest estimates using monitored concentrations 0.96% (95%CI: 0.62, 1.30%) for the subset of counties where these data were available.

CONCLUSIONS

Effect estimates varied by a factor of almost four across methods to model exposures, likely due to varying degrees of exposure measurement error. Nonetheless, we observed a consistently harmful association between PM_2.5 and CVD admissions, regardless of model choice.