Assessing the Effects of Stove Use Patterns and Kitchen Chimneys on Indoor Air Quality during a Multiyear Cookstove Randomized Control Trial in Rural India

We conducted indoor air quality (IAQ) measurements during a multiyear cookstove randomized control trial in two rural areas in northern and southern India. A total of 1205 days of kitchen PM_2.5 were measured in control and intervention households during six ∼3 month long measurement periods across two study locations. Stoves used included traditional solid fuel (TSF), improved biomass, and liquefied petroleum gas (LPG) models. Intent-to-treat analysis indicates that the intervention reduced average 24 h PM_2.5 and black carbon in only one of the two follow-up measurement periods in both areas, suggesting mixed effectiveness. Average PM_2.5 levels were ∼50% lower in households with LPG (for exclusive LPG use: >75% lower) than in those without LPG. PM_2.5 was 66% lower in households making exclusive use of an improved chimney stove versus a traditional chimney stove and TSF-exclusive kitchens with a built-in chimney had ∼60% lower PM_2.5 than those without a chimney, indicating that kitchen ventilation can be as important as the stove technology in improving IAQ. Diurnal trends in real-time PM_2.5 indicate that kitchen chimneys were especially effective at reducing peak concentrations, which leads to decreases in daily PM_2.5 in these households. Our data demonstrate a clear hierarchy of IAQ improvement in real world, "stove-stacking" households, driven by different stove technologies and kitchen characteristics.

Cardiopulmonary Mortality and Fine Particulate Air Pollution by Species and Source in a National U.S. Cohort

The purpose of this study was to estimate cardiopulmonary mortality associations for long-term exposure to PM_2.5 species and sources (i.e., components) within the U.S. National Health Interview Survey cohort. Exposures were estimated through a chemical transport model for six species (i.e., elemental carbon (EC), primary organic aerosols (POA), secondary organic aerosols (SOA), sulfate (SO₄), ammonium (NH₄), nitrate (NO₃)) and five sources of PM_2.5 (i.e., vehicles, electricity-generating units (EGU), non-EGU industrial sources, biogenic sources (bio), "other" sources). In single-pollutant models, we found positive, significant (p < 0.05) mortality associations for all components, except POA. After adjusting for remaining PM_2.5 (total PM_2.5 minus component), we found significant mortality associations for EC (hazard ratio (HR) = 1.36; 95% CI [1.12, 1.64]), SOA (HR = 1.11; 95% CI [1.05, 1.17]), and vehicle sources (HR = 1.06; 95% CI [1.03, 1.10]). HRs for EC, SOA, and vehicle sources were significantly larger in comparison to those for remaining PM_2.5 (per unit μg/m³). Our findings suggest that cardiopulmonary mortality associations vary by species and source, with evidence that EC, SOA, and vehicle sources are important contributors to the PM_2.5 mortality relationship. With further validation, these findings could facilitate targeted pollution regulations that more efficiently reduce air pollution mortality.

Ambient Air Pollution and Socioeconomic Status in China

BACKGROUND

Air pollution disparities by socioeconomic status (SES) are well documented for the United States, with most literature indicating an inverse relationship (i.e., higher concentrations for lower-SES populations). Few studies exist for China, a country accounting for 26% of global premature deaths from ambient air pollution.

OBJECTIVE

Our objective was to test the relationship between ambient air pollution exposures and SES in China.

METHODS

We combined estimated year 2015 annual-average ambient levels of nitrogen dioxide (NO 2 ) and fine particulate matter [PM ≤ 2.5 μ m in aerodynamic diameter (PM 2.5 )] with national demographic information. Pollution estimates were derived from a national empirical model for China at 1 -km spatial resolution; demographic estimates were derived from national gridded gross national product (GDP) per capita at 1 -km resolution, and (separately) a national representative sample of 21,095 individuals from the China Health and Retirement Longitudinal Study (CHARLS) 2015 cohort. Our use of global data on population density and cohort data on where people live helped avoid the spatial imprecision found in publicly available census data for China. We quantified air pollution disparities among individual's rural-to-urban migration status; SES factors (education, occupation, and income); and minority status. We compared results using three approaches to SES measurement: individual SES score, community-averaged SES score, and gridded GDP per capita.

RESULTS

Ambient NO 2 and PM 2.5 levels were higher for higher-SES populations than for lower-SES population, higher for long-standing urban residents than for rural-to-urban migrant populations, and higher for the majority ethnic group (Han) than for the average across nine minority groups. For the three SES measurements (individual SES score, community-averaged SES score, gridded GDP per capita), a 1-interquartile range higher SES corresponded to higher concentrations of 6 - 9   μ g / m 3 NO 2 and 3 - 6   μ g / m 3 PM 2.5 ; average concentrations for the highest and lowest 20th percentile of SES differed by 41-89% for NO 2 and 12-25% for PM 2.5 . This pattern held in rural and urban locations, across geographic regions, across a wide range of spatial resolution, and for modeled vs. measured pollution concentrations.

CONCLUSIONS

Multiple analyses here reveal that in China, ambient NO 2 and PM 2.5 concentrations are higher for high-SES than for low-SES individuals; these results are robust to multiple sensitivity analyses. Our findings are consistent with the idea that in China's current industrialization and urbanization stage, economic development is correlated with both SES and air pollution. To our knowledge, our study provides the most comprehensive picture to date of ambient air pollution disparities in China; the results differ dramatically from results and from theories to explain conditions in the United States. https://doi.org/10.1289/EHP9872.

Global, high-resolution, reduced-complexity air quality modeling for PM2.5 using InMAP (Intervention Model for Air Pollution)

Each year, millions of premature deaths worldwide are caused by exposure to outdoor air pollution, especially fine particulate matter (PM2.5). Designing policies to reduce these deaths relies on air quality modeling for estimating changes in PM2.5 concentrations from many scenarios at high spatial resolution. However, air quality modeling typically has substantial requirements for computation and expertise, which limits policy design, especially in countries where most PM2.5-related deaths occur. Lower requirement reduced-complexity models exist but are generally unavailable worldwide. Here, we adapt InMAP, a reduced-complexity model originally developed for the United States, to simulate annual-average primary and secondary PM2.5 concentrations across a global-through-urban spatial domain: “Global InMAP”. Global InMAP uses a variable resolution grid, with horizontal grid cell widths ranging from 500 km in remote locations to 4km in urban locations. We evaluate Global InMAP performance against both measurements and a state-of-the-science chemical transport model, GEOS-Chem. Against measurements, InMAP predicts total PM2.5 concentrations with a normalized mean error of 62%, compared to 41% for GEOS-Chem. For the emission scenarios considered, Global InMAP reproduced GEOS-Chem pollutant concentrations with a normalized mean bias of 59%–121%, which is sufficient for initial policy assessment and scoping. Global InMAP can be run on a desktop computer; simulations here took 2.6–8.4 hours. This work presents a global, open-source, reduced-complexity air quality model to facilitate policy assessment worldwide, providing a screening tool for reducing air pollution-related deaths where they occur most.

Historical Redlining Is Associated with Present-Day Air Pollution Disparities in U.S. Cities

Communities of color in the United States are systematically exposed to higher levels of air pollution. We explore here how redlining, a discriminatory mortgage appraisal practice from the 1930s by the federal Home Owners' Loan Corporation (HOLC), relates to present-day intraurban air pollution disparities in 202 U.S. cities. In each city, we integrated three sources of data: (1) detailed HOLC security maps of investment risk grades [A ("best"), B, C, and D ("hazardous", i.e., redlined)], (2) year-2010 estimates of NO₂ and PM_2.5 air pollution levels, and (3) demographic information from the 2010 U.S. census. We find that pollution levels have a consistent and nearly monotonic association with HOLC grade, with especially pronounced (>50%) increments in NO₂ levels between the most (grade A) and least (grade D) preferentially graded neighborhoods. On a national basis, intraurban disparities for NO₂ and PM_2.5 are substantially larger by historical HOLC grade than they are by race and ethnicity. However, within each HOLC grade, racial and ethnic air pollution exposure disparities persist, indicating that redlining was only one of the many racially discriminatory policies that impacted communities. Our findings illustrate how redlining, a nearly 80-year-old racially discriminatory policy, continues to shape systemic environmental exposure disparities in the United States.

Historical Redlining Is Associated with Present-Day Air Pollution Disparities in U.S. Cities

Communities of color in the United States are systematically exposed to higher levels of air pollution. We explore here how redlining, a discriminatory mortgage appraisal practice from the 1930s by the federal Home Owners' Loan Corporation (HOLC), relates to present-day intraurban air pollution disparities in 202 U.S. cities. In each city, we integrated three sources of data: (1) detailed HOLC security maps of investment risk grades [A ("best"), B, C, and D ("hazardous", i.e., redlined)], (2) year-2010 estimates of NO₂ and PM_2.5 air pollution levels, and (3) demographic information from the 2010 U.S. census. We find that pollution levels have a consistent and nearly monotonic association with HOLC grade, with especially pronounced (>50%) increments in NO₂ levels between the most (grade A) and least (grade D) preferentially graded neighborhoods. On a national basis, intraurban disparities for NO₂ and PM_2.5 are substantially larger by historical HOLC grade than they are by race and ethnicity. However, within each HOLC grade, racial and ethnic air pollution exposure disparities persist, indicating that redlining was only one of the many racially discriminatory policies that impacted communities. Our findings illustrate how redlining, a nearly 80-year-old racially discriminatory policy, continues to shape systemic environmental exposure disparities in the United States.

Disparities in Air Pollution Exposure in the United States by Race/Ethnicity and Income, 1990-2010

BACKGROUND

Few studies have investigated air pollution exposure disparities by race/ethnicity and income across criteria air pollutants, locations, or time.

OBJECTIVE

The objective of this study was to quantify exposure disparities by race/ethnicity and income throughout the contiguous United States for six criteria air pollutants, during the period 1990 to 2010.

METHODS

We quantified exposure disparities among racial/ethnic groups (non-Hispanic White, non-Hispanic Black, Hispanic (any race), non-Hispanic Asian) and by income for multiple spatial units (contiguous United States, states, urban vs. rural areas) and years (1990, 2000, 2010) for carbon monoxide (CO), nitrogen dioxide (NO 2 ), ozone (O 3 ), particulate matter with aerodynamic diameter ≤ 2.5 μ m (PM 2.5 ; excluding year-1990), particulate matter with aerodynamic diameter ≤ 10 μ m (PM 10 ), and sulfur dioxide (SO 2 ). We used census data for demographic information and a national empirical model for ambient air pollution levels.

RESULTS

For all years and pollutants, the racial/ethnic group with the highest national average exposure was a racial/ethnic minority group. In 2010, the disparity between the racial/ethnic group with the highest vs. lowest national-average exposure was largest for NO 2 [54% (4.6  ppb )], smallest for O 3 [3.6% (1.6  ppb )], and intermediate for the remaining pollutants (13%-19%). The disparities varied by U.S. state; for example, for PM 2.5 in 2010, exposures were at least 5% higher than average in 63% of states for non-Hispanic Black populations; in 33% and 26% of states for Hispanic and for non-Hispanic Asian populations, respectively; and in no states for non-Hispanic White populations. Absolute exposure disparities were larger among racial/ethnic groups than among income categories (range among pollutants: between 1.1 and 21 times larger). Over the period studied, national absolute racial/ethnic exposure disparities declined by between 35% (0.6 6 μ g / m 3 ; PM 2.5 ) and 88% (0.35  ppm ; CO); relative disparities declined to between 0.99 × (PM 2.5 ; i.e., nearly zero change) and 0.71 × (CO; i.e., a ∼ 29 % reduction).

DISCUSSION

As air pollution concentrations declined during the period 1990 to 2010, absolute (and to a lesser extent, relative) racial/ethnic exposure disparities also declined. However, in 2010, racial/ethnic exposure disparities remained across income levels, in urban and rural areas, and in all states, for multiple pollutants. https://doi.org/10.1289/EHP8584.

National Empirical Models of Air Pollution Using Microscale Measures of the Urban Environment

National-scale empirical models of air pollution (e.g., Land Use Regression) rely on predictor variables (e.g., population density, land cover) at different geographic scales. These models typically lack microscale variables (e.g., street level), which may improve prediction with fine-spatial gradients. We developed microscale variables of the urban environment including Point of Interest (POI) data, Google Street View (GSV) imagery, and satellite-based measures of urban form. We developed United States national models for six criteria pollutants (NO₂, PM_2.5, O₃, CO, PM₁₀, SO₂) using various modeling approaches: Stepwise Regression + kriging (SW-K), Partial Least Squares + kriging (PLS-K), and Machine Learning + kriging (ML-K). We compared predictor variables (e.g., traditional vs microscale) and emerging modeling approaches (ML-K) to well-established approaches (i.e., traditional variables in a PLS-K or SW-K framework). We found that combined predictor variables (traditional + microscale) in the ML-K models outperformed the well-established approaches (10-fold spatial cross-validation (CV) R² increased 0.02-0.42 [average: 0.19] among six criteria pollutants). Comparing all model types using microscale variables to models with traditional variables, the performance is similar (average difference of 10-fold spatial CV R² = 0.05) suggesting microscale variables are a suitable substitute for traditional variables. ML-K and microscale variables show promise for improving national empirical models.

Local- and regional-scale racial and ethnic disparities in air pollution determined by long-term mobile monitoring

Disparity in air pollution exposure arises from variation at multiple spatial scales: along urban-to-rural gradients, between individual cities within a metropolitan region, within individual neighborhoods, and between city blocks. Here, we improve on existing capabilities to systematically compare urban variation at several scales, from hyperlocal (<100 m) to regional (>10 km), and to assess consequences for outdoor air pollution experienced by residents of different races and ethnicities, by creating a set of uniquely extensive and high-resolution observations of spatially variable pollutants: NO, NO2, black carbon (BC), and ultrafine particles (UFP). We conducted full-coverage monitoring of a wide sample of urban and suburban neighborhoods (93 km2 and 450,000 residents) in four counties of the San Francisco Bay Area using Google Street View cars equipped with the Aclima mobile platform. Comparing scales of variation across the sampled population, greater differences arise from localized pollution gradients for BC and NO (pollutants dominated by primary sources) and from regional gradients for UFP and NO2 (pollutants dominated by secondary contributions). Median concentrations of UFP, NO, and NO2 are, for Hispanic and Black populations, 8 to 30% higher than the population average; for White populations, average exposures to these pollutants are 9 to 14% lower than the population average. Systematic racial/ethnic disparities are influenced by regional concentration gradients due to sharp contrasts in demographic composition among cities and urban districts, while within-group extremes arise from local peaks. Our results illustrate how detailed and extensive fine-scale pollution observations can add new insights about differences and disparities in air pollution exposures at the population scale.

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.

High-Spatial-Resolution Estimates of Ultrafine Particle Concentrations across the Continental United States

There is growing evidence that ultrafine particles (UFP; particles smaller than 100 nm) are likely more toxic than larger particles. However, the health effects of UFP remain uncertain due in part to the lack of large-scale population-based exposure assessment. We develop a national-scale empirical model of particle number concentration (PNC; a measure of UFP) using data from mobile monitoring and fixed sites across the United States and a land-use regression (LUR) modeling framework. Traffic, commercial land use, and urbanicity-related variables explain much of the spatial variability of PNC (base model R² = 0.77, RMSE = 2400 cm^-3). Model predictions are robust across a diverse set of evaluations [random 10-fold holdout cross-validation (HCV): R² = 0.72, RMSE = 2700 cm^-3; spatially defined HCV: R² = 0.66, RMSE = 3000 cm^-3; evaluation against an independent data set: R² = 0.54, RMSE = 2600 cm^-3]. We apply our model to predict PNC at ∼6 million residential census blocks in the contiguous United States. Our estimates are annual average concentrations for 2016-2017. The predicted national census-block-level mean PNC ranges between 1800 and 26 600 cm^-3 (population-weighted average: 6500 cm^-3), with hotspots in cities and near highways. Our national PNC model predicts large urban-rural, intra-, and inter-city contrasts. PNC and PM_2.5 are moderately correlated at the city scale, but uncorrelated at the regional/national scale. Our high-spatial-resolution national PNC estimates are useful for analyzing population exposure (socioeconomic disparity, epidemiological health impact) and environmental policy and regulation.

Air quality-related health damages of food

Agriculture is a major contributor to air pollution, the largest environmental risk factor for mortality in the United States and worldwide. It is largely unknown, however, how individual foods or entire diets affect human health via poor air quality. We show how food production negatively impacts human health by increasing atmospheric fine particulate matter (PM2.5), and we identify ways to reduce these negative impacts of agriculture. We quantify the air quality-related health damages attributable to 95 agricultural commodities and 67 final food products, which encompass >99% of agricultural production in the United States. Agricultural production in the United States results in 17,900 annual air quality-related deaths, 15,900 of which are from food production. Of those, 80% are attributable to animal-based foods, both directly from animal production and indirectly from growing animal feed. On-farm interventions can reduce PM2.5-related mortality by 50%, including improved livestock waste management and fertilizer application practices that reduce emissions of ammonia, a secondary PM2.5 precursor, and improved crop and animal production practices that reduce primary PM2.5 emissions from tillage, field burning, livestock dust, and machinery. Dietary shifts toward more plant-based foods that maintain protein intake and other nutritional needs could reduce agricultural air quality-related mortality by 68 to 83%. In sum, improved livestock and fertilization practices, and dietary shifts could greatly decrease the health impacts of agriculture caused by its contribution to reduced air quality.

Changes in criteria air pollution levels in the US before, during, and after Covid-19 stay-at-home orders: Evidence from regulatory monitors

The widespread and rapid social and economic changes from Covid-19 response might be expected to dramatically improve air quality. However, national monitoring data from the US Environmental Protection Agency for criteria pollutants (PM2.5, ozone, NO2, CO, PM10) provide inconsistent support for that expectation. Specifically, during stay-at-home orders, average PM2.5 levels were slightly higher (~10% of its multi-year interquartile range [IQR]) than expected; average ozone, NO2, CO, and PM10 levels were slightly lower (~30%, ~20%, ~27%, and ~1% of their IQR, respectively) than expected. The timing of peak anomaly, relative to the stay-at-home orders, varied by pollutant (ozone: 2 weeks before; NO2, CO: 3 weeks after; PM10: 2 weeks after); but, by 5-6 weeks after stay-at-home orders, the concentration anomalies appear to have ended. For PM2.5, ozone, CO, and PM10, no US state had lower-than-expected pollution levels for all weeks during stay-at-home-orders; for NO2, only Arizona had lower-than-expected levels for all weeks during stay-at-home orders. Our findings show that the enormous changes from the Covid-19 response have not lowered PM2.5 levels across the US beyond their normal range of variability; for ozone, NO2, CO, and PM10 concentrations were lowered but the reduction was modest and transient.

PM2.5 polluters disproportionately and systemically affect people of color in the United States

Racial-ethnic minorities in the United States are exposed to disproportionately high levels of ambient fine particulate air pollution (PM_2.5), the largest environmental cause of human mortality. However, it is unknown which emission sources drive this disparity and whether differences exist by emission sector, geography, or demographics. Quantifying the PM_2.5 exposure caused by each emitter type, we show that nearly all major emission categories-consistently across states, urban and rural areas, income levels, and exposure levels-contribute to the systemic PM_2.5 exposure disparity experienced by people of color. We identify the most inequitable emission source types by state and city, thereby highlighting potential opportunities for addressing this persistent environmental inequity.

In-use emissions from biomass and LPG stoves measured during a large, multi-year cookstove intervention study in rural India

We conducted an emission measurement campaign as a part of a multiyear cookstove intervention trial in two rural locations in northern and southern India. 253 uncontrolled cooking tests measured emissions in control and intervention households during three ~3-month-long measurement periods in each location. We measured pollutants including fine particulate matter (PM_2.5), organic and elemental carbon (OC, EC), black carbon (BC) and carbon monoxide (CO) from stoves ranging from traditional solid fuel (TSF) to improved biomass stoves (rocket, gasifier) to liquefied petroleum gas (LPG) models. TSF stoves showed substantial variability in pollutant emission factors (EFs; g kg^-1 wood) and optical properties across measurement periods. Multilinear regression modeling found that measurement period, fuel properties, relative humidity, and cooking duration are significant predictors of TSF EFs. A rocket stove showed moderate reductions relative to TSF. LPG stoves had the lowest pollutant EFs, with mean PM_2.5 and CO EFs (g MJ_delivered^-1) >90% lower than biomass stoves. However, in-home EFs of LPG were substantially higher than lab EFs, likely influenced by non-ideal combustion performance, emissions from food and possible influence from other combustion sources. In-home emission measurements may depict the actual exposure benefits associated with dissemination of LPG stoves in real world interventions.

Fine Particulate Matter Air Pollution and Mortality Risk Among US Cancer Patients and Survivors

Background

Exposure to fine particulate matter (PM_2.5) air pollution has been linked to increased risk of mortality, especially cardiopulmonary and lung cancer mortality. It is unknown if cancer patients and survivors are especially vulnerable to PM_2.5 air pollution exposure. This study evaluates PM_2.5 exposure and risk for cancer and cardiopulmonary mortality in cohorts of US cancer patients and survivors.

Methods

A primary cohort of 5 591 168 of cancer patients and a 5-year survivor cohort of 2 318 068 was constructed using Surveillance, Epidemiology, and End Results Program data from 2000 to 2016, linked with county-level estimates of long-term average concentrations of PM_2.5. Cox proportional hazards models were used to estimate PM_2.5-mortality hazard ratios controlling for age-sex-race combinations and individual and county-level covariables.

Results

Of those who died, 26% died of noncancer causes, mostly from cardiopulmonary disease. Minimal PM_2.5-mortality associations were observed for all-cause mortality (hazard ratio [HR] = 1.01, 95% confidence interval [CI] = 1.00 to 1.03) per 10 µg/m³ increase in PM_2.5. Substantial adverse PM_2.5-mortality associations were observed for cardiovascular (HR = 1.32, 95% CI = 1.26 to 1.39), chronic obstructive pulmonary disease (HR = 1.10, 95% CI = 1.01 to 1.20), influenza and pneumonia (HR = 1.55, 95% CI = 1.33 to 1.80), and cardiopulmonary mortality combined (HR = 1.25, 95% CI = 1.21 to 1.30). PM_2.5-cardiopulmonary mortality hazard ratio was higher for cancer patients who received chemotherapy or radiation treatments.

Conclusions

Air pollution is adversely associated with cardiopulmonary mortality for cancer patients and survivors, especially those who received chemotherapy or radiation treatment. Given ubiquitous and involuntary air pollution exposures and large numbers of cancer patients and survivors, these results are of substantial clinical and public health importance.

Fine Particulate Matter Exposure and Cancer Incidence: Analysis of SEER Cancer Registry Data from 1992-2016

BACKGROUND

Previous research has identified an association between fine particulate matter (PM 2.5 ) air pollution and lung cancer. Most of the evidence for this association, however, is based on research using lung cancer mortality, not incidence. Research that examines potential associations between PM 2.5 and incidence of non-lung cancers is limited.

OBJECTIVES

The primary purpose of this study was to evaluate the association between the incidence of cancer and exposure to PM 2.5 using > 8.5  million cases of cancer incidences from U.S. registries. Secondary objectives include evaluating the sensitivity of the associations to model selection, spatial control, and latency period as well as estimating the exposure-response relationship for several cancer types.

METHODS

Surveillance, Epidemiology, and End Results (SEER) program data were used to calculate incidence rates for various cancer types in 607 U.S. counties. County-level PM 2.5 concentrations were estimated using integrated empirical geographic regression models. Flexible semi-nonparametric regression models were used to estimate associations between PM 2.5 and cancer incidence for selected cancers while controlling for important county-level covariates. Primary time-independent models using average incidence rates from 1992-2016 and average PM 2.5 from 1988-2015 were estimated. In addition, time-varying models using annual incidence rates from 2002-2011 and lagged moving averages of annual estimates for PM 2.5 were also estimated.

RESULTS

The incidences of all cancer and lung cancer were consistently associated with PM 2.5 . The incident rate ratios (IRRs), per 10 - μ g / m 3 increase in PM 2.5 , for all and lung cancer were 1.09 (95% CI: 1.03, 1.14) and 1.19 (95% CI: 1.09, 1.30), respectively. Less robust associations were observed with oral, rectal, liver, skin, breast, and kidney cancers.

DISCUSSION

Exposure to PM 2.5 air pollution contributes to lung cancer incidence and is potentially associated with non-lung cancer incidence. https://doi.org/10.1289/EHP7246.

Cancer mortality risk, fine particulate air pollution, and smoking in a large, representative cohort of US adults

PURPOSE

Air pollution and smoking are associated with various types of mortality, including cancer. The current study utilizes a publicly accessible, nationally representative cohort to explore relationships between fine particulate matter (PM2.5) exposure, smoking, and cancer mortality.

METHODS

National Health Interview Survey and mortality follow-up data were combined to create a study population of 635,539 individuals surveyed from 1987 to 2014. A sub-cohort of 341,665 never-smokers from the full cohort was also created. Individuals were assigned modeled PM2.5 exposure based on average exposure from 1999 to 2015 at residential census tract. Cox Proportional Hazard models were utilized to estimate hazard ratios for cancer-specific mortality controlling for age, sex, race, smoking status, body mass, income, education, marital status, rural versus urban, region, and survey year.

RESULTS

The risk of all cancer mortality was adversely associated with PM2.5 (per 10 µg/m3 increase) in the full cohort (hazard ratio [HR] 1.15, 95% confidence interval [CI] 1.08-1.22) and the never-smokers' cohort (HR 1.19, 95% CI 1.06-1.33). PM2.5-morality associations were observed specifically for lung, stomach, colorectal, liver, breast, cervix, and bladder, as well as Hodgkin lymphoma, non-Hodgkin lymphoma, and leukemia. The PM2.5-morality association with lung cancer in never-smokers was statistically significant adjusting for multiple comparisons. Cigarette smoking was statistically associated with mortality for many cancer types.

CONCLUSIONS

Exposure to PM2.5 air pollution contributes to lung cancer mortality and may be a risk factor for other cancer types. Cigarette smoking has a larger impact on cancer mortality than PM2.5 , but is associated with similar cancer types.

Comparison of Mobile and Fixed-Site Black Carbon Measurements for High-Resolution Urban Pollution Mapping

Urban concentrations of black carbon (BC) and other primary pollutants vary on small spatial scales (<100m). Mobile air pollution measurements can provide information on fine-scale spatial variation, thereby informing exposure assessment and mitigation efforts. However, the temporal sparsity of these measurements presents a challenge for estimating representative long-term concentrations. We evaluate the capabilities of mobile monitoring in the represention of time-stable spatial patterns by comparing against a large set of continuous fixed-site measurements from a sampling campaign in West Oakland, California. Custom-built, low-cost aerosol black carbon detectors (ABCDs) provided 100 days of continuous measurements at 97 near-road and 3 background fixed sites during summer 2017; two concurrently operated mobile laboratories collected over 300 h of in-motion measurements using a photoacoustic extinctiometer. The spatial coverage from mobile monitoring reveals patterns missed by the fixed-site network. Time-integrated measurements from mobile lab visits to fixed-site monitors reveal modest correlation (spatial R² = 0.51) with medians of full daytime fixed-site measurements. Aggregation of mobile monitoring data in space and time can mitigate high levels of uncertainty associated with measurements at precise locations or points in time. However, concentrations estimated by mobile monitoring show a loss of spatial fidelity at spatial aggregations greater than 100 m.

Personal exposure to particulate matter in peri-urban India: predictors and association with ambient concentration at residence

Scalable exposure assessment approaches that capture personal exposure to particles for purposes of epidemiology are currently limited, but valuable, particularly in low-/middle-income countries where sources of personal exposure are often distinct from those of ambient concentrations. We measured 2 × 24-h integrated personal exposure to PM_2.5 and black carbon in two seasons in 402 participants living in peri-urban South India. Means (sd) of PM_2.5 personal exposure were 55.1(82.8) µg/m³ for men and 58.5(58.8) µg/m³ for women; corresponding figures for black carbon were 4.6(7.0) µg/m³ and 6.1(9.6) µg/m³. Most variability in personal exposure was within participant (intra-class correlation ~20%). Personal exposure measurements were not correlated (R_spearman < 0.2) with annual ambient concentration at residence modeled by land-use regression; no subgroup with moderate or good agreement could be identified (weighted kappa ≤ 0.3 in all subgroups). We developed models to predict personal exposure in men and women separately, based on time-invariant characteristics collected at baseline (individual, household, and general time-activity) using forward stepwise model building with mixed models. Models for women included cooking activities and household socio-economic position, while models for men included smoking and occupation. Models performed moderately in terms of between-participant variance explained (38-53%) and correlations between predictions and measurements (R_spearman: 0.30-0.50). More detailed, time-varying time-activity data did not substantially improve the performance of the models. Our results demonstrate the feasibility of predicting personal exposure in support of epidemiological studies investigating long-term particulate matter exposure in settings characterized by solid fuel use and high occupational exposure to particles.

Developing a Low-Cost Passive Method for Long-Term Average Levels of Light-Absorbing Carbon Air Pollution in Polluted Indoor Environments

We propose a low-cost passive method for monitoring long-term average levels of light-absorbing carbon air pollution in polluted indoor environments. Building on prior work, the method here estimates the change in reflectance of a passively exposed surface through analysis of digital images. To determine reproducibility and limits of detection, we tested low-cost passive samplers with exposure to kerosene smoke in the laboratory and to environmental pollution in 20 indoor locations. Preliminary results suggest robust reproducibility (r = 0.99) and limits of detection appropriate for longer-term (~1-3 months) monitoring in households that use solid fuels. The results here suggest high precision; further testing involving "gold standard" measurements is needed to investigate accuracy.

Optimizing Emissions Reductions from the U.S. Power Sector for Climate and Health Benefits

Improved air quality and human health are often discussed as "co-benefits" of mitigating climate change, yet they are rarely considered when designing or implementing climate policies. We analyze the implications of integrating health and climate when determining the best locations for replacing power plants with new wind, solar, or natural gas to meet a CO₂ reduction target in the United States. We employ a capacity expansion model with integrated assessment of climate and health damages, comparing portfolios optimized for benefits to climate alone or both health and climate. The model estimates county-level health damages and accounts for uncertainty by using a range of air quality models (AP3, EASIUR, and InMAP) and concentration-response functions (American Cancer Society and Harvard Six Cities). We find that reducing CO₂ by 30% yields $21-68 billion in annual health benefits, with an additional $9-36 billion possible when co-optimizing for climate and health benefits. Additional benefits accrue from prioritizing emissions reductions in counties with high population exposure. Total health benefits equal or exceed climate benefits across a wide range of modeling assumptions. Our results demonstrate the value of considering health in climate policy design and the need for interstate cooperation to achieve additional health benefits equitably.

Personal exposure to particulate air pollution and vascular damage in peri-urban South India

OBJECTIVE

Air pollution is a leading preventable risk factor for cardiovascular diseases. Previous studies mostly relied on concentrations at residence, which might not represent personal exposure. Personal air pollution exposure has a greater variability compared with levels of ambient air pollution, facilitating evaluation of exposure-response functions and vascular pathophysiology. We aimed to evaluate the association between predicted annual personal exposure to PM2.5 and black carbon (BC) and three vascular damage markers in peri-urban South India.

METHODS

We analyzed the third wave of the APCAPS cohort (2010-2012), which recruited participants from 28 villages. We used predicted personal exposure to PM2.5 and BC derived from 610 participant-days of 24 h average gravimetric PM2.5 and BC measurements and predictors related to usual time-activity. Outcomes included carotid intima-media thickness (CIMT), carotid-femoral pulse wave velocity (cf-PWV) and augmentation index (AIx). We fit linear mixed models, adjusting for potential confounders and accounting for the clustered data structure. We evaluated nonlinear associations using generalized additive mixed models.

RESULTS

Of the 3017 participants (mean age 38 years), 1453 (48%) were women. The average PM2.5 exposure was 51 µg/m3 (range 13-85) for men, and 61 µg/m3 (range 40-120) for women, while the average BC was 4 µg/m3 (range 3-7) for men and 8 µg/m3 (range 3-22) for women. A 10 μg/m3 increase of PM2.5 was positively associated with CIMT (0.026 mm, 95% CI 0.014, 0.037), cf-PWV (0.069 m/s, 95% CI 0.008, 0.131) and AIx (0.8%, 95% CI 0.3, 1.3) among men. The exposure-response function for PM2.5 and AIx among men showed non-linearity, particularly within the exposure range dominated by tobacco smoking and occupational exposures. Both PM2.5 and BC were positively associated with AIx among women (0.6%, 95% CI 0.2, 1.0, per 10 μg/m3 PM2.5; 0.5%, 95% CI 0.1, 0.8, per 2 μg/m3 BC).

CONCLUSIONS

Personal exposure to particulate matter was associated with vascular damage in a peri-urban population in South India. Personal exposure to particulate matter appears to have gender-specific effects on the type of vascular damage, potentially reflecting differences in sources of personal exposure by gender.

Estimating long-term pollution exposure effects through inverse probability weighting methods with Cox proportional hazards models

BACKGROUND

Fine particulate matter (PM2.5) is associated with negative health outcomes in both the short and long term. However, the cohort studies that have produced many of the estimates of long-term exposure associations may fail to account for selection bias in pollution exposure as well as covariate imbalance in the study population; therefore, causal modeling techniques may be beneficial.

METHODS

Twenty-nine years of data from the National Health Interview Survey (NHIS) was compiled and linked to modeled annual average outdoor PM2.5 concentration and restricted-use mortality data. A series of Cox proportional hazards models, adjusted using inverse probability weights, yielded causal risk estimates of long-term exposure to ambient PM2.5 on all-cause and cardiopulmonary mortality.

RESULTS

Covariate-adjusted estimated relative risks per 10 μg/m3 increase in PM2.5 exposure were estimated to be 1.117 (1.083, 1.152) for all-cause mortality and 1.232 (1.174, 1.292) for cardiopulmonary mortality. Inverse probability weighted Cox models provide relatively consistent and robust estimates similar to those in the unweighted baseline multivariate Cox model, though they have marginally lower point estimates and higher standard errors.

CONCLUSIONS

These results provide evidence that long-term exposure to PM2.5 contributes to increased mortality risk in US adults and that the estimated effects are generally robust to modeling choices. The size and robustness of estimated associations highlight the importance of clean air as a matter of public health. Estimated confounding due to measured covariates appears minimal in the NHIS cohort, and various distributional assumptions have little bearing on the magnitude or standard errors of estimated causal associations.

Identifying predictors of personal exposure to air temperature in peri-urban India

Characterizing personal exposure to air temperature is critical to understanding exposure measurement error in epidemiologic studies using fixed-site exposure data and to identify strategies to protect public health. To date, no study evaluating personal air temperature in the general population has been conducted in a low-and-middle income country. We used data from the CHAI study consisting of 50 adults monitored in up to six non-consecutive 24 h sessions in peri-urban south India. We quantified the agreement and association between fixed-site ambient and personal air temperature, and identified predictors of personal air temperature based on housing assessment, self-reported, GPS, remote sensing, and wearable camera data. Mean (SD) daytime (6 am-10 pm) average personal air temperature was 31.2 (2.6) °C and mean nighttime (10 pm-6 am) average temperature was 28.8 (2.8) °C. Agreement between average personal air and fixed-site ambient temperatures was limited, especially at night when personal air temperatures were underestimated by fixed-site temperatures (MBE = -5.6 °C). The proportion of average personal nighttime temperature variability explained by ambient fixed-site temperatures was moderate (R²_mar = 0.39); daytime associations were stronger for women (R²_mar = 0.51) than for men (R²_mar = 0.3). Other predictors of average nighttime personal air temperature included residential altitude, ceiling height, and household income. Predictors of average daytime personal air temperature included roof materials, GPS-tracked altitude, time working in agriculture (for women), and time travelling (for men). No biomass cooking, urban heat island, or greenspace effects were identified. R²_mar between ambient fixed-site and personal air temperature indicate that ambient fixed-site temperature is only a moderately useful proxy of personal air temperature in the context of peri-urban India. Our findings suggest that people living in houses at lower altitude, with lower ceiling height and asbestos roofing sheets might be more vulnerable to heat. We also identified households with higher income, women working in agriculture and men with long commutes as disproportionately exposed to high temperatures.