The Role of Neighborhood Air Pollution in Disparate Racial and Ethnic Asthma Acute Care Use.

RATIONALE: The share of Black or Latinx residents in a census tract remains associated with asthma-related Emergency Department visit rates after controlling for socioeconomic factors. The extent to which evident disparities relate to within-city heterogeneity of long-term air pollution exposure remains unclear. OBJECTIVES: To investigate the role of intraurban spatial variability of air pollution in asthma acute care use disparity. METHODS: An administrative database was used to define census tract population-based incidence rates of asthma-related Emergency Department visits. We estimate the association between census tract incidence rates and (a) average fine and coarse particulate matter (PM2.5, PM10), nitrogen dioxide (NO2), and sulfur dioxide (SO2); and (b) racial/ethnic composition using generalized linear models controlling for socioeconomic and housing covariates. We additionally examine for attenuation of incidence risk ratios (IRR) associated with race/ethnicity when controlling for air pollution exposure. MEASUREMENTS AND MAIN RESULTS: PM2.5, PM10, and SO2 are each associated with census tract-level incidence rates of asthma-related ED visits and multipollutant models show evidence of independent risk associated with PM10 and SO2. Association between census tract incidence rates and Black resident share (IRR [CI] = 1.51 [1.48-1.54]) is attenuated by 24% when accounting for air pollution (1.39 [1.35-1.42]), and the association with Latinx resident share (1.11 [1.09-1.13]) is attenuated by 32% (1.08 [1.06-1.10]). CONCLUSIONS: Neighborhood-level rates of asthma acute care use are associated with local air pollution. Controlling for air pollution attenuates associations with census tract racial/ethnic composition, suggesting that intracity variability in air pollution could contribute to neighborhood-to-neighborhood asthma morbidity disparities.

Racial and Ethnic Identity and Vulnerability to Upper Respiratory Viral Infections Among US Children.

BACKGROUND: It is unclear whether there are racial/ethnic disparities in the risk of upper respiratory viral infection acquisition and/or lower respiratory manifestations. METHODS: We studied all children and children with asthma aged 6 to 17 years in the National Health and Nutrition Examination Survey (2007-2012) to evaluate (1) the association between race/ethnicity and upper respiratory infection (URI) and (2) whether race/ethnicity is a risk factor for URI-associated pulmonary eosinophilic inflammation or decreased lung function. RESULTS: Children who identified as Black (adjusted odds ratio [aOR], 1.38; 95% CI, 1.10-1.75) and Mexican American (aOR, 1.50; 95% CI, 1.16-1.94) were more likely to report a URI than those who identified as White. Among those with asthma, Black children were more than twice as likely to report a URI than White children (aOR, 2.28; 95% CI, 1.31-3.95). Associations between URI and pulmonary eosinophilic inflammation or lung function did not differ by race/ethnicity. CONCLUSIONS: Findings suggest that there may be racial and ethnic disparities in acquiring a URI but not in the severity of infection. Given that upper respiratory viral infection is tightly linked to asthma exacerbations in children, differences in the risk of infection among children with asthma may contribute to disparities in asthma exacerbations.

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.

Demographic and Psychosocial Characteristics, Air Pollution Exposure, and Housing Mobility of Mexican Immigrant Families.

OBJECTIVE: People of color and lower socioeconomic status groups in the USA, including those of Mexican origin, are exposed to higher concentrations of air pollution, including fine particulate matter (PM2.5). Associations were examined between neighborhood air pollution levels and the psychosocial and demographic characteristics of linguistically isolated Mexican-origin immigrant families. Housing mobility and changes in air pollution levels due to changes in residence were also examined. METHODS: A sample of 604 linguistically isolated Mexican-origin families in central TX provided data on demographic and psychosocial experiences. Outdoor air pollution concentrations at participants' home addresses were based on high-resolution estimates of fine particulate matter (PM2.5) and its constituents. Movers were identified as families whose residential addresses changed during the study period; these participants were further grouped and compared based on the change in their residential PM2.5 concentration from before to after their move. RESULTS: Lower PM2.5 concentrations were associated with reports of more ethnic discriminatory experiences, higher socioeconomic status, and higher perceived neighborhood safety. Among the 23% of families who changed residences, PM2.5 concentrations were generally lower at the new family address. Families with mothers reporting a greater sense of neighborhood safety or acculturation levels tended to move from one area low in air pollutants to another, and mothers reporting the lowest levels of neighborhood safety or acculturation tended to move from one area high in air pollutants to another. CONCLUSION: There are limits to assimilation for Mexican immigrant families. Living in more advantaged neighborhoods is associated with experiencing better air quality, but this advantage may come at the cost of experiencing more ethnic discrimination.

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.

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 (PM2.5), 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 PM2.5 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 PM2.5 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.

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.

Mapping Air Pollution with Google Street View Cars: Efficient Approaches with Mobile Monitoring and Land Use Regression.

Air pollution measurements collected through systematic mobile monitoring campaigns can provide outdoor concentration data at high spatial resolution. We explore approaches to minimize data requirements for mapping a city's air quality using mobile monitors with "data-only" versus predictive modeling approaches. We equipped two Google Street View cars with 1-Hz instruments to collect nitric oxide (NO) and black carbon (BC) measurements in Oakland, CA. We explore two strategies for efficiently mapping spatial air quality patterns through Monte Carlo analyses. First, we explore a "data-only" approach where we attempt to minimize the number of repeated visits needed to reliably estimate concentrations for all roads. Second, we combine our data with a land use regression-kriging (LUR-K) model to predict at unobserved locations; here, measurements from only a subset of roads or repeat visits are considered. Although LUR-K models did not capture the full variability of on-road concentrations, models trained with minimal data consistently captured important covariates and general spatial air pollution trends, with cross-validation R2 for log-transformed NO and BC of 0.65 and 0.43. Data-only mapping performed poorly with few (1-2) repeated drives but obtained better cross-validation R2 than the LUR-K approach within 4 to 8 repeated drive days per road segment.