Daily Satellite Observations of Nitrogen Dioxide Air Pollution Inequality in New York City, New York and Newark, New Jersey: Evaluation and Application

Inequality in Hazardous Air Pollutant Emissions and Concentrations Measured Over Los Angeles

In Los Angeles, air pollution disproportionately impacts communities of color and low-income residents. Routine city-wide measurements of hazardous air pollutants (HAPs), of concern for health and contributing to urban air pollution, are notably lacking. In this study, we use the highest spatially resolved (∼2 km) measurements of emissions and concentrations ever reported of HAPs while covering a whole megacity and combine observations with US Census information. We observe higher concentrations and emissions of 17 measured HAPs, such as benzene, naphthalene, and p-chlorobenzotrifluoride (PCBTF), in California-designated Disadvantaged Communities (DACs) and census tracts with low-income Hispanics and Asians. These groups share an unequal burden from traffic-related emissions, with benzene, nitrogen oxides (NOx), and carbon monoxide (CO) concentrations up to 60% higher. However, in DACs and census tracts with large Hispanic populations (>50%), we observe toluene-to-benzene emission ratios above 3, pointing to inequalities in other HAPs primarily caused by non-traffic emission sources such as industry and solvents. In these communities, regulatory inventories also significantly underestimate emissions. We find that efforts to address HAP inequalities and environmental justice concerns in Los Angeles will need to consider contributions from volatile chemical products, which represent a growing source of emissions driving inequalities in impacted communities.

Air pollution exposure disparities among ethnic groups in high-income countries: A scoping review

BACKGROUND

The adverse health effects of air pollution are well-established. Previous reviews have highlighted disparities in air pollution exposure between minoritized ethnic groups and majoritized ethnic groups. However, these reviews primarily focused on proximity to pollution sources rather than objectively measured concentrations. This scoping review aims to provide an overview of the extent of inequalities in objectively measured air pollution exposure among ethnic groups in high-income countries.

METHODS

We systematically searched PubMed, Scopus, Web of Science, and Google Scholar for studies published in English, that reported on objectively measured air pollution exposure stratified by ethnic groups in high-income countries. Data on study characteristics and air pollution exposure were extracted.

RESULTS

The majority of all 55 included studies were conducted in North America (n = 46), followed by Europe (n = 8), and Oceania (n = 1). Across studies, 25 ethnic groups were identified, with African American, Hispanic, Latino, and Asian populations being the most studied minoritized ethnic groups. PM2.5 was the most studied (n = 38) air pollutant. Eighteen studies statistically tested differences in exposure across ethnic groups. Thirteen studies reported significantly higher air pollution exposure among minoritized ethnic populations compared to the majoritized ethnic population, and five studies showed mixed evidence.

CONCLUSIONS

This review highlights the extent of disparities in air pollution exposure among ethnic groups in high-income countries. It underscores the need for further research to understand the contributing factors and potential interventions to address these disparities.

Soil Emissions of Reactive Oxidized Nitrogen Reduce the Effectiveness of Anthropogenic Source Control in China

Nitrogen dioxide (NO2) has decreased by ∼33% across over 1200 monitoring sites in China during 2015-2023, following a series of clean air policies. However, most of these sites are located in or near cities, leading to uncertainties in NO2 trends beyond urban regions due to limited observations. Here, we used satellite measurements to examine the differences in NO2 trends between urban and rural China. In urban areas, NO2 columns decreased by 4.0% per annum (a-1) during summer 2011-2023, consistent with bottom-up anthropogenic emission inventory and in situ measurements. In contrast, rural NO2 columns showed a slower than expected reduction (-2.6 to -0.0% a-1) during the same period. Model simulations with updates in the soil reactive oxidized nitrogen (Nr) scheme indicated that increasing soil Nr emissions can be an important factor contributing to the observed slow NO2 decrease in rural areas. This unregulated source increased summertime pollutant levels, partially offsetting the national efforts to mitigate NO2, ozone (O3), and particulate nitrate (NO3-) levels by 20.9%, 15.4%, and 4.7%, respectively, from 2011 to 2020. In the agriculture-intensive North China Plain, the increase in soil Nr emissions offset 46.6% of the NO2 reductions achieved by clean air policies. Our results highlight the increasing significance of soil emissions and the need to control them in future air-quality policies.

Neighborhood-Level Nitrogen Dioxide Inequalities Contribute to Surface Ozone Variability in Houston, Texas

In Houston, Texas, nitrogen dioxide (NO2) air pollution disproportionately affects Black, Latinx, and Asian communities, and high ozone (O3) days are frequent. There is limited knowledge of how NO2 inequalities vary in urban air quality contexts, in part from the lack of time-varying neighborhood-level NO2 measurements. First, we demonstrate that daily TROPOspheric Monitoring Instrument (TROPOMI) NO2 tropospheric vertical column densities (TVCDs) resolve a major portion of census tract-scale NO2 inequalities in Houston, comparing NO2 inequalities based on TROPOMI TVCDs and spatiotemporally coincident airborne remote sensing (250 m × 560 m) from the NASA TRacking Aerosol Convection ExpeRiment-Air Quality (TRACER-AQ). We further evaluate the application of daily TROPOMI TVCDs to census tract-scale NO2 inequalities (May 2018-November 2022). This includes explaining differences between mean daily NO2 inequalities and those based on TVCDs oversampled to 0.01° × 0.01° and showing daily NO2 column-surface relationships weaken as a function of observation separation distance. Second, census tract-scale NO2 inequalities, city-wide high O3, and mesoscale airflows are found to covary using principal component and cluster analysis. A generalized additive model of O3 mixing ratios versus NO2 inequalities reproduces established nonlinear relationships between O3 production and NO2 concentrations, providing observational evidence that neighborhood-level NO2 inequalities and O3 are coupled. Consequently, emissions controls specifically in Black, Latinx, and Asian communities will have co-benefits, reducing both NO2 disparities and high O3 days city wide.

PM2.5 exposure disparities persist despite strict vehicle emissions controls in California

As policymakers increasingly focus on environmental justice, a key question is whether emissions reductions aimed at addressing air quality or climate change can also ameliorate persistent air pollution exposure disparities. We examine evidence from California's aggressive vehicle emissions control policy from 2000 to 2019. We find a 65% reduction in modeled statewide average exposure to PM2.5 from on-road vehicles, yet for people of color and overburdened community residents, relative exposure disparities increased. Light-duty vehicle emissions are the main driver of the exposure and exposure disparity, although smaller contributions from heavy-duty vehicles especially affect some overburdened groups. Our findings suggest that a continued trend of emissions reductions will likely reduce concentrations and absolute disparity but may not reduce relative disparities without greater attention to the systemic factors leading to this disparity.

Capturing Exposure Disparities with Chemical Transport Models: Evaluating the Suitability of Downscaling Using Land Use Regression

High resolution exposure surfaces are essential to capture disparities in exposure to traffic-related air pollution in urban areas. In this study, we develop an approach to downscale Chemical Transport Model (CTM) simulations to a hyperlocal level (∼100m) in the Greater Toronto Area (GTA) under three scenarios where emissions from cars, trucks and buses are zeroed out, thus capturing the burden of each transportation mode. This proposed approach statistically fuses CTMs with Land-Use Regression using machine learning techniques. With this proposed downscaling approach, changes in air pollutant concentrations under different scenarios are appropriately captured by downscaling factors that are trained to reflect the spatial distribution of emission reductions. Our validation analysis shows that high-resolution models resulted in better performance than coarse models when compared with observations at reference stations. We used this downscaling approach to assess disparities in exposure to nitrogen dioxide (NO2) for populations composed of renters, low-income households, recent immigrants, and visible minorities. Individuals in all four categories were disproportionately exposed to the burden of cars, trucks, and buses. We conducted this analysis at spatial resolutions of 12, 4, 1 km, and 100 m and observed that disparities were significantly underestimated when using coarse spatial resolutions. This reinforces the need for high-spatial resolution exposure surfaces for environmental justice analyses.

Air pollution impacts from warehousing in the United States uncovered with satellite data

Regulators, environmental advocates, and community groups in the United States (U.S.) are concerned about air pollution associated with the proliferating e-commerce and warehousing industries. Nationwide datasets of warehouse locations, traffic, and satellite observations of the traffic-related pollutant nitrogen dioxide (NO2) provide a unique capability to evaluate the air quality and environmental equity impacts of these geographically-dispersed emission sources. Here, we show that the nearly 150,000 warehouses in the U.S. worsen local traffic-related air pollution with an average near-warehouse NO2 enhancement of nearly 20% and are disproportionately located in marginalized and minoritized communities. Near-warehouse truck traffic and NO2 significantly increase as warehouse density and the number of warehouse loading docks and parking spaces increase. Increased satellite-observed NO2 near warehouses underscores the need for indirect source rules, incentives for replacing old trucks, and corporate commitments towards electrification. Future ground-based monitoring campaigns may help track impacts of individual or small clusters of facilities.

Air Pollution Inequality in the Denver Metroplex and its Relationship to Historical Redlining

Prior studies have shown that people of color (POC) in the United States are exposed to higher levels of pollution than non-Hispanic White people. We show that the city of Denver, Colorado, displays similar race- and ethnicity-based air pollution disparities by using a combination of high-resolution satellite data, air pollution modeling, historical demographic information, and areal apportionment techniques. TROPOMI NO2 columns and modeled PM2.5 concentrations from 2019 are higher in communities subject to redlining. We calculated and compared Spearman coefficients for pollutants and race at the census tract level for every city that underwent redlining to contextualize the disparities in Denver. We find that the location of polluting infrastructure leads to higher populations of POC living near point sources, including 40% higher Hispanic and Latino populations. This influences pollution distribution, with annual average PM2.5 surface concentrations of 6.5 μg m-3 in census tracts with 0-5% Hispanic and Latino populations and 7.5 μg m-3 in census tracts with 60-65% Hispanic and Latino populations. Traffic analysis and emission inventory data show that POC are more likely to live near busy highways. Unequal spatial distribution of pollution sources and POC have allowed for pollution disparities to persist despite attempts by the city to rectify them. Finally, we identify the core causes of the pollution disparities to provide direction for remediation.

Dynamic Traffic Data in Machine-Learning Air Quality Mapping Improves Environmental Justice Assessment

Air pollution poses a critical public health threat around many megacities but in an uneven manner. Conventional models are limited to depict the highly spatial- and time-varying patterns of ambient pollutant exposures at the community scale for megacities. Here, we developed a machine-learning approach that leverages the dynamic traffic profiles to continuously estimate community-level year-long air pollutant concentrations in Los Angeles, U.S. We found the introduction of real-world dynamic traffic data significantly improved the spatial fidelity of nitrogen dioxide (NO2), maximum daily 8-h average ozone (MDA8 O3), and fine particulate matter (PM2.5) simulations by 47%, 4%, and 15%, respectively. We successfully captured PM2.5 levels exceeding limits due to heavy traffic activities and providing an "out-of-limit map" tool to identify exposure disparities within highly polluted communities. In contrast, the model without real-world dynamic traffic data lacks the ability to capture the traffic-induced exposure disparities and significantly underestimate residents' exposure to PM2.5. The underestimations are more severe for disadvantaged communities such as black and low-income groups, showing the significance of incorporating real-time traffic data in exposure disparity assessment.

Satellite data for environmental justice: a scoping review of the literature in the United States

In support of the environmental justice (EJ) movement, researchers, activists, and policymakers often use environmental data to document evidence of the unequal distribution of environmental burdens and benefits along lines of race, class, and other socioeconomic characteristics. Numerous limitations, such as spatial or temporal discontinuities, exist with commonly used data measurement techniques, which include ground monitoring and federal screening tools. Satellite data is well poised to address these gaps in EJ measurement and monitoring; however, little is known about how satellite data has advanced findings in EJ or can help to promote EJ through interventions. Thus, this scoping review aims to (1) explore trends in study design, topics, geographic scope, and satellite datasets used to research EJ, (2) synthesize findings from studies that use satellite data to characterize disparities and inequities across socio-demographic groups for various environmental categories, and (3) capture how satellite data are relevant to policy and real-world impact. Following PRISMA extension guidelines for scoping reviews, we retrieved 81 articles that applied satellite data for EJ research in the United States from 2000 to 2022. The majority of the studies leveraged the technical advantages of satellite data to identify socio-demographic disparities in exposure to environmental risk factors, such as air pollution, and access to environmental benefits, such as green space, at wider coverage and with greater precision than previously possible. These disparities in exposure and access are associated with health outcomes such as increased cardiovascular and respiratory diseases, mental illness, and mortality. Research using satellite data to illuminate EJ concerns can contribute to efforts to mitigate environmental inequalities and reduce health disparities. Satellite data for EJ research can therefore support targeted interventions or influence planning and policy changes, but significant work remains to facilitate the application of satellite data for policy and community impact.

Ethnoracial Disparities in Nitrogen Dioxide Pollution in the United States: Comparing Data Sets from Satellites, Models, and Monitors

In the United States (U.S.), studies on nitrogen dioxide (NO2) trends and pollution-attributable health effects have historically used measurements from in situ monitors, which have limited geographical coverage and leave 66% of urban areas unmonitored. Novel tools, including remotely sensed NO2 measurements and estimates of NO2 estimates from land-use regression and photochemical models, can aid in assessing NO2 exposure gradients, leveraging their complete spatial coverage. Using these data sets, we find that Black, Hispanic, Asian, and multiracial populations experience NO2 levels 15-50% higher than the national average in 2019, whereas the non-Hispanic White population is consistently exposed to levels that are 5-15% lower than the national average. By contrast, the in situ monitoring network indicates more moderate ethnoracial NO2 disparities and different rankings of the least- to most-exposed ethnoracial population subgroup. Validating these spatially complete data sets against in situ observations reveals similar performance, indicating that all these data sets can be used to understand spatial variations in NO2. Integrating in situ monitoring, satellite data, statistical models, and photochemical models can provide a semiobservational record, complete geospatial coverage, and increasingly high spatial resolution, enhancing future efforts to characterize, map, and track exposure and inequality for highly spatially heterogeneous pollutants like NO2.

State-of-the-Science Data and Methods Need to Guide Place-Based Efforts to Reduce Air Pollution Inequity

BACKGROUND

Recently enacted environmental justice policies in the United States at the state and federal level emphasize addressing place-based inequities, including persistent disparities in air pollution exposure and associated health impacts. Advances in air quality measurement, models, and analytic methods have demonstrated the importance of finer-scale data and analysis in accurately quantifying the extent of inequity in intraurban pollution exposure, although the necessary degree of spatial resolution remains a complex and context-dependent question.

OBJECTIVE

The objectives of this commentary were to a) discuss ways to maximize and evaluate the effectiveness of efforts to reduce air pollution disparities, and b) argue that environmental regulators must employ improved methods to project, measure, and track the distributional impacts of new policies at finer geographic and temporal scales.

DISCUSSION

The historic federal investments from the Inflation Reduction Act, the Infrastructure Investment and Jobs Act, and the Biden Administration's commitment to Justice40 present an unprecedented opportunity to advance climate and energy policies that deliver real reductions in pollution-related health inequities. In our opinion, scientists, advocates, policymakers, and implementing agencies must work together to harness critical advances in air quality measurements, models, and analytic methods to ensure success. https://doi.org/10.1289/EHP13063.