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Air pollution exposure disparities by race/ethnicity and socioeconomic status have been analyzed using data aggregated at various spatial scales. Our research question is this: To what extent does the spatial scale of data aggregation impact the estimated exposure disparities? We compared disparities calculated using data spatially aggregated at five administrative scales (state, county, census tract, census block group, census block) in the contiguous United States in 2010. Specifically, for each of the five spatial scales, we calculated national and intraurban disparities in exposure to fine particles (PM2.5) and nitrogen dioxide (NO2) by race/ethnicity and socioeconomic characteristics using census demographic data and an empirical statistical air pollution model aggregated at that scale. We found, for both pollutants, that national disparity estimates based on state and county scale data often substantially underestimated those estimated using tract and finer scales; in contrast, national disparity estimates were generally consistent using tract, block group, and block scale data. Similarly, intraurban disparity estimates based on tract and finer scale data were generally well correlated for both pollutants across urban areas, although in some cases intraurban disparity estimates were substantially different, with tract scale data more frequently leading to underestimates of disparities compared to finer scale analyses.

Existing regulatory pollutant monitoring networks rely on a small number of centrally located measurement sites that are purposefully sited away from major emission sources. While informative of general air quality trends regionally, these networks often do not fully capture the local variability of air pollution exposure within a community. Recent technological advancements have reduced the cost of sensors, allowing air quality monitoring campaigns with high spatial resolution. The 100×100 black carbon (BC) monitoring network deployed 100 low-cost BC sensors across the 15 km(2) West Oakland, CA community for 100 days in the summer of 2017, producing a nearly continuous site-specific time series of BC concentrations which we aggregated to one-hour averages. Leveraging this dataset, we employed a hierarchical spatio-temporal model to accurately predict local spatio-temporal concentration patterns throughout West Oakland, at locations without monitors (average cross-validated hourly temporal R(2)=0.60). Using our model, we identified spatially varying temporal pollution patterns associated with small-scale geographic features and proximity to local sources. In a sub-sampling analysis, we demonstrated that fine scale predictions of nearly comparable accuracy can be obtained with our modeling approach by using ~30% of the 100×100 BC network supplemented by a shorter-term high-density campaign.

We report on the long-term performance of a popular low-cost PM2.5 sensor, the PurpleAir PA-II, at multiple sites in India, with the aim of identifying robust calibration protocols. We established 3 distinct sites in India (North India: Delhi, Hamirpur; South India: Bangalore), where we collocated PA-II with reference beta-attenuation monitors to characterize sensor performance and to model calibration relationships between PA-IIs and reference monitors for hourly data. Our sites remained in operation across all major seasons of India. Without calibration, the PA-IIs had high precision (Normalized Root Mean Square Error [NRMSE] among replicate sensors ≤ 15 %) and tracked the overall seasonal and diurnal signals from the reference instruments well (Pearson’s r ≥ 0.9) but were inaccurate (NRMSE ≥ 40 %). We used a comprehensive feature selection process to create optimized site-specific calibrations. Relative to the uncalibrated data, parsimonious least-squares long-term calibration models improved PA-II performance at all sites (cross-validated NRMSE: 20–30 %, R2: 0.82–0.95), particularly by reducing seasonal and diurnal biases. Because aerosol properties and meteorology vary regionally, the form of these long-term models differed by site. Likewise, using a moving-window calibration, we find a calibration scheme using seasonally specific information somewhat improves performance relative to a static long-term calibration model. In contrast, we demonstrate that a successful short-term calibration exercise for one season may not transfer reliably to other seasons. Overall, we demonstrate how the PA-II, when paired with a careful calibration scheme, can provide actionable information on PM2.5 in India with only modest irreducible uncertainty.

Air pollution levels in the United States have decreased dramatically over the past decades, yet national racial-ethnic exposure disparities persist. For ambient fine particulate matter ([Formula: see text]), we investigate three emission-reduction approaches and compare their optimal ability to address two goals: 1) reduce the overall population average exposure ("overall average") and 2) reduce the difference in the average exposure for the most exposed racial-ethnic group versus for the overall population ("national inequalities"). We show that national inequalities in exposure can be eliminated with minor emission reductions (optimal: ~1% of total emissions) if they target specific locations. In contrast, achieving that outcome using existing regulatory strategies would require eliminating essentially all emissions (if targeting specific economic sectors) or is not possible (if requiring urban regions to meet concentration standards). Lastly, we do not find a trade-off between the two goals (i.e., reducing overall average and reducing national inequalities); rather, the approach that does the best for reducing national inequalities (i.e., location-specific strategies) also does as well as or better than the other two approaches (i.e., sector-specific and meeting concentration standards) for reducing overall averages. Overall, our findings suggest that incorporating location-specific emissions reductions into the US air quality regulatory framework 1) is crucial for eliminating long-standing national average exposure disparities by race-ethnicity and 2) can benefit overall average exposures as much as or more than the sector-specific and concentration-standards approaches.

Air pollution levels in the United States have decreased dramatically over the past decades, yet national racial-ethnic exposure disparities persist. For ambient fine particulate matter ( PM 2.5 ), we investigate three emission-reduction approaches and compare their optimal ability to address two goals: 1) reduce the overall population average exposure (“overall average”) and 2) reduce the difference in the average exposure for the most exposed racial-ethnic group versus for the overall population (“national inequalities”). We show that national inequalities in exposure can be eliminated with minor emission reductions (optimal: ~1% of total emissions) if they target specific locations. In contrast, achieving that outcome using existing regulatory strategies would require eliminating essentially all emissions (if targeting specific economic sectors) or is not possible (if requiring urban regions to meet concentration standards). Lastly, we do not find a trade-off between the two goals (i.e., reducing overall average and reducing national inequalities); rather, the approach that does the best for reducing national inequalities (i.e., location-specific strategies) also does as well as or better than the other two approaches (i.e., sector-specific and meeting concentration standards) for reducing overall averages. Overall, our findings suggest that incorporating location-specific emissions reductions into the US air quality regulatory framework 1) is crucial for eliminating long-standing national average exposure disparities by race-ethnicity and 2) can benefit overall average exposures as much as or more than the sector-specific and concentration-standards approaches.

Present methodologies for source apportionment assume fixed source profiles. Since meteorology and human activity patterns change seasonally and diurnally, application of source apportionment techniques to shorter rather than longer time periods generates more representative mass spectra . Here, we present a new method to conduct source apportionment resolved by time of day using the underlying approach of positive matrix factorization (PMF). We call this approach “time-of-day PMF” and statistically demonstrate the improvements in this approach over traditional PMF. We report on source apportionment conducted on four example time periods in two seasons (winter and monsoon 2017), using organic aerosol measurements from an Aerosol Chemical Speciation Monitor (ACSM). We deploy the EPA PMF tool with the underlying Multilinear Engine (ME-2) as the PMF solver. Compared to the traditional seasonal PMF approach, we extract a larger number of factors as well as PMF factors that represent the expected sources of primary organic aerosol using time-of-day PMF. By capturing diurnal time series patterns of sources at a low computational cost, time-of-day PMF can utilize large datasets collected using long-term monitoring and improve the characterization of sources of organic aerosol compared to traditional PMF approaches that do not resolve by time of day.

India experiences some of the highest levels of ambient PM2.5 aerosol pollution in the world. However, due to the historical dearth of in situ measurements, chemical transport models that are often used to estimate PM2.5 exposure over the region are rarely evaluated. Here, we conduct a novel model comparison with speciated airborne measurements of fine aerosol, revealing large biases in the ammonium and nitrate simulations. To address this, we incorporate process-level changes to the model and use satellite observations from the Cross-track Infrared Sounder (CrIS) and the TROPOspheric Monitoring Instrument (TROPOMI) to constrain ammonia and nitrogen oxide emissions. The resulting simulation demonstrates significantly lower bias (NMBModified: 0.19; NMBBase: 0.61) when validated against the airborne aerosol measurements, particularly for the nitrate (NMBModified: 0.08; NMBBase: 1.64) and ammonium simulation (NMBModified: 0.49; NMBBase: 0.90). We use this validated simulation to estimate a population-weighted annual PM2.5 exposure of 61.4 μg m-3, with the RCO (residential, commercial, and other) and energy sectors contributing 21% and 19%, respectively, resulting in an estimated 961,000 annual PM2.5-attributable deaths. Regional exposure and sectoral source contributions differ meaningfully in the improved simulation (compared to the baseline simulation). Our work highlights the critical role of speciated observational constraints in developing accurate model-based PM2.5 aerosol source attribution for health assessments and air quality management in India.

We assess impacts of the 2020 COVID-19 lockdown on ambient air quality in Delhi, building on over three years of real-time measurements of black carbon (BC) and nonrefractory submicrometer aerosol (NR-PM1) composition from the Delhi Aerosol Supersite and public data from the regulatory monitoring network. We performed source apportionment of organic aerosol (OA) and robust statistical analyses to differentiate lockdown-related impacts from baseline seasonal and interannual variability. The primary pollutants NOx, CO, and BC were most reduced, primarily due to lower transportation emissions. Local and regional emissions such as agricultural burning decreased during the lockdown. PM2.5 declined but remained well above WHO guidelines. Despite the lockdown, NR-PM1 changed only moderately compared to prior years. Differences in the trends of hydrocarbon-like OA and BC suggest that some sources of primary aerosol may have increased. Despite notable reductions in some primary pollutants, the lockdown restrictions led to rather small perturbations in the primary fraction of NR-PM1, with secondary aerosol continuing to dominate. Overall, our results demonstrate the impact of secondary and primary pollution on Delhi's air quality and show that large changes in emissions within Delhi alone are insufficient to bring about needed improvements in air quality. © 2021 American Chemical Society.

The interaction between water vapor and atmospheric aerosol leads to enhancement in aerosol water content, which facilitates haze development, but its concentrations, sources, and impacts remain largely unknown in polluted urban environments. Here, we show that the Indian capital, Delhi, which tops the list of polluted capital cities, also experiences the highest aerosol water yet reported worldwide. This high aerosol water promotes secondary formation of aerosols and worsens air pollution. We report that severe pollution events are commonly associated with high aerosol water which enhances light scattering and reduces visibility by 70%. Strong light scattering also suppresses the boundary layer height on winter mornings in Delhi, inhibiting dispersal of pollutants and further exacerbating morning pollution peaks. We provide evidence that ammonium chloride is the largest contributor to aerosol water in Delhi, making up 40% on average, and we highlight that regulation of chlorine-containing precursors should be considered in mitigation strategies.

India experiences some of the highest levels of ambient PM

Air quality in the United States has dramatically improved, yet exposure to air pollution is still associated with 100000–200000 deaths annually. Reducing the number of deaths effectively, efficien...

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 PM2.5 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 PM2.5 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.

Delhi, India, experiences extremely high concentrations of primary organic aerosol (POA). Few prior source apportionment studies on Delhi have captured the influence of biomass burning organic aerosol (BBOA) and cooking organic aerosol (COA) on POA. In a companion paper, we develop a new method to conduct source apportionment resolved by time of day using the underlying approach of positive matrix factorization (PMF). We call this approach “time-of-day PMF” and statistically demonstrate the improvements of this approach over traditional PMF. Here, we quantify the contributions of BBOA, COA, and hydrocarbon-like organic aerosol (HOA) by applying positive matrix factorization (PMF) resolved by time of day on two seasons (winter and monsoon seasons of 2017) using organic aerosol measurements from an aerosol chemical speciation monitor (ACSM). We deploy the EPA PMF tool with the underlying Multilinear Engine (ME-2) as the PMF solver. We also conduct detailed uncertainty analysis for statistical validation of our results. HOA is a major constituent of POA in both winter and the monsoon. In addition to HOA, COA is found to be a major constituent of POA in the monsoon, and BBOA is found to be a major constituent of POA in the winter. Neither COA nor the different types of BBOA were resolved in the seasonal (not time-resolved) analysis. The COA mass spectra (MS) profiles are consistent with mass spectral profiles from Delhi and around the world, particularly resembling MS of heated cooking oils with a high m/z 41. The BBOA MS have a very prominent m/z 29 in addition to the characteristic peak at m/z 60, consistent with previous MS observed in Delhi and from wood burning sources. In addition to separating the POA, our technique also captures changes in MS profiles with the time of day, a unique feature among source apportionment approaches available. In addition to the primary factors, we separate two to three oxygenated organic aerosol (OOA) components. When all factors are recombined to total POA and OOA, our results are consistent with seasonal PMF analysis conducted using EPA PMF. Results from this work can be used to better design policies that target relevant primary sources of organic aerosols in Delhi.

Optical PM

Exposure to nitrogen dioxide (NO(2)), black carbon (BC), and ultrafine particles (UFP) during pregnancy may increase risk of preeclampsia, but previous studies have not assessed hyper-localized differences in pollutant levels which may cause exposure misclassification. We used data from Google Street View cars with mobile air monitors that repeatedly sampled NO(2), BC, and UFP every 30-meters in Downtown and West Oakland neighborhoods during 2015–2017. Data were linked to electronic health records of pregnant women in the 2014-2016 Sutter Health population who resided within 120-meters of monitoring data (N=1,095) to identify preeclampsia cases. We used G-computation with log-binomial regression to estimate risk differences (RD) associated with hypothetical interventions reducing pollutant levels to 25(th) percentiles observed in our sample on preeclampsia risk, overall and stratified by race/ethnicity. Prevalence of preeclampsia was 6.8%. Median (interquartile range) levels of NO(2), BC, and UFP were 10.8 ppb (9.0, 13.0), 0.34 μg/m(3) (0.27, 0.42), and 29.2 # × 10(3)/cm(3) (26.6, 32.6), respectively. Changes in the risk of preeclampsia achievable by limiting each pollutant to the 25(th) percentile was NO(2) RD =−1.5 per 100 women (95% confidence interval (CI): −2.5, −0.5); BC RD=−1.0 (95% CI: −2.2, 0.02); and UFP RD = −0.5 (95% CI: −1.8, 0.7). Estimated effects were largest for non-Latina Black mothers: NO(2) RD=−2.8 (95% CI: −5.2, −0.3) and BC RD=−3.0 (95% CI: −6.4, 0.4).

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.

Significance Wildfires are an increasingly large source of particulate matter (PM 2.5 ) in the western United States. Previous characterizations of exposure to wildfire smoke particles were based mainly on outdoor concentrations of PM 2.5 . Since people mainly shelter indoors during smoke events, the infiltration of wildfire PM 2.5 into buildings determines exposure. We present analysis of infiltration of wildfire PM 2.5 into more than 1,400 buildings in California using more than 2.4 million sensor hours of data from the PurpleAir sensor network. Our study reveals that infiltration of PM 2.5 during wildfire days was substantially reduced compared with non-fire days, due to people’s behavioral changes. These results improve understanding of exposure to wildfire particles and facilitate informing the public about effective ways to reduce their exposure.

Significance It is known, to researchers and heavily impacted communities, that people of color face a higher average burden of air pollution. It was unknown whether racial/ethnic disparities were caused by spatial heterogeneities at the level of city blocks, neighborhoods, or urban regions. Our approach leverages a unique set of highly local observations, covering every city block of 13 cities and urban districts that are home to 450,000 people. We find that even for pollutants with steep localized gradients, differences in average outdoor concentrations among racial/ethnic groups are driven by regional variability. However, localized peaks indicate opportunities to reduce extremes within groups. The methods and findings of this study can inform strategies to reduce disparities in urban air pollution exposure., 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 (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.

City-wide air pollution assessments have typically relied on a small number of widely separated regulatory monitoring sites or land use regression (LUR) models built using time-integrated samples to assess annual average population-scale exposure. However, air pollutant concentrations often exhibit significant spatial and temporal variability depending on local sources and features of the built environment. In 2016, the Center for Air, Climate, and Energy Solutions (CACES) Air Quality Observatory was launched at Carnegie Mellon University to better understand urban spatial and temporal pollution gradients on the 8 h) above the regional background. Compared to the non-decomposed total pollutant signal, the short-lived or persistent enhancement pollutant signals, which should come from local sources, were better correlated with covariates used in LUR model construction. For example, Pearson r between total vehicle counts in a 100 m buffer and NO2 increased from 0.57 using the total pollutant signal to 0.83 using the persistent enhancement only. The findings from this study support building more accurate and higher time resolution (e.g., daily, hourly) LURs using low-cost sensors.

This study presents land-use regression (LUR) models for submicron particulate matter (PM1) components from an urban area. Models are presented for mass concentrations of inorganic species (SO4, NO...

Delhi, India, routinely experiences some of the world's highest urban particulate matter concentrations. We established the Delhi Aerosol Supersite study to provide long-term characterization of the ambient submicron aerosol composition in Delhi. Here we report on 1.25 years of highly time-resolved speciated submicron particulate matter (PM1) data, including black carbon (BC) and nonrefractory PM1 (NR-PM1), which we combine to develop a composition-based estimate of PM1 (“C-PM1” = BC + NR-PM1) concentrations. We observed marked seasonal and diurnal variability in the concentration and composition of PM1 owing to the interactions of sources and atmospheric processes. Winter was the most polluted period of the year, with average C-PM1 mass concentrations of ∼210 µg m−3. The monsoon was hot and rainy, consequently making it the least polluted (C-PM1 ∼50 µg m−3) period. Organics constituted more than half of the C-PM1 for all seasons and times of day. While ammonium, chloride, and nitrate each were ∼10 % of the C-PM1 for the cooler months, BC and sulfate contributed ∼5 % each. For the warmer periods, the fractional contribution of BC and sulfate to C-PM1 increased, and the chloride contribution decreased to less than 2 %. The seasonal and diurnal variation in absolute mass loadings were generally consistent with changes in ventilation coefficients, with higher concentrations for periods with unfavorable meteorology – low planetary boundary layer height and low wind speeds. However, the variation in C-PM1 composition was influenced by temporally varying sources, photochemistry, and gas–particle partitioning. During cool periods when wind was from the northwest, episodic hourly averaged chloride concentrations reached 50–100 µg m−3, ranking among the highest chloride concentrations reported anywhere in the world. We estimated the contribution of primary emissions and secondary processes to Delhi's submicron aerosol. Secondary species contributed almost 50 %–70 % of Delhi's C-PM1 mass for the winter and spring months and up to 60 %–80 % for the warmer summer and monsoon months. For the cooler months that had the highest C-PM1 concentrations, the nighttime sources were skewed towards primary sources, while the daytime C-PM1 was dominated by secondary species. Overall, these findings point to the important effects of both primary emissions and more regional atmospheric chemistry on influencing the extreme particle concentrations that impact the Delhi megacity region. Future air quality strategies considering Delhi's situation in both a regional and local context will be more effective than policies targeting only local, primary air pollutants.

With much of the world's population residing in urban areas, an understanding of air pollution exposures at the city level can inform mitigation approaches. Previous studies of global urban air pollution have not considered trends in air pollutant concentrations nor corresponding attributable mortality burdens. We aimed to estimate trends in fine particulate matter (PMWe use high-resolution annual average PMAlthough regional averages of urban PMOur study showed that, between the years 2000 and 2019, most of the world's urban population lived in areas with unhealthy levels of PMNASA, Wellcome Trust.

Background US preterm-birth rates are 1.6 times higher for Black mothers than for White mothers. Although traffic-related air pollution (TRAP) may increase the risk of preterm birth, evaluating its effect on preterm birth and disparities has been challenging because TRAP is often measured inaccurately. This study sought to estimate the effect of TRAP exposure, measured at the street level, on the prevalence of preterm birth by race/ethnicity. Methods We linked birth-registry data with TRAP measured at the street level for singleton births in sampled communities during 2013–2015 in Oakland and San Jose, California. Using logistic regression and marginal standardization, we estimated the effects of exposure to black carbon, nitrogen dioxide and ultrafine particles on preterm birth after confounder adjustment and stratification by race/ethnicity. Results There were 8823 singleton births, of which 760 (8.6%) were preterm. Shifting black-carbon exposure from the 10th to the 90th percentile was associated with: 6.8%age point higher risk of preterm birth (95% confidence interval = 0.1 to 13.5) among Black women; 2.1%age point higher risk (95% confidence interval = –1.1 to 5.2) among Latinas; and inconclusive null findings among Asian and White women. For Latinas, there was evidence of a positive association between the other pollutants and risk of preterm birth, although effect sizes were attenuated in models that co-adjusted for other TRAP. Conclusions Exposure to TRAP, especially black carbon, may increase the risk of preterm birth for Latina and Black women but not for Asian and White women.

Fine particle air pollution (PM2.5) is the largest global environmental risk factor for ill-health and is implicated in >7% of all human deaths. Improved air quality is a key policy goal for cities, yet in-situ PM2.5 measurements are missing for >50% of the world’s urban population. Here, we apply satellite remote sensing to develop a 21-year time series of ground-level PM2.5 concentrations for the 4231 urban areas with populations >100,000 (2.9 billion people) from 1998 -2018. Globally, we find the most polluted cities are generally small (2.5 monitors. Since 1998, we observe a growing divide in urban air quality between cities in lower and higher-income regions, with the PM2.5 disparity increasing by >50% (from 25 to 39 µg m-3) between the highest- and lowest income quartiles of world cities. Within Asia, a sharp divergence is underway, with sustained PM2.5 increases in South Asian cities (+48%) contrasted against dramatic improvements in Chinese cities (-40% since 2011). While 85% of the world’s urban population experiences PM2.5 higher than World Health Organization guidelines, urban PM2.5 concentrations are tightly linked to regional conditions, suggesting that city-level efforts alone may be insufficient to address this major health threat.

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.

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 deaths relies on air quality modeling for estimating changes in PM2.5 concentrations from many policy scenarios at high spatial resolution. However, air quality modeling typically has high 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 4 km horizontal grid cell widths in cities. We evaluate Global InMAP performance both against measurements and a state-of-the-science chemical transport model, GEOS-Chem. For the emission scenarios considered, Global InMAP reproduced GEOS-Chem pollutant concentrations with a normalized mean bias of 59%–121%. Global InMAP can be run on a desktop computer; simulations here took 2.6–4.4 hours. This work presents a global, open-source, reduced-complexity air quality model to facilitate air pollution policy assessment worldwide, providing a tool for reducing the deaths where they occur most.

Background: Air pollution-attributable disease burdens reported at global, country, state, or county levels mask potential smaller-scale geographic heterogeneity driven by variation in pollution levels and disease rates. Capturing within-city variation in air pollution health impacts is now possible with high-resolution pollutant concentrations. Objectives: We quantified neighborhood-level variation in air pollution health risks, comparing results from highly spatially resolved pollutant and disease rate data sets available for the Bay Area, California. Methods: We estimated mortality and morbidity attributable to nitrogen dioxide (NO2), black carbon (BC), and fine particulate matter [PM ≤2.5μm in aerodynamic diameter (PM2.5)] using epidemiologically derived health impact functions. We compared geographic distributions of pollution-attributable risk estimates using concentrations from a) mobile monitoring of NO2 and BC; and b) models predicting annual NO2, BC and PM2.5 concentrations from land-use variables and satellite observations. We also compared results using county vs. census block group (CBG) disease rates. Results: Estimated pollution-attributable deaths per 100,000 people at the 100-m grid-cell level ranged across the Bay Area by a factor of 38, 4, and 5 for NO2 [mean=30 (95% CI: 9, 50)], BC [mean=2 (95% CI: 1, 2)], and PM2.5, [mean=49 (95% CI: 33, 64)]. Applying concentrations from mobile monitoring and land-use regression (LUR) models in Oakland neighborhoods yielded similar spatial patterns of estimated grid-cell–level NO2-attributable mortality rates. Mobile monitoring concentrations captured more heterogeneity [mobile monitoring mean=64 (95% CI: 19, 107) deaths per 100,000 people; LUR mean=101 (95% CI: 30, 167)]. Using CBG-level disease rates instead of county-level disease rates resulted in 15% larger attributable mortality rates for both NO2 and PM2.5, with more spatial heterogeneity at the grid-cell–level [NO2 CBG mean=41 deaths per 100,000 people (95% CI: 12, 68); NO2 county mean=38 (95% CI: 11, 64); PM2.5 CBG mean=59 (95% CI: 40, 77); and PM2.5 county mean=55 (95% CI: 37, 71)]. Discussion: Air pollutant-attributable health burdens varied substantially between neighborhoods, driven by spatial variation in pollutant concentrations and disease rates. https://doi.org/10.1289/EHP7679

New Delhi, India is the most polluted megacity in the world and routinely experiences high particulate matter (PM) concentrations. As part of the Delhi Aerosol Supersite Study, we have been measuring PM, concentration and composition in Delhi continuously since January 2017. This paper focuses on autumn, one of the most polluted seasons in Delhi when PM, concentrations steadily increase throughout the season and can exceed 1000 mu g m(-3) during episodic events. Positive matrix factorization on the organic aerosol (OA) spectrum suggests comparable seasonal average contributions from hydrocarbon-like OA (HOA), biomass burning OA (BBOA), and oxidized OA (OOA), with BBOA dominating during episodic events. We demonstrate the influence of regional sources such as agricultural burning during this season through temporal trends of pollutants, PMF factors, meteorology, and nonparametric wind regression analysis. We use inorganic fragment ratios to show the influence of metals during the festival of Diwali. Furthermore, we demonstrate the influence of transitioning meteorology in governing PM, composition through the season. Overall, our analysis provides novel insights into the factors controlling PM, during one of the most polluted seasons in Delhi.

In the absence of routine monitoring of ultrafine particles (UFP, Dp 1000 h of coverage of a range of road types and land uses. Across all fixed sites, PN concentrations show prominent mid-day peaks during the summer – characteristic of new particle formation – which are not observed for other co-emitted pollutants (NOx, BC, CO). While we find moderate correlation in diurnal patterns of NOx and UFP at sites with high traffic, the correlation drops significantly for low traffic areas, especially during high insolation (e.g., summer daytime) periods. Mobile monitoring data yields similar results: NOx is observed to have weaker correlation with PN for non-highway roads during high insolation periods. The spatiotemporal profiles of UFP can differ strongly from other co-emitted air pollutants when new particle formation contributes a significant share of UFP.

Background: With much of the world’s population residing in urban areas, an understanding of city-level air pollution exposures can inform mitigation approaches. Prior studies of global urban air pollution have not considered trends in air pollutant concentrations nor corresponding attributable health burdens. Methods: We use high-resolution annual average PM2.5 concentrations, epidemiologically derived concentration response functions, and country-level baseline disease rates to estimate population-weighted PM2.5 concentrations and attributable cause-specific mortality in 13,160 urban centers between 2000 and 2019. Findings: While regional averages of urban PM2.5 concentrations decreased over this time period, we find considerable heterogeneity in trends of PM2.5 concentrations between urban areas. Approximately 86% of urban inhabitants (2·5 billion people) lived in urban areas exceeding the World Health Organization’s (WHO) guideline for annual average PM2.5 (10 µg/m3) resulting in an excess of 1·34-2·3 million deaths in 2019. Regional averages of PM2.5-attributable deaths increased in all regions except for Europe and the Americas, driven by population, age structure, and disease rate changes. In some cities, PM2.5-attributable health burdens increased despite decreases in PM2.5 concentrations, resulting from shifting age distributions and non-communicable disease rates. Interpretation: We find the majority of the world’s urban population lived in areas with unhealthy levels of PM2.5 between 2000 and 2019, leading to substantial contributions to non-communicable disease burdens. Results highlight that avoiding the large public health burden from urban PM2.5 will require strategies that reduce exposure through emissions mitigation as well as strategies that reduce vulnerability to PM2.5 by improving overall public health. Funding Information: NASA Grant No. 80NSSC19K0193 and Wellcome Trust Grant No. 216075-Z-19-Z. RVM acknowledges support from NASA Grant No. 80NSSC21K0508. Declaration of Interests: The authors declare they have no actual or potential competing financial interests.

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., Graphical abstract Unlabelled Image, Highlights • Impacts of stay-at-home orders on air pollution were evaluated using EPA monitoring data from 100s of stations across the US. • During stay-at-home orders, ozone, NO2, CO and PM10 were lower and PM2.5 were higher than expected levels by 1%-30% of their IQR. • Concentration anomalies ended only 5-6 weeks after stay-at-home orders were issued. • Ozone, NO2, and CO concentrations returned to expected levels and PM2.5 and PM10 levels were higher than expected. • Reductions in ozone, NO2, and CO levels were modest and short-lived. PM10 levels did not change and PM2.5 levels increased.

Racial-ethnic minorities in the United States are exposed to disproportionately high levels of ambient fine particulate air pollution (PM