Large reductions of sulfur and nitrogen oxide emissions in the United States have led to considerable improvements in air quality, though recent analyses in the Southeastern United States have shown little response of aerosol pH to these reductions. This study examines the effects of reduced emissions on the trend of aerosol acidity in fine particulate matter (PM), at a nationwide scale, using ambient concentration data from three monitoring networks-the Ammonia Monitoring Network (AMoN), the Clean Air Status and Trends network (CASTNET) and the Southeastern Aerosol Research and Characterization Network (SEARCH), in conjunction with thermodynamic (ISORROPIA-II) and chemical transport (CMAQ) model results. Sulfate and ammonium experienced similar and significant decreases with little change in pH, neutralization ratio ( f = [NH]/2[SO] + [NO]), or nitrate. Oak Grove, MS was the only SEARCH site showing statistically significant pH changes in the Southeast region where small increases in pH (0.003-0.09 pH units/year) were observed. Of the five regions characterized using CASTNET/AMoN data, only California exhibited a statistically significant, albeit small pH increase of +0.04 pH units/year. Furthermore, statistically insignificant (α = 0.05) changes in ammonia were observed in response to emission and PM speciation changes. CMAQ simulation results had similar responses, showing steady ammonia levels and generally low pH, with little change from 2001 to 2011.

Environmental burdens such as air pollution are inequitably distributed with groups of lower socioeconomic statuses, which tend to comprise of large proportions of racial minorities, typically bearing greater exposure. Such groups have also been shown to present more severe health outcomes which can be related to adverse pollution exposure. Air pollution exposure, especially in urban areas, is usually impacted by the built environment, such as major roadways, which can be a significant source of air pollution. This study aims to examine inequities in prevalence of cardiovascular and respiratory diseases in the Atlanta metropolitan region as they relate to exposure to air pollution and characteristics of the built environment. Census tract level data were obtained from multiple sources to model health outcomes (asthma, chronic obstructive pulmonary disease, coronary heart disease, and stroke), pollution exposure (particulate matter and nitrogen oxides), demographics (ethnicity and proportion of elderly residents), and infrastructure characteristics (tree canopy cover, access to green space, and road intersection density). Conditional autoregressive models were fit to the data to account for spatial autocorrelation among census tracts. The statistical model showed areas with majority African-American populations had significantly higher exposure to both air pollutants and higher prevalence of each disease. When considering univariate associations between pollution and health outcomes, the only significant association existed between nitrogen oxides and COPD being negatively correlated. Greater percent tree canopy cover and green space access were associated with higher prevalence of COPD, CHD, and stroke. Overall, in considering health outcomes in connection with pollution exposure infrastructure and ethnic demographics, demographics remained the most significant explanatory variable.

No abstract

Cities are increasingly advancing multiple societal goals related to environmental sustainability, health, well‐being, and equity. However, there are few comprehensive data sets that address social inequality and equity across multiple infrastructure sectors, determinants, and outcomes, particularly at fine intra‐urban spatial scales. This paper: (1) Offers an overarching conceptualization of inequality and equity in multi‐sector urban systems; (2) Introduces a broad data framework to assess inequality and equity across social (S), ecological (E), infrastructural (I), and urban (U) form determinants (SEIU) and environment (E), health (H), well‐being (W), and economy and security (E) outcomes (EHWE), identifying a universe of >110 SEIU–EHWE data layers (variables) of interest; (3) Provides an illustrative data case study of a US city that synthesizes publicly available sources of the associated SEIU–EHWE data attributes, noting their availability/gaps at fine spatial scales, important to inform social inequality; (4) Discusses analytic methods to quantify inequality and spatial correlates across SEIU determinants and EHWE outcomes; and, (5) Demonstrates several use‐cases of the data framework and companion analytic methods through real‐world applied case studies that inform equity planning in applications ranging from energy sector investments to air pollution and health. The US data case study reveals data availability (covering 41 of the 113 data layers) as well as major gaps associated with EHWE outcomes at fine spatial scales, while the application examples demonstrate practical use. Overall, the SEIU–EHWE data framework provides an anchor for systematically gathering, analyzing, and informing multiple dimensions of inequality and equity in sustainable urban systems.