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Mounting evidence over the past several decades has demonstrated inequitable distribution of pollutants of ambient origin between sociodemographic groups in the United States. Most environmental inequality studies to date are cross-sectional and used proximity-based methods rather than modeled air pollution concentrations, limiting the ability to examine trends over time or the factors that drive exposure inequalities. In this paper, we use 1 km 2 modeled PM 2.5 and NO 2 concentrations in Massachusetts over an 8-year period and Census demographic data to quantify inequality between sociodemographic groups and to develop a more nuanced understanding of the drivers and trends in longitudinal air pollution inequality. Annual-average population-weighted PM 2.5 and NO 2 concentrations were highest for urban non-Hispanic black populations (11.8 µg/m 3 in 2003 and 8.4 µg/m 3 in 2010, vs. 11.3 µg/m 3 and 8.1 µg/m 3 for urban non-Hispanic whites) and urban Hispanic populations (15.9 ppb in 2005 and 13.0 ppb in 2010, vs. 13.0 ppb and 10.2 ppb for urban non-Hispanic whites), respectively. While population groups experienced similar absolute decreases in exposure over time, disparities in population-weighted concentrations increased over time when quantified by the Atkinson Index, a relative inequality measure. Exposure inequalities were approximately one order of magnitude greater for NO 2 compared to PM 2.5 , were more pronounced in urban compared to rural geographies, and between racial/ethnic groups compared to income and educational attainment groups. Our results also revealed similar longitudinal PM 2.5 and NO 2 inequality trends using Census 2000 and Census 2010 data, indicating that spatio-temporal shifts in air pollution may best explain observed trends in inequality. These findings enhance our understanding of factors that contribute to persistent inequalities and underscore the importance of targeted exposure reduction strategies aimed at vulnerable populations and neighborhoods. [ABSTRACT FROM AUTHOR]

The effectiveness of air pollution emission control policies can be evaluated by examining ambient pollutant concentration trends that are observed at a large number of ground monitoring sites over time. In this paper, we used ground monitoring measurements in conjunction with satellite aerosol optical depth (AOD) data to investigate fine particulate matter (PM 2.5 ; particulate matter with aerodynamic diameter≤2.5 µm) trends and their spatial patterns over a large U.S. region, New England, during 2000–2008. We examined the trends in rural and urban areas to get a better insight about the trends of regional and local source emissions. Decreases in PM 2.5 concentrations (µg/m 3 ) were more pronounced in urban areas than in rural ones. In addition, the highest and lowest PM 2.5 decreases (µg/m 3 ) were observed for winter and summer, respectively. Together, these findings suggest that primary particle concentrations decreased more relative to secondary ones. This is also supported by the analysis of the speciation data which showed that downward trends of primary pollutants including black carbon were stronger than those of secondary pollutants including sulfate. Furthermore, this study found that ambient primary pollutants decreased at the same rate as their respective source emissions. This was not the case for secondary pollutants which decreased at a slower rate than that of their precursor emissions. This indicates that concentrations of secondary pollutants depend not only on the primary emissions but also on the availability of atmospheric oxidants which might not change during the study period. This novel approach of investigating spatially varying concentration trends, in combination with ground PM 2.5 species trends, can be of substantial regulatory importance. [ABSTRACT FROM AUTHOR]

The majority of studies that have investigated the relationship between particulate matter (PM) air pollution and mortality have assumed a linear dose–response relationship and have used either a single-day''s PM or a 2-or 3-day moving average of PM as the measure of PM exposure. Both of these modeling choices have come under scrutiny in the literature, the linear assumption because it does not allow for non-linearities in the dose–response relationship, and the use of the single-or multi-day moving average PM measure because it does not allow for differential PM–mortality effects spread over time. These two problems have been dealt with on a piecemeal basis with non-linear dose–response models used in some studies and distributed lag models (DLMs) used in others. In this paper, we propose a method for investigating the shape of the PM–mortality dose–response relationship that combines a non-linear dose–response model with a DLM. This combined model will be shown to produce satisfactory estimates of the PM–mortality dose–response relationship in situations where non-linear dose response models and DLMs alone do not; that is, the combined model did not systemically underestimate or overestimate the effect of PM on mortality. The combined model is applied to ten cities in the US and a pooled dose–response model formed. When fitted with a change-point value of 60μg/m3, the pooled model provides evidence for a positive association between PM and mortality. The combined model produced larger estimates for the effect of PM on mortality than when using a non-linear dose–response model or a DLM in isolation. For the combined model, the estimated percentage increase in mortality for PM concentrations of 25 and 75μg/m3 were 3.3% and 5.4%, respectively. In contrast, the corresponding values from a DLM used in isolation were 1.2% and 3.5%, respectively. [Copyright &y& Elsevier]