The field of air pollution accountability continues to grow in importance to a number of stakeholders. Two frameworks, the classical accountability chain and direct accountability, have been used to estimate impacts of regulatory actions, and both require careful attention to confounders and uncertainties. Researchers should continue to develop and evaluate both methods as they investigate current and future air pollution regulations.

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.

The effectiveness of air pollution regulations and controls are evaluated based on measured air pollutant concentrations. Air pollution levels, however, are highly sensitive to both emissions and meteorological fluctuations. Therefore, an assessment of the change in air pollutant levels due to emissions controls must account for these meteorological fluctuations. Two empirical methods to quantify the impact of meteorology on pollutant levels are discussed and applied to the 13-year time period between 2000 and 2012 in Atlanta, GA. The methods employ Kolmogorov-Zurbenko filters and linear regressions to detrended pollutant signals into longterm, seasonal, weekly, short-term, and white-noise components. The methods differ in how changes in weekly and holiday emissions are accounted for. Both can provide meteorological adjustments on a daily basis for future use in acute health analyses. The meteorological impact on daily signals of ozone, NO x , CO, SO 2 , PM 2.5 , and PM species are quantified. Analyses show that the substantial decreases in seasonal averages of NO x and SO 2 correspond with controls implemented in the metropolitan Atlanta area. Detrending allows for the impacts of some controls to be observed with averaging times of as little as 3 months. Annual average concentrations of NO x , SO 2 , and CO have all fallen by at least 50% since 2000. Reductions in NO x levels, however, do not lead to uniform reductions in ozone. While average detrended summer average maximum daily average 8 hour ozone (MDA8h O 3) levels fell by 4% (2.2 ± 2 ppb) between 2000 and 2012, winter averages have increased by 12% (3.8 ± 1.4 ppb), providing further evidence that high ozone levels are NO x-limited and lower ozone concentrations are NO x-inhibited. High ozone days (with MDA8h O 3 greater than 60ppb) decreased both in number and in magnitude over the study period.

In anticipation of the expanding appreciation for air quality models in health outcomes studies, we develop and evaluate a reduced-complexity model for pollution transport that intentionally sacrifices some of the sophistication of full-scale chemical transport models in order to support applicability to a wider range of health studies. Specifically, we introduce the HYSPLIT average dispersion model, HyADS, which combines the HYSPLIT trajectory dispersion model with modern advances in parallel computing to estimate ZIP code level exposure to emissions from individual coal-powered electricity generating units in the United States. Importantly, the method is not designed to reproduce ambient concentrations of any particular air pollutant; rather, the primary goal is to characterize each ZIP code's exposure to these coal power plants specifically. We show adequate performance towards this goal against observed annual average air pollutant concentrations (nationwide Pearson correlations of 0.88 and 0.73 with SO 4 2 − and PM 2.5 , respectively) and coal-combustion impacts simulated with a full-scale chemical transport model and adjusted to observations using a hybrid direct sensitivities approach (correlation of 0.90). We proceed to provide multiple examples of HyADS's single-source applicability, including to show that 22% of the population-weighted coal exposure comes from 30 coal-powered electricity generating units.