BackgroundIn deciding among competing approaches for emissions control, debates often hinge on the potential tradeoffs between efficiency and equity. However, previous health benefits analyses have not formally addressed both dimensions.ObjectivesWe modeled the public health benefits and the change in the spatial inequality of health risk for a number of hypothetical control scenarios for power plants in the United States to determine optimal control strategies.MethodsWe simulated various ways by which emission reductions of sulfur dioxide (SO2), nitrogen oxides, and fine particulate matter (particulate matter < 2.5 μm in diameter; PM2.5) could be distributed to reach national emissions caps. We applied a source–receptor matrix to determine the PM2.5 concentration changes associated with each control scenario and estimated the mortality reductions. We estimated changes in the spatial inequality of health risk using the Atkinson index and other indicators, following previously derived axioms for measuring health risk inequality.ResultsIn our baseline model, benefits ranged from 17,000–21,000 fewer premature deaths per year across control scenarios. Scenarios with greater health benefits also tended to have greater reductions in the spatial inequality of health risk, as many sources with high health benefits per unit emissions of SO2 were in areas with high background PM2.5 concentrations. Sensitivity analyses indicated that conclusions were generally robust to the choice of indicator and other model specifications.ConclusionsOur analysis demonstrates an approach for formally quantifying both the magnitude and spatial distribution of health benefits of pollution control strategies, allowing for joint consideration of efficiency and equity.

BackgroundThe relationship between traffic emissions and mobile-source air pollutant concentrations is highly variable over space and time and therefore difficult to model accurately, especially in urban settings with complex terrain. Regression-based approaches using continuous real-time mobile measurements may be able to characterize spatiotemporal variability in traffic-related pollutant concentrations but require methods to incorporate temporally varying meteorology and source strength in a physically interpretable fashion.ObjectiveWe developed a statistical model to assess the joint impact of both meteorology and traffic on measured concentrations of mobile-source air pollutants over space and time.MethodsIn this study, traffic-related air pollutants were continuously measured in the Williamsburg neighborhood of Brooklyn, New York (USA), which is affected by traffic on a large bridge and major highway. One-minute average concentrations of ultrafine particulate matter (UFP), fine particulate matter [≤ 2.5 μm in aerodynamic diameter (PM2.5)], and particle-bound polycyclic aromatic hydrocarbons were measured using a mobile-monitoring protocol. Regression modeling approaches to quantify the influence of meteorology, traffic volume, and proximity to major roadways on pollutant concentrations were used. These models incorporated techniques to capture spatial variability, long- and short-term temporal trends, and multiple sources.ResultsWe observed spatial heterogeneity of both UFP and PM2.5 concentrations. A variety of statistical methods consistently found a 15–20% decrease in UFP concentrations within the first 100 m from each of the two major roadways. For PM2.5, temporal variability dominated spatial variability, but we observed a consistent linear decrease in concentrations from the roadways.ConclusionsThe combination of mobile monitoring and regression analysis was able to quantify local source contributions relative to background while accounting for physically interpretable parameters. Our results provide insight into urban exposure gradients.

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The use of electronic nicotine delivery systems continues to gain popularity, and there is concern for potential health risks from inhalation of aerosol and vapor produced by these devices. An analytical method was developed that provided quantitative and qualitative chemical information for characterizing the volatile constituents of bulk electronic cigarette liquids (e-liquids) using a static headspace technique. Volatile organic compounds (VOCs) were screened from a convenience sample of 146 e-liquids by equilibrating 1 g of each e-liquid in amber vials for 24 h at room temperature. Headspace was transferred to an evacuated canister and quantitatively analyzed for 20 VOCs as well as tentatively identified compounds using a preconcentrator/gas chromatography/mass spectrometer system. The e-liquids were classified into flavor categories including brown, fruit, hybrid dairy, menthol, mint, none, tobacco, and other. 2,3-Butanedione was found at the highest concentration in brown flavor types, but was also found in fruit, hybrid dairy, and menthol flavor types. Benzene was observed at concentrations that are concerning given the carcinogenicity of this compound (max 1.6 ppm in a fruit flavor type). The proposed headspace analysis technique coupled with partition coefficients allows for a rapid and sensitive prediction of the volatile content in the liquid. The technique does not require onerous sample preparation, dilution with organic solvents, or sampling at elevated temperatures. Static headspace screening of e-liquids allows for the identification of volatile chemical constituents which is critical for identifying and controlling emission of potentially hazardous constituents in the workplace.