In environmental risk management, there are often interests in maximizing public health benefits (efficiency) and addressing inequality in the distribution of health outcomes. However, both dimensions are not generally considered within a single analytical framework. In this study, we estimate both total population health benefits and changes in quantitative indicators of health inequality for a number of alternative spatial distributions of diesel particulate filter retrofits across half of an urban bus fleet in Boston, Massachusetts. We focus on the impact of emissions controls on primary fine particulate matter (PM2.5) emissions, modeling the effect on PM2.5 concentrations and premature mortality. Given spatial heterogeneity in baseline mortality rates, we apply the Atkinson index and other inequality indicators to quantify changes in the distribution of mortality risk. Across the different spatial distributions of control strategies, the public health benefits varied by more than a factor of two, related to factors such as mileage driven per day, population density near roadways, and baseline mortality rates in exposed populations. Changes in health inequality indicators varied across control strategies, with the subset of optimal strategies considering both efficiency and equality generally robust across different parametric assumptions and inequality indicators. Our analysis demonstrates the viability of formal analytical approaches to jointly address both efficiency and equality in risk assessment, providing a tool for decision-makers who wish to consider both issues.
In evaluating risks from air pollution, health impact assessments often focus on the magnitude of the impacts without explicitly considering the distribution of impacts across subpopulations. In this study, we constructed a model to estimate the magnitude and distribution of health benefits associated with emission controls at five older power plants in the Washington, DC, area. We used the CALPUFF atmospheric dispersion model to determine the primary and secondary fine-particulate-matter (< 2.5 micro m in aerodynamic diameter) concentration reductions associated with the hypothetical application of "Best Available Control Technology" to the selected power plants. We combined these concentration reductions with concentration-response functions for mortality and selected morbidity outcomes, using a conventional approach as well as considering susceptible subpopulations. Incorporating susceptibility had a minimal effect on total benefits, with central estimates of approximately 240 fewer premature deaths, 60 fewer cardiovascular hospital admissions (CHA), and 160 fewer pediatric asthma emergency room visits (ERV) per year. However, because individuals with lower education appear to have both higher background mortality rates and higher relative risks for air-pollution-related mortality, stratifying by educational attainment implies that 51% of the mortality benefits accrue among the 25% of the population with less than high school education. Similarly, diabetics and African Americans bear disproportionate shares of the CHA and ERV benefits, respectively. Although our ability to characterize subpopulations is constrained by the available information, our analysis demonstrates that incorporation of susceptibility information significantly affects demographic and geographic patterns of health benefits and enhances our understanding of individuals likely to benefit from emission controls.
Designing air quality policies that improve public health can benefit from information about air pollution health risks and impacts, which include respiratory and cardiovascular diseases and premature death. Several computer-based tools help automate air pollution health impact assessments and are being used for a variety of contexts. Expanding information gathered for a May 2014 World Health Organization expert meeting, we survey 12 multinational air pollution health impact assessment tools, categorize them according to key technical and operational characteristics, and identify limitations and challenges. Key characteristics include spatial resolution, pollutants and health effect outcomes evaluated, and method for characterizing population exposure, as well as tool format, accessibility, complexity, and degree of peer review and application in policy contexts. While many of the tools use common data sources for concentration-response associations, population, and baseline mortality rates, they vary in the exposure information source, format, and degree of technical complexity. We find that there is an important tradeoff between technical refinement and accessibility for a broad range of applications. Analysts should apply tools that provide the appropriate geographic scope, resolution, and maximum degree of technical rigor for the intended assessment, within resources constraints. A systematic intercomparison of the tools' inputs, assumptions, calculations, and results would be helpful to determine the appropriateness of each for different types of assessment. Future work would benefit from accounting for multiple uncertainty sources and integrating ambient air pollution health impact assessment tools with those addressing other related health risks (e.g., smoking, indoor pollution, climate change, vehicle accidents, physical activity).
Benefit-cost and regulatory impact analyses often use atmospheric dispersion models with coarse resolution to estimate the benefits of proposed mobile source emission control regulations. This approach may bias health estimates or miss important intra-urban variability for primary air pollutants. In this study, we estimate primary fine particulate matter (PM2.5) intake fractions (iF; the fraction of a pollutant emitted from a source that is inhaled by the population) for each of 23 398 road segments in the Boston Metro Core area to evaluate the potential for intra-urban variability in the emissions-to-exposure relationship. We estimate iFs using the CAL3QHCR line source model combined with residential populations within 5000 m of each road segment. The annual average values for the road segments range from 0.8 to 53 per million, with a mean of 12 per million. On average, 46% of the total exposure is realized within 200 m of the road segment, though this varies from 0 to 93% largely due to variable population patterns. Our findings indicate the likelihood of substantial intra-urban variability in mobile source primary PM2.5 iF that accounting for population movement with time, localized meteorological conditions, and street-canyon configurations would likely increase.
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