My research is at the intersection of air quality engineering and public health: understanding how much pollution people breathe, and how to reduce those exposures. My specific areas of focus are
(1) Mechanistic and empirical modeling of air pollution, to understand how concentrations vary in space and time, and how concentrations and health impacts would change in response to changes in emissions.
(2) Measuring and modeling air pollution exposures in developing countries (at present, mainly India), including how exposures change in response to interventions.
(3) Environmental justice: understanding who is more exposed or less exposed to air pollution, how those exposures correlate with demographic attributes such as race and income, and how exposure disparities might shift if emissions from specific sources were to increase or decrease.
Three main focus areas, and specific projects for each area, are listed below. Our research is supported by the National Science Foundation, the Environmental Protection Agency, and the National Institutes of Health.
The World Health Organization estimates that outdoor urban air pollution is responsible for 81% of environmental health risks in high-income countries such as the U.S., and 17% of environmental health risks in low- and middle-income countries. Urban air pollution is one of the top 15 causes of death globally, and one of the top 10 causes in high- and middle-income countries. Overall, urban air pollution is responsible for ~ 1.7% of deaths annual (2.1% in high-income countries, 1.7% in low- and middle-income countries).1 Comparison of costs and benefits of federal regulation in the United States2 conclude that monetized benefits are greater for the Clean Air Act than for all other federal regulations combined; those health benefits are mainly attributable to reduced mortality from regulating fine particles, starting in 2007.
My group studies how much pollution people breathe from specific sources and what would be the health benefits from reducing those emissions.
The goal of our modeling is to understand how concentrations vary in space and time, and how concentrations and health impacts would change in response to changes in emissions. Our empirical models are regression models, which combine EPA monitoring data, satellite data, and land-use data. EPA measures air pollution in specific locations across the country; these models aim to predict concentrations at all of the locations where EPA does not have measurements. Our mechanistic models investigate specific technology scenarios, such as displacement of conventional fossil fuels by biofuels, to understand the air pollution and health impacts. More information can be found at www.spatialmodel.com.
We have conducted several studies to measure air pollution in India, including as part of a large epidemiological study on the progression of cardiovascular disease and as part of a randomized control trial on a new cookstove. Current work involves mechanistic modeling of air pollution, and mobile monitoring. I am also involved in teaching in India, via the Grand Challenges Impact Lab.
Our goal is understanding who is more exposed or less exposed to air pollution, how those exposures correlate with demographic attributes such as race and income, and how exposure disparities might shift if emissions from specific sources were to increase or decrease. To study those topics, we use mechanistic models that allow for wide coverage yet with good spatial precision in urban areas. We also use regression models --- again, which have wide coverage yet with good spatial precision – to understand present-day and historical exposure disparities across the US.
1 CD Mathers, G Stevens, M Masarenhas. "Global Health Risks: Mortality and Burden of Disease Attributable to Selected Major Risks". World Health Organization, Geneva, Switzerland, 2009
2 Reports available here. See, for example, page 15 of the 2012 draft report.
