Jonathan Buonocore, Sc.D., Program Leader of our Climate, Energy, and Health Program, along with director Jack Spengler, Xinyi Dong and Joshua Fu, at University of Tennessee, Knoxville, and Jonathan Levy, at Harvard and Boston University, published a paper in Environmental International that estimates the health impacts of emissions from electrical power plants in the mid-Atlantic and Great Lakes regions. This statistical model can be used to estimate the impacts and benefits of changes to the electrical grid and different policy choices, to help policy-makers design optimal policies and interventions.
Background
Ambient fine particular matter or PM2.5 (2.5 micrometers or less in diameter) has been linked to a higher risk of mortality. Fine particulates are small pieces of solid and liquid aerosol matter suspended in the air. One study estimated excess deaths in 2005 due to PM2.5 to be between 130,000 to 320,000. A substantial portion of these emissions, and consequently public health burden, comes from electricity generation. Understanding the impact of individual sources of PM2.5 is critical to understanding the public health impact of different policy options, interventions, and electricity generation choices. To further aid benefit-cost analyses and an understanding of the implications of different choices, these impacts can be monetized.
One of the best available tools for assessing the public health impacts of PM2.5 is the Community Multiscale Air Quality model (CMAQ). However, this model is extremely time and resource intensive, so many have developed simplified methods to perform these analyses. Previously developed simplified models may either omit complex chemistry and interactions of background sources of PM2.5 or only be able to estimate impacts of sources based on region or source type (i.e. power plants in the Chicago area, vehicles in the Los Angeles area, etc). As a result, the simplified models may mischaracterize impacts, particularly at long distances from the emissions sources since they do not include much of the complex chemistry, but the regional-based model may mischaracterize more local impacts since they aggregate sources to region and type. Here, we used CMAQ to develop a model that attempts to include the strengths of both approaches.
Summary
We estimated the public health impacts of set of power plants the mid-Atlantic and Great Lakes regions of the United States using CMAQ, monetized these impacts, and then developed a statistical model to predict these impacts based on population distribution around each power plant. We found that there was substantial variability in the impacts of these power plants and that our statistical model was able to reasonably predict the impacts. Additionally, we found that the impacts of NOx emissions are approximately a factor of three higher than other models predict, likely due to including these complex chemical pathways. This model can be used to predict the impacts of other emission sources in and around this region and can be used to estimate the impacts and benefits of changes to the electrical grid, and different policy choices, and help to design optimal policies and interventions.
Citation
Buonocore, J., Dong, X., Spengler, J. Fu, J., Levy, J., Environment International, Volume 68, July 2014, 200–208.
