An LUR/BME framework to estimate PM2.5 explained by on road mobile and stationary sources
Jeanette Reyes†, Marc L. Serre†
†Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina
Knowledge of Particulate Matter concentrations < 2.5 microns in diameter (PM2.5) across the United States is limited due to sparse monitoring across space and time. Epidemiological studies need accurate exposure estimates in order to properly investigate potential morbidity and mortality. Previous works have used geostatistics and Land Use Regression (LUR) separately to quantify exposure. This work combines both methods by incorporating a large area variability LUR model that accounts for on road mobile emissions and stationary source emissions along with data that take into account incompleteness of PM2.5 monitors into the modern geostatistical Bayesian Maximum Entropy (BME) framework to estimate PM2.5 across the United States from 1999-2009. A cross-validation was done to determine the improvement of the estimate due to the LUR incorporation into BME. These results were applied to known diseases to determine predicted mortality coming from total PM2.5 as well as PM2.5 explained by major contributing sources. Combining LUR and geostatistics resulted in a 21.89% reduction of mean squared error over using geostatistics alone. PM2.5 explained by on road mobile emissions contributed to nearly 568,090 deaths across the United States from 1999-2007.
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Total yearly PM2.5 concentrations across the
(estimated using increasingly more accurate methods)