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

 

Abstract

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.

 

The materials provided below are supplementary information for a paper with the same title as that listed above.

 

Total yearly PM2.5 concentrations across the contiguous US
(estimated using increasingly more accurate methods)

Total yearly PM2.5 estimated using constant offset / hard data

Total yearly PM2.5 estimated using LUR offset / hard data

Total yearly PM2.5 estimated using LUR offset / hard and soft data