title: Estimating ground-level PM(2.5) concentrations in the southeastern U.S. using geographically weighted regression.
abstract: Most of currently reported models for predicting PM(2.5) concentrations from satellite retrievals of aerosol optical depth are global methods without considering local variations, which might introduce significant biases into prediction results. In this paper, a geographically weighted regression model was developed to examine the relationship among PM(2.5), aerosol optical depth, meteorological parameters, and land use information. Additionally, two meteorological datasets, North American Regional Reanalysis and North American Land Data Assimilation System, were fitted into the model separately to compare their performances. The study area is centered at the Atlanta Metro area, and data were collected from various sources for the year 2003. The results showed that the mean local R(2) of the models using North American Regional Reanalysis was 0.60 and those using North American Land Data Assimilation System reached 0.61. The root mean squared prediction error showed that the prediction accuracy was 82.7% and 83.0% for North American Regional Reanalysis and North American Land Data Assimilation System in model fitting, respectively, and 69.7% and 72.1% in cross validation. The results indicated that geographically weighted regression combined with aerosol optical depth, meteorological parameters, and land use information as the predictor variables could generate a better fit and achieve high accuracy in PM(2.5) exposure estimation, and North American Land Data Assimilation System could be used as an alternative of North American Regional Reanalysis to provide some of the meteorological fields.
authors.affiliation: Department of Environmental Health, Rollins School of Public Health, Emory University, 1518 Clifton Road NE, Atlanta, GA 30322, USA.
authors.forename: Xuefei
authors.initials: X
authors.lastname: Hu
authors.forename: Lance A
authors.initials: LA
authors.lastname: Waller
authors.forename: Mohammad Z
authors.initials: MZ
authors.lastname: Al-Hamdan
authors.forename: William L
authors.initials: WL
authors.lastname: Crosson
authors.forename: Maurice G
authors.initials: MG
authors.lastname: Estes
authors.forename: Sue M
authors.initials: SM
authors.lastname: Estes
authors.forename: Dale A
authors.initials: DA
authors.lastname: Quattrochi
authors.forename: Jeremy A
authors.initials: JA
authors.lastname: Sarnat
authors.forename: Yang
authors.initials: Y
authors.lastname: Liu
mesh_terms: D000336:Aerosols / Q000032:analysis*; D000393:Air Pollutants / Q000032:analysis*; D000397:Air Pollution / Q000032:analysis / Q000706:statistics & numerical data; D004781:Environmental Exposure; D004784:Environmental Monitoring / Q000379:methods; D008685:Meteorological Concepts; D008962:Models, Theoretical; D052638:Particulate Matter / Q000032:analysis*; D012044:Regression Analysis; D015203:Reproducibility of Results; D015144:Southeastern United States
chemical_list: D000336:Aerosols; D000393:Air Pollutants; D052638:Particulate Matter