Application of a Fusion Method for Gas and Particle Air Pollutants between Observational Data and Chemical Transport Model Simulations Over the Contiguous United States for 2005–2014

Niru Senthilkumar, Mark Gilfether, Francesca Metcalf, Armistead G. Russell, James A. Mulholland and Howard H. Chang
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Niru Senthilkumar: School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
Mark Gilfether: School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
Francesca Metcalf: School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
Armistead G. Russell: School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
James A. Mulholland: School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
Howard H. Chang: Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA

IJERPH, 2019, vol. 16, issue 18, 1-15

Abstract: Accurate spatiotemporal air quality data are critical for use in assessment of regulatory effectiveness and for exposure assessment in health studies. A number of data fusion methods have been developed to combine observational data and chemical transport model (CTM) results. Our approach focuses on preserving the temporal variation provided by observational data while deriving the spatial variation from the community multiscale air quality ( CMAQ ) simulations, a type of CTM. Here we show the results of fusing regulatory monitoring observational data with 12 km resolution CTM simulation results for 12 pollutants (CO, NOx, NO 2 , SO 2, O 3 , PM 2.5 , PM 10 , NO 3 − , NH 4 + , EC, OC, SO 4 2− ) over the contiguous United States on a daily basis for a period of ten years (2005–2014). An annual mean regression between the CTM simulations and observational data is used to estimate the average spatial fields, and spatial interpolation of observations normalized by predicted annual average is used to provide the daily variation. Results match the temporal variation well ( R 2 values ranging from 0.84–0.98 across pollutants) and the spatial variation less well ( R 2 values 0.42–0.94). Ten-fold cross validation shows normalized root mean square error values of 60% or less and spatiotemporal R 2 values of 0.4 or more for all pollutants except SO 2 .

Keywords: spatiotemporal pollutant fields; data fusion; air pollution; CMAQ; particulate species; gas species (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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