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Article

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

1
School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
2
Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2019, 16(18), 3314; https://doi.org/10.3390/ijerph16183314
Received: 14 July 2019 / Revised: 2 September 2019 / Accepted: 4 September 2019 / Published: 9 September 2019
(This article belongs to the Special Issue Air Quality and Health Predictions)
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, NO2, SO2, O3, PM2.5, PM10, NO3, NH4+, EC, OC, SO42−) 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 (R2 values ranging from 0.84–0.98 across pollutants) and the spatial variation less well (R2 values 0.42–0.94). Ten-fold cross validation shows normalized root mean square error values of 60% or less and spatiotemporal R2 values of 0.4 or more for all pollutants except SO2. View Full-Text
Keywords: spatiotemporal pollutant fields; data fusion; air pollution; CMAQ; particulate species; gas species spatiotemporal pollutant fields; data fusion; air pollution; CMAQ; particulate species; gas species
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MDPI and ACS Style

Senthilkumar, N.; Gilfether, M.; Metcalf, F.; Russell, A.G.; Mulholland, J.A.; Chang, H.H. 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. Int. J. Environ. Res. Public Health 2019, 16, 3314. https://doi.org/10.3390/ijerph16183314

AMA Style

Senthilkumar N, Gilfether M, Metcalf F, Russell AG, Mulholland JA, Chang HH. 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. International Journal of Environmental Research and Public Health. 2019; 16(18):3314. https://doi.org/10.3390/ijerph16183314

Chicago/Turabian Style

Senthilkumar, Niru, Mark Gilfether, Francesca Metcalf, Armistead G. Russell, James A. Mulholland, and Howard H. Chang 2019. "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" International Journal of Environmental Research and Public Health 16, no. 18: 3314. https://doi.org/10.3390/ijerph16183314

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