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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

Author

Listed:
  • 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)

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 .

Suggested Citation

  • Niru Senthilkumar & Mark Gilfether & Francesca Metcalf & Armistead G. Russell & James A. Mulholland & 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," IJERPH, MDPI, vol. 16(18), pages 1-15, September.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:18:p:3314-:d:265561
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    References listed on IDEAS

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    1. Veronica J. Berrocal & Alan E. Gelfand & David M. Holland, 2012. "Space-Time Data fusion Under Error in Computer Model Output: An Application to Modeling Air Quality," Biometrics, The International Biometric Society, vol. 68(3), pages 837-848, September.
    2. Montserrat Fuentes & Hae-Ryoung Song & Sujit K. Ghosh & David M. Holland & Jerry M. Davis, 2006. "Spatial Association between Speciated Fine Particles and Mortality," Biometrics, The International Biometric Society, vol. 62(3), pages 855-863, September.
    3. Gavin Shaddick & Matthew L. Thomas & Amelia Green & Michael Brauer & Aaron van Donkelaar & Rick Burnett & Howard H. Chang & Aaron Cohen & Rita Van Dingenen & Carlos Dora & Sophie Gumy & Yang Liu & Ran, 2018. "Data integration model for air quality: a hierarchical approach to the global estimation of exposures to ambient air pollution," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(1), pages 231-253, January.
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    2. Ji Hyun Kim & Hae Dong Woo & Sunho Choi & Dae Sub Song & Jung Hyun Lee & Kyoungho Lee, 2022. "Long-Term Effects of Ambient Particulate and Gaseous Pollutants on Serum High-Sensitivity C-Reactive Protein Levels: A Cross-Sectional Study Using KoGES-HEXA Data," IJERPH, MDPI, vol. 19(18), pages 1-19, September.

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