Articles | Volume 19, issue 2
https://doi.org/10.5194/acp-19-1097-2019
https://doi.org/10.5194/acp-19-1097-2019
Research article
 | 
28 Jan 2019
Research article |  | 28 Jan 2019

Estimation of ground-level particulate matter concentrations through the synergistic use of satellite observations and process-based models over South Korea

Seohui Park, Minso Shin, Jungho Im, Chang-Keun Song, Myungje Choi, Jhoon Kim, Seungun Lee, Rokjin Park, Jiyoung Kim, Dong-Won Lee, and Sang-Kyun Kim

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Cited articles

AirKorea: https://www.airkorea.or.kr/, last access: 24 January 2019. 
Amani, M., Salehi, B., Mahdavi, S., Granger, J., and Brisco, B.: Wetland classification in Newfoundland and Labrador using multi-source SAR and optical data integration, GISci. Remote Sens., 54, 779–796, 2017. 
Baek, B. H., Seppanen, C., and Houyoux, M.: SMOKE v2. 5 User's manual, https://www.cmascenter.org/smoke/documentation/2.5/html/, last access: 24 January 2019. 
Bartell, S. M., Longhurst, J., Tjoa, T., Sioutas, C., and Delfino, R. J.: Particulate air pollution, ambulatory heart rate variability, and cardiac arrhythmia in retirement community residents with coronary artery disease, Environ. Health Persp., 121, 1135–1141, https://doi.org/10.1289/ehp.1205914, 2013. 
Borlina, C. S. and Rennó, N. O.: The Impact of a Severe Drought on Dust Lifting in California's Owens Lake Area, Sci. Rep., 7, 1784, https://doi.org/10.1038/s41598-017-01829-7, 2017. 
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This study proposed machine-learning-based models to estimate ground-level particulate matter concentrations using satellite observations and numerical model-derived data. Aerosol optical depth was identified as the most significant for estimating ground-level PM concentrations, followed by wind speed and solar radiation. The results show that the proposed models produced better performance than the existing approaches, particularly in improving on the biases of the process-based models.
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