Estimation of spatially continuous daytime particulate matter concentrations under all sky conditions through the synergistic use of satellite-based AOD and numerical models

Seohui Park; Junghee Lee; Jungho Im; Chang-Keun Song; Myungje Choi; Jhoon Kim; Seungun Lee; Rokjin Park; Sang-Min Kim; Jongmin Yoon; Dong-Won Lee; Lindi J Quackenbush

doi:10.1016/j.scitotenv.2020.136516

Estimation of spatially continuous daytime particulate matter concentrations under all sky conditions through the synergistic use of satellite-based AOD and numerical models

Sci Total Environ. 2020 Apr 15:713:136516. doi: 10.1016/j.scitotenv.2020.136516. Epub 2020 Jan 8.

Authors

Seohui Park¹, Junghee Lee¹, Jungho Im², Chang-Keun Song¹, Myungje Choi³, Jhoon Kim⁴, Seungun Lee⁵, Rokjin Park⁵, Sang-Min Kim⁶, Jongmin Yoon⁶, Dong-Won Lee⁶, Lindi J Quackenbush⁷

Affiliations

¹ School of Urban & Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea.
² School of Urban & Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, Republic of Korea. Electronic address: ersgis@unist.ac.kr.
³ Jet Propulsion Laboratory, NASA, Pasadena, CA 91109, USA.
⁴ Department of Atmospheric Sciences, Yonsei University, Seoul 03722, Republic of Korea.
⁵ School of Earth and Environmental Sciences, Seoul National University, Seoul 08826, Republic of Korea.
⁶ Environmental Satellite Centre, Climate and Air Quality Research Department, National Institute of Environmental Research, Incheon 22689, Republic of Korea.
⁷ Department of Environmental Resources Engineering, State University of New York, College of Environmental Science and Forestry, Syracuse, NY 13210, USA.

PMID: 31951839
DOI: 10.1016/j.scitotenv.2020.136516

Abstract

Satellite-derived aerosol optical depth (AOD) products are one of main predictors to estimate ground-level particulate matter (PM₁₀ and PM_2.5) concentrations. Since AOD products, however, are only provided under high-quality conditions, missing values usually exist in areas such as clouds, cloud shadows, and bright surfaces. In this study, spatially continuous AOD and subsequent PM₁₀ and PM_2.5 concentrations were estimated over East Asia using satellite- and model-based data and auxiliary data in a Random Forest (RF) approach. Data collected from the Geostationary Ocean Color Imager (GOCI; 8 times per day) in 2016 were used to develop AOD and PM models. Three schemes (i.e. G1, A1, and A2) were proposed for AOD modeling according to target AOD data (GOCI AOD and AERONET AOD) and the existence of satellite-derived AOD. The A2 scheme showed the best performance (validation R² of 0.74 and prediction R² of 0.73 when GOCI AOD did not exist) and the resultant AOD was used to estimate spatially continuous PM concentrations. The PM models with location information produced successful estimation results with R² of 0.88 and 0.90, and rRMSE of 26.9 and 27.2% for PM₁₀ and PM_2.5, respectively. The spatial distribution maps of PM well captured the seasonal and spatial characteristics of PM reported in the literature, which implies the proposed approaches can be adopted for an operational estimation of spatially continuous AOD and PMs under all sky conditions.

Keywords: AOD; Machine learning; Particulate matter; Random Forest; Satellite.