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Im, Jungho
Intelligent Remote sensing and geospatial Information Science Lab.
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Estimation of TROPOMI-derived Ground-level SO2 Concentrations Using Machine Learning Over East Asia

Alternative Title
기계학습을 활용한 동아시아 지역의 TROPOMI 기반 SO2 지상농도 추정
Author(s)
Choi, HyunyoungKang, YoojinIm, Jungho
Issued Date
2021-04
DOI
10.7780/kjrs.2021.37.2.8
URI
https://scholarworks.unist.ac.kr/handle/201301/52959
Citation
Korean Journal of Remote Sensing, v.37, no.2, pp.275 - 290
Abstract
Sulfur dioxide (SO2) in the atmosphere is mainly generated from anthropogenic emission sources. It forms ultra-fine particulate matter through chemical reaction and has harmful effect on both the environment and human health. In particular, ground-level SO2 concentrations are closely related to human activities. Satellite observations such as TROPOMI (TROPOspheric Monitoring Instrument)-derived column density data can provide spatially continuous monitoring of ground-level SO2 concentrations. This study aims to propose a 2-step residual corrected model to estimate ground-level SO2 concentrations through the synergistic use of satellite data and numerical model output. Random forest machine learning was adopted in the 2-step residual corrected model. The proposed model was evaluated through three cross-validations (i.e., random, spatial and temporal). The results showed that the model produced slopes of 1.14-1.25, R values of 0.55-0.65, and relative root-mean-square-error of 58-63%, which were improved by 10% for slopes and 3% for R and rRMSE when compared to the model without residual correction. The model performance by country was slightly reduced in Japan, often resulting in overestimation, where the sample size was small, and the concentration level was relatively low. The spatial and temporal distributions of SO2 produced by the model agreed with those of the in-situ measurements, especially over Yangtze River Delta in China and Seoul Metropolitan Area in South Korea, which are highly dependent on the characteristics of anthropogenic emission sources. The model proposed in this study can be used for long-term monitoring of ground-level SO2 concentrations on both the spatial and temporal domains.
Publisher
대한원격탐사학회
ISSN
1225-6161
Keyword (Author)
ground-level SO2 concentrationsTROPOMImachine learningresidual correction

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