Estimation of surface-level NO2 and O3 concentrations using TROPOMI data and machine learning over East Asia. (1st November 2021)

• Record Type: Journal Article
• Authors: Kang, Yoojin; Choi, Hyunyoung; Im, Jungho; Park, Seohui; Shin, Minso; Song, Chang-Keun; Kim, Sangmin
• Is Part Of: Environmental pollution. Volume 288(2021)
• Publication Date: 2021-11-01
• Subjects: Air quality -- Satellite -- Trace gas concentration -- Spatial and temporal distribution; Pollution -- Periodicals; Pollution -- Environmental aspects -- Periodicals; Environmental Pollution -- Periodicals; Pollution -- Périodiques; Pollution -- Aspect de l'environnement -- Périodiques; Pollution -- Effets physiologiques -- Périodiques; Pollution; Pollution -- Environmental aspects; Periodicals; Electronic journals; 363.73
• DOI: 10.1016/j.envpol.2021.117711
• Languages: English
• ISSNs: 0269-7491

Abstract

Abstract: In East Asia, air quality has been recognized as an important public health problem. In particular, the surface concentrations of air pollutants are closely related to human life. This study aims to develop models for estimating high spatial resolution surface concentrations of NO2 and O3 from TROPOspheric Monitoring Instrument (TROPOMI) data in East Asia. The machine learning was adopted by fusion of various satellite-based variables, numerical model-based meteorological variables, and land-use variables. Four machine learning approaches—Support Vector Regression (SVR), Random Forest (RF), Extreme Gradient Boost (XGB), and Light Gradient Boosting Machine (LGBM)—were evaluated and compared with Multiple Linear Regression (MLR) as a base statistical method. This study also modeled the NO2 and O3 concentrations over the ocean surface (i.e., land model for scheme 1 and ocean model for scheme 2). The estimated surface concentrations were validated through three cross-validation approaches (i.e., random, temporal, and spatial). The results showed that the NO2 model produced R 2 of 0.63–0.70 and normalized root-mean-square-error (nRMSE) of 38.3–42.2% and the O3 model resulted in R 2 of 0.65–0.78 and nRMSE of 19.6–24.7% for scheme 1. The indirect validation based on the stations near the coastline for scheme 2 showed slight decrease (~0.3–2.4%) in nRMSE when compared to scheme 1. The contributions of input variables to the models were analyzed based on SHapely Additive exPlanations (SHAP) values. The NO2 vertical column density among the TROPOMI-derived variables showed the largest contribution in both the NO2 and O3 models. Graphical abstract: Image 1 Highlights: Surface NO2 /O3 concentrations were modeled for both land and ocean in East Asia. TROPOMI-derived products were used to estimate NO2 and O3 with machine learning. Spatiotemporal transferability of the proposed approaches was carefully evaluated. Potential of the proposed methods for coastal air quality monitoring was examined.