Incorporating satellite-derived data with annual and monthly land use regression models for estimating spatial distribution of air pollution. (April 2019)

• Record Type: Journal Article
• Authors: Huang, Chun-Sheng; Lin, Tang-Huang; Hung, Hung; Kuo, Cheng-Pin; Ho, Chi-Chang; Guo, Yue-Liang; Chen, Kwang-Cheng; Wu, Chang-Fu
• Is Part Of: Environmental modelling & software. Volume 114(2019)
• Page Start: 181
• Page End: 187
• Publication Date: 2019-04
• Subjects: Land use -- Fine particulate matter -- Nitrogen dioxide -- Principle component analysis -- Aerosol optical depth; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2019.01.010
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: The purpose of this study was to assess the performance of annual and monthly land use regression (LUR) models for estimating the spatial distribution of NO2 and PM2.5 in Taiwan. Samples were collected at 73 air quality monitoring sites in 2015. Data transformation coupled with extracting principle components and satellite-derived data were integrated with LUR modeling and applied to increase PM2.5 model performance. Results indicated that NO2 exhibited more robust model performance compared with PM2.5 . Leave-one-out cross validation (LOOCV) R 2 of NO2 annual model was 0.76 and ranged from 0.56 to 0.81 for monthly models. The LOOCV R 2 of PM2.5 annual model was improved from 0.13 to 0.56 by applying principle component analysis and adding satellite data (i.e., percentage of sunshine coverage and aerosol optical depth). These approaches also improved the performance of PM2.5 monthly models. The median LOOCV R 2 increased from 0.12 to 0.49. Graphical abstract: Image 1 Highlights: NO2 showed better model performance than PM2.5, both in annual or monthly models. Data transformation coupled with PCA in modeling improves PM2.5 model performance. Sunshine coverage derived from satellite data was found beneficial for modeling.