Simplicity versus accuracy for estimation of the PM2.5 concentration: a comparison between LUR and GWR methods across time scales. Issue 2 (4th May 2021)

• Record Type: Journal Article
• Authors: Zou, Bin; Fang, Xin; Feng, Huihui; Zhou, Xiang
• Is Part Of: Journal of spatial science. Volume 66:Issue 2(2021)
• Page Start: 279
• Page End: 297
• Publication Date: 2021-05-04
• Subjects: Fine particulate matter (PM2.5) -- land use regression (LUR) -- geographically weighted regression (GWR) -- mapping -- model performance; Geographic information systems -- Periodicals; Cartography -- Periodicals; Surveying -- Periodicals; Geodesy -- Periodicals; Photogrammetry -- Periodicals; Cartography; Geodesy; Geographic information systems; Photogrammetry; Surveying; Periodicals; 526.05
• DOI: 10.1080/14498596.2019.1624203
• Languages: English
• ISSNs: 1449-8596

Abstract

ABSTRACT: This study compares land use regression (LUR) and geographically weighted regression (GWR) models in PM2.5 concentration mapping over California (USA). Results show that R 2 values of LUR model are 0.78, 0.74 and 0.74 times lower than those of GWR model at annual, seasonal and monthly scales. Relative errors are 2.18, 1.79 and 1.60 times higher, and root mean square error (RMSE) are 1.48, 1.32 and 1.28 higher. Furthermore, performance difference is significant under polluted conditions, but is minor under clean conditions. It demonstrates that LUR model is effective under low concentration at short time scales.