Incorporating satellite-derived data with annual and monthly land use regression models for estimating spatial distribution of air pollution
Journal
Environmental Modelling and Software
Journal Volume
114
Pages
181-187
Date Issued
2019
Author(s)
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) R2 of NO2 annual model was 0.76 and ranged from 0.56 to 0.81 for monthly models. The LOOCV R2 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 R2 increased from 0.12 to 0.49.
Publisher
Elsevier Ltd
Type
journal article