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

Chun Sheng Huang, Tang Huang Lin, Hung Hung, Cheng Pin Kuo, Chi Chang Ho, Yue Liang Guo, Kwang Cheng Chen, Chang Fu Wu

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

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.

Original languageEnglish
Pages (from-to)181-187
Number of pages7
JournalEnvironmental Modelling and Software
Volume114
DOIs
StatePublished - Apr 2019

Keywords

  • Aerosol optical depth
  • Fine particulate matter
  • Land use
  • Nitrogen dioxide
  • Principle component analysis

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