Estimating monthly global ground-level NO2 concentrations using geographically weighted panel regression

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Abstract

Predicting long-term ground-level nitrogen dioxide (NO2) is important globally to support environmental and public health research and to provide information to governments and society for air pollution control policies. The ozone monitoring instrument (OMI), onboard Aura Satellite, detects monthly global tropospheric column amounts (TrCA) of NO2 molecules. However, the relationship between the ground-level NO2 concentration and TrCA of NO2 molecules remains elusive because NO2 molecules in the air are not evenly distributed vertically. We use geographically weighted panel regression (GWPR) to examine the relationship between satellite-derived data, measured ground-level NO2 concentrations, and several controlling meteorological variables from January 2015 to October 2021. The GWPR can analyze unbalanced panel data and capture the spatial variability of the relationship. Based on the GWPR estimation, 82 monthly global ground-level NO2 concentrations are predicted from January 2015 to October 2021. The GWPR is reliable, as indicated by the 10-fold cross-validation. The accuracy of the raster prediction of global ground-level NO2 from January 2015 to October 2021 is 69.61%. The coefficient of correlation, root mean square error and mean absolute error between globally predicted and measured ground-level NO2 are 0.838, 7.84 μg/m3 and 4.07 μg/m3, respectively, while the mean of globally measured ground-level NO2 is 19.47 μg/m3. Overall, this research provides critical basic data to environmental and public health science and valuable information for governments and societies to make more reasonable policies.

Original languageEnglish
Article number113152
JournalRemote Sensing of Environment
Volume280
DOIs
Publication statusPublished - Oct 2022

All Science Journal Classification (ASJC) codes

  • Soil Science
  • Geology
  • Computers in Earth Sciences

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