A comprehensive review of the development of land use regression approaches for modeling spatiotemporal variations of ambient air pollution: A perspective from 2011 to 2023

Land use regression (LUR) models are widely used in epidemiological and environmental studies to estimate humans' exposure to air pollution within urban areas. However, the early models, developed using linear regressions and data from fixed monitoring stations and passive sampling, were primarily designed to model traditional and criteria air pollutants and had limitations in capturing high-resolution spatiotemporal variations of air pollution. Over the past decade, there has been a notable development of multi-source observations from low-cost monitors, mobile monitoring, and satellites, in conjunction with the integration of advanced statistical methods and spatially and temporally dynamic predictors, which…

• NN
  National Natural Science Foundation of China

  Awards: 41871317, 42201469
• CS
  China Scholarship Council

  Award: 202208610078
• SP
  Shaanxi Provincial Science and Technology Department

  Award: 2021JM-388/01
• AR
  Australian Research Council

  Award: LP180100516
• SP
  Shanxi Provincial Key Research and Development Project

  Award: 2021SF-435
• KR
  Key Research and Development Projects of Shaanxi Province

  Award: 2021SF-435