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

Author:

Ma XuyingORCID,Zou Bin,Deng Jun,Gao Jay,Longley Ian,Xiao Shun,Guo Bin,Wu Yarui,Xu Tingting,Xu Xin,Yang Xiaosha,Wang Xiaoqi,Tan Zelei,Wang Yifan,Morawska LidiaORCID,Salmond Jennifer

Funder

Australian Research Council

National Natural Science Foundation of China

China Scholarship Council

Shaanxi Provincial Science and Technology Department

Shanxi Provincial Key Research and Development Project

Publisher

Elsevier BV

Subject

General Environmental Science

Reference298 articles.

1. Air pollution measurements and land-use regression in urban Sub-Saharan Africa using low-cost sensors—possibilities and pitfalls;Abera;Atmosphere,2020

2. Spatiotemporal modeling of ozone levels in Quebec (Canada): a comparison of kriging, land-use regression (LUR), and combined Bayesian maximum entropy–LUR approaches;Adam-Poupart;Environmental Health Perspectives,2014

3. Spatial modelling of particulate matter air pollution sensor measurements collected by community scientists while cycling, land use regression with spatial cross-validation, and applications of machine learning for data correction;Adams;Atmospheric Environment,2020

4. LURTools: ArcGIS Toolbox for Land Use Regression (LUR) Model;Akita;Available Online at,2014

5. The moving-window Bayesian maximum entropy framework: estimation of PM2.5 yearly average concentration across the contiguous United States. Journal of Exposure Science & Environmental;Akita;Epidemiology,2012

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