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-Reference-Cited by-同舟云学术

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

Published:2024-01 Issue: Volume:183 Page:108430
ISSN:0160-4120
Container-title:Environment International
language:en
Short-container-title:Environment International

Author:

Ma Xuying^ORCID,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 Lidia^ORCID,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

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

Cited by 11 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A novel ensemble machine learning exposure model system for ground-level ozone at the national scale: A case of mainland China from 2013 to 2020;Environmental Impact Assessment Review;2024-11

2. Spatiotemporal characteristics of PM2.5 concentrations and responses to land-use change in Urumqi, China;Journal of Applied Remote Sensing;2024-08-06

3. Urban spatial structure and air quality in the United States: Evidence from a longitudinal approach;Environment International;2024-08

4. Machine learning applications for electrospun nanofibers: a review;Journal of Materials Science;2024-07-30

5. High-resolution urban air pollution mapping;Science;2024-07-26