Spatiotemporal analysis of PM2.5 in large coastal domains by combining Land Use Regression and Bayesian Maximum Entropy

被引:0
|
作者
Jiang, Qu-Tu [1 ]
He, Jun-Yu [1 ]
Wang, Zhan-Shan [2 ]
Ye, Guan-Qiong [1 ]
Chen, Qian [3 ]
Xiao, Lu [1 ]
机构
[1] Institute of Island & Coastal Ecosystems, Zhejiang University, Zhoushan,316021, China
[2] Beijing Municipal Environmental Monitoring Center, Beijing,100048, China
[3] School of Geographic and Environment, Jiangxi Normal University, Nanchang,330022, China
关键词
Average concentration - Bayesian maximum entropies - Human exposures - Land use regression - Normalized difference vegetation index - PM2.5 - Root mean square errors - Spatiotemporal analysis;
D O I
暂无
中图分类号
学科分类号
摘要
引用
收藏
页码:424 / 431
相关论文
共 50 条
  • [31] Land Use Regression Model for Exposure Assessment to PM2.5 and PM10 in Rio de Janeiro, Brazil
    Oliveira, M.
    Santana, M.
    Marques, M.
    Griep, R.
    Fonseca, M.
    Moreno, A.
    Magalhaes, M.
    Ponce de Leon, A.
    EUROPEAN JOURNAL OF PUBLIC HEALTH, 2020, 30
  • [32] Spatial distribution characteristics of PM2.5 and PM10 in Xi'an City predicted by land use regression models
    Han, Li
    Zhao, Jingyuan
    Gao, Yuejing
    Gu, Zhaolin
    Xin, Kai
    Zhang, Jianxin
    SUSTAINABLE CITIES AND SOCIETY, 2020, 61 (61)
  • [33] Analysis of spatiotemporal variation and relationship to land use - landscape pattern of PM2.5 and O3 in typical arid zone
    Chen, Zewei
    Zhang, Zhe
    SUSTAINABLE CITIES AND SOCIETY, 2024, 113
  • [34] Estimation of Groundwater Radon in North Carolina Using Land Use Regression and Bayesian Maximum Entropy
    Messier, Kyle P.
    Campbell, Ted
    Bradley, Philip J.
    Serret, Marc L.
    ENVIRONMENTAL SCIENCE & TECHNOLOGY, 2015, 49 (16) : 9817 - 9825
  • [35] Estimate annual and seasonal PM1, PM2.5 and PM10 concentrations using land use regression model
    Miri, Mohammad
    Ghassoun, Yahya
    Dovlatabadi, Afshin
    Ebrahimnejad, Ali
    Loewner, Marc-Oliver
    ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY, 2019, 174 : 137 - 145
  • [36] Land use regression models for the oxidative potential of fine particles (PM2.5) in five European areas
    Gulliver, John
    Morley, David
    Dunster, Chrissi
    McCrea, Adrienne
    van Nunen, Erik
    Tsai, Ming-Yi
    Probst-Hensch, Nicoltae
    Eeftens, Marloes
    Imboden, Medea
    Ducret-Stich, Regina
    Naccarati, Alessio
    Galassi, Claudia
    Ranzi, Andrea
    Nieuwenhuijsen, Mark
    Curto, Ariadna
    Donaire-Gonzalez, David
    Cirach, Marta
    Vermeulen, Roel
    Vineis, Paolo
    Hoek, Gerard
    Kelly, Frank J.
    ENVIRONMENTAL RESEARCH, 2018, 160 : 247 - 255
  • [37] A hybrid land use regression/AERMOD model for predicting intra-urban variation in PM2.5
    Michanowicz, Drew R.
    Shmool, Jessie L. C.
    Tunno, Brett J.
    Tripathy, Sheila
    Gillooly, Sara
    Kinnee, Ellen
    Clougherty, Jane E.
    ATMOSPHERIC ENVIRONMENT, 2016, 131 : 307 - 315
  • [38] Spatial modeling of PM2.5 concentrations using an optimized land use regression method in Jiangsu, China
    Wang, Xintong
    Qian, Yu
    THIRD INTERNATIONAL CONFERENCE ON ENERGY ENGINEERING AND ENVIRONMENTAL PROTECTION, 2019, 227
  • [39] Incorporating Local Land Use Regression And Satellite Aerosol Optical Depth In A Hybrid Model Of Spatiotemporal PM2.5 Exposures In The Mid-Atlantic States
    Kloog, Itai
    Nordio, Francesco
    Coull, Brent A.
    Schwartz, Joel
    ENVIRONMENTAL SCIENCE & TECHNOLOGY, 2012, 46 (21) : 11913 - 11921
  • [40] Spatial variation of ambient PM2.5 and PM10 in the industrial city of Arak, Iran: A land-use regression
    Karimi, Behrooz
    Shokrinezhad, Behnosh
    ATMOSPHERIC POLLUTION RESEARCH, 2021, 12 (12)