Spatiotemporal modeling of PM 2.5 concentrations at the national scale combining land use regression and Bayesian maximum entropy in China-Reference-Cited by-同舟云学术

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Spatiotemporal modeling of PM 2.5 concentrations at the national scale combining land use regression and Bayesian maximum entropy in China

Published:2018-07 Issue: Volume:116 Page:300-307
ISSN:0160-4120
Container-title:Environment International
language:en
Short-container-title:Environment International

Author:

Chen Li,Gao Shuang,Zhang Hui,Sun Yanling,Ma Zhenxing,Vedal Sverre,Mao Jian,Bai Zhipeng

Funder

National Key R&D Program

Nature Science Foundation of Tianjin

Chinese Academy of Sciences and Tianjin City

Universities of Tianjin, China

Tianjin Normal University

Publisher

Elsevier BV

Subject

General Environmental Science

Reference34 articles.

1. Large scale air pollution estimation method combining land use regression and chemical transport modeling in a geostatistical framework;Akita;Environ. Sci. Technol.,2014

2. Insights into measurements of ambient air PM2.5 in China;Bai;Trends Environ. Anal. Chem.,2017

3. A hybrid approach to estimating national scale spatiotemporal variability of PM2.5 in the United States;Beckerman;Environ. Sci. Technol.,2013

4. Mapping urban air pollution using GIS: a regression-based approach;Briggs;Int. J. Geogr. Inf. Sci.,1997

5. An integrated risk function for estimating the global burden of disease attributable to ambient fine particulate matter exposure;Burnett;Environ. Health Perspect.,2014

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