Rights statement: This is the author’s version of a work that was accepted for publication in Science of the Total Environment. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Science of the Total Environment, 778, 2021 DOI: 10.1016/j.scitotenv.2021.146305
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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - The application of land use regression model to investigate spatiotemporal variations of PM2.5 in Guangzhou, China
T2 - Implications for the public health benefits of PM2.5 reduction
AU - Mo, Yangzhi
AU - Booker, Douglas
AU - Zhao, Shizhen
AU - Tang, Jiao
AU - Jiang, Hongxing
AU - Shen, Jin
AU - Duohong, Chen
AU - Li, Jun
AU - Jones, Kevin
AU - Zhang, Gan
N1 - This is the author’s version of a work that was accepted for publication in Science of the Total Environment. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Science of the Total Environment, 778, 2021 DOI: 10.1016/j.scitotenv.2021.146305
PY - 2021/7/15
Y1 - 2021/7/15
N2 - Understanding the intra-city variation of PM2.5 is important for air quality management and exposure assessment. In this study, to investigate the spatiotemporal variation of PM2.5 in Guangzhou, we developed land use regression (LUR) models using data from 49 routine air quality monitoring stations. The R2, adjust R2 and 10-fold cross validation R2 for the annual PM2.5 LUR model were 0.78, 0.72 and 0.66, respectively, indicating the robustness of the model. In all the LUR models, traffic variables (e.g., length of main road and the distance to nearest ancillary) were the most common variables in the LUR models, suggesting vehicle emission was the most important contributor to PM2.5 and controlling vehicle emissions would be an effective way to reduce PM2.5. The predicted PM2.5 exhibited significant variations with different land uses, with the highest value for impervious surfaces, followed by green land, cropland, forest and water areas. Guangzhou as the third largest city that PM2.5 concentration has achieved CAAQS Grade II guideline in China, it represents a useful case study city to examine the health and economic benefits of further reduction of PM2.5 to the lower concentration ranges. So, the health and economic benefits of reducing PM2.5 in Guangzhou was further estimated using the BenMAP model, based on the annual PM2.5 concentration predicted by the LUR model. The results showed that the avoided all cause mortalities were 992 cases (95% CI: 221–2140) and the corresponding economic benefits were 1478 million CNY (95% CI: 257–2524) (willingness to pay approach) if the annual PM2.5 concentration can be reduced to the annual CAAQS Grade I guideline value of 15 μg/m3. Our results are expected to provide valuable information for further air pollution control strategies in China.
AB - Understanding the intra-city variation of PM2.5 is important for air quality management and exposure assessment. In this study, to investigate the spatiotemporal variation of PM2.5 in Guangzhou, we developed land use regression (LUR) models using data from 49 routine air quality monitoring stations. The R2, adjust R2 and 10-fold cross validation R2 for the annual PM2.5 LUR model were 0.78, 0.72 and 0.66, respectively, indicating the robustness of the model. In all the LUR models, traffic variables (e.g., length of main road and the distance to nearest ancillary) were the most common variables in the LUR models, suggesting vehicle emission was the most important contributor to PM2.5 and controlling vehicle emissions would be an effective way to reduce PM2.5. The predicted PM2.5 exhibited significant variations with different land uses, with the highest value for impervious surfaces, followed by green land, cropland, forest and water areas. Guangzhou as the third largest city that PM2.5 concentration has achieved CAAQS Grade II guideline in China, it represents a useful case study city to examine the health and economic benefits of further reduction of PM2.5 to the lower concentration ranges. So, the health and economic benefits of reducing PM2.5 in Guangzhou was further estimated using the BenMAP model, based on the annual PM2.5 concentration predicted by the LUR model. The results showed that the avoided all cause mortalities were 992 cases (95% CI: 221–2140) and the corresponding economic benefits were 1478 million CNY (95% CI: 257–2524) (willingness to pay approach) if the annual PM2.5 concentration can be reduced to the annual CAAQS Grade I guideline value of 15 μg/m3. Our results are expected to provide valuable information for further air pollution control strategies in China.
KW - PM2.5
KW - Land use regression model
KW - BenMAP
KW - Guangzhou
KW - Health benefit
U2 - 10.1016/j.scitotenv.2021.146305
DO - 10.1016/j.scitotenv.2021.146305
M3 - Journal article
VL - 778
JO - Science of the Total Environment
JF - Science of the Total Environment
SN - 0048-9697
M1 - 146305
ER -