Estimating spatiotemporal distribution of PM1 concentrations in China with satellite remote sensing, meteorology, and land use information

Chen, Gongbo, Knibbs, Luke D., Zhang, Wenyi, Li, Shanshan, Cao, Wei, Guo, Jianping, Ren, Hongyan, Wang, Boguang, Wang, Hao, Williams, Gail, Hamm, N. A. S. and Guo, Yuming (2017) Estimating spatiotemporal distribution of PM1 concentrations in China with satellite remote sensing, meteorology, and land use information. Environmental Pollution, 233 1086-1094. doi:10.1016/j.envpol.2017.10.011


Author Chen, Gongbo
Knibbs, Luke D.
Zhang, Wenyi
Li, Shanshan
Cao, Wei
Guo, Jianping
Ren, Hongyan
Wang, Boguang
Wang, Hao
Williams, Gail
Hamm, N. A. S.
Guo, Yuming
Title Estimating spatiotemporal distribution of PM1 concentrations in China with satellite remote sensing, meteorology, and land use information
Journal name Environmental Pollution   Check publisher's open access policy
ISSN 0269-7491
1873-6424
Publication date 2017-10-13
Year available 2018
Sub-type Article (original research)
DOI 10.1016/j.envpol.2017.10.011
Open Access Status Not yet assessed
Volume 233
Start page 1086
End page 1094
Total pages 9
Place of publication Kidlington, Oxford, United Kingdom
Publisher Pergamon Press
Language eng
Subject 3005 Toxicology
2310 Pollution
2307 Health, Toxicology and Mutagenesis
Abstract Background: PM might be more hazardous than PM (particulate matter with an aerodynamic diameter ≤ 1 μm and ≤2.5 μm, respectively). However, studies on PM concentrations and its health effects are limited due to a lack of PM monitoring data. Objectives: To estimate spatial and temporal variations of PM concentrations in China during 2005-2014 using satellite remote sensing, meteorology, and land use information. Methods: Two types of Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 aerosol optical depth (AOD) data, Dark Target (DT) and Deep Blue (DB), were combined. Generalised additive model (GAM) was developed to link ground-monitored PM data with AOD data and other spatial and temporal predictors (e.g., urban cover, forest cover and calendar month). A 10-fold cross-validation was performed to assess the predictive ability. Results: The results of 10-fold cross-validation showed R and Root Mean Squared Error (RMSE) for monthly prediction were 71% and 13.0 μg/m, respectively. For seasonal prediction, the R and RMSE were 77% and 11.4 μg/m, respectively. The predicted annual mean concentration of PM across China was 26.9 μg/m. The PM level was highest in winter while lowest in summer. Generally, the PM levels in entire China did not substantially change during the past decade. Regarding local heavy polluted regions, PM levels increased substantially in the South-Western Hebei and Beijing-Tianjin region. Conclusions: GAM with satellite-retrieved AOD, meteorology, and land use information has high predictive ability to estimate ground-level PM. Ambient PM reached high levels in China during the past decade. The estimated results can be applied to evaluate the health effects of PM.
Keyword Aerosol optical depth
China
Land use
Meteorology
PM
Q-Index Code C1
Q-Index Status Provisional Code
Grant ID APP1107107
APP1109193
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
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Created: Tue, 24 Oct 2017, 10:07:19 EST by Luke Knibbs on behalf of School of Public Health