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

Gongbo Chen, Luke D. Knibbs, Wenyi Zhang, Shanshan Li, Wei Cao, Jianping Guo, Hongyan Ren, Boguang Wang, Hao Wang, Gail Williams, N. A.S. Hamm, Yuming Guo

Research output: Contribution to journalArticleResearchpeer-review

27 Citations (Scopus)

Abstract

Background PM1 might be more hazardous than PM2.5 (particulate matter with an aerodynamic diameter ≤ 1 μm and ≤2.5 μm, respectively). However, studies on PM1 concentrations and its health effects are limited due to a lack of PM1 monitoring data. Objectives To estimate spatial and temporal variations of PM1 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 PM1 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 R2 and Root Mean Squared Error (RMSE) for monthly prediction were 71% and 13.0 μg/m3, respectively. For seasonal prediction, the R2 and RMSE were 77% and 11.4 μg/m3, respectively. The predicted annual mean concentration of PM1 across China was 26.9 μg/m3. The PM1 level was highest in winter while lowest in summer. Generally, the PM1 levels in entire China did not substantially change during the past decade. Regarding local heavy polluted regions, PM1 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 PM1. Ambient PM1 reached high levels in China during the past decade. The estimated results can be applied to evaluate the health effects of PM1. Satellite-retrieved AOD could be successfully used to predict levels of PM1. The predicted annual mean concentration of PM1 across China was 26.9 μg/m3.

Original languageEnglish
Pages (from-to)1086-1094
Number of pages9
JournalEnvironmental Pollution
Volume233
DOIs
Publication statusPublished - 1 Feb 2018

Keywords

  • Aerosol optical depth
  • China
  • Land use
  • Meteorology
  • PM

Cite this

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. / Estimating spatiotemporal distribution of PM1 concentrations in China with satellite remote sensing, meteorology, and land use information. In: Environmental Pollution. 2018 ; Vol. 233. pp. 1086-1094.
@article{23d38e772f17498f96cb8159f4bebbee,
title = "Estimating spatiotemporal distribution of PM1 concentrations in China with satellite remote sensing, meteorology, and land use information",
abstract = "Background PM1 might be more hazardous than PM2.5 (particulate matter with an aerodynamic diameter ≤ 1 μm and ≤2.5 μm, respectively). However, studies on PM1 concentrations and its health effects are limited due to a lack of PM1 monitoring data. Objectives To estimate spatial and temporal variations of PM1 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 PM1 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 R2 and Root Mean Squared Error (RMSE) for monthly prediction were 71{\%} and 13.0 μg/m3, respectively. For seasonal prediction, the R2 and RMSE were 77{\%} and 11.4 μg/m3, respectively. The predicted annual mean concentration of PM1 across China was 26.9 μg/m3. The PM1 level was highest in winter while lowest in summer. Generally, the PM1 levels in entire China did not substantially change during the past decade. Regarding local heavy polluted regions, PM1 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 PM1. Ambient PM1 reached high levels in China during the past decade. The estimated results can be applied to evaluate the health effects of PM1. Satellite-retrieved AOD could be successfully used to predict levels of PM1. The predicted annual mean concentration of PM1 across China was 26.9 μg/m3.",
keywords = "Aerosol optical depth, China, Land use, Meteorology, PM",
author = "Gongbo Chen and Knibbs, {Luke D.} and Wenyi Zhang and Shanshan Li and Wei Cao and Jianping Guo and Hongyan Ren and Boguang Wang and Hao Wang and Gail Williams and Hamm, {N. A.S.} and Yuming Guo",
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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.; Guo, Yuming.

In: Environmental Pollution, Vol. 233, 01.02.2018, p. 1086-1094.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - Estimating spatiotemporal distribution of PM1 concentrations in China with satellite remote sensing, meteorology, and land use information

AU - Chen, Gongbo

AU - Knibbs, Luke D.

AU - Zhang, Wenyi

AU - Li, Shanshan

AU - Cao, Wei

AU - Guo, Jianping

AU - Ren, Hongyan

AU - Wang, Boguang

AU - Wang, Hao

AU - Williams, Gail

AU - Hamm, N. A.S.

AU - Guo, Yuming

PY - 2018/2/1

Y1 - 2018/2/1

N2 - Background PM1 might be more hazardous than PM2.5 (particulate matter with an aerodynamic diameter ≤ 1 μm and ≤2.5 μm, respectively). However, studies on PM1 concentrations and its health effects are limited due to a lack of PM1 monitoring data. Objectives To estimate spatial and temporal variations of PM1 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 PM1 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 R2 and Root Mean Squared Error (RMSE) for monthly prediction were 71% and 13.0 μg/m3, respectively. For seasonal prediction, the R2 and RMSE were 77% and 11.4 μg/m3, respectively. The predicted annual mean concentration of PM1 across China was 26.9 μg/m3. The PM1 level was highest in winter while lowest in summer. Generally, the PM1 levels in entire China did not substantially change during the past decade. Regarding local heavy polluted regions, PM1 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 PM1. Ambient PM1 reached high levels in China during the past decade. The estimated results can be applied to evaluate the health effects of PM1. Satellite-retrieved AOD could be successfully used to predict levels of PM1. The predicted annual mean concentration of PM1 across China was 26.9 μg/m3.

AB - Background PM1 might be more hazardous than PM2.5 (particulate matter with an aerodynamic diameter ≤ 1 μm and ≤2.5 μm, respectively). However, studies on PM1 concentrations and its health effects are limited due to a lack of PM1 monitoring data. Objectives To estimate spatial and temporal variations of PM1 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 PM1 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 R2 and Root Mean Squared Error (RMSE) for monthly prediction were 71% and 13.0 μg/m3, respectively. For seasonal prediction, the R2 and RMSE were 77% and 11.4 μg/m3, respectively. The predicted annual mean concentration of PM1 across China was 26.9 μg/m3. The PM1 level was highest in winter while lowest in summer. Generally, the PM1 levels in entire China did not substantially change during the past decade. Regarding local heavy polluted regions, PM1 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 PM1. Ambient PM1 reached high levels in China during the past decade. The estimated results can be applied to evaluate the health effects of PM1. Satellite-retrieved AOD could be successfully used to predict levels of PM1. The predicted annual mean concentration of PM1 across China was 26.9 μg/m3.

KW - Aerosol optical depth

KW - China

KW - Land use

KW - Meteorology

KW - PM

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