Gongbo Chen1, Luke D Knibbs2, Wenyi Zhang3, Shanshan Li1, Wei Cao4, Jianping Guo5, Hongyan Ren4, Boguang Wang6, Hao Wang7, Gail Williams2, N A S Hamm8, Yuming Guo9. 1. Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia. 2. School of Public Health, The University of Queensland, Brisbane, Australia. 3. Center for Disease Surveillance & Research, Institute of Disease Control and Prevention, Academy of Military Medical Science, Beijing, China. 4. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China. 5. Sate Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, China. 6. Institute for Environmental and Climate Research, Jinan University, Guangzhou, China. 7. Air Quality Studies, Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hong Kong, China. 8. Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands. 9. Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia. Electronic address: yuming.guo@monash.edu.
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.
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.
Authors: Mona Elbarbary; Trenton Honda; Geoffrey Morgan; Yuming Guo; Yanfei Guo; Paul Kowal; Joel Negin Journal: Int J Environ Res Public Health Date: 2020-05-05 Impact factor: 3.390
Authors: Hasheel Tularam; Lisa F Ramsay; Sheena Muttoo; Rajen N Naidoo; Bert Brunekreef; Kees Meliefste; Kees de Hoogh Journal: Int J Environ Res Public Health Date: 2020-07-27 Impact factor: 3.390
Authors: Muhammad Mansoor Asghar; Zhaohua Wang; Bo Wang; Syed Anees Haider Zaidi Journal: Environ Sci Pollut Res Int Date: 2019-12-13 Impact factor: 5.190