DOI: 10.1016/j.atmosenv.2017.10.055
Scopus记录号: 2-s2.0-85032964288
论文题名: Estimating PM2.5 concentrations based on non-linear exposure-lag-response associations with aerosol optical depth and meteorological measures
作者: Chen Z ; -Y ; , Zhang T ; -H ; , Zhang R ; , Zhu Z ; -M ; , Ou C ; -Q ; , Guo Y
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2018
卷: 173 起始页码: 30
结束页码: 37
语种: 英语
英文关键词: Aerosol optical depth
; China
; Fine particulate matter
; Non-linear exposure-lag-response
Scopus关键词: Aerosols
; Atmospheric aerosols
; Boundary layer flow
; Boundary layers
; Optical properties
; Pollution
; Pollution control
; Radiometers
; Regression analysis
; Remote sensing
; Aerosol optical depths
; China
; Fine particulate matter
; Health impact assessment
; Mean absolute percentage error
; Meteorological information
; Non linear
; Planetary boundary layers
; Particles (particulate matter)
; aerosol
; atmospheric pollution
; concentration (composition)
; meteorology
; MODIS
; optical depth
; particle size
; particulate matter
; pollution monitoring
; remote sensing
; aerosol
; Article
; atmospheric pressure
; environmental temperature
; humidity
; meteorological phenomena
; optical depth
; particulate matter
; priority journal
; seasonal variation
; vapor pressure
; China
; Guangdong
; Guangzhou
Scopus学科分类: Environmental Science: Water Science and Technology
; Earth and Planetary Sciences: Earth-Surface Processes
; Environmental Science: Environmental Chemistry
英文摘要: Background The accurate measurement of particulate matter (PM) provides a crucial basis for health impact assessment and pollution management and control. However, monitoring stations of air pollution are limited worldwide. Recently, some researchers have attempted to estimate the levels of PM based on remote sensing data, but the methods still need to be validated and further improved. Objectives This study aimed to develop a new model, to estimate daily ground-level PM2.5 concentrations using the fused aerosol optical depth (AOD) retrieved by the Moderate Resolution Imaging Spectro radiometer and meteorological information. Methods We combined generalized additive mixed-effects model with the log-linked Gaussian error distribution and non-linear exposure-lag-response model for AOD and meteorological measures, to estimate daily ground-level PM2.5 concentrations in 2014–2015 in Guangzhou, China. Results The PM2.5 concentration was significantly associated with AOD and meteorological measures. Compared to the log-linear model, the non-linear exposure-lag-response model had better model performance with a higher temporal (spatial) cross-validation R–square (0.81 (0.81) vs 0.67 (0.67)), and a smaller mean absolute percentage error (17.65% (16.90%) vs 21.22% (21.01%)). AOD explained about 15% variations of PM2.5 in the mixed-effect model. The planetary-boundary -layer-height-revised AOD and relative-humidity-revised PM2.5 did not significantly improve the model performance. Conclusion Considering the non-linear exposure-lag-response association between PM2.5 and AOD and meteorological factors can significantly increase the modelling ability to estimate PM2.5 concentrations. © 2017 Elsevier Ltd Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/83116
Appears in Collections: 气候变化事实与影响
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作者单位: State Key Laboratory of Organ Failure Research, Department of Biostatistics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University, Guangzhou, China; State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China; Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Melbourne, VIC, Australia
Recommended Citation:
Chen Z,-Y,, Zhang T,et al. Estimating PM2.5 concentrations based on non-linear exposure-lag-response associations with aerosol optical depth and meteorological measures[J]. Atmospheric Environment,2018-01-01,173