DOI: 10.1016/j.atmosenv.2017.07.023
Scopus记录号: 2-s2.0-85019894960
论文题名: Spatiotemporal estimation of historical PM2.5 concentrations using PM10, meteorological variables, and spatial effect
作者: Li L ; , Wu A ; H ; , Cheng I ; , Chen J ; -C ; , Wu J
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2017
卷: 166 起始页码: 182
结束页码: 191
语种: 英语
英文关键词: Historical concentration
; Particulate matter
; PM2.5
; Spatiotemporal model
Scopus关键词: Autocorrelation
; Scales (weighing instruments)
; Epidemiological studies
; Fine particulate matter
; Meteorological variables
; Particulate Matter
; Spatial autocorrelations
; Spatio-temporal models
; Spatiotemporal variability
; Total suspended particulates
; Particles (particulate matter)
; climate conditions
; concentration (composition)
; epidemiology
; estimation method
; health impact
; model validation
; particulate matter
; pollution exposure
; pollution monitoring
; spatiotemporal analysis
; air temperature
; Article
; California
; humidity
; land use
; meteorology
; particulate matter
; precipitation
; priority journal
; traffic
; validation study
; wind
; California
; United States
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: Monitoring of fine particulate matter with diameter <2.5 μm (PM2.5) started from 1999 in the US and even later in many other countries. The lack of historical PM2.5 data limits epidemiological studies of long-term exposure of PM2.5 and health outcomes such as cancer. In this study, we aimed to design a flexible approach to reliably estimate historical PM2.5 concentrations by incorporating spatial effect and the measurements of existing co-pollutants such as particulate matter with diameter <10 μm (PM10) and meteorological variables. Monitoring data of PM10, PM2.5, and meteorological variables covering the entire state of California were obtained from 1999 through 2013. We developed a spatiotemporal model that quantified non-linear associations between PM2.5 concentrations and the following predictor variables: spatiotemporal factors (PM10 and meteorological variables), spatial factors (land-use patterns, traffic, elevation, distance to shorelines, and spatial autocorrelation), and season. Our model accounted for regional-(county) scale spatial autocorrelation, using spatial weight matrix, and local-scale spatiotemporal variability, using local covariates in additive non-linear model. The spatiotemporal model was evaluated, using leaving-one-site-month-out cross validation. Our final daily model had an R2 of 0.81, with PM10, meteorological variables, and spatial autocorrelation, explaining 55%, 10%, and 10% of the variance in PM2.5 concentrations, respectively. The model had a cross-validation R2 of 0.83 for monthly PM2.5 concentrations (N = 8170) and 0.79 for daily PM2.5 concentrations (N = 51,421) with few extreme values in prediction. Further, the incorporation of spatial effects reduced bias in predictions. Our approach achieved a cross validation R2 of 0.61 for the daily model when PM10 was replaced by total suspended particulate. Our model can robustly estimate historical PM2.5 concentrations in California when PM2.5 measurements were not available. © 2017 Elsevier Ltd Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/82416
Appears in Collections: 气候变化事实与影响
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作者单位: State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, China; Department of Preventive Medicine, University of Southern California, United States; Cancer Prevention Institute of California, United States; Stanford Cancer Institute, United States; Program in Public Health, University of California, Irvine, United States
Recommended Citation:
Li L,, Wu A,H,et al. Spatiotemporal estimation of historical PM2.5 concentrations using PM10, meteorological variables, and spatial effect[J]. Atmospheric Environment,2017-01-01,166