DOI: 10.1016/j.atmosenv.2014.07.014
Scopus记录号: 2-s2.0-84904089665
论文题名: A new hybrid spatio-temporal model for estimating daily multi-year PM2.5 concentrations across northeastern USA using high resolution aerosol optical depth data
作者: Kloog I ; , Chudnovsky A ; A ; , Just A ; C ; , Nordio F ; , Koutrakis P ; , Coull B ; A ; , Lyapustin A ; , Wang Y ; , Schwartz J
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2014
卷: 95 起始页码: 581
结束页码: 590
语种: 英语
英文关键词: Aerosol optical depth (AOD)
; Air pollution
; Epidemiology
; Exposure error
; High resolution aerosol retrieval
; MAIAC
Scopus关键词: Air pollution
; Atmospheric aerosols
; Data processing
; Diseases
; Epidemiology
; Rural areas
; Satellite imagery
; Aerosol optical depths
; Aerosol retrieval
; Epidemiological studies
; Exposure errors
; Fine particulate matter
; MAIAC
; Multi-angle implementation of atmospheric corrections
; Satellite data processing
; Forecasting
; aerosol composition
; epidemiology
; MODIS
; optical depth
; particulate matter
; public health
; satellite data
; size distribution
; spatiotemporal analysis
; spectral resolution
; aerosol
; aerosol optical depth
; article
; concentration (parameters)
; epidemiological monitoring
; human
; long term exposure
; optical depth
; particle size
; particulate matter
; prediction
; priority journal
; rural area
; spatiotemporal analysis
; suburban area
; United States
; urban area
; New England
; New Jersey
; New York [United States]
; 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: The use of satellite-based aerosol optical depth (AOD) to estimate fine particulate matter (PM2.5) for epidemiology studies has increased substantially over the past few years. These recent studies often report moderate predictive power, which can generate downward bias in effect estimates. In addition, AOD measurements have only moderate spatial resolution, and have substantial missing data. We make use of recent advances in MODIS satellite data processing algorithms (Multi-Angle Implementation of Atmospheric Correction (MAIAC), which allow us to use 1 km (versus currently available 10 km) resolution AOD data. We developed and cross validated models to predict daily PM2.5 at a 1 × 1 km resolution across the northeastern USA (New England, New York and New Jersey) for the years 2003-2011, allowing us to better differentiate daily and long term exposure between urban, suburban, and rural areas. Additionally, we developed an approach that allows us to generate daily high-resolution 200 m localized predictions representing deviations from the area 1 × 1 km grid predictions. We used mixed models regressing PM2.5 measurements against day-specific random intercepts, and fixed and random AOD and temperature slopes. We then use generalized additive mixed models with spatial smoothing to generate grid cell predictions when AOD was missing. Finally, to get 200 m localized predictions, we regressed the residuals from the final model for each monitor against the local spatial and temporal variables at each monitoring site. Our model performance was excellent (mean out-of-sample R2 = 0.88). The spatial and temporal components of the out-of-sample results also presented very good fits to the withheld data (R2 = 0.87, R2 = 0.87). In addition, our results revealed very little bias in the predicted concentrations (Slope of predictions versus withheld observations = 0.99). Our daily model results show high predictive accuracy at high spatial resolutions and will be useful in reconstructing exposure histories for epidemiological studies across this region. © 2014 Elsevier Ltd. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/81259
Appears in Collections: 气候变化事实与影响
There are no files associated with this item.
作者单位: Department of Geography and Environmental Development, Ben-Gurion University of the Negev, P.O.B. 653, Beer Sheva, Israel; Department of Geography and Human Environment, Tel-Aviv University, Israel; Department of Environmental Health - Exposure, Epidemiology and Risk Program, Harvard School of Public Health, Landmark Center, 401 Park Drive West, Boston, MA 02215, United States; Department of Biostatistics, Harvard School of Public Health, Boston, MA 02215, United States; GEST/UMBC, NASA Goddard Space Flight Center, Baltimore, MD, United States; University of Maryland Baltimore County, Baltimore, MD, United States
Recommended Citation:
Kloog I,, Chudnovsky A,A,et al. A new hybrid spatio-temporal model for estimating daily multi-year PM2.5 concentrations across northeastern USA using high resolution aerosol optical depth data[J]. Atmospheric Environment,2014-01-01,95