DOI: 10.1016/j.atmosenv.2014.02.019
Scopus记录号: 2-s2.0-84896858453
论文题名: Fine particulate matter predictions using high resolution Aerosol Optical Depth (AOD) retrievals
作者: Chudnovsky A ; A ; , Koutrakis P ; , Kloog I ; , Melly S ; , Nordio F ; , Lyapustin A ; , Wang Y ; , Schwartz J
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2014
卷: 89 起始页码: 189
结束页码: 198
语种: 英语
英文关键词: Aerosol Optical Depth (AOD)
; High resolution aerosol retrieval
; Intra-urban pollution
; MAIAC
; Particulate matter
; PM2.5
; Scales of pollution
; Variability in PM2.5 levels
Scopus关键词: Aerosol optical depths
; Aerosol retrieval
; MAIAC
; Particulate Matter
; Variability in PM<sub>2.5</sub> levels
; Atmospheric aerosols
; Particles (particulate matter)
; Forecasting
; aerosol property
; air quality
; algorithm
; atmospheric pollution
; climate prediction
; concentration (composition)
; data set
; diurnal variation
; MODIS
; optical depth
; particulate matter
; satellite imagery
; spatiotemporal analysis
; surface reflectance
; traffic congestion
; urban atmosphere
; urban pollution
; aerosol
; aerosol optical depth
; air monitoring
; air pollution
; air quality
; article
; calibration
; controlled study
; environmental exposure
; Inverse probability weighting
; land use
; meteorological phenomena
; molecular weight
; optics
; particulate matter
; prediction
; priority journal
; probability
; United States
; urban area
; Boston
; Connecticut
; Massachusetts
; New Haven
; 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: To date, spatial-temporal patterns of particulate matter (PM) within urban areas have primarily been examined using models. On the other hand, satellites extend spatial coverage but their spatial resolution is too coarse. In order to address this issue, here we report on spatial variability in PM levels derived from high 1km resolution AOD product of Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm developed for MODIS satellite. We apply day-specific calibrations of AOD data to predict PM2.5 concentrations within the New England area of the United States. To improve the accuracy of our model, land use and meteorological variables were incorporated. We used inverse probability weighting (IPW) to account for nonrandom missingness of AOD and nested regions within days to capture spatial variation. With this approach we can control for the inherent day-to-day variability in the AOD-PM2.5 relationship, which depends on time-varying parameters such as particle optical properties, vertical and diurnal concentration profiles and ground surface reflectance among others. Out-of-sample "ten-fold" cross-validation was used to quantify the accuracy of model predictions. Our results show that the model-predicted PM2.5 mass concentrations are highly correlated with the actual observations, with out-of-sample R2 of 0.89. Furthermore, our study shows that the model captures the pollution levels along highways and many urban locations thereby extending our ability to investigate the spatial patterns of urban air quality, such as examining exposures in areas with high traffic. Our results also show high accuracy within the cities of Boston and New Haven thereby indicating that MAIAC data can be used to examine intra-urban exposure contrasts in PM2.5 levels. © 2014 Elsevier Ltd. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/80653
Appears in Collections: 气候变化事实与影响
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作者单位: Department of Environmental Health, Harvard School of Public Health, Boston, MA, United States; Department of Geography and Human Environment, Tel-Aviv University, Israel; Department of Geography and Environmental Development, Ben-Gurion University, Israel; GEST/UMBC, NASA Goddard Space Flight Center, Baltimore, MD, United States; University of Maryland Baltimore County, Baltimore, MD, United States
Recommended Citation:
Chudnovsky A,A,, Koutrakis P,et al. Fine particulate matter predictions using high resolution Aerosol Optical Depth (AOD) retrievals[J]. Atmospheric Environment,2014-01-01,89