| DOI: | 10.1016/j.atmosenv.2014.03.060
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| Scopus记录号: | 2-s2.0-84897932399
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| 论文题名: | Improved estimation of PM2.5 using Lagrangian satellite-measured aerosol optical depth |
| 作者: | Saunders R; O; , Kahl J; D; W; , Ghorai J; K
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| 刊名: | Atmospheric Environment
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| ISSN: | 0168-2563
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| EISSN: | 1573-515X
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| 出版年: | 2014
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| 卷: | 91 | | 起始页码: | 146
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| 结束页码: | 153
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| 语种: | 英语
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| 英文关键词: | Aerosol optical depth (AOD)
; Air pollution
; MODIS
; Remote sensing
; Trajectory model
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| Scopus关键词: | Air pollution
; Air quality
; Atmospheric aerosols
; Estimation
; Lagrange multipliers
; Linear regression
; Mathematical models
; Radiometers
; Remote sensing
; Uncertainty analysis
; Aerosol optical depths
; Ground level concentrations
; Moderate resolution imaging spectroradiometer satellites
; MODIS
; Multiple linear regression models
; Simple linear regression
; Suspended particulate matters
; Trajectory modeling
; Satellite imagery
; aerosol composition
; air quality
; atmospheric pollution
; epidemiology
; Lagrangian analysis
; MODIS
; optical depth
; remote sensing
; size distribution
; suspended particulate matter
; trajectory
; aerosol
; aerosol optical depth
; air monitoring
; air pollution
; air quality
; article
; atmospheric transport
; autumn
; optical depth
; priority journal
; spring
; summer
; suspended particulate matter
; United States
; winter
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| Scopus学科分类: | Environmental Science: Water Science and Technology
; Earth and Planetary Sciences: Earth-Surface Processes
; Environmental Science: Environmental Chemistry
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| 英文摘要: | Accurate estimates of fine suspended particulate matter (PM2.5) concentrations are important in air quality and epidemiological studies. Aerosol optical depth (AOD) retrieved by the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument has been used as an empirical predictor to estimate ground-level concentrations of PM2.5. However, these estimates usually have large uncertainties. The main objective of this work is to assess the value of upwind (Lagrangian) MODIS-AOD as predictors in empirical models of ground-level PM2.5. We also explored the reconstruction of missing MODIS data and developed a daily average uniformly-gridded AOD product. The empirical models developed in this work were tested in ten different sites across the continental United States. Multiple linear regression models that included Lagrangian AOD along in situ AOD as predictors showed statistically significant improvement over the simple linear regression models (PM2.5 and in situ AOD). A set of seasonal categorical variables was included in the regressions to account for the variability of regression performance with respect to seasons. The extended multiple linear regression models exhibited statistically significant improvement over the simple and multiple linear regression models that only contained AOD as predictors. © 2014 Elsevier Ltd. |
| Citation statistics: |
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| 资源类型: | 期刊论文
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/81104
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| Appears in Collections: | 气候变化事实与影响
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作者单位: | Department of Mathematical Sciences, University of Wisconsin-Milwaukee, P.O. Box 413, Milwaukee, WI 53201, United States
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| Recommended Citation: | |
Saunders R,O,, Kahl J,et al. Improved estimation of PM2.5 using Lagrangian satellite-measured aerosol optical depth[J]. Atmospheric Environment,2014-01-01,91
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