Correlating MODIS aerosol optical thickness data with ground-based PM2.5 observations across Texas for use in a real-time air quality prediction system

被引:69
|
作者
Hutchison, KD [1 ]
Smith, S [1 ]
Faruqui, SJ [1 ]
机构
[1] Space Res Ctr, Austin, TX 78759 USA
基金
美国国家航空航天局;
关键词
remote sensing; MODIS; air quality prediction; aerosol optical thickness;
D O I
10.1016/j.atmosenv.2005.08.036
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Investigations have been conducted at the Center for Space Research (CSR) into approaches to correlate MODIS aerosol optical thickness (AOT) values with ground-based, PM2.5 observations made at continuous air monitoring station locations operated by the Texas Commission on Environmental Quality (TCEQ). These correlations are needed to more fully utilize real-time MODIS AOT analyses generated at CSR in operational air quality forecasts issued by TCEQ using a trajectory-based forecast model developed by NASA. Initial analyses of two data sets collected during 3 months in 2003 and all of 2004 showed linear correlations in the 0.4-0.5 range in the data collected over Texas. Stronger correlations (exceeding 0.9) were obtained by averaging these same data over longer timescales but this approach is considered unsuitable for use in issuing air quality forecasts. Peculiarities in the MODIS AOT analyses, referred to as hot spots, were recognized while attempting to improve these correlations. It is demonstrated that hot spots are possible when pixels that contain surface water are not detected and removed from the AOT retrieval algorithms. An approach to reduce the frequency of hot spots in AOT analyses over Texas is demonstrated by tuning thresholds used to detect inland water surfaces and remove pixels that contain them from the analysis. Finally, the potential impact of hot spots on MODIS AOT-PM2.5 correlations is examined through the analysis of a third data set that contained sufficient levels of aerosols to mask inland water surfaces from the AOT algorithms. In this case, significantly stronger correlations, that exceed the 0.9 value considered suitable for use in a real-time air quality prediction system, were observed between the MODIS AOT observations and ground-based PM2.5 measurements. (c) 2005 Elsevier Ltd. All rights reserved.
引用
收藏
页码:7190 / 7203
页数:14
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    [J]. CANADIAN JOURNAL OF REMOTE SENSING, 2010, 36 (02) : 119 - 128
  • [2] Improving correlations between MODIS aerosol optical thickness and ground-based PM2.5 observations through 3D spatial analyses
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    [J]. ATMOSPHERIC ENVIRONMENT, 2008, 42 (03) : 530 - 543
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    Zhao, M.
    Cao, Y.
    Li, Ch
    Xing, K.
    Deng, X.
    Xie, Ch
    Liu, D.
    [J]. JOURNAL OF APPLIED SPECTROSCOPY, 2021, 88 (04) : 794 - 801
  • [5] Optimal temporal scale for the correlation of AOD and ground measurements of PM2.5 in a real-time air quality estimation system
    Li, Hui
    Faruque, Fazlay
    Williams, Worth
    Al-Hamdan, Mohammad
    Luvall, Jeffrey
    Crosson, William
    Rickman, Douglas
    Limaye, Ashutosh
    [J]. ATMOSPHERIC ENVIRONMENT, 2009, 43 (28) : 4303 - 4310
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    [J]. Environmental Science and Pollution Research, 2022, 29 : 41971 - 41982
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    [J]. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH, 2022, 29 (28) : 41971 - 41982
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    Li, Weifeng
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    Liu, Yonghong
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