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Predicting daily PM2.5 concentrations in Texas using high-resolution satellite aerosol optical depth

机译:使用高分辨率卫星气溶胶光学深度预测德克萨斯州的每日PM2.5浓度

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摘要

Background and objectiveThe regulatory monitoring data of particulate matter with an aerodynamic diameter <2.5μm (PM2.5) in Texas have limited spatial and temporal coverage. The purpose of this study is to estimate the ground-level PM2.5concentrations on a daily basis using satellite-retrieved Aerosol Optical Depth (AOD) in the state of Texas.nMethodsWe obtained the AOD values at 1-km resolution generated through the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm based on the images retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellites. We then developed mixed-effects models based on AODs, land use features, geographic characteristics, and weather conditions, and the day-specific as well as site-specific random effects to estimate the PM2.5concentrations (μg/m3) in the state of Texas during the period 2008–2013. The mixed-effects models' performance was evaluated using the coefficient of determination (R2) and square root of the mean squared prediction error (RMSPE) from ten-fold cross-validation, which randomly selected 90% of the observations for training purpose and 10% of the observations for assessing the models' true prediction ability.nResultsMixed-effects regression models showed good prediction performance (R2values from 10-fold cross validation: 0.63–0.69). The model performance varied by regions and study years, and the East region of Texas, and year of 2009 presented relatively higher prediction precision (R2: 0.62 for the East region; R2: 0.69 for the year of 2009). The PM2.5concentrations generated through our developed models at 1-km grid cells in the state of Texas showed a decreasing trend from 2008 to 2013 and a higher reduction of predicted PM2.5in more polluted areas.nConclusionsOur findings suggest that mixed-effects regression models developed based on MAIAC AOD are a feasible approach to predict ground-level PM2.5in Texas. Predicted PM2.5concentrations at the 1-km resolution on a daily basis can be used for epidemiological studies to investigate short- and long-term health impact of PM2.5in Texas.
机译:背景和目的德克萨斯州空气动力学直径<2.5μm(PM2.5)的颗粒物的监管监测数据具有有限的时空覆盖范围。这项研究的目的是使用得克萨斯州的卫星探测式气溶胶光学厚度(AOD)每天估算地面PM2.5浓度。基于从中分辨率成像光谱仪(MODIS)卫星检索到的图像的大气校正(MAIAC)算法的角度实现。然后,我们根据AOD,土地利用特征,地理特征和天气状况以及特定日期和特定地点的随机效应,开发了混合效应模型,以估算在该状态下PM2.5的浓度(μg/ m3)。德克萨斯州在2008-2013年期间。使用确定系数(R2)和十次交叉验证的均方根预测误差的平方根(RMSPE)来评估混合效果模型的性能,该方法随机选择了90%的观测值用于训练目的,将10个观测值用于随机选择评估模型真实预测能力的观察值的百分比。n结果混合效应回归模型显示出良好的预测性能(十次交叉验证的R2值:0.63-0.69)。模型的性能随地区和研究年份以及德克萨斯州东部地区和2009年的不同而有所差异(2009年东部地区R2:0.62; 2009年R2:0.69)。通过我们开发的模型在德克萨斯州1公里网格单元上产生的PM2.5浓度从2008年到2013年呈下降趋势,而在更多污染地区的预测PM2.5浓度呈更高下降趋势.n结论我们的发现表明,混合效应回归模型基于MAIAC AOD开发的预测德克萨斯州地面PM2.5的可行方法。每天以1公里分辨率估算的PM2.5浓度可用于流行病学研究,以调查德克萨斯州PM2.5的短期和长期健康影响。

著录项

  • 来源
    《The Science of the Total Environment》 |2018年第1期|904-911|共8页
  • 作者单位

    Department of Epidemiology, Human Genetics and Environmental Sciences, The University of Texas Health Science Center at Houston School of Public Health;

    Department of Biostatistics, The University of Texas Health Science Center at Houston School of Public Health;

    Department of Earth and Atmospheric Sciences, University of Houston,Department of Earth System Sciences, Tsinghua University;

    Department of Epidemiology, Human Genetics and Environmental Sciences, The University of Texas Health Science Center at Houston School of Public Health,Department of Biostatistics, The University of Texas Health Science Center at Houston School of Public Health,Southwest Center for Occupational and Environmental Health, The University of Texas Health Science Center at Houston School of Public Health;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    AOD; Land use regression; PM2.5; Remote sensing;

    机译:AOD;土地利用回归;PM2.5;遥感;
  • 入库时间 2022-08-17 13:47:46

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