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Spatiotemporal mixed effects modeling for the estimation of PM_(2.5) from MODIS AOD over the Indian subcontinent

机译：印度次大陆上MODIS AOD估算PM_（2.5）的时空混合效应模型

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摘要

The physical processes associated with the constituents of the troposphere, such as aerosols have an immediate impact on human health. This study employs a novel method to calibrate Aerosol Optical Depth (AOD) obtained from the MODerate resolution Imaging Spectrometer (MODIS - Terra satellite) for estimating surface PM2.5 concentration. The Combined Deep Blue Deep Target daily product from the MODIS AOD data acquired across the Indian Subcontinent was used as input, and the daily averaged PM(2.5)pollution level data obtained from 33 monitoring stations spread across the country was used for calibration. Mixed Effect Models (MEM) is a linear model to deal with non-independent data from multiple levels or hierarchy using fixed and random effects of dependent parameters. MEM was applied to the dataset obtained for the period from January to August 2017. The MEM considers a fixed and random component, where the random components model the daily variations of the AOD - PM2.5 relationships, site-specific adjustment parameters, temporal (meteorological) variables such as temperature, and spatial variables such as the percentage of agricultural area, forest cover, barren land and road density with the resolution of 10 km x 10 km. Estimation accuracy was improved from an R-2 value of 0.66 from our earlier study (when PM2.5 was modeled against only AOD and site-specific parameters) toR(2) value of 0.75 upon the inclusion of spatiotemporal (meteorological) variables with increased % within Expected Error from 18% to 35%, reduced Mean Bias Error from 3.22 to 0.11 and reduced RMSE from 29.11 to 20.09. We also found that spline interpolation performed better than IDW and Kriging inefficiently estimating the PM2.5 concentrations wherever there were missing AOD data. The estimated minimum PM2.5 is 93 +/- 25 mu g/m(3) which itself is in the upper limit of the hazardous level while the maximum is estimated as 170 +/- 70 mu g/m(3). The study has thus made it possible to determine the daily spatial variations of PM2.5 concentrations across the Indian subcontinent utilizing satellite-based AOD data.
机译：与对流层成分（例如气溶胶）相关的物理过程对人类健康具有直接影响。这项研究采用了一种新颖的方法来校准从MODerate分辨率成像光谱仪（MODIS-Terra卫星）获得的气溶胶光学深度（AOD），以估算表面PM2.5浓度。来自印度次大陆的MODIS AOD数据的组合深蓝色深目标日产数据用作输入，并使用从全国33个监测站获得的每日平均PM（2.5）污染水平数据进行校准。混合效应模型（MEM）是一种线性模型，可使用相关参数的固定和随机效应来处理来自多个级别或层次结构的非独立数据。将MEM应用于从2017年1月至2017年8月获得的数据集。MEM考虑了固定和随机成分，其中随机成分模拟AOD-PM2.5关系，站点特定调整参数，时间（气象）变量（例如温度）和空间变量（例如农业面积百分比，森林覆盖率，贫瘠的土地和道路密度），分辨率为10 km x 10 km。估计准确性从我们先前的研究中的R-2值为0.66（当仅针对AOD和站点特定参数对PM2.5进行建模时）提高到R（2）值为0.75（在增加时空（气象）变量时） ％的预期误差从18％降低到35％，平均偏差误差从3.22降低到0.11，RMSE从29.11降低到20.09。我们还发现，在缺少AOD数据的任何地方，样条插值的效果都优于IDW和Kriging，无法有效地估计PM2.5浓度。估计的最小PM2.5为93 +/- 25μg / m（3），其本身处于危险水平的上限，而最大估计值为170 +/- 70μg / m（3）。因此，这项研究使利用基于卫星的AOD数据确定整个印度次大陆PM2.5浓度的每日空间变化成为可能。

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  • 来源
    《GIScience & remote sensing》 |2020年第2期|159-173|共15页
  • 作者

  • 作者单位

    Indian Inst Technol IITB Monash Res Acad Dept Civil Engn Mumbai Maharashtra India;

    Indian Inst Space Sci & Technol Dept Earth & Space Sci Thiruvananthapuram Kerala India;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    MODIS Aerosol Optical Depth; PM2; 5; Mixed Effects Model; Gap Filling;

    机译：MODIS气溶胶光学深度PM2;5;混合效应模型;间隙填充;

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