Estimation of ground-level particulate matter concentrations based on synergistic use of MODIS, MERRA-2 and AERONET AODs over a coastal site in the Eastern Mediterranean
Estimation of ground-level particulate matter concentrations based on synergistic use of MODIS, MERRA-2 and AERONET AODs over a coastal site in the Eastern Mediterranean
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DOI：
10.1016/j.atmosenv.2021.118562
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发表时间：
2021-09
影响因子：
5
通讯作者：
Gizem Tuna Tuygun;S. Gündoğdu;T. Elbir
中科院分区：
文献类型：
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作者：
Gizem Tuna Tuygun;S. Gündoğdu;T. Elbir
卫星气溶胶光学厚度（AOD）产品被广泛用于估算地面颗粒物（PM）的时空特征。基于卫星的方法在PM估计模型的业务使用中存在局限性，例如由于云量、相对湿度和气溶胶垂直分布的影响，基于卫星的变量中存在缺失值。因此，通过使用来自不同平台的具有不同时间分辨率的AOD产品来提高估计模型的时空分辨率。在这项研究中，基于地面的PM10浓度的目的是估计使用不同的间隙填充的AOD数据集在2008年和2016年之间在东地中海沿海地区。在PM10浓度的估算中，主要使用了来自MODIS的逐日气溶胶光学厚度数据。然后，两个不同的间隙填充的MODIS AOD数据集与AERONET和MERRA-2 AODs也分别检查了多年度和多季节的基础上，由于在该地区的MODIS AOD数据的数量是有限的时间。模式识别神经网络（PRNN）模型用于估计PM10浓度。从现场气象站和气溶胶诊断产品从MERRA-2的几个气象参数的气溶胶光学厚度作为输入的估计模型。使用多元线性回归（MLR）在23个自变量中确定了模型的最重要变量。结果表明，最好的估计（R = 0.74），获得了与间隙填充AODMODIS+ MERRA数据集的期间覆盖所有年份，而单独的AODMODIS数据集表现出最差的性能（R = 0.62）。然而，间隙填充数据集的性能随季节而变化。例如，单独的AODMODIS数据集在夏季表现出最好的估计性能（R = 0.67），而间隙填充AODMODIS+ AERONET数据集在冬季表现最好（R = 0.59）。总体结果表明，对于估计地面PM浓度，间隙填充的AOD数据集的估计模型使用的方法是有用的，特别是在雨季，如冬季和秋季，MODIS AOD检索是有限的。
Satellite-derived aerosol optical depth (AOD) products are widely used to estimate the spatial and temporal characteristics of ground-level particulate matters (PM). Satellite-based approaches in the operational use of PM estimation models have limitations such as missing values in satellite-based variables due to the impact of cloud cover, relative humidity, and aerosol vertical distribution. Therefore, the spatial-temporal resolution of the estimation models is forced to be improved by using AOD products having different temporal resolutions from different platforms. In this study, ground-based PM10concentrations were aimed to be estimated using different gap-filled AOD datasets between 2008 and 2016 over a coastal site in the Eastern Mediterranean. For the estimation of PM10concentrations, daily AOD data were mainly used from MODIS. Then, two different gap-filled MODIS AOD datasets with both AERONET and MERRA-2 AODs were also examined separately on both multi-annual and multi-seasonal basis since the number of MODIS AOD data was temporally limited in the region. A pattern recognition neural network (PRNN) model was used for the estimation of PM10concentrations. AOD with several meteorological parameters from an on-site meteorological station and aerosol diagnostic products from MERRA-2 were used as inputs to the estimation model. The most significant variables for the model were identified in 23 independent variables using the multiple linear regression (MLR). The results indicated that the best estimation (R = 0.74) was obtained with the gap-filled AODMODIS+MERRAdataset for the period covering all years whereas the AODMODISdataset alone showed the poorest performance (R = 0.62). However, the performance of the gap-filled datasets varied with the seasons. For example, the AODMODISdataset alone showed the best estimation performance (R = 0.67) in the summer whereas the gap-filled AODMODIS+AERONETdataset had the best performance (R = 0.59) in the winter. Overall results suggest that for estimating ground-level PM concentrations, an approach of gap-filled AOD dataset usage for estimation models is useful especially in rainy seasons such as the winter and autumn where MODIS AOD retrievals are limited.