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首页> 外文期刊>Journal of Environmental Health Science and Engineering >Estimating ground-level PM10 using satellite remote sensing and ground-based meteorological measurements over Tehran
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Estimating ground-level PM10 using satellite remote sensing and ground-based meteorological measurements over Tehran

机译：使用卫星遥感和德黑兰地面气象测量估算地面PM10

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Background and methodologyMeasurements by satellite remote sensing were combined with ground-based meteorological measurements to estimate ground-level PM10. Aerosol optical depth (AOD) by both MODIS and MISR were utilized to develop several statistical models including linear and non-linear multi-regression models. These models were examined for estimating PM10 measured at the air quality stations in Tehran, Iran, during 2009–2010. Significant issues are associated with airborne particulate matter in this city. Moreover, the performances of the constructed models during the Middle Eastern dust intrusions were examined.ResultsIn general, non-linear multi-regression models outperformed the linear models. The developed models using MISR AOD generally resulted in better estimate of ground-level PM10 compared to models using MODIS AOD. Consequently, among all the constructed models, results of non-linear multi-regression models utilizing MISR AOD acquired the highest correlation with ground level measurements (R2 of up to 0.55). The possibility of developing a single model over all the stations was examined. As expected, the results were depreciated, while nonlinear MISR model repeatedly showed the best performance being able to explain up to 38% of the PM10 variability.ConclusionsGenerally, the models didn’t competently reflect wide temporal concentration variations, particularly due to the elevated levels during the dust episodes. Overall, using non-linear multi-regression model incorporating both remote sensing and ground-based meteorological measurements showed a rather optimistic prospective in estimating ground-level PM for the studied area. However, more studies by applying other statistical models and utilizing more parameters are required to increase the model accuracies.
机译：背景和方法将卫星遥感的测量结果与地面气象测量结果相结合，以估算出地面PM10。利用MODIS和MISR的气溶胶光学深度（AOD）来开发几种统计模型，包括线性和非线性多元回归模型。对这些模型进行了评估，以估计2009-2010年在伊朗德黑兰的空气质量站测得的PM10。该城市的空气中颗粒物与重大问题有关。此外，还检验了所构建模型在中东粉尘入侵期间的性能。结果通常，非线性多元回归模型的性能优于线性模型。与使用MODIS AOD的模型相比，使用MISR AOD的已开发模型通常可以更好地估计地面PM10。因此，在所有构建的模型中，利用MISR AOD进行的非线性多回归模型的结果与地面测量值的相关性最高（R2最高为0.55）。研究了在所有站点上开发单个模型的可能性。正如预期的那样，结果被折旧了，而非线性MISR模型反复显示了最佳性能，能够解释高达38％的PM10变异。结论通常，该模型不能很好地反映出广泛的时间浓度变化，尤其是由于水平升高所致在沙尘暴中。总体而言，使用结合了遥感和地面气象测量的非线性多元回归模型，在估算研究区域的地面PM方面显示出相当乐观的前景。但是，需要通过应用其他统计模型并利用更多参数进行更多研究，以提高模型的准确性。

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