Estimating ground-level PM10 using satellite remote sensing and ground-based meteorological measurements over Tehran

Journal of Environmental Health Science Engineering

Mohammad Arhami¹  Saeed Sotoudeheian¹ 

[1] Department of Civil Engineering, Sharif University of Technology, Azadi Ave, Tehran, Iran

关键词: Multivariable regression models;    MISR;    MODIS;    AOD;    Aerosol optical depth;    Remote sensing;    Particulate matter;    PM10;   

Others  :  1164577
DOI  :  10.1186/s40201-014-0122-6

 received in 2014-02-19, accepted in 2014-08-24,  发布年份 2014

【 摘 要 】

Background and methodology

Measurements 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.

Results

In 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.

Conclusions

Generally, 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.

【 授权许可】

   
2014 Sotoudeheian and Arhami; licensee BioMed Central Ltd.

【 图 表 】

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