Investigating the relationship between ground-level particulate matter and aerosol optical depth during dust storm episodes: a case study of Tehran

• Main Authors: Saeed Sotoudeheian, Behnaz Shirazi Rumenan
• Format: Article
• Language: fas
• Published: Tehran University of Medical Sciences 2020-08-01
• Series: سلامت و محیط
• Subjects: particulate matter; aerosol optical depth; dust storms; multivariable linear regression; modis
• Online Access: http://ijhe.tums.ac.ir/article-1-6412-en.html

Background and Objective: During the last few years, air pollution and increasing levels of particulate matters (PMs) have become major public health issues in the megacity of Tehran. The high cost of constructing and maintaining air pollution monitoring stations has made it difficult to achieve adequate spatial-temporal coverage of PM data over various regions. In this regard, the use of remote sensing data such as aerosol optical depth (AOD) can be a simple and cost-effective way to overcome the problem. Materials and Methods: Due to the weakness of univariate linear relationship of PM10-AOD under normal conditions, this relationship has been studied for the time periods of dust storm occurrence during 2007-2010 in Tehran. Satellite product with spatial resolution of 3 and 10 km obtained from MODIS sensor were used to fit the models. Results: Results showed that the best performance of univariate model was achieved for 5 km radius of AOD extraction and daily mean of PM10 concentrations (r = 0.55). Moreover, the use of meteorological auxiliary variables and the development of multivariate linear regression model have improved the performance of the model (r = 0.64). The final model also exhibited accurate capability for prediction of high PM10 concentrations during dusty days. Conclusion: Overall, the obtained univariate linear relationships of PM10-AOD was stronger during dusty episodes than those of normal conditions, suggest a higher correlation between AOD and PM10 from dust activities as compared with PM10 originating from other sources. Furthermore, the final developed model could be used to predict daily level of PM10 concentrations during dusty episodes.