Iranian Journal of

Background and Objective: In the recent decade, critical condition of particulate matters (PMs) concentration is considered as one of the most important issues in Tehran megacity. Due to sparse spatial distribution of air quality monitoring stations and economic considerations, researchers proposed remote sensing technique as a fast and economical way to obtain complete spatial and temporal coverage of PM concentrations.
Materials and Methods: In this study, aerosol optical depth (AOD) retrieved by MODIS along with meteorological parameters were used to develop statistical linear mixed effect (LME) model and estimating ground-level PM_2.5 concentrations. AOD data with a spatial resolution of 3 km from 13 monitoring stations and  meteorological data from 5 synoptic stations were extracted over Tehran during 2013.
Results: The results showed that the proposed model was able to explain about 57%-72% of daily PM_2.5 concentration variations. Temporal analysis of predicted PM_2.5 concentrations could follow the curve trend which was obtained from the observed PM_2.5 measurements with a reasonable level of accuracy. Best performance of the model was in May 2013 during a model-fitting and cross-validation practice. Also, the spatial distribution of the estimated PM_2.5 concentrations was consistent with the measured values in the monitoring stations.
Conclusion: Based on the spatial distribution map of the estimated PM_2.5, central and northern parts of Tehran were the most polluted areas in the study region. The result showed that the LME model using the satellite-derived AOD and meteorological variables could provide an accurate prediction of ground-level PM_2.5 concentrations.
 

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.