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.

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Contribution of different Middle Eastern dust origins to PM10 (PM with aerodynamic diameters less than 10 µm) levels in several receptor large cities in Iran was investigated. Initially, the major regional dust episodes were determined through statistical analysis of recorded PM levels at air quality stations and verified using satellite images. The particles dispersion was simulated by Hybrid Single‐Particle Lagrangian Integrated Trajectory (HYSPLIT) to regenerate PM10 during the dust episodes. The accuracy of the modeled results was rather convincing, with an average squared correlation coefficient (R2) of 0.7 (max = 0.95). Consequently, the contributions of different dust sources to the observed concentrations were determined. Basin of Tigris‐Euphrates Rivers encompasses active dust sources with significant rate of emission due to fluvial deposits. The sources in this basin with approximately 70–95% contribution, by far, had the most influence on PM10 levels at the receptor cities. In a finer resolution, northern and central parts of Iraq had the most influence on PM10 level during the dust episodes. Effect of probable improvement or deterioration of the current dust origin conditions on PM10 levels was analyzed by performing a sensitivity analysis through varying threshold friction velocities. The results demonstrated that 10% increase or decrease in threshold friction velocities of major dust sources could lead to average of 51% decrease or 77% increase in the receptor cities' PM10, respectively. Finally, effects of Lake Urmia desiccation, as a new hydrological prospect dust origin were analyzed. The predicted dust from the prospective dried lake bed could result in ~ 30–60% increase in PM10 of nearby cities during the studied dust episodes.

Abstract. In this article, the relation between meteorological parameters and dust activities in western Iran has been studied. Satellite-based data achieved from TOMS are used to investigate the dust activities within a time period of 30 years. In the rst part of this study, we examine the statistical trend of Aerosol Index (AI) and local meteorological parameters in 15 di erent stations. The same patterns of AI variations in all stations indicate that this region has always been subjected to dust storms which originate from similar sources in the neighboring countries that could be known as a sole dust transfer system. In the second part, we investigate the spatial correlation between the regional meteorological parameters in the Middle East and AI data to determine the contribution of meteorological parameters to dust levels. Broadly, results show that the precipitation in concurrent and antecedent months has a negative correlation with AI parameter of dusty months. Also, notably, we observed that the zonal wind speed in Iraq has a strong positive correlation with AI in our selected stations. This fact veri es that the zonal winds could be identi ed as the major cause of dust transfer system that was noted in the rst part of this study.

In this study, a laboratory-scale biotrickling filter (BTF) is used to remove Triethylamine (TEA) from gaseous wastes. The BTF is made of stainless steel with a height of 210 cm and an internal diameter of 21 cm packed with lava rocks. TEA elimination pattern was evaluated by changing empty bed residence times (EBRTs). The maximum elimination capacity (EC) has been determined to be 87 g/m 3 /h. At all EBRTs 52, 31, 20, and 10 s, contaminant transferring from gas phase to liquid was more than the EC. Also, the removal efficiency was 100 % for a mass loading of 100 g/m 3 /h. While the liquid recirculation velocity of 3.466 m 3 /m 2 /h was maintained, the flow rate was adjusted to 60, 100, 156, and 312 L/ min. The results show that due to the high solubility of TEA in water for all the EBRTs, TEA can be solved in the circulated liquid and then be degraded gradually by microorganisms. Therefore, the least EBRT of 10 s is more appropriate.