A one-year monitoring of spatiotemporal variations of PM2.5-bound PAHs in Tehran, Iran: Source apportionment, local and regional sources origins and source-specific cancer risk assessment.

PM2.5-bound PAHs were analyzed in a total of 135 daily samples collected during four seasons from 2018 to 2019, at three urban sites in Tehran, Iran. This study aims to investigate spatio-temporal variations, source apportionment, potential local and regional sources contributions and lung cancer risks associated with the 16 US EPA priority PAHs. PM2.5 concentrations ranged from 43.8 to 80.3 µg m-3 with the highest concentrations observed in summer. Total PAHs (TPAHs) concentrations ranged between 24.6 and 38.9 ng m-3. Autumn period exhibited the highest average concentration (48.3 ng m-3) followed by winter (29.5 ng m-3), spring (25.9 ng m-3) and summer (16.1 ng m-3). Five PAHs sources were identified by positive matrix factorization (PMF) analysis: diesel exhaust, unburned petroleum-petrogenic, industrial, gasoline exhaust and coal/biomass combustion-natural gas emissions, accounting for 22.3%, 15.6%, 7.5%, 30.9%, and 23.6% of TPAHs, respectively. Site-specific bivariate polar (BP) and conditional bivariate probability function (CBPF) plots were computed to assess PM2.5 and TPAHs local source locations. CBPF pointed out that TPHAs sources are likely of local origin, showing the highest probability close to the sampling sites associated with low wind speed (<5 m s-1). The potential source contribution function (PSCF) and the concentration weighted trajectory (CWT) models were applied to investigate the long-range transport of PM2.5 and TPAHs. In addition to local sources contributions, Eastern areas were highly related to long-distance transport of PM2.5 and the Western areas showed the highest contribution of the total, medium molecular weight (MMW) (4 rings) and high molecular weight (HMW) (5-6 rings) PAHs. The upper bound of incremental lifetime cancer risk (ILCR) via inhalation exposure to PM2.5-bound PAHs was at a moderate risk level (3.14 × 10-4 to 6.17 × 10-4).

Modeling daily suspended sediment load using improved support vector machine model and genetic algorithm.

Prediction of sediment volume and sediment load is always one of the important issues for decision-makers of watershed basins. The present study investigated the daily suspended sediment load in a watershed basin using the improved support vector machine method. Since in most of the previous studies, the coefficients of the support vector machine method had been calculated based on trial and error, in the present study, the combination of the support vector machine and the genetic algorithm is used. In the first step, the unknown parameters of the support vector machine are calculated and then, the sediment load simulation is performed. Two case studies in the present work involve two earth dams in Semnan Province called Veynakeh and Royan. Furthermore, multivariate adaptive regression spline (MARS) and MT tree model (M5T) methods are used for comparison. The results indicated that the input combination of discharge data at the current time and one, two, and three previous days has the best performance for all models. Also, the support vector machine-genetic algorithm (SVM-GA) model has a lower root mean square error (RMSE) and mean absolute error (MAE) compared to the MARS and M5T models for both stations. In addition, comparing observational data with simulation data based on the R2 coefficient suggested that the SVM-GA model offers more accurate results than the other two methods. Accordingly, the SVM-GA method used in this study has a high potential for simulating sediment volume.

Investigating the Role of Wind in the Dispersion of Heavy Metals Around Mines in Arid Regions (a Case Study from Kushk Pb-Zn Mine, Bafgh, Iran).

The Kushk Pb-Zn mine is located in Central Iran and it has been in operation for the last 75 years. To investigate the role of wind dispersion of heavy metal pollutants from the mine area, dust samples were collected during 1 year and topsoil samples were collected around the mine. Results showed that the topsoil is polluted with Pb and Zn to about 1500 m away from the mine. It was also found that there was not a significant difference between the metal concentrations in topsoil and dust samples. The Pb and Zn concentrations in the dust samples exceeded 200 mg kg-1 and their lateral dispersion via wind was estimated to be about 4 km away from the mine. It has been shown that a combination of mining activities and mechanical dispersion via water and wind have caused lateral movement of heavy metals in this area.