Roohollah Noori, Gholamali Hoshyaripour, Khosro Ashrafi, Omran Rasti,
Volume 6, Issue 1 (5-2013)
Abstract
Backgrounds and Objectives: Precise air pollutants prediction, as the first step in facing air pollution problem, could provide helpful information for authorities in order to have appropriate actions toward this challenge. Regarding the importance of carbon monoxide (CO) in Tehran atmosphere, this study aims to introduce a suitable model for predicting this pollutant.
Materials and Method: We used the air pollutants and meteorological data of Gholhak station located in the north of Tehran these data provided 12 variables as inputs for predicting the average CO concentration of the next day. First, support vector machine (SVM) model was used for forecasting CO daily average concentration. Then, we reduced the SVM inputs to seven variables using forward selection (FS) method. Finally, the hybrid model, FS-SVM, was developed for CO daily average concentration forecasting.
Result: In the research, we used correlation coefficient to evaluate the accuracy of both SVM and FS-SVM models. Findings indicated that correlation coefficient for both models in testing step was equal (R~0.88). It means that both models have proper accuracy for predicting CO concentration. However, it is noteworthy that FS-SVM model charged fewer amounts of computational and economical costs due to fewer inputs than SVM model.
Conclusion: Results showed that although both models have relatively equal accuracy in predicting CO concentration, FS-SVM model is the superior model due to its less number of inputs and therefore, less computational burden.
E Hoshyari, N Hassanzadeh, A Charkhestani,
Volume 12, Issue 1 (5-2019)
Abstract
Background and Objective: Nowadays linear alkyl benzene sulfanate (LAS) is widely used in the production of various detergents. The purpose of this study was to assess the health and ecological hazards of this pollutant on target organisms such as fish and daphnia in the Doroodzan Dam water.
Materials and Methods: According to the research objective and given existing restrictions, 21 water samples were collected in September 2018 from 7 selected stations based on the source of contamination in Doroodzan dam. Water quality parameters including pH, Dissolved Oxygen (DO), potential Redox (ORP), Total dissolve solid (TDS) and Electrical conductivity (EC) was measured at the site. The amount of linear alkyl benzenesulfonate (LAS) was measured using an optimized methylene blue method after transferring samples to the lab. Then ecological and health risk assessment was performed by calculating the RQ index (risk index).
Results: The results showed that the mean of pH, EC, TDS, salinity and DO were 8.88, 732.19 µs/cm, 482.49, 366.16 and 6.87 mg/L, respectively. The highest and lowest concentrations of LAS were 0.039 and 0.055 mg/L, respectively. The results also showed that there is a significant relationship between LAS concentration and pH. The results of the risk assessment showed that the health risk index in all stations is less than 0.1, while the ecological risk index except at station 7, are in low risk level.
Conclusion: In general, the results show that the RQ index in the Droodzan Dam water is in appropriate range and in the low risk level. Therefore, it is necessary to conduct long-term studies in this field to ensure the persistence of optimal water conditions in the dam ecologically and health-wise.
