Environmental Health Engineering and Management Journal

55-59

Authors: Dariush Naghipour, Zeinab Shaabaninezhad , Abdoliman Amouei

Background: Marine products, especially fish, play an increasingly significant role in human diets and health. This research evaluated the concentrations of cadmium, lead, chromium, and nickel in liver and muscle tissues of Rutilus frisii kutum along the Caspian Sea coasts of northern Iran. Methods: Thirty samples of fishes were taken from five stations (Astara, Rezvanshahr, Anazali, Ramsar, and Tonekabon) during fishing season. An ICP-OES spectrophotometer was used to determine concentrations of the studied metals. Data was analyzed by SPSS 19 using descriptive statistical index, t test, and analysis of variance (ANOVA) test; P < 0.05 was considered significant. Results: Mean concentrations of cadmium, lead, chromium and nickel in the livers were 0.06, 1.1, 2.1 and 1.5 μg/g respectively and in the muscles were 0.05, 0.8, 1.7 and 1.1 μg/g, respectively. In liver: cadmium= 0.01-0.13; lead= 0.3-2.7; chromium = 0.9-4.5; nickel = 0.1-2.8. In muscle: cadmium = 0.01-0.08; lead = 0.2-1.5; chromium = 0.7-2.7; nickel = 0.5-1.5. Mean contents of these heavy metals were higher in the liver than in muscle. The order of heavy metal concentrations in the liver and muscle tissues were: Cr>Ni>Pb>Cd. The results showed statistically significant differences in the concentrations of these metals in liver and muscle tissues (P < 0.05), but no significant difference was observed between males and females. Conclusion: Based on calculations, the estimated permissible tolerable weekly intakes (PTWIs) of cadmium (0.042 and 0.567 mg) and lead (0.0021 and 0.027 mg) in this study are lower than the levels established by the World Health Organization (WHO) (0.455 mg for cadmium and 1.625 mg for lead).

69-73

Authors: Ali Ahmad Aghapour, Sepideh Nemati, Amir Mohammadi , Heshmatollah Nourmoradi , Sima Karimzadeh

Background: Nitrate is an acute and well-known hazardous contaminant, and its contamination of water sources has been a growing concern worldwide in recent years. This study evaluated the feasibility of nitrate removal from water using the traditional coagulants alum and ferric chloride with lower concentrations than those used in the conventional coagulation process. Methods: In this research, two coagulants, alum and ferric chloride, were compared for their efficiency in removing nitrate in a conventional water treatment system. The removal process was done in a batch system (jar test) to examine the effects of coagulant dosages and determine the conditions required to achieve optimum results. Results: The results revealed that ferric chloride at an initial dose rate of 4 mg/L reduced nitrate concentration from 70 mg/L to less than the World Health Organization (WHO) guideline value (50 mg/L N-NO3). However, the removal efficiency of alum was not salient to significant nitrate reduction. Conclusion: In conclusion, ferric chloride was more effective than alumin removing NO-3, even in common dosage range, and can be considered a cost-effective and worthy treatment option to remediate nitrate-polluted water. Furthermore, the removal of nitrate by coagulation can be simple and more economical than other treatment alternatives.

81-89

Authors: Maryam Khodadadi, Alireza Mesdaghinia, Simin Nasseri, Mohammad Taghi Ghaneian, Mohammad Hassan Ehrampoush , Mahdi Hadi

Background: Data mining (DM) is an approach used in extracting valuable information from environmental processes. This research depicts a DM approach used in extracting some information from influent and effluent wastewater characteristic data of a waste stabilization pond (WSP) in Birjand, a city in Eastern Iran. Methods: Multiple regression (MR) and neural network (NN) models were examined using influent characteristics (pH, Biochemical oxygen demand [BOD5], temperature, chemical oxygen demand [COD], total suspended solids [TSS], total dissolved solid [TDS], electrical conductivity [EC] and turbidity) as the regression input vectors. Models were adjusted to input attributes, effluent BOD5 (BODout) and COD (CODout). The models performances were estimated by 10-fold external cross-validation. An internal 5-fold cross-validation was also used for the training data set in NN model. The models were compared using regression error characteristic (REC) plot and other statistical measures such as relative absolute error (RAE). Sensitivity analysis was also applied to extract useful knowledge from NN model. Results: NN models (with RAE = 78.71 ± 1.16 for BODout and 83.67 ± 1.35 for CODout) and MR models (with RAE = 84.40% ± 1.07 for BODout and 88.07 ± 0.80 for CODout) indicate different performances and the former was better (P < 0.05) for the prediction of both effluent BOD5 and COD parameters. For the prediction of CODout the NN model with hidden layer size (H) = 4 and decay factor = 0.75 ± 0.03 presented the best predictive results. For BODout the H and decay factor were found to be 4 and 0.73 ± 0.03, respectively. TDS was found as the most descriptive influent wastewater characteristics for the prediction of the WSP performance. The REC plots confirmed the NN model performance superiority for both BOD and COD effluent prediction. Conclusion: Modeling the performance of WSP systems using NN models along with sensitivity analysis can offer better understanding on exploring the most significant parameters for the prediction of system performance. The findings of this study could build the foundation for prospective work on the characterization of WSP operations and optimization of their performances with a view to conducting statistical approaches.

99-106

Authors: Aida Tayebiyan , Thamer Ahmad Mohammad

Background: Several reservoir systems have been constructed for hydropower generation around the world. Hydropower offers an economical source of electricity with reduce carbon emissions. Therefore, it is such a clean and renewable source of energy. Reservoirs that generate hydropower are typically operated with the goal of maximizing energy revenue. Yet, reservoir systems are inefficiently operated and manage according to policies determined at the construction time. It is worth noting that with little enhancement in operation of reservoir system, there could be an increase in efficiency of the scheme for many consumers. Methods: This research develops simulation-optimization models that reflect discrete hedging policy (DHP) to manage and operate hydropower reservoir system and analyse it in both single and multi-reservoir system. Accordingly, three operational models (2 single reservoir systems and 1 multi-reservoir system) were constructed and optimized by genetic algorithm (GA). Maximizing the total power generation in horizontal time is chosen as an objective function in order to improve the functional efficiency in hydropower production with consideration to operational and physical limitations. The constructed models, which is a cascade hydropower reservoirs system have been tested and evaluated in the Cameron Highland and Batang Padang in Malaysia. Results: According to the given results, usage of DHP for hydropower reservoir system operation could increase the power generation output to nearly 13% in the studied reservoir system compared to present operating policy (TNB operation). This substantial increase in power production will enhance economic development. Moreover, the given results of single and multi-reservoir systems affirmed that hedging policy could manage the single system much better than operation of the multi-reservoir system. Conclusion: It can be summarized that DHP is an efficient and feasible policy, which could be used for the operation of existing or new hydropower reservoir system.