Modeling the Impacts of Atmospheric Deposition on Water Quality in Lake Ontario Under Climate Change Scenarios

Water quality in urban areas in Canada is a major issue despite the fact that it has excessive resources of freshwater. Current methods of addressing the impacts of atmospheric deposition and climate change on water quality are inadequate. Physical methods are too complex and usually ignore the impacts of atmospheric deposition. Therefore, in this research two categories of data driven models have been developed using artificial neural networks to model the atmospheric deposition and water quality. These models were developed in three regions near Lake Ontario: Toronto, Cobourg, and Grimsby regions which have different characteristics of population and air contamination. The results showed in future, the atmospheric deposition contamination in summers and autumns will become higher than the present situation. However, the precipitation contamination in winters will be lower. Moreover, the atmospheric deposition can not influence the water quality of Lake Ontario considerably

Provide a multi-purpose fuzzy model for stock portfolio optimization

Researchers in the field of portfolio optimization made efforts to decrease uncertainty in future returns. Any disturbance in the parameter values causes the solution to be non-optimal or impossible. This study designs a strong fuzzy-multipurpose model for stock portfolio optimization based on Tehran Stock Exchange market data. At the end of the paper, the created model is compared with the results of the multi-objective model. The results show that the fuzzy multi-objective optimization model has relative stability and model compared to the multi-purpose optimization model is strong

Modeling the Impacts of Atmospheric Deposition on Water Quality in Lake Ontario Under Climate Change Scenarios

Water quality in urban areas in Canada is a major issue despite the fact that it has excessive resources of freshwater. Current methods of addressing the impacts of atmospheric deposition and climate change on water quality are inadequate. Physical methods are too complex and usually ignore the impacts of atmospheric deposition. Therefore, in this research two categories of data driven models have been developed using artificial neural networks to model the atmospheric deposition and water quality. These models were developed in three regions near Lake Ontario: Toronto, Cobourg, and Grimsby regions which have different characteristics of population and air contamination. The results showed in future, the atmospheric deposition contamination in summers and autumns will become higher than the present situation. However, the precipitation contamination in winters will be lower. Moreover, the atmospheric deposition can not influence the water quality of Lake Ontario considerably

A newly isolated Streptomyces rimosus strain capable of degrading deltamethrin as a pesticide in agricultural soil

Chemical pesticides or insecticides with complex structures are highly abundant in the biosphere and have inevitable side effects on farmland, natural resources, and human health. Deltamethrin is the most popular and widely used pesticide that disrupts the cellular calcium channels. In the present study, isolated strains of bacteria were examined to determine the ones that were capable of degrading deltamethrin. Different species of bacteria were evaluated in terms of the capability to degrade deltamethrin. It is important to note that Streptomyces rimosus was able to degrade up to 200 mg/L deltamethrin concentration and could be grown in mineral salt medium agar containing deltamethrin to be used as a source of carbon and energy. The results demonstrated that there is a diversity of deltamethrin-degrading bacteria in agricultural soil ecosystems. The application of these bacteria, especially S. rimosus, might be used as a bioremediation technique to decrease pesticide contamination of the ecosystem. © 2020 WILEY-VCH Verlag GmbH & Co. KGaA, Weinhei