Reduction of the environmental and health consequences of cigarette butt recycling by removal of toxic and carcinogenic compounds from its leachate.

Cigarette butt is a hazardous waste, and its management faces many challenges. The generation of leachate containing many pollutants including heavy metals is one of the limitations of recycling cigarette butts. The aim of this study was to reduce organic compounds and heavy metals in leachate resulting from cigarette butt recycling in an electrocoagulation reactor. For this purpose, two samples, including freshly smoked cigarette butts and littered cigarette butts, were processed and the treatment of leachate resulting from them was studied in an electrocoagulation reactor. The efficiency of leachate treatment was investigated in the treatment time of 10-40 min and current intensity of 20-100 mA. The results showed that the minimum reduction of chemical oxygen demand and turbidity was 25.3% and 33.4%, respectively. Increasing the current intensity and treatment time increased the efficiency of removing turbidity and chemical oxygen demand up to 47.1% and 41.2%, respectively, in optimum conditions. The reduction of nickel, chromium, cadmium, and lead in the lowest current density and minimum treatment time was more than 90%, which increased to more than 99% with the increase of the current density and treatment time. The use of electrocoagulation is a suitable solution to reduce heavy metals in leachate resulting from cigarette butt recycling, while the proper reduction of organic compounds in this type of leachate requires further treatment processes.

The improvement of wavelet-based multilinear regression for suspended sediment load modeling by considering the physiographic characteristics of the watershed.

The aim of this study is to model a relationship between the amount of the suspended sediment load by considering the physiographic characteristics of the Lake Urmia watershed. For this purpose, the information from different stations was used to develop the sediment estimation models. Ten physiographic characteristics were used as input parameters in the simulation process. The M5 model tree was used to select the most important features. The results showed that the four factors of annual discharge, average annual rainfall, form factor and the average elevation of the watershed were the most important parameters, and the multilinear regression models were created based on these factors. Furthermore, it was concluded that the annual discharge was the most influential parameter. Then, the stations were divided into two homogeneous classes based on the selected features. To improve the efficiency of the M5 model, the non-stationary rainfall and runoff signals were decomposed into sub-signals by the wavelet transform (WT). By this technique, the available trends of the main raw signals were eliminated. Finally, the models were developed by multilinear regressions. The model using all four factors had the best performance (DC = 0.93, RMSE = 0.03, ME = 0.05 and RE = 0.15).