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In the present study, the noise pollution from different compositions of biodiesel, bioethanol, and diesel fuels in MF285 Tractor was studied in the second and third gears from two positions: driver and bystander, at 1000 and 1600 r/min, and running on 10 different fuel levels. For data analysis, the ANFIS network, neural network, and response surface methodology were applied. Comparing the means of noise pollution at different levels demonstrated that the B 25 E 6 D 69 fuel, made up of 25% biodiesel and 6% bioethanol, had the lowest noise pollution. The lowest noise pollution was at 1000 r/ min. Although the noise pollution emitted in the third gear was a little more than that emitted in the second gear. All the resultant models, laid by response surface methodology, neural network, and ANFIS had excellent results. Considering the statistical criteria, the best models with high correlation coefficients and low mean square errors were ANFIS, response surface methodology, and artificial neural network models, respectively.

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Geochemical and hydrogeochemical studies were conducted to assess the origin and geochemical mechanisms driving lead enrichment in groundwaters of semi-arid regions in Central Iran. In this study, 149 water samples were analyzed using inductively coupled plasma mass spectrometry (ICP-MS). Concentrations of Pb and As in about 68% and 27% of the samples, respectively, exceeded WHO guidelines. Analyzing the results of ICP-MS of parental rocks and aquifer sediments shows that unweathered volcanic rocks were the primary source for lead mobilizing to groundwaters.

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