Biochemical oxygen demand (BOD) has been shown to be an important variable in water quality management and planning. However, BOD is difficult to measure and needs longer time periods (5 days) to get results. Artificial neural networks (ANNs) are being used increasingly to predict and forecast water resource variables. The objective of this research was to develop an ANNs model to estimate daily BOD in the inlet of wastewater biochemical treatment plants. The plantscale data set (364 daily records of the year 2005) was obtained from a local wastewater treatment plant. Various combinations of daily water quality data, namely chemical oxygen demand (COD), water discharge (Q w ), suspended solid (SS), total nitrogen (N), and total phosphorus (P) are used as inputs into the ANN so as to evaluate the degree of effect of each of these variables on the daily inlet BOD. The results of the ANN model are compared with the multiple linear regression model (MLR). Mean square error, average absolute relative error, and coefficient of determination statistics are used as comparison criteria for the evaluation of the model performance. The ANN technique whose inputs are COD, Q w , SS, N, and P gave mean square errors of 708.01, average absolute relative errors of 10.03%, and a coefficient of determination 0.919, respectively. On the basis of the comparisons, it was found that the ANN model could be employed successfully in estimating the daily BOD in the inlet of wastewater biochemical treatment plants.

No abstract

This study was aimed to evaluate the water quality and pollution sources in Sapanca Lake and its tributaries by applying multivariate statistical techniques to physicochemical parameters and toxic metals. For this purpose, the multivariate statistical methods such as principal component analysis (PCA) and absolute principal component score-multiple linear regression (APCS-MLR) model have been employed. It was tried to determine the seasonal pollution sources of physicochemical parameters and toxic metals obtained from 22 different sampling points between the years of 2015 and 2017. PCA was applied to the datasets, and 6 varimax factors describing 84%, 80%, 76%, and 79% of the total variance for each season were extracted. The obtained factors were analyzed using the APCS-MLR model for the apportionment of various pollution sources affecting physicochemical parameters and toxic metals. The results show that the natural soil structure, municipal-industrial wastewater, agricultural-atmospheric runoff, highways, and seasonal effects are the major pollution sources for toxic metals and physicochemical parameters. The material contribution of pollutant sources to toxic metals and physicochemical parameters was calculated and verified by the concentrations analyzed. Consequently, multivariate statistical techniques are useful to determine the physicochemical parameters and toxic metals through reciprocal correlation and assess the seasonal impact of pollutant sources in the basin. This study also provides a basis for the creation of measurement programs, determination of pollution sources, and provision of sustainable watershed management regarding other water resources.

In this study simultaneous analysis of seven different anions (Fluoride, Chloride, Bromide, Nitrite, Nitrate, Phosphate, and Sulfate) in13 different water samples collected from Sakarya/Turkey and it was conducted with ion chromatography method. Analyzes were performed simultaneously using the ion chromatography method. Some validation tests and the optimum conditions for the determination of anions were studied. The analysis of anions was accomplished by the dilution of the sample injection device. Samples were used to adjust the terms of the device and the results were recorded. Ion chromatography (IC) is now considered as an excellent technique for the analysis of ions in many samples.

Sapanca Lake is an important drinking water source located by D-100 highway in the north and E-80 (TEM Anatolian Highway) and a railway line in the south. Heavy metal concentrations in roadside soils result from vehicle exhausts and the corroding metal parts of vehicles. Due to the difficulty of removing heavy metals from the soil, a significant pollution problem arises and this pollution also affects the water resources by means of rain. Although there are several industries, the highway located near the lake is the most important pollutant source for Sapanca Basin. Therefore, this study evaluated heavy metal concentrations, the chemical fractions of the metals and ecological risks (by using C f , RAC and PERI) in the soil samples collected seasonally between 2015 and 2017 in Sapanca Lake Basin. Al and Fe were determined at very high amounts in all stations and the relationship between mean concentrations of other metals was determined to be Zn>Ba>As>Ni>Cr>Pb>Cu>Co>Cd. However, Zn, Ba, As, Ni, Cr, Pb, Cu, Co and Cd mean concentrations were determined as 87.63 mg/kg, Ba 86.87 mg/kg, 80.40 mg/kg, 58.62 mg/kg, 50.42 mg/kg, 41.90 mg/kg, 38.16 mg/kg, 13.98 mg/kg, 2.89 mg/kg respectively. Al, Fe, Co, Cr, Cu, Ni and Zn are mainly found in residual fractions in soil. According to the environmental risk assessment on the basis of soil stations, the 7 th station has the highest GC f value, which means that the soil sample presents the highest environmental risk according to the contamination factors. On the other hand, Cd has serious potential ecological risk and As has considerable potential ecological risk in all stations.