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Introduction: Microbial contamination prediction through detecting the indicator bacteria in natural waters is the first health and environmental step for preventing the transmission of water-associated diseases. This study was designed to determine the correlation between Escherichia coli as the indicator bacterium, on the one hand, and Salmonella and Shigella Spp. As the pathogenic bacteria, on the other hand. Materials and Methods: Totally, 60 natural water samples were collected from natural rivers in Gorgan during summer and autumn of 2016. In order to detect Escherichia coli, the membrane filter method with Endo agar and IMVIC test was used and, in order to detect Salmonella and Shigella, the 9-tube fermentation method and biochemical tests with selenite F medium, XLD agar, TSI, urea, and SIM were utilized. For the statistical analysis, Pearson’s correlation test was used at the significance coefficient of < 0.05. Results: Results showed that all of the analyzed bacteria were detected in the water samples. The frequency of Salmonella, Shigella, Escherichia coli, Klebsiella-Enterobacter, and Citrobacter was 9.5, 22.2, 4.8, 22.2, and 65.1%, respectively. The statistical analysis demonstrated no statistical correlation between Escherichia coli, on the one hand, and Salmonella and Shigella, on the other hand; but the relationship between Salmonella and Shigella was significant. Conclusion: Judging the microbial quality of water supplies cannot be sufficient only based on the presence or absence of Escherichia coli bacterium. Therefore, it seems that the use of other secondary indicator bacteria such as fecal streptococci and supplementary sulphite-reducing clostridia will be advised.

Jinan City is the first pilot city for the construction of a hydroecological civilisation in China. Fifty-eight representative river sampling stations were selected through field trips and surveys, and fish were sampled in the spring, summer, and autumn of 2015. An index of fish biological integrity in Jinan City was constructed and to evaluate the hydroecological health of rivers. Canonical correlation analysis was used to select key driving factors that affect the health of the fish community. The results show that the key physical factor affecting water quality was turbidity, the key chemical factor affecting water quality was chemical oxygen demand (COD) and the key hydrological factor affecting water quality was discharge. Of all the driving factors, COD had the greatest effect on the health of the fish community, followed by discharge and turbidity. Macropodus chinensis Bloch was sensitive to changes in COD; Saurogobio dumerili Bleeker and Pseudolaubuca engraulis Nichols were sensitive to the hydrological factors of discharge and flow velocity; and Saurogobio gymnocheilus Lo and Squaliobarbus ourriculus Richardson were sensitive only to discharge. COD and discharge had a strong effect on fish survival, whereas turbidity affected fish survival but was not a major factor affecting the spatial distribution of river health. The findings can provide a reference for aquatic ecological rehabilitation in developing countries. Through canonical correlation analysis, this study showed the key physical, chemical and hydrological factors affecting water quality and the health of the fish community in the first pilot city for the Water Ecological Civilisation Project in China were turbidity, chemical oxygen demand and discharge respectively. The findings can provide a reference for aquatic ecological rehabilitation in developing countries. [ABSTRACT FROM AUTHOR]

The mechanisms involved in N2O production, consumption and air-sea interactions in fluvial systems are modulated and affected by several factors, including hydrological, chemical and anthropogenic impacts. In recent decades, the basin of the Biobío River (central Chile) has been impacted by an enhanced precipitation deficit and anthropogenic pressure, so the aim of the present work is to understand the spatial and seasonal dynamics of N2O during a megadrought that has been affecting the river since 2010. We also aim to determine the annual contribution of N2O to the atmosphere and the possible response of the Biobío River to projected climatic changes. Seasonal sampling of the water physical-chemical properties was carried out at 15 stations along 280 km of the river and its five tributaries; the stations were distributed between the pristine headwaters (700 m asl) and the outlet of the river at the Pacific Ocean. The longitudinal distribution of oxygen, nutrients, and nitrous oxide evidenced agricultural, urban and industrial impacts on the river water. Headwater areas presented the highest oxygen concentration and the lowest nutrient and N2O concentrations, all of which fluctuated with the variability in water discharge with the different seasons. In the middle river section, where agricultural, industrial and urban activities impacted the river, the nutrient and N2O concentrations increased up to 20 and 1.2 times, respectively, compared to those in the headwaters, and the outlet area showed the highest nutrient and N2O concentrations. Throughout the entire river, N2O oversaturation exhibited a pronounced seasonal cycle with maxima occurring during the dry season. Our results suggest that urban activities had the greatest impact on the Biobío River at its outlet. Furthermore, the inverse relationship between the N2O concentration and water discharge suggests that the predicted future decrease in water discharge may result in higher N2O values in the Biobío River that would expectedly enhance global warming further, through a positive feedback.

Biomarkers are measurements within an organism that respond to environmental effects and are used as tools in bioassessment programs since they reflect physiological changes induced by exposure to pollutants. The hepatosomatic index (HSI) and the condition factor (CF) in three fish species Geophagus brasiliensis, Hypostomus affinis and Hypostomus auroguttatus were used to assess effects of pollutant stress in a tropical river during two seasons (dry and wet). Fish from the least disturbed sites had the lowest condition factor (CF) and the lowest HSI, whereas those from sites near pollutant discharges had the highest CF and HSI. Higher HSI and lower CF occurred during the dry season. It is suggested that species adapted to stressful conditions take advantage on food availability from organic loads or unoccupied niches by lesser tolerant species, increasing CF. We concluded that higher HSI values were directly associated with environmental stress whereas the higher CF values are related to availability of food resources derived from organic loads or other sources. CF and HSI are simple and cost-effective, thus suitable as routine screening tools in pollution monitoring, but caution should be taken in relating changes in physiological measurements and any specific pollutant to prevent misinterpretation.

Live bed scour experiments were conducted using four types of non-cohesive bed sediments with median diameters d = 0.26 mm, 0.42 mm, 1.06 mm and 1.92 mm and five 45°wing-wall abutments of lengths l = 0.04 m, 0.06 m, 0.08 m, 0.10 m and 0.12 m. The tests were conducted in the range of flow velocity varies from 0.71 to 5 times of the critical velocity. The plotting of non-dimensional scour depths (scour depths/abutment lengths) vs non-dimensional flow intensity (flow intensity/critical velocity) show one peak value of scour depth close to the threshold velocity and thereafter, it reduces and again attain a live-bed maximum in the range of flow intensity 3 to 5 times of the critical velocity. It is also observed that the scour depth increases with the increase in sediment sizes up to threshold value of flow for all sediment sizes and abutment lengths. Further, the scour depth decreases with the increase of non-uniformity under all flow conditions. A design equation is proposed for estimating maximum scour depth at 45° wing-wall abutment under live-bed condition. The calculated values of scour depths using proposed equation, and also with three different live-bed local scour equations, are compared with the measured values of scour depths. [ABSTRACT FROM AUTHOR]

A new approach for predicting local scour downstream of grade-control structures based on neural networks is presented. An explicit neural networks formulation (ENNF) is developed using a transfer function (sigmoid) and optimal weights obtained from a training process. A genetic algorithm was used to optimize the neural network architecture and the optimal weights for input and output parameters were obtained using the Levenberg–Marquardt back-propagation algorithm. Experimental data available in the literature, including large-scale results were used for training and validation of the proposed model. The predictive performance of the ENNF was found superior to other regression-based equations and the robustness of ENNF was evaluated using field data. [ABSTRACT FROM AUTHOR]

The article couples neural networks and fuzzy logic in order to enrich the description of the basin saturation state for flood forecasting purposes. The proposed framework first classifies the basin saturation state providing a set of fuzzy memberships, and then issues the forecast exploiting a set of neural predictors, each specialized on certain basin saturation condition by means of a weighted least-square training algorithm. The framework has been tested on an Italian catchment and may overperform classical neural networks approaches.