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Maintaining normal pH levels in the body fluids is essential for homeostasis and represents one of the most tightly regulated physiological processes among vertebrates. Fish are generally ammoniotelic and inhabit diverse aquatic environments that present many respiratory, acidifying, alkalinizing, ionic and osmotic stressors to which they are able to adapt. They have evolved flexible strategies for the regulation of acid-base equivalents (H+, NH4+, OH− and HCO3−), ammonia and phosphate to cope with these stressors. The gills are the main regulatory organ, while the kidneys play an important, often overlooked accessory role in acid-base regulation. Here we outline the kidneys role in regulation of acid-base equivalents and two of the key ‘urinary buffers’, ammonia and phosphate, by integrating known aspects of renal physiology with recent advances in the molecular and cellular physiology of membrane transport systems in the teleost kidneys. The renal transporters (NHE3, NBC1, AE1, SLC26A6) and enzymes (V-type H+ATPase, CAc, CA IV, ammoniagenic enzymes) involved in H+ secretion, bicarbonate reabsorption, and the net excretion of acidic and basic equivalents, ammonia, and inorganic phosphate are addressed. The role of sodium-phosphate cotransporter (Slc34a2b) and rhesus (Rh) glycoproteins (ammonia channels) in conjunction with apical V-type H+ ATPase and NHE3 exchangers in these processes are also explored. Nephrocalcinosis is an inflammation-like disorder due to the precipitation of calcareous material in the kidneys, and is listed as one of the most prevalent pathologies in land-based production of salmonids in recirculating aquaculture systems. The causative links underlying the pathogenesis and etiology of nephrocalcinosis in teleosts is speculative at best, but acid-base perturbation is probably a central pathophysiological cause. Relevant risk factors associated with nephrocalcinosis are hypercapnia and hyperoxia in the culture water. These raise internal CO2 levels in the fish, triggering complex branchial and renal acid-base compensations which may promote formation of kidney stones. However, increased salt loads through the rearing water and the feed may increase the prevalence of nephrocalcinosis. An increased understanding of the kidneys role in acid-base and ion regulation and how this relates to renal diseases such as nephrocalcinosis will have applied relevance for the biologist and aquaculturist alike. [ABSTRACT FROM AUTHOR]

The selective detection of pollutants in water in a laboratory scenario has been presented by authors exploiting low-cost optical biosensors based on plastic optical fibers (POFs) and biological or biomimetic receptors. So, for instance, the detection in water of naphthalene, perfluoroalkyl substances (PFAs) and polyfluoroalkyl substances have been investigated with interesting detection limits when compared to those obtained by using different expensive traditional approaches (e.g., liquid chromatography-mass spectrometry with high performances). In this article, we have developed and tested a novel approach used in a smart measuring system to use POF sensors in situ for the remote measures of pollutants in water for smart cities applications. More specifically, we have used different water–glycerin solutions to test the novel sensor system based on a Raspberry Pi connected to the Internet and to a spectrometer, a light source, a POF sensor, and two computers connected to Internet used as client and server. [ABSTRACT FROM AUTHOR]

The River Ganga (2,525 km long) is the largest River basin in India, covering 26.2 percent of India's total geographical area and recently granted living entity status by the court. It is the holiest River and also among the dirtiest in the world. That’s why it is mandatory to maintain its water quality (WQ). Though, monitoring and assessment of WQ of a River is a very challenging task. In this research work, Soft Computing (SC) based popular and commononly used Artificial Neural Network (ANN) technique has been used for modelling the WQ of the Ganga River by developing a prediction model based on six different training functions. Five sampling stations along this River stretch were selected from DEVPRAYAG to ROORKEE in the Uttarakhand state of India. The monthly data sets of five water quality parameters temperature, pH, dissolved oxygen (DO), biochemical oxygen demand (BOD) and total coliform (TC) for the time period from 2001 to 2015 have been taken. The feed forward error back propagation neural network method has been used to develop the WQ-prediction model by conducting various experiments following a neural network structure of 5-10-1, 0.1 as a training goal and various training functions. Using the Mean square error (MSE) statistical method the prediction performance of the developed model was evaluated. The model developed with traincgp (Conjugate Gradient with Polak-Ribiere Restarts) comes out to be the worst one (MSE=0.786) while the other model with trainlm (Levenberg-Marquardt backpropagation) rule proved to be the best one (MSE=0.163) among others. Consequently, it is found that ANNs are capable of predicting WQ of the River Ganga with acceptable results.

This paper proposes a novel statistical approach for blending source waters in a public water distribution system to improve water quality (WQ) by minimizing the release of heavy metals (HMR). Normally, introducing a new source changes the original balanced environment and causes adverse effects on the WQ in a water distribution system. One harmful consequence of blending source water is the release of heavy metals, including lead, copper and iron. Most HMR studies focus on the forecasting of unfavorable effects using precise and complicated nonlinear equations. This paper uses a statistical multiple objectives optimization, namely Multiple Source Waters Blending Optimization (MSWBO), to find optimal blending ratios of source waters for minimizing three HMRs in a water supply system. In this paper, three response surface equations are applied to describe the reaction kinetics of HMR, and three dual response surface equations are used to track the standard deviations of the three response surface equations. A weighted sum method is performed for the multi-objective optimization problem to minimize three HMRs simultaneously. Finally, the experimental data of a pilot distribution system is used in the proposed statistical approach to demonstrate the model’s applicability, computational efficiency, and robustness. [ABSTRACT FROM AUTHOR]