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Assessing river water quality is crucial for human and ecological needs because of the deterioration of the river and escalated water pollution under the threats of anthropogenic activities. In order to assess river water quality, the Damodar River water was evaluated from the perspectives of spatio-temporal dynamics of ecological (organic pollution index or OPI and eutrophication index or EI), bacteriological (coliform count and comprehensive bathing water quality index or CBWQI), and overall water quality assessments (water quality index or WQI and comprehensive pollution index or CPI). The OPI reveals that 44.66% of water samples have heavy organic pollution; however, EI depicts that almost all water samples of Damodar River have severe eutrophication, especially in the pre- and post-monsoon seasons. Moreover, the fecal coliform count and CBWQI indicate the unsuitability of river water for bathing. The overall WQI portrays that 21.56%, 33.59%, and 22.47% of water samples have heavy pollution in pre-monsoon, monsoon, and post-monsoon, respectively. Moreover, 73.39% of water samples have low CPI indicating slight comprehensive pollution. This study also reveals that the pollution level in the Damodar River downstream of the Durgapur barrage is high among the other stations. The major reasons behind the severe contamination of Damodar River water are urban-industrial and agricultural effluents mixing into the river that lead to higher concentrations of BOD, DO, fecal coliform, COD, fluoride TSS, and turbidity in the river water. Thus, this study carries appreciated information on policy recommendations for the different stakeholders of the Damodar River basin including regional planners, agri-engineers, and ecological river engineers for sustainable river management. [ABSTRACT FROM AUTHOR]

Water contamination with faecal matter is usually the main cause of microbial waterborne diseases. Such diseases are an alarming situation for small cities in developing countries like India. In this research, to check the microbiological status of drinking water in Solan, Himachal Pradesh (India), water samples were collected from baories/stepwells (n = 14), handpumps (n = 9), and the municipal water distribution system (MWDS) (n = 2) in alternative months of the year (covering three main seasons). In 6 months, 150 samples were collected, and they were all examined for the presence of total coliforms and other bacterial pathogens. The associations between the isolates' ecological and seasonal prevalence were also examined. The coliforms were detected by the Most Probable Number (MPN) method, whose range was noticed from the 2–540/100-ml MPN index. The colony forming unit (CFU) count for different samples at the base log 10 value ranged from 3.03 to 6.19. Different genera isolated and identified were Escherichia coli, Salmonella enteric subsp. enterica, Pseudomonas spp., Klebsiella spp., and Staphylococcus aureus. Overall, 74% of the isolates identified in water samples were from the Enterobacteriaceae family. E. coli was about 42.67% (n = 102), followed by Salmonella enterica subsp. enterica 20.92% (n = 50), Staphylococcus aureus 13.38% (n = 32), Pseudomonas spp. 12.55% (n = 30), and Klebsiella spp. 10.46% (n = 25) amongst the total of 239 isolates. The seasonal impact and the dependency of the occurrence of bacteria on one another were determined to be insignificant in the Spearman correlation test. These results showed that external factors (anthropogenic activities) are mainly responsible for the presence of these bacteria in water resources. The occurrence of bacterial isolates has been noticed in all water samples, irrespective of collecting site or season. [ABSTRACT FROM AUTHOR]

Natural springs are the main source of water supply for domestic and agricultural use for humans living in the mountainous regions of Asia. Increasing anthropogenic activities with associated waste load, coupled with inadequate sanitation, and contamination of natural water resources and the environment are emerging as important public health issues. We performed a prospective microbiological and physicochemical investigation of water samples from seven distinct natural springs situated at an altitude of 1615 m in the Bhaderwah region of Jammu and Kashmir, India. Bacterial groups belonging to opportunistic pathogens such as members of Moraxellaceae (Acinetobacter), Arcobacteraceae (Pseudoarcobacter), Pseudomonadaceae (Pseudomonas), Oxalobacteraceae (Massilia), and Flavobacteriaceae (Flavobacterium) were observed. The total coliform test indicated an intermediate level of risk of fecal contamination of the springs, except for one site. Through a questionnaire-based survey of the local population, we discovered that around 40% of participants had suffered from waterborne diseases including typhoid (~14%) and diarrhea (~11%). Our data suggests that increased surveillance of fecal contamination and heterotrophic opportunistic pathogens is needed to enhance water quality and reduce health risks for people living in mountainous regions. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of controlling turbidity, dissolved oxygen, ammonium‐ion concentration, and water‐temperature to maintain the desired water quality in an intensive aquaculture plant. The research is carried out to fit into the present scenario of intensive aquaculture in India. The plant model along with uncertain parameters are derived using physical laws and the data acquired from an aquaculture pond in Assam, India. A pre‐compensated multi‐variable quantitative feedback theory (QFT)‐based fully populated robust matrix controller is proposed to mitigate the control challenge. A novel method is used to design the pre‐compensator to enhance the diagonal dominance of plant transfer function matrix. The desired robust stability, reference tracking, and sensitivity specifications are fulfilled by the proposed QFT‐based robust controller despite the parametric uncertainty. The effectiveness of the proposed method is validated through numerical simulation. A comparative study against a H∞$$ {H}_{\infty } $$ controller shows that the proposed controller strategy gives a satisfactory performance compared to it. [ABSTRACT FROM AUTHOR]

The present study evaluates the impact of the COVID-19 lockdown on the water quality of a tropical lake (East Kolkata Wetland or EKW, India) along with seasonal change using Landsat 8 and 9 images of the Google Earth Engine (GEE) cloud computing platform. The research focuses on detecting, monitoring, and predicting water quality in the EKW region using eight parameters—normalized suspended material index (NSMI), suspended particular matter (SPM), total phosphorus (TP), electrical conductivity (EC), chlorophyll-α, floating algae index (FAI), turbidity, Secchi disk depth (SDD), and two water quality indices such as Carlson tropic state index (CTSI) and entropy‑weighted water quality index (EWQI). The results demonstrate that SPM, turbidity, EC, TP, and SDD improved while the FAI and chlorophyll-α increased during the lockdown period due to the stagnation of water as well as a reduction in industrial and anthropogenic pollution. Moreover, the prediction of EWQI using an artificial neural network indicates that the overall water quality will improve more if the lockdown period is sustained for another 3 years. The outcomes of the study will help the stakeholders develop effective regulations and strategies for the timely restoration of lake water quality. [ABSTRACT FROM AUTHOR]

The potential of four different neuro-fuzzy embedded meta-heuristic algorithms, particle swarm optimization, genetic algorithm, harmony search, and teaching–learning-based optimization algorithm, was investigated in this study in estimating the water quality of the Yamuna River in Delhi, India. A cross-validation approach was employed by splitting data into three equal parts, where the models were evaluated using each part. The main aim of this study was to find an accurate prediction model for estimating the water quality of the Yamuna River. It is worth noting that the hybrid neuro-fuzzy and LSSVM methods have not been previously compared for this issue. Monthly water quality parameters, total kjeldahl nitrogen, free ammonia, total coliform, water temperature, potential of hydrogen, and fecal coliform were considered as inputs to model chemical oxygen demand (COD). The performance of hybrid neuro-fuzzy models in predicting COD was compared with classical neuro-fuzzy and least square support vector machine (LSSVM) methods. The results showed higher accuracy in COD prediction when free ammonia, total kjeldahl nitrogen, and water temperature were used as inputs. Hybrid neuro-fuzzy models improved the root mean square error of the classical neuro-fuzzy model and LSSVM by 12% and 4%, respectively. The neuro-fuzzy models optimized with harmony search provided the best accuracy with the lowest root mean square error (13.659) and mean absolute error (11.272), while the particle swarm optimization and teaching–learning-based optimization showed the highest computational speed (21 and 24 min) compared to the other models. [ABSTRACT FROM AUTHOR]

Classification of water quality is a valuable method for reporting quality status of a water body that can help to control pollution in monitored sites. In this study water quality of rivulet Holy Kali Bein in the state of Punjab, India was analysed by using water quality index (WQI) and multivariate statistical analysis on the bases of measured water quality parameters from five sampling sites (K1-K5) during the period 2017–2018. Discriminant analysis (DA) and WQI were applied to examine spatiotemporal changes and to spot possible pollution sources. These changes in water quality parameters were analysed by using stepwise DA method. The first spatial discriminate function (DF1) accounted for 96.9%, second discriminate function (DF2) accounted 2.1%, DF3 accounted for 0.8% and DF4 accounted only 0.2% of the total spatial variance. Similarly, the first temporal DF1 accounted for 80.1% and DF2 accounted for 19.9% of total temporal variance. The water quality index (WQI) score showed good water quality at sites K1 (30.67), K2 (43.98) and K3 (51.25). The sites K4 (64.50) and K5 (62.89) showed poor water quality status. The present investigation represents the first of its type undertaken on the rivulet Holy Kali Bein and shows that the water quality of the rivulet is good at origin and when we move towards its confluence point at Harike wetland (Ramsar site) water quality becomes poor due to input of municipal wastes and agricultural activities discharge at the bank of the rivulet. This observation is supported by the calculated WQI for different sites. The work is original in nature based on the primary data collected by the authors personally. [ABSTRACT FROM AUTHOR]

The lower course of the Damodar River in West Bengal is one of the most polluted stretches in the Ganga River basin. There is a lack of research along the whole course of the Damodar, and parameter level analysis receives little attention. Eleven monitoring sites were chosen based on the potential sources of pollution for 6 years (2014–2019). Multivariate statistical techniques (factor analysis (FA), cluster analysis (CA), and discriminate analysis (DA)) evaluate the spatial and temporal variation of Damodar River water quality by considering 24 parameters. Factor analysis extracts the most influential seasonal parameters, and stepwise DA extracts ammonia, DO, potassium, temperature, total coliform, TFS, and turbidity, which are the most responsible parameters for seasonal variation of the water quality. CA classify sampling stations into three groups helping to identify the spatial variation of water quality. Ammonia, BOD, calcium, chloride, conductivity, DO, sodium, sulfate, temperature, Alkalinity, TDS, hardness, TSS, and turbidity are the most influential variables for spatial variation extracted through stepwise DA. Monsoon season shows a higher pollution level due to the contribution from both point and non-point sources. Due to high-density urban areas and large-scale industries, the middle course is more polluted. The Canadian Council of Ministers of the Environment (CCME) water quality index (WQI) accesses the water quality in temporal and spatial scales. The resultant water quality pattern is matched with the derived result from multivariate analysis. Poor water quality is regular at all sample sites in all seasons. [ABSTRACT FROM AUTHOR]

Haryana is one of the leading states in India in the agricultural and industrial production. With the expansion of these sectors, a continuous increase in water demand is leading to water crises arising from overexploitation and quality deterioration of the available water. Contamination of aquifer resources is a significant concern, because majority of population depends on the groundwater for various agricultural, industrial, and domestic needs. This review article provides an overview of groundwater contamination, associated health risks with different contaminants with regions severely affected by poor water quality, and delves in identifying the sources, by observing and recognising the types of industries dominant in the state with types of effluents discharge. It further suggests the possible mitigation measures such as advanced remedial technologies and proper management practices from the consequent contamination sources. It has been observed during the perusal of various studies and data that the degree of contamination was considerably higher in districts with heavy agro-industrial activities. The groundwater resources in three highly industrialized districts were found to be gravely contaminated with toxic heavy metals. Alongwith heavy metals, the salinity, hardness, nitrate, and fluoride are also posing significant problems in the aquifer resources of Haryana state. The article also discusses various technologies for remediation of different pollutants from groundwater so it can be made potable after treatment. [Display omitted] [ABSTRACT FROM AUTHOR]

Achieving and maintaining suitable water quality is one of the important parameters to ensure health and well-being of the human as well as ecosystems. Among the various aquatic ecosystems, riverine ecosystems are more prone to pollution and therefore needs to be monitored frequently and on regular time intervals. In this context, real-time water quality monitoring system offers excellent opportunity to keep track of the water quality on a continuous basis; which not only helps to identify the affected location and pollution source, but also creates alert enabling the authorities to take immediate action. One such real-time water quality monitoring system was installed in the River Ganga (India), considering the fragility and significance of the Gangetic ecosystem. In this paper, we have presented the details of the real-time water quality monitoring system installed in River Ganga and results obtained through it for various parameters. The results have also been compared with the standard values. Additionally, based on this preliminary investigation, limitations and recommendations have also been presented to further enhance the utility of the system. [Display omitted] • RTWQMS based on IoT was developed and installed in River Ganga, India. • Up to 17 number of water quality parameters could be monitored in real time. • Values measured through RTWQMS were in good approximation with laboratory values. • Robustness of RTWQMS may be improved to enhance the accuracy and applicability. [ABSTRACT FROM AUTHOR]

Sewage is one of the biggest contributors to coastal pollution. The study was aimed to assess the impact of sewage on coastal water quality of Kanyakumari, the southernmost part of India. A bacteriological survey was made on distribution and abundance of fecal indicators and human pathogenic bacteria and seasonal influence on the bacterial load and antibiotic resistance of the isolates. Samples were collected from sewage discharge point along the eastern shore of Kanyakumari Coast from February 2019 to January 2020. Nine pollution indicator bacteria and pathogenic species such as Escherichia coli , Klebsiella spp., Enterococcus faecalis , Aeromonas spp., Proteus mirabilis , Salmonella typhi , Vibrio cholerae , Shigella spp. and Flavobacterium spp. were isolated from the samples. These isolates were tested against 10 antibiotics, using Kirby Bauer method. All the isolates were resistant to at least two antibiotics. The presence of antibiotic resistant bacteria has been used as bio-indicators of pollution. Hence it is clear that the domestic sewage entering the coast is untreated which might lead a serious impact on human and marine wildlife along coastlines. • Kanyakumari coastline is highly contaminated with fecal indicator bacteria. • Untreated sewage discharge is the main source of pathogenic bacteria. • Concentrations of bacterial loads in seasons and sites were significantly different (P < 0.001). • Untreated sewage may introduce antibiotic resistant bacteria to the coastal environment. • It is mandatory to spread awareness to the society on the impacts of sewage on potentially coastal ecosystem. [ABSTRACT FROM AUTHOR]