Evaluation of spatio-temporal variation of water quality and source identification of conducive parameters in Damodar River, India

Dissolved load, chemical weathering, and CO2 uptake dynamics of small tropical mountainous rivers of Southern Granulite Terrain, Karamana and Vamanpuram, Western Ghats, India

Chemical weathering in a river basin is a key geochemical process that controls fluxes between the atmosphere, land, and ocean, playing a crucial role in regulating Earth's climate, particularly the small tropical mountainous rivers with high weathering yield. This study delves into the dissolved solute chemistry of two small tropical mountainous rivers in India's Southern Granulite Terrain (SGT): the Karamana River Basin (KRB) and Vamanapuram River Basin (VRB), together referred as KVRB. Seventy-two water samples (36 per river) that are collected across three seasons are analysed for various physio-chemical parameters using standard methods. A chemical mass balance (CMB) model was employed to quantify solute sources, while silicate weathering rates (SWR), and CO2 consumption rates (CCR) were estimated separately. Results show that silicate weathering is the dominant process, followed by anthropogenic and atmospheric contributions, particularly at the river outlets. At the KRB outlet, SWR is 30.96 t km⁻² yr⁻¹, and CCR is 1.16 × 10⁶ mol km⁻² yr⁻¹, whereas VRB shows lesser values of 22.89 t km⁻² yr⁻¹ and 3.20 × 10⁵ mol km⁻² yr⁻¹. The lower Arrhenius activation energies of 27 kJ mol⁻¹ for KRB and 25 kJ mol⁻¹ for VRB are evident for intense silicate weathering Additionally, the physical weathering rates (PWR), evaluated using total suspended solids (TSS) concentrations, are more significant in KRB, particularly during the monsoon, due to it's steep terrain and humid climate; while VRB experiences less physical weathering. The study emphasizes the complex interaction of silicate and physical weathering processes with climate and geomorphological conditions of KVRB.

Development of AI-based hybrid soft computing models for prediction of critical river water quality indicators

Prediction of river water quality indicators (RWQIs) using artificial intelligence (AI)-based hybrid soft computing modeling techniques could provide essential predictions required for efficient river health planning and management. The study described the development of a novel AI-based relative weighted ensemble (AIRWE) hybrid model for predicting critical RWQIs, i.e., biochemical oxygen demand (BOD) and total coliform (TC). The study involved comprehensive water quality (WQ) monitoring from 30 locations along the Damodar River to establish the baseline data and delineate the WQ. The representative input features showing a strong association with BOD and TC were identified using Spearman's rank-coupled orthogonal linear transformation (SOT). The relative weighted ensemble (RWE) method was applied to determine the relative weights for base learners in the AIRWE model. The statistical analysis of the developed model revealed that it was most efficient and accurate for predicting BOD (R2, 0.97; RMSE, 0.06; MAE, 0.04) and TC (R2, 0.98; RMSE, 0.06; MAE, 0.05) over the traditional techniques. The tstat (BOD 0.02 and TC 0.47) was lesser than tcrit (1.672), confirming its unbiased predictions. The SOT technique removed the data noise and multicollinearity, whereas RWE curtailed the individual model's limitations and predicted more reliable results. The model resulted 97% accuracy with high precision (96%) in classifying the river water quality for various end uses. The study describes a novel approach for researchers, scientists, and decision-makers for modeling and predicting various environmental attributes.

Evaluation of Surface Water Quality in the Betwa River Basin through the Water Quality Index Model and Multivariate Statistical Techniques

The Betwa River Basin (BRB), a sub-basin of the River Yamuna, is the oldest flowing water system in Central India. The water quality of the rivers are under stress, hence regular monitoring and appraisal is required to know the health of the rivers. Factor analysis and principal component analysis (FA/PCA) multivariate statistical techniques were used to extract three and four varimax factors that explained 96.408 and 100.00 percent of the total variance in water quality, respectively. Cluster analysis (CA) categorizes observed items into distinct quality categories based on correlations between stations and years. Point industrial/sewage effluents, diffuse pollution as runoff from arable land, erosion, and natural source pollution contribute to the pollution of the BRB. As a result, water quality is threatened or impaired, and conditions often departed from natural or desirable levels at Rajghat, Garrauli, Mohana, and Shahijina stations. According to the Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI), the surface water quality at the Rajghat and Mohana stations corresponds to fair ecological status. However, the surface water quality of the Garrauli and Shahijina stations has a marginal water quality as per CCME-WQI. From 1985 to 2018, the Shahijina had the most considerable load of nutrients and organic matter, as determined by the CCME-WQI and by comparing the water quality data. A thorough examination had revealed a fluctuating trend in the BRB pollution, particularly at all stations. Results indicate that between 1985 and 2018, the only defense mechanism of the river was the auto purification mechanism, which is strongly influenced by the drought, point pollution source, and extreme meteorological events that probably cause these fluctuations.

Assessment and interpretation of surface water quality in Jhelum River and its tributaries using multivariate statistical methods

Water is an essential part of human life, and its pollution is a hotly debated topic on both national and international scales. Surface waterbodies in the beautiful Kashmir Himalayas are already deteriorating. In this study, fourteen physio-chemical parameters were tested in water samples taken during the spring, summer, autumn, and winter seasons from twenty-six different sampling points. The findings showed a consistent decline in the water quality of river Jhelum and its adjoining tributaries. The upstream section of the river Jhelum had the least pollution, whereas the Nallah Sindh had the poorest water quality. The water quality of Jhelum and Wular Lake was strongly impacted by the water quality of all the adjoining tributaries. To examine the link between the selected water quality indicators, descriptive statistics and a correlation matrix were used. Analysis of variance (ANOVA) and principal component analysis/factor analysis (PCA/FA) were used to identify the key variables that influenced seasonal and sectional water quality fluctuations. The ANOVA analysis revealed that there were significant differences in water quality characteristics among the twenty-six sampling locations throughout all four seasons. The PCA findings identified four principal components that accounted for 75.18% of the total variance and could be utilized to evaluate all data. The study revealed that chemical, conventional, organic, and organic pollutants were significant latent factors influencing the water quality of rivers in the region. The findings of this study could contribute to the vital management of surface water resources in Kashmir's ecology and environment.

Hemato-biochemical alteration in the bronze featherback Notopterus notopterus (Pallas, 1769) as a biomonitoring tool to assess riverine pollution and ecology: a case study from the middle and lower stretch of river Ganga

Fishes are poikilothermic animals and are rapid responders to any sort of ecological alteration. The responses in the fish can be easily assessed from their hematological and biochemical responses. To study the variation in the hemato-biochemical parameters in retort to ecological alteration and ecological regime, a study was conducted at six different sampling stations of the middle and lower stretches of river Ganga. Various hematological and biochemical responses of fishes were also monitored in response to multiple ecological alterations. For the assessment of ecological alteration, various indices were calculated such as the water pollution index (WPI), National Sanitation Foundation-water quality index (NSF-WQI), and Nemerow's pollution index (NPI) has been calculated based on various water quality parameters such as dissolved oxygen (DO), pH, total dissolved solids (TDS), total alkalinity (TA), total hardness (TH), electrical conductivity (EC), biochemical oxygen demand (BOD), chlorinity (CL), total nitrogen (TN), and total phosphorus (TP). The hematological parameters such as WBC, RBC, platelet, hemoglobin, and hematocrit were monitored. The serum biochemical parameters such as SGPT, SGOT, ALP, amylase, bilirubin, glucose, triglyceride (TRIG), and cholesterol (CHOL) were investigated. The study revealed that NSF-WQI varied from 45.08 at Buxar to 110.63 at Rejinagar and showed a significantly positive correlation with SGPT, SGOT, ALP, TRIG, CHOL, and WBC, whereas a significantly negative correlation was observed between TRIG and RBC. WPI varied from 19 to 23 and showed a significant positive correlation with SGOT and a negative correlation was observed with total nitrogen. The PCA analysis illustrated the significance of both natural as well as anthropogenic factors on riverine ecology. Strong positive loading was observed with SGPT, SGOT, ALP, and platelet. The study signified the need for monitoring the hemato-biochemical responses of fishes in response to alterations in the ecological regime.

A comprehensive review of water quality indices (WQIs): history, models, attempts and perspectives

Water quality index (WQI) is one of the most used tools to describe water quality. It is based on physical, chemical, and biological factors that are combined into a single value that ranges from 0 to 100 and involves 4 processes: (1) parameter selection, (2) transformation of the raw data into common scale, (3) providing weights and (4) aggregation of sub-index values. The background of WQI is presented in this review study. the stages of development, the progression of the field of study, the various WQIs, the benefits and drawbacks of each approach, and the most recent attempts at WQI studies. In order to grow and elaborate the index in several ways, WQIs should be linked to scientific breakthroughs (example: ecologically). Consequently, a sophisticated WQI that takes into account statistical methods, interactions between parameters, and scientific and technological improvement should be created in order to be used in future investigations.

Impact of COVID-19 lockdown on the water quality of the Damodar River, a tributary of the Ganga River in West Bengal

The COVID-19 (SARS-CoV-2) pandemic is wreaking havoc on the planet, yet control of waste materials comforted the ecosystem during the lockdown restricting human activities. Damodar is the most important tributary of the lower Ganga River in West Bengal. It flows through an industrially developed, agriculturally flourished populated area. Different methods are applied to identify the changing pattern of water quality during the lockdown. BOD graph shows an increase in pollution levels in residential areas but a sharp decline in coliform levels in urban residential sites. The National Sanitation Foundation Water Quality Index (NSFWQI) shows the same pattern of water quality throughout the course. Irrigation suitability of water is examined using sodium percentage (%Na), sodium absorption ratio (SAR), potential salinity (PS), magnesium absorption ratio (MAR), and Kelly's ratio (KR). Mujhermana (received maximum pollutants from industries and residential areas) station shows a decrease in ions concentration and subsequent improvement in agriculture water quality during the COVID-19 period. According to Kelly's ratio, the water at this sample site is unfit for agricultural use; however, the water quality improved and became acceptable for cultivation during the lockdown period. Cluster analysis is used to understand the similar pollution concentration of eleven sampling stations in different periods. Mujhermana site makes a separate cluster due to its high pollution load compared to other sampling sites before the COVID-19. But during the lockdown period, this site was clustered with the most petite contaminated sites.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s40899-022-00790-2.

Evaluation and Analysis of Bridge Modal Parameters Under Intelligent Monitoring Environment

After the bridge is completed, the structural materials will be gradually eroded or aged under the influence of climate, temperature, and building environment. Under long-term static and dynamic loads, the structural strength and stiffness of bridge structures, including bridge deck and bridge support, will decrease with the accumulation of time. Bridge modal parameter identification is not only the premise and foundation of health monitoring, but also the main part of bridge structure dynamic identification. Therefore, this paper proposes a bridge modal parameter identification model based on Bayesian method. The model fully considers the uncertainty of parameters and the selection of modal parameters, and identifies more local information through the probability distribution of model parameters and a posteriori confidence. The reliability of the bridge is monitored in real time through the Bayesian dynamic model, and the monitoring error is only 0.01, which can realize high-precision bridge modal parameter identification.