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This study provides a comprehensive review of the efforts utilized in the measurement of water quality parameters (WQPs) with a focus on total dissolved solids (TDS) and total suspended solids (TSS). The current method used in the measurement of TDS and TSS includes conventional field and gravimetric approaches. These methods are limited due to the associated cost and labor, and limited spatial coverages. Remote Sensing (RS) applications have, however, been used over the past few decades as an alternative to overcome these limitations. Although they also present underlying atmospheric interferences in images, radiometric and spectral resolution issues. Studies of these WQPs with RS, therefore, require the knowledge and utilization of the best mechanisms. The use of RS for retrieval of TDS, TSS, and their forms has been explored in many studies using images from airborne sensors onboard unmanned aerial vehicles (UAVs) and satellite sensors such as those onboard the Landsat, Sentinel-2, Aqua, and Terra platforms. The images and their spectral properties serve as inputs for deep learning analysis and statistical, and machine learning models. Methods used to retrieve these WQP measurements are dependent on the optical properties of the inland water bodies. While TSS is an optically active parameter, TDS is optically inactive with a low signal–noise ratio. The detection of TDS in the visible, near-infrared, and infrared bands is due to some process that (usually) co-occurs with changes in the TDS that is affecting a WQP that is optically active. This study revealed significant improvements in incorporating RS and conventional approaches in estimating WQPs. The findings reveal that improved spatiotemporal resolution has the potential to effectively detect changes in the WQPs. For effective monitoring of TDS and TSS using RS, we recommend employing atmospheric correction mechanisms to reduce image atmospheric interference, exploration of the fusion of optical and microwave bands, high-resolution hyperspectral images, utilization of ML and deep learning models, calibration and validation using observed data measured from conventional methods. Further studies could focus on the development of new technology and sensors using UAVs and satellite images to produce real-time in situ monitoring of TDS and TSS. The findings presented in this review aid in consolidating understanding and advancement of TDS and TSS measurements in a single repository thereby offering stakeholders, researchers, decision-makers, and regulatory bodies a go-to information resource to enhance their monitoring efforts and mitigation of water quality impairments.

One of the most polluting industries in the world is the distillery industry, which produces strong distillery wastewater that has an adverse impact on the environment since it contains high organic matter. Using various combinations of electrodes in the electrocoagulation process, to examined the operational variables, viz., total dissolved solids (TDS), electrical conductivity (EC) and turbidity from the distillery spent wash. With a constant pH of 7, an agitation speed of 500 RPM, and optimizing the operating parameters, viz., varying the electrode distance (2, 3, 4, 5, and 6 cm), voltage (5–25 volts) current density of 1.5 A/cm2, and electrolysis time (30–150 min). Aluminium (Al) electrodes were observed to perform slightly better than iron (Fe) and zinc (Zn) electrodes, with punched Al electrodes outperforming plane Al electrodes. The highest removal efficiencies were achieved with punched Al electrodes in an optimized condition of voltage 25 volts, electrode distance (2 cm), and electrolysis time 150 minutes, with removal efficiencies of 91% for TDS, 92% for EC and 92% for turbidity. This study highlights the potential of electrocoagulation with optimized parameters for improving the purification of distillery spent wash and enhancing the removal of TDS, EC, and turbidity.

Lake Mead located in the Arizona–Nevada region of the Mohave Dessert is a unique and complex water system whose flow follows that of a warm monomictic lake. Although monomictic lakes experience thermal stratification for almost the entire year with a period of complete mixing, the lake on occasion deviates from this phenomenon, undergoing incomplete turnovers categorized with light stratifications every other year. The prolonged drought and growing anthropogenic activities have the potential to considerably impact the quality of the lake. Lake Mead and by extension the Boulder Basin receive cooler flow from the Colorado River and flow with varying temperatures from the Las Vegas Wash (LVW), which impacts its stratification and complete turnovers. This study analyzes four key water quality parameters (WQPs), namely, total dissolved solids (TDS), total suspended solids (TSS), temperature, and dissolved oxygen (DO), using statistical and spatial analyses to understand their variations in light of the lake stratifications and turnovers to further maintain its overall quality and sustainability. The study also evaluates the impacts of hydrological variables including in and out flows, storage, evaporation, and water surface elevation on the WQPs. The results produced from the analysis show significant levels of TDS, TSS, and temperature from the LVW and Las Vegas Bay regions compared with the Boulder Basin. LVW is the main channel for conveying effluents from several wastewater treatment facilities into the lake. We observed an increase in the levels of TDS, TSS, and temperature water quality in the epilimnion compared with the other layers of the lake. The metalimnion and the hypolimnion layer, however, showed reduced DO due to depletion by algal blooms. We observed statistically significant differences in the WQPs throughout various months, but not in the case for season and year, an indication of relatively consistent variability throughout each season and year. We also observed a no clear trend of influence of outflows and inflows on TDS, temperature, and DO. TSS concentrations in the lake, however, remained constant, irrespective of the inflows and outflows, possibly due to the settling of the sediments and the reservoir capacity.

In this paper, an attempt has been made to develop a statistical model based on Internet of Things (IoT) for water quality analysis of river Krishna using different water quality parameters such as pH, conductivity, dissolved oxygen, temperature, biochemical oxygen demand, total dissolved solids and conductivity. These parameters are very important to assess the water quality of the river. The water quality data were collected from six stations of river Krishna in the state of Karnataka. River Krishna is the fourth largest river in India with approximately 1400 km of length and flows from its origin toward Bay of Bengal. In our study, we have considered only stretch of river Krishna flowing in state of Karnataka, i.e., length of about 483 km. In recent years, the mineral-rich river basin is subjected to rapid industrialization, thus polluting the river basin. The river water is bound to get polluted from various pollutants such as the urban waste water, agricultural waste and industrial waste, thus making it unusable for anthropogenic activities. The traditional manual technique that is under use is a very slow process. It requires staff to collect the water samples from the site and take them to the laboratory and then perform the analysis on various water parameters which is costly and time-consuming process. The timely information about water quality is thus unavailable to the people in the river basin area. This creates a perfect opportunity for swift real-time water quality check through analysis of water samples collected from the river Krishna. IoT is one of the ways with which real-time monitoring of water quality of river Krishna can be done in quick time. In this paper, we have emphasized on IoT-based water quality monitoring by applying the statistical analysis for the data collected from the river Krishna. One-way analysis of variance (ANOVA) and two-way ANOVA were applied for the data collected, and found that one-way ANOVA was more effective in carrying out water quality analysis. The hypotheses that are drawn using ANOVA were used for water quality analysis. Further, these analyses can be used to train the IoT system so that it can take the decision whenever there is abnormal change in the reading of any of the water quality parameters.

Total dissolved solids (TDS) concentration determination in water bodies is sophisticated, time-consuming, and involves expensive field sampling and laboratory processes. TDS concentration has, however, been linked to electrical conductivity (EC) and temperature. Compared to monitoring TDS concentrations, monitoring EC and temperature is simpler, inexpensive, and takes less time. This study, therefore, applied several machine learning (ML) approaches to estimate TDS concentration in Lake Mead using EC and temperature data. Standalone models including the support vector machine (SVM), linear regressors (LR), K-nearest neighbor model (KNN), the artificial neural network (ANN), and ensemble models such as bagging, gradient boosting machine (GBM), extreme gradient boosting (XGBoost), random forest (RF), and extra trees (ET) models were used in this study. The models’ performance were evaluated using several performance metrics aimed at providing a holistic assessment of each model. Metrics used include the coefficient of determination (R2), mean absolute error (MAE), percent mean absolute relative error (PMARE), root mean square error (RMSE), the scatter index (SI), Nash–Sutcliffe model efficiency (NSE) coefficient, and percent bias (PBIAS). Results obtained showed varying model performance at the training, testing, and external validation stage of the models, with obtained R2 of 0.77–1.00, RMSE of 2.28–37.68 mg/L, an MAE of 0.14–22.67 mg/L, a PMARE of 0.02–3.42%, SI of 0.00–0.06, NSE of 0.77–1.00, and a PBIAS of 0.30–0.97 across all models for the three datasets. We utilized performance rankings to assess the model performance and found the LR to be the best-performing model on the external validation datasets among all the models (R2 of 0.82 and RMSE of 33.09 mg/L), possibly due to the established existence of a relationship between TDS and EC, although this may not always be linear. Similarly, we found the XGBoost to be the best-performing ensemble model based on the external validation with R2 of 0.81 and RMSE of 34.19 mg/L. Assessing the overall performance of the models across all the datasets, however, revealed GBM to produce a superior performance based on the ranks, possibly due to its ability to reduce overfitting and improve generalizations. The findings from this study could be employed in assisting water resources managers and stakeholders in effective monitoring and management of water resources to ensure their sustainability.

Water contamination is indeed a worldwide problem that threatens public health, environmental protection, and agricultural productivity. The distinctive attributes of machine learning (ML)-based modelling can provide in-depth understanding into increasing water quality challenges. This study presents the development of a multi-expression programming (MEP) based predictive model for water quality parameters, i.e., electrical conductivity (EC) and total dissolved solids (TDS) in the upper Indus River at two different outlet locations using 360 readings collected on a monthly basis. The optimized MEP models were assessed using different statistical measurements i.e., coefficient-of-determination (R2), root-mean-square error (RMSE), mean-absolute error (MAE), root-mean-square-logarithmic error (RMSLE) and mean-absolute-percent error (MAPE). The results show that the R2 in the testing phase (subjected to unseen data) for EC-MEP and TDS-MEP models is above 0.90, i.e., 0.9674 and 0.9725, respectively, reflecting the higher accuracy and generalized performance. Also, the error measures are quite lower. In accordance with MAPE statistics, both the MEP models shows an “excellent” performance in all three stages. In comparison with traditional non-linear regression models (NLRMs), the developed machine learning models have good generalization capabilities. The sensitivity analysis of the developed MEP models with regard to the significance of each input on the forecasted water quality parameters suggests that Cl and HCO3 have substantial impacts on the predictions of MEP models (EC and TDS), with a sensitiveness index above 0.90, although the influence of the Na is the less prominent. The results of this research suggest that the development of intelligence models for EC and TDS are cost effective and viable for the evaluation and monitoring of the quality of river water.

This study aims to optimize solar photocatalysis for textile wastewater using ZnO-ED NPs produced from Eleocharis dulcis (E. dulcis) by RSM. The maximum decolorization (87.34%) and COD removal (100%) were recorded at pH 7, time (60 min), ZnO-ED NPs dosage (2 g/L), and 10% of color concentrations with R2 coefficient of 0.78 at P < 0.05. FESEM analysis showed the presence of granules with smaller diameters than the diameter of the granules before SPD. EDX analysis revealed the presence of impurities like copper (Cu). XRD analysis indicated the purity of ZnO-ED NPs after SPD. The results of an AFM analysis presented that agglomerations of ZnO-ED NPs were somewhat homogeneous in size, nature, and dispersion. According to the FTIR study, OH, CH, C=O, C=C, and C-O-C appear to be the primary functional groups of ZnO NPs that contributed to SPD. ZnO-ED NPs' increased surface roughness was seen in their Raman spectra. Aromatic intermediates were produced, like aromatic amines or phenolic compounds, and led to the complete conversion of CO2 and H2O. These results indicated that ZnO-ED NPs play an important role in raw textile wastewater treatment and the possibility of reusability of ZnO-ED NPs over four different cycles.

Our research project specifically focuses on evaluating groundwater quality in six West Texas counties. We aim to determine whether environmental changes have any impact on the levels of Total Dissolved Solids (TDS) in the water supplied to the public. To achieve this goal, we will be utilizing advanced machine learning algorithms to analyze TDS levels and create geospatial maps for each year between the 1990s and 2010s. To ensure the accuracy of our data, we have gathered information from two trusted sources: the Texas Water Development Board (TWDB) and the Groundwater Database (GWDB). We have analyzed the TDS and other elemental analyses from TWDB–GWDB lab reports and compared them with the quality cutoff set by the World Health Organization (WHO). Our approach involves a thorough examination of the data to identify any emerging patterns. The machine learning algorithm has been successfully trained and tested, producing highly accurate results that effectively predict water quality. Our results have been validated through extensive testing, highlighting the potential of machine learning approaches in the fields of environmental research. Overall, our findings will contribute to the development of more effective policies and regulations in predicting groundwater quality and improving water resource management in Texas. Therefore, this research provides important information for groundwater protection and the development of plans for water resource use in the future.

Saline intrusion constitutes a significant menace to freshwater resources in coastal aquifer systems, particularly in regions undergoing intensive hydrocarbon exploration and exploitation. This study investigated the spatial distribution of saline intrusion within a major crude oil production region in Southern Nigeria. The investigation was undertaken by integrating geoelectrical methods with hydrogeological analysis to characterize subsurface conditions and aquifer vulnerability. Geo-electrostratigraphic surveys were conducted across the area to delineate subsurface lithology and variations in resistivity. The data acquired were interpreted to derive the Dar-Zarrouk parameters and water quality indicator computations of electrical conductivity (EC) and total dissolved solids (TDS). The investigations revealed the presence of three unique subterranean layers. Maps of longitudinal conductance and transverse resistance highlighted the spatial variability in aquifer protective capacity, susceptibility to salinization, and hydro-potential. The Dar-Zarrouk parameters indicated that the aquifer was highly vulnerable to contamination. Saline incursion was most pronounced at depths less than 30 m, with near-surface strata exhibiting reduced resistivity values indicative of elevated salinity levels. Groundwater EC ranged from 660.3 to 729.5 μS/cm (mean 715.6 μS/cm), while TDS concentrations spanned 422.6–466.9 mg/L (mean 457.9 mg/L). Although the international permissible standards for potable water appeared to be met, the aesthetic quality of water in this region is poor. The elevated saline values have also contributed to accelerated corrosion of metallic infrastructure and degradation of concrete foundations. These findings have broad implications for structural integrity and human health. The findings also offer valuable insights into groundwater flow dynamics and can inform sustainable water resource management strategies in similar coastal environments undergoing anthropogenic pressure. Policymakers should implement robust groundwater monitoring systems, regulate groundwater extraction, and utilize artificial barriers to preclude further groundwater salinization.

Surface seawater intakes are popular method in most of seawater reverse osmosis (SWRO) desalination plants which pose fouling-sensitive membranes. Seawater characteristics, especially those near ports, and residential and commercial areas, can be strongly affected by climatic conditions, topology, and human activities such as dredging, fishing, sea traffic, and sewage discharge and consequently are related to the designed pretreatment method and its cost. Therefore, choosing the right location for a SWRO desalination plants is essential to use feed water of appropriate quality and with minimum risk of change. In this research, influence of the dredging process in the Shahid Beheshti port (phase 2) on the characteristics of surface seawater has been investigated as intake water of SWRO desalination plant. Parameters such as temperature, TDS, silt density index (SDI), pH, and chlorophyll a were studied. The results indicate significant changes in some water characteristics in locations very close to the dredging activity, and this is not good news mainly for membrane processes. High amount of SDI and chlorophyll a (as fouling and biofouling indicator) will imply the high fouling possibility near the dredging activity places.