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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.

The estuaries of Banten Bay have sandy sediment, and their main activity is fisheries. In order to maintain fishery productivity, good water quality is required. The sandy sediment in this location can affect water conditions at the point of the residue content. This study aims to reveal solids content in the estuaries of Banten Bay. A survey was conducted in four estuaries, namely Karangantu, Wadas, Cengkok, and Pamong. TDS were measured in situ in April and October 2021. TSS was analyzed in May, July, and October 2013, as well as in April and October 2021. Other physical and chemical parameters were analyzed in the laboratory or in situ. The results were compared with the standard for sea aquatic biota. Furthermore, the regression method was used to determine the correlation of TSS and TDS with other parameters. The physical and chemical parameters that affect TDS and TSS were analyzed using Principal Component Analysis (PCA). The results show that TSS correlated with Oxidative Reductive Potential (ORP) and turbidity. PCA showed that TSS correlated with TDS, nitrate, ammonium, ORP, water depth, and water current. Although TSS exceeded the threshold, sufficient dissolved oxygen and higher nutrient still maintained water quality for fisheries in four estuaries.

The Colorado River is a principal source of water for 40 million people and farmlands in seven states in the western US and the Republic of Mexico. The river has been under intense pressure from the effects of climate change and anthropogenic activities associated with population growth leading to elevated total dissolved solid (TDS) and total suspended solid (TSS) concentrations. Elevated TDS- and TSS-related issues in the basin have a direct negative impact on the water usage and the ecological health of aquatic organisms. This study, therefore, analyzed the spatiotemporal variability in the TDS and TSS concentrations along the river. Results from our analysis show that TDS concentration was significantly higher in the Upper Colorado River Basin while the Lower Colorado River Basin shows a generally high level of TSSs. We found that the activities in these two basins are distinctive and may be a factor in these variations. Results from the Kruskal–Wallis significance test show there are statistically significant differences in TDSs and TSSs from month to month, season to season, and year to year. These significant variations are largely due to seasonal rises in consumptive use, agriculture practices, snowmelts runoffs, and evaporate rates exacerbated by increased temperature in the summer months. The findings from this study will aid in understanding the river’s water quality, detecting the sources and hotspots of pollutions to the river, and guiding legislative actions. The knowledge obtained forms a strong basis for management and conservation efforts and consequently helps to reduce the economic damage caused by these water quality parameters including the over USD 300 million associated with TDS damages.

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.

The qualities of the treated final effluents of a wastewater treatment plant located in a rural community of the Eastern Cape Province of South Africa were assessed over the duration of 12 months. Parameters measured include pH, temperature, electrical conductivity, salinity, turbidity, total dissolved solid, dissolved oxygen, chemical oxygen demand , nitrate, nitrite and orthophosphate levels and these were simultaneously monitored in the treated final effluents and the receiving watersheds using standard methods. Unacceptably, high levels of the assayed parameters were observed in many cases for chemical oxygen demand (7.5-248.5 mg/L), nitrate (1.82-13.14 mg/L), nitrite (0.09-1.3 mg/L), orthophosphate (0.07 - 4.81 mg/L), dissolved oxygen (4.15-11.22 mg/L) and turbidity (3.68-159.06 NTU) during the study period and are severally outside the compliance levels of the South African Guidelines and World Health Organization tolerance limits for effluents intended for discharge through public sewers into receiving watersheds. The study has revealed that there was an adverse impact on the physico-chemical characteristics of the receiving watershed as a result of the discharge of inadequately treated effluents from the wastewater treatment facility. This poses a health risk to several rural communities which rely on the receiving water bodies primarily as their sources of domestic water. There is need for the intervention of appropriate regulatory agencies to ensure production of high quality treated final effluents by wastewater treatment facilities in rural communities of South Africa.

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.

Microbial desalination cell (MDC) has great potential toward direct electricity generation from wastewater and concurrent desalination through potential difference developed due to microbial activity. Degradation of phenol by isolate Pseudomonas aeruginosa in anodic chamber and simultaneous desalination of water in middle desalination chamber of multichamber MDC is demonstrated in this study. Performance of the MDCs with different anodic inoculum conditions, namely pure culture of P. aeruginosa (MDC-1), 50 % v / v mixture of P. aeruginosa and anaerobic mixed consortia (MDC-2) and anaerobic mixed consortia (MDC-3), was evaluated to compare the phenol degradation in anodic chamber, bioelectricity generation, and simultaneous total dissolved solids (TDS) removal from saline water in desalination chamber. Synergistic effect between P. aeruginosa and mixed anaerobic consortia as inoculum was evident in MDC-2 demonstrating phenol degradation of 90 %, TDS removal of 75 % in 72 h of reaction time along with higher power generation of 27.5 mW/m 2 as compared to MDC-1 (95 %, 64 %, 12.8 mW/m 2 , respectively) and MDC-3 (58 %, 52 %, 4.8 mW/m 2 , respectively). The results illustrate that the multichamber MDC-2 is effective for simultaneous removal of phenol and dissolved solids contained in industrial wastewaters.

Total Dissolved Solids, one of the most extensively used indicators for assessing groundwater quality, it useful to estimate salinity and hardness in water. The objective of the present study is to develop accurate and dependable machine learning models for forecasting the total dissolved solids, parameter; as well to evaluate and explain the relationship of total dissolved solids with the mineral salts. Four machine learning models Decision tree, Random forest, Adaboost and support victor regression SVR have been successfully employed for modeling the total dissolved solids using Electrical Conductivity (EC) and concentrations of major elements (Ca 2+ , Mg 2+ , Na + , K + , Cl – , SO , HCO , NO ) of the groundwater aquifer in upper Cheliff plain (the northwestern of Algeria). One hundred ninety-one of observations collected from wells by the ANRH (national water resources agency, Algeria) for a period of 8 years between 2008 and 2016, were randomly divided into training and validation sets. The overall prediction performance results indicated that the models provided satisfactory estimation with priority to the support vector regression model, based on the four parameters including: EC, Na + , SO , and Cl – , with the best support vector machine results of RMSE = 0.0328; NS = 0.9455. Feature selection method revealed that the correlation analysis results were reliable and could be utilized as a first step in selecting the optimum input data for forecasting groundwater quality parameters. Generally, the proposed models are useful in predicting groundwater quality parameters and may aid decision-makers in developing and managing groundwater plans.

The present study evaluates the potential of an integrated Constructed Wetland and Microbial Fuel Cell (CW-MFC) in removing Chemical Oxygen Demand (COD), Total Dissolved Solids (TDS), nitrate and phosphate along with generating electricity from the polluted water of Ana Sagar Lake, Ajmer. Two vertical downward flow CW-MFCs have been investigated (with and without plant) in batch mode for a period of 12 months. The performance of CW-MFC with the plant (system A) was better than the CW-MFC without the plant (system B), both in terms of COD, TDS, nitrate and phosphate removal and electricity generation. Dracaena trifasciata plant species were used in this study. The maximum power density of 157 mW/m 2 , current density of 0.65 mA/m 2 , COD removal efficiency (72%), TDS reduction (74%), nitrate removal (69%) and phosphate removal (69%) were obtained with System A. The germination rate of Vigna radiata seeds was 100% with the effluent from system A. At the end of the experiment the FTIR analysis revealed the structural changes occurring on the CW-MFC media (sand) after removal of pollutants present in the lake water.

Pharmaceutical effluent disposal is a serious problem in the present times. The manufacturing process involves the use of both organic and inorganic compounds, which contribute to high chemical oxygen demand and dissolved solids. The common techniques used to extract available salts and to produce reusable waters are evaporation and cooling. Evaporators are equipment used for evaporation which is a kind of heat transfer system in which transfer mechanism is controlled by natural or forced convection. Multi-effect evaporators in many industries are used for volume reduction and cutting down the waste handling cost. This paper focusses on studying the efficiency of multi-effect evaporators in the pharmaceutical industry for the treatment of high total dissolved solids (HTDS) waste streams. The feed and condensate parameters were monitored for three years. Competence of the treatment process is presented in terms of reduction in TDS and COD. The current study evaluates the efficiency of MEE in terms of removal of total dissolved solids and chemical oxygen demand. Removal efficiencies are more than 98% for TDS and 50% for COD.