Towards Water Circular Economy

Water pressure is increasing continuously with environmental change, pollution, and an increasing population. This work examines the suitability of the water potability of surface and groundwater sources in Bhopal City presently. The seasonal changes have been seen in physicochemical parameters on a monthly basis for six months. The potability of the water has also been checked for water sources that have not been used for drinking purposes. Sixteen physicochemical properties were analyzed of water that was collected from the tube wells and reservoirs located in Bhopal, the capital city of Madhya Pradesh. Nine water samples were tested according to APHA’s (American Public health Association) 23rd edition on selected parameters. The findings were subjected to comparative study and mathematical modeling to draw conclusions. The high iron (Fe) content of three groundwater sites, viz, Kolua Khurd (3.01 mg/l), Chhawani Pathar (3.08 mg/l) and Kerwa (2.20 mg/l) exceeded the permissible limit (1.0 mg/l) given by World Health Organization (WHO). The COD value (55.75 mg/l) for Upper Lake indicated contamination of principle drinking water source due to highly oxidizable pollutants from the city. The physicochemical data was also checked for Gibbs and Piper plot and it was inferred that both water sources belong to rock dominance with Mg(HCO₃)₂ hydrochemical facies. The other parameters assured safe potability of water. The results showed that the water sources in Bhopal are still healthy for drinking purposes. They are safe to use and may be stored for future needs. The dams that have been used for fulfilling irrigation needs can also be considered for household requirements.

Seawater intrusion (SWI), a serious environmental issue threatens the availability of fresh groundwater resources in coastal aquifers. An effort has been made to evaluate and quantify the influence of seawater on groundwater chemistry through the molar ionic ratios in heavily inhabited coastal areas of Jagatsinghpur district of eastern Odisha. Water samples were collected from different sources and the chemical analysis indicated the fresh to saline nature of the groundwater in the region with higher electrical conductivity (EC) values. WQI values have classified 50% of water samples as good water and 50% as poor water. The molar ratios of Na⁺/Cl⁻ and K⁺/Cl⁻ demonstrate the seawater intrusion mechanism. This study shows that anthropogenic activities and seawater intrusion affects the water quality of the area, which needs regular monitoring and assessment.

The exploitation of coastal aquifers to meet groundwater demand for drinking, farming, and industry is lowering groundwater levels and aquifer potential. Therefore, mapping the groundwater potential areas is important along the coastline to examine the groundwater scenario. The Groundwater Potential Zone (GWPZ) map was developed using Remote sensing, Geographic information system (GIS), and Multi-Influencing Factor (MIF) techniques along Odisha’s coast. The geomorphology, geology, rainfall, landcover/land use, slope, drainage density, and soil thematic maps were used for determining GWPZ. Approximately 78% of the study region is a “good” GWPZ. The output was cross-validated with Central Groundwater Board (CGWB) water level data. Prospects rated MIF’S GWPZ map with 88% reliability. This research will aid in the preparation of a sustainable groundwater planning map and policy.

River systems globally face challenges of excessive flooding and prolonged droughts, impacting socio-economic and environmental conditions. Kerala state in India, characterized by wet climate, experienced dry spells and heavy rainfall events leading to devastating consequences. To manage extreme events, reservoir operations play a crucial role by diverting the water during monsoons. This study focuses on the Kakki and Pamba reservoirs in Pamba basin in Kerala. The HEC-HMS and HEC-ResSim models were employed together, simulating reservoir inflow and operational decisions of reservoir operators. The efficiency of the models was verified by comparing simulated and observed discharge data, yielding satisfactory Nash-Sutcliffe Efficiency (NSE) values. Results demonstrated the potential of the integrated approach to predict reservoir elevations under different hydrological scenarios, aiding decision-makers in developing sustainable reservoir operation plans for release of water for flood control and hydropower generation.

In agricultural practices, the widely embraced plant-based index for irrigation scheduling is the Crop Water Stress Index (CWSI). However, the conventional estimation of CWSI often operates under the assumption of clear skies and abundant sunlight. This research endeavors to explore the applicability of CWSI across various sky conditions. In a semi-arid region of Uttar Pradesh, an experiment was conducted involving wheat crops subjected to five distinct irrigation treatments across five plots. Plots 1, 2, and 3 received 50% maximum allowable depletion (MAD) through drip irrigation, 25% MAD via drip irrigation, and 50% MAD through flood irrigation, respectively. Plot 4 was designated as a rain-fed plot, while plot 5 served as a replication of a farmer’s field. The study data underwent classification based on relative sunshine duration (n/N), offering insights into cloud cover. Lower baselines of the plant-stress index were derived from plot 2. Building upon this, an empirical CWSI was calculated for all plots. The research explored three distinct cases: a regular case encompassing all cloud conditions, and two additional cases (case 1 and case 2) representing specific cloud conditions with 1 > n/N > 0.7 and 0.5 < n/N < 0.7, respectively. Unique lower baselines were developed for each case, expressed as \(-1.29 \left(VPD\right)-2.91\) for the regular case, \(-1.47\left(VPD\right)-1.69\) for case 1, and \(-1.36\left(VPD\right)-1.06\) for case 2. Here, VPD (vapor pressure deficit), a function of air temperature and relative humidity, played a crucial role. Mean CWSI values varied across the cases: for the regular case, they were 0.09, 0, 0.33, 0.95, and 0.40 for plots 1, 2, 3, 4, and 5, respectively. In case 1, the mean CWSI for the same plots were 0.16, 0.02, 0.38, 0.92, and 0.52, and in case 2, they were −0.11, −0.05, 0.28, 0.97, and 0.23. The linear relationship between CWSI and soil moisture depletion yielded R2 values of 0.59 (regular case), 0.70 (case 1), and 0.55 (case 2), providing insights into the statistical significance of the findings.

Changes in climatic parameters are observed globally. According to Intergovernmental Panel on Climate Change sixth Assessment Report (IPCC AR6), climate change is due to a change in carbon dioxide (CO2) concentration in the atmosphere. The main purpose of this study is to detect the trends of the climate variables associated with the Land, Water and Atmosphere between the periods of 1971 to 2022 using Mann-Kendall trend analysis. In this study, trend analysis has been carried out for various climate variables associated with the change in climate, i.e., Air Temperature, Mean Total Precipitation Rate, Relative Humidity, Sea Level Pressure, Sea Surface Temperature, Horizontal wind and Vertical Wind. The study relies on daily data from ECMWF and NCEP/NCAR Reanalysis 1 for Kerala, India. Mann-Kendall’s trend analysis determines the significant trends in these variables. The results indicate a general upward trend in air temperature, relative humidity, sea level pressure, and sea level temperature. In contrast, mean total precipitation rate exhibits varying trends across different months. Conclusions drawn from the analysis attribute trends observed in the time series that can be taken as an indicator of climate change. The long-term changes of these variables could lead to the occurrence of extreme events, necessitating further studies to establish it.

Consumption of safe water and adequate sanitation are the most basic needs for human survival. According to the World Health Organization (WHO) in 2022, unsafe water leads to 1.7 million mortalities and 4 billion cases of waterborne diseases globally. The scarcity of safe water due to acceleration in urbanization with increasing populations, industrialization, untreated sewage disposal, and industrial toxic runoffs leads to several deadly diseases. Monitoring potable water quality is a difficult task owing to the limited and diminishing water resources and the adverse impact of pollutants. Manual quality monitoring approaches often exacerbate the quality of potable water vividly. The integration of cutting-edge technologies such as the Internet of Things (IoT), sensing probes, and communication systems has the ability to transform the water quality monitoring (WQM) system. It enables efficient water resource management, prediction, and proactive responses to ecological challenges. The key objective of this work is to develop an IoT-based water quality observing system to measure physiochemical parameters for a rural drinking water distribution system (hereafter called AquaNet). The key hardware part of AquaNet comprises standard sensors (i.e., probes), a powerful controller, a seamless communication network, a fixed sink node (an access point or base station), and a personalized anchored float (buoyage). It measures critical quality parameters of drinking water including temperature, conductivity, turbidity, and pH in a predetermined time interval. Besides, it provides cloud storage for collected data and engenders an alarm to the registered clients through smartphones when quality measures are abnormal. The proposed AquaNet is implemented in the water tanks of TWAD (Tamilnadu Water Supply and Drainage Board) in Nagapattinam district. The accuracy of the sensors were assessed by comparing its measured data values to that of a commercial multi-parameter water checker, the Horiba® probe. The higher performance of the sensors, the data transmission system, and visualization tools demonstrate that the AquaNet can be used for monitoring the quality of drinking water to help users or concerned authorities make better decisions by delivering accurate and instantaneous information.

One of the indicators of degraded stream water is the increased levels of harmful algal blooms (HABs), which consume oxygen and block sunlight, and eventual oxygen deficit in water is detrimental for aquatic life. The chlorophyll a molecule, a key biochemical component responsible for photosynthesis in algae, is used as an indicator for nutrient pollution in river water. In this study, we used a simple method for determining wet and dry algal biomass and chlorophyll a of freshwater algae. Measurements were performed with spectrophotometry, and the extraction method involved cell disruption by mortar and pestle, and solvent based extraction. The algal biomass determination process used a two-step approach for estimating the wet biomass weight by centrifugation and pelletization, and dry biomass weight by oven drying at 104 ºC. In addition, viable cells were enumerated using a hemocytometer. A strong correlation was found between dry and wet biomass (R2 = 0.86), and the relationship between cell counts and turbidity of water was reasonable (R2 = 0.51). The novelty of this study is that five different methods were used to asses chlorophyll, and algal biomass in river water samples, and attempts were made to derive simplified relationships and methods to determine chlorophyll a. Further, the novelty of this study is unfolding the challenges and limitations of each approach utilized for chlorophyll a in ambient water.

Groundwater is the most precious natural resource in modern days. The extreme extraction of groundwater is causing a decline in the groundwater level. The groundwater level mostly depends on various climatic factors i.e., rainfall and temperature. The pattern of these factors changes over the years due to climate change. These variations of climatic factors affect the groundwater level over the seasons. Compared to conventional hydrological modeling, today’s most intriguing model is built on a deep learning environment and exhibits higher interest in precise predictions. The study area of Chennai Basin is a coastal area where we apply the deep learning-based model to forecast groundwater levels. Using only two parameters and some hyper-parameters this model performs well. This study considers 4 SSP scenarios, SSP126, SSP245, SSP370, and SSP585. The forecasted groundwater level in SSP585 is approximately 12 m which is the maximum. The R² and NSE are used to check the accuracy. This season-wise accurate forecasting will help India’s govt. And the farmers to plan their upcoming actions. The farmers will have an initial idea about the available groundwater level. So, they can irrigate their land according to the available crop-water ratio. Also, CGWB can plan its yearly plan for precious groundwater use in the locality. This action can help to avoid the extreme events of water scarcity.

Soil Conservation Service Curve Number (SCS-CN) method currently known as Natural Resources Conservation Services curve number (NRCS-CN) method, is one of the extensively used reliable, simple, and attractive model in practical hydrology for direct surface runoff (rainfall-excess) prediction of a given storm, initially developed for direct surface runoff estimation in small and medium agricultural watersheds of the USA, later extended to other geographical regions of different land use land cover, and climatic conditions of different parts of the earth viz, to rural, urban, forest, experimental. Besides of various improvements, the method has also been extended to a number of hydrological application beyond its initial purpose. This study evaluates incorporation of antecedent precipitation (P5) in place of the antecedent moisture (Mishra and Singh 2004 models) in the Soil Conservation Services-Curve Number (SCS-CN) method using a large set of rainfall-runoff data from 234 small to large experimental watersheds from USDA-ARS. Three variants of the proposed models (M3, M6, and M9), Mishra and Singh models (M2, M5, and M8), and existing SCS-CN (M1, M4, and M7), out of which the first two models of proposed, Mishra and Singh, and existing models are one parameter or CN based and the third model of each are two parameters (CN and λ) models are considered. Employing the widely used performance evaluation goodness of fit (GoF) criterion of root mean square error (RMSE) and ranking based grading system indicate that all the variants of proposed models in turn performing far better than variants of Mishra and Singh model followed by variants existing model, respectively.

G-Filters can contribute to the circular economy by promoting sustainable water management practices. G-Filters provide gravity based filtration and an affordable clay ceramic solution for household water treatment in India. These filters are made from clayey soils with organic pore forming material. As a result, ceramic water filters (CWF) reduce the need for expensive water treatment facilities and the associated energy and resource consumption. G-Filters can be easily cleaned and reused, reducing the amount of waste generated by single-use water filters. They can also be reused a porous irrigation vessels (PIV) for desalting soils. This contributes to the circular economy by promoting resource efficiency and minimizing waste. Furthermore, G-Filters can also help promote local economic development by using locally-sourced materials and manufacturing processes, and creating job opportunities for local communities. By incorporating G-Filters into water management practices, we can move towards a more sustainable and circular water economy.

Regardless of challenges in estimation of chlorophyll-a (Chl-a) by remote sensing, Chl-a assessment by remote sensing offers the potential for significant understanding of algal pollution in ambient waterbodies. In this study, the fluorescence enveloped area (FEA) was estimated from remotely sensed hyperspectral data to determine chlorophyll-a (Chl-a) in river water. The FEA was estimated by integrating the fluorescence height and fluoresce peak positions. The research is built on the work, which requires both lab-scale and field-scale analytical capabilities to determine Chl-a concentrations. Firstly, field water samples were collected from a river, and a portable spectroradiometer was used to generate hyperspectral data of ambient water samples. Secondly, these samples were processed in lab using spectrophotometer to determine actual Chl-a concentrations in water samples. This measurement requires using conventional analytical methods that involves algae cell destruction, and Chl-a extraction using acetone solvent. Finally, relationships between FEA, and actual Chl-a concentrations, and wet biomass were determined to evaluate the capabilities of FEA-based approach to determine the Chl-a concentrations in river water. This approach was applied to Putah Creek for retrieval of Chl-a concentrations from hyperspectral data obtained from water samples collected in July 10 & July 26, 2023. The relationships between actual Chl-a concentrations, and retrieved Chl-a concentrations from hyperspectral showed that the approach used here can be applied to evaluate the algal biomass and Chl-a in river waters, particularly high Chl-a concentrations.

The paper presents recent trends in smart metering solution for management of water grids. Unlike power grids, water grids span over a massive territory spread across a GPS enabled network map. In order to effectively manage the same, IBM smart grid maturity model uses Netcool/Omnibus probe for advanced metering infrastructure (AMI) which addresses all important dimensions of Technology, Process & Security. It’s not only intelligent, interconnected & instrumented for all the layers of functioning of smart water grid model from procuring sources, planning capacity, distribution network & finally consumption but also enables proactive fixation of grid issues by preventive flow mechanism. With introduction of IOT driven sensor replacing 2G/3G mobile network nodes, the data acquisition & processing can be further brought down near the Edge of the network for quicker analysis & faster resolution. Currently, the machine language (ML) reinforcement model algorithm is tested with live data stream for one large CSP in India with accuracy of ~90%.