Akshay Kumar Chaudhry

This study proposes the evaluation of GIS‐based groundwater quality index (GQI) and spatial variability with a hybrid system that combines Fuzzy Logic with GIS‐based GQI. A case study in the semi‐arid area of Punjab, namely Rupnagar, illustrates the proposed hybrid outline. Two GIS‐based GQI models GQI‐10 (using ten water quality parameters (WQPs)) and GQI‐6 (using six “apprehensive/crucial” WQPs), as well as hybrid Fuzzy Logic GIS‐based GQI (FGQI) (using six crucial WQPs) models, were developed using trapezoidal membership functions and rule sets based on expert's knowledge. Performance comparison of GQI models showed that the FGQI model predicts improved groundwater quality than the GQI‐10 and GQI‐6. FGQI model variance, RMSE, and R² values are 84.25, 6.49, 0.89, respectively. Results have shown that the developed FGQI model is successful and GQI modelling suggests the use of northern and central groundwater without treatment to be unsafe for drinking purposes.

Groundwater is the main source of water in the study area (Rupnagar District, Punjab), and its quality is essential since it is the primary determinant of the suitability of groundwater for drinking and irrigation purposes. In this study, data from 28 years have been used to evaluate the adequacy of groundwater for domestic and irrigation purposes and assess the potential human health impacts of nitrate contaminants. Results of sodium adsorption ratio, percentage sodium, magnesium hazard ratio, Kelley ratio, and residual sodium carbonate illustrate that most of the sampling locations were suitable for irrigational purposes and drinking water quality of the region mostly belonged to the ‘good’ class. The maximum nitrate concentration was observed in the northern and north-east parts of the area. Among the three age groups, children > female > male was found to be more prone to health risks with oral ingestion of nitrate. Uncertainties in the risk estimates were quantified using Monte Carlo simulation and sensitivity analyses. Thus, a proper management plan should be adopted by the decision-makers to improve the quality of drinking water in this area to avoid major health problems in the near future.

COVID-19 outbreak was declared a pandemic by the WHO on 12 March 2020. As of 27 May 2020, WHO statistics exhibited that more than five million confirmed cases have been reported globally. Much remains unclear about the fate and impact of SARS-CoV-2, the novel coronavirus 2019, in wastewater. SARS-CoV-2 infection, the etiologic agent of the current COVID-19 pandemic, is followed by virus shedding in the stool. The quantification of SARS-CoV-2 in wastewater, therefore, enables monitoring of the prevalence of infections among the population through wastewater-based epidemiology. This review discusses the possible spread of the SARS-CoV-2 virus in wastewater and its impact on human health, if any. The information and resources outlined in this paper are based on recently published studies and provide information to decision-makers on the successful management of COVID-19 and reduce the risk of human exposure to COVID-19. Additionally, systems-based approaches to curtail COVID-19 spread are also discussed.

Delineation of the groundwater potential zones (GWPZs) is one of the most vital practices for the sustainable management of groundwater resources. In the present study, the delineation of GWPZs is done using an integrated approach of geospatial technique and fuzzy-analytic hierarchy process (a hybrid method that combines fuzzy set theory and AHP). Seven major thematic layers like geology, slope, rainfall, landuse/landcover, soil type, lineament density, and drainage density are used to generate GWPZs map of the area. The final map is delineated into three different GWPZs, namely, high (20.44% of total study region), moderate and low zones both covering more than 79.55% of the total study region. Validation was done by overlaying the existing well yield (WY) and groundwater depth (GD) data on the final map. Results show that an accuracy of 81.81% and 75.75% was found between WY and GD data, respectively.

The rising population, contamination and mismanagement of groundwater worldwide require sustainable management techniques and strategies to prevent misuse of groundwater resources especially in the semi-arid regions of the world. The aim of the present study is to assess the distribution of contaminants in groundwater at a spatial level by using a geostatistical method, namely ordinary kriging. For this, a physico-chemical parameter data set at 14 sampling locations for a period over 25 years was assessed. Three semi-variogram models, namely exponential, Gaussian and spherical, fitted well for the data set and were cross-validated using predictive statistics. Based on nugget/sill ratio, which characterizes the overall spatial dependence of water quality parameters, it was observed that, apart from nitrate, all the other parameters showed moderate to weak spatial dependence (i.e. total hardness), indicating significant influence of urbanization, fertilization and industrialization. Spatial distribution maps of all the parameters were generated. Concentration of most of the parameters reported high values in the northern region, while silicon dioxide and potassium recorded high values in the southern and central regions of the study area respectively. The study highlighted the depleting groundwater resources in various regions of the study area, indicating that the groundwater quality is in a declining state.