Revealing soil microbial ecophysiological indicators in acidic environments laden with heavy metals via predictive modeling: Understanding the impacts of black diamond excavation.

Appraising the activity of soil microbial community in relation to soil acidity and heavy metal (HM) content can help evaluate it's quality and health. Coal mining has been reported to mobilize locked HM in soil and induce acid mine drainage. In this study, agricultural soils around coal mining areas were studied and compared to baseline soils in order to comprehend the former's effect in downgrading soil quality. Acidity as well as HM fractions were significantly higher in the two contaminated zones as compared to baseline soils (p < 0.01). Moreover, self-organizing and geostatistical maps show a similar pattern of localization in metal availability and soil acidity thereby indicating a causal relationship. Sobol sensitivity, cluster, and principal component analyses were employed to enunciate the relationship between the various metal and acidity fractions with that of soil microbial properties. The results indicate a significant negative impact of metal bioavailability, and acidity on soil microbial activity. Lastly, Taylor diagrams were employed to predict soil microbial quality and health based on soil physicochemical inputs. The efficiency of several machine learning algorithms was tested to identify Random Forrest as the best model for prediction. Thus, the study imparts knowledge about soil pollution parameters, and acidity status thereby projecting soil quality which can be a pioneer in sustainable agricultural practices.

Evaluation of groundwater quality by adopting a multivariate statistical approach and indexing of water quality in Sagar Island, West Bengal, India.

In the vicinity of the coast, predominantly groundwater is the sole reliable resource for potable purposes as the surface water sources are highly saline and unfit for human consumption. However, the groundwater in Sagar Island is highly vulnerable to saltwater intrusion. The majority of drinking water comes from government-owned hand pump-equipped tube wells. But during the summer season, many of these tube wells yield significantly less water. Hence, in the current scenario, water quality assessment has become important to the quantity available. Total of 31 samples of deep tube wells (groundwater) are collected at variegated locations during pre-monsoon season throughout Sagar, and then, the physical and chemical quality parameters of these water samples are analysed. Furthermore, a multivariate statistical technique is executed with the aid of the SPSS program. The hydro-chemical parameters that are taken into account for the quality analysis are pH, salinity, electrical conductivity (EC), total dissolved solids (TDS), total hardness, aluminium, arsenic, bi-carbonate, cadmium, iron, chloride, copper, chromium, cobalt, lead, magnesium, manganese, nickel, potassium, sulphate, zinc, and sodium. Then, the analysed data evaluates the water quality index (WQI). Five components are identified through the principal component analysis (PCA) technique, and 82.642% total variance is found. The outcomes of the quality assessment study illustrate that about 54.84% of collected samples come in the "excellent" water quality class when calculated by the "weighted arithmetic WQI method," and 90.32% of collected groundwater samples come in the "good" water quality class when computed using the "modified weighted arithmetic WQI method." This study helps for the interpretation of WQI to assess groundwater quality.

Understanding the exchange process between ground and surface water using mini drive point piezometer and mathematical models to identify suitable managed aquifer recharge sites.

In the current study, subsurface characteristics within the complex formation of the Shilabati basin system of West Bengal, India, extending over an area of 3888 km2, have been estimated using a cost-effective piezometer and MIKE FEFLOW package based on a steady-state numerical model. Pore size and fine particle content of streambeds are affected by two opposing flow contraptions. Such opposite flow conditions are likely to affect the hydraulic conductivity of the streambed. However, analogies of the hydraulic conductivity (Kh) of streambeds for losing and gaining streams have not been well documented in the recent past. The Kh value from the piezometer has been highest at the Dakshin Pairachali site (6.765 m/day), with the stream gaining water from the discharge of the local aquifer. Analysis of the stream-aquifer interaction using the FEFLOW model has allowed us to understand the groundwater water head of the basin ranging from 160.33 to 0.32 m.a.s.l (meters above sea level). The present study also constitutes the first attempt for the identification of suitable sites for the implementation of managed aquifer recharge (MAR) technology in West Bengal, India, to manage extreme drought events. The suitable sites have been identified by means of three fuzzy multi-criteria decision analysis based on nine criteria: river discharge, moisture content, porosity, drainage type, rainfall, land use type, geology, aquifer material, and hydraulic conductivity. To design a radial collector well and infiltration gallery for the selected site in an anisotropic, homogeneous, unconfined, and semi-infinite aquifer near a fully penetrating stream, a pumping test has been conducted to optimize a safe yield of 12.096 MLD (megaliters per day).

Application of fuzzy C-means clustering and fuzzy EDAS to assess groundwater irrigation suitability and prioritization for agricultural development in a complex hydrogeological basin.

The current research is focused on detecting a river basin suitable for agriculture and priority for management using a new clustering tool of groundwater quality with fuzzy logic technique in R and Geographical Information System. A new fuzzy clustering-soft computing technique has been executed to determine the different hydrochemical zones considering 13 essential parameters such as electrical conductivity, hardness, chloride, sodium adsorption ratio, residual sodium carbonate, soluble sodium percent, magnesium hazard, permeability index, potential salinity, residual sodium bicarbonate, Kelly's ratio, synthetic harmful coefficient, and exchangeable sodium percentage. The derived fuzzy C-mean clustering (FCM) outperformed other available hard computing techniques like hierarchical clustering, K-means clustering, and agglomerative clustering. It divided the sampling sites into 2 clustering groups (FCM I and FCM II) which has been validated using fuzzy silhouette index (0.85), the partition coefficient (0.76), the partial entropy (0.68), and the modified partition coefficient (0.52). The hydrogeochemical analysis confirmed that the rock-water interaction, chemical weathering, and ion exchange process are predominant in the aquifer system of the study area. According to the correlation plots, the studied groundwater samples largely evolved from [Formula: see text], mixed [Formula: see text] types, and [Formula: see text] types. The spatial distribution map and the hydrochemical analysis also gives a clear depiction of the fluoride (> 1.0 mg/l) and high iron (> 0.3 mg/l) contamination in groundwater quality, making it unsuitable for both drinking and irrigation. A fuzzy EDAS priority map has been prepared based on all the irrigation suitability parameters which concludes that the groundwater at the upstream and downstream section of the basin requires the most attention. Based on the highest priority for management, five zones have been delineated: very high (5.98%), high (22.31%), medium (16.39%), low (32.30%), and very low (23.02). The findings of this study will be beneficial to planners and policymakers as they can develop schemes to solve similar problems across the country.

Study of water productivity model on wetlands: a case study of Bhagirathi-Hooghly sub-catchment basin, Southern West Bengal, India.

In this article, we explored how to determine the water productivity of wetlands in a compact and evolving basis with geographic information systems and agro-climatic models. Our research aimed to use the AquaCrop model to determine the extent to which the wetlands in the Bhagirathi-Hooghly Basin in South West Bengal, India, have used their water productivity to meet human needs. The wetland capacity and productivity are declining over time due to pressure from the growing population with declining land area. Various statistical data obtained from the field are calibrated based on the AquaCrop model. The validity of the information collected from the model depends on the prediction error (Pe), coefficient of determination (R2), index of agreement (d) and model efficiency (ME) and based on which the goodness of fit develops. It is concluded that the economic dependence of the people living along the wetland has shifted from rice production to fishing and other aquatic plant production. The water productivity of aquatic resources is 7% higher than the water productivity of rice. The study marks the success of a future strategy for wetland development and urges the state government to undertake management policies to create a healthy wetland environment.