Hydrogeochemical characterization based water resources vulnerability assessment in India's first Ramsar site of Chilka lake

Hydro-chemical based assessment of groundwater vulnerability in the Holocene multi-aquifers of Ganges delta

Determining the degree of high groundwater arsenic (As) and fluoride (F-) risk is crucial for successful groundwater management and protection of public health, as elevated contamination in groundwater poses a risk to the environment and human health. It is a fact that several non-point sources of pollutants contaminate the groundwater of the multi-aquifers of the Ganges delta. This study used logistic regression (LR), random forest (RF) and artificial neural network (ANN) machine learning algorithm to evaluate groundwater vulnerability in the Holocene multi-layered aquifers of Ganges delta, which is part of the Indo-Bangladesh region. Fifteen hydro-chemical data were used for modelling purposes and sophisticated statistical tests were carried out to check the dataset regarding their dependent relationships. ANN performed best with an AUC of 0.902 in the validation dataset and prepared a groundwater vulnerability map accordingly. The spatial distribution of the vulnerability map indicates that eastern and some isolated south-eastern and central middle portions are very vulnerable in terms of As and F- concentration. The overall prediction demonstrates that 29% of the areal coverage of the Ganges delta is very vulnerable to As and F- contents. Finally, this study discusses major contamination categories, rising security issues, and problems related to groundwater quality globally. Henceforth, groundwater quality monitoring must be significantly improved to successfully detect and reduce hazards to groundwater from past, present, and future contamination.

Modelling groundwater vulnerability in a vulnerable deltaic coastal region of Sundarban Biosphere Reserve, India

Groundwater is the most reliable source of freshwater for human well-being. Significant toxic contamination in groundwater, particularly in the aquifers of the Ganges delta, has been a substantial source of arsenic (As). The Sundarban Biosphere Reserve (SBR), located in the southwestern part of the world's largest Ganges delta, suffers from As contamination in groundwater. Therefore, assessment of groundwater vulnerability is essential to ensure the safety of groundwater quality in SBR. Three data-driven algorithms, i.e. "logistic regression (LR)", "random forest (RF)", and "boosted regression tree (BRT)", were used to assess groundwater vulnerability. Groundwater quality and hydrogeochemical characteristics were evaluated by Piper, United States Salinity Laboratory (USSL), and Wilcox's diagram. The result of this study indicates that among the applied models, BRT (AUC = 0.899) is the best-fit model, followed by RF (AUC = 0.882) and LR (AUC = 0.801) to assess groundwater vulnerability. In addition, the result also indicates that the general quality of the groundwater in this area is not very good for drinking purposes. The applied methods of this study can be used to evaluate the groundwater vulnerability of the other aquifer systems.

Insights into long-term changes of groundwater levels in the typical region of Zhangjiakou City, China

Little information is available on the long-term changes of groundwater levels and their associated influencing factors. Zhangjiakou City was chosen as a case to reveal the temporal and spatial dynamics of groundwater level and its driving factors in the long term. Herein, the observation data of groundwater level from 56 wells was investigated from 1981 to 2015, including the collected meteorological data, socio-economic data, and groundwater resource exploitation situation. Results showed that the groundwater level in Zhangjiakou City tended to be decreased, and the decrease rate was gradually accelerated. In the past 35 years, the groundwater level of Bashang Plateau has decreased by 3.59 m < 3.6 m in Yuyang Basin < 7.17 m in Zhuohuai Basin < 20.41 m in Chaixuan Basin. The dynamic changes of groundwater level in four geomorphic units in Zhangjiakou City were significant correlation between the total population and other socio-economic factors, including primary industry production value; common cultivated land area; effective irrigation area; total grain yield; total vegetable yield; total production of pork, beef, and mutton; secondary industry production value; tertiary industry production value; and year-end total population. Furthermore, the principal component analysis of groundwater level variation in Zhangjiakou city showed that the variance contribution rates of the first principal component in the characteristic indicators of the Bashang Plateau, Chaixuan Basin, Zhuohuai Basin, and Yuyang Basin were 75.7%, 83.9%, 66.1%, and 77.8%, respectively, which mainly reflect the information of socio-economic factors. This indicated that socio-economic factors were the main driving factor influencing the continuous decline of groundwater levels in Zhangjiakou City. The obtained findings can provide new insights into the sustainable development of social economy and the rational utilization and allocation of regional water resources.

Hydrogeochemical evaluation with emphasis on nitrate and fluoride in urban and rural drinking water resources in western Isfahan province, central Iran

Nitrate (NO3-) and fluoride (F-) are two major potential contaminants found in the groundwater of Iran. These contaminants are highly dangerous to humans if consumed more than the safe limit prescribed by the WHO. Therefore, in this study, the urban and rural drinking water resources of Isfahan province (central Iran) were investigated to evaluate the quality of groundwater from the perspective of NO3- and F-. The calculated saturation index (SI) shows that the majority of samples are mainly undersaturated or in equilibrium with respect to potential minerals. The most likely interpretation for undersaturation with respect to most minerals is either that the minerals are not present if they are reactive or if they are present, then they are not reactive. This study reveals that the majority of the groundwater samples belong to the Ca-Mg-HCO3 water type. Further, in this study, potential physicochemical variables have been used to calculate entropy weighted water quality index (EWQI). The EWQI reveals that the majority of the groundwater in the area is of good quality. Results show that the water chemistry in the area is largely governed by the water-rock interaction. This study based on large data sets reveals that the majority of drinking water resources are uncontaminated by F-. However, the groundwater is found to be largely contaminated by NO3-. The bivariate plot suggests that the unscientific farming practices and overuse of manures and fertilizers are largely responsible for high content of NO3-. Therefore, emphasis should be given on the cost-effective environmentally friendly fertilizers. The findings from this study will aid the governing authorities and concerned stakeholders to understand the hydrogeochemical evolution of groundwater in this region. The results will help formulate policies in the area for sustainable water supply.

Analyzing past and future trends in Pakistan's groundwater irrigation development: implications for environmental sustainability and food security

Since the last four decades, groundwater irrigation has played a critical role in improving crop production and rural livelihoods. However, the flawed policies have allowed farmers to install private tube wells relentlessly, resulting in a slew of water quality and environmental issues. This study aims to investigate the key trends in temporal development of groundwater irrigation and its consequences in Pakistan. The dataset, which spanned 38 years (1981 to 2018), included variables such as the number of tube wells, wheat area and production, farm size, total cultivated area, and total irrigated area in Punjab province. Our results show that, while the number of government-installed tube wells has decreased over time, the number of private tube wells has increased by 579% since 1981. About 85% of these privately owned tube wells are diesel tube wells, while the remaining 15% are electric tube wells. The ARDL regression results show that groundwater development, as a result of growth in private tube wells, has significantly aided wheat production in both the short and long run. However, the results of ARIMA model show that, in the absence of any regulatory mechanism to limit private tube well growth, the number of private tube wells in Punjab will increase by 43% over the next two decades, potentially jeopardizing the country's groundwater sustainability and food security. To ensure the sustainability of groundwater use, farmers' incomes, and the food security of the population, there is an urgent need to devise policy options to limit the growth of probate tube wells in the province. In addition, the new regulations should consider the equity implications and the economic shock to poor farms and households.

Effects of elevated arsenic and nitrate concentrations on groundwater resources in deltaic region of Sundarban Ramsar site, Indo-Bangladesh region

An attempt has been adopted to predict the As and NO₃^- concentration in groundwater (GW) in fast-growing coastal Ramsar region in eastern India. This study is focused to evaluate the As and NO₃^- vulnerable areas of coastal belts of the Indo-Bangladesh Ramsar site a hydro-geostrategic region of the world by using advanced ensemble ML techniques including NB-RF, NB-SVM and NB-Bagging. A total of 199 samples were collected from the entire study area for utilizing the 12 GWQ conditioning factors. The predicted results are certified that NB-Bagging the most suitable and preferable model in this current research. The vulnerability of As and NO₃^- concentration shows that most of the areas are highly vulnerable to As and low to moderately vulnerable to NO₃. The reliable findings of this present study will help the management authorities and policymakers in taking preventive measures in reducing the vulnerability of water resources and corresponding health risks.

Hydro-chemical assessment of coastal groundwater aquifers for human health risk from elevated arsenic and fluoride in West Bengal, India

The vulnerability of groundwater in the coastal regions in terms of As, F^-, and NO₃^- exposure is growing rapidly. Hence, the present study focused on assessing groundwater quality, ecological richness, and HR in the coastal districts of West Bengal by applying field-based CD, GWQI, ERI, and HRI techniques. After assessing the GW vulnerability, it is stated that approximately 40-50 % area of the two selected coastal district's GW is poor to very poor in quality, the ecology of GW is threatened, and human health is faced serious risk for both dry and wet season. The Wilcox and USSL diagram verified that nearly 50 % GW aquifers of coastal district of West Bengal are not fit for irrigation and drinking. The findings of this study will be beneficial to manage and control groundwater vulnerability in the coastal regions for water scientists, policy makers, and researchers as well in sustainable way.

Influencing Factors and Risk Assessment of Precipitation-Induced Flooding in Zhengzhou, China, Based on Random Forest and XGBoost Algorithms

Due to extreme weather phenomena, precipitation-induced flooding has become a frequent, widespread, and destructive natural disaster. Risk assessments of flooding have thus become a popular area of research. In this study, we studied the severe precipitation-induced flooding that occurred in Zhengzhou, Henan Province, China, in July 2021. We identified 16 basic indicators, and the random forest algorithm was used to determine the contribution of each indicator to the Zhengzhou flood. We then optimised the selected indicators and introduced the XGBoost algorithm to construct a risk index assessment model of precipitation-induced flooding. Our results identified four primary indicators for precipitation-induced flooding in the study area: total rainfall for three consecutive days, extreme daily rainfall, vegetation cover, and the river system. The Zhengzhou storm and flood risk evaluation model was constructed from 12 indicators: elevation, slope, water system index, extreme daily rainfall, total rainfall for three consecutive days, night-time light brightness, land-use type, proportion of arable land area, gross regional product, proportion of elderly population, vegetation cover, and medical rescue capacity. After streamlining the bottom four indicators in terms of contribution rate, it had the best performance, with an accuracy rate reaching 91.3%. Very high-risk and high-risk areas accounted for 11.46% and 27.50% of the total area of Zhengzhou, respectively, and their distribution was more significantly influenced by the extent of heavy rainfall, direction of river systems, and land types; the medium-risk area was the largest, accounting for 33.96% of the total area; the second-lowest-risk and low-risk areas together accounted for 27.09%. The areas with the highest risk of heavy rainfall and flooding in Zhengzhou were in the Erqi, Guanchenghui, Jinshui, Zhongyuan, and Huizi Districts and the western part of Xinmi City; these areas should be given priority attention during disaster monitoring and early warning and risk prevention and control.

Risk assessment of cadmium pollution in selenium rich areas based on machine learning in the context of carbon emission reduction

Machine learning is of great value for the situation analysis and scientific prevention and control of soil heavy metal pollution risk. In this paper, taking the selenium rich area as the research object, the improved Genetic Algorithm (GA)–Back Propagation (BP) algorithm was used to construct the risk assessment model of Cd pollution in this area. Firstly, the content of Cd and Se in the soil of the study area was statistically analyzed based on descriptive statistics and correlation analysis. Then, a three-layer BP neural network structure was designed and optimized by GA algorithm. The individual coding length was calculated by connecting weights and thresholds of Cd and Se elements. Based on 97 groups of field data in this area, the experimental results show that the BP model optimized by GA has faster convergence speed, maintains good generalization ability on the test sample points. Compared with multiple linear regression model (MLRM), GA-BP reduces RMSE by 64.84, 52.12, 49.53, and 63.18% compared with M5. The accuracy of estimating Cd pollution status in different areas by GA-BP neural network model is higher than the other three regression models on the whole. In the whole research region, the samples in the safe interval, relatively safe interval, light pollution interval, moderate pollution interval and severe pollution interval accounted for 4.12, 8.24, 42.26, 17.52 and 27.86%, respectively, and the prediction results of soil Cd pollution level showed that only 12.36% of the samples were in a safe state without the risk of Cd pollution, while most of the samples were in a mild state. Because of the huge potential of carbon sequestration and emission reduction in agriculture, planting se-rich and Cd-low crops in these areas can not only promote the development of local Se-rich industries but also achieve carbon sequestration and emission reduction.