Assessment of groundwater safety surrounding contaminated water storage sites using multivariate statistical analysis and Heckman selection model: a case study of Kazakhstan

Unraveling the hydrogeochemical evolution and pollution sources of shallow aquifer using multivariate statistical analysis and hydrogeochemical techniques: a case study of the Quaternary aquifer in Beni Suef area, Egypt

This study integrates multivariate statistical analysis and hydrogeochemical modeling to investigate the processes controlling the groundwater composition of a shallow aquifer where increased pumping rates and anthropogenic impacts were prevalent. Eighteen groundwater samples were collected and analyzed for major elements and selected heavy metals. The data were classified on the basis of multivariate statistical analysis into three clusters: C1 (Na-Cl facies), C2 (Ca-SO₄ facies), and C3 (Ca-HCO₃ facies). The application of factor analysis gave four factors affecting the groundwater chemistry, namely the salinization factor, anthropogenic/secondary enrichment factor, the secondary and the micro-nutrient fertilizers, and the aluminum fertilizer factor. The hydrogeochemical study of the groundwater revealed that the processes controlling the groundwater chemistry in the study area are mainly affected by the groundwater occurrence either to the east or to the west of Bahr Youssef Canal. Generally, the dominant hydrogeochemical processes affecting the groundwater are silicate weathering, ion exchange, irrigation return flow, gypsum applications in soil, and evaporation. The groundwater quality evaluation shows that water quality varies from fair to excellent for drinking purposes, where the best water is located in the northern and central parts of the study area. The suitability of groundwater for irrigation was evaluated using several indices indicating that groundwater is suitable for irrigation in the northwest and western parts of the study area. As some groundwater samples lie in high salinity classes on the US Salinity diagram, it is recommended to use this water for plants with good salt tolerance under good drainage conditions. The integration between the statistical and geochemical tools helps reveal the dominant processes through data reduction and classification.

Farmers' adoption behavior of conservation tillage technology: a multidimensional heterogeneity perspective

Adopting conservation tillage technology can promote sustainable agriculture development. There have been many studies on all farmers' conservation tillage behaviors, but few studies are based on a farmer's multidimensional heterogeneity perspective. Given the background, we conduct an empirical evaluation using micro survey data from 819 households in the middle and upper reaches of the Yellow River Basin. This paper uses the entropy method to classify farm households into three types: economically dominant, resource dominant, and socially dominant. Furthermore, we use the Heckman sample selection model to discuss the factors that affect the adoption of conservation tillage technology by different types of farmers. The results show that land fragmentation degree can inhibit economically dominant farmers conservation tillage technology adoption behavior. Social relations can positively influence resource dominant farmers. The share of non-farm income will positively impact socially dominant farmers. This paper further proposes policy implications, based on the findings that different types of farmers have various factors influencing conservation tillage technology adoption behavior.

Preliminary risk assessment of regional industrial enterprise sites based on big data

An accurate and inexpensive preliminary risk assessment of industrial enterprise sites at a regional scale is critical for environmental management. In this study, we propose a novel framework for the preliminary risk assessment of industrial enterprise sites in the Yangtze River Delta, which is one of the fastest economic development and most prominent contaminated regions in China. Based on source-pathway-receptors, this framework integrated text and spatial analyses and machine learning, and its feasibility was validated with 8848 positive and negative samples with a calibration and validation set ratio of 8:2. The results indicated that the random forest performed well for risk assessment; and its accuracy, precision, recall, and F1 scores in the calibration set were all 1.0, and the four indicators for the validation set ranged from 0.97 to 0.98, which was better than that for the other models (e.g., logistic regression, support vector machine, and convolutional neural network). The preliminary risk ranking of industrial enterprise sites by the random forest showed that high risks (probabilities) were mainly distributed in Shanghai, southern Jiangsu, and northeastern Zhejiang from 2000 to 2015. The relative importance of the site industrial, production, and geographical features in the random forest was 69%, 22%, and 9%, respectively. Our study highlights that we could quickly and effectively establish a priority (or ranking) list of industrial enterprise sites that require further investigations, using the proposed framework, and identify potentially contaminated sites.

Essential Trace Elements in Scalp Hair of Residents across the Caspian Oil and Gas Region of Kazakhstan

Most of the country’s oil and gas fields are situated in West Kazakhstan, mainly on the Caspian Sea coast, causing significant damage to the local environment and contributing to an imbalance in the trace element composition of the human body. The study is aimed to evaluate the relationship between the concentration of essential trace elements in scalp hair of the western Kazakhstan adult population and the remoteness of their residence from oil and gas fields. The concentration of essential trace elements (Co, Cu, Fe, I, Mn, Se, Zn) in the hair of 850 individuals aged 18−60 years was determined by inductively coupled plasma mass spectrometry. In residents of settlements located at a distance of >110 km from oil and gas fields, the concentration of Cu and I in hair was significantly higher than in those closer to 110 km (p < 0.001). The content of Cu and I were associated with the distance to oil and gas fields (0.072 (95% CI: 0.050; 0.094)) and (0.121 (95% CI: 0.058; 0.185)), respectively. We detected a significant imbalance in the distribution of some essential trace elements in residents’ scalp hair from the Caspian region of western Kazakhstan, living near oil and gas fields. The concentrations of Cu and I were significantly interrelated with the distance to oil and gas fields. The level of copper in the hair of both inhabitants of the area most remote from oil and gas facilities and the entire population of western Kazakhstan as a whole remains significantly low. The data obtained provide evidence of the possible impact of pollutants generated by the oil and gas facilities on a shortage of essential trace elements and associated subsequent health risks.

Appraisal of groundwater from lithological diversity of the western coastal part, Maharashtra, India: An integrated hydrogeochemical, geospatial and statistical approaches

The present study attempts to decipher the seasonal variations in hydro-geochemistry of groundwater in the Terekhol River Basin, western coastal region, Maharashtra, India. A total of 65 groundwater samples of post-monsoon (POMS) and pre-monsoon (PRMS) seasons were collected and analyzed for major ion composition using standard analytical procedures of APHA. Piper and Gibbs plots is used to elucidate the controlling factors which altering the groundwater composition. Scatter plots of ions indicate that major ions from lithologies exposed in the study area and anthropogenic activities are altering the groundwater chemistry. Statistical analysis includes correlation, factor analysis and cluster analysis used to interpret the hydrochemical data. As compared to the WHO drinking standards, all the groundwater samples are fit for drinking. Irrigation water suitability was ascertained based on SAR, %Na and KR indices. Overall, the groundwater chemistry in study area is reflects changes in natural processes rather than anthropogenic inputs.

Characterization of drinking groundwater quality in rural areas of Inner Mongolia and assessment of human health risks

Groundwater is an important natural resource of drinking water in rural areas in Inner Mongolia, China. In this study, 4438 drinking groundwater samples were collected from the rural areas of 81 counties in Inner Mongolia, and were analyzed for 16 parameters, including pH, total hardness (TH), chemical oxygen demand (COD), total dissolved solids (TDS), sulfate (SO42-), chloride (Cl-), fluoride (F-), iron (Fe), manganese (Mn), arsenic (As), cadmium (Cd), hexavalent chromium (Cr), lead (Pb), aluminum (Al), cuprum (Cu), zinc (Zn). The groundwater quality was evaluated with water quality index (WQI) and human health risk assessment (HRA). Monte Carlo simulation were applied for the uncertainty and sensitivity analysis in the health risk assessment. The spatial map was employed based on the inverse distance weighted (IDW) interpolation technique. The results reveal that while the hazard quotient (HQ) suggests that the risk of single element contamination is feeble, the hazard index (HI) indicates a potential health risk for the local population. The observed cumulative carcinogenic risk (CCR) indicates a probable risks of carcinogenic health hazards in the study area. The sensitivity analysis revealed that daily ingestion rate (IR), exposure frequency (EF), and the concentrations of As, Mn, F-, and Cr are the most influential parameters for health hazards. The highly polluted areas are mainly distributed in the central and western regions of Inner Mongolia, including Xianghuangqi, New Barag Zuoqi, and Togtoh. It is observed that the groundwater may cause a potential health risk after long-term ingestion. The results of this study will contribute to groundwater management and protection in Inner Mongolia.

Health Impact of Drying Aral Sea: One Health and Socio-Economical Approach

Once one of the largest saline lakes, the Aral Sea, was recognized as a significant environmental disaster as the water level decreased dramatically. Water level decrease increases water salinity, affecting biodiversity. Exposed lake beds become the source for fine dust picked up by the dust storms and spread across a long distance, affecting people’s health in surrounding areas. This review paper attempts to evaluate the potential links between the Aral Sea shrinking and the existing health issues in the case of Kazakhstan. The literature-based research revealed that the population of the Aral Sea basin region has been suffering from exposure to various pollutant residues for a long time. There is an apparent increase in morbidity and mortality rates in the region, especially in people suffering from chronic illness. Furthermore, the catastrophic desiccation of the Aral Sea has led to the sharp deterioration in living conditions and negative trends in the socio-economic situation of the region’s population. While the dust storms spread the polluted salts from the exposed bottom across the Aral Sea region, specific contaminants define the relevance and importance of public health problems linked to the basin rather than the Aral Sea drying process. There is, however, no clear evidence that associated dust storms are the only primary source of the deterioration of people’s health. Moreover, One Health approach seems to play a crucial role in achieving better outcomes in the health of people and the health of the environment.

Simulating contaminant transport in unsaturated and saturated groundwater zones

The demand for clean and adequate water is rising rapidly with increasing population. This growing demand for water necessitates the measurement of the quantity and quality of water. Simulation modeling has become increasingly popular in the last two decades largely because of their predictive ability. This paper reviews the approaches for simulation modeling in groundwater resources management, focusing on models that have been used to simulate contaminant transport through the aquifer system. Recent research papers that have integrated the models for unsaturated and saturated zones have also been studied and described. Integrated models require assessment of the complex interactions between the groundwater zones and the movement of water and solute through them. Due to this, integrated models provide a more accurate modeling approach than models that have been independently developed for saturated and unsaturated zones. Application of such models is encouraged at the regional level to arrive at the best groundwater management decisions. PRACTITIONER POINTS: In the past few decades, modeling of contaminant transport in groundwater systems has seen tremendous applications. A number of models exist that independently simulate flow and solute transport in unsaturated and saturated zones. Recently, focus has been given on developing advanced coupled modeling approaches that require less inputs and run times.