Evaluating the relationship between groundwater quality and land use in an urbanized watershed.

Understanding the impact of urbanization on groundwater quality is critical. Effective water management requires understanding the relationship between land use and water quality. The study's goals were to compare the effects of land use, identify the types of land that impact hydrochemistry, and define how different land use affects water quality. For this purpose, the comparative relationship between groundwater quality, land use classes and landscape metrics were established for the years 2016 and 2021. Water samples were collected from 42 wells, and different hydro-chemical variables were considered to calculate the water quality index (WQI). The WQI value in 2016 ranged from 26.49 to 151.03 and 29.65 to 155.62 in 2021. The results indicate that the water quality in most parts of the study area is moderate for drinking and domestic purpose use. The google earth engine platform was used and radiometrically corrected and orthorectified Sentinel-2 satellite images were processed to classify land use classes for selected years. Five buffer zones were established within a 2-km watershed along each well site, and the effects of land use types and landscape metrics on water quality in the buffer zones were analyzed. Results revealed that the effects of land use types on water quality were mainly reflected in buffer 1 (B1), buffer 4 (B4), buffer 5 (B5) in 2016 and B1, buffer 3 (B3), and B5 in 2021. The impacts of landscape-level metrics on water quality are mainly reflected in buffer 2 (B2) and B3 in 2021, while at the class-level, they are mainly reflected in B1 and B4 in 2021. The redundancy analysis revealed that different hydro-chemical variables behaved differently with the land use classes and landscape metrics in the various buffer zones.

A comprehensive review on pollution status and associated health risk assessment of human exposure to selected heavy metals in road dust across different cities of the world.

In order to expound on the present situation and potential risk of road dust heavy metals in major cities, a total of 114 literatures mainly over the past two decades, involving more than 5000 sampling sites in 61 cities of 21 countries, were screened through the collection and analysis of research papers. The concentration, sources, distribution, health risk, sample collection, and analytical methods of heavy metal research on road dust in cities around the world are summarized. The results show that Cd, Zn, and Cu in many urban road dusts in the world are higher than the grade II of the Chinese maximum allowable concentration of potentially toxic elements in the soil. Geo-accumulation index values show that Pb > Cd > Zn > Cu had the highest contamination levels. Hazard index assessment indicates Pb and Cr had the highest potential health risk, especially for children in which ingestion was found as the main exposure pathway. Moreover, through comparative analysis, it is found that some pollutants are higher in developed and industrialized cities and transport (53%) followed by industrial emissions (35%) provide the major contributions to the sources of heavy metals.

Mapping of groundwater productivity potential with machine learning algorithms: A case study in the provincial capital of Baluchistan, Pakistan.

Although groundwater (GW) potential zoning can be beneficial for water management, it is currently lacking in several places around the world, including Pakistan's Quetta Valley. Due to ever increasing population growth and industrial development, GW is being used indiscriminately all over the world. Recognizing the importance of GW potential for sustainable growth, this study used to 16 GW drive factors to evaluate their effectiveness by using six machine learning algorithms (MLA's) that include artificial neural networks (ANN), random forest (RF), support vector machine (SVM), K- Nearest Neighbor (KNN), Naïve Bayes (NB) and Extreme Gradient Boosting (XGBoost). The GW yield data were collected and divided into 70% for training and 30% for validation. The training data of GW yields were integrated into the MLA's along with the GW driver variables and the projected results were checked using the Receiver Operating Characteristic (ROC) curve and the validation data. Out of six ML algorithms, ROC curve showed that the XGBoost, RF and ANN models performed well with 98.3%, 96.8% and 93.5% accuracy respectively. In addition, the accuracy of the models was evaluated using the mean absolute error (MAE), root mean square error (RMSE), F-score and correlation-coefficient. Hydro-chemical data were evaluated, and the water quality index (WQI) was also calculated. The final GW productivity potential (GWPP) maps were created using the MLA's output and WQI as they identify the different classification zones that can be used by the government and other agenciesto locate new GW wells and provide a basis for water management in rocky terrain.

Spatial and temporal variation of dissolved heavy metals in the Lijiang River, China: implication of rainstorm on drinking water quality.

Lijiang River is an essential drinking water source and natural scenery in the Guilin City. For the first time, implications of rainstorm were taken into consideration by investigating spatial and temporal variation of dissolved heavy metals (HMs) in the Lijiang River water. A total of 68 water samples were collected during low flow (normal) season and high flow (rainstorm) season from 34 sampling sites. Dissolved HMs including Cr, Mn, Co, Cu, Zn, As, Cd, Sb, and Pb were found to meet the respective drinking water standards, while comparatively higher concentration was observed after the rainstorm season, except for Cr. Multivariate statistical analysis showed that Co, Cu, Cr, Zn, Sb, and Pb in normal season were mainly controlled by anthropogenic sources. Furthermore, higher concentrations of Mn, Cu, Cd, Pb, Co, and Zn during the high flow season were attributed to rainstorm. The water quality index (WQI) showed good grades and comparatively lower in rainstorm season. The results of health risk assessment revealed that HMs in Lijiang River posed limited health risk; however, As posed potential health risk specially in rainstorm season. It is suggested to adopt preventive measures for mining activities and industrial waste-water discharge at the river's upstream and downstream.

Efficient performance of magnesium oxide loaded biochar for the significant removal of Pb2+ and Cd2+ from aqueous solution.

Lead (Pb) and cadmium (Cd) are considered as a typical heavy metals in aqueous solution, which may pose adverse health effects on human beings. For the removal of these two pollutants, magnesium oxide (MgO) was successfully immobilized onto eucalyptus biochar (BC) matrix via simple and cost-effective pyrolysis process of MgCl2-pretreated eucalyptus biomass under high temperature (500 °C). Synthesized MgO nanoparticles-biochar composites (MBC) exhibited superior removal performance for target pollutants, and achieve 99.9% removal efficiency for Pb(II) and Cd(II) at optimum conditions (0.02 g, pH in range of 4-7, and reaction time 120, 240 min). Furthermore, the maximum theoretical adsorbing amount of MBC was 829.11 mg/g for Pb(II) and 515.17 mg/g for Cd(II). Pseudo-second-order model and Langmuir models were well-determined for isotherm and adsorption kinetics. FTIR, XRD, and XPS analysis revealed that precipitation and ion exchange was of great importance for the removal of contaminants. Besides, cation-π interaction and complexation from the carbon-containing functional groups should not be neglected. Considering the advantage of low-cost, facile preparation, and brilliant adsorption capacity, it is anticipated that MBC has a promising prospect for the broad application in Pb(II)/Cd(II)-containing wastewater treatment.

Hydrogeochemical Characterization and Suitability Assessment of Groundwater: A Case Study in Central Sindh, Pakistan.

Groundwater is the most important water resource, on which depends human geo-economic development and survival. Recent environmental changes and anthropogenic activities render groundwater severely vulnerable. Groundwater in Central Sindh, Pakistan, is facing a similar situation. Hydrogeochemical characteristics of the groundwater in the said region were investigated by analyzing 59 groundwater samples via agricultural and drinking indices, using various statistical methods and graphical approaches to identify factors affecting groundwater. Major reactions occurring in the groundwater system were quantified by hydrogeochemical modeling. A statistical summary reveals the abundance of cations is Na⁺ > Ca2+ > Mg2+ > K⁺, while the abundance of anions is HCO3- > Cl- > SO4². Groundwater chemistry is mainly of rock dominance. Correlation analysis and graphical relationships between ions reveal that ion exchange and rock weathering such as the dissolution of halite, albite, and dissolution of carbonate minerals are important rock⁻water interactions, governing the evolution of groundwater chemistry. Hydrochemical facies are predominantly of mixed CaMgCl and Na-Cl type, with few samples of Ca-HCO3 type, which constitutes fresh recharged water. Based on the Water Quality Index (WQI), 28.82% samples were found to be unsuitable for drinking. A United States Salinity Laboratory (USSL) diagram, Wilcox diagram, and other agricultural indices indicate that majority of the groundwater samples fall within the acceptable range for irrigation purposes.