Arsenic Removal from Groundwater Using Iron Pyrite: Influence Factors and Removal Mechanism

Baloch, Muhammad Yousuf Jat; Su, Chunli; Talpur, Shakeel Ahmed; Iqbal, Javed; Bajwa, Kulvinder

doi:10.1007/s12583-022-1698-x

Cited by 11 publications

(2 citation statements)

References 76 publications

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“…This indicates that there is no factor with a high load. Except for SO 4 2− (0.776) and Cd (0.592), almost all factors were in the negative or very low factor loading range, indicating that anthropogenic activities, such as sewage, landfill, metal industry, and mining may contaminate the groundwater [45]. F1 and F2 are the main high-loading factors; thus, these factors were built in a biplot and demonstrated 68.46% variability (Figure 5a).…”

Section: Principal Component Analysis and Pearson Correlation Analysismentioning

confidence: 99%

Groundwater Contamination and Risk Assessment in Greater Palm Springs

Khalid,

Jat Baloch,

Ali

et al. 2023

Water

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Groundwater is an essential resource for drinking water, but its contamination with potentially toxic elements and arsenic (As) is a global issue. To evaluate As and its levels in the Coachella Valley, the US Geological Survey (USGS) collected 17 groundwater samples. This study looked into the arsenic distribution, enrichment, hydrogeochemical behavior, and health risks associated with the samples. The comparative analysis between groundwater contamination in Greater Palm Springs and similar regions, could provide valuable insights into regional differences and common challenges. The hydrogeochemical facies showed the dominance of calcium and magnesium-bicarbonate-carbonate, indicating permanent hardness and salt deposits of residual carbonate. The Gibbs plot demonstrated that chemical weathering of rock-forming minerals and evaporation are the primary forces impacting groundwater chemistry. Geochemical modeling revealed saturation for calcite and dolomite, and under-saturation for halite. Principal component analysis identified the potential contributory sources for contamination of groundwater. The carcinogenic and non-carcinogenic potentials of the toxic elements arsenic, cadmium, chromium (VI), and lead were calculated using a human health risk assessment model. For both adults and children, the highest non-carcinogenic mean value was observed for arsenic (8.52 × 10−1), with the lowest for cadmium (1.32 × 10−3). Children had the highest cumulative non-carcinogenic risk from potentially toxic elements. Our research offers crucial baseline data for assessing arsenic in groundwater at the regional level, which is important for health risk reduction and remediation programs. The data show that preventative action must be taken to reduce the potential health risks in the study area from drinking groundwater, particularly for children.

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Section: Principal Component Analysis and Pearson Correlation Analysismentioning

confidence: 99%

Groundwater Contamination and Risk Assessment in Greater Palm Springs

Khalid,

Jat Baloch,

Ali

et al. 2023

Water

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“…Rising populations, industrial development, and modern lifestyles have increased water use [4][5][6]. The most accessible source of fresh water on the planet is groundwater, and it is essential for human health and industrial and agricultural purposes [7,8]. Chemicals and microbial contamination frequently challenge water safety [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Algorithms for Predicting the Water Quality Index

Hussein,

Jat Baloch,

Nigar

et al. 2023

Water

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Groundwater is one of the water resources used to preserve natural water sources for drinking, irrigation, and several other purposes, especially in industrial applications. Human activities related to industry and agriculture result in groundwater contamination. Therefore, investigating water quality is essential for drinking and irrigation purposes. In this work, the water quality index (WQI) was used to identify the suitability of water for drinking and irrigation. However, generating an accurate WQI requires much time, as errors may be made during the sub-index calculations. Hence, an artificial intelligence (AI) prediction model was built to reduce both time and errors. Eighty data samples were collected from Sakrand, a city in the province of Sindh, to investigate the area’s WQI. The classification learners were used with raw data samples and the normalized data to select the best classifier among the following decision trees: support vector machine (SVM), k-nearest neighbors (K-NN), ensemble tree (ET), and discrimination analysis (DA). These were included in the classification learner tool in MATLAB. The results revealed that SVM was the best raw and normalized data classifier. The prediction accuracy levels for the training data were 90.8% and 89.2% for the raw and normalized data, respectively. Meanwhile, the prediction accuracy levels for the testing data were 86.67 and 93.33% for the raw and normalized data, respectively.

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