Arsenic in drinking water—recent examples and updates from Southeast Asia

Uppal, Jagdeesh S.; Zheng, Qi; Le, X. Chris

doi:10.1016/j.coesh.2019.01.004

Cited by 94 publications

(44 citation statements)

References 61 publications

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“…These findings revealed that the water quality is has been changed in comparison of previous studies from the selected area and with the passage of time the water quality is degrading. So far, there is need to monitor the drinking water quality, which is helpful to avoid the negative health impacts (Boone et al, 2019;Brunner et al, 2019;Charuaud et al, 2019;Khanoranga and Khalid, 2019;Mintenig et al, 2019;Mohammed, 2019;Nnorom et al, 2019;Perrin et al, 2019;Rocha-Melogno et al, 2019;Ruiz-Beviá and Fernández-Torres, 2019;Shakoor et al, 2019;Sharma et al, 2019;Uppal et al, 2019;Xiao et al, 2019).…”

Section: Resultsmentioning

confidence: 99%

Ground Water Quality of Selected Areas of Punjab and Sind Provinces, Pakistan: Chemical and Microbiological Aspects

et al. 2019

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The assessment of groundwater is essential for the estimation of suitability of water for safe use. An attempt has been made to study the groundwater of selected areas of Punjab (Sheikhupura & Sahiwal) and Sindh (Sindh, Jawar Dharki and Dharki), Pakistan. The results indicate that pH, color and odor were all within limits of WHO that is pH ranges 6.5-8.5, colorless and odorless, respectively. The high values of suspended solids were observed in the Sindh-1 and Dharki samples. Microbiologically only Sahiwal and Jawar Dharki were found fit for drinking purpose. Trace metals analysis of Sheikhupura-1 and Sindh-1 showed that values do not fall within limits of WHO for Iron. The ionic concentration analysis showed that high bicarbonate (HCO3-), ions are present in the samples of Sahiwal and Dharki; Sindh-1 and Jawar Dharki samples showed very high concentration for chloride ions, all samples were satisfactory level for sulphate (SO4 2-), sodium, magnesium and phosphate ions except samples of Sindh-1 and Jawar Dharki. High concentration of calcium and potassium ions was observed in samples of Sindh-1, while all other samples were found fit for drinking purposes in respect of nitrate, nitrite and ammonium ions. The high concentration of Fluoride was found only in Sheikhupura-2 samples.

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Section: Resultsmentioning

confidence: 99%

Ground Water Quality of Selected Areas of Punjab and Sind Provinces, Pakistan: Chemical and Microbiological Aspects

et al. 2019

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“…Regarding risk to population, Uppal, Zheng, and Le (2019) presented a review on contamination of As in drinking water, and based on the revised studies, the authors reported that the risk of being exposed to arsenic levels higher than the allowed by the WHO threats more than 200 million people in the World. In line with that, Kumar, Patel, et al (2019) reported that water in Latin America is also contaminated by As and in two studies reviewed by these authors is stated that around 14 million people in this region depend on contaminated water with concentration >10 μg/L (the threshold allowed by WHO).…”

Section: Arsenic: General Aspects and Contamination Around The Worldmentioning

confidence: 99%

Use of plants in the remediation of arsenic‐contaminated waters

Berg

Borges

2020

Water Environment Research

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Arsenic-contaminated waters represent environmental and public health problems. The use of plants has emerged as a viable, cost-effective, and low environmental impact alternative to treat these polluted waters. This review presents studies published in the year 2019 on phytoremediation covering the effects observed on plants, trends in plant species selection and treatment of arsenic-rich biomass. Studies on application of this technology are exposed through constructed (treatment) wetlands, and a brief contextualization on the impacts of arsenic contamination was also performed.

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“…The water resources contamination with arsenic (As) has been studied worldwide [1,2]. Due to severe health concerns associated with arsenic polluted water, maximum contaminant level was changed from 50 to 10 ppb in 2001 by United States Environmental Protection Agency (EPA) [3].…”

Section: Introductionmentioning

confidence: 99%

Experimental and neural network modeling of micellar enhanced ultrafiltration for arsenic removal from aqueous solution

Yaqub

Lee

2020

Environmental Engineering Research

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The optimization of micellar-enhanced ultrafiltration (MEUF) of arsenic (As) contaminated aqueous solution using cetylpyridinium chloride (CPC) as surfactant was studied through experimental and artificial neural network (ANN) modeling. Experimental studies were carried out by varying operational conditions such as time, pressure, molar ratio of CPC to As, concentration of As and pH of feed solution. Root mean square error (RMSE) and coefficient of determination (R<sup>2</sup>) were considered as performance criterion to evaluate the predicted results of ANN model. The experimental studies provided optimum operating parameters such as pressure 1.8 bar, molar ratio of CPC to As was 5:1, As concentration 1 mM and pH 8.0 of feed solution. ANN model presented reliable results with RMSE values 0.259, 0.553 and 0.623 for training, validation and testing datasets, respectively, while R<sup>2</sup> values for training, validation and testing dataset were noted as 0.962, 0.942 and 0.932, respectively. The proposed ANN model traced input-output relationship to predict As removal efficiency (RE) of MEUF process. Therefore, ANN model can be considered as a competitive, powerful and fast alternate because of its high computational speed, accuracy and economics in MEUF process optimization without doing laborious experimental work.

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Arsenic in drinking water—recent examples and updates from Southeast Asia

Cited by 94 publications

References 61 publications

Ground Water Quality of Selected Areas of Punjab and Sind Provinces, Pakistan: Chemical and Microbiological Aspects

Ground Water Quality of Selected Areas of Punjab and Sind Provinces, Pakistan: Chemical and Microbiological Aspects

Use of plants in the remediation of arsenic‐contaminated waters

Experimental and neural network modeling of micellar enhanced ultrafiltration for arsenic removal from aqueous solution

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