Removal of Arsenic in Groundwater Using Fe(III) Oxyhydroxide Coated Sand: A Case Study in Mekong Delta, Vietnam

Because of its threat to the quality of freshwater resources and human health, arsenic (As) pollution is important to scientific communities and policymakers around the world. The Mekong Delta, Vietnam, is one hotspot of As pollution. Its risk assessment of different environmental components has been well documented; however, very few studies focus on As removal techniques. Considering this information gap, this study aimed to investigate the performance of an innovative and low-cost treatment system using Fe(III)-oxyhydroxide (FeOOH) coated sand to remove As(III) from aqueous solution. Batch and column experiments were conducted at a laboratory scale in order to study removal kinetics and efficiency. Experimental results indicated that the adsorption isotherm of As(III) on FeOOH coated sand using Langmuir and Freundlich models have high regression factors of 0.987 and 0.991, respectively. The batch adsorption experiment revealed that contact time was approximately 8 h for rough saturation (kinetic test). The concentration of As(III) in effluents at flow rates of 0.6 L/h, 0.9 L/h, and 1.8 L/h ranged from 1.1 µg/L to 1.7 µg/L. Results from this study indicated that FeOOH coated sand columns were effective in removing As(III) from water, with a removal efficiency of 99.1%. Ultimately, FeOOH coated sand filtration could be a potential treatment system to reduce As(III) in the domestic water supply in remote areas of the Vietnamese Mekong Delta.

Predicting the distribution of arsenic in groundwater by machine learning technique in two worst hit districts of Assam, India: a risk to public health

Arsenic (As) is a well-known human carcinogen and a significant chemical contaminant in groundwater. The spatial heterogeneity in the distribution of As in groundwater makes it difficult to predict the location of safe areas for tube well installations for consumption and agricultural use. Geospatial machine learning techniques have been used to predict the location of safe and unsafe areas of groundwater As contaminations. Here we used a similar machine learning approach to determine the risk and extent of As >10 ug/L in groundwater at a finer spatial resolution (250m x 250m) in two worst-hit districts of Assam, India, to advise policymakers for targeted campaigning for mitigation. Random Forest Model was employed in Python environments to predict probabilities of the occurrences of As at concentrations >10 ug/L using several intrinsic and extrinsic predictor variables. The selection of predictor variables was based on their inherent relationship with the occurrence of As in groundwater. The relationships between predictor variables and proportions of As occurrences >10 ug/L follow the well-documented processes leading to As release in groundwater. We identified extensive areas of potential As hotspots based on the probability of 0.7 for As >10 ug/L. These identified areas include areas that were not previously surveyed and extended beyond previously known As hotspots. Twenty-five percent of the land area (1,500 km2) was identified as a high-risk zone with an estimated population of 155,000 potentially consuming As through drinking water or food cooked with water containing As >10 ug/L. The ternary hazard map (i.e., high, moderate, and low risk for As >10 ug/L) could inform the policymakers to target the regions by establishing newer drinking water treatment plants and supplying safe drinking water.

Variations of arsenic in groundwater from a transect in Van Phuc village, Hanoi

In this study, the author report detailed results of the variation of arsenic in groundwater along a transect in an area near the Hanoi city centre. The results showed that 64% of collected samples exceeded the WHO guideline value for arsenic concentration in drinking water. The arsenic concentration varied in a wide range, strongly depending on the sediment characteristics of each zone along the transect. Aside from As, groundwater in this area also was contaminated by elevated concentrations of Fe, Mn, and ammonium. The study also pointed out a positive correlation between As and reductive chemical species, namely DOC, NH4+, and CH4 in groundwater. Although there is no clear trend in the correlation between As and Fe, Mn, it can be concluded that the formation of arsenic in groundwater in the study area was due to the reductive dissolution of As-bearing iron minerals under the presence of organic matter.

Spatial distribution of arsenic in groundwater in the northwestern Hanoi

Arsenic contamination in groundwater is commonly found in alluvial plains of major river basins, in which the Red river delta has also been reported to be contaminated with high levels of arsenic. In this study, groundwater from 50 household wells was collected to study the spatial distribution of arsenic in northwestern Hanoi. The results showed that arsenic concentration in groundwater varied in a wide range of less than 5 to 334 μg/l, of which up 62% of the wells exceeded the WHO guideline value of 10 μg/l for arsenic content in drinking water. Arsenic groundwater in this area is unevenly distributed throughout the area, high arsenic concentrations are found in a narrow band between Red river and Day river. This pattern of arsenic distribution is strongly related to the sediment age, sedimentary processes, and it is also modified by local groundwater flow parts and the occurrence of hydraulic connection between aquifers, which are observed in the study area. Arsenic is released into the groundwater during the reductive dissolution of arsenic-bearing minerals under the presence of organic matter.

Influence of chemical fertilizers on arsenic mobilization in the alluvial Bengal delta plain: a critical review

Arsenic contamination of alluvial aquifers of the Bengal delta plain causes a serious threat to human health for over 75 million people. The study aimed to explore the impacts of chemical fertilizer on arsenic mobilization in the sedimentary deposition of the alluvial Bengal delta plain. It selected ten comparatively higher affected Districts and the least affected two Divisions as a referral study site. The countrywide pooled concentration of arsenic in groundwater was 109.75 μg/L (52.59, 166.91) at a 95% confidence interval, which was double the national guideline value (50 μg/L). The analysis results showed a strong positive correlation (r ≥ 0.5) of arsenic with NO3, NH4, PO4, SO4, Ca, and K, where a portion of those species originated from fertilizer leaching into groundwater. The results showed that PO4 played a significant influence in arsenic mobilization, but the role of NO3, SO4, and NH4 was not clear at certain lithological conditions. It also showed that clay, peat, silt-clay, and rich microbial community with sufficiently organic carbon loaded soils could lead to an increase in arsenic mobilization. Finally, the study observed that the overall lithological conditions are the main reason for the high arsenic load in the study area.