Arsenic contamination, induced symptoms, and health risk assessment in groundwater of Lahore, Pakistan

Hydrogeochemical and sediment parameters improve predication accuracy of arsenic-prone groundwater in random forest machine-learning models

The relative importance of groundwater geochemicals and sediment characteristics in predicting groundwater arsenic distributions was rarely documented. To figure this out, we established a random forest machine-learning model to predict groundwater arsenic distributions in the Hetao Basin, China, by using 22 variables of climate, topographic features, soil properties, sediment characteristics, groundwater geochemicals, and hydraulic gradients of 492 groundwater samples. The established model precisely captured the patchy distributions of groundwater arsenic concentrations in the basin with an AUC value of 0.84. Results suggest that Fe(II) was the most prominent variable in predicting groundwater arsenic concentrations, which supported that the enrichment of arsenic in groundwater was caused by the reductive dissolution of Fe(III) oxides. The high relative importance of SO42- indicated that sulfate reduction was also conducive to groundwater arsenic enrichment in inland basins. Nevertheless, parameters of climate variables, sediment characteristics, and soil properties showed secondly important roles in predicting groundwater arsenic concentrations. The other two models, which excluded parameters of groundwater geochemicals and/or sediment characteristics, showed much worse predictions than the model considering all variables. This highlights the importance of variables of groundwater geochemicals and sediment characteristics in improving the precision and accuracy of predicting results. Future studies should probe a method constructing the random forest predicting model with high precision based on the limited number of groundwater samples and sediment samples.

GIS based hotspot analysis and health risk assessment of groundwater arsenic from an unconfined deep aquifer of Lahore, Pakistan

Use of groundwater for drinking purpose poses serious hazards of arsenic contamination particularly in plains of western Himalayan region. Therefore, current study was designed to investigate the level of Arsenic (As) in the water obtained from tubewells in a metropolitan city of Lahore, Pakistan and assess the human health risk. So, a total of 73 tubewells were sampled randomly in the manner that the whole study region was covered without any clustering. The water samples were analyzed for As using atomic absorption spectrophotometer. These samples were also tested for total dissolved solids, chlorides, pH, alkalinity, turbidity, hardness and calcium. GIS based hotspots analysis technique was used to investigate the spatial distribution patterns. Our results revealed that only one sample out of total 73 had arsenic level below the WHO guideline of 10 μg/L. The spatial distribution map of arsenic revealed that the higher concentrations of arsenic are present in the north-western region of Lahore. The cluster and outlier analysis map using Anselin Local Moran's I statistic indicated the presence of an arsenic cluster in the west of River Ravi. Furthermore, the optimized hotspot analysis based on Getis-Ord Gi* statistics confirmed the statistical significance (P < 0.05) and (P < 0.01) of these samples from the vicinity of River Ravi. Regression analysis showed that variables such as turbidity, alkalinity, hardness, chlorides, calcium and total dissolved solids were significantly (all P < 0.05) associated with level of Arsenic in tubewells. Whereas, PH and electrical conductivity and other variables like town, year of installation, depth and diameter of the wells were not significantly associated with Arsenic concentrations in tubewells. Principal component analysis (PCA) exhibited that the random distribution of tubewell samples showed no distinct clustering with towns studied. Health risk assessment based on hazard and Cancer risk index revealed serious risk of developing carcinogenic and non-carcinogenic diseases particularly in children. The health risk due to prevalence of high As concentration in tubewells' water need to be mitigated immediately to avoid worst consequences in future.

Use of Microbial Consortia in Bioremediation of Metalloid Polluted Environments

Metalloids are released into the environment due to the erosion of the rocks or anthropogenic activities, causing problems for human health in different world regions. Meanwhile, microorganisms with different mechanisms to tolerate and detoxify metalloid contaminants have an essential role in reducing risks. In this review, we first define metalloids and bioremediation methods and examine the ecology and biodiversity of microorganisms in areas contaminated with these metalloids. Then we studied the genes and proteins involved in the tolerance, transport, uptake, and reduction of these metalloids. Most of these studies focused on a single metalloid and co-contamination of multiple pollutants were poorly discussed in the literature. Furthermore, microbial communication within consortia was rarely explored. Finally, we summarized the microbial relationships between microorganisms in consortia and biofilms to remove one or more contaminants. Therefore, this review article contains valuable information about microbial consortia and their mechanisms in the bioremediation of metalloids.

Health risk assessment and geospatial analysis of arsenic contamination in shallow aquifer along Ravi River, Lahore, Pakistan

The exposure variation of arsenic from different ground and surface water sources has remained unpredictable which may cause severe human health problems. The current study is, therefore, designed to analyze the spatial variability of arsenic contamination in shallow aquifer and assess the potential human health risks. For this purpose, a total of 55 groundwater, 10 drain water, 4 river water, and 6 sediment samples were collected along zero to 5 km stretch of the River Ravi, Lahore. All water samples were tested for As, pH, and total dissolved solids (TDS), whereas sediments were only tested for As. Health risk models were used to predict cancer and non-cancer risk in adults and children. Among water samples, highest median (minimum-maximum) concentrations (µg/L) of As were recorded 53.32 (1.98-1555) in groundwater, followed by 53.04 (1.58-351.5) in drain water, and 4.80 (2.13-8.67) in river water, respectively, whereas As concentration (mg/kg) in river sediments was 6.03 (5.56-13.92). Variation of As in groundwater was non-significant (P > 0.05) among every 1-km stretch from the Ravi River. However, maximum median concentrations (µg/L) of 60.18 and 60.08 were recorded between 2-3 and 0-1 km from River Ravi, respectively, reflecting possible mixing of river water with shallow aquifers. A very high cancer and non-cancer risk (HI > 1.0 × 10^-4) through groundwater As exposure was predicted for both children and adults. The current study concluded that prevalence of As above WHO prescribed limits in shallow aquifer along the urban stretch of the River Ravi is posing serious health risk to the exposed population.