Distribution, geochemical behavior, and risk assessment of arsenic in different floodplain aquifers of middle Gangetic basin, India

Geostatistical assessment of groundwater arsenic contamination in the Padana Plain

Arsenic (As) in groundwater from natural and anthropogenic sources is one of the most common pollutants worldwide affecting people and ecosystems. A large dataset from >3600 wells is employed to spatially simulate the depth-averaged As concentration in phreatic and confined aquifers of the Padana Plain (Northern Italy). Results of in-depth geostatistical analysis via PCA and simulations within a Monte Carlo framework allow the understanding of the variability of As concentrations within the aquifers. The most probable As contaminated zones are located along the piedmont areas in the confined aquifers and in the lowland territories in the phreatic aquifers. The distribution of the As contaminated zones has been coupled with hydrogeological, geological, and geochemical information to unravel the sources and mechanisms of As release in groundwater. The reductive dissolution of Fe oxyhydroxides and organic matter mineralization under anoxic conditions resulted to be the major drivers of As release in groundwater. This phenomenon is less evident in phreatic aquifers, due to mixed oxic and reducing conditions. This large-scale study provides a probabilistic perspective on As contamination, e.g. quantifying the spatial probability of exceeding national regulatory limits, and to outline As major sources and drivers.

Metabolomic profiling reveals key factors and associated pathways regulating the differential behavior of rice (Oryza sativa L.) genotypes exposed to geogenic arsenic

Arsenic (As) toxicity is an escalating problem; however, information about the metabolic events controlling the varied pattern of As accumulation in rice genotypes within their natural environment is still lacking. The present study is thus an advancement in unravelling the response of such rice genotypes. Soil-water-rice samples were analyzed for As accumulation using ICP-MS. Furthermore, we implemented metabolomics through LC-MS/MS and UHPLC to identify metabolic signatures regulating As content by observing the metalloid's composition in rice agrosystem. Results showed that rice genotypes differed significantly in their levels of metabolites, with Mini mansoori and Pioneer having the highest levels. Mini mansoori contained least As which might have been regulated by Ala, Ser, Glu, Phe, Asn, His, Ile, Lys, Gln, Trp, Tyr, chlorogenic, p-coumaric, trans-ferulic, rutin, morin, naringenin, kampferol, and myricetin, while Asp, Arg, Met, syringic, epigalocatechin, and apigenin contributed to the greater As acclimatization ability of Pioneer. Multivariate tools separated the rice genotypes into two major clusters: Pioneer-Mini mansoori and Damini-Sampoorna-Chintu. KEGG identified three major metabolic pathways (aminoacyl-tRNA, phenylpropanoid, and secondary metabolites biosynthesis route) linked with As tolerance and adaptation mechanisms in rice. Overall, these two genotypes symbolize their As hostile and accommodating attitudes probably due to the accumulated metabolites and the physicochemical attributes of the soil-water. Thus, thorough understanding of the metabolic reactions to As may facilitate the emergence of As tolerant/resilient genotypes. This will aid in the selection of molecular markers to cultivate healthier rice genotypes in As-contaminated areas.

Occurrence, speciation, and controls on arsenic mobilization in the alluvial aquifer system of the Ghaghara basin, India

High concentrations of arsenic (As) in groundwater are among the long-standing environmental problems on the planet. Due to adverse impacts on the human and aquatic system, characterization and quantification of individual inorganic As species are crucial in understanding the occurrence, environmental fate, behaviour, and toxicity in natural waters. This study presents As concentration and its speciation As(III) and As(V) data, including the interrelationship with other major and trace aqueous solutes from parts of the Ghaghara basin, India. More than half (57%) of the groundwater samples exhibited elevated As concentrations (> 10 μg/L), whereas 67.4% of samples have higher As(III) values relative to As(V), signifying a potential risk of As(III) toxicity. The elevated concentration of As was associated with higher Fe, Mn, and HCO3-, especially in samples from shallow well depth. PHREEQC modeling demonstrates the presence of mineral phases such as hematite, goethite, rhodochrosite, etc. Therefore, it is inferred that the release of As from sediment particles into pore water via microbially mediated Fe/Mn oxyhydroxides, and As(V) reduction processes mainly control high As concentrations. The heavy metal pollution indices (HPI) and (HEI) values revealed heavy metal pollution in low-lying areas deposited by relatively younger sediments along the Ghaghara River. Large-scale agricultural practices, overexploitation of groundwater, and indiscriminate sewage disposal, in addition to geogenic factors, cannot be ruled out as potential contributors to As mobilization in the region. This study recommends conducting seasonal hydrogeochemical monitoring and investigating regional natural background levels of As, to precisely understand the controlling mechanistic pathways of As release.

Application of Monte Carlo simulation for carcinogenic and non-carcinogenic risks assessment through multi-exposure pathways of heavy metals of river water and sediment, India

Heavy metal contamination has severe detrimental impacts on the entire river ecosystem's quality and causes potential risks to human health. An integrated approach comprising deterministic and probabilistic (Monte Carlo simulation) models with sensitivity analysis was adopted to determine heavy metals' chronic daily intake (CDI) and their associated health risks from the riverine ecosystem. Both carcinogenic and non-carcinogenic risks of water and sediment were estimated through multi-exposure pathways. The analytical results indicated that the concentration patterns of heavy metals in sediment (Fe > Mn > Sr > Zn > Cr > Cu > Cd) were slightly different and higher than in water (Fe > Zn > Cr > Sr > Mn > Cu > Cd). The potential carcinogenic risks of Cr and Cd in sediment (5.06E-02, 5.98E-04) were significantly (p < 0.05) higher than in water (9.08E-04, 8.97E-05). Moreover, 95th percentile values of total cancer risk (TCR) for sediment (1.80E-02, 3.37E-02) were about 22 and 143 times higher than those of water (8.10E-04, 2.36E-04) for adults and children, respectively. The analysis of non-carcinogenic risk revealed a significantly higher overall hazard index (OHI) for both sediment (adults: 1.26E+02, children: 1.11E+03) and water (adults: 3.26E+00, children: 9.85E+00) than the USEPA guidelines (OHI ≤ 1). The sensitivity analysis identified that the concentration of heavy metals was the most influencing input factor in health risk assessment. Based on the reasonable maximum exposure estimate (RME), the study will be advantageous for researchers, scientists, policymakers, and regulatory authorities to predict and manage human health risks.