Groundwater Salinity Across India: Predicting Occurrences and Controls by Field-Observations and Machine Learning Modeling.

Elevated groundwater salinity is unsuitable for drinking and harmful to crop production. Thus, it is crucial to determine groundwater salinity distribution, especially where drinking and agricultural water requirements are largely supported by groundwater. This study used field observation (n = 20,994)-based machine learning models to determine the probabilistic distribution of elevated groundwater salinity (electrical conductivity as a proxy, >2000 µS/cm) at 1 km2 across parts of India for near groundwater-table conditions. The final predictions were made by using the best-performing random forest model. The validation performance also demonstrated the robustness of the model (with 77% accuracy). About 29% of the study area (including 25% of entire cropland areas) was estimated to have elevated salinity, dominantly in northwestern and peninsular India. Also, parts of the northwestern and southeastern coasts, adjoining the Arabian Sea and the Bay of Bengal, were assessed with elevated salinity. The climate was delineated as the dominant factor influencing groundwater salinity occurrence, followed by distance from the coast, geology (lithology), and depth of groundwater. Consequently, ∼330 million people, including ∼109 million coastal populations, were estimated to be potentially exposed to elevated groundwater salinity through groundwater-sourced drinking water, thus substantially limiting clean water access.

Chronic Exposure to Drinking Water Arsenic and Gallbladder Cancer Risk: Preliminary Evidence from Endemic Regions of India.

BACKGROUND: Evidence linking arsenic in drinking water to digestive tract cancers is limited. We evaluated the association between arsenic levels in groundwater and gallbladder cancer risk in a case-control study (2019-2021) of long-term residents (≥10years) in two arsenic-impacted and high gallbladder cancer risk states of India-Assam and Bihar. METHODS: We recruited men and women aged 30 to 69 years from hospitals (73.4% women), with newly diagnosed, biopsy-confirmed gallbladder cancer (N = 214) and unrelated controls frequency-matched for 5-year age, sex, and state (N = 166). Long-term residential history, lifestyle factors, family history, socio-demographics, and physical measurements were collected. Average-weighted arsenic concentration (AwAC) was extrapolated from district-level groundwater monitoring data (2017-2018) and residential history. We evaluated gallbladder cancer risk for tertiles of AwAC (µg/L) in multivariable logistic regression models adjusted for important confounders [Range: 0-448.39; median (interquartile range), T1-0.45 (0.0-1.19); T2-3.75 (2.83-7.38); T3-17.6 (12.34-20.54)]. RESULTS: We observed a dose-response increase in gallbladder cancer risk based on AwAC tertiles [OR = 2.00 (95% confidence interval, 1.05-3.79) and 2.43 (1.30-4.54); Ptrend = 0.007]. Participants in the highest AwAC tertile consumed more tubewell water (67.7% vs. 27.9%) and reported more sediments (37.9% vs. 18.7%) with unsatisfactory color, odor, and taste (49.2% vs. 25.0%) than those in the lowest tertile. CONCLUSIONS: These findings suggest chronic arsenic exposure in drinking water at low-moderate levels may be a potential risk factor for gallbladder cancer. IMPACT: Risk factors for gallbladder cancer, a lethal digestive tract cancer, are not fully understood. Data from arsenic-endemic regions of India, with a high incidence of gallbladder cancer, may offer unique insights. Tackling 'arsenic pollution' may help reduce the burden of several health outcomes.

Prediction of elevated groundwater fluoride across India using multi-model approach: insights on the influence of geologic and environmental factors.

Elevated fluoride in groundwater is a severe problem in India due to its extensive occurrence and detrimental health impacts on the large population that thrives on groundwater. Although fluoride is primarily a geogenic pollutant, existing model-based studies lack the amalgamation of the influence of geologic factors, specifically tectonics, for identifying groundwater fluoride distribution. This drawback encourages the present study to investigate the association of the tectonic framework with fluoride in a multi-model approach. We have applied three machine learning models (random forest, boosted regression tree, and logistic regression) to predict elevated groundwater fluoride based on fluoride measurements across India. The random forest model outperformed other models with an accuracy of 93%. Tectonics was found to be one of the most important predictors alongside "depth to water table." Two major areas of high risk identified were the northwest parts and the south-southeast cratonic peninsular region. The random forest model also performed significantly well over the validation dataset. We estimate that nearly 257 million people are exposed to elevated fluoride risk in India. We endeavor that the findings of our study would be an effective tool for identifying the areas at risk of elevated fluoride and also assist in undertaking effective groundwater management strategies.

Predicting Potential Climate Change Impacts on Groundwater Nitrate Pollution and Risk in an Intensely Cultivated Area of South Asia.

One of the potential impacts of climate change is enhanced groundwater contamination by geogenic and anthropogenic contaminants. Such impacts should be most evident in areas with high land-use change footprint. Here, we provide a novel documentation of the impact on groundwater nitrate (GWNO3 ) pollution with and without climate change in one of the most intensely groundwater-irrigated areas of South Asia (northwest India) as a consequence of changes in land use and agricultural practices at present and predicted future times. We assessed the probabilistic risk of GWNO3 pollution considering climate changes under two representative concentration pathways (RCPs), i.e., RCP 4.5 and 8.5 for 2030 and 2040, using a machine learning (Random Forest) framework. We also evaluated variations in GWNO3 distribution against a no climate change (NCC) scenario considering 2020 status quo climate conditions. The climate change projections showed that the annual temperatures would rise under both RCPs. The precipitation is predicted to rise by 5% under RCP 8.5 by 2040, while it would decline under RCP 4.5. The predicted scenarios indicate that the areas at high risk of GWNO3 pollution will increase to 49 and 50% in 2030 and 66 and 65% in 2040 under RCP 4.5 and 8.5, respectively. These predictions are higher compared to the NCC condition (43% in 2030 and 60% in 2040). However, the areas at high risk can decrease significantly by 2040 with restricted fertilizer usage, especially under the RCP 8.5 scenario. The risk maps identified the central, south, and southeastern parts of the study area to be at persistent high risk of GWNO3 pollution. The outcomes show that the climate factors may impose a significant influence on the GWNO3 pollution, and if fertilizer inputs and land uses are not managed properly, future climate change scenarios can critically impact the groundwater quality in highly agrarian areas.

Vulnerability of groundwater from elevated nitrate pollution across India: Insights from spatio-temporal patterns using large-scale monitoring data.

Agriculture-sourced, non-point groundwater contamination (e.g., nitrate) is a serious concern from the drinking water crisis aspect across the agrarian world. India is one of the largest consumers of nitrogen fertilizers in South-Asia as well as in the world but groundwater nitrate lacks critical attention as a wide-scale drinking water pollutant in the country. Our study provides the first documentation of the distribution of groundwater nitrate and the extent of elevated nitrate contamination across India, along with the delineation of the temporal trends and the natural and anthropogenic factors that influence such occurrence of groundwater nitrate. High resolution, annual-scale spatio-temporal variability of groundwater nitrate concentration and consequent contamination was delineated using groundwater nitrate measurements from ~3 million drinking water wells spread across 7038 administrative blocks between 2010 and 2017 in India. An average 8% of the studied blocks were found affected by elevated groundwater nitrate (> 45 mg/L). Depth-dependent trend demonstrated that nitrate concentrations were about 14% higher in shallow water wells (≤ 35 m) than deep wells (>35 m). The overall temporal trend of groundwater nitrate concentration was decreasing slightly nationwide in the study period. The correlation tests and causality test results indicated that the spatial distribution of groundwater nitrate was significantly associated with agricultural N-fertilizer usage, whereas the decreasing temporal trend corresponded with the overall reduced N-fertilizer usage during the study period. Spatial autocorrelation analysis identified the clustering of high nitrate areas in central, north, and southern India, specifically in areas with higher fertilizer usage. We estimate about 71 million Indians possibly exposed to elevated groundwater nitrate concentrations and the majority of them reside in rural areas. Thus, this study provides the previously unrecognized, wide-scale, anthropogenic, diffused groundwater nitrate contamination across India.

Impact of Covid-19 Lockdown on Availability of Drinking Water in the Arsenic-Affected Ganges River Basin.

The 2020 COVID-19 pandemic has not only resulted in immense loss of human life, but it also rampaged across the global economy and socio-cultural structure. Worldwide, countries imposed stringent mass quarantine and lockdowns to curb the transmission of the pathogen. While the efficacy of such lockdown is debatable, several reports suggest that the reduced human activities provided an inadvertent benefit by briefly improving air and water quality. India observed a 68-days long, nation-wide, stringent lockdown between 24 March and 31 May 2020. Here, we delineate the impact of the lockdown on groundwater and river sourced drinking water sustainability in the arsenic polluted Ganges river basin of India, which is regarded as one of the largest and most polluted river basins in the world. Using groundwater arsenic measurements from drinking water wells and water quality data from river monitoring stations, we have studied ~700 km stretches of the middle and lower reaches of the As (arsenic)-polluted parts of the river for pre-lockdown (January-March 2020), syn-lockdown (April-May), and post-lockdown periods (June-July). We provide the extent of As pollution-free groundwater vis-à-vis river water and examine alleviation from lockdown as an opportunity for sustainable drinking water sources. The overall decrease of biochemical oxygen demand (BOD) and chemical oxygen demand (COD) concentrations and increase of pH suggests a general improvement in Ganges water quality during the lockdown in contrast to pre-and-post lockdown periods, potentially caused by reduced effluent. We also demonstrate that land use (agricultural/industrial) and land cover (urban-periurban/rural) in the vicinity of the river reaches seems to have a strong influence on river pollutants. The observations provide a cautious optimistic scenario for potentially developing sustainable drinking water sources in the arsenic-affected Ganges river basin in the future by using these observations as the basis of proper scientifically prudent, spatially adaptive strategies, and technological interventions.

Occurrence, predictors and hazards of elevated groundwater arsenic across India through field observations and regional-scale AI-based modeling.

Existence of wide spread elevated concentrations of groundwater arsenic (As) across South Asia, including India, has endangered a huge groundwater-based drinking water dependent population. Here, using high-spatial resolution As field-observations (~3 million groundwater sources) across India, we have delineated the regional-scale occurrence of elevated groundwater As (≥10 µg/L), along with the possible geologic-geomorphologic-hydrologic and human-sourced predictors that influence the spatial distribution of the contaminant. Using statistical and machine learning method, we also modeled the groundwater As concentrations probability at 1 Km resolution, along with probabilistic delineation of high As-hazard zones across India. The observed occurrence of groundwater As was found to be most strongly influenced by geology-tectonics, groundwater-fed irrigated area (%) and elevation. Pervasive As contamination is observed in major parts of the Himalayan mega-river Indus-Ganges-Brahmaputra basins, however it also occurs in several more-localized pockets, mostly related to ancient tectonic zones, igneous provinces, aquifers in modern delta and chalcophile mineralized regions. The model results suggest As-hazard potential in yet-undetected areas. Our model performed well in predicting groundwater arsenic, with accuracy: 82% and 84%; area under the curve (AUC): 0.89 and 0.88 for test data and validation datasets. An estimated ~90 million people across India are found to be exposed to high groundwater As from field-observed data, with the five states with highest hazard are West Bengal (28 million), Bihar (21 million), Uttar Pradesh (15 million), Assam (8.6 million) and Punjab (6 million). However it can be much more if the modeled hazard is considered (>250 million). Thus, our study provides a detailed, quantitative assessment of high groundwater As across India, with delineation of possible intrinsic influences and exogenous forcings. The predictive model is helpful in predicting As-hazard zones in the areas with limited measurements.

Modeling regional-scale groundwater arsenic hazard in the transboundary Ganges River Delta, India and Bangladesh: Infusing physically-based model with machine learning.

For the last few decades, toxic levels of arsenic (As) in groundwater from the aquifers of the Ganges River delta, India and Bangladesh, have been known to cause serious public health concerns. Innumerable studies have advocated the control of geomorphologic, geologic, hydrogeologic, biogeochemical, and anthropogenic factors on arsenic mobilization, flow, and distribution patterns within the Ganges River delta. We have developed transboundary regional-scale models for computing the probability of groundwater As concentrations to exceed the WHO permissible thresholds for drinking water of 10 µg/L within the Ganges River delta as a function of the various geomorphologic-(hydro)geologic-hydrostratigraphic-anthropogenic controlling factors, using statistical methods and artificial intelligence (AI) [i.e., machine learning] techniques namely, Random Forest (RF), Boosted Regression Trees (BRT) and Logistic Regression (LR) algorithms, followed by probabilistic delineation the high As-hazard zones within the delta. A "hybrid multi-modeling approach" was adapted for this study, which involved the introduction of hydrostratigraphic parameters (aquifer connectivity and surficial aquitard thickness) derived from a high-resolution transboundary hydrostratigraphic model developed for the Ganges River delta aquifer system, as predictors for modeling groundwater As probabilities within the delta. The RF model outperforms the BRT and LR model in terms of model performance. Model outputs suggest the dominant influence of surficial aquitard thickness and groundwater-fed irrigated area (%) on groundwater As. While, the north-central and southern regions of the Ganges River delta show low As-hazard (<10 µg/L), the western and north-eastern regions demonstrate elevated hazard level (>10 µg/L). An estimated 30.3 million people are found to be exposed to elevated groundwater As within the study area. Thus, our study demonstrates that such hybrid, predictive models are not only helpful in delineating the regional-scale distribution of groundwater As-hazard zones in the areas with limited As data but is also useful in identifying the possible exogenous forcing that may have led to the worst, natural pollution in human history.

Achieving Sustainable Development Goal for Clean Water in India: Influence of Natural and Anthropogenic Factors on Groundwater Microbial Pollution.

Worldwide, >2 billion people (~1/3 world population), mostly living in economically stressed areas of Africa and South Asia, still do not have access to basic sanitation, and ~1 billion still practice open defecation. Water pollution due to open defecation may primarily be linked to economy, and other factors such as social and hygiene practices, land use and hydrogeological parameters could also have sufficient influence. The present study describes the effect of human development index (HDI, 2001-2015) and economic development (NL, 1992-2013) on groundwater microbial pollution (FC, 2002-2017) across India. Economic development pattern suggested discernable inverse relationship with FC in most areas, although areas with inferior water quality, improper human practices were found to outweigh economic development. Vulnerability modelling, using these data, along with measured FC in groundwater-sourced drinking water locations (n = 235) demonstrated the heterogeneity of FC distribution potential in areas of homogenous economy, social practices, and land use. High-resolution numerical modelling of the advective transport of the hypothetical FC particles in the aquifers, suggest up to ~24 times faster movement of pollutants under irrigation-induced pumping regimes. Hence, the results of our study highlight and quantify the potential pitfalls that are possible hindrance for achieving the United Nations sustainable development goal, despite social and economic development, across the spatial scales.

Distribution and mobilization of heavy metals at an acid mine drainage affected region in South China, a post-remediation study.

Dabaoshan Mine Site (DMS) is the largest polymetallic mine in South China. The Hengshi River flowing next to DMS receives acid mine wastes leaching from the tailings pond and run-off from a treatment plant, which flows into the Wengjiang River. This study focuses on spatiotemporal distribution and mobilization of As, Cd, Pb, and Zn along the Hengshi River, groundwater, fluvial sediments, and soils, with a focus on As due to its high toxicity and the fact that mining is one of the main sources of contamination. Geochemical analyses (heavy metals, grain-size, X-ray diffraction, organic carbon and sulfur content) followed by geochemical modeling (PHREEQC) and statistical assessment were done to determine the physicochemical characteristics, toxicity risks, and behavior of heavy metals. Near the tailings pond, heavy metal concentrations in surface water were 2-100 times higher than the Chinese surface water standard for agriculture. Although water quality during the dry season has improved since the wastewater treatment plant started, heavy metal concentrations were high during rainy season. In groundwater, heavy metal concentrations were low and pose little risks. Soils along the Hengshi River were disturbed and they did not show any specific trends. The potential ecological risk of heavy metals was ranked as Cd > As > Cu > Pb > Zn in sediments and Cd > Cu > Pb > As > Zn in soils indicating multi-metal contamination and toxicity. As(III) was the predominant species in surface water during the dry season, whereas As(V) dominated during the rainy season. Arsenic levels in most sites exceeded the Chinese soil standard. Although As is assumed to have a moderate ecological risk in sediments and low risk in soils, anthropogenic activities, such as mining and land-use changes contribute to the release of As and other heavy metals and pose a risk for local residents.

Author Correction: Impact of sanitation and socio-economy on groundwater fecal pollution and human health towards achieving sustainable development goals across India from ground-observations and satellite-derived nightlight.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Nonmagnetic single-molecule spin-filter based on quantum interference.

Key spin transport phenomena, including magnetoresistance and spin transfer torque, cannot be activated without spin-polarized currents, in which one electron spin is dominant. At the nanoscale, the relevant length-scale for modern spintronics, spin current generation is rather limited due to unwanted contributions from poorly spin-polarized frontier states in ferromagnetic electrodes, or too short length-scales for efficient spin splitting by spin-orbit interaction and magnetic fields. Here, we show that spin-polarized currents can be generated in silver-vanadocene-silver single molecule junctions without magnetic components or magnetic fields. In some cases, the measured spin currents approach the limit of ideal ballistic spin transport. Comparison between conductance and shot-noise measurements to detailed calculations reveals a mechanism based on spin-dependent quantum interference that yields very efficient spin filtering. Our findings pave the way for nanoscale spintronics based on quantum interference, with the advantages of low sensitivity to decoherence effects and the freedom to use non-magnetic materials.

Impact of sanitation and socio-economy on groundwater fecal pollution and human health towards achieving sustainable development goals across India from ground-observations and satellite-derived nightlight.

Globally, ~1 billion people, mostly residing in Africa and South Asia (e.g. India), still lack access to clean drinking water and sanitation. Resulting, unsafe disposal of fecal waste from open-defecation to nearby drinking water sources severely endanger public health. Until recently, India had a huge open-defecating population, leading declining public health from water-borne diseases like diarrhoea by ingesting polluted water, mostly sourced to groundwater. However, in recent past, sanitation development to achieve Sustainable Development Goals (SDGs) has been encouraged throughout India, but their effect to groundwater quality and human health conditions are yet-unquantified. Here, for the first time, using long term, high-spatial resolution measurements (>1.7 million) across India and analyses, we quantified that over the years, groundwater fecal coliform concentration (2002-2017, -2.56 ± 0.06%/year) and acute diarrheal cases (1990-2016, -3.05 ± 0.01%/year) have significantly reduced, potentially influenced by sanitation development (1990-2017, 2.63 ± 0.01%/year). Enhanced alleviation of groundwater quality and human health have been observed since 2014, with initiation of acceletated constructions of sanitation infrastructures through Clean India (Swachh Bharat) Mission. However, the goal of completely faecal-pollution free, clean drinking water is yet to be achieved. We also evaluated the suitability of using satellite-derived night-time light (NLan, 1992-2013, 4.26 ± 0.05%/year) as potential predictor for such economic development. We observed that in more than 80% of the study region, night-time light demonstrated to be a strong predictor for observed changes in groundwater quality, sanitation development and water-borne disease cases. While sanitation and economic development can improve public health, poor education level and improper human practices can strongly influence on water-borne diseases loads and thus health in parts of India.

Systematic modification of the indium tin oxide work function via side-chain modulation of an amino-acid functionalization layer.

Controlled modification of the semiconductor surface work function is of fundamental importance for improvements in the efficiency of (opto-)electronic devices. Binding amino acids to a semiconductor surface through their common carboxylic group offers a versatile tool for modulation of surface properties by the choice of their side chain. This approach is demonstrated here by tailoring the surface work function of indium tin oxide, one of the most abundant transparent electrodes in organic optoelectronic devices. We find that the work function can be systematically tuned by the side chain of the amino acid, resulting in either an increase or a decrease of the work function, over a large range of ∼250 meV. This side chain effect is mostly due to alteration of the dipole component perpendicular to the surface, with a generally smaller contribution for changes in surface band bending. These findings also shed light on electronic interactions at the interface between proteins and semiconductors, which are of importance for future bio-electronic devices.

Separation of enantiomers by their enantiospecific interaction with achiral magnetic substrates.

It is commonly assumed that recognition and discrimination of chirality, both in nature and in artificial systems, depend solely on spatial effects. However, recent studies have suggested that charge redistribution in chiral molecules manifests an enantiospecific preference in electron spin orientation. We therefore reasoned that the induced spin polarization may affect enantiorecognition through exchange interactions. Here we show experimentally that the interaction of chiral molecules with a perpendicularly magnetized substrate is enantiospecific. Thus, one enantiomer adsorbs preferentially when the magnetic dipole is pointing up, whereas the other adsorbs faster for the opposite alignment of the magnetization. The interaction is not controlled by the magnetic field per se, but rather by the electron spin orientations, and opens prospects for a distinct approach to enantiomeric separations.

Electronic structure of dipeptides in the gas-phase and as an adsorbed monolayer.

Peptide-based molecular electronic devices are promising due to the large diversity and unique electronic properties of biomolecules. These electronic properties can change considerably with peptide structure, allowing diverse design possibilities. In this work, we explore the effect of the side-chain of the peptide on its electronic properties, by using both experimental and computational tools to detect the electronic energy levels of two model peptides. The peptides include 2Ala and 2Trp as well as their 3-mercaptopropionic acid linker which is used to form monolayers on an Au surface. Specifically, we compare experimental ultraviolet photoemission spectroscopy measurements with density functional theory based computational results. By analyzing differences in frontier energy levels and molecular orbitals between peptides in gas-phase and in a monolayer on gold, we find that the electronic properties of the peptide side-chain are maintained during binding of the peptide to the gold substrate. This indicates that the energy barrier for the peptide electron transport can be tuned by the amino acid compositions, which suggests a route for structural design of peptide-based electronic devices.

Enhanced Magnetoresistance in Molecular Junctions by Geometrical Optimization of Spin-Selective Orbital Hybridization.

Molecular junctions based on ferromagnetic electrodes allow the study of electronic spin transport near the limit of spintronics miniaturization. However, these junctions reveal moderate magnetoresistance that is sensitive to the orbital structure at their ferromagnet-molecule interfaces. The key structural parameters that should be controlled in order to gain high magnetoresistance have not been established, despite their importance for efficient manipulation of spin transport at the nanoscale. Here, we show that single-molecule junctions based on nickel electrodes and benzene molecules can yield a significant anisotropic magnetoresistance of up to ∼200% near the conductance quantum G0. The measured magnetoresistance is mechanically tuned by changing the distance between the electrodes, revealing a nonmonotonic response to junction elongation. These findings are ascribed with the aid of first-principles calculations to variations in the metal-molecule orientation that can be adjusted to obtain highly spin-selective orbital hybridization. Our results demonstrate the important role of geometrical considerations in determining the spin transport properties of metal-molecule interfaces.

Cold denaturation induces inversion of dipole and spin transfer in chiral peptide monolayers.

Chirality-induced spin selectivity is a recently-discovered effect, which results in spin selectivity for electrons transmitted through chiral peptide monolayers. Here, we use this spin selectivity to probe the organization of self-assembled α-helix peptide monolayers and examine the relation between structural and spin transfer phenomena. We show that the α-helix structure of oligopeptides based on alanine and aminoisobutyric acid is transformed to a more linear one upon cooling. This process is similar to the known cold denaturation in peptides, but here the self-assembled monolayer plays the role of the solvent. The structural change results in a flip in the direction of the electrical dipole moment of the adsorbed molecules. The dipole flip is accompanied by a concomitant change in the spin that is preferred in electron transfer through the molecules, observed via a new solid-state hybrid organic-inorganic device that is based on the Hall effect, but operates with no external magnetic field or magnetic material.

Probing the orbital origin of conductance oscillations in atomic chains.

We investigate periodical oscillations in the conductance of suspended Au and Pt atomic chains during elongation under mechanical stress. Analysis of conductance and shot noise measurements reveals that the oscillations are mainly related to variations in a specific conduction channel as the chain undergoes transitions between zigzag and linear atomic configurations. The calculated local electronic structure shows that the oscillations originate from varying degrees of hybridization between the atomic orbitals along the chain as a function of the zigzag angle. These variations are highly dependent on the directionally and symmetry of the relevant orbitals, in agreement with the order-of-magnitude difference between the Pt and Au oscillation amplitudes observed in experiment. Our results demonstrate that the sensitivity of conductance to structural variations can be controlled by designing atomic-scale conductors in view of the directional interactions between atomic orbitals.