The stakes are enormous. More than two billion people worldwide depend on groundwater as their primary drinking source, and it supplies roughly 43 percent of global agricultural water demand. In Iran, where abstraction already exceeds natural recharge, groundwater accounts for over 55 percent of total water consumption. Population growth, intensified agriculture, urbanisation, and climate change are pushing these aquifers toward declining water tables, land subsidence, saltwater intrusion, and contamination that is often irreversible. Yet traditional monitoring — accurate as it is — remains slow, expensive, and too sparse to capture the spatial patterns that determine where problems actually emerge.

The core challenge the researchers faced is fundamental: groundwater is invisible from space. Unlike lakes and rivers, whose surfaces reflect light in ways satellites can decode directly, aquifers lie buried under soil and rock. The team, led by Mozhgan Yarahmadi with Ali Haghizadeh and Leila Ghasemi, circumvented this with an indirect, surrogate-based strategy. Their reasoning was that groundwater quality is tightly coupled to what happens at the surface — land use, vegetation, soil moisture, temperature, and rainfall. If satellites can see the surface, and machine learning can learn the hidden relationships between surface signals and underground chemistry, then the orbiting sensors become proxies for what lies below.

To build those proxies, the researchers turned to Sentinel-2, the European Space Agency’s twin-satellite Copernicus mission offering thirteen spectral bands at 10 to 60 metre resolution and a five-day revisit cycle. They extracted eight bands — from blue through near-infrared — for dates matching field sampling within a ten-day window, and computed five spectral indices designed to capture chlorophyll content, turbidity, and surface moisture. These were fused with climatic variables from the ERA5-Land reanalysis dataset, including precipitation and land surface temperature. Around each of five piezometer stations, a five-kilometre buffer zone averaged the pixel values, smoothing the mismatch between point-based well measurements and satellite footprints. The result was a dataset of 56 valid samples collected seasonally from 2015 to 2024, each paired with sixteen environmental predictor variables.

With the dataset assembled, the team deployed nine competing models: five base algorithms — Random Forest, XGBoost, Support Vector Regression, Multi-Layer Perceptron, and Linear Regression — and four hybrid architectures that combined them, including a Voting Ensemble averaging four base models and a Stacking Ensemble feeding base-model outputs into a Random Forest meta-model. The standouts were striking. The Voting Ensemble achieved a coefficient of determination of 0.986 for sodium at the Sabour station and 0.974 for electrical conductivity at Gosheh, while the SVR+MLP hybrid reached 0.900 for total dissolved solids at Robat. Hybrid models claimed five of the six top-performing station-model-parameter combinations, confirming that blending complementary algorithms consistently outperforms any single learner.

The pattern behind those numbers is physically meaningful. Parameters the researchers classify as optically active — sodium, electrical conductivity, total dissolved solids, and chloride — achieved the highest accuracies, because dissolved salts alter the refractive index and light absorption of water in ways Sentinel-2’s spectrometer can detect. Semi-optical parameters such as calcium, magnesium, and total hardness landed in a moderate range, while non-optical parameters like pH, sodium percentage, and sulfate proved hardest to predict, lacking any direct spectral signature. Cross-validation told an honest story: while point-specific performance soared above 0.97, stable regional performance settled between 0.52 and 0.55 for the main parameters — respectable given the modest sample size and the inherent spatial variability of aquifer chemistry.

The study’s most novel contribution lies in how it weighted the importance of each quality parameter. Rather than relying on a single importance metric, the team combined SHAP — SHapley Additive exPlanations, a technique rooted in cooperative game theory that fairly distributes each prediction among contributing features — with classical Shapley values derived from linear regression. The final weight for each parameter was the normalised average of the two. The verdict was unambiguous: electrical conductivity dominated with 50.47 percent of the total weight, followed by total dissolved solids at 32.40 percent. Total hardness and sodium percentage ranked third and fourth at 7.87 and 7.56 percent, while the remaining six parameters each fell below half a percent. In other words, just two indicators — both optically active and strongly correlated with satellite reflectance — carry more than 82 percent of the explanatory power.

The researchers then stress-tested their rankings. Seasonal analysis revealed that while conductivity and dissolved solids held steady across spring, summer, and autumn, total hardness surged in summer — climbing from 17.12 percent in spring to 21.15 percent — likely driven by higher temperatures intensifying carbonate dissolution in the alluvial aquifer. Sodium percentage also crept upward in summer, a plausible fingerprint of agricultural fertiliser use during the growing season. Bootstrap resampling with 1,000 iterations quantified the uncertainty: conductivity and dissolved solids showed the lowest coefficients of variation, around 9.7 to 9.9 percent, confirming their weights are stable and reliable, while chloride and pH carried the highest uncertainty, consistent with their weak spectral linkage.

The authors are candid about limitations. Fifty-six samples is a small dataset, and reanalysis-based climatic data may diverge from ground stations; atmospheric correction remains tricky under cloudy skies, and models trained on Khorramabad’s geology cannot simply be transplanted elsewhere without local retraining. Yet the practical implications are compelling. Because Sentinel-2 imagery and ERA5-Land data are freely available and continuously refreshed, the trained models can be extrapolated to unmonitored areas, generating spatial maps of conductivity, dissolved solids, sodium, and chloride where no wells exist. For water managers in semi-arid regions, the framework offers an interpretable, low-cost decision-support tool: prioritise conductivity and dissolved solids as headline indicators, intensify hardness monitoring during summer, and treat satellite-based assessment as a powerful complement — not a replacement — for laboratory sampling. As aquifers worldwide face mounting pressure, watching them from orbit may soon become standard practice.

Subject of Research: Prediction and weighting of groundwater quality parameters using Sentinel-2 remote sensing, hybrid machine learning models, and SHAP-game theory analysis in Khorramabad, Iran

Article Title: Groundwater Quality Parameter Weighting Using Hybrid Machine Learning and SHAP-Game Theory Based on Sentinel-2 Data (Case Study: Khorramabad, Iran)

Article References: Yarahmadi, M., Haghizadeh, A., & Ghasemi, L. (2026). Groundwater Quality Parameter Weighting Using Hybrid Machine Learning and SHAP-Game Theory Based on Sentinel-2 Data (Case Study: Khorramabad, Iran). Environmental Advances, Article 100770. https://doi.org/10.1016/j.envadv.2026.100770

Image Credits: AI Generated

DOI: 10.1016/j.envadv.2026.100770

Keywords: groundwater quality, Sentinel-2, machine learning, SHAP, game theory, remote sensing, hybrid models, Iran, water monitoring, electrical conductivity, total dissolved solids, semi-arid regions

The scale of the land transformation emerges clearly from the satellite record. Between 2005 and 2024, agricultural land in and around Dehradun declined by 34 percent, forest cover fell by 14 percent, and grassland buffers diminished by 39 percent. Each of these losses matters for reasons that go beyond scenery. Farmland conversion removes the peri-urban soils that once absorbed rainfall and recharged aquifers; forest loss erodes slope stability and biodiversity habitat in a seismically and hydrologically sensitive Himalayan setting; and disappearing grasslands strip away the transitional zones that filter runoff and moderate local climate. The study attributes much of this conversion to sprawling, low-density development rather than compact growth, a pattern long associated with inefficient infrastructure provision and greater per-capita environmental burdens. In Dehradun’s case, the sprawl proceeded with little of the regulatory coordination that planners might have hoped the city’s master plan and smart city proposals would impose.

The most direct human consequence documented by the team is the deterioration of groundwater, the resource on which much of the city depends. Total dissolved solids in the city’s groundwater increased by more than 70 percent over the study period. Water hardness rose from 142.5 milligrams per liter to 197.4 milligrams per liter, climbing toward and past thresholds that consumers and regulators find concerning. Nitrate concentrations were found to frequently exceed permissible limits, a classic signature of sewage infiltration and fertilizer leaching, and the researchers recorded widespread microbial contamination across monitored wells. For a city perched above porous Himalayan foothill aquifers, these numbers describe more than a technical problem. They indicate that the same ground on which houses and roads now stand is transmitting waste-derived contaminants directly into the drinking water supply, with potential consequences for public health that the indicators only begin to capture.

Air quality tells a parallel story of growth outpacing environmental capacity. Across Dehradun’s major urban corridors, PM10 concentrations increased by 45 to 60 percent and PM2.5 concentrations by 40 to 80 percent over the study period. Particulate matter at these sizes penetrates deep into the lungs and enters the bloodstream, and epidemiological research has linked long-term exposure to cardiovascular and respiratory disease. In a valley city where topography can trap pollutants during temperature inversions, rising particulate loads compound an already vulnerable atmospheric setting. The study’s statistical modeling makes the connection explicit: built-up expansion correlated strongly with PM2.5 levels, with a correlation coefficient of 0.84, among the strongest associations reported in the analysis. In other words, the spatial pattern of new construction does not merely coincide with dirtier air; the two move together closely enough that land-use policy is, in effect, air-quality policy.

Water and air are not the only media under stress. Noise monitoring revealed that levels exceeded regulatory standards at more than 65 percent of the locations surveyed, a burden concentrated along the transport corridors where traffic, construction, and commercial activity converge. Chronic noise exposure is associated with sleep disturbance, elevated stress hormones, and cardiovascular risk, yet it is among the least regulated dimensions of urban environmental quality in fast-growing Indian cities. Meanwhile, the city’s material flows have swelled beyond its capacity to process them. Municipal solid waste generation reached 280 tonnes per day, but processing efficiency stands at only 70 percent, meaning roughly a third of the daily waste stream is unaccounted for in terms of treatment, with predictable consequences for land, water, and air. Sewage treatment capacity, likewise, remains below the volume of wastewater the city actually generates, leaving untreated effluent to find its way into groundwater and surface channels.

The statistical backbone of the study gives these observations their force. By correlating built-up expansion against a suite of environmental indicators, the researchers demonstrated that degradation is not randomly distributed but systematically tied to the geography and pace of development. Groundwater TDS correlated with built-up growth at 0.82, and nitrate concentration at 0.77, alongside the 0.84 coefficient for PM2.5. In environmental research, correlation coefficients of this magnitude across independent indicators are notable, because they suggest a common driver rather than a collection of unrelated local problems. The authors frame this as evidence that unregulated spatial growth functions as an integrated stressor: the same expansion that paves over recharge zones also concentrates traffic, generates waste, and outpaces the sewerage network. Addressing any single indicator in isolation, the analysis implies, will fail so long as the underlying land-use trajectory remains unchecked.

Set against the Sustainable Development Goals, the picture darkens further. SDG 11 calls for cities that are inclusive, safe, resilient, and sustainable, with specific targets covering air quality, waste management, water and sanitation, and access to green public space. The study finds substantial deviations from these targets in Dehradun on nearly every dimension it measured: particulate pollution rising rather than falling, waste processing short of full coverage, sewage treatment lagging behind generation, water quality deteriorating, and the green and agricultural land base contracting. The researchers’ assessment of urban infrastructure performance, drawing on census data, municipal datasets, and environmental monitoring records, indicates that infrastructure augmentation has simply not kept pace with the physical growth of the city. This is a pattern familiar across Himalayan hill towns, where steep terrain, limited flat land, and ecological fragility magnify the costs of every unplanned extension of the urban edge.

The methodological approach itself carries lessons for other cities in the region. By combining freely available Landsat and Sentinel imagery with supervised classification and post-classification change detection, the team produced a two-decade change record without requiring expensive bespoke data collection. Anchoring the satellite analysis in census data, municipal records, and environmental monitoring allowed the researchers to move from mapping to causally suggestive statistics, connecting the where of urban growth to the what of environmental harm. For city governments with limited analytical budgets, this kind of indicator-based geospatial framework offers a replicable template: it can flag which corridors are degrading fastest, which aquifers are failing, and which SDG targets are most off-track, providing an evidence base for prioritizing interventions before conditions become irreversible.

The authors’ prescription is correspondingly integrated rather than piecemeal. They call for strengthened land-use regulation, ecological conservation of the remaining forest and grassland buffers, augmentation of water, sewage, and waste infrastructure, and climate-resilient urban planning to steer Dehradun back toward sustainable development. None of these measures is novel in itself; what the study adds is quantitative proof that in Dehradun they are interdependent. Every year of continued sprawl simultaneously worsens the air residents breathe, the water they drink, and the noise they endure, while eroding the natural systems that could buffer the city against floods, landslides, and a warming climate. As Himalayan foothill cities across India and neighboring countries absorb some of the world’s fastest urban growth, Dehradun’s two-decade satellite record stands as a clear-eyed account of what unmanaged expansion costs, and a data-driven argument for why the next two decades must be different.

Subject of Research: Urban sprawl and its environmental impacts in Dehradun, India, assessed through geospatial and environmental indicator analysis against SDG 11

Article Title: Assessing urban sprawl, environmental vulnerability, and SDG 11 alignment using geospatial and environmental indicator-based analysis in Dehradun, India

Article References: Assessing urban sprawl, environmental vulnerability, and SDG 11 alignment using geospatial and environmental indicator-based analysis in Dehradun, India. (n.d.). https://doi.org/10.1007/s43545-026-01827-5

Image Credits: AI Generated

DOI: 10.1007/s43545-026-01827-5

Keywords: urban sprawl, Dehradun, land use land cover change, remote sensing, groundwater quality, air pollution, noise pollution, SDG 11, sustainable cities, Himalayan foothills, environmental indicators, urban planning

The research team set out to close a persistent gap in the literature. Most earlier investigations of groundwater quality have examined drinking suitability, irrigation suitability, or hydrochemical characteristics in isolation, rather than weaving them together into a single framework. For Alibag, a coastal taluka of roughly 510 square kilometers bounded by the Arabian Sea to the west, the Amba River to the north, and the Kundalika River to the south, no comprehensive study had simultaneously evaluated hydrochemical characteristics, spatial variability, and contamination sources for both domestic and agricultural uses. The new work addresses that gap by combining hydrochemical analysis, spatial mapping, and suitability evaluation across the entire tehsil.

Methodologically, the study rests on secondary data drawn from India’s official monitoring apparatus: the Central Ground Water Board and the Groundwater Surveys and Development Agency. Records from 17 groundwater monitoring wells, collected during the pre-monsoon seasons of 2014, 2018, and 2022, formed the backbone of the analysis. Thirteen wells had data for all three years, while four additional wells at Alibag, Borli, Kihim, and Navedar Kolgaon entered the dataset only in 2022. The parameters examined spanned the classic hydrogeochemical toolkit: pH, electrical conductivity, total dissolved solids, total hardness, the major cations calcium, magnesium, sodium, and potassium, and the major anions bicarbonate, chloride, sulfate, nitrate, and fluoride.

To translate these raw measurements into a verdict on drinkability, the researchers employed the weighted arithmetic Water Quality Index, a method originally proposed by Horton in 1965 and later modified by Brown and colleagues. The approach assigns each parameter a unit weight inversely proportional to its recommended drinking water standard, meaning that constituents with lower permissible limits and higher health significance, such as fluoride and nitrate, exert greater influence on the final score. Quality ratings for each parameter are calculated relative to standards set by the Bureau of Indian Standards and the World Health Organization, then summed into a single index. Scores below 25 denote excellent water, 25 to 50 good, 50 to 75 poor, 75 to 100 very poor, and anything above 100 is deemed unsuitable for drinking.

The results were reassuring on that front. Every sample from 2014 and 2018 fell into the excellent category, and by 2022 approximately 53 percent remained excellent while the remaining 47 percent had slipped into the good class. Not a single sample crossed into the poor, very poor, or unsuitable ranges across the entire study period. That slight decline in 2022, the authors note, tracks closely with localized increases in electrical conductivity, dissolved solids, hardness, and major ions at several coastal locations, a signature consistent with creeping salinity rather than industrial or sewage contamination.

The chemical detail behind the index reveals the aquifer’s trajectory. pH values ranged from 6.26 to 8.20, with averages between 7.51 and 7.90, indicating water that stayed neutral to slightly alkaline throughout. Electrical conductivity, a proxy for dissolved ionic load, climbed from a 2014 range of 112 to 1516 microsiemens per centimeter to 206 to 1878 by 2022, with the highest values concentrated in northern and coastal zones. Total dissolved solids followed the same path, rising from 71 to 955 milligrams per liter to 132 to 1202, with elevated readings around Poynad, Kude, Nagaon, Vadhav BK, and Navedar Kolgaon. Total hardness, spanning 55 to 620 milligrams per liter, exceeded desirable limits at several sites including Bhilji, Poynad, Kude, Nagaon, Valvade Shahabaj, and Vadhav BK, marking water that ranges from hard to very hard.

The cation chemistry reinforces the picture of active rock-water interaction. Calcium concentrations rose from 10 to 104 milligrams per liter in 2014 to 22 to 200 in 2022, exceeding desirable limits at Nagaon, Vadhav BK, and Alibag in the final year. Magnesium peaked early, with values up to 87.5 milligrams per liter in 2014 before declining, though localized enrichment persisted in northern and western areas. Sodium climbed steadily from less than 20 milligrams per liter to as much as 160 by 2022, concentrated in coastal zones, while potassium, negligible in the early years, jumped to 33 milligrams per liter at a few northwestern sites in 2022, a pattern the authors attribute to marine influence together with agricultural and anthropogenic inputs. Among the anions, bicarbonate rose sharply from 18 to 189 milligrams per liter in 2014 to 110 to 476 in 2022, signaling enhanced carbonate weathering, while chloride reached 347 milligrams per liter at Dapoli in 2022. Sulfate, nitrate, and fluoride, by contrast, remained safely below permissible limits throughout, ruling out widespread contamination from fertilizers, wastewater, or fluoride-bearing minerals.

Two graphical techniques pinned down the processes driving this chemistry. Piper trilinear diagrams showed that in 2014 roughly 62 percent of samples belonged to the calcium-magnesium-bicarbonate facies, indicative of temporary hardness, with the rest classified as calcium-magnesium-chloride-sulfate type. By 2018 every sample had converged on the calcium-magnesium-bicarbonate type, and by 2022 about 29 percent had shifted toward a mixed facies, a drift the researchers link to localized seawater mixing and ion exchange. Gibbs diagrams, which plot ionic ratios against total dissolved solids, placed nearly all samples squarely in the rock-dominance field, confirming that mineral weathering and rock-water interaction, rather than atmospheric precipitation or evaporation, are the principal engines of groundwater evolution in Alibag.

For agriculture, the team deployed five complementary indices: the Sodium Adsorption Ratio, Residual Sodium Carbonate, Percentage Sodium, Magnesium Hazard, and Permeability Index. The news was largely favorable. SAR values ranged from just 0.034 to 2.847, far below the excellent threshold of 10, indicating negligible risk of soil sodicity. Residual Sodium Carbonate placed nearly all samples in the safe category, with only one sample each from Poynad in 2014 and Kihim in 2022 falling into the marginal range. Percentage sodium stayed below 50 for every sample, classifying the water as good for irrigation. The exceptions emerged with Magnesium Hazard, which exceeded recommended limits in about 61 percent of samples in 2014 before improving to 85 percent suitability in 2018 and 88 percent in 2022, and the Permeability Index, which placed most samples in the moderately suitable Class II, with roughly 23 percent unsuitable in 2014 improving to just a handful of restricted sites by 2022. The authors conclude that magnesium enrichment and soil permeability, not sodium, are the principal constraints on irrigation in the tehsil, and that a few areas still require treatment before agricultural use.

The broader message is one of cautious vigilance. Groundwater in Alibag is governed predominantly by natural geogenic processes, with localized seawater intrusion and human activity contributing to a gradual, measurable mineralization trend along the coast. The study’s authors acknowledge limitations, including the absence of seasonal sampling within years, a monitoring network that may not capture local-scale heterogeneity, and the exclusion of trace elements, heavy metals, pesticides, and microbiological contaminants. Yet the direction of travel is clear: salinity-related parameters are rising in coastal zones, and the researchers argue that periodic monitoring, regulation of groundwater abstraction, and sustainable management practices are essential to prevent future deterioration. For a region where 215 villages dot the landscape and roughly 25 sit directly on the sea’s edge, the aquifer’s slow chemical drift is a warning worth heeding while the water is still, by every index applied, good to drink.

Subject of Research: Groundwater quality assessment for drinking and irrigation suitability in a coastal aquifer in Alibag Tehsil, Maharashtra, India

Article Title: Assessment of groundwater quality for drinking and irrigation purpose of Alibag Tehsil, Raigad district, Maharashtra

Article References: Bar, R., & Dhorde, A. A. (2026). Assessment of groundwater quality for drinking and irrigation purpose of Alibag Tehsil, Raigad district, Maharashtra. Discover Geoscience, 4(1), Article 299. https://doi.org/10.1007/s44288-026-00671-7

Image Credits: AI Generated

DOI: 10.1007/s44288-026-00671-7

Keywords: groundwater quality, hydrogeochemistry, water quality index, coastal aquifer, seawater intrusion, irrigation suitability, drinking water, Maharashtra, Alibag, SAR, Piper diagram, Gibbs diagram

The Karamana River rises at Chemmunji Mottai, a 1,717 meter peak in the Western Ghats, and flows through the coastal plains of Thiruvananthapuram district before emptying into the Arabian Sea. Classified among the world’s small mountainous rivers, it drains a landscape dominated by agriculture, with plantation crops such as coconut, rubber, plantain, and tapioca covering roughly 64 percent of the basin, particularly in its middle and lower reaches. Forest survives mainly within the Peppara reservoir catchment, while the Aruvikkara watershed hosts intensive farming and expanding settlements. Because the basin supplies a substantial share of the drinking and domestic water for Thiruvananthapuram city, understanding what controls its groundwater chemistry is not an academic exercise but a matter of public health.

The geological setting matters enormously here. The basin sits on a complex crystalline basement of Archaean to Proterozoic age, dominated by the Khondalite Group, chiefly garnetiferous-biotite-sillimanite gneiss, interspersed with pyroxene granulite bands that often contain graphite, along with granites, gneisses, and intrusions of gabbro, dolerite, pegmatite, and quartz veins. Quaternary deposits of coastal sands, pebble beds, and river alluvium mantle the Mio-Pliocene sediments along the coast. Rainfall is equally variable, averaging 2,600 millimeters per year across the basin but ranging from less than 1,400 millimeters in the southwest to more than 4,200 millimeters in the northeast. Despite Kerala’s reputation for abundant rain, acute drinking water shortages strike every summer, making groundwater the critical buffer between wet and dry seasons.

The field team measured pH, total dissolved solids, and temperature on site with handheld meters, recorded GPS coordinates at every well, and carried samples to the laboratory for determination of the major dissolved ions: calcium, magnesium, sodium, potassium, bicarbonate, chloride, sulfate, and nitrate. Ionic balance errors were kept within plus or minus five percent, a standard quality check in hydrochemistry. Trace elements, including iron, chromium, nickel, cadmium, cobalt, copper, and zinc, were measured in unfiltered samples using flame and graphite furnace atomic absorption spectrophotometry. Spatial distribution maps for every parameter were then generated with inverse distance weighting interpolation in a geographic information system, allowing the researchers to visualize how water chemistry changes as one moves from the highland headwaters toward the coast.

The physicochemical results paint a picture of generally mild but spatially uneven conditions. Groundwater temperatures ranged from 26 to 30 degrees Celsius, and pH values spanned 6.3 to 8.0 with an average of 7.26, indicating slightly acidic to mildly alkaline water, a condition the authors attribute to natural buffering processes combined with anthropogenic influences and possible seawater intrusion near the coast. Total dissolved solids ranged from 73 to 373 milligrams per liter, within drinking water norms. Electrical conductivity, however, told a more troubling story: applying a standard classification of water by dissolved solids, only about 56 percent of the samples qualified as fresh, while more than 43 percent fell into the brackish category, a striking signal of spatial heterogeneity likely driven by coastal proximity, saline encroachment, and dissolved solid inputs from human activity.

Among the major ions, sodium emerged as the dominant cation, followed by calcium, potassium, and magnesium, while bicarbonate was the most abundant anion, ahead of chloride, sulfate, and nitrate. This sodium-bicarbonate dominance is a classic fingerprint of silicate weathering in crystalline terrain, where feldspar minerals slowly dissolve and release sodium while consuming carbon dioxide. To decode the facies, the team used the Piper trilinear diagram, which plots cations and anions on two triangular fields and classifies water types on a central diamond. The samples spread across several cationic groups, including sodium-rich, sodium-magnesium, and sodium-calcium-magnesium waters, and anionic groups ranging from bicarbonate to mixed bicarbonate-chloride types, confirming that no single process governs the basin’s chemistry.

Two further graphical tools sharpened the interpretation. The Gibbs plot, which relates the ratio of sodium plus potassium to calcium against total dissolved solids, placed the great majority of samples squarely in the rock-water interaction field, indicating that mineral weathering, not precipitation or evaporation, is the primary engine of ion enrichment, though a few samples drifted toward the evaporation field. The Wilcox plot, which graphs electrical conductivity against percent sodium to judge irrigation suitability, showed that most samples fall in the very good to good range, with sodium adsorption ratios between 30 and 70 percent and conductivity mostly below 1,000 microsiemens per centimeter, meaning salinity and sodicity hazards for agriculture remain limited even where drinking quality is marginal.

The trace metal results are where the study raises its most serious flags. Cadmium averaged 0.018 milligrams per liter, chromium and cobalt each averaged 0.052, copper 0.023, iron 0.040, nickel 0.037, and zinc 0.062, with zinc showing the widest spread, from 0.004 to 0.319 milligrams per liter, a variability the authors link to localized pollution sources. Several samples exceeded drinking water standards for these metals, and the researchers note that prolonged exposure to cadmium, chromium, and nickel can pose health risks. They also highlight the role of iron and manganese hydrous oxides, which form in the aquifer and act as carriers, sorbing and transporting other heavy metals such as zinc, cobalt, nickel, and copper through the groundwater system. While the conventional parameters, pH, TDS, and the major ions, generally complied with World Health Organization and Bureau of Indian Standards guidelines, the metal data demanded a more cautious verdict on drinkability.

To synthesize all of this, the team computed a weighted arithmetic Water Quality Index, assigning each parameter a weight from one to five based on its importance to drinking water quality, converting concentrations into quality ratings against WHO standards, and summing the sub-indices. The resulting map shows most of the basin in the intermediate quality range of 44 to 72, with better water, scoring 15 to 44, around several wells and a clear decline in quality toward the middle and southern parts of the basin. Critically, a handful of sites near wells designated GW4, GW13, GW17, GW18, GW19, and GW20 scored above 100, falling into the very poor to unsuitable category. The authors identify agricultural runoff, domestic sewage, and localized industrial activity as the principal contamination sources behind these hotspots.

The statistical backbone of the study came from correlation analysis, principal component analysis, and hierarchical cluster analysis, and together they disentangled natural from human influences. TDS showed strong positive correlations with all the major ions, confirming dissolved solids as the master variable of ionic composition, with moderate links to nickel and other trace elements suggesting that salinity and metal mobilization are connected. PCA extracted three components explaining 48.6, 14.8, and 11.3 percent of the variance respectively: the first, loaded heavily on the major ions, reflects natural processes of rock-water interaction, silicate weathering, and evaporation; the second, loaded on sulfate, cadmium, and fluoride, points toward anthropogenic inputs; and the third, loaded on cobalt, nickel, cadmium, and zinc, reinforces the human contamination signal. Cluster analysis of the twenty-two samples using Ward’s method produced three distinct geochemical groups that corroborated the PCA picture. The overall message is one of cautious reassurance paired with vigilance: the Karamana basin’s groundwater remains broadly usable, but its brackish fringes and metal-laden hotspots demand continuous monitoring, targeted treatment, and land-use management if this vital coastal aquifer is to keep supplying a growing city sustainably.

Subject of Research: Hydrogeochemical and multivariate statistical assessment of groundwater quality in the Karamana River Basin, Kerala, Southern India

Article Title: Multi-faceted approach for comprehensive groundwater quality assessment of Karamana river basin in Kerala, Southern India

Article References: Shanmugasundharam, A., Anto, A. M., & Krishna, R. S. A. (2026). Multi-faceted approach for comprehensive groundwater quality assessment of Karamana river basin in Kerala, Southern India. Discover Geoscience, 4(1), Article 318. https://doi.org/10.1007/s44288-026-00677-1

Image Credits: AI Generated

DOI: 10.1007/s44288-026-00677-1

Keywords: groundwater quality, hydrogeochemistry, Karamana River Basin, Kerala, heavy metals, Water Quality Index, principal component analysis, seawater intrusion, silicate weathering, trace elements, drinking water standards, coastal aquifers

The research team, led by Kwabina Ibrahim of the University of Ghana, set out to do something that groundwater studies in the region have rarely attempted: evaluate water quality from every angle at once. Rather than measuring a handful of physicochemical parameters and stopping there, the researchers combined hydrochemical characterisation, a suite of six water quality and pollution indices, microbiological indicators, ecological risk diagnostics and quantitative microbial risk assessment into a single management-oriented framework. Every operational borehole providing domestic water in the three communities was sampled during September 2022, in the late wet season, giving a census-style snapshot of the sources residents actually rely on rather than a statistical subset. Samples were analysed at the Council for Scientific and Industrial Research’s Water Research Institute in Accra, following standard methods of the American Public Health Association, with field duplicates, procedural blanks and certified reference standards used to verify accuracy.

The chemistry told a story of young, actively recharging groundwater shaped primarily by the rocks it passes through. The aquifers of the Kibi-Winneba belt belong to Ghana’s Proterozoic Birimian terrain, hard crystalline metavolcanic and metasedimentary rocks whose only usable water storage lies in weathered regolith and fracture networks. Water in these systems is typically slightly acidic and lightly mineralised, and the samples matched that profile. pH values ranged from 5.38 to 7.29 with a mean of 6.49, electrical conductivity averaged 173.5 microsiemens per centimetre, and total dissolved solids averaged just 135.4 milligrams per litre, indicating fresh water with short residence times. Bicarbonate was the dominant anion, calcium and magnesium dominated the cations, and fluoride concentrations were uniformly far below the World Health Organization guideline of 1.5 milligrams per litre.

To trace the origins of this chemistry, the team deployed the classic tools of hydrogeochemical forensics. Piper trilinear diagrams placed nearly all samples in a calcium-magnesium-sulphate-chloride facies, while Gibbs diagrams confirmed that rock weathering, not evaporation or atmospheric deposition, controls the water’s composition. Bivariate ion plots revealed evidence of both normal cation exchange, in which sodium is stripped from solution onto clay surfaces, and reverse ion exchange, in which calcium is adsorbed and sodium released, processes typical of prolonged water-rock interaction in clay-rich weathered aquifers. Saturation indices calculated with the PHREEQC geochemical model came back negative for every mineral phase examined, including calcite, dolomite, gypsum, halite and fluorite, showing that the groundwater remains undersaturated and actively dissolving minerals under open-system conditions. Correlation analysis reinforced the picture: pH tracked bicarbonate almost perfectly at a Spearman coefficient of 0.97, and iron correlated with manganese at 0.90, a signature of coupled mobilisation under reducing conditions rather than industrial pollution.

Principal component analysis distilled the dataset into three components explaining 94.3 percent of total variance. The first, accounting for 71.2 percent, captured the dominant mineralisation engine of carbonate dissolution and silicate weathering. The second, at 12.8 percent, isolated chloride and sulphate, pointing to secondary salinity enrichment from sulphide oxidation, local wastewater or agricultural return flows, with Segyemase samples showing the widest dispersion and the strongest association with these vectors, consistent with localised mining influence. The third component was dominated by potassium, likely reflecting fertiliser inputs or silicate weathering. Notably, the composite pollution indices correlated with one another at coefficients of 0.98 to 0.99, a coherence the authors flag as a caution: highly correlated indices may offer less independent information than their parallel use implies.

When the chemical data were folded into the Water Quality Index, every single borehole landed in the excellent category, below the threshold of 50 that separates excellent from good water. The General Pollution Index ranged from 0.116 to 0.543, the Comprehensive Pollution Index placed all samples in the clean class, and the Ecological Risk Index classified every sample as low ecological risk, far below the threshold of 40. The Trace Metals Pollution Index, however, told a more complicated story, with 14 of 16 samples classified as very high metal pollution, driven largely by iron and manganese whose concentrations, reaching 5.94 and 1.18 milligrams per litre respectively, reflect natural reducing conditions and dissolution of iron-manganese oxyhydroxides rather than direct anthropogenic inputs. The Water Nutrient Pollution Index identified two boreholes with severe nutrient loading, hinting at sanitation leakage or fertiliser runoff at specific spots.

Then came the microbiology, and the picture inverted. Total coliforms were found in 13 of the 16 boreholes, with counts ranging from 5 to a staggering 2,790 colony-forming units per 100 millilitres and an average of 554. Against the WHO guideline of zero coliforms per 100 millilitres, 81 percent of the boreholes failed. Faecal coliforms and E. coli, the definitive markers of recent faecal contamination, were detected in two boreholes, JSB05 and JSB06, at counts reaching 1,202 and 1,116 CFU per 100 millilitres. Salmonella and Vibrio species were not detected anywhere, offering some reassurance against immediate cholera or typhoid risk, but the presence of E. coli in drinking water is a red flag in its own right. The contamination tracked turbidity, total suspended solids and proximity to latrines and animal enclosures, consistent with shallow well construction, inadequate sealing and surface ingress rather than deep aquifer pollution.

To translate counts into consequences, the team applied a quantitative microbial risk assessment using a Beta-Poisson dose-response model, assuming a typical adult ingestion of two litres per day. For borehole JSB05, with an estimated daily E. coli dose of 22,320 CFU, the model yielded an annual probability of infection of 58 percent, a figure that rivals the risk profiles of notoriously unsafe water supplies. Even JSB06, with a far lower dose of 520 CFU per day, carried a 2 percent annual infection probability, which the authors note becomes hazardous over years of habitual consumption. A Microbial Contamination Index was then used to rank every borehole, creating a tiered priority list: the two E. coli-positive sources at the top for immediate remediation, high-coliform sources in the middle for sanitary upgrades, and microbially clean boreholes at the bottom for routine monitoring only.

The paradox at the heart of the study, chemically excellent water harbouring dangerous microbes, is not unique to these three communities. Comparable patterns have been documented across Ghana and neighbouring Burkina Faso, where microbial contamination persists even when physicochemical testing passes. But the integrated framework deployed here makes the divergence impossible to ignore and, crucially, actionable. The authors recommend structural interventions for the high-risk boreholes, including sealing of borehole annuli, construction of sanitary aprons and improved surface drainage, complemented by chlorination and point-of-use treatment. Longer term, they advocate sanitary protection zones around wells, enforcement of construction standards, hygienic household water handling and low-cost technologies such as biosand filtration and solar disinfection. Community engagement was woven into the study itself, with results shared through workshops run by a collaborating non-governmental organisation and state institutions.

The policy implications reach beyond Eastern Ghana. The authors argue that groundwater governance should adopt the World Health Organization’s Water Safety Plan approach, which embeds hazard identification and risk management across the entire supply chain rather than relying on end-point compliance testing. Regulatory frameworks, they contend, must explicitly integrate chemical, microbial and risk-based data, because monitoring programmes that emphasise physicochemical compliance alone will systematically miss the faecal pathways that actually cause waterborne disease. The study acknowledges its own limits, notably a single-season sampling campaign and the absence of age-stratified risk scenarios for children, and calls for multi-seasonal monitoring to capture how dilution, recharge and runoff shift both chemistry and contamination through the year. Still, its central message stands with unusual clarity: a borehole can pass every chemical test a laboratory can throw at it and still deliver pathogens in every glass. Protecting the invisible infrastructure of wellheads, seals and sanitary zones may matter as much as the aquifer itself, and for the residents of Juaso, Segyemase and Nsuapemso, that insight could not arrive soon enough.

Subject of Research: Integrated hydrochemical and microbiological assessment of groundwater quality in mining- and agriculture-influenced communities of Ghana

Article Title: Integrated hydrochemical and microbiological assessment of groundwater quality for early contamination detection and preventive management

Article References: Ibrahim, K., Kazapoe, R. W., Asare, R., Yankson, A. A., Addai, M. O., & Sagoe, S. D. (2026). Integrated hydrochemical and microbiological assessment of groundwater quality for early contamination detection and preventive management. Discover Geoscience, 4(1), Article 358. https://doi.org/10.1007/s44288-026-00729-6

Image Credits: AI Generated

DOI: 10.1007/s44288-026-00729-6

Keywords: groundwater quality, hydrogeochemistry, water quality index, E. coli, coliform contamination, Ghana, Birimian aquifers, microbial risk assessment, pollution indices, water safety plans, borehole sanitation, mining impact

The stakes in this landscape could hardly be higher. Lake Urmia, once one of the largest hypersaline lakes on Earth, has shrunk dramatically over recent decades, and the aquifers surrounding it supply drinking water, irrigation, and ecosystems across a semi-arid basin. As the lake desiccates, saline water and changing recharge patterns push hydrochemical conditions in nearby groundwater into flux. Water managers need to know not just where quality stands today, but where it is heading, so that agricultural planning and environmental protection can respond before wells turn brackish. The study was designed to give them exactly that foresight.

The researchers assembled records from 54 monitoring stations spanning 2008 to 2021, drawing on the physical and chemical parameters measured at each site. From these they calculated an annual WQI for every station, a single composite number that condenses multiple chemical indicators into one interpretable score of overall water quality. To visualize how quality varied across the landscape and through time, they mapped the index values using inverse distance weighting, a geostatistical interpolation method that estimates values at unsampled locations based on the weighted influence of nearby measurements. The resulting maps exposed clear spatial and temporal gradients, with the northeastern region standing out as the area of greatest concern by the end of the study period.

With the historical picture established, the team turned to prediction. Three models were trained to forecast the WQI for the year following each observation. The baseline was a standard artificial neural network, or ANN, a flexible but relatively simple learner. The second was a hybrid convolutional neural network paired with a long short-term memory network, known as CNN-LSTM. The convolutional layer excels at extracting local patterns and features from input sequences, while the LSTM component is specifically designed to retain information over long time lags, making it well suited to the slow-moving, memory-laden dynamics of groundwater systems. This combination has proven powerful in other hydrological forecasting problems, and it outperformed the plain ANN here as well.

The third model added a crucial preprocessing step that proved decisive. Successive Variational Mode Decomposition, or SVMD, is a signal decomposition technique that breaks a complicated, noisy time series into a set of distinct intrinsic mode functions, each representing a cleaner, more regular component of the original signal. Groundwater quality data are notoriously non-stationary: they drift with drought cycles, pumping regimes, and salinity intrusion, all superimposed on measurement noise. By decomposing the input before it reaches the deep learning stage, SVMD reduces that non-stationarity and strips away noise, presenting the network with simpler, more learnable patterns. The idea echoes a broader trend in hydrological machine learning, where decomposition-based hybrids have improved forecasts of everything from dissolved oxygen to lake levels.

The performance numbers tell a striking story. The CNN-LSTM model already beat the ANN, achieving a root mean square error of 46.881, a coefficient of determination of 0.921, a Nash–Sutcliffe efficiency of 0.791, and a mean absolute percentage error of 0.097. Each of these metrics captures a different facet of accuracy: RMSE penalizes large mistakes, R-squared measures how much of the variance the model explains, NSE compares predictions against the simple benchmark of using the observed mean, and MAPE expresses typical error in relative terms. Then the SVMD-CNN-LSTM pushed every metric further, delivering an RMSE of 29.654, an R-squared of 0.967, an NSE of 0.946, and a MAPE of just 0.032. Relative to the ANN, the full hybrid reduced error by 58.28 percent, and relative to CNN-LSTM alone, by 36.75 percent.

Those gains matter because hydrochemical time series in this region are short and data-limited. Fourteen annual observations per station is a modest dataset by deep learning standards, and noisy, non-stationary signals make every data point harder to exploit. The study demonstrates that signal decomposition before deep learning substantially enhances predictive accuracy precisely in these challenging conditions. In practical terms, a water manager using the SVMD-CNN-LSTM model gets a forecast close enough to act on, whether that means adjusting irrigation allocations, flagging wells at risk of exceeding salinity thresholds, or prioritizing monitoring investment in the most vulnerable zones.

The spatial findings add urgency to the modeling. By 2021, WQI values in the northeastern part of the basin had fallen into the very poor category, a deterioration the authors link to the broader environmental stress afflicting the Lake Urmia system. Previous work in the region has documented how drought and land-use change degrade aquifer quality around the shrinking lake, and the new maps and forecasts provide a quantitative, station-by-station record of that decline. For a semi-arid, saline lake basin, the ability to anticipate which areas will cross critical quality thresholds gives planners a window for intervention that historical monitoring alone cannot offer.

Beyond its immediate regional value, the framework is designed to travel. The authors describe it as a transferable approach that can be extended to other basins, integrated with additional hydrogeological drivers such as recharge estimates or aquifer properties, and coupled with uncertainty quantification to support decision-making under changing environmental conditions. As climate change intensifies drought pressure on aquifers worldwide, tools that convert sparse monitoring data into reliable one-year-ahead quality forecasts are likely to move from research novelty to operational necessity. For the communities around Lake Urmia, the new model offers something rare: a quantitative glimpse of their groundwater’s future, early enough to change it.

Subject of Research: Hybrid deep learning prediction of spatiotemporal groundwater quality index in the Eastern Lake Urmia region

Article Title: A novel SVMD-CNN-LSTM hybrid model for spatiotemporal groundwater quality index prediction in the ecologically sensitive Eastern Lake Urmia Region

Article References: Abdi, E., Asadi, E., Zarrintan, N., & Ibrahim, O. R. (2026). A novel SVMD-CNN-LSTM hybrid model for spatiotemporal groundwater quality index prediction in the ecologically sensitive Eastern Lake Urmia Region. Earth Science Informatics, 19(10), Article 172. https://doi.org/10.1007/s12145-026-02228-1

Image Credits: AI Generated

DOI: 10.1007/s12145-026-02228-1

Keywords: groundwater quality, Water Quality Index, Lake Urmia, SVMD, CNN-LSTM, machine learning, signal decomposition, hydrochemistry, spatiotemporal prediction, semi-arid basin, deep learning, water resource management

The headline figure concerns recharge efficiency. Across the compiled literature, drywells achieved an average recharge efficiency of 83.4 percent, a figure 2.22 times higher than the 37.5 percent average recorded for surface-based recharge methods such as percolation ponds, spreading basins and infiltration reservoirs. The difference stems from fundamental physics. Surface systems lose substantial volumes of applied water to evaporation before it can percolate downward, and they depend on the slow, often limiting vertical hydraulic conductivity of surface soils. Drywells bypass the most restrictive near-surface layers entirely, injecting water through a borehole into permeable vadose zone materials where infiltration occurs along the well’s entire wetted depth. The result is a structure that converts a far greater fraction of captured stormwater or treated wastewater into actual aquifer storage.

Speed of operation tells an equally dramatic story. The review found that drywells required between 1 and 20 days to initiate measurable recharge after water was introduced, whereas surface reservoirs needed anywhere from 81 to 450 days before recharge began in earnest. For water managers confronting intense but brief storm events, monsoon pulses or episodic snowmelt, that temporal gap can determine whether a capture opportunity is exploited or lost entirely. Evaporative losses compound the contrast: drywells lost less than 2 percent of applied water to evaporation, since water is stored and infiltrated below ground, while surface methods routinely surrendered 10 to 40 percent of their inflow to the atmosphere. In arid and semi-arid regions where every cubic meter counts, the review’s authors argue that such losses represent a systemic inefficiency that buried infiltration structures can largely eliminate.

Longevity and land use further favor the subsurface approach. Drywells in the reviewed studies exhibited service lives of 25 to 35 years, between 60 and 250 percent longer than comparable surface systems, which are prone to sediment accumulation, vegetation encroachment and structural degradation. Their footprint advantage is even more dramatic. Because a drywell concentrates infiltration into a vertical column rather than spreading it across a horizontal plane, the technology required 40 to 4,000 times less land area than surface basins delivering equivalent recharge. In densely built urban environments where open land is scarce and expensive, this compactness transforms managed aquifer recharge from a land-intensive rural strategy into a viable component of city-scale stormwater and water supply infrastructure. The vadose zone itself adds a bonus: as water migrates from the well through unsaturated soil and rock, natural filtration and attenuation processes improve water quality before it reaches the aquifer.

These performance advantages come with engineering caveats, and the review dedicates considerable attention to design specifications distilled from the literature. The authors recommend drywell depths of 15 to 30 meters, diameters of 0.9 to 1.5 meters, and a separation of more than 10 meters between the base of the structure and the water table. That vertical buffer is not arbitrary. It preserves an unsaturated treatment zone through which recharged water must pass, providing residence time for pathogen die-off, filtration of suspended solids and attenuation of chemical contaminants. It also maintains a hydraulic gradient that sustains infiltration capacity. Site-specific hydrogeology remains decisive: the thickness and heterogeneity of the vadose zone, the hydraulic conductivity of the receiving layers and the quality of the source water all shape how a given drywell will perform, and the review emphasizes that careful site characterization should precede any installation.

Clogging emerges as the technology’s principal operational enemy, and the review identifies pretreatment systems and intermittent operation as the most effective countermeasures documented across the 71 studies. Suspended sediments, organic matter, microbial growth and chemical precipitates can progressively seal the walls and base of an infiltration well, throttling recharge rates over time. Settling basins, sand filters and other pretreatment units intercept the worst offenders before water enters the drywell, while alternating wet and dry operational cycles allow the infiltration surface to aerate and partially regenerate between recharge events. The literature also documents more active interventions, including backflushing concepts tested in laboratory settings to restore lost infiltration capacity. The review’s synthesis suggests that with disciplined maintenance regimes, the long service lives reported for well-managed drywells are achievable rather than exceptional.

Contamination risk is the question that most often shadows drywell deployment, particularly when the injected water is urban stormwater carrying hydrocarbons, metals, nutrients, PFAS or pathogens. The reviewed studies confront this concern directly, noting that the vadose zone provides meaningful but not unlimited protection, and that virus transport modeling and field assessments of stormwater drywells have informed guidelines on setback distances and acceptable source water quality. The authors stress that the same 10-meter minimum separation from the water table that safeguards recharge performance also functions as a water quality safeguard, and that pairing drywells with robust pretreatment substantially reduces the mass of pollutants reaching the aquifer. Nevertheless, the review is candid that unclear regulatory frameworks remain one of the chief barriers to wider adoption, with many jurisdictions lacking explicit permitting pathways, monitoring requirements or technical guidance for vadose zone injection structures.

That regulatory ambiguity helps explain a geographic paradox the authors highlight: despite superior measured performance, drywell technology remains confined to specific regions, notably parts of the United States, India, Israel and a handful of other countries with active managed aquifer recharge programs. Where surface spreading dominates, the choice often reflects institutional familiarity and established permitting rather than hydrogeological superiority. The review argues that the evidence base now assembled, quantifying efficiency, longevity, land savings and treatment benefits across six decades of research, provides regulators and engineers with the technical foundation needed to write the standards and design manuals that have been missing. Standardized design specifications, coupled with documented pretreatment and maintenance protocols, could allow drywells to move from a niche solution to a mainstream instrument of groundwater management.

The implications arrive at a moment of acute global water stress. Previous assessments have estimated that roughly four billion people experience severe water scarcity for at least one month each year, and declining aquifers underpin agriculture and drinking water supplies on every inhabited continent. Managed aquifer recharge, in which surplus surface water, stormwater or treated wastewater is deliberately stored underground, has been recognized for decades as a proven technology for water supply resilience. What this review adds is a rigorous, quantitative case that the subsurface branch of that family, the humble drywell, deserves far more prominent placement in the toolkit. With recharge efficiencies more than double those of surface basins, startup times measured in days rather than seasons, evaporative losses nearly eliminated and land requirements slashed by orders of magnitude, drywells offer a rare alignment of hydrogeological performance and urban practicality. The challenge, the authors conclude, is no longer proving that the technology works, but building the regulatory and technical frameworks that will let it work everywhere it is needed.

Subject of Research: Performance, design and implementation strategies of artificial recharge drywell technologies for managed groundwater recharge.

Article Title: Review: Performance, design and implementation strategies for artificial recharge drywell technologies

Article References: Devappa, Veerabadran, R., Selvaraj, S., Kaliaperumal, R., Kannan, B., Alagirisamy, B., & Balasubramaniam, S. (2026). Review: Performance, design and implementation strategies for artificial recharge drywell technologies. Hydrogeology Journal. https://doi.org/10.1007/s10040-026-03165-5

Image Credits: AI Generated

DOI: 10.1007/s10040-026-03165-5

Keywords: artificial recharge, drywell technology, vadose zone infiltration, groundwater management, managed aquifer recharge, recharge efficiency, clogging prevention, stormwater infiltration, hydrogeology, water scarcity, groundwater quality, Review