Background Brevibacillus , a spore-forming and resilient bacterium, thrives in diverse environments. This study investigated the plastic-degrading potential of three Brevibacillus spp. (SM1, SM2, and SM3) isolated from landfill soil near Dhaka, Bangladesh. Method The isolated strains were first characterized based on their optimal growth conditions, morphology, and biochemical properties. 16S rRNA gene sequencing and phylogenetic analysis were conducted to identify the isolates. Results All three isolates exhibited optimal growth at an alkaline pH of 8-8.5 and tolerated extreme conditions up to pH 11.5 and temperature 55 °C. SM1 and SM3 grew best at 30-37 °C, while SM2 favored 40-45 °C. Microscopy confirmed all isolates as Gram-positive, rod-shaped, and motile. Biochemical tests yielded positive results for oxidase, catalase, and citrate in all strains. SM1 exhibited the highest lipase activity, while esterase activity was higher in SM2 and SM3. A halo around SM2 on polyethylene glycol plates indicated potential degradation. All strains demonstrated the ability to utilize PET, LDPE, and LLDPE as sole carbon sources in degradation assays. The weight-loss analysis indicated greater LLDPE degradation efficiency over time in SM1 and SM3 compared to SM2. The DCPIP assay confirmed degradation of PET and LDPE by SM2. Molecular identification (16S rRNA and BLASTn) revealed 97% similarity to Brevibacillus species ( B. parabrevis , B. reuszeri , and B. brevis ). MSA and phylogenetic analyses confirmed their taxonomic placement within the Brevibacillus genus. Conclusion These findings highlight the potential of wild-type Brevibacillus spp . isolated from Bangladeshi landfill soil for application in plastic bioremediation.
Background Brevibacillus , a spore-forming and resilient bacterium, thrives in diverse environments. This study investigated the plastic-degrading potential of three Brevibacillus spp. (SM1, SM2, and SM3) isolated from landfill soil near Dhaka, Bangladesh. Method The isolated strains were first characterized based on their optimal growth conditions, morphology, and biochemical properties. 16S rRNA gene sequencing and phylogenetic analysis were conducted to identify the isolates. Results All three isolates exhibited optimal growth at an alkaline pH of 8-8.5 and tolerated extreme conditions up to pH 11.5 and temperature 55 °C. SM1 and SM3 grew best at 30-37 °C, while SM2 favored 40-45 °C. Microscopy confirmed all isolates as Gram-positive, rod-shaped, and motile. Biochemical tests yielded positive results for oxidase, catalase, and citrate in all strains. SM1 exhibited the highest lipase activity, while esterase activity was higher in SM2 and SM3. A halo around SM2 on polyethylene glycol plates indicated potential degradation. All strains demonstrated the ability to utilize PET, LDPE, and LLDPE as sole carbon sources in degradation assays. The weight-loss analysis indicated greater LLDPE degradation efficiency over time in SM1 and SM3 compared to SM2. The DCPIP assay confirmed degradation of PET and LDPE by SM2. Molecular identification (16S rRNA and BLASTn) revealed 97% similarity to Brevibacillus species ( B. parabrevis , B. reuszeri , and B. brevis ). MSA and phylogenetic analyses confirmed their taxonomic placement within the Brevibacillus genus. Conclusion These findings highlight the potential of wild-type Brevibacillus spp . isolated from Bangladeshi landfill soil for application in plastic bioremediation.
Toxic metals and metalloids (TMs) are inorganic chemical contaminants that cause adverse health outcomes. Drinking water is an important exposure route for TMs such as arsenic (As), cadmium (Cd), lead (Pb), and manganese (Mn). Arsenic and manganese are geogenic contaminants that can also arise from localized anthropogenic and industrial sources. Lead leaching from Pb-containing pipes and fittings is of particular concern. Occurrence of these and other TMs has been reported in drinking water in Mexico, but the evidence has not been synthesized. Following PRISMA Guidelines, we conducted a systematic review to understand the distribution of TM occurrence in Mexican drinking water. We searched PubMed, EBSCO Global Health, and Web of Science to identify studies published in English after 1968 that measured any TM in drinking water globally, identifying a set of 20,052 papers for screening, last searched in March 2025. A total of 134 papers were included that report quantitative data on TMs in Mexican drinking water. We found that 62%, 20%, 14%, and 18% of Mexican drinking water samples exceeded the World Health Organization’s Guideline Values for As, Pb, Mn, and Cd, respectively. Overall, there is sufficient evidence to recommend preventive measures to reduce TM exposures from drinking water in Mexico, but there is insufficient data to inform targeted remediation efforts. Across Pb, Mn, and Cd, we found that sample sites were overwhelmingly in urban settings, leaving rural populations underrepresented. While arsenic is well characterized across the country and should certainly be addressed, we found that Pb, Mn, and Cd are understudied outside of the region surrounding Mexico City. We have developed a GIS layer for researchers to identify areas where research has not been conducted. This study can inform the efforts of public health practitioners, policy-makers, and researchers to identify understudied regions for further monitoring, surveillance, and review.
Summer savory ( Satureja hortensis L.) is a valuable annual herb sensitive to drought stress. This greenhouse study investigated whether amending soil with natural bentonite clay (0, 50, or 100 g kg -1 ) could mitigate the effects of three irrigation regimes (100, 75 and 50% of field capacity, FC). Growth, physiological and biochemical parameters were measured using a factorial based on completely randomized design to assess plant respons. Results showed that severe drought (50% FC) reduced shoot biomass by 25.8% and leaf relative water content (RWC) by 37.2%, while increasing root growth and oxidative stress markers (malondialdehyde by 50.8%). Bentonite amendment at 50 g kg -1 significantly alleviated these stress symptoms: it restored leaf RWC by 42.4% in stressed plants, increased the activity of key antioxidant enzymes (guaiacol peroxidase and polyphenol oxidase) by up to 45.5% and enhanced the uptake of essential nutrients (e.g., N, K, Zn). Consequently, this optimal bentonite rate improved the biomass of drought-stressed plants by up to 19.0%. The higher application rate (100 g kg -1 ) was less effective or even detrimental, likely due to reduced soil aeration. Our findings demonstrate that moderate bentonite application (50 g kg -1 ) effectively enhances drought tolerance in summer savory by improving soil water retention, nutrient availability and the plant’s antioxidant capacity. This strategy offers a practical and sustainable approach to cultivate this high-value medicinal herb under water-limited conditions.
This study provides a spatially resolved assessment of the abundance, composition, and ecological risk of large microplastics (LMPs, 1–5 mm) and mesoplastics (MSPs, 5–25 mm) in surface sediments at Lido Morelli Beach, southern Italy. Fifteen replicated sediment samples distributed across five shoreline-parallel transects were taken from the upper 5 cm of sediment within 0.25 m 2 quadrats, covering approximately 800 m 2 of beach surface. Plastic particles were visually sorted, size-classified, and chemically identified using ATR–FTIR spectroscopy. A total of 764 LMPs and 229 MSPs were collected, with maximum concentrations for both size classes occurring along storm berm transects. Polymer composition was dominated by polyethylene and polypropylene, and a strong positive correlation between LMP and MSP abundances ( R 2 = 0.86) indicated co-accumulation. Plastic pollution was assessed using four ecological indices. Pollution Load Index values >1 for both size classes and exceptionally high Pellet Pollution Index values suggest a probable pellet industrial source, while hazard-based indices (Hazard Index) indicated low polymer toxicity. The results provide actionable evidence for environmental regulators, port authorities, industrial pellet producers, and coastal management agencies, supporting targeted pellet-loss prevention, improved port and supply-chain controls, and monitoring strategies aligned with forthcoming EU regulations on the release of plastic pellets.
Climate change is reshaping freshwater availability, quality, and distribution, posing major challenges to water security. Increasing hydroclimatic variability, extreme events, glacier retreat, salinity intrusion, and shifting precipitation are disrupting water systems globally, with disproportionate impacts on vulnerable populations. Despite growing research, key gaps remain in understanding compound risks, cross-sectoral interactions, and adaptation effectiveness under uncertainty. This editorial synthesizes current evidence on climate impacts on water security, focusing on hydrological variability, infrastructure resilience, governance, and inequality. It highlights gaps in integrated assessment, data systems, and science–policy translation. Priority research areas include improved climate–water modeling, nature-based solutions, adaptive governance, and scalable innovations in storage and reuse. Aligned with Sustainable Development Goal 6 (SDG 6), this Special Collection calls for interdisciplinary contributions that advance both theory and practice toward climate-resilient water management.
Introduction: The demand for freshwater is increasing around the globe due to population growth and urbanization, exacerbating pressure on available water resources. Consequently, understanding the factors influencing the price per cubic meter of water at a public Ecuadorian university operating in US dollars is crucial for sustainable resource management. Aim: To estimate water cost at the Universidad de las Fuerzas Armadas ESPE. Although the university has relevant data, such as maintenance, operation, energy, and water analysis costs. Results: This research focused on identifying the various elements contributing to the cost of water, including expenses associated with maintenance ($14,201.60 annually), water quality measurements ($250.00 annually), human resources ($980.00 monthly), chemical inputs ($8.00 monthly), electricity consumption ($464.38), operational and maintenance costs ($30.00 monthly), and the implementation of an ultraviolet (UV) system at the wastewater treatment plant ($186.05 per year). The study concluded that the cost per cubic meter of water at the university was 1.07 USD.
Pharmaceutical and dye residues are emerging contaminants of concern in various aquatic ecosystems, and they may pose risks to the health of these ecosystems. Adsorption is recognized as an excellent and versatile strategy for remediating these contaminants. In this study, a nanocomposite adsorbent of Cu-rich Ag-Cu, denoted as Ag 25 Cu 75 , was used to adsorb diclofenac sodium (DCF), a popularly used pharmaceutical compound, and crystal violet (CV), a model of a cationic dye. Ag 25 Cu 75 nanoparticles were synthesized by the NaBH 4 reduction method and characterized by X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), electron microscopy (TEM), and UV-Vis spectroscopy (UV-Vis.), and Batch adsorption experiments were conducted to investigate the effects of pH, temperature, adsorbent dosage, contact time, and initial concentrations of contaminants. The adsorption efficiency of Ag-Cu nanocomposites for DCF and CV contaminants was found to be high, with 85% adsorption efficiency for DCF within 60 min and 80% adsorption efficiency for CV within 150 min at pH 3 and 252°C. Thermodynamic calculations showed that adsorption of both contaminants by Ag-Cu nanocomposites was spontaneous and entropy-driven, with endothermic adsorption of DCF and CV. The point of zero charge (PZC = 9.0 ± 0.1) was also responsible for the favorable adsorption of CV under acidic conditions and the amphoteric adsorption behavior of DCF. The binary adsorption tests indicated minimal competitive effects, indicating the independent adsorption on heterogeneous surfaces. The fixed-bed column tests with Sand-Ag-Cu nanoparticle composites were superior to the sand-alone columns, which further demonstrated the potential of the Ag-Cu nanoparticle material for water treatment. This study demonstrated the effectiveness of Ag-Cu nanoparticles for the removal of pharmaceutical and dye contaminants.
Root zone soil moisture (RZSM) is critical for irrigation management, as it directly affects plant water availability, crop growth, and irrigation scheduling. However, modeling RZSM is challenging due to the high variability and nonlinearity of soil moisture patterns. Physically based models, such as those solving Richards’ equation, offer detailed soil dynamics but require extensive hydrological parameters and significant computational resources. In contrast, statistical approaches are more efficient but lack physical interpretability. This study proposes an event-based framework that models soil moisture increases following individual water input events (e.g., precipitation, irrigation). Water balance models are designed to simulate moisture changes within each soil layer, while an XGBoost ensemble captures interlayer interactions. By embedding the machine learning model within physically structured equations, the approach ensures both accuracy and interpretability. The model was applied using soil moisture sensor data collected from weather stations with and without crops in Florida. Results show strong accuracy in event-pattern identification and the modelling of event-scale duration and magnitude behavior across locations.
Access to safe drinking water remains a major public health issue in low- and middle-income countries. Local geological materials offer an affordable alternative to costly conventional treatments. This study evaluates the effectiveness of basalt, quartzite, and granitic weathered rock in purifying surface water in western Cameroon. Materials collected in Dschang were crushed, sieved (0.3 mm), washed, and sterilized, then used to make gravity filters. Filtered river water was analyzed before and after treatment for physicochemical and bacteriological parameters according to APHA methods. Data were compared using the Kruskal-Wallis test (α = 5%). All materials reduced bacterial load and turbidity. Granitic alterationite performed best, with total elimination of fecal streptococci and Salmonella spp., and greater than 99% for Vibrio spp. A significant decrease in turbidity, conductivity, TDS, and nitrates was observed, while basalt showed a tendency to release ions. Granitic alterationite is the most effective material for making surface water drinkable. These results confirm the potential of local geological materials as simple and sustainable solutions for access to drinking water in resource-limited environments.
Lebanon’s prolonged economic collapse has fundamentally altered the functioning of its water and energy service systems, exposing deep spatial inequalities and accelerating shifts in resource provisioning. Drawing on municipal-level data from 150 municipalities across nine governorates for pre-crisis (2019) and crisis period (2023) conditions, this study examines how the crisis reshaped domestic, industrial, and total water supply; energy use for water extraction and distribution; and the adoption of decentralized renewable technologies. Through an integrated statistical analysis, we identify marked regional disparities, with Akkar experiencing the steepest declines in water supply and Baalbek-Hermel retaining comparatively higher domestic availability. A small set of municipalities, including Tripoli and Qaa, consistently emerge as extreme outliers, underscoring the need for highly localized rather than uniform national interventions. The results also reveal a rapid transition toward decentralized resilience: solar photovoltaic systems and solar water heaters have become increasingly central to sustaining water services amid grid failure and rising diesel costs. Demographic dynamics play differentiated roles, with Lebanese population pressures closely linked to industrial water demand, while refugee presence correlates with domestic supply patterns shaped by humanitarian support. By linking these crisis-period transformations to SDG 6 (clean water) and SDG 7 (clean energy), the study contributes rare empirical evidence on how economic freefall restructures water and energy systems and identifies concrete entry points for adaptive, spatially differentiated governance.
Atrazine, a triazine-class herbicide widely used in U.S. Corn and soybean production is highly soluble, persistent and prone to runoff. It poses risks such as reduced aquatic productivity and biological endocrine disruption. Observation-driven geostatistical models can extend sparse monitoring networks by estimating contaminant levels across space and time. We evaluated all available Iowa surface water atrazine measurements from 1986 to 2025 ( n = 13,247), identifying sparse and inconsistent sampling before 2000 and after 2004, with a period of sustained high sampling intensity from 2000 to 2004 (>1,000 samples per year). In this study, we applied a geostatistical, Bayesian Maximum Entropy (BME) framework to model log-transformed atrazine concentrations in Iowa surface waters from 2000 to 2004 using 6,478 observations from 673 monitoring sites. We compared a traditional spatial-only geostatistical framework with a fully spatiotemporal BME model incorporating both spatial and temporal data. Spatiotemporal dependence was modeled using an additive, nested space–time, exponential covariance structure. This allows spatial and temporal autocorrelation to jointly inform prediction while preserving localized contamination patterns. Model performance was evaluated using leave-one-out cross-validation. Results show spatiotemporal BME significantly outperformed spatial-only approaches, improving predictive power ( R 2 = 0.73 vs. 0.64) and reducing mean squared error (0.318 vs. 0.432) log µg 2 /L. Performance gains were largest for error metrics sensitive to extreme values, with spatiotemporal BME substantially reducing mean squared error relative to spatial-only models, indicating improved representation of episodic high-concentration events. Our findings demonstrate atrazine exposure risk appears to be predictable and likely aligned with seasonal application, chemical half-life, and delayed runoff. These processes likely produce localized hotspots often exceeding the U.S. EPA’s 3 µg/L drinking water standard. Atrazine correlation showed strong spatial (mobility 25–150 km) and temporal persistence (~20–50 days). This approach complements mechanistic models, offering a flexible, data-driven framework for environmental health monitoring where data are sparse or irregular.
Hakaluki Haor, the largest freshwater wetland in Bangladesh, plays a crucial ecological and socio-economic role by supporting inland capture fisheries and sustaining local livelihoods; however, its morphology and hydrological dynamics have undergone significant transformations over recent decades. This study integrated remote sensing and geographic information systems techniques with time-series statistical analyses to quantify water area changes from 1990 to 2020 and examine their implications for fish production and community livelihoods. Landsat imagery and the Modified Normalized Difference Water Index were applied to assess waterbody dynamics, while Mann–Kendall trend tests, Sen’s slope estimator, Pettitt’s change-point detection, and Spearman correlation were used to quantify long-term trends and structural shifts. Results revealed significant water area changes in Hakaluki Haor between 1990 and 2020, mainly due to erratic rainfall and discharge from transboundary rivers. Fish catch showed a statistically significant decreasing trend (Sen’s slope = −83.68 MT/year, p < .001), while water area exhibited a significant increasing trend (Sen’s slope = 19.55 ha/year, p = .001). Fish catch and abundance in the haor were influenced by a combination of climatic factors and anthropogenic pressure, with a notable decline between 1990 and 2015 associated with erratic rainfall, temperature fluctuation, beel siltation, and excessive use of destructive fishing gears. Change-point analysis indicated significant breaks in the fish production around 2004 to 2005 and near 2015, coinciding with extreme rainfall events ( z -score = 1.25 in 2015), which adversely affected fisher livelihoods, reducing fishing participation, prompting occupational diversification, seasonal migration, and gradual socio-economic transitions. Post-2015, fish catch improved following government-led re-excavation of beels and canals, undertaken in collaboration with non-governmental organizations. Sustainable management of haor wetlands requires restoring hydrological connectivity, mitigating destructive fishing, and strengthening climate-resilient fisheries governance.
Debris flows are destructive movements of water and earth, strongly influenced by environmental factors. In South America, extensive tropical forests, mountain chains, and high annual rainfall create favorable conditions for these geodynamic processes. However, there is still a need for further research on the triggers, soil mechanics, climate influence, and travel distances of such events. This study explores three physically based models for simulating the propagation of shallow landslides and associated flows in data-scarce regions, with the aim of developing a comprehensive analysis methodology for these events, the affected zones, and the processes that cause them. Using the 2015 Salgar landslide in Antioquia, Colombia, as a calibration case, where over 40 landslides triggered by a storm resulted in significant loss of life and property damage, three models were calibrated and evaluated: RAMMS, Flow R, and GPP from SAGA GIS. Following this, the models were validated using a similar event in the La Argelia basin, Carmen de Atrato, Chocó. Statistical analyses and ROC curve evaluations were conducted to assess the predictive performance of these models, considering the available data and event documentation. The findings suggest that while the models are useful for hazard assessments in tropical mountain basins, further refinement and data collection are essential to improve predictive accuracy. This research contributes to understanding landslide dynamics in tropical regions and offers insights for future hazard mitigation efforts.
Microplastics (MPs) are a growing environmental concern due to their widespread occurrence, persistence, and ecological impacts. However, data on MP pollution in freshwater-dominated subtropical estuaries, particularly in South Asia, remain scarce. This study aimed to assess the spatial distribution, characteristics, sources, and ecological risks of MP contamination in the Feni River Estuary, Bangladesh. MPs were isolated by density separation, characterized by stereomicroscopy, and Fourier Transform Infrared spectroscopy. MP abundances ranged from 426.66 ± 140.23 to 546.66 ± 136.11 items/m 3 (mean: 468.33 ± 105.76 items/m 3 ), with no statistically significant variation among stations ( p > .05). MPs were dominated by fibers, followed by fragments and films, with minor foams (porous plastics) and microbeads (spherical primary plastics). Polymer characterization identified polyethylene terephthalate (PET, 78%) as the dominant type, with polyethylene (PE), polypropylene (PP), and polystyrene (PS) also detected, reflecting strong consumer plastic inputs and secondary fragmentation processes. Contamination indices (CF) indicated non-negligible pollution, with both the contamination factor (CF, 1 ⩽ CF < 3) and pollution load index (PLI > 1) confirming moderate MP contamination. polymer hazard index (PHI) scores ranged from 0.1 to 9.1, placing polymers into risk categories I (<1) and II (1–10). Notably, PS (9.1), PET (8.6), and PE (7.3) were classified as moderate ecological risks (Category II), whereas PP (0.1) posed a negligible risk (Category I). Multivariate analyses using principal component analysis (PCA) and hierarchical cluster analysis (HCA) revealed spatial heterogeneity in MP distribution, influenced by municipal discharge, aquaculture, industrial inputs, and estuarine hydrodynamics.
Despite advances in remote sensing, vineyard yield prediction often suffers from limited multi-source data integration, low spatial resolution, and a lack of reproducibility in Mediterranean environments. This study addresses these gaps by presenting an innovative, standardized methodology that integrates multi-temporal satellite data (Sentinel-2, PlanetScope, and VHR), high-resolution UAV imagery (1–3 cm 2 ), and soil property measurements with machine learning (ML) algorithms to improve yield prediction and crop management. The research was conducted in a 9.2 ha experimental vineyard in Villamena, Granada (Spain). A statistically significant difference was detected between vegetation indices obtained from different sensors ( p < .05), highlighting that sensor type strongly affects the measured index values. Vegetation indices and soil variables were analyzed using advanced ML algorithms (KNN, SVM, DT, and RF) to identify key patterns influencing crop vigor and performance. Model evaluation metrics included Pearson’s correlation coefficient, Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). Among the tested models, KNN achieved the highest accuracy, particularly when using PlanetScope imagery ( r = .9988, RMSE = 0.0105). The findings demonstrate the practical value of data integration: UAVs provide precise, site-specific monitoring during key growth phases, while satellite imagery supports broader temporal and spatial coverage. By applying this methodology, farmers and agronomists can make informed decisions regarding fertilization, irrigation, and harvest timing, optimizing resource use and enhancing sustainability. Furthermore, the developed models are transferable to other Mediterranean vineyard regions. This work provides a concise framework for implementing precision viticulture strategies, bridging the gap between advanced remote sensing analytics and actionable agricultural management.
Camelina is a resilient oilseed crop, but its productivity in acidic soils is limited by phosphorus fixation and poor soil fertility. This study examined the combined use of coffee husk biochar and triple superphosphate (TSP) fertilizer to improve soil properties, oil yield, and the nutritional quality of camelina seed cake in the acidic soils of the Hadiya Zone, Central Ethiopia. A split–split plot design was implemented during the 2024 cropping season using two cultivars (Zeytee-1 and Syria), four TSP rates (0, 23, 46, and 69 kg ha -1 ), and four biochar rates (0, 10, 12, and 14 t ha -1 ). Integrated biochar and TSP application significantly enhanced soil pH, total nitrogen, available phosphorus, and cation exchange capacity, with the highest amendment levels increasing available P to 45.3 ppm and CEC to 18.4 cmol kg -1 . Oil content improved with increasing TSP, reaching 45.7% in the Syria cultivar, while biochar reduced peroxide value, indicating better oil stability. Seed cake quality also improved, with crude protein rising to 39.2% and fiber fractions (ADF and NDF) increasing under higher amendment rates. Economic analysis showed that the greatest net benefit was achieved with moderate inputs (23 kg TSP ha -1 and 14 t biochar ha -1 for Syria), highlighting that economic optima differ from maximum yields. Overall, integrating biochar with TSP effectively ameliorated soil acidity, enhanced soil fertility, and improved both the quantity and quality of camelina products, offering a sustainable intensification strategy for acidic agroecosystems.
This study presents a newly developed pilot-scale water treatment system that integrates oxidation, aeration, and multi-stage filtration (birm, activated carbon, and sand) for efficient iron reduction from drinking water to acceptable levels. Synthetic water samples were prepared with different iron concentrations and passed through different treatment stages. The iron removal efficiency is affected by various variables, including chlorine dose, oxidation time, aeration time, filtration time, and the number of filtering times. A comparison was conducted between different treatment scenarios with different treatment stages. The result showed that the iron removal efficiency could reach 95% at an initial iron concentration of 1.5 mg/L, a chlorine dose of 0.63 mg/L, oxidation time of 240 s, aeration time of 15 min, and double filtration of 60 s each. The results also showed that the iron concentration could reach the allowable limit of 0.4 mg/L when applying a chlorine dose of 0.63 mg/L, oxidation time of 120 s, and single filtration of 60 s. It is estimated that the operation cost of iron removal by the proposed system under the optimal conditions will be about 0.1 USD per cubic meter. An artificial neural network (ANN) was used to predict iron removal efficiency. Modeling results showed that the ANN with R 2 -value of 0.995 is reliable in describing the iron removal. This system can be scaled up to be integrated into any riverbank filtration system.
Despite significant advancements in improving access to safe drinking water, marginalized communities, particularly those relying on rivers and handpumps, continue to face challenges related to water contamination. This study provides comprehensive seasonal water quality assessments from the river and handpump in Barapita, a marginalized village in Odisha, India, using a novel Targeted Parameter Adjustment Strategy (TPAS). TPAS ranks pollutants by their Water Quality Index (WQI) contribution, then iteratively lowers the top contributor(s) to World Health Organization (WHO) permissible levels—recomputing WQI after each step—until the score falls below the “Good” (< 50) threshold, efficiently channelling resources towards the parameter whose incremental correction most improves overall water quality. When full compliance with all WHO guidelines is unrealistic, TPAS provides a practical route to safer water. The use of Hierarchical Cluster Analysis (HCA) enhanced understanding of the contamination sources, grouping parameters into clusters linked to geogenic and anthropogenic sources. We employed sensitivity analysis to identify the parameters influencing water quality during the four distinct seasons: winter, pre-monsoon, monsoon, and post-monsoon. This innovative combination of TPAS, HCA, and sensitivity analysis provides deeper insights into seasonal water quality variations, providing tailored interventions in each season to improve drinking water quality in the village. The findings revealed significant seasonal variations, with the monsoon season exhibiting the highest WQI values due to runoff-induced contamination, while pre-monsoon values were lowest due to reduced anthropogenic interference. Iron, turbidity, colour, and nitrates were identified as critical parameters influencing water quality. The study’s adaptive approach effectively reduced WQI to acceptable levels by sequentially targeting the most impactful parameters, demonstrating the feasibility of low-cost, community-focused interventions in rural contexts.
