Journal of Water Resources and Pollution Studies

Variations in Physicochemical Parameters of Wastewater from Different Sources in Odisha and their Environmental Impact

Aakankshya Pradhan — 2026-02-04

Wastewater originating from domestic, industrial, and agricultural activities represents a major source of environmental pollution and poses significant risks to public health and aquatic ecosystems. The present study investigated variations in physicochemical characteristics of wastewater collected from different sources across selected regions of Odisha, India. Nine wastewater samples were analysed, comprising three samples each from industrial, domestic, and agricultural origins. Key physicochemical parameters, including pH, colour, turbidity, total dissolved solids (TDS), total alkalinity, total hardness, ammonia, nitrite, chloride, fluoride, sodium, potassium, and calcium, were determined using standard analytical methods. Industrial wastewater exhibited pronounced variability, with effluents from ACC Cement and Shyam Metals showing elevated pH, turbidity, hardness, and fluoride levels, indicating substantial inorganic contamination. Domestic wastewater samples were characterized by lower pH and significantly higher concentrations of ammonia, fluoride, colour, and turbidity, with Rajendrapada showing the highest contamination among domestic sources. Agricultural wastewater displayed moderate pollution levels; however, rice field samples showed comparatively higher concentrations of TDS, alkalinity, ammonia, and fluoride, reflecting nutrient enrichment from agricultural practices. Several parameters exceeded acceptable and, in certain cases, permissible limits across all wastewater categories, highlighting widespread deterioration of water quality. Findings emphasize the need for better wastewater management and treatment technologies to reduce environmental and health risks.

Environmental Defense: Reconciling Indigenous Sensory Metrics with Laboratory Standards in Coastal Communities of Akwa Ibom State, Nigeria

Jimmy U. J — 2026-04-16

Coastal communities in the Niger Delta, including those in Akwa Ibom State, face an escalating water security crisis driven by climate-induced saltwater intrusion and industrial environmental pressures. While conventional water management relies exclusively on laboratory metrics, this research pioneers an interdisciplinary approach by documenting and validating indigenous water quality indicators. Using a phenomenological qualitative design, the study explores the sensory perceptions of coastal residents in Akwa Ibom State, focusing on taste thresholds, lathering capacity, and food discoloration as diagnostic tools. Using 8 shoreline communities, a total of 360 participants were selected for in-depth engagement at various communities. Findings reveal a significant perceptual chemical gap: community undrinkability thresholds for sodium reach 200 mg/L, nearly four times higher than World Health Organization (WHO) aesthetic guidelines. This physiological adaptation masks a severe “poverty trap,” where vulnerable households spend up to 25% of their daily income on sachet water to avoid the physical and financial stressors of saline groundwater. The study synthesizes these data into an integrated water quality perception framework, which aligns technical salinity standards with indigenous sensory metrics. This research concludes that technical water interventions often fail due to a lack of sensory alignment with local expectations. By institutionalizing community-led “Green Squad” monitoring and adopting sensory acceptance protocols, policymakers can ensure higher project sustainability. This work provides a scalable model for decolonizing water science and enhancing climate resilience in marginalized coastal environments globally.

Comparative Evaluation of Physicochemical Parameter Dynamics during Crude Oil Degradation in Freshwater and Saltwater Media

Ozioko Fabian Chidiebere — 2026-03-10

Crude oil contamination of aquatic environments presents serious ecological and environmental challenges, particularly in freshwater and marine systems. This study investigates the physicochemical behavior of crude oil degradation in freshwater and saltwater media under controlled laboratory conditions at a constant temperature of 15°C. Key physicochemical parameters—including total dissolved solids, conductivity, total hardness, sulphate, chloride, alkalinity, pH, nitrate, turbidity, oil and grease, dissolved oxygen, and iron—were monitored over increasing contact time. Results revealed slight but consistent variations in parameter concentrations with time in both media, indicating active degradation processes. Temperature was observed to play a critical role in influencing substrate availability and microbial activity, which subsequently affected degradation efficiency. Comparative analysis showed that both freshwater and saltwater systems respond differently to crude oil contamination due to variations in ionic composition and physicochemical stability. These findings highlight the importance of physicochemical monitoring in understanding petroleum hydrocarbon degradation and optimizing bioremediation strategies in aquatic environments.

Microplastics in Industrial Wastewater: Emerging Detection Methods and Control Approaches

Ritesh G Upadhyay — 2026-01-22

Microplastic pollution in industrial wastewater has emerged as a significant environmental concern due to the persistence of plastic particles and their potential impacts on ecosystems and human health. Industrial activities such as textile manufacturing, plastic processing, paint and coating production, cosmetics manufacturing, and wastewater treatment operations contribute substantially to the release of microplastics into aquatic environments. These particles exhibit wide variability in size, shape, and polymer composition, which complicates their detection and control. This review critically examines conventional and emerging techniques for the detection and characterization of microplastics in industrial effluents, including microscopic analysis, Fourier-transform infrared spectroscopy (FTIR), Raman spectroscopy, and pyrolysis-GC/MS. The limitations of traditional methods and the advantages of emerging approaches such as hyperspectral imaging, laser-induced breakdown spectroscopy, and machine-learning-assisted identification are also discussed. In addition, the performance of established and advanced wastewater treatment technologies, including membrane bioreactors, sand filtration, dissolved air flotation, electrocoagulation, and adsorption-based methods, is evaluated. Reported removal efficiencies range from approximately 50 to over 95%, depending on the treatment technology and microplastic characteristics. The review highlights key methodological and regulatory gaps, particularly the lack of standardized sampling protocols and microplastic-specific discharge limits. Overall, the findings emphasize the need for integrated detection strategies, effective treatment combinations, and supportive regulatory frameworks to mitigate microplastic pollution from industrial sources.

Deep Learning Approach for Marine Plastic Waste Detection in Autonomous Robots

Shruti Hemant More — 2026-04-02

Marine plastic pollution is a major global environmental issue that affects marine ecosystems, biodiversity, and human health. Each year, large amounts of plastic waste enter oceans through rivers, coastal activities, industrial discharge, and improper waste disposal. These plastics remain in the environment for long periods, harming marine life through ingestion, entanglement, and habitat damage. Over time, they break down into microplastics, which contaminate water and enter the food chain, posing long-term ecological and health risks. Traditional methods of monitoring marine plastic waste, such as manual inspection and satellite observation, are often slow, expensive, and limited in real-time effectiveness. To address these challenges, this research proposes a deep learning-based system combined with autonomous robotics for efficient plastic waste detection. The system uses cameras mounted on robots to capture real-time ocean images, which are analysed using Convolutional Neural Networks (CNNs) to detect and classify plastic items like bottles and bags. The methodology involves dataset collection, pre-processing, model training, and robotic integration. Techniques like image resizing, normalisation, and data augmentation improve model performance. Advanced object detection models such as YOLO, Faster R-CNN, and SSD are evaluated, with YOLO achieving up to 92% accuracy and strong performance even under challenging conditions like reflections and waves. This system enables real-time monitoring, reduces human effort, and supports large-scale ocean cleanup operations. It also allows continuous data collection for analysing pollution patterns and supports better environmental decision-making. While challenges such as microplastic detection and environmental variability remain, this approach offers a scalable and effective solution for tackling marine plastic pollution and promoting sustainable environmental conservation.