Microplastics are very small particles of plastics, usually smaller than 5 mm. Microplastic pollution has emerged as a rising and challenging issue worldwide, posing serious threats to aquatic and terrestrial ecosystems and human health. Because of global demand and frequent use in daily routines, including clothing, packaging, and household items, the production of plastic is increasing annually. This study provides a comprehensive overview of the source, classification (based on shape, color, polymer), transportation, and impact of microplastic pollution. Depending upon size, mass, and density, microplastics can be transported to the environment via air and water. However, microplastics can be inhaled and ingested by humans, causing various health issues; for example, aquatic organisms like small fish ingest microplastics, which accumulate through the food chain and end up in the human body. This can lead to physiological harm, including inflammation, digestion tract obstruction, biomagnification throughout the food chain, and reproductive failure. This study further highlighted initiatives taken by government agencies to address plastic and microplastic pollution across India; for example, The Ministry of Environment Forest and Climate Change (MoEFCC) has formulated and amended the Plastic Waste Management (PWM) rules, Mission LiFE (LiFEStyle for Environment) launched campaigns such as “Say No to Single Use Plastic” and “One Nation, One Mission: End Plastic Pollution” to create awareness at the grassroot level, and institutions like the Food Safety and Standards Authority of India (FSSAI) have initiated a project to detect microplastics in food products. In addition, the National Green Tribunal (NGT) has instructed the Central Pollution Control Board (CPCB) to actively take measures to address microplastic pollution across Indian cities, focusing on key parameters like air, water, food, and humans. This study presents several recommendations, including detection and removal techniques (conventional, advanced, and removal); strengthening legislative policies such as Extended Producer Responsibility (EPR); research collaboration and monitoring with institutions such as CSIR-IITR, ICAR-CIFT, and BITS-Pilani; integrating EPR and Material Recovery Facilities (MRF) to develop a circular economy model; and mass awareness through government initiatives like the Swachh Bharat and Smart City programs to foster long-term behavioral change.

The ecosystem of Dal Lake, an important freshwater lake in Srinagar, India, has been rapidly degraded in recent decades due to intensified eutrophication. The main causes of eutrophication were determined to be different types of human activities in the catchment area and its inappropriate development as well as excessive loads of pollutants introduced into the lake. The heightened algal blooms brought significant water quality deterioration, a reduction in indigenous fish populations, and a general disturbance of the ecological balance of the lake. Such changes adversely influenced the living conditions of the inhabitants depending on the lake for tourism, fishing, and other economic pursuits. The aim of the research was the evaluation of the specificity of the course and spatio-temporal dynamics of Dal Lake eutrophication process on the base of accurate assessment of its actual trophic state. The applied assessment methodology was based on the biotic balance approach. As an indicator of the biotic balance in water, the Index of Trophic State (ITS) was chosen and adopted for the conditions of the lake ecosystem in humid subtropical climate conditions. The assessment was based on data from a five-year lake monitoring period (2019–2023) and analyzed for four lake basins: Hazaratbal, Nishat, Nagin, and Gagribal. The results indicated a steady increase in the lake’s trophic status, with the Hazaratbal basin evolving from mesotrophic to eutrophic, while the other basins progressed from meso-eutrophic to eutrophic during the research period. At the end of the research period, the whole lake was classified as eutrophic, with a modest inclination towards heightened eutrophication severity. The research underscores the pressing need for elaboration of a holistic lake management approach, where ITS, which has proven to be a valuable and reliable express-monitoring tool, can be used for obtaining information necessary for solving different applied tasks for protection and conservation strategies.

Effective water resource management is contingent on sound water legislation, which plays a crucial role in shaping worldwide water management strategies. The objective of this review is to analyze water policies and legislation in India, and pinpoint issues in water policy implementation and management. Additionally, the review includes an examination of the European Water Framework Directive (WFD) to explore the differences between European water legislation assumptions and water management, which can aid in adapting certain EU assumptions to Indian legislation. India faces urgent water-related problems due to inadequate water resource management in individual states and nationwide, which can be mitigated by implementing coherence in water policies and collaborating with policymakers, practitioners, and stakeholders from various economic sectors to enhance water governance strategies. Comparative analysis revealed that the European Water Framework Directive adopts a more specific and integrated approach, supported by precise legal regulation, wide-ranging stakeholder involvement, and investments in technology and infrastructure to achieve its set water management goals. In contrast, the Indian water legislation framework lacks a systemic approach, and the main gap lies in the inconsistency between national and state water legislation and the lack of proper coordination between the state and central governments. Unifying legal regulations at the national and state levels in cooperation with responsible legislative and executive institutions at appropriate governmental levels can help solve water management problems and achieve set goals.

The Dal Lake ecosystem is a vital freshwater body situated in the heart of Srinagar, Kashmir, India. It is not only a natural asset but also a cornerstone of environmental health, economic vitality, cultural heritage, and urban sustainability. In the last few decades, the condition of the lake ecosystem and water quality has deteriorated significantly owing to the intensification of the eutrophication process. Effective integrated management of the lake is crucial for the long-term sustainable development of the region and the communities that rely on it for their livelihoods. The main reasons for eutrophication are the substantial quantity of anthropogenic pollution, especially nutrients, discharged from the catchment area of the lake and the overexploitation of the lake space and its biological resources. The research presented in this paper aimed to diagnose the state of the lake by analysing trends in eutrophication development and its long-term changes related to the catchment area and lake ecosystem relationships. The research period was 25 years, from 1997 to 2023. Land use and land cover data and water quality monitoring data, which are the basis for trophic state assessment, allowed us to analyze the long-term dynamics of eutrophication in the reservoir. For these purposes, GIS-generated thematic maps were created by using QGIS software version 3.44.1, and an appropriate methodology for quantifying eutrophication was chosen and adapted to the specifics of Dal Lake. The obtained results provide a foundation for a eutrophication management strategy that considers the specificity of the Dal Lake ecosystem and the impact of the catchment area. The outcomes highlighted the varied trophic conditions in different lake basins and the dominance of eutrophic conditions during the study period. The research highlights the complexity of the problem and underscores the need for a comprehensive lake management system.

Dal Lake, the freshwater lake in Srinagar, Jammu and Kashmir, has experienced significant water quality changes over the last two decades due to anthropogenic activities, intensifying eutrophication and threatening its ecological integrity. The study aimed to identify the main limiting factors of eutrophication process in Dal Lake and to rate the impact of traditional eutrophication drivers, i.e. total nitrogen (TN), ammonium (NH₄⁺), total phosphorus (TP), orthophosphate (PO₄³⁻), WT (water temperature), T (transparency), and chemical oxygen demand (COD), Artificial neural networks (ANNs) were employed to identify key drivers to develop predictive models of eutrophication dynamics. Three ANN models with different input combinations were developed. In an initial train–test split, the most complex configuration (model 3) yielded the highest correlation with the index of trophic state (ITS) (training R² = 0.24, testing R² = 0.20, r = 0.46–0.49), whereas 5-fold cross-validation showed that the simpler model 2 achieved the lowest average root mean square error (RMSE) and mean absolute error (MAE) but the highest mean R². Overall explanatory power was modest (R² ≤ 0.20), indicating that the ANNs captured only a small proportion of ITS variability. Permutation-based sensitivity analysis showed that COD and TP are consistently the most influential predictors of ITS, while the remaining nutrient variables (TN, PO₄³⁻, NH₄⁺) contributed weakly in this dataset. Thus, the ANN approach yields only partially informative insights into the relationships between water-quality variables and ITS in Dal Lake and is not yet suitable as a stand-alone forecasting tool for eutrophication dynamics.

The aim of this study was to determine the factors that influence eutrophication. The factors causing eutrophication are widely known, but identifying the primary threat for a specific water body remains challenging. The study objects were the warm monomictic urban Dal Lake in Kashmir, India, and the artificial dam reservoir Dobczyce in Poland. Data analysis methods, including multiple regression and artificial neural networks (NNET and NeuralNet) from the R package [ver. 4.5.2], were used. Regarding Dal Lake, the factor most influencing the trophic change was total nitrogen. In contrast, for the Dobczyce dam reservoir, water temperature was the dominant factor. Although the regression method did not provide clear results, neural networks enabled the identification of the limiting factors; therefore, the proposed approach may be useful for determining the factors limiting the eutrophication process. The core novelty of this research lies in demonstrating the potential of artificial neural networks to identify key factors causing eutrophication, particularly under conditions of limited data.