Studies on Physicochemical Parameters of Kalvati Lake in Ambajogai
AbstractThe physicochemical properties of freshwater bodies are essential indicators of the health and water quality of aquatic ecosystems. These parameters not only reflect the overall state of the water but also provide critical insights into potential threats to biodiversity and public health. This study investigates the seasonal variations in physicochemical parameters of Kalvati Lake, located in Ambajogai City, District Beed, Maharashtra, aiming to assess the lake's ecological status. Key parameters such as pH, biochemical oxygen demand (BOD), total dissolved solids (TDS), temperature, and nutrient levels (nitrates and phosphates) were analyzed over the course of different seasons to provide a comprehensive understanding of water quality fluctuations. The findings revealed significant pollution levels in the lake, primarily due to anthropogenic activities such as agricultural runoff, domestic sewage, and religious practices that contribute to nutrient overload and organic waste. Elevated levels of nitrates and phosphates were particularly concerning, indicating a high potential for eutrophication, which can lead to algal blooms, oxygen depletion, and further degradation of aquatic life. Seasonal variations in parameters like temperature and TDS also demonstrated how changes in environmental conditions influence the water’s ability to maintain ecological balance. The study emphasizes the urgent need for effective conservation strategies to address the pollution sources, such as the implementation of proper waste management systems, reduction of agricultural runoff, and community engagement in lake protection initiatives. Additionally, policy measures at the local and regional levels are essential to curb further degradation of water quality. By promoting sustainable practices and restoring the ecological balance of Kalvati Lake, the health of this vital freshwater ecosystem can be safeguarded.
Institutions
- Maharishi Mahesh Yogi Vedic University (IN)
- Russian Academy of Arts (RU)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2025-03-30
- DOI
- https://doi.org/10.5281/zenodo.15322396
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00