Application of remodeled water quality indices for the appraisal of water quality in a Himalayan lake.

Natural and anthropogenic pollution influence the general hydrochemistry of freshwater sources. Effective management strategies need an accurate evaluation of the water quality parameters, and inferences extracted from the data should be based on the most appropriate statistical methods. Conventional water quality indices (WQI) being related to a large number of water quality parameters results in significant variability and analytical costs. The focus of this study was to develop a remodeled water quality index (WQImin) based on the localized trends in water quality and demonstrate it to understand water quality variations of Dal Lake (a freshwater lake in the Himalayan region). Spatio-temporal changes and trends of 14 water quality parameters were investigated that were arbitrated from the samples collected at 11 sampling locations during the water quality monitoring across the Dal Lake from September 2017 to August 2020. The results signify that the general mean WQI value was 81.9, and seasonal average WQI values ranges from 79.44 to 84.55. The water quality showed seasonal variance, with lowest values in summer, succeeded by autumn and winter, and highest in spring. Moreover, the results from stepwise multiple regression analysis indicated that the WQImin significantly correlates with six water quality parameters (ammonia, dissolved oxygen, chemical oxygen demand, temperature, turbidity, and nitrate) in Dal Lake. The WQImin model predicted the water quality of the Dal Lake with a coefficient of determination (R2) value of 0.96, root mean square error (RMSE) value of 4.1, and percentage error (PE) of 5.3%. The developed WQImin model can be applied as a cost-effective and efficacious approach to determine the water quality of fresh surface water bodies.

Are we underestimating stormwater? Stormwater as a significant source of microplastics in surface waters.

Stormwater represent a critical pathway for transporting microplastics (MPs) to surface waters. Due to complex dynamics of MPs in stormwater, its dispersion, weathering, risk, and transport are poorly understood. This review bridges those gaps by summarizing the latest findings on sources, abundance, characteristics, and dynamics involved in stormwater MP pollution. Weathering starts before or after MPs enter stormwater and is more pronounced on land due to continuous heat and mechanical stress. Land use patterns, rainfall intensity, MPs size and density, and drainage characteristics influence the transport of MPs in stormwater. Tire and road wear particles (TRWPs), littering, and road dust are major sources of MPs in stormwater. The concentrations of MPs varies from 0.38-197,000 particles/L globally. Further MP concentrations showed regional variations, highlighting the importance of local monitoring efforts needed to understand local pollution sources. We observed unique signatures associated with the shape and color of MPs. Fibers and fragments were widely reported, with transparent and black being the predominant colors. We conclude that the contribution of stormwater to MP pollution in surface waters is significantly greater than wastewater treatment plant effluents and demands immediate attention. Field and lab scale studies are needed to understand its behavior in stormwater and the risk posed to the downstream water bodies.

Selection of priority emerging contaminants in surface waters of India, Pakistan, Bangladesh, and Sri Lanka.

The challenge of emerging contaminants (ECs) in global surface water bodies and particularly in low- and middle-income countries such as India, Pakistan, Bangladesh, and Sri Lanka, is evident from the literature. The complexity arises from the high costs involved in EC analysis and the extensive list of ECs, which complicates the selection of essential compounds for scientific and regulatory investigations. Consequently, monitoring programs often include ECs that may have minimal significance within a region and do not pose known or suspected ecological or human health risks. This study aims to address this issue by employing a multi-risk assessment approach to identify priority ECs in the surface waters of the aforementioned countries. Through an analysis of occurrence levels and frequency data gathered from published literature, an optimized risk quotient (RQ) was derived. The findings reveal a priority list of 38 compounds that exhibit potential environmental risks and merit consideration in future water quality monitoring programs. Furthermore, the majority of antibiotics in India (12 out of 17) and Pakistan (7 out of 17) exhibit a risk quotient for antimicrobial resistance selection (RQAMR) greater than 1, highlighting the need for devising effective strategies to mitigate the escalation of antibiotic resistance in the environment.