Frequent and frequently deliberate release of plastics leads to accumulation of plastic waste in the environment which is an ever increasing ecological threat.

The focus of this study was to investigate fate and transport of toluene, a light non-aqueous phase liquids (LNAPL), in subsurface under dynamic groundwater table conditions. A series of experiments were conducted using two-dimensional (2-D) sand tank setup having the dimension of 125cm L×90cm H×10cm W and integrated with an auxiliary column of inner diameter 14 cm and height 120 cm. In the beginning a steady state flow and LNAPL transport experiment was conducted under stable groundwater table condition. Thereafter, three groundwater table fluctuation experiments were conducted by rising and falling groundwater table in 2, 4 and 8 hours to maintain a rapid, general, and slow fluctuation conditions respectively. Pure phase of toluene was injected at the rate of 1mL/minute for a total duration of 5 minutes. The soil-water and soil vapor samples were periodically collected and analyzed for toluene concentrations. Later, the representation of 2-D sand tank setup was numerically simulated to get the response of flow and the LNAPL transport under varying groundwater table conditions. The analysis of the results show that a large LNAPL pool area (250 cm 2) gets developed under rapid fluctuating groundwater condition which significantly enhances the dissolution rate and contributes for a high concentration of

India is facing the worst water crisis in its history, and major Indian cities which accommodates about 50% of its population will be among highly groundwater stressed cities by 2020. In past few decades, the urban groundwater resources declined significantly due to over exploitation, urbanization, population growth and climate change. To understand the role of these variables on groundwater level fluctuation, we developed a machine learning based modelling approach considering singular spectrum analysis (SSA), mutual information (MI), genetic algorithm (GA), artificial neural network (ANN), and support vector machine (SVM). The developed approach was used to predict the groundwater levels in Bengaluru, a densely populated city with declining groundwater water resources. The input data consist of groundwater levels, rainfall, temperature, NOI, SOI, NIÑO3 and monthly population growth rate, and were pre-processed using mutual information, genetic algorithm and lag analysis. Later, the optimized input sets were used in ANN and SVM to predict monthly groundwater level fluctuations. The results suggest that the machine learning based approach with data pre

Microplastic disposal into riverine ecosystems is an emergent ecological hazard that mainly originated from land-based sources. This paper presents a comprehensive review on physical processes involved in microplastics transport in riverine ecosystems. Microplastic transport is governed by physical characteristics (e.g., plastic particle density, shape, and size) and hydrodynamics (e.g., laminar and turbulent flow conditions). High-density microplastics are likely to prevail near riverbeds, whereas low-density particles float over river surfaces. Microplastic transport occurs either due to gravity-driven (vertical transport) or settling (horizontal transport) in river ecosystems. Microplastics are subjected to various natural phenomena such as suspension, deposition, detachment, resuspension, and translocation during transport processes. Limited information is available on settling and rising velocities for various polymeric plastic particles. Therefore, this paper highlights how appropriately empirical transport models explain vertical and horizontal distribution of microplastic in riverine ecosystems. Microplastics interact, and thus feedback loops within the environment govern their fate, particularly as these ecosystems are under increasing biodiversity loss and climate change threat. This review provides outlines for fate and transport of microplastics in riverine ecosystems, which will help scientists, policymakers, and stakeholders in better monitoring and mitigating microplastics pollution.

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