Simulation and Prediction of the Groundwater Pollution Caused by the Leakage from a Sewage Plant in an Aluminum Factory Under the Action of Ocean Tide Support

In order to predict the impact of wastewater from an aluminum plant treatment station on the groundwater environment under abnormal conditions (i.e., sewage leakage accident). Through the investigation of hydrogeological conditions, and then the permeability coefficient of the aquifer was measured through borehole injection tests. Finally, the groundwater pollution transport halo was obtained by numerical simulation based GMS software. The simulation results showed that the groundwater aquifer will be seriously polluted by COD and fluoride (F-) after the sudden sewage seepage accident. What’s more, the simulation results showed that the pollution concentration is getting higher and higher with time, which is analyzed to be caused by the small permeability of the water-bearing medium in the aquifer and the groundwater flow field was supported by seawater tide.

Organization of parallel-pipeline calculations for modeling the pollutant distribution process in a reservoir

One of the most acute problems for today is the water pollution. For rapid decision in emergency situations, it is necessary to develop effective software and algorithmic tools that allow us to make accurate forecasts of the environmental situation changing of coastal systems. Water pollution of the Azov and Black Seas by storm drains and human waste products leads to an increase of toxic substances concentrations that significantly exceed the maximum permissible values. The pollution transport problem is solved on the basis of the Navier-Stokes and the diffusion-convection-reaction equations. As a result of discretization of the continuous problem of transport of pollutants using the finite-difference approach for a rectangular grid, we obtain a system of linear algebraic equations (SLAE) of large dimension, which require significant time costs. To increase the efficiency of calculations (to reduce the time) on a multiprocessor computer system (MCS), there is a need to develop effective parallel algorithms for solving SLAE. The decomposition method for a two-dimensional computational domain is proposed in this paper, which allows organizing a parallel-pipeline process of calculations as follows: at each stage of calculations, each processor core simultaneously processes fragments of the computational domain that are offset from each other. This process is described in the form of a graph, in which each node corresponds to fragments of the computational domain, and the edges – a sign of the adjacency of fragments.

Plastic Pollutions in Aquatic Environment- A Review

Plastic pollution in aquatic ecosystems is a growing environmental concern, as it has the potential to harm ecology, imperil aquatic organisms and cost ecological damage. Although rivers and other freshwater environments are known to play an important role in carrying land-based plastic trash to the world's seas, riverine ecosystems are also directly impacted by plastic pollution. A detailed understanding of the origin, movement, fate, and effects of riverine plastic waste is critical for better quantifying worldwide plastic pollution transport and effectively reducing sources and dangers. In this review, we emphasize the current scientific state of plastic debris in rivers, as well as the existing knowledge gaps, providing a basic overview of plastics and the types of polymers commonly found in rivers and the threat they bring to aquatic ecosystems. We also go through the origins and fates of riverine plastics, as well as the mechanisms and factors that affect plastic debris transit and spatiotemporal variation. We give an overview of riverine plastic transport monitoring and modeling activities, as well as examples of typical values from throughout the world. Finally, we discuss what the future holds for riverine plastic research.

Mathematical modeling of the pollutant distribution process in the Gelendzhik Bay

Currently, the pollution problem of coastal sea waters in the resort areas of the Black Sea is becoming increasingly urgent. Thousands of chemicals, industrial and household waste enter the water basins every year, which significantly worsens the state of marine waters. Storm drains are saturated with pollutants at precipitation, washing out various chemical compounds, garbage and transporting them to the sea. In addition, a separate problem is poorly self-cleaning of the Gelendzhik Bay. A complete water change occurs in the period from 1 to 6 days. This paper covers the development, research and numerical implementation of a mathematical model of the pollution transport, including petroleum products, in the Gelendzhik Bay taking into account a number of important hydrodynamic and hydrophysical factors, methods of its numerical implementation, which allow predictive modeling of the pollution spread in shallow water systems in a limited time. A hydrobiological model of a coastal system characterized by significant depth differences has been developed. A three-dimensional mathematical model is designed to research the transformation of the phosphorus, nitrogen and silicon forms in the plankton dynamics problem. It takes into account the convective and diffusive transports, absorption and release of nutrients by phytoplankton, salinity, temperature, oxygen regime, etc. Using a spatial-three-dimensional hydrodynamics model, taking into account the physical properties of water environment of the coastal system, calculation results are used as input data for the development of scenarios for the dynamics of transport processes and the transformation of pollution biogenic elements in the water.

Impact of Sediment Layer on Longitudinal Dispersion in Sewer Systems

Experiments focused on pollution transport and dispersion phenomena in conditions of low flow (low water depth and velocities) in sewers with bed sediment and deposits are presented. Such conditions occur very often in sewer pipes during dry weather flows. Experiments were performed in laboratory conditions. To simulate real hydraulic conditions in sewer pipes, sand of fraction 0.6–1.2 mm was placed on the bottom of the pipe. In total, we performed 23 experiments with 4 different thicknesses of sand sediment layers. The first scenario is without sediment, the second is with sediment filling 3.4% of the pipe diameter (sediment layer thickness = 8.5 mm), the third scenario represents sediment filling 10% of the pipe diameter (sediment layer thickness = 25 mm) and sediment fills 14% of the pipe diameter (sediment layer thickness = 35 mm) in the last scenario. For each thickness of the sediment layer, a set of tracer experiments with different flow rates was performed. The discharge ranges were from (0.14–2.5)·10−3 m3·s−1, corresponding to the range of Reynolds number 500–18,000. Results show that in the hydraulic conditions of a circular sewer pipe with the occurrence of sediment and deposits, the value of the longitudinal dispersion coefficient Dx decreases almost linearly with decrease of the flow rate (also with Reynolds number) to a certain limit (inflexion point), which is individual for each particular sediment thickness. Below this limit the value of the dispersion coefficient starts to rise again, together with increasing asymmetricity of the concentration distribution in time, caused by transient (dead) storage zones.

Uncertainty Quantification of Granular Computing-neural Network Model for Prediction of Pollutant Longitudinal Dispersion Coefficient in Aquatic Streams

Discharge of pollution loads into natural water systems remains a global challenge that threatens water/food supply as well as endangers ecosystem services. Natural rehabilitation of the polluted streams is mainly influenced by the rate of longitudinal dispersion (Dx), a key parameter with large temporal and spatial fluctuates that characterizes pollution transport. The large uncertainty in estimation of Dx in streams limits evaluation of water quality in natural streams and design of water quality enhancement strategies. This study develops a sophisticated model coupled with granular computing and neural network models (GrC-ANN) to provide robust prediction of Dx and its uncertainty for different flow-geometric conditions with high spatiotemporal variability. Uncertainty analysis of Dx GrC-ANN model was based on the alteration of training data fed to tune the model. Modified bootstrap method was employed to generate different training patterns through resampling from a 503 global database of tracer experiments in streams. Comparison between the Dx values estimated by GrC-ANN to those determined from tracer measurements show the appropriateness and robustness of the proposed method in determining the rate of longitudinal dispersion. GrC-ANN model with the narrowest bandwidth of estimated uncertainty (bandwidth-factor =0.56) that brackets the most percentage of true Dx data (i.e., 100%) is the best model to compute Dx in streams. Given considerable inherent uncertainty reported in other Dx models, the Dx GrC-ANN model is suggested as a proper tool for further studies of pollutant mixing in turbulent flow systems such as streams.