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Based On GMS, A Groundwater Environmental Impact Prediction Study Of A Hazardous Waste Warehouse

Posted on:2019-01-23Degree:MasterType:Thesis
Country:ChinaCandidate:N ZhaoFull Text:PDF
GTID:2351330548961702Subject:Environmental Engineering
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Groundwater as a significant part of water resources,the quality of it directly affects the normal operation and development of ecosystems,it also plays a key role in protecting residential water and promoting social and economic development.Groundwater is difficult to recover after contaminated due to the fact that groundwater is buried beneath the surface of earth.Contaminated groundwater will directly cause serious impact to human health.In recent years,lead-acid battery have been widely used in the automobile,communications and electronics industries as due to their long service life and heavy load discharge.Furthermore,the application amount of lead-acid battery is increasing yearly.According to the“Policy for Prevention and Control of Waste Battery Pollution”promulgated by China,used lead-acid batteries are classified as hazardous wastes.Therefore,they must be stored in qualified hazardous waste warehouses.The poor management of hazardous waste warehouses could result the leakage of lead-acid batteries,and the leaked liquid will cause serious pollution to groundwater.Based on GMS,this paper evaluates and predicts groundwater pollution that may be caused during the storage of lead-acid batteries stored in a hazardous waste warehouse.First of all,based on the fully understanding of hydrogeological conditions of the study area,the current status of groundwater under a hazardous waste warehouse which is storing lead-acid storage batteries was evaluated by hydrogeological investigation and detection of groundwater environment quality.Based on this,according to the aquifer structure,groundwater flow and hydraulic characteristics of the study area,the groundwater system was systematically generalized into heterogeneous anisotropic,two-dimensional stable groundwater flow system.The corresponding data model was established through GMS,based on actual data such as stratum and source strength.Data model was revised by monitored water level data,and the dynamics of the actual water level and water flow were reproduced by the established model.Showed by fitting results of the measured flow field and the simulated flow field,the groundwater flow field was generally aligning with the measured flow field.Therefore,the established mathematical model met the requirements of model precision,and it can be used in the following study which to predict SO42-solute transport of pollutant.SO42-is the most abundant of many ions in lead-acid batteries electrolyte,which was selected as the characteristic pollution factor to be predicted and analyzed,and The temporal and spatial variation of SO42-in the groundwater system was predicted in the accident condition of the study area.After 100 days,sulfate scope reaches 21842m2,the plume will forward transport downstream 64m,1000 days,to expand the scope sulfate 34170m2,the plume will forward the downstream transport 124m,10 years later,sulfate scope expanded 73328m2,downstream migration 307m,20 years later,sulfate scope expanded 137248m2,downstream migration 606m.It can be seen that,the influence scope of groundwater was continuously expanded as the passage of time,and gradually move downstream to affected the downstream water quality.Therefore,according to the predications,the prevention and treatment measures should be put forward.In conclusion,propose pollution prevention measures and put forward reasonable ground-level anti-seepage measures and groundwater monitoring programs.The groundwater environmental assessment prediction can provide scientific and reasonable reference and suggestion basis for the environmental management of such lead-acid battery hazardous waste storage projects,and is of great significance to the protection of groundwater around the research area.
Keywords/Search Tags:environmental impact assessment of groundwater, waste warehouse, SO42-, prediction, lead-acid batteries
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