Pollution source identification methods and remediation technologies of groundwater: A review

Groundwater is an important source of drinking water.Groundwater pollution severely endangers drinking water safety and sustainable social development.In the case of groundwater pollution,the top priority is to identify pollution sources,and accurate information on pollution sources is the premise of efficient remediation.Then,an appropriate pollution remediation scheme should be developed according to information on pollution sources,site conditions,and economic costs.The methods for identifying pollution sources mainly include geophysical exploration,geochemistry,isotopic tracing,and numerical modeling.Among these identification methods,only the numerical modeling can recognize various information on pollution sources,while other methods can only identify a certain aspect of pollution sources.The remediation technologies of groundwater can be divided into in-situ and ex-situ remediation technologies according to the remediation location.The in-situ remediation technologies enjoy low costs and a wide remediation range,but their remediation performance is prone to be affected by environmental conditions and cause secondary pollution.The ex-situ remediation technologies boast high remediation efficiency,high processing capacity,and high treatment concentration but suffer high costs.Different methods for pollution source identification and remediation technologies are applicable to different conditions.To achieve the expected identification and remediation results,it is feasible to combine several methods and technologies according to the actual hydrogeological conditions of contaminated sites and the nature of pollutants.Additionally,detailed knowledge about the hydrogeological conditions and stratigraphic structure of the contaminated site is the basis of all work regardless of the adopted identification methods or remediation technologies.

Analysis of Pollution Sources and Countermeasures against Water Pollution in a Small River Basin of Chaohu Lake——A Case Study of the Shiwulihe River

The Shiwulihe River, the inflow river of Chaohu Lake in Hefei City, was taken as an example. Based on the current status of water quali- ty and analysis of pollution sources in the Shiwulihe River, countermeasures and suggestions of controlling water pollution were proposed to provide effective ways for the control of water pollution and restoration of aquatic ecosystem in the Shiwulihe River and other similar rivers flowing into lakes.

Analysis on the Infectious Characteristics of African Swine Fever Virus

African swine fever virus(ASFV)is the only pathogen that causes African swine fever(ASF).The virus has strong viability outside of pigs,easily causing cross-border transmission,and bringing great harm to the pig industry.This paper mainly introduced the biological characteristics and transmission methods of ASFV,and provides a certain theoretical basis for preventing ASF.

Identification of pollution sources in rivers using a hydrodynamic diffusion wave model and improved Bayesian-Markov chain Monte Carlo algorithm

Water quality restoration in rivers requires identification of the locations and discharges of pollution sources,and a reliable mathematical model to accomplish this identification is essential.In this paper,an innovative framework is presented to inversely estimate pollution sources for both accident preparedness and normal management of the allowable pollutant discharge.The proposed model integrates the concepts of the hydrodynamic diffusion wave equation and an improved Bayesian-Markov chain Monte Carlo method(MCMC).The methodological framework is tested using a designed case of a sudden wastewater spill incident(i.e.,source location,flow rate,and starting and ending times of the discharge)and a real case of multiple sewage inputs into a river(i.e.,locations and daily flows of sewage sources).The proposed modeling based on the improved Bayesian-MCMC method can effectively solve high-dimensional search and optimization problems according to known river water levels at pre-set monitoring sites.It can adequately provide accurate source estimation parameters using only one simulation through exploration of the full parameter space.In comparison,the inverse models based on the popular random walk Metropolis(RWM)algorithm and microbial genetic algorithm(MGA)do not produce reliable estimates for the two scenarios even after multiple simulation runs,and they fall into locally optimal solutions.Since much more water level data are available than water quality data,the proposed approach also provides a cost-effective solution for identifying pollution sources in rivers with the support of high-frequency water level data,especially for rivers receiving significant sewage discharges.