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Environmental Multimedia Modeling System (EMMS) for Health Risk Assessment: Key Influencing Factors and Uncertainties Researches.

Author(s): Yuan J, Chen Z, Ding S, Zhang Q, Jia Y

In this paper, a former developed Environmental Multimedia Modeling System (EMMS) combining with risk assessment Monte Carlo Method (MCM) was further used for exploring the interaction between groundwater and surface water. When using this EMMS-MCM simulati...

Article GUID: 33410257
Simulation and computer modeling of asphaltene in different solvents on oil-water interfaces using a molecular dynamic methodology.

Author(s): Yuan J, Elektorowicz M, Chen Z, Segun GA, Vakili M, Zhong L, Wang B, Zhu J, Wu Y

J Mol Graph Model. 2019 Sep 13;93:107450 Authors: Yuan J, Elektorowicz M, Chen Z, Segun GA, Vakili M, Zhong L, Wang B, Zhu J, Wu Y

Article GUID: 31542686
Extended environmental multimedia modeling system assessing the risk carried by pollutants in interacted air-unsaturated-groundwater zones.

Author(s): Yuan J, Elektorowicz M

J Hazard Mater. 2019 Jul 22;381:120852 Authors: Yuan J, Elektorowicz M

Article GUID: 31376662
Title:Environmental Multimedia Modeling System (EMMS) for Health Risk Assessment: Key Influencing Factors and Uncertainties Researches.
Authors:Yuan J,  Chen Z,  Ding S,  Zhang Q,  Jia Y
Link:https://www.ncbi.nlm.nih.gov/pubmed/33410257
DOI:10.1002/ieam.4387
Category:Integr Environ Assess Manag
PMID:33410257
Dept Affiliation: ENCS
1 Green Intelligence Environmental School, Yangtze Normal University, Chong Qing, China.
2 University of Alberta, Edmonton, Alberta, Canada.
3 Department of Building, Civil, and Environmental Engineering, Concordia University, Montreal, Quebec, Canada.
4 Institute of Energy and Environment, Anhui University of Technology, Ma'anshan, Anhui, China.

Description:

Environmental Multimedia Modeling System (EMMS) for Health Risk Assessment: Key Influencing Factors and Uncertainties Researches.

Integr Environ Assess Manag. 2021 Jan 06; :

Authors: Yuan J, Chen Z, Ding S, Zhang Q, Jia Y

Abstract

In this paper, a former developed Environmental Multimedia Modeling System (EMMS) combining with risk assessment Monte Carlo Method (MCM) was further used for exploring the interaction between groundwater and surface water. When using this EMMS-MCM simulation, this paper concentrate on combining several influencing factors on its permeability coefficient and other parameters, such as hydraulic conductivity, density, recharge, layers, and depth. Those influencing factors are key factors to the uncertain natural geological media and hydrographical factors, causing big challenges related to the optimal allocation of water. Especially, the system working with an EMMS-MCM simulation combined with Risk Quotient (RQ) will be the source of quantitative analysis and evaluation of environmental risk hazards, and the environmental risk hazards are within the acceptable range when considering optimal water allocation. The EMMS-MCM is verified and analyzed by comparing the data of hazardous sites with the investigation results considering those influencing factors. A case study of benzene emissions from Trail Road Landfill Site shows that the upper and middle aquifers flow in the northwest direction. In the south and near the west of the Trail Road landfill, groundwater flows north. About 60% of the predicted concentration of benzene falls within the range of 1.15E-6 to 2.65E-6 kg/m3 . RQ risk assessment for the groundwater indicates that risk level decrease from 2016 to 2026. The use of EMMS-MCM simulation will help to make hazardous field tests and future environmental management as reasonable as possible and enable one to predict the possible consequences of hazardous groundwater contamination after considering some water allocation factors. The integration of the EMMS-MCM and RQ will be the best tool for a faster and more accurate assessment of the long-term risks to pollutant diffusion areas, especially surface and groundwater environmental management. This article is protected by copyright. All rights reserved.

PMID: 33410257 [PubMed - as supplied by publisher]