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A web-based Decision Support System (DSS) for preliminary risk assessment of Brownfield sites

Mahammedi, Charf

A web-based Decision Support System (DSS) for preliminary risk assessment of Brownfield sites Thumbnail


Authors

Charf Mahammedi



Abstract

Reusing brownfield sites always raises concern for the health and safety of site workers involved in site renovations and redevelopments and, subsequently, for the residents or occupants of the sites. As a minimum, a preliminary risk assessment is necessary to determine whether a brownfield site is contaminated and, if so, ensure any redevelopment is safe and suitable for its intended use. For instance, a developer may decide to redevelop brownfield site based on a preliminary risk assessment alone without any further investigation and detailed quantitative risk assessment, provided the developer is confident that any hazards present can be addressed using appropriate measures, and the acquisition brings broad commercial benefits. Despite growing interest in the progression of risk assessment tools, there are limited instruments available to brownfield site assessors to consult when conducting investigations at the preliminary risk assessment stage. This research seeks to bridge this gap and provide a Decision Support System (DSS) PRAofBS (Preliminary Risk Assessment of Brownfield sites) Tool to assist investigators and different stakeholders by identifying potential hazards associated with brownfield sites at the preliminary stage. As a result, a clear and comprehensive conceptual framework was developed to guide research in this field and shape the DSS Tool development. Experts validated the framework through a questionnaire survey. While the validation of the DSS Tool was facilitated by two approaches, firstly the DSS was uploaded to the internet, which involved testing tool by experts in terms of a graphical user interface (GUI), level of information, quality of data. The second approach was carried out by functional testing, which involved testing the tool’s outputs against real–life case studies to confirm information agreement. The framework validation results can be divided into two parts. Firstly, the statistical analyses revealed that the top information to identify the source of hazards is site history, made ground, invasive species, previous mining, storage of materials and old tanks, presence of radon, underground services and buildings and other structures. Furthermore, site geology, site hydrology, site hydrogeology, and site topography were rated as top information to identify the contaminants' pathway movement. At the same time, future site use scenario is critical to identify the critical receptor of the population most likely to be exposed and/or susceptible to soil contamination. Secondly, the likelihood of potential hazards was calculated by using a Voting Analytic Hierarchy Process (VAHP), which was useful for prioritizing and generally distinguishing potential hazards from more likely to least likely threats. On the subject of DSS validation, most of the participants reported that they found DSS easy to use. They were also positive about the level and quality of information and knowledge provided by the decision support system. In particular, they found it to be helpful for the preliminary risk assessment process of brownfield site. Finally, it is anticipated that, with some modifications, the DSS Tool could become a commercially viable interface.

Citation

Mahammedi, C. A web-based Decision Support System (DSS) for preliminary risk assessment of Brownfield sites. (Thesis). University of the West of England. Retrieved from https://uwe-repository.worktribe.com/output/7067723

Thesis Type Thesis
Deposit Date Feb 4, 2021
Publicly Available Date Mar 29, 2024
Keywords Brownfield Sites, Contaminated sites, Site investigation, Preliminary risk assessment, Conceptual framework, Decision–making, VAHP, Statistical analysis, Hazard, Risk assessment. Pollutant linkage model, Decision Support System (DSS)
Public URL https://uwe-repository.worktribe.com/output/7067723
Award Date Jun 29, 2021

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