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A framework and simple decision support tool for groundwater contamination assessment in an urban redevelopment precinct

Hydrogeology Journal, ISSN: 1435-0157, Vol: 27, Issue: 6, Page: 1911-1928
2019
  • 4
    Citations
  • 0
    Usage
  • 56
    Captures
  • 0
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    4
    • Citation Indexes
      4
  • Captures
    56

Article Description

Overcoming barriers to large-scale urban redevelopment on brownfield land is an essential step in the global drive toward achieving the UN’s sustainable development goals. Arguably the most significant barrier is site contamination. Decision support tools (DSTs) are potentially effective ways to assess contaminated sites and evaluate different remediation/management strategies as appropriate to local conditions. Whilst there has been extensive development of DSTs for single contaminated sites, only a limited number are available for assessing contamination at the ‘precinct’ (district) scale. This paper presents a framework and simple DST for the assessment of contaminated groundwater, using Australia’s largest urban redevelopment project, Fishermans Bend in Melbourne, as a case study. The value of the inclusion of precinct-wide data into individual site-scale investigations is demonstrated via the DST. Common contaminant sources identified across Fishermans Bend include fill contaminated with heavy metals, legacy landfill leachate containing heavy metals and per- and poly-fluoroalkyl substances (PFAS), and naturally occurring ammonia and methane in organic-rich sediments. By comparison, point sources of heavy metals and PFAS appear related to industrial sources. By using the DST, environmental practitioners and auditors can more effectively separate background conditions from point sources, characterise the ambient conditions of the aquifer and their relationships to natural and anthropogenic processes, and use contaminant data to inform remedial options assessment. Where these increasingly complex and varied environmental datasets can be collected, stored and managed within one central database, cross- or inter-disciplinary collaborations may drive improvements in solving environmental problems which typically arise during the redevelopment process.

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