The design and validation of a decision support system (DSS) for the preliminary risk assessment of brownfield sites (PRABS)
Bibliographic record
Abstract
Purpose This study aims to design and validate a decision support system (DSS), named preliminary risk assessment of brownfield sites (PRABS). It is intended that the proposed DSS will aid the identification of potential hazards and, in doing so, highlight challenges facing those stakeholders dealing with the decision-making on brownfield site redevelopments, where the examples of diverse stakeholders would include, for instance, risk assessors, local planning authorities, regulator, developers, civil engineers, architectures, landowners, investors and alike. Moreover, the DSS will enable them to promote safer redevelopment and minimise the risks to future occupants of brownfield sites and neighbouring lands, on the top of the tool being communal platform of an effective communication between them as it is for both experts and non-experts. Design/methodology/approach This research employs a comprehensive five-stage process, integrating both quantitative and qualitative methods and utilizing mixed methods for a nuanced exploration of data. The initial stage involves an in-depth examination of contemporary risk assessment tools for contaminated sites, setting the foundation and benchmarks for subsequent stages. Stage two focuses on creating a conceptual framework using insights from existing literature to guide the development of the DSS tool. Stage three introduces a validation mechanism through a questionnaire administered to experts. Stage four involves the active development of the DSS tool, transforming theoretical constructs into a practical application. The final stage, stage five, employs quantitative data analysis and case studies to validate, refine and enhance the DSS tool’s applicability in real-world scenarios, ensuring its approval. Findings This study presents PRABS, a user-friendly DSS for the PRABS. Validation through a quantitative online survey indicates strong support for PRABS, with around 80% of participants willing to recommend it due to its ease of use and information quality. Qualitative data analysis using real-life case studies further demonstrates the tool’s effectiveness. PRABS proves valuable in identifying hazards during the preliminary stage, accurately predicting potential contaminants despite limited input data in the case studies. The tool’s hazard identification aligns well with expert judgments and case study reports, confirming its practical utility. Practical implications This study has several limitations. First, the DSS identifies only hazards associated with one layer of site geology, even though sites may include multiple layers, which limits the comprehensiveness of the hazard identification process. Second, adopting an online survey approach posed challenges in achieving a high response rate and gathering a representative sample, making it uncertain how the results might vary with a higher number of professional participants. This limitation affects the generalisability of the findings. Finally, while this study identified 65 potential hazards associated with brownfield sites, this number could be expanded to include hazards related to plants, animals and air, indicating the need for a more inclusive approach to hazard identification. Given these limitations, future research should focus on addressing these gaps. Originality/value The contributions of this study offer practical benefits. Firstly, it enables the initial risk assessment process to be more comprehensive and integrated and reduces complexity in the risk assessment process by ensuring that all probabilities, along with their significance, are identified at the initial stage of the risk assessment. This could be a strong starting point for successfully conducting a more detailed risk assessment and remediation. Secondly, the developed PRABS can promote effective environmental communication among stakeholders, which should speed up the planning process and help develop brownfield sites more efficiently and effectively, while preserving the natural environment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".