Spatially Explicit Risk Modeling Framework for Tracking Subsurface Contaminant Migration in Data-Limited Remediation Sites

• Author(s): Azeez Lamidi Olamide; Omolola Badmus
• Paper ID: 1713080
• Page: 178-198
• Published Date: 30-11-2019
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 2 Issue 6 December-2018

Abstract

This study presents a Spatially Explicit Risk Modeling Framework designed to track and predict subsurface contaminant migration in data-limited remediation sites, where sparse monitoring networks, incomplete hydrogeological characterization, and constrained resources often undermine effective decision-making. The framework integrates geospatial analysis, probabilistic risk assessment, and simplified transport modeling to generate spatially resolved risk surfaces that support remediation planning under uncertainty. Rather than relying on dense datasets or computationally intensive simulations, the approach leverages readily available site information, including limited borehole logs, historical land-use records, shallow groundwater observations, and proxy environmental indicators, to construct a defensible representation of contaminant behavior in the subsurface. At the core of the framework is a modular architecture that combines conceptual site modeling with spatial interpolation techniques and Bayesian inference to estimate contaminant plume evolution and associated exposure risks. Uncertainty is explicitly quantified and propagated through the model using scenario-based simulations, allowing practitioners to identify high-risk zones, prioritize sampling locations, and evaluate remedial alternatives with transparent confidence bounds. The spatially explicit structure enables visualization of risk gradients across heterogeneous subsurface conditions, facilitating communication between technical experts, regulators, and community stakeholders. The framework is particularly suited to early-stage site assessment and adaptive management contexts, where rapid screening and iterative refinement are required. By emphasizing scalability and transferability, the model can be applied across diverse contaminated land settings, including abandoned industrial sites, legacy petroleum facilities, and informal waste disposal areas. A hypothetical case application demonstrates how the framework can guide targeted data acquisition, reduce uncertainty over time, and improve the efficiency of remediation strategies despite limited input data. Overall, the proposed Spatially Explicit Risk Modeling Framework offers a practical and robust tool for enhancing subsurface contaminant tracking and risk-informed decision-making in data-constrained environments. It bridges the gap between qualitative conceptual models and data-intensive numerical simulations, supporting more resilient, transparent, and cost-effective remediation outcomes. The framework also aligns with contemporary sustainability and environmental governance objectives by promoting evidence-based prioritization, minimizing unnecessary intrusive investigations, and enabling more equitable allocation of remediation resources in low-capacity settings, thereby strengthening long-term environmental protection and public health safeguards. This approach supports adaptive learning and compliance.

Keywords

Spatial Risk Modeling; Subsurface Contamination; Contaminant Migration; Data-Limited Sites; Geospatial Analysis; Uncertainty Quantification; Remediation Planning

Citations

IRE Journals:
Azeez Lamidi Olamide, Omolola Badmus "Spatially Explicit Risk Modeling Framework for Tracking Subsurface Contaminant Migration in Data-Limited Remediation Sites" Iconic Research And Engineering Journals Volume 2 Issue 6 2018 Page 178-198

IEEE:
Azeez Lamidi Olamide, Omolola Badmus "Spatially Explicit Risk Modeling Framework for Tracking Subsurface Contaminant Migration in Data-Limited Remediation Sites" Iconic Research And Engineering Journals, 2(6)