GIS-Integrated Digital Twin Framework for Dynamic Environmental Site Assessment and Contaminated Plume Delineation in Petroleum Hydrocarbon Spill Zones

Abstract

This study addresses the persistent problem of uncertainty in dynamic environmental site assessment and contaminated plume delineation in petroleum hydrocarbon spill zones, where static and fragmented assessment methods often limit the accuracy of contamination boundary identification, monitoring continuity, and remediation planning. The purpose of the research was to examine whether a GIS-integrated digital twin framework could improve spatial interpretation, dynamic environmental monitoring, and decision-support reliability in petroleum hydrocarbon spill contexts. The study adopted a quantitative, cross-sectional, case-based design and collected data from 210 professionals drawn from cloud-enabled and enterprise-oriented environmental assessment cases, including environmental engineers, GIS specialists, hydrogeologists, remediation consultants, and petroleum site managers. The key independent variables were GIS integration, digital twin responsiveness, and real-time environmental data integration, while the dependent variables were spatial confidence in plume delineation, dynamic monitoring effectiveness, and decision-support reliability. Data were gathered using a structured 5-point Likert-scale questionnaire and analyzed through descriptive statistics, Cronbach’s alpha reliability testing, correlation analysis, and multiple regression modeling. The findings showed strong support for the proposed framework, with high mean scores for GIS integration (M = 4.32, SD = 0.61), digital twin responsiveness (M = 4.26, SD = 0.66), real-time environmental data integration (M = 4.18, SD = 0.69), spatial confidence in plume delineation (M = 4.29, SD = 0.58), dynamic monitoring effectiveness (M = 4.21, SD = 0.64), and decision-support reliability (M = 4.35, SD = 0.56). Correlation results indicated strong positive relationships, including GIS integration with spatial confidence (r = .71, p < .001) and digital twin responsiveness with monitoring effectiveness (r = .74, p < .001). Regression results further showed that GIS integration significantly predicted plume confidence (β = .41, p < .001, R² = .58), digital twin responsiveness strongly predicted monitoring effectiveness (β = .46, p < .001, R² = .61), and the combined framework significantly explained decision-support reliability (R² = .64, F = 52.87, p < .001). The study implies that integrating geospatial intelligence, responsive digital representation, and timely environmental data can substantially enhance contamination assessment accuracy, plume interpretation, and evidence-based remediation planning in petroleum hydrocarbon spill management.