An integrated modelling framework for multiple pollution source identification in surface water

A DiffeRential Evolution Adaptive Metropolis (DREAM)-based inverse model for continuous release source identification in river pollution incidents: Quantitative evaluation and sensitivity analysis

The identification of continuous pollution sources for rivers is of great concern for emergency response. Most studies focused on instantaneous river pollution sources and associated incidents. There is a dire need to address continuous pollution sources, as pollutant discharge may impose a major impact on the water ecosystem. Therefore, in this study, a novel inverse model is proposed to identify the continuous point sources in river pollution incidents that would estimate the source strength, location, release time, and spill time. The proposed inverse model combines the advanced DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm and the forward transport advection-dispersion equation to infer the posterior probability distribution of source parameters for quantifying uncertainties. In addition, the performance of the DREAM-based model is compared with those of the Metropolis-Hastings (MH)-based and genetic algorithm (GA)-based models. The results show that the DREAM-based model performs accurately for both the hypothetical and the field tracer cases. The comparative analysis shows that the DREAM-based model performs better in saving computation time, improving the accuracy of results, and reconstructing pollutant concentrations. Observation errors significantly influence the accuracy of the identification results from the DREAM-based model. In addition, a comprehensive sensitivity analysis of the DREAM-based model is conducted. The identification results from the DREAM-based model are sensitive to the dispersion coefficient and river velocity. The accuracy of the inverse model could be improved by increasing the monitoring number and by monitoring locations closer to the spill site. The findings of this study can improve decision-making during emergency responses to sudden river pollution incidents.

Assessing petrochemical effluent effect on heavy metal pollution in Musa Estuary: A numerical modeling approach

The objective of this study is to assess the effect of petrochemical effluent on heavy metal pollutant in the Musa Estuary ecosystem in the North-western region of the Persian Gulf, through numerical modeling. The outfall of 30 petrochemical plants poses a potential threat to the estuary's seawater and sediment quality, environment, and public health. A combined hydrodynamic and ecologic modeling framework is applied to predict the spatial distribution of BOD and hazardous heavy metals in this estuary. MIKE 21 Flow Model (FM) CFD software is applied to simulate the tidal waves hydrodynamics, next to applying the MIKE ECO Lab models to predict the distribution of BOD and heavy metals in ambient water. The accuracy of the modeling framework is validated against measured water level, current speed, and water quality data. The results reveal that the level of lead concentration corresponds with the national standard, while the BOD, arsenic, molybdenum and vanadium exceed the limit in some areas, particularly in the tidal zone. The optimal outlet locations that effectively meet the standard concentrations of the heavy metals in the ambient water of the estuary are determined. The results confirm that the new outlet configuration corresponds with the standards: 0.198 μg/L for arsenic concentrations, 0.182 μg/L for molybdenum, 1.530 μg/L for vanadium, and 1.132 mg/L for BOD, at maximum. This study contributes to the perception of estuarine dynamics and provides practical implications for estuarine sustainable management and pollution control.