Integrated framework for assessing the impact of inland oil spills on a river basin: Model and case study in China

Wang, Jiahui; Wang, Shu; Zhu, Zhenduo; Yang, Yufeng; Zhang, Qiang; Xu, Sheng; Yan, Jianing

doi:10.1016/j.ecolind.2024.111576

Cited by 1 publication

(1 citation statement)

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“…Despite the smaller scale of inland river pollution, the cleanup and restoration efforts may be constrained by geographical location and environmental conditions, resulting in more challenging and complex cleanup processes. Therefore, in-depth research on inland river oil spills is imperative [2,24,25].…”

Section: Introductionmentioning

confidence: 99%

Study on the Oil Spill Transport Behavior and Multifactorial Effects of the Lancang River Crossing Pipeline

Lu,

Chen,

2024

Applied Sciences

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As the number of long-distance oil and gas pipelines crossing rivers increases, so does the risk of river oil spills. Previous research on oil spills in water mainly focuses on the oceans, and there are relatively few studies on oil spills in rivers. This study established two-dimensional hydrodynamic and oil spill models for the Lancang River crossing pipeline basin and verified the model’s accuracy. The oil spill transport process under different scenarios was simulated, and the oil spill transport state data set was established. The effects of river flow, wind, and leakage mode on the transport behavior of oil spills were studied. The results show that an increase in flow rate accelerates the migration, diffusion, and longitudinal extension behavior of oil spills; Changes in wind speed have less effect on the transport behavior of oil spills under downwind and headwind conditions. The mode of leakage mainly affects the diffusion and longitudinal extension of the oil spill. The oil spill transport state prediction model was established using machine learning combination algorithms. The three combined machine learning algorithms, PSO-SVR, GA-BPNN, and PSO-BPNN, have the best performance in predicting the oil spill migration distance, oil spill area, and the length of the oil spill contamination zone, respectively, with the coefficient of determination (R2) and the 1-Mean Absolute Percentage of Error (1-MAPE) above 0.971, and the prediction model has excellent accuracy. This study can provide support for the rapid development of emergency response plans for river crossing pipeline oil spill accidents.

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Section: Introductionmentioning

confidence: 99%