Mechanism of nearshore sediment-facilitated oil transport: New insights from causal inference analysis

• Published in: Journal of hazardous materials Vol. 465; p. 133187
• Main Authors: Fu, Hongrui, Kang, Qiao, Sun, Xiaojun, Liu, Wei, Li, Yang, Chen, Bing, Zhang, Baiyu, Bao, Mutai
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 05.03.2024
• Subjects: Machine learning; Oil-particle aggregate; Spilled oil transport; Structural causal models; Structural causal models; Oil-particle aggregate; Spilled oil transport; Machine learning
• ISSN: 0304-3894; 1873-3336
• DOI: 10.1016/j.jhazmat.2023.133187

A quantitative understanding of spilled oil transport in a nearshore environment is challenging due to the complex physicochemical processes in aqueous conditions. The physicochemical processes involved in oil sinking mainly include oil dispersion, sediment settling, and oil-sediment interaction. For the first time, this work attempts to address the sinking mechanism in petroleum contaminant transport using structural causal models based on observed data. The effects of nearshore salinity distribution from the estuary to the ocean on those three processes are examined. The causal inference reveals sediment settling is the crucial process for oil sinking. Salinity indirectly affects oil sinking by promoting sediment settling rather than directly affecting oil-sediment interaction. The increase of salinity from 0‰ to 35‰ provides a natural enhancement for sediment settling. Notably, unbiased causal effect estimates demonstrate the strongest positive causal effect on the settling efficiency of sediments is posed by increasing oil dispersion effectiveness, with a normalized value of 1.023. The highest strength of the causal relationship between oil dispersion and sediment settling highlights the importance of the dispersing characteristics of spilled oil to sediment-facilitated oil transport. The employed logic, a data-driven method, will shed light on adopting advanced causal inference tools to unravel the complicated contaminants’ transport. [Display omitted] •The method of data-driven causal inference is first used to interpret oil transport.•Causal analysis unravels that sediment settling is the crucial process for oil sinking.•Salinity indirectly affects oil sinking by promoting sediment settling.•Unbiased causal effect estimates (1.023) highlight the importance of oil dispersion.•Multiple droplet-type OPAs tends to form in saltwater, which trapped more oil.