Machine learning-aided causal inference for unraveling chemical dispersant and salinity effects on crude oil biodegradation

Autor: Yiqi Cao, Qiao Kang, Baiyu Zhang, Zhiwen Zhu, Guihua Dong, Qinhong Cai, Kenneth Lee, Bing Chen
Rok vydání: 2022
Předmět:
Zdroj: Bioresource Technology. 345:126468
ISSN: 0960-8524
DOI: 10.1016/j.biortech.2021.126468
Popis: Chemical dispersants have been widely applied to tackle oil spills, but their effects on oil biodegradation in global aquatic systems with different salinities are not well understood. Here, both experiments and advanced machine learning-aided causal inference analysis were applied to evaluate related processes. A halotolerant oil-degrading and biosurfactant-producing species was selected and characterized within the salinity of 0-70 g/L NaCl. Notably, dispersant addition can relieve the biodegradation barriers caused by high salinities. To navigate the causal relationships behind the experimental data, a structural causal model to quantitatively estimate the strength of causal links among salinity, dispersant addition, cell abundance, biosurfactant productivity and oil biodegradation was built. The estimated causal effects were integrated into a weighted directed acyclic graph, which showed that overall positive effects of dispersant addition on oil biodegradation was mainly through the enrichment of cell abundance. These findings can benefit decision-making prior dispersant application under different saline environments.
Databáze: OpenAIRE
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