期刊
BIORESOURCE TECHNOLOGY
卷 345, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.biortech.2021.126468
关键词
Oil biodegradation; Corexit; Salinity; Causal inference; Machine learning
资金
- Natural Sciences and Engineering Research Council of Canada (NSERC)
- Canada Research Chairs (CRC) program
- Fisheries and Oceans Canada (DFO)
- Canada Foundation for Innovation (CFI)
Through experiments and causal inference analysis, it was found that the addition of dispersants can alleviate the barriers of oil biodegradation in high-salinity environments, mainly by increasing cell abundance to enhance oil biodegradation.
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.
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