使用 PyCaret 建立绿藻对盐度和石油污染的生长反应模型,用于原油生物修复。

IF 2.2 4区 环境科学与生态学 Q3 ENVIRONMENTAL SCIENCES
Environmental Technology Pub Date : 2025-03-01 Epub Date: 2024-07-07 DOI:10.1080/09593330.2024.2374027
Mohamed Abbas, Lixiao Ni, Cunhao Du
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引用次数: 0

摘要

原油泄漏严重影响了水生生态系统,因此需要创新的修复策略。以微藻类为基础的生物修复,尤其是利用小球藻,提供了一种前景广阔的解决方案。本研究引入了一个新的框架,评估选定环境压力因素对微藻适应性的综合影响,超越了传统的孤立因素分析。通过将因子实验设计与使用 PyCaret AutoML 和 SHAP 值的机器学习方法相结合,我们详细研究了原油浓度、盐度和暴露持续时间如何影响绿藻的生长。Extra Trees 回归模型在预测生物量浓度这一关键适应性指标方面具有很高的准确性,其 MAE 为 0.0202,RMSE 为 0.029,R² 为 0.8875。SHAP 分析强调盐度和原油是影响生长的重要因素,而暴露持续时间的影响较小。值得注意的是,C. vulgaris 对盐度的敏感性高于对原油的敏感性,这表明它可能面临高盐度的挑战,但同时对石油污染物也有很强的耐受性。这些发现加深了我们对微藻类在污染环境中的反应的理解,并建议利用环境因素的协同作用和机器学习的洞察力,改进受石油泄漏影响的盐碱水域的生物修复方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using PyCaret to model Chlorella vulgaris's growth response to salinity and oil contamination for crude oil bioremediation.

Crude oil spills significantly impact aquatic ecosystems, necessitating innovative remediation strategies. Microalgae-based bioremediation, particularly with Chlorella vulgaris, offers a promising solution. This study introduces a novel framework that evaluates the combined effects of selected environmental stressors on microalgal adaptability, advancing beyond traditional isolated factor analyses. By integrating a factorial experimental design with a machine learning approach using PyCaret AutoML and SHAP values, we provide a detailed examination of how crude oil concentration, salinity, and exposure duration affect C. vulgaris growth. The Extra Trees Regressor model emerged as highly accurate in predicting biomass concentration, a crucial adaptability indicator, achieving an MAE of 0.0202, RMSE of 0.029, and an R² of 0.8875. SHAP analysis highlighted salinity and crude oil as significant growth influencers, with exposure duration playing a minor role. Notably, C. vulgaris exhibited more sensitivity to salinity than to crude oil, indicating potential high-salinity challenges but also a strong tolerance to oil pollutants. These findings enhance our understanding of microalgal responses in polluted environments and suggest improved bioremediation approaches for saline waters affected by oil spills, leveraging the synergy of environmental factors and machine learning insights.

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来源期刊
Environmental Technology
Environmental Technology 环境科学-环境科学
CiteScore
6.50
自引率
3.60%
发文量
0
审稿时长
4 months
期刊介绍: Environmental Technology is a leading journal for the rapid publication of science and technology papers on a wide range of topics in applied environmental studies, from environmental engineering to environmental biotechnology, the circular economy, municipal and industrial wastewater management, drinking-water treatment, air- and water-pollution control, solid-waste management, industrial hygiene and associated technologies. Environmental Technology is intended to provide rapid publication of new developments in environmental technology. The journal has an international readership with a broad scientific base. Contributions will be accepted from scientists and engineers in industry, government and universities. Accepted manuscripts are generally published within four months. Please note that Environmental Technology does not publish any review papers unless for a specified special issue which is decided by the Editor. Please do submit your review papers to our sister journal Environmental Technology Reviews at http://www.tandfonline.com/toc/tetr20/current
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