| 机器学习在微塑料识别与环境风险评估中的应用研究进展 |
| 摘要点击 1337 全文点击 220 投稿时间：2023-02-15 修订日期：2023-05-11 |
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| 中文关键词 微塑料 机器学习 识别 污染特征 环境效应 |
| 英文关键词 microplastics machine learning identification pollution characteristics environmental effects |
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| 中文摘要 |
| 微塑料是一种新型污染物，可以在环境中长期存在并造成生态风险.目前，微塑料污染已成为全球性的重大环境问题.借助新的技术途径，提高微塑料识别的简便性和可靠性，并系统分析各类环境介质中微塑料的污染特征，明确微塑料的环境效应，对科学准确评价微塑料污染的环境风险具有重要意义.机器学习技术通过学习和解析大量数据建立结果评估或预测模型，目前已广泛应用于微塑料领域的相关研究.机器学习的应用可以提高视觉和光谱识别微塑料的自动化程度和识别效率，为微塑料污染溯源提供方法支撑并有助于揭示微塑料的复杂环境效应机制.通过综述机器学习技术在微塑料识别与环境风险评估中的应用研究进展，概括了机器学习在上述方向的应用特点和局限性，为机器学习在相关方向的发展和应用提出建议与展望. |
| 英文摘要 |
| Microplastics are an emerging contaminant that can persist in the environment for extended periods， posing risks to ecological systems. Recently， microplastic pollution has emerged as a major global environmental problem. In order to ensure accurate and scientific evaluation of the ecological risks associated with microplastic pollution， it is of paramount importance to improve the simplicity and reliability of microplastic identification， systematically analyze the pollution characteristics of microplastics in various environmental media， and clarify their environmental impacts. Machine learning technology has gained widespread attention in microplastic research by learning and analyzing large volumes of data to establish result evaluation or prediction models. The use of machine learning can enhance the automation and identification efficiency of visual and spectral identification of microplastics， provide scientific support for tracing the sources of microplastic pollution， and help reveal the complex environmental effects of microplastics. This review provides a summary of the application characteristics and limitations of machine learning in the aforementioned areas by reviewing the progress made in research that employs machine learning technology in microplastic identification and environmental risk assessment. Furthermore， the findings of the review will provide suggestions and prospects for the development and application of machine learning in related areas. |
