摘要
环境激素类物质因其对生态系统和人类健康的潜在威胁，已成为环境科学领域的研究热点。本研究旨在系统探讨环境激素类物质的检测方法及其在自然环境中的行为特征。通过综合运用高效液相色谱-质谱联用技术（HPLC-MS/MS）、气相色谱-质谱联用技术（GC-MS）以及分子生物学手段，建立了高灵敏度、高特异性的检测体系，并成功应用于多种典型环境样本中目标化合物的定量分析。同时，结合实验室模拟与野外监测数据，深入解析了环境激素类物质在水体、土壤及大气中的迁移转化规律及其影响因素。研究发现，部分环境激素类物质具有较强的生物累积性和持久性，且其环境行为受光照、温度和微生物群落结构等多重因素的协同调控。此外，本研究创新性地提出了一种基于机器学习算法的预测模型，用于评估环境激素类物质的暴露风险及其生态效应，为相关环境管理政策的制定提供了科学依据。总体而言，本研究不仅完善了环境激素类物质的检测技术体系，还揭示了其复杂的环境行为机制，为后续研究奠定了重要基础。
关键词：环境激素；检测方法；环境行为；机器学习；生态效应
Abstract
Environmental hormones have become a research focus in environmental science due to their potential threats to ecosystems and human health. This study aims to systematically investigate the detection methods of environmental hormones and their behavior characteristics in natural environments. By integrating high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS), gas chromatography-mass spectrometry (GC-MS), and molecular biological techniques, a highly sensitive and specific detection system was established and successfully applied to the quantitative analysis of target compounds in various typical environmental samples. Meanwhile, combining laboratory simulations with field monitoring data, this study thoroughly analyzed the migration and transformation patterns of environmental hormones in water bodies, soil, and atmosphere, as well as their influencing factors. The findings indicate that some environmental hormones possess strong bioaccumulation and persistence, and their environmental behaviors are synergistically regulated by multiple factors such as light, temperature, and microbial community structure. Furthermore, this study innovatively proposed a predictive model based on machine learning algorithms to evaluate the exposure risks and ecological effects of environmental hormones, providing a scientific basis for the formulation of relevant environmental management policies. Overall, this study not only improves the technical system for detecting environmental hormones but also reveals their complex environmental behavior mechanisms, laying an important foundation for future research.
Keywords：Environment Hormone; Detection Method; Environmental Behavior; Machine Learning; Ecological Effect
目 录
摘要 I
Abstract II
一、绪论 1
(一) 环境激素类物质的研究背景与意义 1
(二) 国内外研究现状分析 1
(三) 本文研究方法与技术路线 2
二、环境激素类物质的检测方法研究 2
(一) 检测方法的分类与原理 2
(二) 常用检测技术的优缺点分析 3
(三) 新型检测技术的发展与应用 3
三、环境激素类物质的环境行为分析 4
(一) 环境中的迁移与转化机制 4
(二) 生物累积效应及其影响因素 4
(三) 环境激素在水体中的分布特征 5
四、环境激素类物质的风险评估与控制策略 5
(一) 风险评估模型的构建与应用 5
(二) 控制技术的选择与优化 6
(三) 政策建议与未来发展方向 7
结 论 8
参考文献 9
