一种用于识别水生环境特征的创新方法:构建水质指纹频谱,并与二维相关性光谱分析相结合
Kuotian Lu1, Xiaobo Gao2, Dongping Liu1
1College of Water Sciences, Beijing Normal University, Beijing, 100875, China; State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China.
Environmental research
|May 14, 2025
概括
一种新的水质指纹频谱方法分析了大型数据集,以确定化学氧气需求 (COD),总 (TN) 和总 (TP) 等关键污染参数. 这种方法揭示了旅游业和补水是主要影响因素.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 水资源管理 水资源管理
背景情况:
- 随着水质数据的规模和复杂性不断增加,需要先进的分析方法.
- 目前的多变量统计方法在处理多维数据和对水质波动的因果分析方面存在困难.
- 现有的方法在处理大规模,复杂的水质数据集和确定因果关系方面存在局限性.
研究的目的:
- 为复杂的水质数据开发先进的分析和可视化方法.
- 引入水质指纹频谱,用于多尺度和大量数据集分析.
- 提出工作流程,以确定水质管理中的优先控制区域,时期,参数和因果因素.
主要方法:
- 水质指纹频谱的构建.
- 开发一个由三个模块组成的工作流程:数据准备,数据解释和因果分析.
- 该方法应用于沙户湖792个水质数据点.
主要成果:
- 确定化学氧气需求 (CODCr),总 (TN) 和总 (TP) 作为沙湖的优先控制参数.
- 确定旅游活动和补水是影响水质的关键因素.
- 因果分析显示,补水通过抑制藻类生长和稀释污染物来改善水质 (总效果: -0.464).
- 因果分析表明,旅游活动对水质产生负面影响 (总影响:0.562).
结论:
- 水质指纹光谱为分析大型复杂数据集提供了强大的方法.
- 拟议的工作流程有效地确定了水质管理的优先控制参数和因果因素.
- 这种创新方法提高了对时空水质变化的理解,并为实际的管理策略提供了信息.
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