一个开源的工作流程,通过连续的水质监测和时间序列数据处理使用R和美国EPA CANARY的数据处理来识别河流中的水力动力水质事件
L Cronin1,2, C M Taylor2, C Briciu Burghina3
1School of Chemical Sciences, Glasnevin, Dublin 9, Ireland.
MethodsX
|August 14, 2025
概括
本研究介绍了使用传感器数据进行高频水质监测的实际工作流程. 这种可重复的方法有效地检测河流中的污染事件,有助于水质管理.
科学领域:
- 环境科学 环境科学
- 水资源管理 水资源管理
- 分析化学 分析化学
背景情况:
- 由于分散的污染,特别是来自农业的污染,全球地表水质量正在下降.
- 水气候事件加剧了污染物出口和水质恶化.
- 高频水质监测至关重要,但在数据管理和公民科学分析方面面临挑战.
研究的目的:
- 为高频水质监测提供一个实用,可重复的工作流程.
- 使用开源工具演示传感器部署,数据管理和分析.
- 验证工作流在检测水质事件方面的有效性.
主要方法:
- 在临时监测站部署水质探测器.
- 使用R和R标记的数据收集,验证和管理.
- 美国环保署Canary软件用于事件检测和数据可视化.
主要成果:
- 在两个监测站上,CANARY软件成功识别了100%和97%的手动识别的度事件.
- 呈现的工作流确保了可重复性,并适合多个用户.
- 该方法有助于分析大型水质数据集.
结论:
- 开发的工作流程为高频水质监测提供了强大的和可重复的方法.
- 这种方法支持公民科学家和研究人员管理和分析水质数据.
- 有效的监测协议对于改善欧洲地表水质量和管理污染事件至关重要.
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