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使用数据驱动的集群方法,在农业运河中进行水化学分析和污染源识别,使用数据驱动的集群方法

Yashaswi Songara¹, Anupam Singhal², Rahul Dev Garg³

¹Department of Civil Engineering, Birla Institute of Technology and Science, Pilani, Rajasthan, 333031, India.

Scientific reports

|July 10, 2025

概括

这项研究评估了印度半干旱地区的运河水质,发现尽管存在农业化学风险,但用于灌的水质"好"到"非常好". 它提供了利用时空监测和多变量统计数据管理水资源的框架.

科学领域:

环境科学环境科学
农业科学农业科学
水资源管理水资源管理

背景情况:

运河网络对于半干旱地区的农业至关重要.
水质受到农业排水和废水排放的威胁.
关于运河水质的时空动态存在有限的数据.

研究的目的:

评估运河水的物理化学参数的时空变化.
确定灌道中的污染驱动因素和污染热点.
通过灌水质指数 (IWQI) 评估运河水是否适合灌.

主要方法:

印度哈里亚纳州Charkhi Dadri区11个月的十个运河地点的每月采样.
分析了16个物理化学参数,包括重金属和离子.
应用多变量统计技术 (PCA,聚类,ANOVA) 和IWQI计算.

主要成果:

两个主要成分解释了72.6%的差异,其中,铁和铜是主要贡献者.
对于这些金属,观察到显著的季节性变化 (p < 0.05).
集群分析确定了污染热点,IWQI得分 (67.3-85.5) 表示灌用水质量"好"至"非常好".

结论:

该研究提供了一个可扩展的框架,用于在数据有限的半干旱地区管理灌运河.
综合时空监测和多变量统计对于评估水质是有效的.
研究区域的运河水适合灌,但建议对季节性金属转移进行监测.

关键词:

这是一个ANOVA,一个ANOVA.运河系统运河系统集群分析就是对集群进行分析.建模建模模型是什么水的质量水的质量

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