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Using electronic conductivity and hardness data for rapid assessment of stream water quality

机译:使用电子电导率和硬度数据快速评估溪流水质

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摘要

A graphical screening method was previously developed by Kney and Brandes (2007) for assessing stream water quality data using electronic conductivity (EC) and alkalinity data. The method was aimed at providing citizen scientists involved in stream monitoring programs with a relatively simple way to interpret EC data. The method utilizes a plot of EC against concurrent alkalinity data, and is used to distinguish EC values for impacted or degraded streams from those that can be considered background values in a particular geologic setting. The method performs well in areas underlain by carbonate bedrock, as streams in those areas characteristically have EC values that are strongly correlated with alkalinity. However, in areas of low stream alkalinity (less than approximately 50 mg/L as CaCO_3), the Kney and Brandes (2007) method was found to be much less effective in identifying impacted streams. This paper extends the graphical screening approach to streams with low alkalinity, specifically regions underlain by clastic sedimentary or crystalline bedrock, by using the strong correlation between EC and total hardness (TH). A baseline relationship of EC vs. TH is developed using surface water chemistry data from Hydrologic Benchmark Network streams (deemed as having minimal anthropogenic impacts) and regional groundwater quality data. The usefulness of the method is demonstrated by application to publicly available stream chemistry data and to field data collected from streams of eastern Pennsylvania under baseflow conditions. Results demonstrate that for streams with alkalinity <75 mg/L as CaCO_3, the TH-based graphical screening method should be used rather than the alkalinity-based method of Kney and Brandes (2007).
机译:Kney and Brandes(2007)先前开发了一种图形筛选方法,用于使用电子电导率(EC)和碱度数据评估溪流水质数据。该方法旨在为参与流监视程序的公民科学家提供一种相对简单的解释EC数据的方法。该方法利用EC对同时存在的碱度数据的绘图,并用于区分受影响或退化的流的EC值与在特定地质环境中可以视为背景值的EC值。该方法在碳酸盐岩床下的地区表现良好,因为这些地区的水流特征在于EC值与碱度密切相关。然而,在低碱度地区(CaCO_3小于约50 mg / L),发现Kney and Brandes(2007)方法在识别受影响的河流方面效果不佳。本文利用EC和总硬度(TH)之间的强相关性,将图形筛选方法扩展到了低碱度的流,特别是碎屑沉积或结晶基岩层下的区域。利用水文基准网数据流(被认为具有最小的人为影响)和区域地下水水质数据,开发了EC与TH的基线关系。该方法的实用性通过应用于公开可用的流化学数据以及在基流条件下从宾夕法尼亚州东部流采集的现场数据得到了证明。结果表明,对于碱度<75 mg / L的CaCO_3物流,应使用基于TH的图形筛选方法,而不是Kney and Brandes(2007)的基于碱度的方法。

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