Endress+Hauser (Schweiz) AG, Kägenstrasse 2, 4153 Reinach, Switzerland.
Department of Environmental Microbiology, Eawag - Swiss Federal Institute for Aquatic Science and Technology, Dübendorf, Switzerland; Department of Environmental Systems Science, Institute of Biogeochemistry and Pollutant Dynamics, ETH Zürich, Zürich, Switzerland.
Sci Total Environ. 2017 Dec 1;599-600:227-236. doi: 10.1016/j.scitotenv.2017.04.204. Epub 2017 May 3.
We have studied the dynamics of water quality in three karst springs taking advantage of new technological developments that enable high-resolution measurements of bacterial load (total cell concentration: TCC) as well as online measurements of abiotic parameters. We developed a novel data analysis approach, using self-organizing maps and non-linear projection methods, to approximate the TCC dynamics using the multivariate data sets of abiotic parameter time-series, thus providing a method that could be implemented in an online water quality management system for water suppliers. The (TCC) data, obtained over several months, provided a good basis to study the microbiological dynamics in detail. Alongside the TCC measurements, online abiotic parameter time-series, including spring discharge, turbidity, spectral absorption coefficient at 254nm (SAC254) and electrical conductivity, were obtained. High-density sampling over an extended period of time, i.e. every 45min for 3months, allowed a detailed analysis of the dynamics in karst spring water quality. Substantial increases in both the TCC and the abiotic parameters followed precipitation events in the catchment area. Differences between the parameter fluctuations were only apparent when analyzed at a high temporal scale. Spring discharge was always the first to react to precipitation events in the catchment area. Lag times between the onset of precipitation and a change in discharge varied between 0.2 and 6.7h, depending on the spring and event. TCC mostly reacted second or approximately concurrent with turbidity and SAC254, whereby the fastest observed reaction in the TCC time series occurred after 2.3h. The methodological approach described here enables a better understanding of bacterial dynamics in karst springs, which can be used to estimate risks and management options to avoid contamination of the drinking water.
我们利用新技术的发展研究了三个岩溶泉的水质动态，这些技术能够实现细菌负荷（总细胞浓度：TCC）的高分辨率测量以及非生物参数的在线测量。我们开发了一种新的数据分析方法，使用自组织映射和非线性投影方法，使用非生物参数时间序列的多元数据集来近似 TCC 动态，从而为水供应商提供了一种可在在线水质管理系统中实施的方法。在几个月的时间里，（TCC）数据为详细研究微生物动态提供了良好的基础。除了 TCC 测量外，还获得了在线非生物参数时间序列，包括泉水流量、浊度、254nm 光谱吸收系数（SAC254）和电导率。长时间内的高密度采样，即每 45 分钟采集一次，持续 3 个月，允许对岩溶泉水水质的动态进行详细分析。TCC 和非生物参数都在集水区的降水事件后大幅增加。只有在高时间尺度上进行分析时，才能观察到参数波动之间的差异。泉水流量总是第一个对集水区的降水事件做出反应。降水开始与流量变化之间的滞后时间在 0.2 到 6.7 小时之间变化，具体取决于泉水和事件。TCC 大多反应第二或与浊度和 SAC254 大致同时发生，其中在 TCC 时间序列中观察到的最快反应发生在 2.3 小时后。这里描述的方法可以更好地了解岩溶泉中的细菌动态，从而可以用来估计风险和管理选项以避免饮用水污染。
