Online analysis: Deeper insights into water quality dynamics in spring water

被引:24
作者
Page, Rebecca M. [1 ]
Besmer, Michael D. [2 ,3 ]
Epting, Jannis [4 ]
Sigrist, Jurg A. [2 ]
Hammes, Frederik [2 ]
Huggenberger, Peter [4 ]
机构
[1] Endress Hauser Schweiz AG, Kagenstr 2, CH-4153 Reinach, Switzerland
[2] Eawag Swiss Fed Inst Aquat Sci & Technol, Dept Environm Microbiol, Dubendorf, Switzerland
[3] Swiss Fed Inst Technol, Inst Biogeochem & Pollutant Dynam, Dept Environm Syst Sci, Zurich, Switzerland
[4] Univ Basel, Dept Environm Sci, Appl & Environm Geol, Basel, Switzerland
关键词
Karst spring water; Drinking water; Online monitoring; Online flow cytometry; Multivariate time series analysis; SELF-ORGANIZING MAPS; DRINKING-WATER; KARST AQUIFER; PARTICLE-TRANSPORT; MICROBIAL DYNAMICS; CONDUIT-FLOW; GROUNDWATER; CONTAMINATION; BACTERIA; INDICATOR;
D O I
10.1016/j.scitotenv.2017.04.204
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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 254 nm (SAC254) and electrical conductivity, were obtained. High-density sampling over an extended period of time, i.e. every 45 min for 3 months, 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 6.7 h, 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 2.3 h. 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. (C) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码:227 / 236
页数:10
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