RESUMO
Early warning systems monitoring the quality of drinking water need to distinguish between normal quality fluctuations and those caused by contaminants. Thus, to decrease the number of false positive events, normal water quality fluctuations, whether periodic or aperiodic, need to be characterized. For this, we used a novel flow-imaging particle counter, a light-scattering particle counter, and electrochemical sensors to monitor the drinking water quality of a pressure zone in a building complex for 109 days. Data were analyzed to determine the feasibility of the sensors and particle counters to distinguish periodic and aperiodic fluctuations from real-life contaminants. The concentrations of particles smaller than 10 µm and N, Small, Large, and B particles showed sudden changes recurring daily, likely due to the flow rate changes in the building complex. Conversely, the concentrations of larger than 10 µm particles and C particles, in addition to the responses of electrochemical sensors, remained in their low typical values despite flow rate changes. The aperiodic events, likely resulting from an abnormally high flow rate in the water mains due to maintenance, were detected using particle counters and electrochemical sensors. This study provides insights into choosing water quality sensors by showing that machine learning-based particle classes, such as B, C, F, and particles larger than 10 µm are promising in distinguishing contamination from aperiodic and periodic fluctuations while the use of other particle classes and electrochemical sensors may require dynamic baseline to decrease false positive events in an early warning system.
RESUMO
Contamination detection in drinking water is crucial for water utilities in terms of public health; however, current online water quality sensors can be unresponsive to various possible contaminants consisting of particulate and dissolved content or require a constant supply of reagents and sample preparation. We used a two-line test environment connected to a drinking water distribution system with flow-imaging particle counters and conventional sensors to assess their responses to the injection of contaminants into one line, including stormwater, treated wastewater, wastewater, well water, and Escherichia coli, while simultaneously measuring responses to normal water quality fluctuations in the other line. These water quality fluctuations were detected with all of the conventional sensors (except conductivity) and with 3 out of 5 of the size- and shape-derived particle classes of the flow-imaging particle counter. The flow-imaging particle counter was able to detect all of the studied contaminants, e.g. municipal wastewater at 0.001% (v/v), while the oxidation-reduction potential sensor outperformed other conventional sensors, detecting the same wastewater at 0.03% (v/v). The presence of particles less than 1 µm in size was shown to be a generic parameter for the detection of particulates present in the studied contaminants; however, they manifested a considerable response to fluctuations which led to lower relative response to contaminants in comparison to larger particles. The particle size and class distributions of contaminants were different from those of drinking water, and thus monitoring particles larger than 1 µm or specific particle classes of flow-imaging particle counter, which are substantially more abundant in contaminated water than in pure drinking water, can improve the detection of contamination events. Water utilities could optimize contamination detection by selecting water quality parameters with a minimal response to quality fluctuations and/or a high relative response to contaminants.