Automatic optimization of temporal monitoring schemes dealing with daily water contaminant concentration patterns

The semi-arbitrary selection of water monitoring frequencies and sampling instants conducted by water utilities and regulatory agencies does not guarantee the identification of the maximum contaminant concentration or the extent of the daily variations present in fast-responding water systems, potentially leading to erroneous evaluations of process performances or human health risk. Hence, this work proposes two novel methods to optimize temporal monitoring schemes dealing with daily contaminant concentration patterns to select the sampling instants characterized by the maximum concentration or the maximum daily variation, while, coincidentally, limiting the number of samples analysed. The corresponding algorithms, based on the multi-armed bandit framework, were termed Seq(GP-UCB-SW) and Seq(GP-UCB-CD). While the first algorithm passively adapts to daily pattern changes, the other actively monitors the sampled concentrations providing change detection alerts. The algorithms' application to monitoring of drinking water distribution systems has been compared against traditional schemes on two synthetic scenarios derived from full-scale monitoring campaigns regarding chemical or microbiological contaminants and directly employing high-frequency flow-cytometry data. Compared to traditional schemes, the algorithms demonstrate better performances, providing lower differences between the observed and true target values (i.e., maximum concentration or maximum concentration variation) with a reduced number of samples per day, being also resilient to pattern changes. Following a sensitivity analysis, we provide practical guidance for their usage and discuss their applicability to other water matrices and highlight possible modifications to handle different usage scenarios and other pattern types. The application of the developed algorithms results in lower monitoring costs while providing detailed water contamination characterization.