Several Small or Single Large? Quantifying the Catchment-Wide Performance of On-Site Wastewater Treatment Plants with Inaccurate Sensors

On-site wastewater treatment plants (OSTs) often lack monitoring, resulting in unreliable treatment performance. They thus appear to be a stopgap solution despite their potential contribution to circular water management. Low-maintenance but inaccurate soft sensors are emerging that address this concern. However, how their inaccuracy impacts the catchment-wide treatment performance of a system of many OSTs has not been quantified. We develop a stochastic model to estimate catchment-wide OST performances with a Monte Carlo simulation. In our study, soft sensors with a 70% accuracy improved the treatment performance from 66% of the time functional to 98%. Soft sensors optimized for specificity, indicating the true negative rate, improve the system performance, while sensors optimized for sensitivity, indicating the true positive rate, quantify the treatment performance more accurately. This new insight leads us to suggest programming two soft sensors in practical settings with the same hardware sensor data as input: one soft sensor geared to high specificity for maintenance scheduling and one geared to high sensitivity for performance quantification. Our findings suggest that a maintenance strategy combining inaccurate sensors with appropriate alarm management can vastly improve the mean catchment-wide treatment performance of a system of OSTs

Characterization of Pathogenic <i>Escherichia coli</i> in River Water by Simultaneous Detection and Sequencing of 14 Virulence Genes

The occurrence of pathogenic <i>Escherichia coli</i> in environmental waters increases the risk of waterborne disease. In this study, 14 virulence genes in 669 <i>E. coli</i> isolates (549 isolates from the Yamato River in Japan, and 30 isolates from each of the following hosts: humans, cows, pigs, and chickens) were simultaneously quantified by multiplex PCR and dual index sequencing to determine the prevalence of potentially pathogenic <i>E. coli</i>. Among the 549 environmental isolates, 64 (12%) were classified as extraintestinal pathogenic <i>E. coli</i> (ExPEC) while eight (1.5%) were classified as intestinal pathogenic <i>E. coli</i> (InPEC). Only ExPEC-associated genes were detected in human isolates and pig isolates, and 11 (37%) and five (17%) isolates were classified as ExPEC, respectively. A high proportion (63%) of cow isolates possessed Shiga-toxin genes (<i>stx1</i> or <i>stx2</i>) and they were classified as Shiga toxin-producing <i>E. coli</i> (STEC) or enterohemorrhagic <i>E. coli</i> (EHEC). Among the chicken isolates, 14 (47%) possessed <i>iutA</i>, which is an ExPEC-associated gene. This method can determine the sequences as well as the presence/absence of virulence genes. By comparing the sequences of virulence genes, we determined that sequences of <i>iutA</i> were different among sources and may be useful for discriminating isolates, although further studies including larger numbers of isolates are needed. Results indicate that humans are a likely source of ExPEC strains in the river