Background: Wastewater-based epidemiology (WBE) has become crucial for early microbial outbreak detection and public health surveillance globally, underscored by the COVID-19 pandemic. However, despite advancements in sampling and analyses, interpreting results and estimating infection rates pose challenges. Enhancements in sewer system engineering, understanding the wastewater environment, and addressing the impact of the environment on the accuracy of results are needed. Objective: This scoping review aims to identify engineering knowledge gaps in WBE to guide future study designs. Design: Research on “wastewater-based epidemiology” involving “engineering”, published between 2015 and 2023, was extracted from the Scopus database. Results: This scoping review examines elements influencing WBE’s precision and reliability, especially in identifying and measuring SARS-CoV-2 RNA. It identifies significant effects of engineering, analytical practices, and the wastewater’s composition on WBE performance. Conclusions: This review calls for further investigation into economical evaluation methods of these factors to enhance WBE data normalization and interpretation, utilizing existing wastewater treatment plant data used for treatment control, which could be a cost-effective approach over more expensive population biomarkers. This approach, aside from SARS-CoV-2, holds potential for application to a broader number and types of diseases, as well as population consumption habits.

Concerns about drinking water (DW) quality contamination during water distribution raise a need for real-time monitoring and rapid contamination detection. Early warning systems (EWS) are a potential solution. The EWS consist of multiple conventional sensors that provide the real-time measurements and algorithms that allow the recognizing of contamination events from normal operating conditions. In most cases, these algorithms have been established with artificial data, while data from real and biological contamination events are limited. The goal of the study was the event detection performance of the Mahalanobis distance method in combination with on-line DW quality monitoring sensors and manual measurements of grab samples for potential DW biological contamination scenarios. In this study three contamination scenarios were simulated in a pilot-scale DW distribution system: untreated river water, groundwater and wastewater intrusion, which represent realistic contamination scenarios and imply biological contamination. Temperature, electrical conductivity (EC), total organic carbon (TOC), chlorine ion (Cl-), oxidation–reduction potential (ORP), pH sensors and turbidity measurements were used as on-line sensors and for manual measurements. Novel adenosine-triphosphate and flow cytometric measurements were used for biological water quality evaluation. The results showed contamination detection probability from 56% to 89%, where the best performance was obtained with manual measurements. The probability of false alarm was 5–6% both for on-line and manual measurements. The Mahalanobis distance method with DW quality sensors has a good potential to be applied in EWS. However, the sustainability of the on-line measurement system and/or the detection algorithm should be improved.

The aim of the study is to train a machine learning (ML) model for drinking water contamination detection and compare performance to statistical methods and existing anomaly detection solutions. A pilot drinking water supply system was made and equipped with drinking water quality sensors and a contamination dosing system. The results from this study demonstrated that using the statistical Mahalanobis distance (MD) method to predict the classification of drinking water measurements yields a 99% accuracy, 23% precision, and 28% F-score result (for wastewater contamination); however, the ML model yields a 99% accuracy, 98% precision, and a 98% F-score result. The results show that the application of ML methods can improve drinking water contamination detection speed and accuracy.

Antimicrobial resistance (AMR) poses a global health threat, with urban wastewater systems serving as key reservoirs for resistance dissemination. This study aimed to investigate the relationships among urban environments, bacterial communities, and AMR patterns, and evaluate the specific municipal-scale drivers of resistance gene distribution. Shotgun metagenomic analysis was conducted on 45 wastewater samples collected from 15 municipalities across Latvia to determine the composition of the resistome and its correlation with local factors. The analysis identified 417 distinct antibiotic resistance genes (ARGs) belonging to 108 families, with geographic location serving as the primary driver of ARG distribution, which explained 65.87% of community variation (p = 0.001). Local industrial factors demonstrated significant effects, with food industry wastewater significantly influencing both bacterial taxonomy and ARG profiles (p < 0.05). While the presence of a regional hospital did not shape the overall municipal resistome, hospital-associated wastewater showed 19 overlapping ARGs, including clinically critical carbapenemases. Municipal wastewater systems function as geographically structured reservoirs of AMR that are shaped by localized industrial and healthcare outputs. These findings support wastewater-based AMR surveillance as a valuable tool for tracking specific resistance sources.

The presence of excess water in centralized sewerage systems is known to have a multitude of unfavorable effects on the daily operation of the wastewater infrastructure. The additional volume of I/I-water decreases the hydraulic capacity of wastewater collection networks, reduces the efficiency of wastewater treatment processes, and increases the costs of transporting and treating the wastewater. Currently, most I/I studies in Latvia are conducted on the scale of the wastewater treatment plant service area and determine only the common performance indicators for a given year. However, data of such resolution are not sufficient to identify problem areas within the networks and to introduce cost-effective measures. The contribution of private sewer laterals to the overall I/I volume is an area of particular interest. Although it is possible to locate and quantify I/I from individual house connections, in practice, given the financial and time constraints, it is not feasible to apply a case-by-case approach. Thus, a simple method to predetermine the problematic parts of the system before conducting on-site inspections is needed. This study investigates the link between groundwater levels and observed night-time wastewater flows on a sub-catchment scale by performing a linear regression analysis (940 data points in total). The results show a direct correlation (R > 0.70 in all cases) between said parameters and highlight the impacts of poorly built and ill-maintained house connections. The presented approach can be widely adopted by system operators to help identify potential sources of diluted wastewater and to aid in the development of priority-based renovation plans.