Machine Learning for Detecting Virus Infection Hotspots Via Wastewater-Based Epidemiology: The Case of SARS-CoV-2 RNA.

Wastewater-based epidemiology (WBE) has been proven to be a useful tool in monitoring public health-related issues such as drug use, and disease. By sampling wastewater and applying WBE methods, wastewater-detectable pathogens such as viruses can be cheaply and effectively monitored, tracking people who might be missed or under-represented in traditional disease surveillance. There is a gap in current knowledge in combining hydraulic modeling with WBE. Recent literature has also identified a gap in combining machine learning with WBE for the detection of viral outbreaks. In this study, we loosely coupled a physically-based hydraulic model of pathogen introduction and transport with a machine learning model to track and trace the source of a pathogen within a sewer network and to evaluate its usefulness under various conditions. The methodology developed was applied to a hypothetical sewer network for the rapid detection of disease hotspots of the disease caused by the SARS-CoV-2 virus. Results showed that the machine learning model's ability to recognize hotspots is promising, but requires a high time-resolution of monitoring data and is highly sensitive to the sewer system's physical layout and properties such as flow velocity, the pathogen sampling procedure, and the model's boundary conditions. The methodology proposed and developed in this paper opens new possibilities for WBE, suggesting a rapid back-tracing of human-excreted biomarkers based on only sampling at the outlet or other key points, but would require high-frequency, contaminant-specific sensor systems that are not available currently.

Microbiological Health Risk Assessment of Water Conservation Strategies: A Case Study in Amsterdam.

The aim of this study was to assess the health risks that may arise from the implementation of greywater reuse and rainwater harvesting for household use, especially for toilet flushing. In addition, the risk of cross connections between these systems and the drinking water system was considered. Quantitative microbial risk assessment (QMRA) is a method that uses mathematical modelling to estimate the risk of infection when exposure to pathogens happens and was used in this study to assess the health risks. The results showed that using rainwater without prior treatment for toilet flushing poses an annual infection risk from L. pneumophila at 0.64 per-person-per-year (pppy) which exceeds the Dutch standard of 10-4 pppy. The use of untreated greywater showed a risk that is below the standard. However, treatment is recommended due to the ability of P. aeruginosa to grow in the reuse system. Moreover, showering and drinking with cross-connected water has a high annual infection risk that exceeds the standard due to contact with Staphylococcus aureus and E. coli O157:H7. Several measures can be implemented to mitigate the risks such as treating the greywater and rainwater with a minimum of 5-log removal, closing the toilet lid while flushing, good design of greywater and rainwater collection systems, and rigorous plumbing installation procedures.

Life cycle assessment of nutrient recycling from wastewater: A critical review.

Recovering resources from wastewater systems is increasingly being emphasised. Many technologies exist or are under development for recycling nutrients such as nitrogen and phosphorus from wastewater to agriculture. Planning and design methodologies are needed to identify and deploy the most sustainable solutions in given contexts. For the environmental sustainability dimension, life cycle assessment (LCA) can be used to assess environmental impact potentials of wastewater-based nutrient recycling alternatives, especially nitrogen and phosphorus recycling. This review aims to evaluate how well the LCA methodology has been adapted and applied for assessing opportunities of wastewater-based nutrient recycling in the form of monomineral, multimineral, nutrient solution and organic solid. We reviewed 65 LCA studies that considered nutrient recycling from wastewater for agricultural land application. We synthesised some of their insights and methodological practices, and discussed the future outlook of using LCA for wastewater-based nutrient recycling. In general, more studies suggested positive environmental outcomes from wastewater-based nutrient recycling, especially when chemical inputs are minimised, and source separation of human excreta is achieved. The review shows the need to improve methodological consistency (e.g., multifunctionality, fertiliser offset accounting, contaminant accounting), ensure transparency of inventory and methods, consider uncertainty in comparative LCA context, integrate up-to-date cross-disciplinary knowledge (e.g., agriculture science, soil science) into LCA models, and consider the localised impacts of recycled nutrient products. Many opportunities exist for applying LCA at various scales to support decisions on wastewater-based nutrient recycling - for instance, performing "product perspective" LCA on recycled nutrient products, integrating "process perspective" LCA with other systems approaches for selecting and optimising individual recovery processes, assessing emerging nutrient recovery technologies and integrated resource recovery systems, and conducting systems analysis at city, national and global level.