A long-term passive sampling approach for wastewater-based monitoring of SARS-CoV-2 in Leipzig, Germany

Wastewater-based monitoring of SARS-CoV-2 has become a promising and useful tool in tracking the potential spread or dynamics of the virus. Its recording can be used to predict how the potential number of infections in a population will develop. Recent studies have shown that the use of passive samplers is also suitable for the detection of SARS-CoV-2 genome copies (GC) in wastewater. They can be used at any site, provide timely data and may collect SARS-CoV-2 GC missed by traditional sampling methods. Therefore, the aim of this study was to evaluate the suitability of passive samplers for the detection of SARS-CoV-2 GC in wastewater in the long-term at two different scales. Polyethylene-based plastic passive samplers were deployed at the city-scale level of Leipzig at 13 different locations, with samples being taken from March 2021 to August 2022. At the smaller city district level, three types of passive samplers (cotton-cloth, unravelled polypropylene plastic rope and polyethylene-based plastic strips) were used and sampled on a weekly basis from March to August 2022. The results are discussed in relation to wastewater samples taken at the individual passive sampling point. Our results show that passive samplers can indicate at a city-scale level an accurate level of positive infections in the population (positive-rate: 86 %). On a small-scale level, the use of passive samplers was also feasible and effective to detect SARS-CoV-2 GC easily and cost-effectively, mirroring a similar trend to that at a city-scale level. Thus, this study demonstrated that passive samplers provide reproducible SARS-CoV-2 GC signals from wastewater and a time-integrated measurement of the sampled matrix with greater sensitivity compared to wastewater. We thus recommend the use of passive samplers as an alternative method for wastewater-based epidemiology. Passive samplers can in particular be considered for a better estimation of infections compared to incidence levels.