To assist public health responses to COVID-19, wastewater-based epidemiology (WBE) is being utilised internationally to monitor SARS-CoV-2 infections at the community level. However, questions remain regarding the sensitivity of WBE and its use in low prevalence settings. In this study, we estimated the total number of COVID-19 cases required for detection of SARS-CoV-2 RNA in wastewater. To do this, we leveraged a unique situation where, over a 4-month period, all symptomatic and asymptomatic cases, in a population of approximately 120,000, were precisely known and mainly located in a single managed isolation and quarantine facility (MIQF) building. From 9 July to 6 November 2020, 24-hr composite wastewater samples (n = 113) were collected daily from the sewer outside the MIQF, and from the municipal wastewater treatment plant (WWTP) located 5 km downstream. New daily COVID-19 cases at the MIQF ranged from 0 to 17, and for most of the study period there were no cases outside the MIQF identified. SARS-CoV-2 RNA was detected in 54.0% (61/113) at the WWTP, compared to 95.6% (108/113) at the MIQF. We used logistic regression to estimate the shedding of SARS-CoV-2 RNA into wastewater based on four infectious shedding models. With a total of 5 and 10 COVID-19 infectious cases per 100,000 population (0.005 % and 0.01% prevalence) the predicated probability of SARS-CoV-2 RNA detection at the WWTP was estimated to be 28 and 41%, respectively. When a more realistic proportional shedding model was used, this increased to 58% and 87% for 5 and 10 cases, respectively. In other words, when 10 individuals were actively shedding SARS-CoV-2 RNA in a catchment of 100,000 individuals, there was a high likelihood of detecting viral RNA in wastewater. SARS-CoV-2 RNA detections at the WWTP were associated with increasing COVID-19 cases. Our results show that WBE provides a reliable and sensitive platform for detecting infections at the community scale, even when case prevalence is low, and can be of use as an early warning system for community outbreaks.

Odors from wastewater treatment plants (WWTPs) have frequently been attributed primarily to hydrogen sulfide (H2S). Low-to-medium cost hydrogen sulfide sensors have been utilized as odor indicators. However, other odorous species are usually present that may have lower odor thresholds than hydrogen sulfide. Hydrogen sulfide is not always present in odorous environments and the correlation of hydrogen sulfide to odor at a treatment facility is inconsistent. Such factors determine hydrogen sulfide an inconsistent indicator and more sophisticated measurement techniques are required to accurately predict odor intensity from complex gaseous mixes. In this paper, the performance of a direct mass spectrometric technique, selected ion flow tube mass spectrometry (SIFT-MS), is evaluated for analysis of odors from diverse sources at a modern WWTP. The soft chemical ionization employed in SIFT-MS provides detection and quantification of a wide range of potential odorants to below, or close to, the human odor detection threshold (ODT). The results presented demonstrate that methyl mercaptan is almost always a more significant odorant at this WWTP than hydrogen sulfide and confirm that the relative abundances of these odorants vary significantly. Parallel SIFT-MS chemical analysis and human sensory analysis (olfactometry) was conducted in this study. Good agreement was observed for samples of moderate to strong “sewage” or “chemical” character. However, in samples that were otherwise low in odor intensity, sensory analysis did not attribute “sewage” odor notes as the predominant odor character. Chemicals attributed with this odor character were present significantly above the ODTs in the mixed samples and were detected by SIFT-MS. A weak correlation was obtained between total odor activity values measured using SIFT-MS and the odor concentration (in odor units per cubic meter) determined using dilution olfactometry. The complexity of the wastewater matrix and complexity of human odor recognition from mixed samples is thought to be the underlying cause of less-than-ideal correlation, perturbing both olfactometry and SIFT-MS analyses.