Monitoring SARS-CoV-2 variants alterations in Nice neighborhoods by wastewater nanopore sequencing.

BACKGROUND: Wastewater surveillance was proposed as an epidemiological tool to define the prevalence and evolution of the SARS-CoV-2 epidemics. However, most implemented SARS-CoV-2 wastewater surveillance projects were based on qPCR measurement of virus titers and did not address the mutational spectrum of SARS-CoV-2 circulating in the population. METHODS: We have implemented a nanopore RNA sequencing monitoring system in the city of Nice (France, 550,000 inhabitants). Between October 2020 and March 2021, we monthly analyzed the SARS-CoV-2 variants in 113 wastewater samples collected in the main wastewater treatment plant and 20 neighborhoods. FINDINGS: We initially detected the lineages predominant in Europe at the end of 2020 (B.1.160, B.1.177, B.1.367, B.1.474, and B.1.221). In January, a localized emergence of a variant (Spike:A522S) of the B.1.1.7 lineage occurred in one neighborhood. It rapidly spread and became dominant all over the city. Other variants of concern (B.1.351, P.1) were also detected in some neighborhoods, but at low frequency. Comparison with individual clinical samples collected during the same week showed that wastewater sequencing correctly identified the same lineages as those found in COVID-19 patients. INTERPRETATION: Wastewater sequencing allowed to document the diversity of SARS-CoV-2 sequences within the different neighborhoods of the city of Nice. Our results illustrate how sequencing of sewage samples can be used to track pathogen sequence diversity in the current pandemics and in future infectious disease outbreaks. TRANSLATION: For the French translation of the abstract see Supplementary Materials section.

Use of fuzzy logic models for prediction of taste and odor compounds in algal bloom-affected inland water bodies.

Mechanistic modeling of how algal species produce metabolites (e.g., taste and odor compounds geosmin and 2-methyl isoborneol (2-MIB)) as a biological response is currently not well understood. However, water managers and water utilities using these reservoirs often need methods for predicting metabolite production, so that appropriate water treatment procedures can be implemented. In this research, a heuristic approach using Adaptive Network-based Fuzzy Inference System (ANFIS) was developed to determine the underlying nonlinear and uncertain quantitative relationship between observed cyanobacterial metabolites (2-MIB and geosmin), various algal species, and physical and chemical variables. The model is proposed to be used in conjunction with numerical water quality models that can predict spatial-temporal distribution of flows, velocities, water quality parameters, and algal functional groups. The coupling of the proposed metabolite model with the numerical water quality models would assist various utilities which use mechanistic water quality models to also be able to predict distribution of taste and odor metabolites, especially when monitoring of metabolites is limited. The proposed metabolite model was developed and tested for the Eagle Creek Reservoir in Indiana (USA) using observations over a 3-year period (2008-2010). Results show that the developed models performed well for geosmin (R (2) = 0.83 for all training data and R (2) = 0.78 for validation of all 10 data points in the validation dataset) and reasonably well for the 2-MIB (R (2) = 0.82 for all training data and R (2) = 0.70 for 7 out of 10 data points in the validation dataset).

Impact of sampling strategy on stream load estimates in till landscape of the Midwest.

Accurately estimating various solute loads in streams during storms is critical to accurately determine maximum daily loads for regulatory purposes. This study investigates the impact of sampling strategy on solute load estimates in streams in the US Midwest. Three different solute types (nitrate, magnesium, and dissolved organic carbon (DOC)) and three sampling strategies are assessed. Regardless of the method, the average error on nitrate loads is higher than for magnesium or DOC loads, and all three methods generally underestimate DOC loads and overestimate magnesium loads. Increasing sampling frequency only slightly improves the accuracy of solute load estimates but generally improves the precision of load calculations. This type of investigation is critical for water management and environmental assessment so error on solute load calculations can be taken into account by landscape managers, and sampling strategies optimized as a function of monitoring objectives.

Assessment of source water pathogen contamination.

During the last decade, the source to tap risk-based approach to pathogens in drinking water has been largely promoted. This paper addresses the issue of source water pathogen contamination, which is the first step of quantitative microbial risk assessment. It is focused on a selection of pathogens considered to be a major risk to human health. Source water quality is highly variable and understanding the reasons for this variability is important as it will influence the requirements for treatment, treatment efficiency and the resulting health risk associated with the finished water. A framework for source water microbial quality assessment based on catchment surveys and monitoring programmes was set and tested on ten water sources. The monitoring programmes included faecal indicators and pathogens, during both baseline and hazardous event conditions. Concentrations varied greatly within and between systems. Faecal indicators were shown to be poor surrogates for pathogen presence and concentrations. There was no recurring evidence that the pathogens correlated together and links between microbial parameters appeared to be very site specific. Such variability between systems shows the importance of running local monitoring programs for use in risk assessment. Finally, pathogen detection methods are not yet optimal due to their sensitivity and to the lack of knowledge on viability and infectivity of pathogens. A great effort needs to be made in the future to ensure better quality data as this may have large implications in the statistical risk assessment calculations.

Is dew water potable? Chemical and biological analyses of dew water in Ajaccio (Corsica Island, France).

To determine to what extent dew water is potable without further treatment, a thorough set of chemical and biological analyses were performed on 10 samples of dew water collected on a large scale radiative collector (29.83 m2) in Ajaccio (Corsica Island, France), between 21 May 2002 and 5 Mar. 2003. Samples were collected following four protocols according to the dew volume amount and 48 parameters (ions, minerals, and bacteria) were analyzed and compared to French and European Union legislation and also World Health Organization (WHO) recommendations. Aluminum and Fe were the main pollutants whose concentrations were significantly larger than recommended. Their presence is due to local deposition of aerosols coming from the Sahara (a characteristic of the Mediterranean basin). A large number of biologically cultivable microorganisms were found, together with bacteria typical of fecal contamination. For dew water to be potable with respect to present legislation at the Ajaccio site, it should be disinfected and treated for turbidity.