Impact of vacuum UV on natural and algal organic matter from cyanobacterial impacted waters - Visentin, Flavia; Bhartia, Siddharth; Mohseni, Madjid; Peldszus, Sigrid; Dorner, Sarah; Barbeau, Benoit (Environmental Science: Water Research & Technology, 2020)
Cyanobacterial bloom events produce toxins and taste and odor issues, disturbing drinkable water quality. Vacuum UV (VUV) is a promising advanced oxidation process used to treat impacted water, with potential applicability in small and remote communities. , Cyanobacterial blooms are a growing concern around the world. A feasible approach for small treatment plants fed by sources contaminated with cyanobacteria is vacuum UV (VUV). VUV is a promising advanced oxidation process used to treat water impacted by cyanobacterial blooms, with potential applicability in small and remote communities because of its simplicity. In this work, water samples from three Canadian lakes periodically affected by cyanobacteria were used to assess the impact of natural and algal organic matter (NOM/AOM) on treatment with VUV. NOM and AOM were characterized before and after VUV treatment by size exclusion chromatography (SEC) and fluorescence emission–excitation matrix (FEEM). FEEM spectra were analyzed with the parallel factor analysis (PARAFAC) tool. As a result, we found seven principal components describing the whole dataset. Disinfection by-product (DBP) formation after VUV treatment was analyzed and trihalomethanes (THM) yield was calculated. THM yield increased by 15–20% after VUV treatment. Regarding DBP formation and NOM/AOM fractions from SEC, we found that humic substances are the most important fraction causing the increase in DBP formation with at least 3 times higher yield than the other fractions: biopolymers, building blocks, low weight molecular acids and neutrals.
Keywords: Dorner_Sarah

Detection of prevalent SARS-CoV-2 variant lineages in wastewater and clinical sequences from cities in Québec, Canada - N’Guessan, Arnaud; Tsitouras, Alexandra; Sanchez-Quete, Fernando; Goitom, Eyerusalem; Reiling, Sarah J.; Galvez, Jose Hector; Nguyen, Thanh Luan; Loan Nguyen, Ha Thanh; Visentin, Flavia; Hachad, Mounia; Krylova, Kateryna; Matthews, Sara; et al. (2022)
ABSTRACT Wastewater-based epidemiology has emerged as a promising tool to monitor pathogens in a population, particularly when clinical diagnostic capacities become overwhelmed. During the ongoing COVID-19 pandemic caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), several jurisdictions have tracked viral concentrations in wastewater to inform public health authorities. While some studies have also sequenced SARS-CoV-2 genomes from wastewater, there have been relatively few direct comparisons between viral genetic diversity in wastewater and matched clinical samples from the same region and time period. Here we report sequencing and inference of SARS-CoV-2 mutations and variant lineages (including variants of concern) in 936 wastewater samples and thousands of matched clinical sequences collected between March 2020 and July 2021 in the cities of Montreal, Quebec City, and Laval, representing almost half the population of the Canadian province of Quebec. We benchmarked our sequencing and variant-calling methods on known viral genome sequences to establish thresholds for inferring variants in wastewater with confidence. We found that variant frequency estimates in wastewater and clinical samples are correlated over time in each city, with similar dates of first detection. Across all variant lineages, wastewater detection is more concordant with targeted outbreak sequencing than with semi-random clinical swab sampling. Most variants were first observed in clinical and outbreak data due to higher sequencing rate. However, wastewater sequencing is highly efficient, detecting more variants for a given sampling effort. This shows the potential for wastewater sequencing to provide useful public health data, especially at places or times when sufficient clinical sampling is infrequent or infeasible.
Keywords: Dorner_Sarah