Systematic recovery of building plumbing-associated microbial communities after extended periods of altered water demand during the COVID-19 pandemic.
Vosloo, S.; Huo, L.; Chauhan, U.; Cotto, I.; Gincley, B.; Vilardi, K. J.; Yoon, B.; Pieper, K. J.; Stubbins, A.; Pinto, A. J.
Show abstract
Building closures related to the coronavirus disease (COVID-19) pandemic resulted in increased water stagnation in commercial building plumbing systems that heightened concerns related to the microbiological safety of drinking water post re-opening. The exact impact of extended periods of reduced water demand on water quality is currently unknown due to the unprecedented nature of widespread building closures. We analyzed 420 tap water samples over a period of six months, starting the month of phased reopening (i.e., June 2020), from sites at three commercial buildings that were subjected to reduced capacity due to COVID-19 social distancing policies and four occupied residential households. Direct and derived flow cytometric measures along with water chemistry characterization were used to evaluate changes in plumbing-associated microbial communities with extended periods of altered water demand. Our results indicate that prolonged building closures impacted microbial communities in commercial buildings as indicated by increases in microbial cell counts, encompassing greater proportion cells with high nucleic acids. While flushing reduced cell counts and increased disinfection residuals, the microbial community composition in commercial buildings were still distinct from those at residential households. Nonetheless, increased water demand post-reopening enhanced systematic recovery over a period of months, as microbial community fingerprints in commercial buildings converged with those in residential households. Overall, our findings suggest that sustained and gradual increases in water demand may play a more important role in the recovery of building plumbing-associated microbial communities as compared to short-term flushing, after extended periods of altered water demand that result in reduced flow volumes.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Extended spectrum β-lactamase and carbapenemase genes are substantially and sequentially reduced during conveyance and treatment of urban sewage 96%
- Benchmarking concentration and direct extraction methods for wastewater-based surveillance of eight human respiratory viruses: implications for rapid application to novel pathogens 96%
- Dehalobacter dechlorinates dichloroanilines and contributes to the natural attenuation of dichloronitrobenzenes at a complex industrial site 96%
Similar papers in this journal
- Mitigation of Antimicrobial Resistance Genes in Greywater Treated at Household Level 97%
- Ecological theory applied to environmental metabolomes reveals compositional divergence despite conserved molecular properties 96%
- Characterisation of the bacterial microbiota of a landfill-contaminated confined aquifer undergoing intrinsic remediation 94%
Similar papers in this journal
- Influence of Copper Dose on Mycobacterium avium and Legionella pneumophila Growth in Premise Plumbing 96%
- Press xenobiotic disturbance favors deterministic assembly with a shift in function and structure of bacterial communities in sludge bioreactors 96%
- Within-Day Variability of SARS-CoV-2 RNA in Municipal Wastewater Influent During Periods of Varying COVID-19 Prevalence and Positivity 96%
Similar papers in this journal
Similar papers in this journal
- Operationalizing a routine wastewater monitoring laboratory for SARS-CoV-2 95%
- Effect of SARS-CoV-2 digital droplet RT-PCR assay sensitivity on COVID-19 wastewater based epidemiology 95%
- Environmental surveillance of soil-transmitted helminths and other enteric pathogens in settings without networked wastewater infrastructure 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.