Partitioning and inactivation of enveloped and nonenveloped viruses in activated sludge, anaerobic and microalgae-based wastewater treatment systems

Viral RNA reduction from wastewaters using microalgae-based treatments: Elucidating the effect of light and zero-valent iron nanoparticles

Microalgae-based systems can potentially inactivate E. coli and viruses. In this work, batch algal-bacterial photobioreactors were operated to elucidate the effect of zero-valent iron (ZVI) nanoparticles and light intensity on the reduction of viral RNA (MS2, Phi6 and Bovine coronavirus, BCoV) and uidA gene (E. coli) during secondary wastewater treatment. Biodegradation and abiotic control photoreactors were operated at high light intensity (1100 µE m-2 s-1), with and without ZVI-nanoparticles addition (HLNP and HL) and low light intensity (450 µE m-2 s-1), without nanoparticles (LL). After 72 h, HLNP matched or increased the reductions of at least 99.9 % for viruses and 99 % for uidA achieved within 192 h in HL and LL. Oxidative reactions in the presence of ZVI-nanoparticles seemed to mediate the decay of viral RNA and uidA. This work demonstrated for the first time the potential for enhanced reduction of viral RNA and E. coli by ZVI-nanoparticles during microalgae-based wastewater treatment.

Epidemiological and Molecular Surveillance of Aichi Virus A at Different Stages of Sewage Treatment: A One-Year Study in the Southeast of Brazil

Enteric viruses, such as the Aichi virus (AiV), pose a potential health risk due to their high excretion rates through fecal elimination, limited removal during treatment processes, and prolonged survival, highlighting the need to assess the potential for exposure and disease transmission through sanitation systems. This study investigated the prevalence of AiV at three key stages of sewage treatment in the city of São José do Rio Preto, São Paulo state, Brazil, as well as its viral concentrations, infectious potential, and molecular characterization. The data were also analyzed for potential correlations with reported diarrheal disease cases in the city and the physicochemical properties of sewage. The methodology employed included Nested PCR, qPCR, Sanger Sequencing, and phylogenetic analysis, as well as infectivity testing in cell cultures. The prevalence of AiV throughout the year in raw sewage samples was 90.4%, 78.8% in post-anaerobic biological treatment, and 71.1% in post-chemical treatment, totaling 125 positive samples out of 156, being characterized as AiV genotype A. The virus also demonstrated persistence and infectious potential at all three stages analyzed. The AiV-A mean concentration ranged from 2.05 log10 to 4.64 GC/mL, 2.31 to 4.72 log10 GC/mL, and 2.13 to 2.85 log10 GC/mL for the same treatment stages, respectively. A significant difference (p ≤ 0.05) suggests higher viral concentrations in summer at the three sewage process points analyzed, while lower viral concentrations were observed in post-chemical treatment samples (p ≤ 0.01). Additionally, no statistically significant relationship was observed between the virus occurrence in samples and cases of acute diarrheal diseases in the city. In conclusion, this study highlights that much remains to be understood about AiV while providing valuable insights into the relationship between AiV, environmental factors, and public health.

Unveiling the inactivation mechanisms of different viruses in sludge anaerobic digestion based on factors identification and damage analysis

Despite anaerobic digestion having potential for pathogen reduction in sewage sludge, the behaviors of viruses as the primary health concern are rarely studied. This study investigated the inactivation kinetics and mechanisms of four typical virus surrogates with different structures in mesophilic (MAD) and thermophilic (TAD) anaerobic digestion of sludge. Virus inactivation in MAD was virus-type-dependent correspondingly to different function loss. Temperature drove the faster inactivation proceeding for enveloped Phi6, while temperature and ammonia were the critical inactivation factors for nonenveloped MS2, causing genome degradation and protein functional damage. Interaction with sludge solids played critical role in DNA viruses T4 and Phix174 inactivation via inducing host binding function damage. By comparison, TAD enhanced viral protein denaturation, bringing efficient inactivation with reducing heterogeneity among nonenveloped viruses. These insights into unique virus behaviors in anaerobic digestion systems can provide guidance for developing more effective disinfection protocols and improving sludge biosafety.

Wastewater-based epidemiology for COVID-19 using dynamic artificial neural networks

Global efforts in vaccination have led to a decrease in COVID-19 mortality but a high circulation of SARS-CoV-2 is still observed in several countries, resulting in some cases of severe lockdowns. In this sense, wastewater-based epidemiology remains a powerful tool for supporting regional health administrations in assessing risk levels and acting accordingly. In this work, a dynamic artificial neural network (DANN) has been developed for predicting the number of COVID-19 hospitalized patients in hospitals in Valladolid (Spain). This model takes as inputs a wastewater epidemiology indicator for COVID-19 (concentration of RNA from SARS-CoV-2 N1 gene reported from Valladolid Wastewater Treatment Plant), vaccination coverage, and past data of hospitalizations. The model considered both the instantaneous values of these variables and their historical evolution. Two study periods were selected (from May 2021 until September 2022 and from September 2022 to July 2023). During the first period, accurate predictions of hospitalizations (with an overall range between 6 and 171) were favored by the correlation of this indicator with N1 concentrations in wastewater (r = 0.43, p < 0.05), showing accurate forecasting for 1 day ahead and 5 days ahead. The second period's retraining strategy maintained the overall accuracy of the model despite lower hospitalizations. Furthermore, risk levels were assigned to each 1 day ahead prediction during the first and second periods, showing agreement with the level measured and reported by regional health authorities in 95 % and 93 % of cases, respectively. These results evidenced the potential of this novel DANN model for predicting COVID-19 hospitalizations based on SARS-CoV-2 wastewater concentrations at a regional scale. The model architecture herein developed can support regional health authorities in COVID-19 risk management based on wastewater-based epidemiology.