Machine learning estimation of biodegradable organic matter concentrations in municipal wastewater

This study investigates whether a novel estimation method based on machine learning can feasibly predict the readily biodegradable chemical oxygen demand (RB-COD) and slowly biodegradable COD (SB-COD) in municipal wastewater from the oxidation-reduction potential (ORP) data of anoxic batch experiments. Anoxic batch experiments were conducted with highly mixed liquor volatile suspended solids under different RB-COD and SB-COD conditions. As the RB-COD increased, the ORP breakpoint appeared earlier, and fermentation occurred in the interior of the activated sludge, even under anoxic conditions. Therefore, the ORP decline rates before and after the breakpoint were significantly correlated with the RB-COD and SB-COD, respectively (p < 0.05). The two biodegradable CODs were estimated separately using six machine learning models: an artificial neural network (ANN), support vector regression (SVR), an ANN-based AdaBoost, a SVR-based AdaBoost, decision tree, and random forest. Against the ORP dataset, the RB-COD and SB-COD estimation correlation coefficients of SVR-based AdaBoost were 0.96 and 0.88, respectively. To identify which ORP data are useful for estimations, the ORP decline rates before and after the breakpoint were separately input as datasets to the estimation methods. All six machine learning models successfully estimated the two biodegradable CODs simultaneously with accuracies of ≥0.80 from only ORP time-series data. Sensitivity analysis using the Shapley additive explanation method demonstrated that the ORP decline rates before and after the breakpoint obviously contributed to the estimation of RB-COD and SB-COD, respectively, indicating that acquiring the ORP data with various decline rates before and after the breakpoint improved the estimations of RB-COD and SB-COD, respectively. This novel estimation method for RB-COD and SB-COD can assist the rapid control of biological wastewater treatment when the biodegradable organic matter concentration dynamically changes in influent wastewater.

Occurrence and contamination profile of legacy and emerging per- and polyfluoroalkyl substances (PFAS) in Belgian wastewater using target, suspect and non-target screening approaches

With the growing concern regarding the health risks of per- and polyfluoroalkyl substances (PFAS), there is an increasing demand for the identification of emerging PFAS. This study provides a comprehensive investigation of legacy and emerging PFAS in 16 wastewater treatment plants (WWTPs) in Belgium using target, suspect, and non-target screening methods. Perfluorobutanoic acid (PFBA) and perfluoropentanoic acid (PFPeA) were the dominant compounds in most locations, whereas perfluorooctanoic acid (PFOA) was the most predominant PFAS in WWTP Deurne (Antwerp region). Using a suspect screening approach, 14 PFAS were annotated as confidence level (CL) of 4 or higher and 4 PFAS were annotated as CL 2a and 2b, including aqueous film forming foam (AFFF)-derived PFAS. The compound group of n:3 unsaturated fluorotelomer carboxylic acid was found using non-target screening in the wastewater from WWTP Deurne. Population exposure in a catchment area estimated using population-normalized mass loads (PNML) showed the highest value in the catchment area of WWTP Deurne, implying a potentially higher exposure to PFAS in this community.

Analytical methodologies for the detection of SARS-CoV-2 in wastewater: Protocols and future perspectives

In March 2020 the World Health Organization announced a pandemic outbreak. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative pathogen for the coronavirus disease-19 (COVID-19) pandemic. The authorities worldwide use clinical science to identify infected people, but this approach is not able to track all symptomatic and asymptomatic cases due to limited sampling capacity of the testing laboratories. This drawback is eliminated by the Wastewater-Based Epidemiology (WBE) approach. In this review, we summarized the peer-reviewed published literature (available as of September 28, 2020), in the field of WBE. The commonly used steps (sampling, storage, concentration, isolation, detection) of the analytical protocols were identified. The potential limitations of each stage of the protocols and good practices were discussed. Finally, new methods for the efficient detection of SARS-CoV-2 were proposed.

Natural continuous influent nitrifier immigration effects on nitrification and the microbial community of activated sludge systems

Two sequencing batch reactors (SBRs) were operated for 100days under aerobic conditions, with one being fed with unsterilized municipal wastewater (USBR), and the other fed with sterilized municipal wastewater (SSBR). Respirometric assays and fluorescence in situ hybridization (FISH) results show that active nitrifiers were present in the unsterilized influent municipal wastewater. The maximum ammonia utilization rate (AUR) and nitrite utilization rate (NUR) of the unsterilized influent were 0.32±0.12mg NH₄⁺-N/(L·hr) and 0.71±0.18mg NO₂^--N/(L·hr). Based on the maximum utilization rates, the estimated seeding intensity for the ammonia oxidizing bacteria (AOB) and nitrite oxidizing bacteria (NOB) of the USBR was 0.08g AOB/(g AOB·day) and 0.20g NOB/(g NOB·day) respectively. The fraction of nitrifiers/total bacteria in the influent was 5.35%±2.1%, the dominant AOB was Nitrosomonas spp., Nitrosococcus mobilis hybridizated with Nsm156, and the dominant NOB was Nitrospira hybridizated with Ntspa662. The influent nitrifiers potentially seeded the activated sludge of the bioreactor and hence demonstrated a mitigation of the acclimatization times and instability during start-up and early operation. The AUR and NUR in the USBR was 15% and 13% higher than the SSBR respectively during the stable stage, FISH results showed that nitrifiers population especially the Nitrospira in the USBR was higher than that in the SSBR. These results indicate that the natural continuous immigration of nitrifiers from municipal influent streams may have some repercussions on the modeling and design of bioreactors.

Spatial and temporal trends in alcohol consumption in Belgian cities: A wastewater-based approach

BACKGROUND

In recent years, scientific evidence has emerged that wastewater-based epidemiology can deliver complementary information concerning the use of different substances of abuse. In this study, the potential of wastewater-based epidemiology in monitoring spatial and temporal trends in alcohol consumption in different populations in Belgium has been examined.

METHODS

Concentrations of ethyl sulphate, a minor Phase-II metabolite of ethanol, in 163 influent wastewater samples from eight wastewater treatment plants in Belgium in the period 2013-2015 were measured with liquid chromatography coupled to tandem mass spectrometry and used to estimate alcohol consumption.

RESULTS

The highest levels of alcohol consumption were detected in the metropoles Antwerp and Brussels compared to smaller villages. Annual variations were detected, with a higher alcohol consumption measured in 2013 compared with 2014. The weekly pattern showed a clear week and weekend difference in alcohol use, with intermediate levels on Monday and Friday. The results were extrapolated and a use of 5.6L pure alcohol per year per inhabitant aged 15+ has been estimated in Belgium. The comparison with available information on drinking habits of the Belgian population further demonstrated the usefulness of the wastewater-based epidemiology approach.

CONCLUSIONS

This is the largest wastewater-based epidemiology study monitoring alcohol consumption to date, demonstrating that objective and quick information on spatio-temporal trends in alcohol consumption on a local and (inter)national scale can be obtained.

Influence of influent wastewater communities on temporal variation of activated sludge communities

Continuously feeding influent wastewater containing diverse bacterial species to a wastewater treatment activated sludge bioreactor may influence the activated sludge bacterial community temporal dynamics. To explore this possibility, this study tracked influent wastewater and activated sludge bacterial communities by pyrosequencing 16S rRNA genes from four full-scale wastewater treatment plants over a 9-month period. The activated sludge communities showed significantly higher richness and evenness than the influent wastewater communities. Furthermore, the two communities were different in composition and temporal dynamics. These results demonstrate that the impact of the influent wastewater communities on the activated sludge communities was weak. Nevertheless, 4.3-9.3% of the operational taxonomic units (OTUs) detected in the activated sludge were shared with the influent wastewater, implying contribution from influent wastewater communities to some extent. However, the relative OTU abundance of the influent wastewater was not maintained in the activated sludge communities (i.e., weak neutral assembly). In addition, the variability of the communities of the shared OTUs was moderately correlated with abiotic factors imposed to the bioreactors. Taken together, temporal dynamics of activated sludge communities appear to be predominantly explained by species sorting processes in response to influent wastewater communities.