Wastewater

Aerobic granular sludge (AGS) is usually considered to be a biofilm system consisting of granules only, although practical experience suggests that flocs and granules of various sizes co-exist. This study thus focused on understanding the contribution of flocs and granules of various sizes to nitrification in a full-scale AGS-based wastewater treatment plant (WWTP) operated as a sequencing batch reactor (SBR). The size distribution in terms of total suspended solids (TSS) and the distribution of the nitrifying communities and activities were monitored over 14 months. Our results indicate that AGS is a hybrid system in which flocs (<0.25 mm) play a critical role in nitrification. AGS consisted of 36 % flocs and 50 % large granules (>2 mm) at a TSS concentration of 4.7 ± 0.7 gTSS L^-1. The growth of ammonia oxidizing bacteria (AOB) and nitrite oxidizing bacteria (NOB) in large granules was limited due to the high mass transfer limitation in biofilm and the high solids retention time (SRT) of flocs, where favorable conditions for the growth of nitrifiers were maintained during the warm season. The specific activities of the small aggregates (<1 mm) were 5 to 15 times higher than those of large granules. As a result, flocs contributed >50 % to nitrification during the warm season, whereas granules >1 mm contributed <20 %. Such predominance of flocs in nitrification became problematic in the cold season when the minimum SRT of NOB increased to values similar to the floc SRT, resulting in 79 % loss of the NOB. Consequently, NOB activities dropped, and elevated effluent nitrite concentrations of several mgN L^-1 were monitored. We suggest operating AGS systems similarly to hybrid systems in order to promote the enrichment of NOB in the granules by controlling the floc SRT at low values smaller than the minimum SRT of NOB throughout the year.

A currently increasing interest in water reuse is met with the concern about water quality. Excitation-emission matrix (EEM) measurements, which are widely implemented in laboratory analysis, emerge as a promising tool for characterizing both microbial and chemical water qualities in the online monitoring of water reuse systems. However, the robustness of EEM measurements has been rarely validated in actual online monitoring campaigns where predictions are made for new samples independent of those used to establish EEM analysis models, including the popular parallel factor analysis (PARAFAC). In this study, two strategies of conducting PARAFAC were examined for the online monitoring of a greywater reuse system using two EEM datasets from two monitoring periods for model establishment and model testing respectively. With the first strategy that is commonly used in laboratory analyses, an entire EEM datasets from one period was used to establish one PARAFAC model, and the maximum fluorescence intensity (F_max) of a PARAFAC component was used to predict total cell count (TCC) in another period. However, under the disturbance of dissolved organic matter (DOM) fluorescence in the background, F_max gave unreliable predictions in model testing. To address this problem, a second and novel strategy was proposed using an EEM clustering and PARAFAC component shift mining technique. This unsupervised algorithm, named K-PARAFACs, automatically groups EEMs into K clusters and on each cluster establishes a cluster-specific PARAFAC model with distinct component shapes. With this method, multiple PARAFAC models were established on one EEM dataset, with each model representing samples with certain TCC ranges and DOM compositions. In model testing, these cluster-specific PARAFAC models served as EEM classifiers. A new sample was not characterized by F_max but by the cluster-specific model that best fitted the EEM signal of the sample with the least numerical error. The proposed strategy demonstrates its robustness by successfully predicting the TCC trend in test datasets. Our findings suggest that K-PARAFACs is a promising tool that enables robust qualitative monitoring of water reuse systems with background DOM variability.

Wastewater-based epidemiology has proven to be an important public health asset during the COVID-19 pandemic. It can provide less biassed and more cost-effective population-level monitoring of the disease burden as compared to clinical testing. An essential component of SARS-CoV-2 wastewater monitoring is next-generation sequencing, providing genomic data to identify and quantify circulating viral strains rapidly. However, the specific choice of sequencing method influences the quality and timeliness of generated data and hence its usefulness for wastewater-based pathogen surveillance. Here, we systematically benchmarked Illumina Novaseq 6000, Element Aviti, ONT R9.4.1 MinION flow cell, and ONT R9.4.1 Flongle flow cell sequencing data to facilitate the selection of sequencing technology. Using a time series of wastewater samples from influent of six wastewater treatment plants throughout Switzerland, along with spike-in experiments, we show that higher sequencing error rates of ONT Nanopore sequencing reduce the accuracy of estimates of the relative abundance of viral variants, but the overall trend is in good concordance among all technologies. We find that the sequencing runtime for ONT Nanopore flow cells can be reduced to as little as five hours without significant impact on the quality of variant estimates. Our findings suggest that SARS-CoV-2 variant tracking is readily achievable with all tested technologies, albeit with different tradeoffs in terms of cost, timeliness and accuracy.