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Effects of imposed disturbance and SRT on QQ-based antifouling efficacyFigure 1 shows the fouling rate profiles of MBRs over time under different operating conditions, summarizing the average fouling rates of each phase. Two representative transmembrane pressure (TMP) profiles, the average fouling time, and the number of MBR runs at each phase are also provided in Supplementary Fig. 1 and Supplementary Table 1. In Phase 1, the average fouling rates for Reactors 1 and 2 were nearly the same, i.e., there was no statistically significant difference between the reactors. This confirms that the MBRs were in identical states, in terms of fouling behavior when operated in the conventional mode. In Phase 2, the average fouling rates of Reactors 1 and 2 were also almost the same, showing that the QQ effect on fouling mitigation appears to be insignificant. Unlike previous reports on the QQ effect on antifouling efficacy35, it was unclear whether QQ played a role in fouling control under Phase 2 conditions. One possible reason for the difference is SRT, which will be further investigated and discussed later. After disturbance (2 d starvation with a high shear rate of 103 s−1) was applied to both MBRs at the beginning of Phase 3, both MBRs experienced severe fouling phenomena, with sharp increases in the average fouling rate at >40 kPa/d. No QQ effect was observed in this phase either. The applied disturbance may have caused a drastic change in mixed liquor characteristics, probably including the microbial community structure, while aggravating fouling propensities, which will be discussed further in later sections.Fig. 1: Membrane-fouling rate.a Its variations with time for Reactor 1 (R1) and Reactor 2 (R2). Each data point indicates the average fouling rate for each MBR run. b Average fouling rates with t-test probability (p) values for each phase. Error bars indicate 1 SD. Details on the experimental conditions are provided in Table 2.Full size imageTo examine how SRT affects membrane fouling in the MBRs, we increased SRT from 50 to 75 d in Phase 4. The average fouling rate of Reactor 1 slightly decreased with vacant beads (which contain no QQ bacteria), whereas that of Reactor 2 decreased more significantly with QQ beads (corresponding to 47% of that of Reactor 1). With the longer SRT, it appears that QQ affected biofouling mitigation. When Reactor 1 was switched to conventional mode in Phase 5, its membrane fouling rate was slightly reduced compared to Phase 4. This implies that the vacant beads had no effect on fouling mitigation and in fact may have caused membrane fouling. A previous study also reported that membrane fouling increased when the media added were trapped inside the membrane fibers36. However, Reactor 2 exhibited a notably slower fouling rate, which corresponds to 55% of that of Reactor 1. Thus, the biofouling control due to QQ was evident at long SRT (75 d). The effect of SRT on QQ will be discussed further in later sections, along with the time-series data of MBR operational performance and microbial community.Effects of QQ, disturbance, and SRT on biopolymer productionFigure 2a–d show EPS and SMP variations during MBR operations with and without QQ at different SRT values. The EPS and SMP data normalized to mixed liquor suspended solids (MLSS) are also provided in Supplementary Fig. 2. During Phase 1, when the MBRs were operated in the conventional mode, the EPS-carbohydrate (EPS-C) and EPS-protein (EPS-P) levels were similar in both MBRs (~20 and 80 mg/L, respectively). Notably, however, there was only ~3% probability that the EPS-P level between Reactors 1 and 2 occurs by chance. One possible explanation is that the microbial communities in both the reactors should change as a result of the provision of synthetic wastewater, leading to alterations in metabolic products. A previous study also reported that fluctuations in EPS-P level at the beginning of MBR operation were observed due to bacterial acclimation to new environments13. In Phase 2, the EPS-C concentration decreased by ~13.5% in Reactor 1 compared with that of Phase 1, but decreased by 33.1% in Reactor 2. However, the EPS-P concentrations in both reactors remained virtually unchanged. It seemed that the lower EPS levels with QQ did not virtually contribute to fouling mitigation, possibly because its levels were still too high to make a perceptible reduction in membrane fouling. In Phase 3, the EPS-C concentration in Reactor 1 increased by ~6%, whereas it increased by ~39.3% in Reactor 2. This substantial EPS-C increase may have resulted in severe membrane fouling, even in the presence of QQ beads. A previous study reported that increased EPS production was strongly correlated with environmental stresses such as shear and starvation37. It was thought that the disturbance at the beginning of Phase 3 may have caused a similar phenomenon. When the SRT was increased to 75 d in Phase 4, the EPS levels in the two MBRs decreased. In Reactor 1, EPS-C and EPS-P concentrations declined by ~15.6% and 11%, respectively, whereas their concentrations decreased by ~74.4% and 21.65%, respectively, in Reactor 2. Similarly, previous studies also reported that EPS production was reduced with long SRT values11,38. In Phase 5, EPS-C and EPS-P contents continued to decrease in Reactor 2; however, no further decrease was observed in Reactor 1. The higher EPS content caused preferential attachment of biomass onto the membrane surface so as to form cake layers39. The reduced EPS production associated with the QQ strategy correlates with previous findings35,40. It is thus believed that membrane fouling could be mitigated by the presence of QQ media.Fig. 2: Biopolymer concentrations and mixed liquor characteristics according to phases in the two MBRs.a EPS-carbohydrates (EPS-C); b EPS-proteins (EPS-P); c SMP-carbohydrates (SMP-C); d SMP-proteins (SMP-P); e mixed liquor suspended solids (MLSS); and f floc size. The box is determined by the 25th and 75th percentiles, whereas the whiskers are determined by the 5th and 95th percentiles. The white square symbol inside each bar represents the average value of each parameter.Full size imageThe SMP-carbohydrate (SMP-C) and SMP-protein (SMP-P) levels were also similar in both reactors in Phase 1 (~5 and 4.5 mg/L, respectively). In Phase 2, there were slight changes in both. When disturbance was applied in Phase 3, the SMP levels in both reactors significantly increased. The SMP-C and SMP-P concentrations in Reactor 1 increased by ~45.3% and ~36.1%, respectively, and their respective increases in Reactor 2 were more significant at ~62.4% and ~110%. These results agree well with previous studies, which reported that the disturbance imposed on microorganisms induced the release of microbial polymeric substances28,37, in addition to substrate limitations41. When the SRT was increased to 75 d in Phases 4 and 5, the SMP-C and SMP-P concentrations started decreasing, and the decline was more significant with QQ. For instance, in Phase 5, Reactor 2 had the lowest SMP-C and SMP-P levels, at ~54.2% and ~62.4% lower than these respective values in Phase 4. In addition, the longer SRT contributed to decreased SMP levels when comparing Reactor 2 between Phases 2 and 5. This result is consistent with previous studies35,40, which reported that the presence of QQ media reduced soluble biopolymer contents in MBRs. Another previous study also reported that increasing the SRT in MBRs alleviated biofouling42. Notably, QQ caused the more substantive and immediate decrease of SMP-C than that of SMP-P in Phase 4. This could be associated with the inhibition of protease enzyme secretion in the presence of QQ enzymes leading to reduced degradation of soluble protein43. Overall, it can be concluded that the presence of QQ media reduced biopolymer production more significantly when the SRT was extended.Effects of QQ, disturbance, and SRT on mixed liquor characteristics and biological treatment efficienciesMixed liquor characteristics, such as MLSS and floc size, were monitored over time (Fig. 2e, f). The MLSS concentration in both MBRs from Phases 1–3 varied in the range of 2100–2250 mg/L. Disturbance (starvation with shear) at the beginning of Phase 3 caused a slight decrease in biomass concentration compared to that of Phase 2. At the longer SRT (75 d) in Phases 4 and 5, the MLSS concentration increased to 2650–2900 mg/L. It is natural that a longer SRT should increase MLSS levels at the same yield. It appeared that the MLSS levels were a bit higher with QQ than without it as observed from Phase 2 through 5. Microbial growth can be promoted if QS that requires carbon sources is inhibited. A recent finding pointed out that the QQ enzyme (acylase) may increase microbial yield, converting the resource (food) to more biomass44. Operational parameters such as QQ, SRT, and disturbance did not yield significant changes in floc size during the entire study, although fluctuations were possible45. In this study, there was a slight increase in floc size with QQ in Phase 5. The microbial floc size is a function of several factors, such as QS, QQ, nutrients, and operational conditions43. A recent study reported that there was a negative correlation between floc size and EPS level, because the excessive EPS played a role in reducing the hydrophobicity of flocs and, thereby, weakening the cells’ attachment46. It is thus seen that the reduced EPS content may help enhance the floc aggregation, possibly resulting in greater floc sizes.The biological treatment efficiencies of the two MBRs were evaluated in terms of removals of chemical oxygen demand (COD), total organic carbon (TOC), total nitrogen (TN), and total phosphorus (TP) (Supplementary Fig. 3a–d). The effects of SRT and QQ on these removal efficiencies were almost negligible, although disturbance caused a slight decrease in organics removal. The result coincided with the increased SMP level with shear in Phase 3. In short, mixed liquor properties and biological treatment performances seemed to tolerate the effects of QQ and SRT, although they were slightly impacted by disturbance.Microbial community structure changeFigure 3a and Supplementary Table 2 show microbial community variations relative to phases, with clear microbial community structure shifts between phases. Two species were dominant in the seed sludge: Dokdonella immobilis (11.31%) and Sphaerotilus natans (14.91%). After inoculation in the laboratory MBRs, the dominance of these species diminished and other species, being adapted to the synthetic feed, flourished instead (Phase 1). In Phase 2, Thiothrix eikelboomii (15%–18%) and Panacibacter ginsenosidivorans (11%–14%), which were negligible in the seed sludge, became dominant in both reactors. In addition, the relative abundances of Kofleria flava and Flavitalea antarctica increased to 7.57% and 6.95%, respectively. These two species were more abundant in Reactor 1 than Reactor 2. Lastly, the major species of the seed sludge, such as D. immobilis, S. natans, and Terrimonas lutea, became minority species ( 0.7) with SMP-C, SMP-P, and the relative abundance of four individual microbial species (i.e., F. antarctica, P. glucosidilyticus, S. piscinae, and T. carbonis). SMP had a lot stronger correlations with fouling rates than EPS, although the actual amounts of the former were a lot smaller than those of the latter. The result indicates that the soluble biopolymers present in the bulk liquid play a more important role in membrane fouling, possibly due to their direct deposition onto the membrane surface. The strong, negative correlations of fouling rates with COD and TOC removal efficiencies support the above explanation. In particular, P. glucosidilyticus and S. piscinae, which had the highly strong correlations with membrane fouling (r  > 0.87), accordingly exhibited strong correlations with SMP. Notably, T. eikelboomii, which was the most abundant in Reactor 1 of Phases 4 and 5, had a relatively weak negative correlations (r = −0.32) with membrane fouling and so not as strong as did K. flava (r = −0.73). The decrease in the relative abundance of T. eikelboomii in Reactor 2 of Phase 5 should be associated with QQ, but the species might still have been contributing to membrane fouling, as discussed above. As expected, the microbial diversity indices between OTUs and Chao1, as well as Shannon and inverse Simpson, were found to be strongly correlated. However, the fouling rate did not have strong correlations with any of the microbial diversity indices (−0.05 ≤ r ≤ 0.55), although the microbial diversity was always higher in the presence of QQ (see Table 1).Fig. 4: Correlation analysis.Spearman’s correlation coefficients between all the MBR parameters determined in the study.Full size imageOn the other hand, the content of EPS-C and EPS-P had strong, negative correlations with MLSS levels. The biomass increase was accompanied with longer SRT, so the aged sludge produced less EPS amounts leading to the floc size decline (corresponding to a negative correlation, i.e., r = −0.52). Notably, the floc size had a strong, positive correlation (r = 0.84) with TN removal, suggesting that simultaneous nitrification and denitrification possibly occurred with larger biological flocs59. In addition, D. immobilis showed a strong positive correlation (r = 0.84) with TP removal, proposing its role as a potential phosphate uptake strain60. Overall, the relationships between MBR parameters (e.g., fouling rates, mixed liquor characteristics, biological treatment efficiencies, and microbial species dominance) helped better understand the fouling patterns and biological performances in the MBRs with and without QQ.In summary, the QQ effect on MBR antifouling efficacy was clearer when the SRT was extended from 50 to 75 d, although the disturbance (starvation with shear) aggravated membrane fouling, which counteracted the positive QQ effect. QQ yielded a significant biopolymer production decrease with the longer SRT. Accordingly, organic substance removal showed relatively strong, negative correlations with MBR-fouling propensity. MBR microbial communities showed dynamic responses to the feed change, QQ, disturbance, and SRT. With disturbance, F. antarctica, S. piscinae, and P. glucosidilyticus dominated the microbial community leading to substantive membrane fouling. However, the microbial community balance between T. eikelboomii and K. flava, whose relative abundances appeared to be affected by SRT and QQ, played a key role in fouling propensity under stabilized conditions. The correlation analysis showed strong positive relationships between membrane fouling rate and the abundance of several microbial species (F. antarctica, P. glucosidilyticus, S. piscinae, and T. carbonis). However, there was no strong correlation between T. eikelboomii and membrane fouling propensity, possibly due to the antagonism by K. flava, and vice versa. More

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Shape analysis of 3038 daily flow patterns (2016–2020) for WWTP1 and WWTP2
An example effluent flow pattern for a 10-day period at WWTP1 is shown in Fig. 1. EDM detected spilling intervals are overlaid to demonstrate the flattening effect of spilling on the profile of the flow pattern.
Fig. 1: WWTP1: example effluent flow pattern for 10 days annotated with EDM confirmed spilling intervals.

A 24-h (midnight to midnight) daily flow pattern of 96 15-min-interval average flow rates (litres/second) of treated effluent is shown in blue. The black horizontal, linear annotations represent EDM recorded intervals denoting a discharge from a storm tank (i.e., consented spill or potentially unconsented spill of untreated sewage), the shortest being 15 min and the longest over 24-h. Total daily rainfall (mm/d) is provided in green. The first two days, with no detected spills, show diurnal patterns of low flow between midnight (previous day) and the first peak after mid-morning, followed by a lull until a second, smaller peak in the evening. The next seven days (15/12/18 through 21/12/18) involve spill intervals of various length (black EDM line), showing a flattening of flow, which is typical of storm discharge during heavy rainfall. The last day shows elevated flows and a partial return to a diurnal flow pattern with no spills reported.

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For WWTP1 (resp. WWTP2), EDM data were available for 446 (resp. 471) consecutive days for 2018–2020 during which untreated sewage spill intervals of varying lengths had been recorded. For each day, spill intervals were aggregated to the total number of hours of discharge. Of the days used for machine learning for WWTP1 (resp. WWTP2), 339 (resp. 346) involved no EDM recorded spilling incidents and 107 (resp. 125) days had spills with various lengths of which over a third were for 24-h. For WWTP1 (resp. WWTP2), 97 (resp. 117) days with an aggregated ‘spill’ length of at least 3-h were labelled as ‘spill’ and 349 (resp. 354) with an aggregated ‘spill’ length of below 3-h as ‘normal’. A 3-h aggregation period was selected because it guaranteed a reasonable number of ‘spill’ days on which to base the supervised learning and preliminary attempts to predict spilling hours per day were weakest for aggregated daily spills under 3-h. Where no EDM data was available, days were labelled as ‘unknown’. The average ‘normal’ (blue line) and ‘spill’ (black line) daily flow patterns as a proportion of storm overflow rates (red line) are shown in Fig. 2 for each WWTP. The storm overflow rates mark the minimum flow that should be treated before untreated sewage spills can be made in compliance with EA permits to discharge to watercourses.
Fig. 2: Average daily flow patterns.

a WWTP1: black curve for ‘spill’ days (n = 97) and blue curve for ‘normal’ days (n = 349); b WWTP2: black curve for ‘spill’ days (n = 117) and blue curve for ‘normal’ days (n = 354).

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Separate shape models were generated for flow patterns from 2016 to 2020 for WWTP1 (n = 1511) and WWTP2 (n = 1527). The first principle component of shape variation, PCA1, in both models, is associated with magnitude, and temporal shifting of morning flow peak (see Supplementary Video 1.mp4) as well as “seasonal” changes related to daylight saving, public holidays and vacation periods (Supplementary Fig. 1). Despite differences in the population served by WWTP1 and WWTP2, Fig. 3a, b shows similar distributions for scatter plots of PCA1 vs PCA2 for 2121 flows for 2016–2018 without EDM data. Analogous plots of PCA1 vs PCA2 for 917 flows for 2018–2020 with EDM data (Fig. 3c, d) suggest that, for both WWTPs, PCA2 is correlated with shape difference between ‘normal’ flow (open circles) and ‘spill’ affected flow (filled triangles). This spill-related flattening is illustrated by morphing the overall average daily flow pattern for WWTP1 between −1 and +1 standard deviations of PCA2 (Supplementary Video 2.mp4). Interestingly, the area under the receiver-operating characteristics curve associated with using PCA2 alone for ‘normal’/’spill’ discrimination is 0.88 and 0.91 for WTTP1 and WWTP2, respectively (this is the estimated probability of correctly classifying a pair of flow patterns selected randomly, one each, from the ‘normal’ and ‘spill’ labelled subsets).
Fig. 3: PCA1 vs PCA2 for daily flow patterns.

Unknown spill status (grey filled circles); spill confirmed by EDM (filled black triangles); confirmed as normal by EDM (unfilled grey circle) 2016–2018 without EDM data a WWTP1 (n = 1065); b WWTP2 (n = 1056); 2018–2020 with EDM data c WWTP1 (n = 466); d WWTP2 (n = 471).

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Supervised learning of the effect of sewage spills on 917 effluent flow patterns
The performance of 20-folded cross-validation of supervised learning for labelled flow patterns for WWTP1 and WTTP2 is shown in Supplementary Tables 3 and 4 for 20 support vector machine (SVM) variations while retaining up to 15 PCA modes for flow pattern synthesis. The number of PCA modes retained for shape synthesis affects the validity of the reconstruction of each daily flow pattern and hence classification accuracy. For the three best-performing algorithms, Supplementary Fig. 2 shows the variation in classification accuracy of daily flow patterns for different numbers of retained PCA modes estimated as the average area under the 20 receiver-operating characteristic curves associated with the cross-validation folds. For the optimal classifiers, the average area under the receiver-operating characteristic curve was 0.97 for WTTP1 and 0.96 for WWTP2.
For verification, prior to wider application, the optimal ML classifiers defined for each WWTP were used to reclassify the flow patterns used in their derivation. Figure 4 shows these flow patterns in contiguous temporal sequence with annotations for each day reflecting EDM detected spill intervals (horizontal black segments) and ML confirmation of ‘spill’ (unfilled gold circles). During this period there were 97 (resp. 117) days with an EDM confirmed aggregated spill of at least 3-h at WWTP1 (resp. WWTP2). The agreement between optimal ML classification and spill day labels derived from EDM data was extremely high (WTTP1: sensitivity = 0.91, specificity = 0.95; WTTP2: sensitivity = 0.98, specificity = 0.98), as would be expected for such “training” data.
Fig. 4: Daily effluent flow patterns and event duration monitor (EDM) detected spill intervals at WTTP1 and WWTP2 used as training data (Dec’2018–Mar’2020).

The daily flow and EDM spill data are measured at 15 min intervals. Flow is coloured (orange/blue/pink) to distinguish different years. Black horizontal lines delimit EDM detected spill intervals. Daily flows of aggregated spill length of at least/less than 3-h are labelled as ‘spill’/‘normal’ prior to the supervised learning. Gold circles indicate days classified as ‘spill’ following the training of the machine learning (ML) algorithms to produce an optimal classifier for each WWTP. The grey dashed line represents the storm overflow which defines the minimum sewage flow that should be treated even during storm filling or overflow. Additional annotations are telemetry alarms provided by the operator. These alarms have the potential to corroborate ML predictions of ‘spill’ days for the unseen flow patterns from 2009 to 2018 for which there is no EDM data. Similar charts showing the unseen ML classification of the 2009–2018 daily flow patterns overlaid with rainfall and river level data are provided in Supplementary Figs 5–10.

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Figure 4 also includes data from other alarms related to untreated sewage discharges that have the potential to corroborate ML flow pattern classification for historical periods without EDM data. For WWTP1, there is near-perfect agreement (Cohen’s kappa: 0.81–1.00) between the EDM, STO (Storm Tank Overflow) and COL (Consented Overflow Level) alarms and ML classification for Feb ‘19–Feb ‘20 (Fig. 4 and Table 2). For just two months, Dec ‘18 and Jan ‘19, the EDM and COL devices concur with near-perfect agreement (Cohen’s kappa = 0.95), the STO device was largely at odds (Cohen’s kappa ≤ 0), and the ML classifier flagged incidents detected by all three. These results suggest that the STO is a good candidate and the COL alarm is an excellent candidate for corroborating ML detected putative spills at WWTP1 when EDM data is unavailable.
Table 2 Agreement of ML classification, EDM, COL and STO alarms for the supervised learning.
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For WWTP2, there is almost perfect agreement between EDM and COL alarms (Cohen’s kappa = 0.87) and with ML classification (Cohen’s kappa = 0.78) (Fig. 4 and Table 2). No STO alarm data were provided for 2020 and between Dec ‘18 and Dec ‘19 STO showed only chance agreement with other devices and the ML classifier (Cohen’s kappa  More