Abstract
Activated sludge (AS) microbial communities were analyzed for seasonal variation, a disturbancerecovery event, and separated small aggregates (SAG) to study the influent immigration effect using both neutral immigration model and massbalance model with operational parameters. SAG differed with AS, and higher immigration impact on SAG was confirmed by both models. Adding the SAG community segregation in the latter model to evaluate the contribution of influent immigration to community disturbancerecovery showed increased impact of immigration.
Introduction
The activated sludge (AS) wastewater treatment plant provides a welldefined model system to study microbial ecology. The AS community is shaped by deterministic (Niche) factors and stochastic (Neutral) factors (e.g., neutral immigration from wastewater influent)^{1}. Two types of numerical models have been used to quantify the influent immigration effect. One is the neutral community (immigration) model (NIM)^{2}, employing the abundance and frequency of operational taxonomic units (OTUs) in metacommunities to calculate the communitylevel migration probability^{1,3,4,5,6}. The other is massbalance model (MBM) that defines the immigration as the proportion of reactor biomass derived from influent biomass \(\left( {\frac{{{\mathrm{influent}}\;{\mathrm{biomass}}}}{{{\mathrm{influent}}\;{\mathrm{biomass}} + {\mathrm{local}}\;{\mathrm{growth}}}}} \right)\), using abundances of specific OTUs to calculate the immigration rates and net growth rates^{7}. The NIM immigration describes the probability that a dead individual in the reactor is replaced by an immigrant from the influent, differentiated from the reproduction of local community. Intrinsically, the MBM bases on the fractionation of influent biomass and local growth, which also aims to differentiate the influent immigration and local community. The MBM has been increasingly used because of the quantitative measurements of the OTU growth activity^{8}. Although steadystate metacommunities demonstrated that operational parameters (e.g., solid retention time, SRT) are important for the evaluation of immigration^{4,9}, the operation parameters were not directly employed in the original MBM. Recently, Frigon and Wells^{10} and Guo^{11} developed the massflow immigration model, which is a MBM incorporating operational parameters (MBMOP), to calculate the immigration rate of specific OTUs. By using operational parameters, this model is comprehensible for both wastewater engineers and microbiologists.
In wastewater treatment bioreactors, community variation has been shown in relation to the size and status (settled and unsettled) of the aggregates^{12,13}. In conventional AS, the relative importance of influent immigration on differentsized aggregate communities (i.e., easily settleable largesize flocs and lowsettleability smallsize particles) has not been explored. Besides steadystate modeling, the bioreactors often experience disturbances^{9,14}, such as sudden high flow rates which lead to a biomass washout. The nonsteadystate community serves as a good example to study the community recovery and to compare the contributions of local community growth and influent immigration.
Our study aimed at monitoring the longterm variation of AS communities in different seasons and comparing the influent immigration effect on differentsized AS communities using NIM and MBMOP models. In addition, the shortterm nonsteadystate (under operational highflow disturbance) AS community’s recovery attributed to local community growth and influent immigration was evaluated.
AS community variation
The AS communities (between 2013 and 2019) showed that abundant microorganisms were relatively consistent during the longterm observation, and consisted of a core group at the order level (Fig. 1b) and at genus level (Supplementary Fig. S1). Seasonal changes and disturbance did not alter the core group^{14}. The variation between small aggregates (SAG) and AS was clearly shown (Fig. 1b), being consistent in different seasons. The most abundant members in SAG were different from AS communities, but similar with the influent community’s most abundant members, indicating that influent microbiome is an important origin for the SAG. The community richness (number of observed OTUs) was slightly higher in SAG than in AS (Supplementary Fig. S2), inferring that the SAG community was a union of AS and influent communities. The shared numbers and abundances of OTUs are indicators of immigration effect^{7,9,15}. A large number of OTUs were shared among the influent, AS, and SAG (Fig. 1c). The shared OTUs between influent and AS took up 89.1 ± 0.1% of the influent community total number of reads and 54.0 ± 4.3% of the AS community total number of reads, which increased between influent and SAG (97.0 ± 0.1% and 73.8 ± 3.0%, respectively), indicating higher immigration effect for influent–SAG than for influent–AS. The BrayCurtis distances also showed higher similarity between influent and SAG than between influent and AS (Fig. 1d). Permutational multivariate analysis of variance (BrayCurtis distance, permutation = 999) was performed to test the twoway factor effect of sampling time and aggregate size. Our results showed significant effect of aggregated size (p = 0.001), but not sampling time (p = 0.911) or their interaction (p = 0.866).
This phenomenon of community segregation was observed in different forms of biomass in biofilm and granular sludge reactors^{13,16,17}. Less work has been reported for conventional AS. SAG have higher accessibility to substrates and lower retention time and are more subjected to influent immigration effect as revealed in our results. An ecological growth trait of community, weighted rrn gene copy number per genome, showed that SAG has higher average rrn than AS, implying possibly higher growth activity (Supplementary Fig. S3)^{14,18,19}.
The influent immigration impact was quantified using the NIM^{2,3} and MBMOP (Eq. 14) models. The migration probability (m_{NIM}) was higher for influent–SAG (0.40) than for influent–AS (0.33) (Fig. 1e). The distribution of immigration rates for each OTU based on MBMOP (m_{i,MBMOP}) showed that influent–SAG shifted towards higher immigration rate compared to influent–AS (Fig. 1e), in accordance with the observation of m_{NIM}. These observations suggest a neglected fact that the influent immigration to bioreactors is associated with the aggregate sizedifferentiated community. The SAG community is less settleable compared to flocs and more subjected to washing out from the reactor, leading to higher dependency on the influent immigration. Moreover, the influent community that enters the reactor and the SAG community share a more similar suspension status compared to flocs, which may attribute to the higher community similarity (Fig. 1d) and immigration (Fig. 1e).
Immigration contribution to community recovery
The AS community was disturbed by a highflow event which led to a drop in biomass density, shifting away from the steadystate community and recovered after 3 days (Fig. 1d). The recovery could be attributed to different mechanisms, including growth of the indigenous microorganisms^{9,15} and influent immigration^{1,2,9}.
Using the MBMOP immigration model, the influent community can be partitioned to growth and neutral immigration (nongrowth) (Fig. 2a). A conceptual model MBMOPSAG was constructed (Fig. 2c). In both models, OTUs were classified into three groups based on their relative abundances in the influent and in the AS: (i) not in influent but in reactor community, (ii) growth group (calculated net growth rate u_{i,MBMOP} > 0, Eq. 16), (iii) complete immigration group (calculated net growth rate u_{i,MBMOP} ≤ 0, Eq. 16). The relative abundances (Fig. 2b, d) showed that the immigration group was using the MOMOPSAG model more (Fig. 2d) than using the MBMOPAS model (Fig. 2b). For the disturbed and recovered AS communities, the MBMOPAS model showed similar abundances of immigration group (Fig. 2b). The MBMOPSAG model estimated higher contribution of immigration in recovered than in disturbed community (Fig. 2d). The two models differed in certain taxa’s growth rates and immigration rates, resulting in a list of genera classified into growth group by MBMOPAS model while into immigration group by MBMOPSAG model (Supplementary Table S1). Microorganisms such as Methanobacterium, Methanobrevibacter, Clostridiales, Synergistales, and Bifidobacterium are known anaerobic microorganisms and likely to be carried by wastewater from sewer^{20} and not to grow in AS. The MBMOPSAG model was successful to identify these genera as influent immigration microorganisms.
Our results demonstrated AS community variation in differentsized aggregates. NIM and MBMOP models were compared, both resulted in higher immigration impact for the small aggregate (SAG) community than for the AS community. The differentsized community variation led to an approach (MBMOPSAG model) of modeling influent and segregated communities for immigration effects, which was applied to investigate the community recovery. The MBMOPSAG model showed higher immigration contribution to recovery than the MBMOPAS model. This approach could be applied to other bioreactor systems to increase modeling accuracy of microbial growth and immigration, compared to the conventional NIM and MBMOP models.
Methods
AS and influent biomass sampling from fullscale wastewater treatment plant
Influent (treated by primary clarifier) and AS samples under various flow and seasonal conditions were taken from a fullscale biological nutrient removal wastewater treatment plant with a capacity of treating 310 MLD in Edmonton, Canada. For longterm seasonal comparison, AS samples were collected spanning 6 years (2013–2019), which covers seasonal variations and various flow conditions (240–310 MLD). A highflow event resulted in a decrease of mixed liquor volatile suspended solids from 2840 to 2200 mg/L. The most recent two AS samples (Fall 2018 and Spring 2019) were used to analyze differentsized AS communities. AS samples were taken from the aeration tank to the onsite lab and immediately settled for 30 min using a stir tank at 5 rpm. The supernatant (containing SAG), the interface, and the settled AS were transferred to 50 mL sterile centrifuge tubes and stored on ice before centrifugation (within 1 h). The particle size in the supernatant was determined using Zetasizer (model ZEN 1600, Malvern Instruments Ltd.), resulting in a median size of 25 ± 4.5 µm and range of 1.9–29.0 µm.
Influent samples and small aggregate samples were centrifuged in 50 mL tubes at 5000g for 10 min to collect the pellets. The AS samples were centrifuged in 2 mL tubes at 13,000g for 2 min. The samples were stored at −20 °C until DNA extraction.
DNA extraction, 16S rRNA gene amplicon sequencing
DNA was extracted from influent and bioreactor samples using DNeasy PowerSoil Kit (Qiagen Inc., Toronto, Canada) according to the manufacturer’s protocol. The longterm monitoring of the community was conducted using pyrosequencing of the 16S rRNA genes amplified by 28F (5′GAGTTTGATCNTGGCTCAG 3′) and 519R (5′GTNTTACNGCGGCKGCTG3′). The differentsized AS and influent communities were analyzed using Illumina sequencing. The extracted DNA was used for PCR with modified primers with adaptors: 515 forward primer (5′ ACA CTG ACG ACA TGG TTC TAC AGT GYC AGC MGC CGC GGT AA −3′) and 806 reverse primer (5′ TAC GGT AGC AGA GAC TTG GTC TGG ACT ACN VGG GTW TCT AAT −3′)^{21,22}, targeting the V4 region of 16S rRNA genes. The PCR was performed under the following conditions: primer concentration at 0.3 µm, Mg^{2+} at 1.5 mM, dNTP at 200 µm, Taq DNA polymerase at 1.25 U/50 µL reaction (New England Biolabs, Whitby, ON, Canada), initial denaturation at 94 °C for 3 min, then 25 cycles of denaturation at 94 °C for 30 s, annealing at 62 °C for 45 s and elongation at 68 °C for 1 min, then final elongation at 68 °C for 10 min. The PCR products were purified using QIAquick PCR Purification Kit (Qiagen, Toronto, ON, Canada), then sent for barcoding and sequencing at McGill University and Génome Québec Innovation Centre (Montréal, QC, Canada) using Illumina MiSeq PE250 platform. Sequence data were deposited to the National Center for Biotechnology Information (NCBI) GenBank (Bio Project Accession number PRJNA580466).
The raw sequences (2 × 250 nt) were paired, qualityfiltered using DADA2^{23} in Qiime2 pipelines^{24}. The OTUs were defined using the default 100% similarity. The taxonomic identification was assigned using GreenGenes (version gg_13_8) reference database at 99% similarity^{25,26}. Microbial community diversities (alphadiversity, betadiversity) were analyzed using R “vegan” package^{27}. The weighted rrn copy number was estimated by the normalized OTU table using PICRUSt^{28} with metagenome references in Integrated Microbial Genomes (IMG) system^{29}.
where f_{16S_norm,i} is the relative abundance of the ith OTU in total 16S rRNA gene amplicon sequence reads normalized by PICRUSt; N_{rrn,i} is the rrn copy number of the ith OTU in the IMG database.
Neutral immigration model
The probability of influent immigration was defined by Sloan et al^{2}. and calculated using the neutral immigration model. The model described that in a microbial community with N_{T} individuals, an individual die or leave the system and is immediately replaced by an immigrant with probability of m, or by reproduction of a local member of the community with probability of 1–m.
For the i^{th} species (initial number N_{i}), the probability (Pr) of increase by one, stay the same, or decrease by one is expressed, respectively, by the following three equations:
where p_{i} is the relative abundance of the ith species, α_{i} is the advantage parameter of the ith species over the other species.
The relative abundance can be expressed as \(x_i = \frac{{N_i}}{{N_T}}\). Assume that N_{T} is large enough and x_{i} becomes continuous, and α_{i} = 0 so the species has no advantages, then the probability density function of x_{i},ϕ_{i}=(x_{i},t) can be expressed as (for more details please see Sloan et al^{2}.):
where c is a constant so that \({\int\limits_0^1} \phi _i(x_i)dx_i = 1\).
The probability of the ith species observed in any local community is:
where d is the detection threshold.
This equation can be used to calibrate community data (frequency and mean relative abundance plot) and solve for m.
AS and SAG communities included six replicates each. The immigration rate m was estimated for influent and AS communities and influent and SAG communities, using the R codes from Burns et al^{3}. with 100 permutations (R code in Supplementary Information).
Massbalance model with operational parameters
The MBM with operational parameters (massflow immigration model) calculation for net growth rates was described by Guo^{11}.
The mixed liquor heterotrophic biomass is contributed from the influent biomass and the microbial growth from the consumption of substrate resources. The immigration is defined as the fraction of influent biomass contribution:
where m is the massflow immigration rate; X_{OHO,Inf} and X_{OHO,ML} are the active heterotrophic biomass in influent and mixed liquor; f_{OHO,Capt} is the fraction of influent biomass captured by the mixed liquor which is default 1;Y_{OHO} is the heterotrophic growth yield; S_{consumed} is the consumed substrates.
For continuous stirred tank reactors, the heterotrophic biomass based on massbalance calculation^{30} is:
Rearrangement gives:
Under the assumption that \(f_{{\mathrm{OHO,Capt}}} \ast X_{{\mathrm{OHO,Inf}}} \ll Y_{{\mathrm{OHO}}} \ast S_{{\mathrm{consumed}}}\), substitution of Eq. 10 to Eq. 8 gives:
The immigration rate of a specific ith OTU is:
The proportion of the ith OTU in the biomass sample (f_{16S,i}) can be quantified from the 16S rRNA amplicon sequencing data. The biomass concentration of the ith OTU can be defined by:
where X_{OHO,i} is the biomass concentration of the ith OTU; X_{Tot} is the total concentration of solids; γ_{DNA} is the mass of DNA per solids; f_{16S,i} is the proportion of the ith genus in total 16S rRNA gene amplicon sequence reads.
Therefore, the immigration rate of a specific ith OTU is expressed using 16S rRNA amplicon sequencing data:
Note that the maximum value of m_{i} is 1, so the calculated m_{i} values when greater than 1 were set to 1. When m_{i} = 1 (i.e., the growth rate [μ_{OHO,i}] is 0):
The net growth rate equals the growth rate minus decay rate, therefore the net growth rate of the ith OTU is:
The OTUs then can be classified into three groups:

(i)
not in influent but in reactor community

(ii)
growth group (calculated net growth rate u_{OHO,Net,i} > 0)

(iii)
complete immigration group (calculated net growth rate u_{OHO,Net,i} ≤ 0).
Data availability
The raw sequence data of 16S rRNA gene amplicons can be accessed at GenBank (Bio Project Accession number PRJNA580466).
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Acknowledgements
This work was funded by the Canada Research Chair (CRC) in Future Water Services (Y.L.) and Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant (Y.L.).
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B.G. collected and analyzed the samples, carried out data analysis, modeling and wrote the manuscript. Z.S. analyzed the samples and carried out analysis. Y.L. conceived and guided the study and revised the manuscript. All authors read and approved the final manuscript.
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Guo, B., Sheng, Z. & Liu, Y. Evaluation of influent microbial immigration to activated sludge is affected by differentsized community segregation. npj Clean Water 4, 20 (2021). https://doi.org/10.1038/s41545021001127
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