Microbial composition of bioaerosols in indoor wastewater treatment plants by Hamza Mbareche

Aerobiologia (2022) 38:35–50 https://doi.org/10.1007/s10453-021-09732-5

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ORIGINAL PAPER

Microbial composition of bioaerosols in indoor wastewater treatment plants Hamza Mbareche . Marc Veillette . Vanessa Dion-Dupont . Jacques Lavoie . Caroline Duchaine

Received: 29 April 2021 / Accepted: 22 November 2021 / Published online: 8 January 2022 The Author(s), under exclusive licence to Springer Nature B.V. 2021

Abstract Wastewater treatment is one of the major biotechnological processes used to treat municipal and industrial sewage. All the steps involved in the removal of contaminants from wastewaters to treat municipal and industrial sewage represent a reservoir of a dynamic microbial communities with specific key players in the different process types. Aerosolized biological particles, defined as bioaerosols, can be generated during different steps of the wastewater treatment process. The goal of this study is to offer a comprehensive indoor-air microbiota description of numerous wastewater treatments plants using an amplicon-based high-throughput sequencing

Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/ s10453-021-09732-5. H. Mbareche M. Veillette V. Dion-Dupont C. Duchaine (&) Centre de recherche de l’institut universitaire de cardiologie et de pneumologie de Québec, 2725 Chemin Ste-Foy, Québec, QC G1V 4G5, Canada e-mail: Caroline.Duchaine@bcm.ulaval.ca H. Mbareche V. Dion-Dupont C. Duchaine Département de biochimie de microbiologie et de bioinformatique, Faculté des sciences et de génie, Université Laval, Québec, QC G1V0A6, Canada

approach. To fill up the gap of the wastewaterbioaerosol microbial diversity literature, a site-related and seasonal variation of bioaerosol emission in eight indoor wastewater treatments plants is described targeting the 16S rRNA gene for bacterial community analyses. No significant differences were observed between summer and winter in terms of microbial diversity and composition. However, indoor pretreatment and secondary treatment steps suggest the presence of different bacterial taxa, some of them being pathogens or opportunistic pathogens. Gutassociated flora was most abundant in the air collected during the biodegradation of organic matter of the wastewater treatment step and suggests that fecal contamination can persist in aerosols until the last steps of the process. The results suggest that wastewater workers could be exposed to pathogenic and opportunistic pathogenic microorganisms in aerosols released at every treatment step with a peak during degritting and degreasing. This study offers a comprehensive indoor-air microbiota description of waste water treatment plants—concluding a significant potential occupational risk. Keywords Bioaerosols Indoor air Microbiota Wastewater treatment plants Microbial diversity

J. Lavoie Institut de Recherche Robert-Sauvé en Santé et en Sécurité du travail (IRSST), Montreal, QC H3A3C2, Canada

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1 Introduction Wastewater treatment is a process used to return water to its cycle while minimizing the environmental impact. Wastewater treatment is one of the major biotechnological processes used to treat municipal and industrial sewage (Cheremisinoff, 1997). The removal of contaminants from wastewaters represents an important challenge in the field of water pollution (Khopkar, 2007). The treatment process takes place in wastewater treatment plants (WWTP). In general, WWTP involve three steps: pre-treatment, primary and secondary treatments. Pre-treatment includes removal of large materials, primary treatment is the settling of sludge, and secondary treatment concerns the biological degradation of organic matter (Droste, 2018). All these steps involve a reservoir of a dynamic microbial community with specific key players in the different process types (Shu et al., 2015; Wagner et al., 2002). There are differences in the microbial community structure between the different steps of the wastewater process that may be explained by dissolved oxygen, pH, and nitrate-nitrogen content of effluents, diminution of nutrients, and bacteria stuck or associated with removed debris (Cydzik-Kwiatkowska & Zienlinka, 2016; ElNaker et al., 2018). Aerosolized biological particles, defined as bioaerosols, can be generated during different steps of the wastewater treatment process (Sánchez-Monedero et al., 2008). Previous studies quantified microbial bioaerosol concentrations (bacteria, fungi, viruses) and/or detected antibiotic resistance genes from WWTP (Brisebois et al., 2017; Kowalski et al., 2017; Li et al., 2016; Masclaux et al., 2014; Niazi et al., 2015; Wang, Lan, et al., 2018; Wang, Li, et al., 2018). More precisely, some studies demonstrated the presence of bacterial pathogens such as Legionella, Enterobacteriaceae like Klebsiella pneumoniae and Escherichia coli O157:H7, Pseudomonas aeruginosa, Enterococcus faecalis and Clostridium perfringens in air samples collected from WWTP (Caceido et al., 2018; Korzeniewska et al., 2012; Shannon et al., 2007). Bioaerosols generated during the wastewater treatment process can remain in suspension for several hours, depending on their size and air movements, and eventually be inhaled by workers or can settle and be ingested after contact (Uhrbrand et al, 2011). Workers at WWTPs are endangered by the inhalation or the ingestion of potentially harmful infectious

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and non-infectious microorganisms and microbial components (Van Hooste et al., 2010). Few studies already demonstrated an increased incidence of gastrointestinal illnesses among WWTP workers compared to control groups (Khuder et al., 1998; Rylander, 1999; Thorn et al., 2002). Furthermore, symptoms like headaches, weakness, fever, nausea, cough, rhinitis and other respiratory symptoms are identified as sewage worker syndrome and have been associated with WWTP work environment (Thorn and Kerekes, 2001; Rylander et al., 1976). Several other health problems emerged in WWTP workers over time like different infections, systemic symptoms and certain types of cancers (Divizia et al., 2008; Gangamma et al., 2011; Kindzierski & Maal-Bared, 2015; Smit et al., 2005; Thorn & Beijer, 2004; Vidal et al., 2012). Recent work has demonstrated, through the American Thoracic Society (ATS) standard respiratory symptom questionnaire, significantly higher prevalence of respiratory symptoms among WWTP workers compared to non-exposed persons (Jahangiri et al., 2015). The studies cited herein suggest the need for further investigation to ascertain causal agents of WWTP workers adverse health effect. There are no official exposure limits related to bacterial bioaerosols in WWTP, and efficient biomarkers to monitor air quality are needed. Available literature on bioaerosol exposure in WWTPs focused essentially on culture-dependent approaches to quantify cultivable microorganisms and/or on molecular approaches for the detection and quantification of specific pathogens, including viruses (Niazi et al., 2015; Li et al., 2016; Wang, Lan, et al., 2018; Wang, Li, et al., 2018, Brisebois et al., 2017; Caceido et al., 2018; Korzeniewska et al., 2012; Carducci et al., 2018; Courault et al., 2017; Dehghan et al., 2018; Ding et al., 2017; Uhrbrand et al., 2017). Thus, the literature lacks important information on microbial diversity of bioaerosols emitted from the different steps of the wastewater treatment processes. The importance of studying microbial diversity of bioaerosols using high-throughput sequencing (HTS) is highlighted because of the possibility to identify multiple agents that may be responsible of the observed health effects. Also, HTS allows to study the effect of multiple variables (e.g., season, sites, etc.) on the microbial composition of bioaerosols (Mbareche et al., 2017, 2018a, b). In addition, the exhaustive list of bacterial genera that can be drawn following

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HTS can help determine, in the long-term, new biomarkers for a better assessment of future occupational exposure studies. Recent studies that applied HTS approaches on bioaerosol from WWTPs either focused on outdoor installations or were specific to certain stages of the wastewater treatment processes (Han et al., 2018; Wang et al., 2018; Wang, Li, et al., 2018). The Eastern Canadian harsh winter forces indoor setting of wastewater treatment processes. The indoor conditions might affect microbial composition and concentration during summer and winter. These conditions include the use of heating, ventilation and air conditioning (HVAC) systems, and the windows/doors opening and closing. Investigating the effect of the different steps involved in wastewater treatment on the microbial diversity of bioaerosols emitted in indoor WWTPs during summer and winter would help us understand the seasonal impact on indoor air quality in WWTPs. The goal of this study is to offer a comprehensive indoor-air microbiota description of numerous WWTPs using a HTS approach. To fill up the gap of the wastewater-bioaerosol microbial diversity literature, a site-related and seasonal variation of bioaerosols in eight indoor WWTPs is described targeting the 16S rRNA gene for bacterial community analyses.

Table 1 presents a description of the sampling sites and conditions during the sampling campaign. During winter visits, indoor temperature varied from 9.5 to 24 C with the lowest temperature registered in the biofiltration step and the highest in the screening step. Summer indoor temperatures varied between 15.5 C and 23 C with the lowest value noted in the screening step and the highest in the biofiltration. Sampling occurred in the morning from 9AM to 12PM. 2.2 Air sampling Samples were collected with a SASS 3100 (Research International, Inc. Monroe, USA), a high efficiency sampler that collects bioaerosols using a 44-mmdiameter charged electret filter. Between 10 and 30 m3 of air were sampled at 300 L/min. Filters were kept at 4 C until the extraction using the SASS 3010 Particle Extractor (Research International, Inc. Monroe, USA). Filters were eluted in 5 ml of SASS extraction buffer. We followed the SASS 3010 manufacture instructions for handling and function of the sampler, and for the extraction of the filters by the particle extractor blank filters were brought to the sampling site and analyzed using the same procedure used for bacteria or viruses. 2.3 DNA extraction

2 Material and methods 2.1 Field samples Air samples were collected from eight WWTPs located in the province of Quebec in Eastern Canada during summer (June to mid-September) and winter (mid-December to mid-March). Summer visits occurred between September 2015 and July 2016, and winter visits between February 2015 and March 2016. Four indoor steps of the wastewater treatment process were chosen based on where workers perform daily tasks and where the confinement of the space exposes them to higher concentrations of bioaerosols. Air samplers were installed 1 m above the floor and distant from ventilation. Outdoor control samples were taken during summer visits outside each WWTP in the upwind direction to avoid the influence of WWTPs operations. Outdoor control samples cannot be taken during the winter because the temperature is below zero and the air samplers could be damaged.

Each filter elution liquid was divided into three aliquots (1.5 mL each) and centrifuged at 21,000 g for 10 min. All samples were extracted in 50 ll of elution buffer using the PowerLyzer UltraClean Microbial DNA Isolation Kit (Bio-Rad Laboratories, Mississauga, Canada). After the DNA elution, samples were stored at - 20 C until subsequent analyses. 2.4 MiSeq illumina sequencing Amplification of the targeted genes, equimolar pooling and sequencing was performed at the Plateforme d’analyses génomiques (IBIS, Université Laval, Quebec City, Canada). Amplification of the 16S V6-V8 region was performed using the sequence-specific regions described by Comeau and his collaborators (2011) using a two-step dual-indexed PCR approach specifically designed for Illumina instruments by the IBIS team. PCR was performed after the gene-specific sequence was fused to the Illumina TruSeq

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Table 1 Description of the activities related to each treatment step and total number of sites sampled for each step in the eight WWTPs Steps

Treatment steps

Number of sites

Description of activities

Pre-treatment

Screening

Filtration of large and insoluble materials

Degritting

Removal of granular matter and fats, oils and greases (FOGs)

Primary treatment

Primary Settling

Residual particles and sludge settling

Secondary treatment

Biofiltration

Biological degradation of residual organic matter

sequencing primers. Each reaction mixture (total volume of 25 ll) consisted of 1X Q5 buffer (NEB), 0.25 lM of each primer, 200 lM of each dNTPs, 1U of Q5 high-fidelity DNA polymerase (NEB) and 1lL of template cDNA. PCR started with an initial denaturation at 98 C for 30 s followed by 35 cycles of denaturation at 98 C for 10 s, annealing at 55 C for 10 s, extension at 72 C for 30 s and a final extension at 72 C for 2 min. Axygen PCR cleanup kit was used to purify the PCR (Axygen ). Quality of the purified PCR product was verified on a 1% agarose gel. Fifty- to 100-fold dilution of the purified product was used as a template for the second PCR to add barcodes (dual-indexed) and missing sequence required for Illumina sequencing. The second PCR was identical to the first PCR, but with 12 cycles. PCRs were purified as above, checked for quality on a DNA7500 Bioanalyzer chip (Agilent ) and then quantified with the Nanodrop 1000 (Thermo Fisher Scientific). Barcoded amplicons were pooled in equimolar concentration for sequencing on the Illumina MiSeq machine. The oligonucleotide sequences that were used for amplification are presented in Table 2. Please note that primers used in this work contain Illumina -specific sequences protected by intellectual property (Oligonucleotide sequences 2007–2013 Illumina, Inc). All rights reserved. Derivative works created by Illumina customers are authorized for use with Illumina

instruments and products only. All other uses are strictly prohibited. 2.5 Bioinformatic analyses The paired-end sequencing reads were combined using the make.contigs script from mothur v.1.35 (Schloss et al., 2009). The quality filtering was also performed with mothur discarding homopolymers, reads with ambiguous sequences and reads with suspicious length (the ones that did not assemble) using the screen.seqs script. Similar sequences were gathered together to reduce the computational burden, and the number of copies of the same sequence was displayed to keep track of the abundance of each sequence. This dereplication step was performed with vsearch (Rognes et al., 2016). Sequences were aligned with the SILVA reference core alignment using QIIME (Caporaso et al., 2010). This alignment step allowed the production of a phylogenetic tree that was used to include phylogenetic information of the sequences in the microbial diversity analyses. Operational taxonomic units (OTUs) with 97% similarity cutoff were clustered using the UPARSE method implemented in vsearch. Uchime was used to identify and remove chimeric sequences (Edgar et al., 2011). QIIME was used to assign taxonomy to OTUs based on the latest SILVA database v.132 (Quast et al., 2013). A metadata file was generated to separate the samples into groups of two variables: season and

Table 2 Primers used for MiSeq amplification of the 16S rRNA gene First-PCR primer

Forward: 50 -ACACTCTTTCCCTACACGACGCTCTTCCGATCTACGCGHNRACCTTACC -30 Reverse: 50 -GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTACGGGCRGTGWGTRCA -30

Second-PCR primer

Forward: 50 AATGATACGGCGACCACCGATCTACA[index1]ACACTCTTTCCCTACACGAC-30 Reverse: 50 CAAGCAGAAGACGGCATACGAGAT[indexe2]GTGACTGGAGTTCAGACGTGT-30

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treatment steps. The microbial diversity analyses, including statistical analyses, were achieved using QIIME plugins in version 1.9.1 described in QIIME scripts (http://qiime.org/scripts/). 2.6 Microbial diversity analyses Bacterial diversity was described by Illumina MiSeq sequencing of the 16S rRNA gene. After quality and chimera filtering, 1 750 451 clean sequences clustered onto 16 612 OTUs. To confirm that the sequencing depth was enough to cover microbial diversity of air samples, rarefaction analyses were performed using the number of observed OTUs. The lowest depth sample parameter was used to determine the sequencing depth of the rarefaction curves. The higher the sequencing depth, the more likely total microbial coverage will be attained. In this study, samples were rarefied at 4300 sequences per sample. Samples with a sequencing depth lower than 4300 were excluded from analyses. All samples from the eight WWTPs met this criterion and were included in the analyses, except for one sample from the second WWTP that exhibited sequence numbers that were abnormally too low. It is important to exclude outliers from analysis that aims to validate the sequencing depth effect on the diversity collected. Microbial community studies aim at comparing the diversity between samples (beta diversity). QIIME scripts for beta diversity analyses were used to produce the phylogenetic quantitative weighted UniFrac distance matrix. Then, samples are represented in a 2-dimensional space using ordination (Ramette, 2007). The PCoA (principal coordinate analyses) is a common multivariate visualization tool used in microbial ecology to represent ordinations. The weighted UniFrac distance matrix was used as an input for ordination calculation and clustering. Distance matrices obtained from weighted UniFrac measures are well suited for PCoA analyses (Forsberg et al., 2014; Lozupone et al., 2007). The matrices were transformed to coordinates and then plotted using the QIIME principal coordinates script. A heatmap generated with R programming language as described in the Community Ecology Package ‘Vegan’ with R studio was used to compare the aerosol data from the actual study with a custom public database from the European Nucleotide Archive (ENA) and the National Center for Biotechnology

Information (NCBI). The selection criteria of the public data were the use of the keywords ‘‘16S sequencing’’ and ‘‘wastewater treatment.’’ Raw sequencing reads from seven bioprojects were combined and underwent the same bioinformatics protocol as described herein to create the custom public database OTU table. The same version of the SILVA database used for our data served to assign taxonomy to the public database OTU table. 2.7 Statistical analysis Descriptive statistics were used on sequencing data to highlight significant differences in the PD whole tree alpha diversity measure showed with boxplots. The normality was verified by the D0 Agostino and Pearson omnibus normality test, and was not fulfilled. Thus, nonparametric Mann–Whitney U test analyses were performed to highlight significant differences showing a p-value of less than 0.05. The results were analyzed using the software GraphPad Prism 5.03 (GraphPad software, Inc.). To determine the statistical significance of the variation observed with the PCoA, a permutational multivariate analysis of variance (PERMANOVA) test was performed on the weighted UniFrac distance matrix. PERMANOVA test divides the distance matrix in different possible sources of variation and calculates the statistical significance of the sample clustering. It is inspired by the ANOVA statistical test, but, because it is a nonparametric test, it analyzes the variance and determines the significance using permutations (Anderson, 2005). The number of permutations used is 999. P value B 0.05 was considered statistically significant. Two variables were chosen to examine beta diversity more closely: treatment sites and season. The use of multivariate analyses, PCoA, coupled with a PERMANOVA test, offers a robust statistical significance of sample clustering using distance matrices. Both analyses (PCoA and PERMANOVA) resulted in the same conclusions in regard of sample clustering confirming their usefulness as tools to visualize and measure sample clustering. Detailed information about the performance of the test is presented in Results section. The group significance QIIME scripts were used to select OTUs that are differentially more abundant in each one of the four treatment steps. The G-test used for differential abundance in this study is a likelihood-

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ratio statistical test. It compares the ratio of the OTU frequencies in the sample groups to an extrinsic hypothesis about the desired distribution. The extrinsic hypothesis used in this case is that all sample groups have equal OTU frequencies. The test compares the ratio of the observed OTU frequencies in the sample groups to the expected frequencies based on the extrinsic hypothesis (McDonald, 2014). In brief, the test allowed the comparison of OTU frequencies in the groups of samples and to ascertain which OTUs have statistically different abundances in the four wastewater treatment steps. The output contains a test statistic and a p-value for each OTU certifying its significant higher mean frequency in one condition compared to the others.

3 Results 3.1 Alpha diversity of bacterial aerosols in wastewater treatments steps Rarefaction curves were calculated and generated as follows: ten values from 10 to 4 300 were randomly selected, with 4 300 representing the sequencing depth. For each one of these values, the corresponding number of OTUs observed was noted for all of the samples. Standard deviation was produced by calculating the mean observed OTUs for samples from the eight WWTPs at each point for the five conditions. The plateaus observed in the five curves shown in Fig. 1 indicate an efficient coverage of the bacterial diversity, as no more OTUs were observed even with much greater numbers of sequences per sample. In addition, the number of observed OTUs was at least three times lower for outdoor control air samples compared to the four indoor treatment sites air samples. PD Whole Tree is a phylogenetic alpha diversity measure where the diversity of a set of species is equal to the sum of the lengths of all the branches on the tree (Faith & Baker, 2006). A sample with more unique OTUs would have a higher PD since they will contribute to more branches on the tree. Thus, the use of a reliable phylogenetic tree is necessary when applying PD Whole Tree. Values start from zero, and the more diversity in a sample the PD Whole Tree values get higher. For these analyses, samples were grouped into four treatment sites and into two seasons.

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In Fig. 2a, no significant difference in diversity between air samples from the four treatment sites is observed. The majority of the samples are between 200 and 400 PD Whole Tree. Likewise, no significant difference was noted between summer and winter (Fig. 2b). However, winter values are more dispersed than summer. All the samples collected during summer are comprised between 200 and 400 PD Whole Tree, while 5 winter samples are lower than 200 and 1 is higher than 800. The rest is also comprised between 200 and 400 PD Whole Tree. 3.2 Beta diversity of bacterial aerosols in wastewater treatment’s steps Figure 3 shows the two principal coordinate axes capturing 22.43% of the variation in the input data from the 8 WWTPs. Herein, samples were grouped according to the treatment site (Fig. 3a): screening, degritting/degreasing, biofiltration, primary settling and outdoor controls, and according to the sampling season (Fig. 3b): summer and winter. Samples closer to one another are more similar than those ordinated further apart. The best sample grouping can be observed in Fig. 3a as air samples collected during the screening process (purple rectangles) cluster more if compared to samples collected during the biofiltration (red circles), although three samples of the screening process are outliers and clustered separately from the rest. Samples collected during degritting/ degreasing (orange) and during the primary settling (green) were interconnected, close to the screening samples. It is important to notice that outdoor control samples from the eight WWTPs (blue) clustered together further away from indoor air samples collected during the wastewater treatment processes. Only few samples collected during summer (red) and winter (blue) formed separate clusters (Fig. 3b). The other samples are scattered in the figure with no apparent grouping. Using a significance of 0.05 from PERMANOVA, both variables (treatment sites and season) exhibited significant differences in the sample clustering. However, the test statistic and the p value were more significant for the treatment sites (t-statistic = 2.60 and p value = 0.001) compared to the season (t-statistic = 1.53 and p value = 0.03). The PCoA analyses focus on the core microbiome (taxa representing [ 50% of the sample composition), while the

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Fig. 1 Rarefaction curves obtained from the number of observed OTUs and the sequences per sample for air samples from the eight WWTPs visited. Curves hit a plateau after 1000 sequences per sample

Fig. 2 Comparison of PD Whole Tree alpha diversity metrics for bioaerosols collected in eight WWTPs. Samples were grouped according to the four treatment sites chosen for this

study (Fig. 2a) and to the sampling season (Fig. 2b). The statistical significance was tested with the Mann–Whitney U test, and all p-values were higher than 0.05

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Fig. 3 Principal Coordinates Analysis of air samples collected from eight WWTPs. The PCoA was calculated using the weighted UniFrac distance metric. Figure 3a represents the samples colored according to the treatment site (screening in purple rectangles, degritting/degreasing in orange rectangles,

biofiltration in red circles, primary settling in green rectangles). Figure 3b represents samples colored according to the sampling season (winter in blue circles and summer in red squares). The ellipsoid areas indicate the most obvious sample clustering

statistical test analyzing the significance of sample grouping includes also taxa of low relative abundance that are different between samples. This explains the statistical significance of the difference between summer and winter, although the core microbiome is conserved between the two seasons.

level. The first 10 OTUs with more abundance in outdoor control samples had a t-statistic between 181.2 and 21,480.7, with p-values varying from 3,15 9 10–11 to 1.06 9 10–13. In the screening, these values ranged from 688.6 to 2480.3 for the t-statistic and from 1.11 9 10–11 to 6.66 9 10–15 for p-values. During degritting/degreasing, t-statistics of the 10 OTUs were between 744.47 and 1748.31 and p-values between 1.06 9 10–13 and 4.52 9 10–14. For biofiltration, t-statistics varied between 688.2 and 4261 and p-values from 1.35 9 10–9 and 3.64 9 10–13. Lastly, differentially abundant OTUs in the primary settling had a t-statistic varying from 1534.79 to 1571.05 and p-values from 3.17 9 10–6 to 4.44 9 10–16. The list presented in Fig. 4 is not exhaustive as we considered only the first 10 striking examples of differential abundance between the controls and the different wastewater treatment steps. However, the complete output from the statistical G-test is presented in Supplementary file 1. Major differences in the mean number of sequences representing the 10 bacterial genera in each one of the five conditions are observed.

3.3 Differential abundance of bacterial aerosols in wastewater treatment steps With an idea about sample clustering, the next step consists of having a closer look into the taxa that drives the differences observed between the WWTPs steps. The identity of bacterial groups detected in air samples collected from eight WWTPs in different treatment sites was determined by comparing the Illumina sequences to the SILVA database. Figure 4 presents the 10 OTUs that had a significantly higher abundance in each one of the five conditions (outdoor controls, screening, degritting/degreasing, biofiltration, and primary settling). The taxonomic identification was at the genus level and in some cases to the species

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Fig. 4 Bacterial OTUs with statistically significant differential abundances across the samples collected in eight WWTPs during five conditions: outdoor controls, screening, degritting/

degreasing, biofiltration, and primary settling. From bottom to top, 10 OTUs with higher abundance in each one of the five conditions (total of 50 OTUs)

Here are a few examples: Delftia had an occurrence of 5800 sequences in controls taken outdoors compared to fewer than 1000 sequences in the four treatments steps. Acinetobacter had an abundance of 2000 sequences in indoor air samples collected during the screening, 1400 sequences in the degritting/degreasing, and less than 500 sequences in biofiltration and primary settling. Acidovorax was more abundant during the degritting/degreasing with 660 sequences compared to less than 300 sequences in the other conditions. Rhizobiales bacterium is another example of large differential abundance between the conditions because of its detection more than 2000 times in

biofiltration and fewer than 250 times during the other treatment steps. Prevotella paludivivens had a mean number of sequences of 711 sequences in the primary settling air samples, close to 300 sequences in the degritting/degreasing, and fewer than 100 sequences in the other conditions. More details about the rest of the taxa listed are presented in Fig. 5. The presence of pathogenic and opportunistic pathogenic taxa as well as environmental impacts on these differentially abundant taxa is scrutinized in Discussion section.

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Fig. 5 Heatmap comparing bacterial taxa identified in aerosol samples from 4 treatment steps of 8 WWTPs with samples from wastewater treatment projects available publicly. The purple/

pink scale represents high abundance cases, as the light/dark blue one represents low abundance cases

3.4 Heatmap of bacterial aerosols and public database from wastewater treatment

The color fade represents the abundance scale going from blue (low abundance) to pink (high abundance). The intensity of purple/pink color scale in the public database column and in our data suggests that the primary source of airborne microbes is from the wastewater treatment processes. Of the four treatment sites, the screening and biofiltration demonstrated the most similarities with the public database. After a careful analysis of the heatmap, some differences

In order to link bacterial aerosols collected from the four treatment sites of the eight WWTPs visited for this study with the source of aerosolization, a heatmap comparing bacterial abundance of our aerosol data with a custom public database of bacteria detected during wastewater treatment was generated (Fig. 5).

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could be noted in the comparison of our data with the public database. A higher occurrence of Methylobacterium, Rhodopseudomonas and Bradyrhizobium in the public database, and not in our data was noted. In opposite, Mycobacterium, Roseburia, Ruminococcus, Lactococcus, Subdoligranulum, Hypnocyclicus and Pleomorphomonodaceae were more abundant in our data compared to the public database. However, these differences are related to the light/dark blue color scale in Fig. 5, which represents low abundance cases. The shared abundant bacteria identified in aerosol samples from our study and in the water from the public database are predominantly associated with the gut flora—notable examples are Faecalibacterium, Aeromonas, Blautia, Akkermansia, Klebsiella, Bacteroides, Prevotella, Ruminococcus and Roseburia.

4 Discussion Despite the previous work on biological aerosols present in wastewater treatment environments, there is still a lack of information regarding the extent of the microbial diversity emitted during the different wastewater treatment processes, especially in an indoor settings (Niazi et al., 2015; Li et al., 2016; Wang, Lan, et al., 2018; Wang, Li, et al., 2018; Brisebois et al., 2017; Caceido et al., 2018; Korzeniewska et al., 2012; Carducci et al., 2018; Courault et al., 2017; Dehghan et al., 2018; Ding et al., 2017; Uhrbrand et al., 2017; Wang et al., 2018; Wang, Li, et al., 2018; Han et al., 2018). This study demonstrated no significant difference between summer and winter in terms of microbial diversity and composition. However, indoor pre-treatment and secondary treatment steps suggest the presence of different bacterial taxa. In addition, all indoor air samples presented a different microbial composition than control samples taken upwind outside the WWTPs. The indoor air microbiome identified in this work correlated with public data recovered from wastewater treatment samples. 4.1 Bacterial diversity and composition of aerosols in wastewater treatment steps

diversity between pre-treatment, primary and secondary treatments demonstrates the importance of the potential exposure during the whole wastewater treatment process. These observations confirm a recent study where no significant difference was observed in bacterial concentrations from different indoor wastewater treatment sites (unpublished data). Also, the results of the diversity evaluation present in this work (observed OTUs and PD Whole Tree) concur with other richness estimators, and diversity indexes results (Chao1, Shannon and Simpson) used to compare the different treatment steps (data not shown). Although few studies have associated high temperature and relative humidity with a higher microbial exposure, the bacterial richness and diversity remained the same for both seasons. Other studies have already demonstrated that temperature, relative humidity and air exchange rate are not always good predictors of microbial exposure (Frankel et al., 2012; Horve et al., 2020). The multivariate analyses using ordination suggest treated water is the primary bioaerosol source. Previous studies in other environments have linked microbial exposure via aerosols to the potential source of the aerosolized material (Mbareche et al., 2017; Bonifait et al., 2017; Prussin et al., 2015; Taha et al., 2006). The difference between indoor and outdoor is not surprising when an important source of contamination, such as wastewater, is present indoor. Nevertheless, samples from the first step (screening) and the last step (biofiltration) of the wastewater treatment process also presented significant differences in bacterial composition. Sure enough, the physicochemical properties of the wastewater change across the treatment process, which may also influence the microbial composition (Popa et al., 2012). The first treatment step includes removing large and insoluble material from untreated wastewater, while the last step is the biological degradation of residual organic matter. Part of the differences in the bacterial community structure between the first and last step may be explained by dissolved oxygen, pH, and nitrate-nitrogen content of effluents, diminution of nutrients, and bacteria stuck or associated with removed debris (Cydzik-Kwiatkowska & Zienlinka, 2016; ElNaker et al., 2018).

High overall bacterial diversity in aerosols from all the different treatment steps was observed. The fact that there were no significant difference in bacterial

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4.2 Differential abundance of aerosols in wastewater treatment steps The bacterial taxonomic profile from WWTPs bioaerosols resembles the microbial community of wastewater from different geographic areas (Ferrera & Sànchez, 2016; Ju et al., 2014; Sánchez et al., 2013; Shanks et al., 2013). This profile contains an apparent core human fecal microbial signature constituted mainly of taxa from Proteobacteria, Firmicutes and Bacteroidetes (Doherty et al., 2018; Newton et al., 2013). Comparing OTU frequencies across samples is one way to study the differential abundance of the microbial community in bioaerosols (Caporaso et al., 2010). To integrate information about bioaerosol content at each work station, samples were grouped into each one of the four treatment steps. The fact that Acinetobacter was most abundant in aerosols during the screening process may be linked to its essential role in phosphorus removal through an activated sludge process (Kim et al., 1997). Many members of the genus Acinetobacter are emerging as opportunistic pathogens, increasing the threat to human health (Weber et al., 2016). Flavobacterium is another example of higher abundance in aerosols from the screening step. These bacteria are also involved in biological phosphorus removal, acetate production, acid production, degradation of soluble, and floc formation (Gerardi, 2011). Empedobacter falsenii, differentially abundant in the screening step, was previously isolated from the respiratory tract of an immunosuppressed patient (Giordano et al., 2016). Although the clinical significance is yet to be evaluated, the presence of this bacteria in aerosol samples may represent a risk for WWTP workers. Acidovorax and Paludibacter, most abundant in aerosols during degritting/degreasing, were previously isolated from wastewater treatment samples (Shanks et al., 2013). In turn, Klebsiella, Kluyvera and Serratia marcescens, which were also more abundant during degritting/ degreasing, are associated with illness in humans (87, 88, 89). Notably, bacteria that were differentially more abundant in aerosols during the biofiltration are mainly composed of gut flora: Bifidobacterium longum, Lactococcus, Subdoligranulum, and Akkermansia (Lata et al., 2016; Podschun et al., 1998; Sarria et al., 2001). It is surprising to observe gut flora being most abundant during the biodegradation of organic matter of the wastewater treatment step and suggests

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that fecal contamination can persist in aerosols until the last steps of the wastewater treatment process. Similarly, taxa from air samples collected during the primary settling step are related to feces. These taxa include Prevotella, Bacteroides, Ruminococcus and Faecalibacterium (Lin et al., 2017; Shanks et al., 2013). Increased Prevotella abundance is associated with higher mucosal inflammation, and Bacteroides species are significant pathogens found in most anaerobic infections (Larsen, 2017; Wexler, 2007). Differential abundance of these taxa during the primary settling step suggests a higher exposure risk in this workstation. In summary, pathogens and opportunistic pathogens were differentially abundant in aerosols from the four steps. However, the degritting/degreasing step had a higher proportion of opportunistic pathogenic bacteria than the other treatment steps, followed by the primary settling step. These results strongly suggest that workers in WWTPs are exposed to a dynamic microbial community of pathogens in every workstation of the process and that measures to reduce this exposure are mandatory. 4.3 Heatmap of aerosols from wastewater treatment’s steps and a custom public database As only air samples were collected in this study, we hypothesized that the primary source of aerosols is wastewater. This supposition was confirmed due to a similar clustering of the taxa identified in this study with taxa retrieved from wastewater projects, sequencing water samples, deposited in public databases. Looking at the comparison, it is safe to affirm that wastewater has a core microbiota, which is mirrored in aerosols generated from the wastewater treatment steps. Previous studies have shown that sewage reflects the gut microbiomes of human populations (Newton, et al., 2015). In this, we demonstrate that aerosols generated from wastewater treatment processes also reflect a similar gut microbial diversity. Beside the gut microbiota, the comparison between aerosols (this study) and water samples (public database) showed the presence of broad diversity of environmental bacteria from soil (Chryseobacterium), water (Rhodobacter) and plants (Acidovorax)—some of the bacteria cited as examples are ubiquitous in all three environments. The core microbiota identified in this study can be compared to future high-throughput sequencing wastewater aerosol projects. In the long

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term, biomarkers identification and health studies associations will help us provide better bioaerosol exposure assessment.

47 Declarations Conflict of interests financial interests.

The authors declare no competing

5 Conclusion References HTS of aerosol samples from different steps of eight WWTPs allowed a better understanding of the bacterial community of aerosols released during the wastewater treatment process. Different bacterial diversity profiles were noted between the treatment steps, which was different from outdoor control samples. This observation is indicative of workers being potentially exposed to a specific bacterial community coming from the source of wastewater. This statement was confirmed after the similarity of our aerosol data with public data from wastewater was demonstrated. The results suggest that workers could be exposed to pathogens and opportunistic pathogens in aerosols released in every treatment step with a pic during degritting and degreasing. This study offers a comprehensive indoor-air microbiota description of WWTPs with the conclusion that the staff is bearing a potential risk. Therefore, preventive measures should be encouraged and implemented in order to reduce risk of exposure. Acknowledgmenent We are grateful to all of the employees and managers of the WWT plants that participated in this study. We are also grateful to Evelyne Brisebois and to the members of the IRSST and that participated to field sampling for their technical assistance. The authors are thankful to Amanda Kate Toperoff and Michi Waygood for English revision of the manuscript. Funding HM is a recipient of the FRQNT PhD scholarship and received a short internship scholarship from the Quebec Respiratory Health Network and is the recipient of the Lab Exchange Visitor Award from the Canadian Society of Virology. This work was supported by the Institut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail (IRSST Grant 2010–0050). CD is holder of Tier-1 Canada Research Chair on Bioaerosols. Data availability All material requests and correspondence should be addressed to Caroline Duchaine. Raw sequence reads of every sample used in this study and that support its findings have been deposited in the National Center for Biotechnology Information (NCBI) under the BioProject ID: PRJNA722754.

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