Characteristics and interactions of bioaerosol microorganisms from wastewater treatment plants

Journal Pre-proof Characteristics and interactions of bioaerosol microorganisms from wastewater treatment plants Yunping Han, Tang Yang, Guangsu Xu, Lin Li, Junxin Liu

PII:

S0304-3894(20)30244-2

DOI:

https://doi.org/10.1016/j.jhazmat.2020.122256

Reference:

HAZMAT 122256

To appear in:

Journal of Hazardous Materials

Received Date:

24 June 2019

Revised Date:

14 January 2020

Accepted Date:

7 February 2020

Please cite this article as: Han Y, Yang T, Xu G, Li L, Liu J, Characteristics and interactions of bioaerosol microorganisms from wastewater treatment plants, Journal of Hazardous Materials (2020), doi: https://doi.org/10.1016/j.jhazmat.2020.122256

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier.

Characteristics

and

interactions

of

bioaerosol

microorganisms from wastewater treatment plants

Yunping Han1,2,a, *, Tang Yang1,2,a, Guangsu Xu3, Lin Li1,2, Junxin Liu1

[email protected], [email protected], [email protected], [email protected],

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[email protected]

1. State Key Laboratory of Environmental Aquatic Chemistry, Research Center for

Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, PR

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China.

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2. National Engineering Laboratory for VOCs Pollution Control Material & Technology, University of Chinese Academy of Sciences, Beijing 101408, PR China.

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264025, PR China

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3. School of Resources and Environmental Engineering, Ludong University, Yantai

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*Corresponding author: Yunping Han Tel.: 86-10-62911425

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E-mail: [email protected]

a: Yunping Han and Tang Yang contributed equally to this work.

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Highlights

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Graphic abstract

95 and 22 potential bacterial and fungal pathogens were detected in WWTP bioaerosols

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Microbial diversity in WWTP bioaerosols is correlated to region, season and process.

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Serratia, Yersinia, Klebsiella, and Bacillus are discriminative in WWTP bioaerosols.

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Positive and negative correlations exist among bioaerosol pathogens from WWTPs

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

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Abstract

Bacteria and fungi are abundant and ubiquitous in bioaerosols from wastewater

treatment plants (WWTPs). However, the specificity and interactions of bioaerosol microorganism, particularly of potential pathogens, from WWTPs are still poorly understood. In this study, we investigated 9 full-scale WWTPs in different areas of China for 3 years, and found microbial variations in bioaerosols to be associated with 2

regions, seasons, and processes. Relative humidity, total suspended particulates, wind speed, temperature, total organic carbon, NH4+, Cl- and Ca2+ were the major factors influencing this variation, and meteorological factors were more strongly associated with the variation than chemical composition. In total, 95 and 22 potential bacterial and fungal pathogens were detected in bioaerosols, respectively. The linear discriminant analysis effect size method suggested that Serratia, Yersinia, Klebsiella,

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and Bacillus were discriminative genera in bioaerosols on the whole, and were also hub niches in the interactions within potential bacterial pathogens, based on network analysis.

Strong

co-occurrences

such

as

Serratia-Bacillus

and

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Staphylococcus-Candida, and co-exclusions such as Rhodotorula-Cladosporium and

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Pseudomonas-Candida, were found within and between potential bacterial and fungal pathogens in bioaerosols from WWTPs. This study furthers understanding of the

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biology and ecology of bioaerosols from WWTPs, and offers a theoretical basis for

Keywords:

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determining bioaerosol control. Wastewater

treatment

plant;

Bioaerosol;

Potential

pathogen;

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Discriminative taxa; Interaction

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1. Introduction

Wastewater treatment plants (WWTPs) have been identified as one of the major

sources of bioaerosols (Kowalski et al., 2017). These bioaerosols contain a large number of hazardous components, such as pathogenic bacteria, fungi and viruses (Han et al., 2018; Masclaux et al., 2014; Uhrbrand et al., 2017), due to their origin in

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wastewater and sludge. Inhalation, dermal contact, and ingestion of bioaerosols from wastewater treatment are a potential health risk to people within/around WWTPs (Sánchez-Monedero et al., 2008; Stellacci et al., 2010). Respiratory tract infections, gastrointestinal symptoms, hypersensitivity, and allergies caused by exposure to airborne microorganisms among WWTP workers have been reported in some epidemiological studies (Gangamma et al., 2011; Masclaux et al., 2014; Thorn et al.,

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2002). Furthermore, a study suggested a correlation between exposure level of total

bacteria and rod-shaped bacteria and flu-like and respiratory symptoms (Melbostad et al., 1994). Thus, microbial characteristics in bioaerosols from WWTPs have attracted

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increasingly more attention.

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Bacteria and fungi are abundant and ubiquitous in bioaerosols from WWTPs, with the concentration ranging from 8.00 × 101–6.90 × 103 and 5.10 × 102–3.90 × 103

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CFU/m3, respectively (Kowalski et al., 2017). The main escape sites of bioaerosols at

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WWTPs are sludge dewatering room and aeration units (Yang et al., 2019a). Risk assessment based on microorganism concentration indicated high non-carcinogenic

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risk in WWTPs (Han et al., 2019; Yang et al., 2019b). Moreover, microorganisms, especially pathogens such as Staphylococcus aureus and Pseudomonas aeruginosa

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that are associated with the respiratory fraction (size < 3.30 μm) of airborne particulate matter, increased the exposure risk (Yang et al., 2019b). Apart from the microorganism concentration and size distribution, microbial population of the bioaerosols is also one of the important characteristics related to human health. Potential bacterial pathogens such as Acinetobacter, Alcaligenes,

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Bacteroides, Chryseobacterium, Micrococcus, Enterobacter, Pantoea, Pseudomonas, Serratia, and Stenotrophomonas, and fungal pathogens such as Alternaria, Aspergillus, Cladosporium, and Penicillium, have been detected in bioaerosols (Han et al., 2019; Korzeniewska et al., 2009; Korzeniewska, 2011; Yang et al., 2019b). The concentration, size distribution, and microbial population of the bioaerosols that escape from each WWTP are not identical, and depend on the kind of wastewater

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treated, the process selected, and meteorological parameters (Kowalski et al., 2017; Szyłak-Szydłowski et al., 2016).

Identifying biomarkers that characterize the discrepancy among different

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biological conditions or classes is one of the most widely used means in conversion of

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biological data into hygeian practice (Bai et al., 2019; Feng et al., 2018; Segata et al., 2011). Meanwhile, polymicrobial interactions could influence the virulence of

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bacteria or fungi (Cugini et al., 2007; Peleg et al., 2008). Microbial interactions are

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likely the main driver for variation of the microbial population, and the hub taxa (taxa with multiple correlations with other taxa) would be largely responsible for the

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variation of community structure (Barberan et al., 2012; Cordero and Datta, 2016; Lin et al., 2018; Zhang et al., 2019a). Previous studies have provided an increasingly

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intuitive profile of the severity of bioaerosol hazards in WWTPs (Han et al., 2019; Yang et al., 2019b). However, relatively less is known about the discriminative taxa (biomarker) and the microbial interactions of bioaerosol microorganism from WWTPs, which is closely related to exposure risks, bioaerosol control standards, and development of future control technology.

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In the present study, three typical sewage treatment processes, namely, anaerobic-anoxic-oxic (A2/O), oxidation ditch (OD), and sequencing batch reactor (SBR), were selected in three different regions of China, with a total of nine sewage treatment plants, for a three-year field sampling study. This study comprehensively characterized the bacterial and fungal population in bioaerosols from the WWTPs, with special attention to potential pathogens. Discriminative microorganisms of

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WWTP bioaerosols were determined. Interactions within and between bioaerosol

microorganisms (bacteria and fungi) and different factors were further studied to

investigate characteristics of bioaerosol biology and ecology. All these will further

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improve the characteristic understanding of the WWTP bioaerosols, thus providing a

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2. Materials and methods

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scientific basis for the bioaerosol control strategies in WWTPs.

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2.1. Plant description and sample collection

Bioaerosol samples were collected from 9 full-scale WWTPs across Beijing,

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Hefei, Yixing, and Guangzhou (Fig. 1). Sampling time and points at WWTPs were illustrated in Table 1. Air samples from upwind (UW) of each WWTP were also

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collected as control. Total suspended particulates (TSP) were collected using a particulate matter sampler (TH-150C, TianHong, Wuhan, China). Under the suction of the negative pressure pump, the particulate matters are evenly dispersed by the cutter and then enriched on quartz film surface (90 mm in diameter). During this process, the control system controls the flow through the pressure value according to the

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pressure detector and conducts constant-flow sampling (Fig. S1). The sampling flow range of the sampler is 80 – 120 L/min. In this study, the collection was conducted at a flow rate of 100 L/min for 4 h (Yang et al., 2019b and 2019c) and performed at the average human respiratory height (1.50 m above ground level; Niazi et al., 2015). Careful attention was paid to avoid cross-contamination. Apart from bioaerosols from different treatment units at WWTPs, wastewater in corresponding units was also

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collected.

The panel data consisted of 27 air samples from UW, 90 bioaerosol samples, and 90 wastewater samples from WWTPs. The collected samples came from a broad

2.2. Microbial and chemical analyses

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range of space, climates, treatment units, and processes (Table 1).

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After 4 h of collection, the TSP concentration was estimated from one-eighth of

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each enriched film under 45% relative humidity (RH) at 20 °C (Cao et al., 2014). Other enriched films were stored at –80 °C for subsequent analysis. Seven-eighths of

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each enriched film was used for microbial and chemical analysis. The enriched films were pretreated as described previously (Cao et al., 2014; Jiang et al., 2015). After

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filtration using a 0.22-µm Supor 200 Polyethersulfone (PES) membrane, the enriched membrane and filtrate were respectively used for microbial and chemical analyses. The PES filter (microbial enrichment) and wastewater from corresponding treatment units were subjected to DNA extraction using a MO-BIO Power Soil DNA Isolation Kit (USA) according to the manufacturer’s instructions. DNA purity and

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quantity were detected using microspectrophotometry (NanoDrop ND-1000, NanoDrop Technologies, Wilmington, DE). Bacteria and fungi were respectively amplified in triplicate by primers 338F/806R and 0817F/1196R, which amplify the corresponding hypervariable V3 and V4 regions of the 16S rRNA and the hypervariable ITS1 regions of the 18S rRNA (Dennis et al., 2013; Rousk et al., 2010). High-throughput sequencing was performed using an Illumina Miseq PE300 platform

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(Majorbio, Shanghai, China) as described in previous studies (Han et al., 2018; Wang et al., 2018).

The filtrate collected after PES membrane filtration and wastewater from

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corresponding treatment units were used for chemical analysis. Total organic carbon

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(TOC) was determined using a total carbon analyzer (Shimadzu, TOC-V CPH, Japan), while Cl-, NO3-, and SO42- levels were measured using an ion chromatograph (Dionex

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ICS 1000 equipped with AS23, Thermo Fisher, USA). The Na+, NH4+, K+, Mg2+, and

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Ca2+ contents were determined using an ion chromatograph (Dionex ICS plus 883 equipped with AS189, Thermo Fisher, USA). The determined data was revised by

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subtraction of the filter blanks, which was subject to the same analytical procedures as

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the field samples. The data has been summarized and listed in Table S1.

2.3. Meteorological monitoring During sampling, RH and temperature (T), solar radiation, and wind speed (WS) were real-time determined using dewpoint thermohygrometer (WD-35612, OAKTON, Germany), irradiance meter (HD2302.0, Delta OHM, Italy) and a portable

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anemometer (HD2303, Delta OHM, Italy), respectively. All of these meteorological conditions have been summarized in Table S2.

2.4. Data analysis Heatmaps were prepared to indicate the dominant bacterial and fungal genera in different samples. Spatial patterns of microbial assemblages were investigated using

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non-metric multidimensional scaling ordination (NMDS). The similarity or

dissimilarity of microbial population within and between the groups were compared using the analysis of similarity (ANOSIM) test and similarity percentage (SIMPER)

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analysis. The multivariate statistical analyses were carried out in PRIMER version 7.0

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(Clarke and Gorley, 2015).

Networks were prepared to visualize the interactions within and between

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bioaerosol microorganisms and factors. The correlation matrix was firstly established

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among bacteria, fungi, meteorological factors, and chemical compositions by computing the Spearman’s rank correlation coefficients (SRCCs). The correlations

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with p ≤ 0.05 and SRCC ≥ 0.60 or ≤ –0.60 were defined as significant and strong (Fan et al., 2017) and visualized using Cytoscape software (version 3.0.2). Variation

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partitioning analysis (VPA) was carried out to assess the relative importance of meteorological factors and chemical compositions in structuring the bacterial or fungal population. CANOCO 4.5 package (Lepš and Šmilauer, 2003) was applied to evaluate the relative roles of factors. The linear discriminant analysis (LDA) effect size (LEfSe) method

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(http://huttenhower.sph.harvard.edu/galaxy),

which

conducts

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non-parametric

Kruskal-Wallis rank sum test followed by LDA (Feng et al., 2018; Segata et al., 2011), was used to identify discriminative types related to the significant differences. Alpha threshold value at 0.05 in non-parametric Kruskal-Wallis rank sum test and LDA score at 4.00 (Pehrsson et al., 2016) were employed in this study.

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3. Results and discussion 3.1. Microbial population and similarity

A total of 14876 OTUs were detected from 207 samples and categorized into

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1375 bacterial genera (Fig. S2), and 3818 OTUs were categorized into 159 fungal

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genera (Fig. S3). In the bioaerosol samples, 95 bacterial and 22 fungal potential pathogens were detected (Table S3 and S4); this number was much more than that

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detected in PM2.5 in previous studies (Du et al., 2018; Gao et al., 2017). The Shannon

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indexes of bacteria in WWTP bioaerosols, UW and wastewater were 4.27, 4.11 and 3.93, and that of fungi were 2.54, 2.73 and 2.51 (Fig. S4), respectively. The evenness

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of microorganism in WWTP bioaerosols, UW and wastewater were compared, the range of Pielou’s evenness of bacteria was 0.64 – 0.67 and that of fungi was 0.55 -

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0.57 (Fig. S4). These suggested that bacteria and fungi in bioaerosols from WWTPs showed higher community diversity than that in wastewater. Yang et al. (2019a) also found high community diversity of bioaerosol bacteria from WWTPs with the mean Shannon index of 5.12. Wastewater was the important source of bioaerosol microorganism in WWTPs (Yang et al., 2019b and 2019c). Biological processes such

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as activated sludge are widely used in WWTPs and activated sludge relies on complex and incomplex defined microbial communities (Global Water Microbiome Consortium, 2019). Meanwhile, bioaerosols in WWTPs were easily susceptible to other particles in the ambient air (Uhrbrand et al., 2017). All these might lead to a higher community diversity in WWTP bioaerosols than that in wastewater. The Illumina Miseq high-throughput sequencing technology can directly provide

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a broad profile of microbial population in complex system, which has been widely used to identify the ‘potential’ pathogens in soil (Hong et al., 2015), wastewater

(Global Water Microbiome Consortium, 2019), urban atmosphere (Cao et al., 2014)

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and WWTP bioaerosols (Han et al., 2020; Uhrbrand et al., 2017). However, the

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relatively short sequence length (~400 bp) can only provide the taxonomic resolution at genus level which is neither accurate nor specific enough to investigate pathogens,

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since not all of the species under the pathogenic genera are pathogens (Cai and Zhang,

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2013; Li et al., 2015). Thus, more accurate and specific methods are imperative in the further study of identifying the pathogens from WWTP bioaerosols, such as

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metagenomic approach.

As shown in NMDS (Fig. 2), the bacterial and fungal population in samples that

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were divided according to sources (UW, bioaerosols and wastewater; Fig. 2a and 2b) clustered distinctly. Bioaerosol samples clustered distinctly according to regions (Beijing, Hefei & Yixing and Guangzhou; Fig. 2c and 2d) and seasons (spring, summer and winter; Fig. 2e and 2f), while the clustering based on processes (A2/O, SBR and OD; Fig. 2g and 2h) was scattered with some overlap.

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In order to quantify the variations in population, ANOSIM and SIMPER analyses were applied to calculate intra-group similarity and inter-group dissimilarity (Fig. S5). On the whole, we found significant variation of bacterial and fungal population among UW, bioaerosols, and wastewater, as indicated by Global test with P = 0.001 (Fig. S5a and S5b). At the inter-group level, significant difference was found in bacteria between bioaerosols and wastewater (dissimilarity = 69.68%; Pairwise test, P

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= 0.001; Fig. S5a), and similar result was obtained for fungi (dissimilarity = 88.56%; Pairwise test, P = 0.001; Fig. S5b). Significant difference was also found in bacteria

between bioaerosols and UW (dissimilarity = 60.11%; Pairwise test, P = 0.005), but

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the difference in case of fungi was not significant (average dissimilarity was as high

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as 67.86%). At intra-group level, higher similarity was determined for bacteria (41.13%) than fungi (29.43%) in bioaerosols. During biological treatment of

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wastewater, wastewater/sludge can be aerosolized to the atmosphere air under the

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drivers of aeration, mechanical agitation and so on. Wastewater was the important source of bioaerosols. However, in the process of microbial aerosolization,

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microorganisms in wastewater have different aerosolization potential to air (Han et al., 2020; Liu et al., 2020). These could lead to the microbial dissimilarities between

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bioaerosols and wastewater. For microorganisms in bioaerosols (Fig. S5c-S5h), significant inter-group

differences in bacteria were found from the perspective of regions, seasons, and processes. In case of fungi, significant inter-group differences were found based on regions and seasons, but not treatment processes. At intra-group levels, the average

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Bray-Curtis similarities of bacterial population in bioaerosols from WWTPs at Beijing, Hefei & Yixing, and Guangzhou were 45.38%, 51.73%, and 56.51% (Fig. S5c), and that of fungal population were 33.72%, 56.82%, and 58.84%, respectively (Fig. S5d). Generally, the intra-group similarity of microbial structure in bioaerosols from WWTPs at these three regions increased with reduction of latitude. Bioaerosol bacteria and fungi from WWTPs both existed significant regional specificity. The

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regional disparity of bioaerosol bacteria from WWTPs has also been reported in previous studies (Han et al., 2018; Yang et al., 2019c). The similarities of bacterial

population in bioaerosols from WWTPs in spring, summer, and winter were 43.15%,

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48.31%, and 53.43% (Fig. S5e), while that of fungal population were 44.55%, 39.87%,

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and 44.98%, respectively (Fig. S5f). Compared with spring and summer, microbial population in winter had the highest similarity in this study. Similar results were also

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confirmed in atmospheric particulate matter (Fan et al., 2019; Zhen et al., 2017).

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Existed knowledge has suggested that the wastewater/sludge is the main source of microorganisms of WWTP bioaerosols, meanwhile the chemical compositions in

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WWTP bioaerosols and meteorological conditions can also affect the microbial populations of WWTP bioaerosols (Yang et al., 2019b, 2019c). In this study, this

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mainly due to the highest similarity of bacteria (52.46%) and fungi (34.38%) in wastewater/sludge (Table S5), and the lowest dissimilarity of chemical compositions in WWTP bioaerosols (average squared distance = 7.46) and meteorological conditions (average squared distance = 3.84; Table S6) in winter compared to spring and summer. From the perspective of process, the highest intra-group similarity of

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bacterial population was found in A2/O process (47.70%) (Fig. S5g), and that of fungal population in OD (35.60%) (Fig. S5h). The intra-group similarities of microorganisms in bioaerosols from different treatment process were different, which mainly due to the difference of driven modes (Han et al., 2020; Sánchez-Monedero et al., 2008).

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3.2. Interactions between airborne microorganisms and factors

In this study, the results of network analysis revealed that the bacterial and fungal

populations were associated with meteorological factors and chemical compositions

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(Fig. 3). Among these verified airborne microorganisms that existed significant

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correlations with meteorological factors (Fig. 3a), 13 out of 17 were significantly positively correlated with RH. Special attention was paid to potential pathogens. The

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relative abundance of Arcobacter, Aeromonas, Bacteroides, Prevotella, Arthrinium

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and Conidiobolus, showed significant positive correlations with RH. Bioaerosols from WWTPs mainly come from wastewater due to aeration, stirring, and sludge

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dewatering, which result in higher RH compared with surrounding environment. Previous studies have indicated that aerosol particulate matters absorb water from

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high humidity environments, and the increased water sorption protects the microorganisms from UV-induced inactivation (O’Gorman and Fuller, 2008; Peccia et al., 2001; Reinthaler et al., 2003). The high RH underlines the probability of microbiological contamination at WWTPs. Eight out of 17 verified airborne microorganisms that existed significant correlations with meteorological factors, were

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significantly positively correlated with TSP. The potential pathogens of Arcobacter, Aeromonas, Bacteroides, and Prevotella were significantly positively correlated with TSP. Since bioaerosol microorganisms mainly adhere to particulate matter and are abundant components of TSP (Smets et al., 2016), a high TSP indicates higher microorganism load. There also existed significant negative correlation between WS and Arcobacter. Wind strength can dilute the particle concentrations (Zhen et al.,

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2017), and the low WS may increase the accumulation of airborne particulate matter. All these indicated that high RH, severe TSP pollution, and low WS, were conducive

to the persistence of certain potential pathogens in WWTP bioaerosols. These

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correlations provide indicative knowledge of that the variations of some

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meteorological factors favor or disfavor persistence of potential pathogens in WWTP bioaerosols.

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Apart from microorganisms, the chemicals are also the important composition

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for WWTP bioaerosols. The effects of chemical compositions on bioaerosol microorganisms were also discussed (Fig. 3a). Among these detected bioaerosol

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microorganisms that existed significant correlations with chemicals, 6, 8, 16, and 2 out of 26 showed significant correlations with TOC, Cl-, NH4+ and Ca2+ (Fig. 3a),

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respectively. For potential pathogens, there was a positive correlation between TOC and Arcobacter, Aeromonas, Acinetobacter, Bacteroides, and Prevotella, and between Cl- and Arcobacter, Aeromonas, Bacteroides, and Prevotella. There also existed negative correlations of NH4+ with Arthrinium. The variations of TOC, NH4+, and Clin WWTP bioaerosols could affect the microbial population, as they act as nutrients

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for growth of microorganisms (Han et al., 2018; Yang et al., 2019a). In this study, significant and strong correlations (especially these positive) were found between chemical compositions and potential pathogens. These indicated that not only the existence of potential pathogens, but also the favorable survival of potential pathogens cased by these chemicals, should be seriously considered in the process of bioaerosols control in WWTPs.

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The relative importance of meteorological factors and chemical composition to bacterial and fungal population was explored using VPA analysis (Fig. 3b and 3c). Meteorological factors was found to account for 27.80% of bacterial variability and

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25.00% of fugal variability, while chemical composition accounted for 19.30% of

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bacterial variability and 15.90% of fugal variability. This indicated that meteorological factors had more impact on airborne bacterial and fungal population.

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In addition, 3.50% of the bacterial variability and 2.90% of the fungal variability were

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simultaneously explained by the two factors, likely due to the correlations within and between meteorological factors and chemical composition (Fig. 4). In total, the

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selected factors explained the 43.60% and 38.00% variability of bacteria and fungi in

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bioaerosols, respectively.

3.3. Discriminative microorganisms Significant dissimilarity of microbial population in WWTP bioaerosols was found compared to that in wastewater and ambient air, and significant inter-group dissimilarity was also found from the respective of region, season and process. These

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were similar to previous studies about WWTP bioaerosols (Wang et al., 2018, 2019; Yang et al., 2019c) and atmospheric aerosols (Fan et al., 2019; Zhen et al., 2017). Differently, discriminative microorganisms associated with bioaerosols from WWTPs, and discriminative bioaerosol microorganisms associated with regions, seasons, and processes, were subsequently determined by LEfSe analysis in this study.

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3.3.1 Discriminative microorganisms associated with bioaerosols from WWTPs

Based on the LEfSe analysis of bacteria (Fig. 5a), Bacillus, Yersinia, Serratia, and Klebsiella at genus level, Enterobacteriaceae and Bacillaceae at family level,

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Bacillales at order level, Bacilli at class level, and Firmicutes at phylum level, were

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discriminative bacteria in bioaerosols from WWTPs, compared with ambient air and

identified (Fig. 5b).

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wastewater. For fungi in bioaerosols from WWTPs, no discriminative taxa were

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It was noteworthy that most of these indicative bacteria were potential pathogens at genus level. Bacillus species are usually found in soil and wastewater; two Bacillus

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species are considered medically significant: B. anthracis and B. cereus cause anthrax and food poisoning, respectively (Okutani et al., 2019). Yersinia is known as a

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potential pathogen of Crohn's disease, an inflammatory autoimmune condition of the gut (Tan et al., 2015). Serratia is responsible for various infections of the urinary tract and lower respiratory tract, surgical wounds, bloodstream (Goldman and Green, 2009). Klebsiella genus is ubiquitous in humans, animals, insects, plants, water, and soil, on account of evolving distinct sub-lineages due to particular niche adaptations (Bagley,

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1985). All Klebsiella species are known pathogens of the respiratory tract (Gangamma et al., 2011). In addition, Enterobacteriaceae, which was also discriminative in bioaerosols on the whole, had been determined by simple culturable method as an indicator of bioaerosol pollution at WWTPs in previous studies (Gotkowska-Płachta et al., 2013; Yang et al., 2019b). These discriminative might serve as the ‘most wanted’ list for future experimental and field steps to realize the risks of WWTP

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bioaerosols and take effective management.

3.3.2. Discriminative bioaerosol microorganisms associated with regions, seasons,

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and processes

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For bacteria in bioaerosols from WWTPs, there existed discriminative taxa associated with regions, seasons, and processes (Fig. S6). At genus level, Kocuria,

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Vibrionimonas and Arcobacter were discriminative in Hefei & Yixing (Fig. S6a),

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spring (Fig. S6b) and OD process (Fig. S6c), respectively. For fungi in bioaerosols from WWTPs, there existed discriminative taxa associated with regions and seasons,

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but none associated with processes (Fig. S7). Aureobasidium and Cladosporium, Sclerotinia, and Arthrobotrys and Cochliobolus were specialized in Beijing, Hefei &

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Yixing (Fig. S7a) and summer (Fig. S7b), respectively. There also existed discriminative taxa at higher taxonomic level. For example, Moraxellaceae at family level was specialized in Beijing (Fig. S6a); Clostridiales at order level was discriminative in summer (Fig. S6b). Notably, Arcobacter species are considered emerging zoonotic pathogens

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associated with human gastroenteritis, and were previously isolated from a wide range of habitats and hosts worldwide (Zhang et al., 2019b). A species of Aureobasidium, A. pullulans, found in soil, water, air, and limestone (Andrews et al., 2002), can cause hypersensitivity pneumonitis (Gostinčar et al., 2014). Cladosporium species, belonging to environmental saprophytes, is reported to cause various invasive and superficial fungal infections worldwide (Batra et al., 2019).

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In this study, the microbial population in the bioaerosols from WWTPs was significantly different from that of wastewater and ambient air. Meanwhile, there existed discriminative taxa in bioaerosols from WWTPs and also associated with

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regions, seasons, and processes. These discriminative taxa could be used as important

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biomarker in the control process. The control strategy should also be associated with

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different regions, seasons, and processes.

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3.4. Interactions within/between bioaerosol bacteria and fungi Apart from discriminative taxa, microbial interaction is also an important

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characteristic of microbial population. At microscales, interactions are likely to be the dominant driver of population structure and dynamics (Cordero and Datta, 2016). In

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this study, network was used to reveal the interactions within/between bacteria and fungi in WWTP bioaerosols (Fig. 6 and 7; Fig. S8). Strong co-occurrences and co-exclusions patterns within bacteria and fungi were found in bioaerosols from WWTPs (Fig. S8). A total of 47 and 26 hub bacteria and fungi were found among the top 50 abundant bioaerosol microorganisms, respectively.

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The predominant hub bacteria and fungi were Arcobacter and Conidiobolus, respectively. In the top 50 abundant bioaerosol microorganisms, 9/47 of hub bacteria (consisting of Arcobacter, Aeromonas, Bacteroides, Prevotella, Acinetobacter, Sphingomonas, Mycobacterium, Pseudomonas, and Gordonia) and 4/26 hub fungi (comprising Conidiobolus, Cladosporium, Arthrinium, and Vorticella) were potential pathogens. A total of 66 hub potential bacterial pathogens (Fig. 6a) and 13 hub

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potential fungal pathogens (Fig. 6b) were determined in the interactions within

bacteria and fungi. Bacteria such as Serratia, Bacteroides, Clostridium, and Corynebacterium, and fungi Rhodotorula were the predominant hub potential

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bacterial and fungal pathogens, respectively. Except Bacteroides, most of the

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predominant hub potential pathogens had low relative abundance in bioaerosol samples (Fig. S2 and S3), with relative abundance of < 3.65%.

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The polymicrobial interactions not only existed within bioaerosol bacteria and

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fungi, but also were found between them, especially the potential pathogens (Fig. 7). Of the 12 hub bacteria, 2 were Staphylococcus and Pseudomonas, and 4/14 of the hub

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fungi were Cladosporium, Arthrinium, Conidiobolus and Candida, these were potential pathogens that showed an interaction between bacteria and fungi. Candida

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showed a positive correlation with Staphylococcus (Fig. 7a) and a negative correlation with Pseudomonas (Fig. 7b), which have been widely reported previously in the clinical environment, in-vitro and in-vivo models (Carlson, 1983; Carlson and Johnson, 1985; de Macedo and Santos, 2005; Hogan and Kolter, 2002; Peleg et al., 2010; Witherden et al., 2017).

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Among the existed significant correlations within/between potential bacterial and fungal pathogens, some of these co-occurrences patterns may represent guilds of potential pathogens performing similar or stimulative, which may augment the health risk for human, such as the co-occurrence of Candida-Staphylococcus (Fig. 7a) and Haemophilus-Streptococcus (Fig. 6a). C. albicans and S. aureus are important pathogens of Candida and Staphylococcus; C. albicans has been reported to protect S.

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aureus, increase its virulence, and facilitate its dissemination (Carlson, 1983; Carlson and Johnson, 1985). For the co-occurrence of Haemophilus-Streptococcus, Margolis

et al. (2010) pointed out that H. influenzae and S. pneumoniae are usually

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acknowledged etiological agents in respiratory tract infections; the presence of S.

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pneumoniae results in an increase of H. influenzae density. Some of these co-exclusions patterns may serve as important indicator for each other in the process

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of detection and control of potential pathogens, such as the co-exclusions of

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Aspergillus-Candida (Fig. 6b) and Candida-Pseudomonas (Fig. 7b), which has also been reported in previous studies (Lorek et al., 2008; Peleg et al., 2010).

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While others may co-occur or co-exclude because of shared, preferred or unfavorable responsible factors (Steele et al., 2011). For example, the genera of

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Conidiobolus and Phaeobacter showed significant negative and positive correlations with NH4+ (Fig. 3a), respectively, and co-exclusions pattern between the two potential pathogens was found (Fig. 7b); the genera of Aeromonas and Arcobacter were both significant positive with RH (Fig. 3a), and co-occurrence pattern between the two taxa was verified in this study (Fig. S8a), similar result was also found among Arthrinium,

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Conidiobolus and RH (Fig. 3a and Fig. S8b).

4. Conclusion The present study focused on the systematic investigation of characteristics, specificity, and interactions of WWTP bioaerosols. The following conclusions can be drawn from the results of this study:

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1) Significant dissimilarity of microorganism (bacteria or fungi) was found in

WWTP bioaerosols compared to wastewater/sludge and ambient air. For WWTP bioaerosols, significant regionality, seasonality and process particularity of microbial

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populations were discovered.

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2) The meteorological factors had more impact on microbial variations in WWTP bioaerosols than chemical compositions.

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3) The hub genera, Serratia, Yersinia, Klebsiella, and Bacillus, identified in the

WWTPs.

positive

correlations

(such

as

Aeromonas-Arcobacter,

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4) Strong

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interactions within potential pathogens, were also discriminative in bioaerosols from

Arthrinium-Conidiobolus, Candida-Staphylococcus and Haemophilus-Streptococcus)

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were found within/between potential bacterial and fungal pathogens in bioaerosols from WWTPs. The existed positive correlations among potential pathogens may augment the health risk for human. 5) Some of these co-occurrences patterns may represent guilds of potential pathogens performing similar, while others may co-occur because of shared, preferred

22

responsible factors.

Contribution of authors

Yunping Han: determining the research plan, field sampling and data analysis, and writing the manuscript Tang Yang: field sampling and data analysis, and writing the manuscript

Lin Li: determining the research plan and field sampling

Declaration of Interest Statement:

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Junxin Liu: determining the research plan

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Guangsu Xu: field sampling and data analysis

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Acknowledgement

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The authors declare no competing financial interest.

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This work was supported by National Natural Science Foundation of China (NO.51308527), Young Scientists Fund of RCEES (RCEES-QN-20130006F), and

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State Key Joint Laboratory of Environment Simulation and Pollution Control

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(Research Center for Eco-environmental Sciences, Chinese Academy of Sciences) (NO. 19Z03ESPCR).

Supplementary data Supplementary data associated with this article can be found in the online version at xxxxxx. 23

Competing interests

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The authors declare no competing financial interests

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Fig. 2.

Fig. 2. NMDS analyses of (a) bacterial and (b) fungal population among UW, bioaerosols, and wastewater. Bioaerosol bacteria and fungi associated with (c, d) 38

regions, (e, f) seasons, and (g, h) processes. UW: upwind; A2/O: anaerobic-anoxic-oxic; OD: oxidation ditch; SBR: sequencing batch reactor.

Fig. 3. Effects of different factors on microbial population in bioaerosols from WWTPs. (a) Interactions between different factors and bioaerosol microorganisms.

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Relative importance of meteorological factors and chemical compositions in structuring the (b) bacterial and (c) fungal population. RH: relative humidity; WS: wind speed; TSP: total suspended particulates; TOC: total organic carbon; SRCC:

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spearman’s rank correlation coefficient.

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Fig. 3.

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Fig. 3. Effects of different factors on microbial population in bioaerosols from

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WWTPs. (a) Interactions between different factors and bioaerosol microorganisms. Relative importance of meteorological factors and chemical compositions in

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structuring the (b) bacterial and (c) fungal population. RH: relative humidity; WS:

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wind speed; TSP: total suspended particulates; TOC: total organic carbon; SRCC: spearman’s rank correlation coefficient.

Fig. 4. Correlation analysis within and between meteorological factors and chemical compositions. RH: relative humidity; WS: wind speed; TSP: Total suspended 40

particulates; TOC: total organic carbon. SRCC: Spearman’s rank correlation

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coefficient.

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Fig. 5. Discriminative (a) bacteria and (b) fungi associated with bioaerosols from

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WWTPs. UW: upwind; WWTPs: wastewater treatment plants.

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Fig. 5.

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Fig. 5. Discriminative (a) bacteria and (b) fungi associated with bioaerosols from

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WWTPs. UW: upwind; WWTPs: wastewater treatment plants.

Fig. 6. Network analysis showing the interactions within potential (a) bacterial and (b) fungal pathogens in bioaerosols from WWTPs.

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Fig. 6.

Fig. 6. Network analysis showing the interactions within potential (a) bacterial and (b)

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fungal pathogens in bioaerosols from WWTPs.

Fig. 7. Network analysis showing the interactions between bacteria and fungi in bioaerosols from WWTPs. (a) Positive correlations; (b) Negative correlations.

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Fig.7.

Fig. 7. Network analysis showing the interactions between bacteria and fungi in bioaerosols from WWTPs. (a) Positive correlations; (b) Negative correlations.

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Table legend

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Table 1 Sampling time and points of bioaerosols from wastewater treatment plants.

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Table 1 Sampling time and points of bioaerosols from wastewater treatment plants.

Treatment Scale

Regions

No.

Coordinates

Yixing Hefei

(×104 m3/d) wastewater

Season

Sampling sites

WWTP1

116.55°E, 39.90°N A2/O

20

DS

Spr, Sum, and Win

UW, FG, AnaT, AerT, and SDR

WWTP2

116.44°E, 39.84°N SBR

3

DS-IS

Spr, Sum, and Win

UW, FG, SBRRT, and SDR

WWTP3

116.52°E, 39.97°N OD

8

DS

Spr, Sum, and Win

UW, FG, ODRT, and SDR

WWTP4

119.86°E, 31.35°N A2/O

7.5

DS-IS

Spr, Sum, and Win

UW, FG, AnaT, AerT, and SDR

WWTP5

117.10°E, 31.85°N SBR

5.5

DS

Spr, Sum, and Win

UW, FG, SBRRT, and SDR

WWTP6

117.22°E, 31.72°N OD

10

DS-IS

Spr, Sum, and Win

UW, FG, ODRT, and SDR

112.92°E, 22.96°N A2/O

15

DS

Spr, Sum, and Win

UW, FG, AnaT, AerT, and SDR

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Beijing

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process

Source of

Guangzhou WWTP7

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WWTP8

113.23°E, 23.13°N SBR

8

DS

Spr, Sum, and Win

UW, FG, SBRRT, and SDR

WWTP9

114.11°E, 22.79°N OD

5

DS

Spr, Sum, and Win

UW, FG, ODRT, and SDR

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WWTP: wastewater treatment plant; A2/O: anaerobic-anoxic-oxic; OD: oxidation ditch; SBR: sequencing batch reactor; DS: domestic sewage;

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IS: industrial sewage; Spr: Spring; Sum: Summer; Win: Winter; UW: upwind; FG; fine grid; AnaT: anaerobic tank; AerT: aerobic tank; SBRRT:

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react tank of sequencing batch reactor; ODRT: react tank of oxidation ditch; SDR: sludge dewatering room.

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