Seasonal variation characteristic of inhalable microbial communities in PM2.5 in Beijing city, China

Science of the Total Environment 610–611 (2018) 308–315

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Seasonal variation characteristic of inhalable microbial communities in PM2.5 in Beijing city, China Pengrui Du a, Rui Du a,⁎, Weishan Ren a, Zedong Lu a, Pingqing Fu b a b

College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China State Key of Laboratory of Atmospheric Boundary Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, People's Republic of China

H I G H L I G H T S

G R A P H I C A L

• Seasonal variations of inhalable microbial communities in PM2.5 were investigated. • Impact of air pollution level on bioaerosol was discussed. • High-throughput sequencing method was used to identified bacteria and fungi. • The bioaerosol in PM2.5 showed significant seasonal variations.

Principal coordinate analysis of the samples using Weighted-UniFrac distance matrix, and classified as Bacteria (a) and fungi (b).

a r t i c l e

a b s t r a c t

i n f o

Article history: Received 29 April 2017 Received in revised form 11 July 2017 Accepted 11 July 2017 Available online xxxx Editor: D. Barcelo Keywords: Bioaerosol PM2.5 Seasonal effect Air pollution level

A B S T R A C T

Bacteria and fungi are primary constituents of airborne microbes in fine particulate matter (PM2.5) and significantly impact human health. However, hitherto, seasonal variation and effect of air pollution on microbial community composition and structure are poorly understood. This study analyzed the bacterial and fungal composition of PM2.5 under different air pollution levels during different seasons in Beijing. We altogether collected 75 PM2.5 samples during four seasons from April 2014 to January 2015, under different air pollution levels and employed highthroughput sequencing methods to analyze microbial composition. The results showed that air pollution decreased species richness and community diversity of bacteria in PM2.5. The variation in bacterial and fungal community composition and structure was significantly related to the season but there was no correlation between their abundance and pollution levels. Pathogenic bacteria and fungi were more abundant in winter than other seasons. To best of our knowledge, this is the first study that demonstrates seasonal variation characteristics of bacteria and fungi in PM2.5 in heavy haze contaminated areas and highlights the effects of air pollution on the atmospheric microbial community. This study would be useful to other bioaerosol studies focusing on the role of the atmospheric particulate matter on human health. © 2017 Elsevier B.V. All rights reserved.

1. Introduction

⁎ Corresponding author. E-mail address: [email protected] (R. Du).

http://dx.doi.org/10.1016/j.scitotenv.2017.07.097 0048-9697/© 2017 Elsevier B.V. All rights reserved.

Fine particulate matter (PM2.5 ≤ 2.5 μm in aerodynamic diameter) has become a critical air pollutant in China during the past 15 years, and Beijing has suffered frequent haze events since entering the new century

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(Chan and Yao, 2008; Duan et al., 2006; He et al., 2001). Haze episodes resulting from atmospheric particulate matter are frequent phenomenon in China in recent years (Huang et al., 2014). PM2.5 plays a significant role in this air pollution phenomenon (Schichtel et al., 2001; Sun et al., 2006), and has attracted the attention of the government and public. A new standard of ambient Air Quality Index (AQI) (see Supplementary data Table S1) was formulated and promulgated in 2012 in which PM2.5 was considered as an indicator of atmospheric pollution. Components of PM2.5 are complicated and include sulfate, nitrate, metal ions, organic compounds, plant debris, and microbes (Després et al., 2012; He et al., 2001). Abiotic inorganic and organic substances in PM2.5 have been extensively investigated for composition, physical and chemical properties, sources, and temporal and spatial variation in previous studies. However, relatively less is known about the variation in biotic components, such as bacteria, fungi, viruses, and pollens, which are known as bioaerosols. Atmospheric particulate matter originating from biological sources, such as bacteria, fungi, pollen, and animal and plant debris, constituted up to 25% of the total atmospheric aerosols (Jaenicke, 2005). Approximately 60% of the aerosol particles b 2.5 μm exhibited fluorescent property, which was mainly due to the emission by biological aerosol particles when excited by light at certain wavelengths (Yue et al., 2016). Extensive studies have showed that bioaerosols can influence air quality, weather, and global climate and contribute to the formation of cloud condensation nuclei or ice nuclei (Fuzzi et al., 2015; Kulmala et al., 2011; O'Sullivan et al., 2014; Pöhlker et al., 2012; Steiner et al., 2015). Additionally, atmospheric microorganisms can impact photochemical and chemical reactions of aerosols through metabolization of organic carbon species and reduction of radicals (Husárová et al., 2011; Vaïtilingom et al., 2009; Vaïtilingom et al., 2013). However, the effects of bioaerosols on human health are of serious concern to the general public. Airborne bacteria and fungi can act as pathogens and triggers to induce various respiratory tract diseases and allergies (Douwes, 2003; Fung and Hughson, 2003; Jahne et al., 2015). Specifically, PM2.5 can penetrate and deposit deeper in the alveolar and bronchial regions, rendering them more harmful than coarse particles, which mainly deposit in the nasal and thoracic regions (Brook et al., 2004; Kawanaka et al., 2009). Moreover, Fröhlichnowoisky (2009) suggested higher proportion of human pathogens and allergens adhered to fine particles and plant pathogens adhered to coarse particles. Therefore, scientific investigations and public discussions in recent years have focused on fine aerosol particles. Numerous studies have devoted considerable efforts to investigate airborne bacteria and fungi. Culture-based methods were widely adopted in previous studies involving bioaerosols (Fang et al., 2008; Gao et al., 2015; Hu et al., 2015). However, characterizations of atmospheric microorganisms employing these methods were limited because only a small fraction (b 1%) of airborne bacteria and fungi could be cultured on mediums (Després et al., 2012; Pace, 1997). Culture-independent methods, such as microscope, microarray, and Sanger sequencing were also employed for studying microbial communities in the atmosphere (Brodie et al., 2007; Fierer et al., 2008; Tong and Lighthart, 2000), although deficiencies in biological classification and the amount of available experimental data existed. Nonetheless, with recent advances in gene sequencing technology, next-generation highthroughput sequencing had become an effective method to research microorganisms associated with aerosols (Bowers et al., 2013; Bowers et al., 2009; Cao et al., 2014). Temporal and spatial variation in bioaerosols was affected by their sources, land-use types, environmental factors, and weather conditions (Bowers et al., 2010; Brodie et al., 2007). Variation in air pollution levels was also an important factor that affected the abundance and composition of bioaerosols (Cao et al., 2014; Gao et al., 2015; Hu et al., 2015). Till date, effects of atmosphere pollution on the bacteria and fungi in PM2.5 have not been well investigated, chiefly due to methodological difficulties. Therefore, we used high-throughput sequencing to characterize

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bacterial and fungal communities in PM2.5 collected under different air pollution levels during all seasons in Beijing. The results highlight the variation in bacteria and fungi in PM2.5 during different seasons and the impact of air pollution on the microbial communities. Furthermore, our results would also contribute to revealing the pathogenic mechanism of PM2.5 on human health. 2. Materials and methods 2.1. Sampling site and PM2.5 collection PM2.5 samples were collected on the rooftop of a two-story building located at the State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC) (39° 58′ 28″N, 116° 22′ 16″ E), Institute of Atmospheric Physics (IAP), Chinese Academy of Science in Beijing. The sample site is located between the north third ring road and the fourth ring road, and surrounded by urban roads, green belts, residential areas, and business districts. A shallow stream named small moon river flows near the north of the site. The underlying surface near the site is covered by sparse grass and trees. A high volume air particulate matter sampler (TE-6070VFC, Tisch, USA) at a flow rate of 1.13 m3/min was used to collect PM2.5 samples on a 203 mm × 254 mm quartz fiber filter (Whatman™, GE, USA) for 23 h 50 min (7:10 AM to 7:00 AM the next day). The filter was pre-sterilized in a muffle furnace at 550 °C for 5 h. A total of 75 PM2.5 samples, that included 19 spring samples (from April 22 to May 13, 2014), 15 summer samples (from June 28 to July 13, 2014), 20 autumn samples (from October 15 to November 5, 2014), and 21 winter samples (from January 1 to January 22, 2015), were collected at the sampling site. A blank filter was also collected during all seasons by shutting the sampler for 5 min. All PM2.5 samples were stored at −20 °C until subsequent analyses. 2.2. DNA extraction and PCR amplification Fig. 1 lists the AQI on the sampling dates acquired from the nearest air monitoring station, located at the Beijing Olympic Sports Center situated about 2 km southwest to our sampling site. For each season, PM2.5 filter samples were divided into different experimental treatment groups according to the AQI value: group 1 included filters collected at AQI lesser than 100, group 2 included filters collected at AQI 101–200, group 3 included filters collected at AQI 201–300, and group 4 included filters

Fig. 1. Daily variations in Air quality index (AQI) on the sampling dates from the nearest air monitoring station, located at the Beijing Olympic Sports Center. The green, blue, and red dash lines correspond to the boundary values of AQI to divide four experimental treatment groups.

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collected at AQI N 300. The four AQI categories respectively represented different air quality and health effects: excellent and good air quality which was moderate for normal outdoor activities, slight and moderate pollution which was unhealthy for sensitive groups, heavy pollution which was very unhealthy for sensitive groups and general population should reduce the time in outdoor, and severe pollution which was hazardous for outdoor activities. Pieces of the same area filters were clipped from each filter in an experimental treatment group and they were merged to extract DNA of the biological components in PM2.5. It should be noted that the clipped area differed between different experimental treatment groups as the number of PM2.5 filter samples were different in each group. Table 1 lists detailed relevant information about the experimental treatment groups in different seasons. Approximately 18 cm2 of PM2.5 filters from experimental treatment group were cut into pieces and placed in an agate mortar sterilized an autoclave sterilizer. We ground PM2.5 filters to powder and loaded them into a bead tube provided with a PowerSoil DNA isolation kit (MoBio Laboratories, Carlsbad, CA, USA). The remaining steps for DNA extraction were performed as the DNA isolation kit protocol. Subsequently, the V3V4 hyper-variable region of the bacterial 16S rRNA gene was amplified using primers 338F (5′-ACTCCTACGGGAGGCAGCA-3′) and 806R (5′GGACTACHVGGGTWTCTAAT-3′) and the internal transcribed spacer 1 (ITS1) of the fungal rRNA gene was amplified using primers 1737F (5′GGAAGTAAAAGTCGTAACAAGG-3′) and 2043R (GCTGCGTTCTGCATCGATGC) through polymerase chain reactions (PCRs). PCRs were performed in a 25 μL volume containing 1× PCR buffer, 0.2 mM dNTPs, 0.2 μM primer, 0.6 Units taq DNA polymerase, and nearly 10 ng template DNA under the following cycling conditions: pre-denaturation at 94 °C for 5 min; 30 cycles of denaturation at 94 °C for 30 s, annealing at 50 °C for 30 s (for bacteria) or 56 °C for 30 s (for fungi), extension at 72 °C for 45 s, with a final extension at 72 °C for 5 min. Every DNA sample had three repeated PCRs and the subsequent products were combined together. Blank filters were also subjected to above operations. However, PCRs results for blank filters did not yield any output. After purification and quantification, PCR products were tested through high-throughput sequencing using the Illumina Miseq PE300 platform. Raw sequencing data were deposited to the NCBI Sequence Read Archive with accession number SRR4963381. 2.3. Sequence analyses The open-source software package Mothur was used to process and analyzed 16S and ITS1 gene sequences. Sequences that were b400 bp or N470 bp (for 16 s rRNA gene), b 200 bp or N 300 bp (for ITS rRNA gene), had homopolymer stretches of N8 bp, contained ambiguous bases, and PCR chimeras identified using the Chimer Uchime command in Mothur were remove from analysis. Subsequent processing was conducted in accordance with the Mothur analysis example described by Kozich et al. (2013). Since alignment databases for ITS rRNA gene were not

available, fungal ITS rRNA gene sequences were aligned using MAFFT version 7 (http://mafft.cbrc.jp/alignment/server), which was a targetindependent alignment tool. We clustered sequences into operational taxonomic units (OTUs) by setting distance measure of 0.03. For fairly comparing the alpha and beta diversities in different samples, we selected minimum number of sequences in all samples as the standard of normalization. The bacterial sequences were assigned to phylotypes using a Bayesian approach against the Ribosomal Database Project (RDP) 16 s rRNA gene training database with a confidence threshold of 70% (Wang et al., 2007). Fungal sequences were classified against UNITE ITS database using the K-Nearest Neighbor algorithm (Abarenkov et al., 2010). Principal coordinate analyses (PCoA) based on the weighted-UniFrac distance matrix were conducted to check the difference and variation in different bacterial and fungal communities in PM2.5. The variations in species richness and community diversity across different seasons and pollution levels were assessed using analysis of variance (ANOVA) test, and their relationships were investigated using Spearman correlation analysis. ANOVA test and correlation analysis were conducted using SPSS 19.0 software. 3. Results 3.1. Species richness and community diversity of bacteria and fungi A total of 257,978 and 344,440 high-quality sequences for bacterial 16S rRNA gene and fungal ITS gene, respectively, were acquired after removing unqualified gene sequences (Table 2 and Table 3). As the standard of normalization, 9595 and 9838 sequences were singled out from each sample to calculate species richness and community diversity for bacterial and fungal communities harbored in PM2.5, respectively. ACE and Chao 1 indexes were applied to compare the species richness and Shannon index was used to compare community diversity in different samples. For bacteria, the winter samples exhibited highest species richness with an average ACE and Chao 1 of 8948 and 5304, respectively, followed by spring samples (average ACE of 7856 and average Chao 1 of 4628), autumn samples (average ACE of 5848 and average Chao 1 of 3435), and summer samples with the lowest average ACE (3527) and Chao 1 (2411) indexes. The results of statistical analysis showed species richness varied significantly in different seasons (ANOVA, P = 0.03, F = 4.78 for ACE; P = 0.01, F = 6.57 for Chao 1). Meanwhile, there were statistically significant relationships between community diversity of bacteria revealed by Shannon indexes and different seasons (ANOVA, P b 0.01, F = 56.09). The order of average Shannon index in the four season samples were winter (6.05), autumn (5.55), summer (5.23), and spring (4.40). Conversely, the ACE, Chao1, and Shannon indexes showed no significant variation at different pollution levels (ANONA, P N 0.05). However, if we assessed the relationship between bacterial communities and air pollution levels only in one season, we found species richness of bacteria was closely related to the pollution levels, especially in summer and

Table 1 Information for dividing different experimental treatment groups in different seasons and under different air pollution levels. Season

Experimental groups

AQI range

Number of filter samples

Area of each filter (cm2)

Sampling date

Spring

Spr-pl1 Spr-pl2 Sum-pl1 Sum-pl2 Sum-pl3 Aut-pl1 Aut-pl2 Aut-pl3 Win-pl1 Win-pl2 Win-pl3 Win-pl4

0–100 101–200 0–100 101–200 201–300 0–100 101–200 201–300 0–100 101–200 201–300 N300

12 7 6 5 4 10 4 6 9 7 4 1

1.5 2.6 3.0 3.6 4.5 1.8 4.5 3.0 2.0 2.6 4.5 18.0

22, 26, 27, 28-Apr, 2014 and 2, 4, 5, 7, 9, 10, 11, 12-May 2014 23, 29, 30-Apr, 2014 and 1, 3, 6, 8-May 2014 28-Jun,2014 and 2, 9, 10, 11, 12-Jul, 2014 29, 30-Jun, 2014 and 1, 5, 8-Jul, 2014 3, 4, 6, 7-Jul, 2014 15, 16, 21, 22, 26, 27, 28-Oct, 2014 and 1,2, 3-Nov, 2014 17, 23, 29-Oct and 4-Nov, 2014 18, 19, 20, 24, 30, 31-Oct, 2014 1, 2, 6, 7, 11, 17, 18, 19, 21-Jan, 2015 5, 8, 9, 10, 12, 16, 20-Jan, 2015 3, 4, 13, 14-Jan, 2015 15-Jan

Summer

Autumn

Winter

The four AQI ranges represented different air quality levels: excellent and good air quality (0−100), slight and moderate pollution (101−200), heavy pollution (201−300), and severe pollution (N300).

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Table 2 Comparison of species richness and community diversity estimators of the bacterial communities in PM2.5 in different samples. Season

Experimental groups

Number of sequencesa

OTUsb

ACE

Chao 1

Shannon

Coverage

Spring

Spr-pl1 Spr-pl2 Sum-pl1 Sum-pl2 Sum-pl3 Aut-pl1 Aut-pl2 Aut-pl3 Win-pl1 Win-pl2 Win-pl3 Win-pl4

16,882 14,213 10,019 12,366 9595 24,691 30,821 24,286 32,377 26,230 27,325 29,173

2181 1880 1607 1408 1005 3052 3084 2158 4884 4328 3527 3723

7543 8170 5533 3176 1873 6895 5909 3648 11,079 11,162 7236 6315

4177 4360 3430 2197 1606 4170 3589 2545 6082 6297 4416 4421

4.56 4.25 5.42 5.22 5.04 5.69 5.51 5.43 6.13 6.13 5.94 6.02

89.41% 89.57% 90.21% 93.35% 95.50% 88.77% 89.93% 92.42% 84.43% 84.34% 87.64% 87.97%

Summer

Autumn

Winter

a b

Sequences after quality controls and chimera removal. The operational taxonomic units (OTUs) were defined with 3% dissimilarity.

autumn, and the ACE and Chao 1 indexes decreased with the aggravation of air pollution. As only two pollution levels were observed in spring, we were unable to assess the impact of pollution levels on the species richness. Spearman correlation coefficients indicated that species richness and community diversity were correlated with air pollution levels during summer, autumn, and winter (∣r∣ N 0.8 in all three seasons). Similarly, the species richness of fungal communities showed significant variation in different seasons (ANOVA, P = 0.02, F = 6.35 for ACE; P b 0.01, F = 14.8 for Chao 1; P b 0.02, F = 25.29 for Shannon), and the fungal communities harbored in PM2.5 collected in winter had the largest average ACE (1069), Chao 1(805), and Shannon (3.12) indexes. Whereas, the summer samples had the lowest species richness (average ACE of 604 and average Chao 1 of 349) and community diversity (average Shannon of 1.03). Unlike bacterial communities in PM2.5, species richness and community diversity of fungal communities were not distinctly related with the pollution levels in any of the seasons. Therefore, the variation in species richness and community diversity of bacteria and fungi harbored in PM2.5 was affected by the seasons; however, air pollution appeared to decrease species richness and community diversity only for bacterial communities.

samples were separated from other season samples though they were not relatively concentrated. Analogously, for fungal communities, these PM2.5 samples were centralized in terms of spring, summer, and winter seasons, but autumn samples were relatively scattered (Fig. 2b).

3.2. Community variation with seasons and pollution levels The variation of bacterial and fungal communities in PM2.5 collected during different seasons and under different pollution levels were analyzed using principal coordinate analysis (PCoA), which correlated the similarity of different microbial communities. PCoA analysis of bacterial communities (Fig. 2a) showed that PM2.5 samples were clustered together according to the spring, autumn, and winter seasons, and the summer

Table 3 Comparison of species richness and community diversity estimators of the fungal communities in PM2.5 in different samples. Season Spring

Experimental Number of OTUsb ACE groups sequencesa

Spr-pl1 Spr-pl2 Summer Sum-pl1 Sum-pl2 Sum-pl3 Autumn Aut-pl1 Aut-pl2 Aut-pl3 Winter Win-pl1 Win-pl2 Win-pl3 Win-pl4 a b

13,180 11,407 12,296 9838 15,736 22,724 24,082 30,523 64,271 54,079 31,204 55,060

221 283 165 154 179 509 364 340 984 964 851 878

529 726 679 740 392 793 566 558 943 1204 1247 884

Chao Shannon Coverage 1 394 543 359 392 297 614 435 365 743 820 931 727

1.82 2.11 0.85 1.3 0.95 2.68 1.95 1.77 2.91 2.91 3.36 3.32

98.96% 98.58% 99.06% 99.05% 99.23% 98.31% 98.78% 98.95% 97.88% 97.77% 97.52% 98.00%

Sequences after quality controls and chimera removal. The operational taxonomic units (OTUs) were defined with 3% dissimilarity.

Fig. 2. Principal coordinate analysis of the samples using Weighted-UniFrac distance matrix, and classified as Bacteria (a) and fungi (b). The label shapes square, circle, upper triangle and lower triangle respectively correspond to the spring, summer, autumn, and winter samples. The colors green, blue, red, and black correspond to the air pollution levels represented by AQI grades 0–100, 101–200, 201–300, N300, respectively. The numbers next to PCO 1 and PCO 2 explain the percentages of community variations.

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However, samples were not clustered on account of pollution levels for bacterial communities or fungal communities. Therefore, the PCoA results demonstrated that microbial community variation was significantly correlated with the season, but air pollutions levels had no apparent effect on the microbial structure. 3.3. Bacterial community composition As many as 25 bacterial phyla were identified in the twelve PM2.5 samples collected during the four seasons (Fig. 3a). In all sequences, Proteobacteria was the most abundant phylum and accounted for 33.03%. Four other dominant phyla were Actinobacteria (28.12%), Firmicutes (19.30%), Bacteroidetes (8.14%), and Cyanobacteria_Chloroplast (7.02%). However, the order of the first three abundant phyla in spring, Cyanobacteria_Chloroplast, Firmicutes, and Actinobacteria, sorted by the relative abundance, was different from other seasons in which their order was Proteobacteria, Actinobacteria, and Firmicutes. The average relative abundance of Cyanobacteria_Chloroplast varied significantly

between the seasons: spring (32.48%), summer (10.09%), autumn (3.29), and winter (2.22%). At the genus level, nearly 831 different genera were identified in the PM2.5 samples. The genus Streptophyta is affiliated to phylum Cyanobacteria_Chloroplast and is mainly derived from plant chloroplast. In spring, the genus Streptophyta was the most abundant and accounted for 28.52% and 35.30% in PM2.5 samples spr-pl1 and spr-pl2, respectively (see Supplementary data Fig. S1). Nevertheless, the average abundance of Streptophyta decreased to 8.94%, 2.24%, and 1.23% in summer, autumn, and winter, respectively. The dominant bacteria were Streptophyta (6.05%), Kocuria (5.70%), Paracoccus (4.08%), Sphingomonas (4.03%), Rubellimicrobium (2.68%), Planococcus (2.63%), Bacillus (2.43%), and Clostridium (2.15%). Bacteria in PM2.5 collected in spring were represented by Planococcus (8.86%), Kocuria (5.43%), and Bacillus (2.04%). The primary genera of bacteria in summer were Kocuria (7.31%), Paracoccus (6.83%), and Bacillus (2.86%). The dominant bacterial genera in autumn were Sphingomonas (6.50%), Kocuria (5.89%), and Paracoccus (5.73%). In winter, the top-three genera were Kocuria (5.18%), Sphingomonas (3.50%),

Fig. 3. Phylogenetic classification of the bacterial communities (a) at the phylum level and fungal communities (b) at the class level. The category “others” denoted that with relative abundance b5% for bacteria and b1% for fungi in every experimental groups. Sequences that could not be classified into any known category were assigned as “unclassified”.

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and Paracoccus (2.73%). Therefore, the categories of major bacteria genera were similar, but their relative abundances varied slightly between seasons. 3.4. Fungal community composition Totally, six fungal phyla were identified in PM2.5. Ascomycota accounted for 95.37%, and were dominant in the fungal community. Other fungal phyla were members of Basidiomycota (1.51%), Zygomycota (0.13%), Chytridiomycota (0.01%), Glomeromycota, and Rozellomycota, and the abundance of the latter two phyla Glomeromycota and Rozellomycota were b0.01%. At the class level, 24 different classes of fungi were detected, but only three classes including Dothideomycetes, Sordariomycetes, and Eurotiomycetes, were N1% (Fig. 3b). Furthermore, 397 different fungal genera were identified in PM2.5, although N71% of the total sequences could not be classified at the genus level. Fig. S2 shows the 20 common fungal genera, and indicats that only seven genera, Epicoccum (5.99%), Penicillium (3.39%), Selenophoma (3.30%), Mycosphaerella (2.50%), Cladosporium (1.74%), Aspergillus (1.70%), and Sarocladium (1.33%), were found at levels N1% in total sequences. However, the relative abundances of these genera varied in different seasons. Mycosphaerella was the most abundant genus collected in spring and summer, but Epicoccum was the richest in autumn and Penicillium was more abundant than other genera in winter. Therefore, we found fungi adhered to PM2.5 were different in the four seasons. 3.5. Potential bacterial and fungal pathogens The directory of pathogenic microorganisms infecting humans promulgated by the Ministry of Health of the People's Republic of China (MOHC) was used for pathogen detection at the genus level as 16S and ITS gene sequences obtained through high-throughput sequencing were insufficient for species level classification. Five bacterial genera and five fungal genera were identified from the PM2.5 samples (see Supplementary data Fig. S3). The pathogenic bacteria comprised of Streptococcus (0.47%), Prevotella (0.10%), Rickettsia (0.01%), Erysipelothrix (0.01%), and Enterobacter (0.001%) and pathogenic fungi included Trichoderma (0.34%), Trichothecium (0.22%), Alternaria (0.20%), Stachybotrys (0.10%), and Arthrinium (0.001%). The total abundance of bacterial pathogens was higher in winter (0.753%) compared to autumn (0.583%), summer (0.319%), and spring (0.244%). Additionally, the winter samples recorded the most fungal pathogens, occupying 1.24% in all the sequences, followed by spring (0.31%), autumn (0.30%), and summer (0.30%). The total proportion of pathogenic bacteria was significantly different in different seasons (ANOVA, P b 0.01, F = 12.05). Similarly, the total abundance of pathogenic fungi was significantly affected seasons (ANOVA, P = 0.01, F = 7.57). Overall, the abundance of pathogens in winter was more than the other seasons. However, the pathogenic bacteria and fungi were not related to the pollution levels (ANOVA, P = 0.53, F = 0.80 for pathogenic bacteria; P = 0.10, F = 2.86 for pathogenic fungi). The variation in abundance of pathogens in any of the seasons was also not related to the pollution levels. 4. Discussion Concentrations of culturable airborne bacteria and fungi have been extensively studied (Fang et al., 2007; Gao et al., 2015; Haas et al., 2013), and these results highlighted the variation in their concentration under different meteorological and geographical factors. Nevertheless, the characteristics involving in species richness of bacteria and fungi attached to airborne particles in different seasons and meteorological conditions were still unclear. In our study, we observed that bacterial and fungal communities in PM2.5 collected in winter had higher species richness than other seasons, and PM2.5 collected in summer hosted the least bacterial and fungal species. In general, winter was regarded as an adverse season for growth and reproduction of microorganisms due to

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low temperature, in contrast to summer, which was conducive for bacterial and fungal growth and reproduction. However, this was contrary to our research results for species richness. We suspected that excessive growth of relatively dominant microbial populations in the warm season inhibited the growth of less dominant bacterial and fungal communities. Under low temperatures, inhibition or competition between different kinds of microorganisms was weak and allowed more species to coexist in the community. Additionally, wind speed and relative humidity were important factors in the release and diffusion of airborne microorganisms from various sources (Kembel et al., 2012; Tang, 2009). Dry air and strong wind were beneficial for the spread of airborne microorganisms, and Beijing has drier air and stronger wind in winter than in other seasons. Thus, the winter PM2.5 samples recorded highest variation for airborne microorganisms, which was represented by species richness and community diversity, as seen in Table 2 and Table 3. Within one season, the variations of species richness and community diversity in different air pollution levels can also be attributed to wind speed. In general, wind speed has negative relation with air pollution level, and the weak wind is unfavorable to the release and diffusion of air microorganisms. Our result also seemed to indirectly indicate that low temperature might limit some dominant bacteria and fungi and assist some non-dominant bacteria and fungi to flourish. Previous studies have shown that several microorganisms thrived in extremely cold environments and played important roles in the global ecosystem (Kandeler et al., 2006; Knelman et al., 2012; Schutte et al., 2010; Sharp et al., 1999; Xiang et al., 2005). Nevertheless, the manner in which the increase in microbial species during low temperatures influenced the atmospheric environment is unclear. Effects of airborne particulate matter on human health are paid comprehensive attention and historical data have fully illustrated the health impacts of particulate matter (Cheng et al., 2013; Pope et al., 2009; Zhang et al., 2010). Bioaerosols are ubiquitous and can cause allergy and asthma (Nayak et al., 2016; Ross et al., 2000; Yamamoto et al., 2012). An understanding of the variation of airborne bacteria and fungi under different air pollution levels and seasons is essential to assess the extent of their health risk. The concentration of bacterial and fungal aerosols are closely related to the air pollution levels (Gao et al., 2015; Li et al., 2015), but microbial composition and community structure changes under different air pollution levels are still elusive. Our results showed variation in bacterial and fungal communities was driven mainly by different seasons and there were no significant correlation between the microbial community structure and air pollution levels. Wei et al. (2016) also indicated insignificant difference in abundance and community structure for dominant bacteria at different air pollution levels. Airborne bacteria and fungi have a wide range of sources, such as soil, vegetation, animals, and water bodies, which are affected by environmental factors such as temperature, relative humidity, precipitation, and speed. In a short time span, the sources of bioaerosols usually remained stable and mainly originated from localized environment unless affected by high wind and precipitation. Moreover, the changes in environmental factors between different seasons were more significant than during the same season. Therefore, influence of air pollution level changes on the microbial community in the PM2.5 during the same season was less than between seasons. Bacterial community in the atmosphere mainly comprised of Proteobacteria, Firmicutes, and Actinobacteria, and Ascomycota was dominant in the fungal community. This was consistent with previous studies (Bowers et al., 2011; Cao et al., 2014; Gou et al., 2016). However, at the genus level, studies conducted at different locations recorded different dominant bacterial and fungal genera (Cao et al., 2014; Wei et al., 2016), and this seemed to prove that geographical variation, which could result in changes in bioaerosol sources, had greater effect than meteorological and seasonal variation. Pathogenic bacteria and fungi in PM2.5 were priority among the researches of bioaerosols, and numerous pathogens had been identified in the air particulate matter (Fröhlichnowoisky, 2009; Gou et al., 2016).

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Cao et al. (2014) indicated that some pathogens appeared to increase with air pollution levels, while our results showed pathogenic bacteria and fungi were relatively more abundant in winter than other seasons, but there was no significant relation between pathogen and air pollution levels. We also found several pathogenic bacterial genera and fungal genera. Streptococcus can induce infections, and often associate with shock, bacteremia, acute respiratory distress syndrome, neonatal sepsis and meningitis (Lin et al., 2003; Stevens, 1992). Prevotella often exist in oral cavity and can cause periodontal disease (Ibrahim et al., 2017). Erysipelothrix, a gram-positive, slender, straight or slightly curved rod, are known to be the causative agent of erysipelas in swine and erysipeloid in humans (Okatani et al., 2000). Enterobacter are common nosocomial pathogens, which are capable of causing opportunistic infections and bacteremia (Bodey et al., 1991; Gaston, 1988). They are the most frequently isolated organisms in intensive care unit (ICU) bloodstream infections and are the most common pathogen isolated in cases of ICU pneumonias (Cosgrove et al., 2002). Rickettsia are the causative agents of numerous diseases of humans, including spotted fever, BrillZinsser disease, and epidemic typhus, and these diseases are generally characterized by sudden onset of nonspecific symptoms, such as severe headache, arthralgia, acute sustained high fever, malaise, and myalgia (Kelly et al., 2002; Perlman et al., 2006). According to the MOHC, five pathogenic fungal genera were identified; however, little research had been conducted to directly investigate their pathogenicity to humans and they mainly cause several plant diseases. Nevertheless, some fungal metabolites may pose human health risks, for instance, trichothecenes produced by some fungal species from the genera Trichothecium, Trichoderma, Stachybotrys, and Fusarium can inhibit protein synthesis and immunomodulatory (Sudakin, 2003). Alternaria can produce a wide range of mycotoxins such as tenuazonic acid, alternariol, and alternariol monomethyl ether, and which are acutely toxic to mice, chicken, and dogs, and are considered as a possible causal factor of Onyalai; however, their toxicological impact on human is still unclear (Vaquera et al., 2016). 3-Nitropropionic acid is a known causative agent produced by Arthrinium, and intake of 3-Nitropropionic in humans may lead to dystonia, torsion spasms, involuntary jerky movements, and cognitive impairment (Kaur et al., 2015; Wei et al., 1994). The total proportion of the above mentioned pathogenic bacteria and fungi was higher in winter than in other seasons, and so bioaerosols in PM2.5 might cause greater impact to human health in winter. Moreover, to a certain extent, maybe our current results explained the “Beijing cough” phenomenon caused by serious haze in Beijing winter. Despite the presence of pathogenic bacteria and fungi, it was still difficult to assess their effects on human health owing to their extremely low proportion. Additionally, due to a paucity of related researches, the relationship between pathogen variation and meteorological and geological factors is not well understood. Moreover, bioaerosols might interact with some chemical substances in the atmosphere and clouds (Wei et al., 2017; Xu et al., 2017), and these resultant chemical reactions may affect the toxicity of various components of airborne particulate matters, such as organic substances, transition metals, nitrates, and sulfates (Ariya et al., 2002). Consequently, the community composition and structure may change since their interaction. However, the impact of these changes on human health is not known. Therefore, further research is still needed to study the variation characteristic of bioaerosols, variation in toxicity of bioaerosols under different weather conditions, relationship between bioaerosols and various meteorological and geological factors, and biological reactions occurred on the airborne particulate matters. 5. Conclusion This study is the first of its kind to demonstrate seasonal variation in bacterial and fungal communities in PM2.5 in Beijing, and analyze the relationship between microbial community structure and air pollution level. The results showed that air pollution decreased species richness

and community diversity of bacteria in PM2.5. The variation in bacterial and fungal community composition and structure was significantly related to the season but there was no correlation between their abundance and pollution levels. Pathogenic bacteria and fungi were more abundant in winter than other seasons; however, we did not detect the activity and toxicity of these mentioned pathogenic bacteria and fungi because they were difficult to culture using current methods. Thus, we still cannot assess their health impacts in humans owing to their extremely low proportion and possible chemical reactions with abiotic components in PM2.5. These observations had implications on how air pollution levels affects microbes in PM2.5, and should be consolidated by more exhaustive sampling campaigns and screening of more time series samples. Our findings would serve as an important reference for researches working on the characteristics and functions of bioaerosols. However, due to this study's focus on one sample site and limited sample number, the aforementioned focus needs further discussion. Moreover, future research should also focus on understanding the relationship between chemical composition and pathogenic microbes, and exploring the reaction between biotic and abiotic components in airborne particulate matters. Specifically, the impact of bioaerosols on the toxicity of transition metals, and organic matter in the PM2.5, and their reaction mechanism should be further investigated. Acknowledgments This research was funded by the National Science Foundation of China (Grant No. 41175135), Beijing Natural Science Foundation (Grant No. 8172045), and the Open Project of the State Key Laboratory of Atmospheric Boundary Physics and Atmospheric Chemistry (LAPC-KF-201408). Appendix A. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.scitotenv.2017.07.097. References Abarenkov, K., Nilsson, R.H., Larsson, K.H., Alexander, I.J., Eberhardt, U., Erland, S., Høiland, K., Kjøller, R., Larsson, E., Pennanen, T., 2010. The UNITE database for molecular identification of fungi – recent updates and future perspectives. New Phytol. 186 (2), 281–285. Ariya, P.A., Oleg, N., Olga, I., Marc, A., 2002. Microbiological degradation of atmospheric organic compounds. Geophys. Res. Lett. 29 (22), 34-31–34-34. Bodey, G.P., Elting, L.S., Rodriguez, S., 1991. Bacteremia caused by Enterobacter: 15 years of experience in a cancer hospital. Clin. Infect. Dis. 13 (4), 550–558. Bowers, R.M., Lauber, C.L., Wiedinmyer, C., Hamady, M., Hallar, A.G., Fall, R., Knight, R., Fierer, N., 2009. Characterization of airborne microbial communities at a high-elevation site and their potential to act as atmospheric ice nuclei. Appl. Environ. Microbiol. 75 (15), 5121–5130. Bowers, R.M., Mcletchie, S., Knight, R., Fierer, N., 2010. Spatial variability in airborne bacterial communities across land-use types and their relationship to the bacterial communities of potential source environments. ISME J. 5 (4), 601–612. Bowers, R.M., Mcletchie, S., Knight, R., Fierer, N., 2011. Spatial variability in airborne bacterial communities across land-use types and their relationship to the bacterial communities of potential source environments. ISME J. 5 (4), 601–612. Bowers, R.M., Clements, N., Emerson, J.B., Wiedinmyer, C., Hannigan, M.P., Fierer, N., 2013. Seasonal variability in bacterial and fungal diversity of the near-surface atmosphere. Environ. Sci. Technol. 47 (21), 12097–12106. Brodie, E.L., Desantis, T.Z., Parker, J.P.M., Zubietta, I.X., Piceno, Y.M., Andersen, G.L., 2007. Urban aerosols harbor diverse and dynamic bacterial populations. Proc. Natl. Acad. Sci. U. S. A. 104 (1), 299–304. Brook, R.D., Franklin, B., Cascio, W., Hong, Y., Howard, G., Lipsett, M., Luepker, R., Mittleman, M., Samet, J., Smith, S.C., 2004. Air pollution and cardiovascular disease: a statement for healthcare professionals from the expert panel on population and prevention science of the American Heart Association. Circulation 109 (21), 2655–2671. Cao, C., Jiang, W., Wang, B., Fang, J., Lang, J., Tian, G., Jiang, J., Zhu, T.F., 2014. Inhalable microorganisms in Beijing's PM2.5 and PM10 pollutants during a severe smog event. Environ. Sci. Technol. 48 (3), 1499–1507. Chan, C.K., Yao, X., 2008. Air pollution in mega cities in China. Atmos. Environ. 42 (1), 1–42. Cheng, Z., Jiang, J., Fajardo, O., Wang, S., Hao, J., 2013. Characteristics and health impacts of particulate matter pollution in China (2001−2011). Atmos. Environ. 65 (3), 186–194. Cosgrove, S.E., Kaye, K.S., Eliopoulous, G.M., Carmeli, Y., 2002. Health and economic outcomes of the emergence of third-generation cephalosporin resistance in Enterobacter species. Arch. Intern. Med. 162 (2), 185–190.

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