Airborne bacteria in the Central Mediterranean: Structure and role of meteorology and air mass transport

Science of the Total Environment 697 (2019) 134020

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

Airborne bacteria in the Central Mediterranean: Structure and role of meteorology and air mass transport Salvatore Romano a, Marco Di Salvo b, Gennaro Rispoli a, Pietro Alifano b, Maria Rita Perrone a, Adelfia Talà b,⁎ a b

Department of Mathematics and Physics, University of Salento, via per Arnesano, 73100 Lecce, Italy Department of Biological and Environmental Sciences and Technologies, University of Salento, Via Monteroni, 73100 Lecce, Italy

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• The structure of the airborne bacterial community of 20 PM10 samples was characterized. • Proteobacteria, Cyanobacteria, Actinobacteria, Firmicutes, Bacteroidetes were the main phyla. • The bacterial community richness and biodiversity increased from winter to spring. • Meteorology, seasons, and advection patterns affected the bacterial community structure. • The bacterial community diversity increased during the advection of African dust particles.

a r t i c l e

i n f o

Article history: Received 23 April 2019 Received in revised form 24 July 2019 Accepted 19 August 2019 Available online 22 August 2019 Editor: Pingqing Fu Keywords: Airborne bacterial community PM10 samples Meteorological parameters Advection patterns Biodiversity indices Principal Component Analysis

⁎ Corresponding author. E-mail address: adelfi[email protected] (A. Talà).

https://doi.org/10.1016/j.scitotenv.2019.134020 0048-9697/© 2019 Published by Elsevier B.V.

a b s t r a c t The 16S rRNA gene metabarcoding approach has been used to characterize the structure of the airborne bacterial community of PM10 samples, and investigate the dependence on meteorology, seasons, and long-range transported air masses. The PM10 samples were collected at a Central Mediterranean coastal site, away from large sources of local pollution. Proteobacteria, Cyanobacteria, Actinobacteria, Firmicutes, and Bacteroidetes, which were found in all samples, were the most abundant phyla. Calothrix, Pseudomonas, and Bacillus were the most abundant genera. The within-sample relative abundance (RA) of each phylum/genus varied from sample to sample. Calothrix was the most abundant genus during the advection of desert dust and Atlantic air masses, Pseudomonas was the most abundant genus when the advected air flows spent several hours over lands or close to lands affected by anthropogenic activities, before reaching the study site. The bacterial community richness and biodiversity of the PM10 samples on average increased from winter to spring, while the sample dissimilarity on average decreased from winter to spring. The spring meteorological conditions over the Mediterranean, which have likely contributed to maintain for longer time the bacterial community in the atmosphere, could have been responsible for the above results. The analysis of the presumptive species-level characterization of the airborne bacterial community has revealed that the abundance of human (opportunistic) pathogens was highly inhomogeneous among samples, without any significant change from winter to spring. We also found that the PM10 samples collected during the advection of desert dust and Atlantic air masses were on average the less enriched in human (opportunistic) pathogenic species. © 2019 Published by Elsevier B.V.

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1. Introduction Primary biological airborne particles or bioaerosols comprise living and dead microorganisms such as bacteria, fungi, virus, bacterial and fungal spores, microbial fragments, pollen, etc. (e.g., FröhlichNowoisky et al., 2016). They are a ubiquitous component of the atmosphere, which plays a fundamental role in their transport and dispersal across the planet (e.g., Yoo et al., 2017; Després et al., 2012). Due to their small size, bacteria have a relatively long atmospheric residence time (approximately several days or more) and they can be transported over long distances (up to thousands of kilometers). Previous studies have shown that bacteria were the dominant airborne microorganisms in the particulate matter (Cao et al., 2014). Airborne bacteria may be suspended as individual cells but are more likely to be attached to other particles, such as soil, or found as agglomerate of many bacterial cells (e.g., Després et al., 2012; Yan et al., 2018). The abundance and speciation of airborne bacteria is of great interest due to their potential impact on human health (e.g., Yoo et al., 2017; Brodie et al., 2007), agriculture, ecosystem's health, biogeochemical cycles, and atmospheric processes (e.g., Morris et al., 2011; Smets et al., 2016; Zhong et al., 2019). Conventional detection of bacteria based on culture-dependent methods is inadequate, due to the existence of non-cultivable species, viable-but not-cultivable status or loss of viability during airborne transfer (Després et al., 2012; Blais-Lecours et al., 2015). The application of molecular biological techniques has represented a major step forward in the study of the airborne biota distribution in the environment (Yoo et al., 2017). Mazar et al. (2016) applied modern genomic techniques to investigate the impact of dust storms on the airborne microbial community in the Eastern Mediterranean. They found that dust storms enriched the ambient airborne microbiome, but they do not seem to be an important vector for the transport of antibiotic resistances genes. Gat et al. (2017) demonstrated that dust storms from different sources presented distinct bacterial communities. Polymenakou et al. (2008) examined by 16S rRNA genes analysis the aerosol microbial quality over the eastern Mediterranean region during an African storm. They found that spore-forming bacteria such as Firmicutes dominated large particle sizes (N3.3 μm), whereas clones affiliated with Actinobacteria and Bacteroidetes gradually increased their abundance in aerosol particles of reduced size (b3.3 μm). Barberán et al. (2015) found at about 1200 locations across the continental United States that the aerosolized bacteria were highly variable across the study area and were correlated with regional environmental factors. Many studies have mainly been devoted to the characterization of the airborne bacterial communities during hazy weather conditions (e.g., Bertolini et al., 2013; Cao et al., 2014; Qi et al., 2018). Li et al. (2018) and Zhen et al. (2017) found that temperature, relative humidity, and air pressure were of great importance in shaping airborne bacterial communities in Beijing (China). Du et al. (2018) observed that air pollution in hazy weather decreased species richness and community diversity of bacteria, which in contrast were related to the season. Only a few studies have characterized bacterial communities at coastal sites away from large sources of local pollution. These studies are essential to better gain information on the characterization of the airborne bacterial community dispersal through the atmosphere, since overall 33–68% of microorganisms could be traced to a marine origin, being transported thousands of kilometers before re-entering the sea, according to Mayol et al. (2017). Moreover, the estimation of the bacterial loads at costal sites could contribute to a better understanding of the air-sea exchanges. In the current study, the 16S rRNA gene metabarcoding approach has been used to characterize the bacterial communities of PM10 samples regularly collected from January to June 2018 at a coastal site in the Central Mediterranean. Airborne bacteria are more likely attached to other particles, such as soil, or found as agglomerate of many bacterial cells, as mentioned. Consequently, the PM10 fraction, i.e. the particles

with aerodynamic diameter ≤10 μm, was selected. The study site is away from large pollution sources and significantly affected by the air mass transport from the surrounding countries, as several studies have demonstrated (e.g., Perrone et al., 2013, 2015, 2019). Therefore, it is ideal to fulfill the aims of the experiment design: (1) deeply characterize the biodiversity of aerosolized bacterial communities collected away from heavy polluted sites, (2) explore the impact of meteorology, seasons, and advection of long range-transported air masses to identify the potential factors responsible for the bacterial community structure. 2. Materials and methods 2.1. Sample collection and meteorological parameters The PM (particulate matter) samples have been collected at about 10 m above the ground level, on the roof of the Mathematics and Physics Department of the University of Salento, which is located in a suburban site (40.3°N; 18.1°E) of the flat Salento's peninsula (Figs. SI1 and SI2 of the Supporting Information (SI) file). The sampling was performed with a low volume (2.3 m3 h−1) HYDRA-FAI dual-sampler that made it possible to simultaneously collect PM10 and PM2.5 particles on two different 47-mm-diameter PTFE (polytetrafluoroethylene) filters (TEFLO W/RING 2 μ from VWR International S.R.L.) that showed excellent collection efficiency according to Burton et al. (2007). Ten samples were collected both in winter and in spring (Table 1). The PM10 mass concentrations of this study are in reasonable agreement with previous data (e.g., Pietrogrande et al., 2018; Perrone et al., 2018). Measurements from a local meteorological station, co-located in space and time with the PM samplings, have been used to calculate mean values of temperature (T), relative humidity (RH), vapor pressure (VP), atmospheric pressure (P), wind direction and speed (WD and WS, respectively), and cumulative rain (CR) during the PM10 sampling time (Table 1). The operator used masks and sterile tweezers during sampling to minimize contamination risks. The PM samples have been collected from January to June 2018 by performing 72- or 48-hour long samplings, once a week. We tested different sampling times to investigate the sensitivity of the 16S metabarcoding analysis for the detection of airborne bacterial communities collected in the sampled PM mass, by assuming that the bacterial growth or decay was negligible during the sampling time. After sampling, each filter was put in a sterile box and stored at −20 °C, since the bacterial growth was unlike at such temperature, according to Mykytczuk et al. (2012). Two control filters, which were not subjected to sampling, but handled and stored in accordance with the same procedure applied to sampled filters, have been used as negative control. 2.2. DNA extraction and 16S rRNA gene metabarcoding Bacteria and debris were recovered from the PM10 filters in aseptic conditions, as described by Radosevich et al. (2002). In more detail, the filter was cut into 10–15 strips, using sterile scissors and tweezers, and placed in a 50 ml conical Falcon tube containing 40 ml phosphate buffer Tween solution (PBT: 0.003% Tween-20, 17 mmol l−l KH2PO4 and 72 mmol l−l K2HPO4). The Falcon tube containing the filter strips in buffer was vortexed for 5 min at maximum power and sonicated at room temperature. The tube was vortexed for an additional 5 s and the suspension was poured into a clean Falcon tube. The wash was repeated with an additional 40 ml PBT to remove any residual material from the filter. Both sample washes were centrifuged for 30 min at 3500g to recover bacteria. The supernatant fluids were immediately drawn off and discarded. The pellets were processed for DNA extraction using the DNeasy PowerSoil kit (Qiagen), according to the manufacturer's specifications. Eluted DNA was precipitated in ice-cold 100% ethanol and sodium acetate and then resuspended in 10 mM TrisHCl, pH 8. Extracted DNA was sent to Genomix4life S.R.L. (Baronissi, Salerno, Italy) for library preparation and sequencing of the V3 and V4 region

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Table 1 PM10 mass concentration, PM2.5/PM10 mass ratio, and meteorological parameters related to the 20 analyzed samples. The sampling time (ST) started at 08.00 UTC of the indicated start date. T, RH, VP, P, WD, and WS show the mean value of air temperature, relative humidity, vapor pressure, atmospheric pressure, wind direction and speed, respectively, during the sampling time interval. CR provides the cumulative rain. The mean values (± standard deviation) of the analyzed parameters in winter (S1–S10) and in spring (S11–S20) are reported in the last two rows of the table. Only for CR, the total sum for winter and spring is reported instead of the mean values. Sample

Start date (UTC)

S1 23/01/18 S2 30/01/18 S3 06/02/18 S4 13/02/18 S5 20/02/18 S6 01/03/18 S7 06/03/18 S8 13/03/18 S9 20/03/18 S10 27/03/18 S11 08/05/18 S12 15/05/18 S13 22/05/18 S14 26/05/18 S15 29/05/18 S16 05/06/18 S17 09/06/18 S18 13/06/18 S19 18/06/18 S20 20/06/18 Winter (mean ± SD) Spring (mean ± SD)

ST (h)

PM10 (μg m−3)

PM2.5/PM10

T (°C)

RH (%)

VP (mbar)

P (mbar)

CR (mm)

WD (deg)

WS (ms−1)

72 72 72 72 72 72 72 72 72 72 48 48 48 48 48 48 48 48 48 48

23 26 33 18 12 23 17 18 12 21 18 12 24 22 26 21 20 15 16 18 20 ± 6 19 ± 4

0.99 0.71 0.41 0.65 0.50 0.16 0.48 0.31 0.40 0.79 0.58 0.34 0.55 0.67 0.66 0.49 0.43 0.55 0.79 0.69 0.54 ± 0.25 0.58 ± 0.13

6.8 10.3 11.3 7.2 10.2 12.9 12.3 12.4 9.7 11.1 17.9 17.0 19.9 22.5 23.5 23.4 23.0 21.7 24.5 24.7 10.4 ± 2.1 21.8 ± 2.7

75 86 81 80 92 80 76 75 84 79 89 66 84 77 76 72 70 74 73 72 81 ± 5 75 ± 7

9.9 12.5 13.4 10.2 12.4 14.9 14.3 14.4 12.0 13.2 20.5 19.3 23.1 27.1 28.9 28.7 28.1 25.9 30.7 31.0 12.7 ± 1.7 26.3 ± 4.1

1022.6 1018.6 1009.5 1008.9 1000.6 1000.8 1003.7 1008.3 1001.8 1010.5 1003.3 1011.9 1011.0 1014.0 1011.5 1008.8 1007.8 1001.3 1009.5 1012.7 1008.5 ± 7.4 1009.2 ± 4.1

0.4 0.0 2.4 0.0 16.2 0.2 10.0 2.8 38.4 0.8 27.9 0.0 26.3 0.0 0.0 4.0 0.6 5.8 0.0 0.0 71.5 64.6

338 180 155 323 127 172 205 192 259 164 194 203 355 348 352 291 322 318 357 330 196 ± 24 326 ± 26

2.5 3.1 3.6 4.2 2.8 4.8 3.8 3.0 4.5 2.3 1.4 2.1 1.9 3.1 1.7 2.2 3.0 2.5 3.4 2.2 3.5 ± 0.9 2.4 ± 0.6

of the 16S rRNA gene, quality control, and bioinformatics analysis. Final yield and quality of extracted DNA were determined using NanoDrop ND-1000 spectrophotometer (Thermo Scientific, Waltham, MA) and Qubit Fluorometer 1.0 (Invitrogen Co., Carlsbad, CA). PCR amplifications were performed with primers: Forward: 5′-CCTACGGGNGGCWGCAG3′ and Reverse: 5′-GACTACHVGGGTATCTAATCC-3′, which target the hypervariable V3 and V4 region of the 16S rRNA gene (Klindworth et al., 2013). No amplification product was observed in the negative control. Each PCR reaction was assembled according to 16S Metagenomic Sequencing Library Preparation (Illumina, San Diego, CA). Libraries were quantified using Qubit fluorometer (Invitrogen Co., Carlsbad, CA) and pooled to an equimolar amount of each index-tagged sample to a final concentration of 4 nM, including the Phix Control Library (Illumina; expected 25%). Pooled samples were subject to cluster generation and sequenced on MiSeq platform (Illumina, San Diego, CA) in a 2 × 250 paired-end format at a final concentration of 10 pmol. The generated raw sequence files underwent quality control analysis with FastQC. Chimeric sequences were identified by the UCHIME algorithm, included in GreenGenes SQL, and removed from the dataset (Edgar et al., 2011). Taxonomic classification of 16S rRNA targeted amplicon reads was performed using ClassifyReads, a high-performance naïve Bayesian classifier of the Ribosomal Database Project (RDP) (Wang et al., 2007; http://rdp.cme.msu.edu/, Roura et al., 2017) available within the Illumina metagenomic analysis software 16S Metagenomics on BaseSpace. In the original RDP classifier, classification was performed to the genus level, in ClassifyReads it has been extended to the species level (https://basespace.illumina.com). ClassifyReads uses a 32-base word-matching strategy to determine the percentage of shared words between a query and the Greengenes taxonomy database (greengenes.secondgenome.com/downloads). This database is currently based on a de novo phylogenetic tree of 408,135 quality-filtered complete sequences calculated using FastTree (McDonald et al., 2012). Taxonomy was assigned to each read by accepting the Greengenes taxonomy string of the best matching Greengenes sequence (127,741 complete bacterial sequences; Werner et al., 2012). The RDP classifier uses a bootstrapping method of randomly subsampling the words in the sequence to determine the classification confidence (Wang et al., 2007). In ClassifyReads, there is no bootstrapping

procedure. This change is primarily due to performance reasons (bootstrapping slowed the algorithm down by 20–50×) and weak correlation between the resulting confidence estimate and the actual classification accuracy. In ClassifyReads, the classification confidence is statistically assigned based on the overall accuracy of the classification algorithm at different taxonomic levels (100% for Kingdom, 100% for phylum, 100% for class, 99.98% for order, 99.97% for family, 99.65% for genus, 98.24% for species). Reads that did not match a reference sequence were considered as unclassified and were also included in the community analysis (Werner et al., 2012; Roura et al., 2017). A total of 7,317,407 reads (138,055–1,537,025 per sample) was assigned to 1738 OTUs (with N0.01% within-sample abundance) at the threshold of 97% sequence similarity (Edgar, 2013). Due to the nature of the 16S rRNA gene, sequences from an organellar origin, such as mitochondria and/or chloroplast DNA, could represent a source of contamination. V3-V4 primer pairs completely eliminate the co-amplification of mitochondria DNA sequences, but retrieve a significant fraction of chloroplast (Beckers et al., 2016). Usually, a high abundance of Cyanobacteria at the phylum level, unidentified at the genus level, in raw sequencing results, could indicate the occurrence of contaminating sequences from chloroplast. These potential contaminating sequences were not removed from the raw dataset, because this might not reveal the real microbiome structures of samples (Beckers et al., 2016; Wang et al., 2018). The within-sample relative abundance (RA) of Cyanobacteria phylum, unidentified at the down taxonomic levels, is reported in Fig. SI3. 2.3. Biodiversity and dissimilarity indices The Shannon index (H′, also termed Shannon-Wiener index) and the Simpson (D) index have commonly been used to quantify and compare biodiversity among samples (Escobar-Zepeda et al., 2015), which depends on the number of present species (species richness), and their relative abundances (termed dominance or evenness). The Shannon index accounts for both richness and evenness of the present species and it is calculated as follows: H0 ¼ −Σi pi ln pi where pi is the proportion of individuals found in species i. For a well-

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sampled community, we can estimate this proportion as pi = ni/N, where ni is the number of individuals in species i and N is the total number of individuals in the community (Krebs, 2014). H′ increases as both the richness and the evenness of the community increase. The most common dominance measure is the Simpson's index (D), which is based on the probability of any two individuals randomly selected from an infinitely large community belonging to the same species: D ¼ Σi ðpi Þ2 D is a measure of dominance, so as D increases, diversity (in the sense of evenness) decreases. The value of D ranges between 0 and 1, where 1 describes a non-diverse community (the probability that any two individuals will be of the same species is 1), whereas 0 describes an infinitely diverse community (Magurran, 2004). The Bray-Curtis dissimilarity indices (BC) have commonly been used to determine the dissimilarity between the species of two samples. It can be calculated by the following formula: BC i; j ¼j Si −S j j = Si þ S j




where i and j identify the two samples and Si and Sj represent the total number of species counted in sample i and j, respectively. The BrayCurtis dissimilarity index is always a number between 0 and 1. If BC = 0, the two samples share all the same species; if BC = 1, they do not share any species. 2.4. Advection patterns Desert dust from northern Africa, polluted particles from urban and industrial areas of North and East Europe, marine aerosols from the Mediterranean Sea and the Atlantic Ocean, and biomass burning particles from forest fires affect the aerosol load over the Central Mediterranean basin and at the study site, as several studies have demonstrated (e.g., Basart et al., 2009; Perrone et al., 2014, 2015, 2019; Mallet et al., 2016; Pietrogrande et al., 2018). Perrone et al. (2015) identified main airflows at the study site by using the 4-day backtrajectories from the HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model version 4.8, from NOAA/ARL (https://www.ready.noaa.gov/; Stein et al., 2015), as shown in Fig. SI2. In addition to the backtrajectory pathways, data from the NMMB/BSC-Dust model (Haustein et al., 2012; https://www.bsc.es/) have also been used to support the advection of dust particles at the study site. 3. Results and discussion 3.1. PM10 mass concentration and local meteorological parameters The Principal Component Analysis (PCA) of the normalized meteorological factors allowed identifying by 95% confidence ellipses two separate clusters representative of samples collected in winter (S1–S10) and spring (S11–S20), as depicted in Fig. 1, where the principal components P1 and P2 explained 53.1% and 27.6% of the variance, respectively. T and VP were the parameters that significantly varied from winter to spring (Table 1) and likely contributed to the sample spread depicted in Fig. 1. Temperature is strongly correlated with VP (e.g., WMO, 2008). Samples S5, S9, S11, and S13, which are detached from the other samples, were collected on heavy rain days (Table 1). The Pearson's correlation coefficients (r) between meteorological parameters, PM10 mass concentrations, and PM2.5/PM10 mass ratios are shown in Table SI1a and SI1b of the Supplementary Information (SI) file for winter and spring, respectively. T was anti-correlated with WD and PM10 was anti-correlated with CR (Table SI1a) suggesting that rain was responsible for the PM10 decrease in winter. In contrast, in spring, T and RH were correlated with WD and CR, respectively (Table SI1b). The PM2.5/PM10 mass ratio increased with pressure in

Fig. 1. Two-dimensional Principal Component Analysis (PCA) of normalized meteorological parameters for the 20 analyzed samples. Winter and spring samples are reported by black and grey circles, respectively. The percentages of the total variance explained by the first and second principal component (P1 and P2, respectively) are also indicated in the plot. The dashed lines represent the 95% confidence ellipses.

winter, which likely favored the accumulation of fine particles at the surface, and decreased with WS, which favored mainly the dispersion of fine mode particles (Perrone and Romano, 2018). In contrast, in spring, the PM2.5/PM10 mass ratio increased with T, since T increases with the increase of the solar radiation reaching the Earth surface, which mainly favors the formation of new fine mode particles (Dulac et al., 2016). Note that the seasonal changes of the meteorological conditions at the study site were quite similar to the ones of the Mediterranean basin (Dulac et al., 2016) because of its geographical location. In fact, the decrease of windy and rainy days and the increase of sunny days and the mean air temperature in spring-summer, all over the Mediterranean and at the study site (Perrone et al., 2015; Pietrogrande et al., 2018; Dulac et al., 2016), have likely contributed to the results reported in Fig. 1 and Table SI1a–b. 3.2. Overview of the phylum bacterial communities The microbial community detected in each sample was evaluated by the Operational Taxonomic Units (OTU), as mentioned, which can be considered as representative of the bacterial community richness. Table 2 provides the number of OTUs and phyla in the 20 independent PM10 samples (S1–S20). The last two lines of Table 2 show the mean value (±1 SD) of the OTU and phylum number for the winter (S1– S10) and spring (S11–S20) samples. Fig. 2a shows the mean relative abundance (±1 SD) of the 17 classified phyla contributing, each, by a percentage N0.01%. The heat map of the within-sample RA of bacterial phyla is shown in Fig. SI3. Proteobacteria, Cyanobacteria, Actinobacteria, Firmicutes, and Bacteroidetes, which overall contributed by 45.6, 18.8, 11.4, 10.5, and 5.4% (Fig. 2a), respectively, were the most abundant phyla and were found in all samples (Fig. 2b). These results are consistent with previous studies (e.g., Cao et al., 2014; Barberán et al., 2015; Gao et al., 2017; Mayol et al., 2017) and the ones by Mazar et al. (2016), Gat et al. (2017), and Polymenakou et al. (2008), which refer to Mediterranean sites. In general, Actinobacteria, Proteobacteria, Firmicutes, Bacteroidetes and Cyanobacteria could be found at relatively high abundance in various soils and aquatic environments. Therefore, it is expected that the main contribution for airborne bacteria would be from land and sea at the study site, being a coastal location. However, the bacterial community composition as observed at a higher hierarchy might still differ significantly among sites and aerosol samples. In fact, the comparison of Fig. 2b with Fig. 6 of the paper by Li et al. (2018), who reported the RA of the airborne bacterial phyla detected in

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Table 2 Number of operational taxonomic units (OTUs), phyla, orders, and genera for the 20 analyzed samples. Shannon and Simpson indices at the phylum and genus level are also reported. The sampling time (ST) started at 08.00 UTC of the indicated start date. The mean values (± standard deviation) of the analyzed parameters in winter (S1–S10) and in spring (S11–S20) are reported in the last two rows of the table. Sample

Start date (UTC)

S1 23/01/18 S2 30/01/18 S3 06/02/18 S4 13/02/18 S5 20/02/18 S6 01/03/18 S7 06/03/18 S8 13/03/18 S9 20/03/18 S10 27/03/18 S11 08/05/18 S12 15/05/18 S13 22/05/18 S14 26/05/18 S15 29/05/18 S16 05/06/18 S17 09/06/18 S18 13/06/18 S19 18/06/18 S20 20/06/18 Winter (mean ± SD) Spring (mean ± SD)

ST (h)

n° OTUs

n° phyla

72 72 72 72 72 72 72 72 72 72 48 48 48 48 48 48 48 48 48 48

337 360 887 250 147 538 104 26 261 285 306 756 738 636 721 839 812 995 652 344 320 ± 240 680 ± 210

11 12 19 11 8 16 10 7 7 10 12 17 18 18 20 19 19 21 18 14 11 ± 4 18 ± 3

At phylum level Shannon index

Simpson index

1.16 1.21 1.72 1.22 0.63 1.55 0.89 0.63 1.03 1.29 1.01 1.53 1.56 1.67 1.74 1.69 1.61 1.73 1.55 0.95 1.13 ± 0.36 1.50 ± 0.29

0.46 0.43 0.21 0.38 0.72 0.26 0.57 0.71 0.51 0.39 0.56 0.29 0.27 0.23 0.24 0.25 0.28 0.24 0.32 0.55 0.46 ± 0.17 0.32 ± 0.13

response to haze events in Beijing (China), shows both that the dominant phyla were the same in both sites and that the phylum RAs were characterized by similar variability ranges. Nevertheless, the bacteria structure at the genus level was rather different in the two sites. One must also be aware that the geographical location and meteorology of the monitoring site, which affect the bacterial dispersion, may significantly contribute to the day-by-day changes of the bacterial community composition, as outlined by Barberán et al. (2015). Anyhow, the atmosphere plays a fundamental role in the dispersal of bacteria across the planet contributing to the worldwide homogenization of the most abundant bacterial communities. Fig. 3 presents by a bar-chart OTU number and phylum-based Shannon and Simpson indices per sample, highlighting the marked day-byday change of the three parameters in the winter samples (S1–S10) and their weak variability in the spring samples (S11–S20). Note also that the phylum-Shannon index follows the day-by-day change of OTU, to which is strongly correlated (r = 0.92). The OTU number per sample varied from 26 (sample S8) to 995 (sample S18) and its mean value increased more than twice from winter to spring (Table 2), suggesting that the sample richness on average increased from winter to spring. The Shannon index value varied from 0.63 (S5 and S8) to 1.74 (S15) and its mean value changed from 1.13 to 1.50 from winter to spring. The phylum-Simpson index varied from 0.72 (S5) to 0.21 (S3) and its mean value decreased from 0.46 to 0.32 from winter to spring. Therefore, Shannon and Simpson indices on average increased and decreased, respectively, from winter to spring and showed that richness and biodiversity were highest in sample S3 (Table 2). In winter, the greater occurring of low air temperatures, cloudy, wet, and stormy days has likely contributed to the marked day-by-day change of the richness and biodiversity of the collected samples (S1–S10), since atmospheric stressors likely contributed to the decrease of the bacteria atmospheric residence time. In contrast, the more stable meteorological conditions occurring in spring-summer all over the Mediterranean basin, which favored the air mass aging, enhanced natural and anthropogenic dust resuspension and limited the removal of atmospheric particles by wet deposition, likely contributed to the increase of the richness and biodiversity of samples S11–S20. Du et al. (2018) also observed that species richness and community diversity of bacteria were dependent on the season. It is noteworthy that previous studies have shown that the chemical composition and the ability to form oxygen

n° orders

n° genera

75 74 102 75 67 88 66 55 66 68 77 95 94 79 97 94 96 100 90 85 74 ± 13 91 ± 8

154 163 397 119 62 253 50 14 95 125 138 355 339 284 343 364 360 441 316 177 143 ± 111 312 ± 91

At genus level Shannon index

Simpson index

2.48 2.28 2.11 1.68 1.85 2.18 1.06 0.66 2.27 2.40 2.52 2.07 2.19 2.26 2.21 2.38 2.24 2.13 2.46 2.44 1.90 ± 0.60 2.29 ± 0.15

0.13 0.15 0.18 0.31 0.23 0.17 0.52 0.64 0.15 0.14 0.11 0.20 0.18 0.19 0.20 0.16 0.19 0.22 0.16 0.11 0.26 ± 0.18 0.17 ± 0.04

reactive species of PM10 particles collected at the study site significantly varied from autumn-winter to spring-summer (e.g., Perrone et al., 2015; Pietrogrande et al., 2018), in accordance with the results shown in Figs. 2 and 3. 3.3. Class, order, and family overview in the PM10 samples The class, order, and family heat maps are shown in Figs. SI4a, SI5, and SI6a, respectively. The corresponding within-sample RAs of the most abundant bacterial classes and families are also reported in Figs. SI4b and SI6b, respectively. The number of orders in the 20 independent PM10 samples (S1–S20) is given in Table 2. The mean RA (± 1 SD) of the 13 orders contributing each with a RA N 2% is shown in Fig. 4a. Fig. 4b shows the corresponding within-sample RAs. Stigonematales, Actinomycetales and Pseudomonadales were the most abundant orders contributing by 16.43, 11.91, and 11.32%, respectively. The similarity between sample S7 and S8 and their dissimilarity with respect to all the other samples are also highlighted by Fig. 4b. 3.4. Genera overview in the PM10 samples Seventy-nine predominant genera, which overall contributed, each, by a percentage N0.01% were identified in the collected PM10 samples. The corresponding heat map is shown in Fig. SI7. The mean RA (±1 SD) of the 12 genera contributing each with a RA N 1% is shown in Fig. 5a, while Fig. 5b shows the corresponding within-sample RAs. Presence and RA of each genus considerably varied in the analyzed samples (Figs. 5b and SI7). High percentages (N5% within-sample RA) of some detected genera were specifically associated with one or two samples: marine Vibrio (S9 and S10), Providencia (S19), aquatic Enhydrobacter (S9), Hymenobacter (S1), Trabulsiella (S20), obligate intracellular Rickettsia (S4), Hyphomicrobium (S10 and S11), Propionibacterium (S16). The increase in RA of “Other” bacterial genus is in accordance with the observed increase in diversity in spring samples. Table 2 shows that the mean value of the number of genera per sample, which is equal to 143 in the winter samples (S1–S10), increased more than twice in the spring samples (S11–S20). The mean value of the Shannon index at the genus level that was equal to 1.90 in the winter samples (S1–S10) increased up to 2.29 in the spring samples (S11– S20). Similarly, the mean value of the Simpson index at the genus

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Fig. 2. (a) Mean percentage contribution (on a logarithmic scale) for the 17 most abundant (N0.01% mean within-sample relative abundance) bacterial phyla and (b) their relative percentage contribution in each sample. The error bars in (a) represent the standard error of the mean. The b0.01% mean within-sample relative abundance and unclassified bacterial phyla (denoted as “Other” and “Unclassified”, respectively) are also represented in each plot.

Fig. 3. Number of predominant (N0.01% within-sample abundance) Operational Taxonomic Units (OTUs, bars with black diagonal lines), in addition to Shannon (grey bars) and Simpson index (bars with black horizontal lines), both at the phylum level, as a function of the PM10 sample.

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Fig. 4. (a) Mean percentage contribution (on a logarithmic scale) for the 13 most abundant (N2% mean within-sample relative abundance) bacterial orders and (b) their relative percentage contribution in each sample. The error bars in (a) represent the standard error of the mean. The b2% mean within-sample relative abundance and unclassified bacterial orders (denoted as “Other” and “Unclassified”, respectively) are also represented in each plot. The corresponding phyla related to each order are also reported in (a).

level decreased from 0.26 in winter to 0.17 in spring (Table 2). Therefore, these last results show that richness and biodiversity of the bacterial communities increased in spring at the study site, as found at the phylum level. The Bray-Curtis indices for the 20 analyzed PM10 samples are depicted in Fig. 6 by the Non-metric MultiDimensional Scaling (NMDS) plot. The Bray-Curtis dissimilarity matrix is given in Table SI2. The comparison of Fig. 6 and Table SI2 highlights that the dissimilarity between two samples on average increases with their distance in the NMDS plot. Sample S8 and S7, which are close in the NMDS plot, are characterized by the highest similarity (BC7,8 = 0.10, Table SI2). In contrast, both samples are detached from all the other samples suggesting that the S7 and S8 bacterial community features were different from those of the other samples (Fig. 6 and Table SI2). Fig. 6 also suggests that the samples in the cluster located in the central-lower part of the NMDS plot share similar bacterial community features. Note also that most of the winter and spring samples are located in the upper and lower part of the NMDS plot, respectively, likely because of the change of the bacterial community properties from winter to spring, as previously outlined. 3.4.1. Relationships between meteorological and bacterial community parameters and genera The relationships between meteorological parameters, PM10 mass concentrations, PM2.5/PM10 mass ratios, total number of OTUs, orders, genera and phyla, Shannon and Simpson indices, both at the phylum and at the genus level, and the relative abundance of the 79 identified genera have been investigated in the spring and winter samples. A better understanding of the bacterial community differences between samples also highlighted in Fig. 6 and Table SI3 represented the main goal of

the analysis. Results are shown in Tables SI3a and SI3b for the winter and spring samples, respectively, by the Pearson's correlation coefficients (r), where statistical significant r-values at the p-level b 0.05 based on the two-tailed t-test are marked in bold. The number of phyla, orders, genera and OTUs did not show any significant correlation with local meteorological parameters for the winter and spring samples (Table SI3a–b, respectively). In contrast, they were correlated with the PM10 mass concentration only in winter, suggesting that the sample richness likely increased with the PM10 mass concentration in that season. The OTU and genus number were weakly anticorrelated with the PM2.5/PM10 mass ratio in spring (Table SI3b). The Shannon and Simpson indices at the genus and phylum level did not show any significant correlation with local meteorological parameters both in winter and in spring. The phylum-Shannon (phylum-Simpson) index was correlated (anti-correlated) with the OTU, phylum, order, and genus number in winter and spring, respectively, and with the PM10 mass only in winter. In contrast, the genus-Shannon (genusSimpson) index was anti-correlated (correlated) with the OTU, phylum, order, and genus number in spring. Both indices did not show any significant correlation with the above parameters in winter. The relationships of meteorological parameters, richness and biodiversity indices with the identified genera varied from winter to spring, as well as the relationships between different genera. As an example, Calothrix that reached the highest mean percentage (16.84%) in the analyzed samples (Fig. 5a) was significantly correlated with the genusSimpson index in winter and spring, suggesting that its RA on average increased with the decrease of the sample biodiversity. In fact, Calothrix reached the highest RA in S8, whose genus-Simpson index was equal to 0.64 (Table 2). In contrast, the lowest Calothrix RAs were found in S1 and

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Fig. 5. (a) Mean percentage contribution (on a logarithmic scale) for the 12 most abundant (N1% mean within-sample relative abundance) bacterial genera and (b) their relative percentage contribution in each sample. The error bars in (a) represent the standard error of the mean. The b1% mean within-sample relative abundance and unclassified bacterial genera (denoted as “Other” and “Unclassified”, respectively) are also represented in each plot. The corresponding phyla related to each genera are also reported in (a).

S20 characterized by a genus-Simpson index equal to 0.13 and 0.11, respectively (Table 2 and Fig. SI7). Calothrix was significantly correlated only with the genera Desulfuromusa and Thermogematispora in winter. In contrast, in spring, it was correlated with many genera (e.g., Hymenobacter, Rubrobacter, Sphingomonas, Acidisphaera) and anti-correlated in particular with Pseudomonas, Enterobacter, and

Fig. 6. NMDS (Non-metric MultiDimensional Scaling) ordination plot based on the BrayCurtis dissimilarity matrix of the bacterial genera relative abundance for the 20 analyzed samples. Winter and spring samples are reported by black and grey circles, respectively.

Chryseobacterium. This last result is likely because Proteobacteria (Pseudomonas and Enterobacter) and Bacteroidetes (Chryseobacterium) are widely distributed in both terrestrial and aquatic environments, in contrast to Cyanobacteria (Calothrix), whose increase in coastal environments is often associated with intense algal bloom, although they can occupy a broad range of habitats. Pseudomonas (9.23% mean relative percentage) was significantly correlated with Enterobacter, Arthrospira, Trabulsiella, Escherichia, Curtobacterium, and Providencia in winter. Many other genera (e.g., Chryseobacterium, Acinetobacter, Brevundimonas, and Sphingobium) were correlated with Pseudomonas in spring, in addition to Enterobacter, Arthrospira, and Trabulsiella. In addition, Pseudomonas that was anti-correlated with Calothrix, as mentioned, was also anticorrelated with genera and parameters correlated with Calothrix. Consequently, in spring, the Pseudomonas RA increased with the sample biodiversity. In fact, the highest Pseudomonas RAs were reached in S11 and S20, both characterized by genus-Simpson indices equal to 0.11. Bacillus, the third most abundant genus (4.27% mean relative percentage), was correlated in winter with the PM10 mass concentration and the OTU and genera number. Moreover, it was correlated with the following genera: Pseudonocardia, Kaistobacter, Paenibacillus, and Nocardioides. In spring, the Bacillus RA was anti-correlated with temperature and, hence, with the PM2.5/PM10 mass ratio and correlated with the following genera: Microcoleus, Escherichia, Erwinia, Candidatus scalindua, and Gluconacetobacter. Several works have highlighted the seasonal variations of the bacterial community properties. At an urban monitoring station in Beijing (China), Du et al. (2018) found that the variations of the bacterial

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community composition and structure were significantly related to the season, but there was no correlation between their abundance and pollution levels. Uetake et al. (2019) evaluated that the seasonal variations of humidity and wind speed were key factors affecting bacterial diversity and, hence, they appeared to be the controlling factors on emissions of bacteria from bay sea water and soil around the study site in Tokyo (Japan). Cáliz et al. (2018) evaluated the atmospheric microbes (bacteria, archaea, protists, and fungi) on samples collected in a high-elevation Long-Term Ecological Research (LTER) Network site in the Central Pyrenees (northeastern Spain) over a 7-year period. They found consistent microbial seasonal patterns with highly divergent summer and winter bacterial communities. In more detail, airborne bacterial communities showed a consistent seasonal trend with the highest diversity indices found in winter, conversely to what was found in our study due to the different site features and meteorological conditions. In conclusion, Tables SI3a–b have highlighted that the bacterial community properties and their inter-genera relationships also varied from winter to spring. However, we are aware that many of the indicated relationships are due to cross-correlations, that is, the high diversity observed in spring, resulting in a different bacterial community composition, is likely to be correlated with the bacterial genera that characterize these samples, but all are a result of the same set of conditions. 4. PCA analysis and impact of long-range transported air flows The PCA was applied to the normalized meteorological parameters (with the exception of VP), OTUs number and genera RA to better identify the factors likely responsible for the richness, biodiversity, and structure similarities/dissimilarities of the airborne bacterial communities collected in the 20 PM10 samples. PCA results are depicted in Fig. 7, where the principal components P1 and P2 explained 39.3% and 21.2% of the variance, respectively. Note that the normalized phyla and genera number, and the Shannon and Simpson indices have not been included in the PCA because of their negligible impact. Two main clusters representative of the winter (S1–S10) and spring (S11–S20) samples located on the left and the right side of the P1 = 0 axis, respectively, are highlighted by Fig. 7, in accordance with previous results/comments, which have shown that the bacterial community properties varied from winter to spring. Note that the samples collected in winter are more spread along the P2 axis than those collected in spring. The larger

Fig. 7. Two-dimensional Principal Component Analysis (PCA) of normalized meteorological parameters (with the exception of vapor pressure), OTUs number, and genera relative abundance for the 20 analyzed samples. Winter and spring samples are reported by black and grey circles, respectively. The percentages of the total variance explained by the first and second principal component (P1 and P2, respectively) are also indicated in the plot.

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day-by-day variability of sample richness, biodiversity, and/or similarity/dissimilarity (Fig. 6) in winter likely contributed to this result. Some detached samples and clusters consisting of a few samples can also be detected in Fig. 7. The Canonical Correspondence Analysis (CCA) plot (Fig. SI8) has led to a data clustering similar to that retrieved from the PCA plot (Fig. 7).

4.1. Heavy rainy day samples: S5, S9, S11, and S13 Samples S5, S9, S11, and S13 (Table 1), each of which is detached from the other three samples in the PCA plot (Fig. 7), were collected in sampling days affected by heavy rain (16 mm ≤ CR ≤ 38 mm) and characterized by high RHs (≥ 84%). The Simpson index at the genus level that varied within the 0.11–0.23 range (Table 2) indicates that the four samples were all characterized by high biodiversity. The NMDS plot also reveals a high dissimilarity between them (Fig. 6). Proteobacteria was the dominant phylum of sample S5, S9, and S11, contributing by 84.28 (the highest within-sample RA), 69.13, and 73.54%, respectively. Note that Amato et al. (2017) found that Proteobacteria was on average the dominant phylum in cloud water samples. However, the genus structure was rather different in S5, S9, and S11 (Fig. 5b). Pseudomonas (36.07%), Enterobacter (26.72%), and Stenotrophomonas (8.94%), which can be found in soil, water, and in living organisms, were the dominant Proteobacteria genera, and reached the highest RA in S5 (Fig. 5b). The second most abundant phylum was Cyanobacteria (6.74%) with the genus Calothrix (6.35%), which can be found in soil and aquatic environment. The pathways of the 4-day analytical backtrajectories from HYSPLIT that reached the study site at 271, 500, and 1000 m above the ground level (AGL) and at 12:00 UTC of each sampling day are shown in Fig. SI9 for (a) 20, (b) 21, and (c) 22 February 2018. Fig. SI9a–c shows that the air masses spent most of the time over the central and western Mediterranean Sea before reaching the study site. Therefore, the advection of air masses from the sea likely contributed to the Pseudomonas, Enterobacter, Stenotrophomonas, and Calothrix RA, being all these genera widely distributed in coastal areas. Vibrio, whose habitat is aquatic environment, was the dominant genus in sample S9, in which also reached the highest RA (30.75%). Then, Pseudomonas, Enhydrobacter, and Enterobacter contributed by 11.2, 7.8, and 3%, respectively. Moreover, Cyanobacteria Calothrix and Arthrospira contributed by 14.75 and 1.39%, respectively. Proteobacteria Pseudomonas, Enterobacter, Hyphomicrobium, and Acinetobacter contributed by 21.49, 13.40, 6.44, and 4.19%, respectively, in S11, while the Calothrix and Arthrospira RA was 3.39 and 6.37%, respectively (Fig. SI5b). The pathways of the 4-day backtrajectories that reached the study site throughout the S9 and S11 sampling days are shown in Figs. SI10 and SI11, respectively. Fig. SI10a–c shows that air masses from the Atlantic that crossed the Mediterranean Sea on average reached the study site during the S9 sampling days. In contrast, Fig. SI11a–b shows that on average air masses at first from northeastern Europe and then from the southern Mediterranean were advected at the study site during the S11 sampling time. The different pathway of the long-range transported air masses that reached the site during the S9 and S11 sampling time likely contributed to the different bacterial communities found in these two samples. The highest RA of Actinobacteria Streptomyces (31.26%), which can be found in soil, was reached in S13. The Streptomyces RA was smaller than 13% in all the other samples of this study. The other dominant genera (RA N 2%) in S13 were Calothrix (7.17%), Sphingomonas (6.53%), Hyphomicrobium (2.18%), and Bacillus (2.37%). Soil is also the Firmicutes Bacillus habitat. The bacterial community structure of S13 is rather similar to that of the samples collected by Gat et al. (2017) during African and Syrian dust events at Rehovot (Israel). Köberl et al. (2011) have demonstrated that in Egyptian and Negev desert soils that were claimed for agriculture, the Actinobacteria RA decreased, while that of

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Firmicutes increased, compared with pristine non-agricultural desert soils. The pathways of the 4-day backtrajectories (Fig. SI12) show that the air masses spent most of the time over the southeastern Mediterranean Sea before reaching the study site and likely they were responsible for the transport of terrestrial bacteria in addition to the ones due to aquatic environment. In fact, Mayol et al. (2017) results showed a substantial load of terrestrial microbes transported over the oceans with abundances declining exponentially with distance from land. It is also worth mentioning that advection patterns similar to the one of Fig. SI12 are unusual at the study site, according to previous studies (Santese et al., 2008; Perrone et al., 2014). 4.2. Desert dust day samples: S3, S6, and S12 Samples S3 and S6, which are rather close in the PCA plot (Fig. 7), deserve special attention being the winter samples with the highest OTU number (Table 2) and characterized by a high similarity (Fig. 6). The Simpson index at the genus level that is equal to 0.18 and 0.17 in S3 and S6, respectively, suggests that both samples were characterized by a rather high biodiversity (Table 2). Results from the NMMB/BSC-Dust model (Fig. SI13a–c) indicated that the Central Mediterranean basin was affected by an African dust outbreak on 6–8 February 2018 and that sample S3 was likely hugely affected by desert dust particles throughout the sampling time. In fact, the modelled dust surface concentration reached the highest value on February 7, at the study site. Backtrajectory pathways (Fig. SI13d–f) support the above comments and show that the 1000 m-arrival-height-backtrajectory crossed northern Africa at quite low heights before reaching the study site at 12:00 UTC of February 7. Sample S6 was mainly affected by African dust on March 2, 2018, according to the NMMB/BSC-Dust model (Fig. SI14b) and the backtrajectory pathways (Fig. SI14d–f). The PM10 mass concentration (Table 1) that reached the highest value in S3 (33 μg m−3) and one of the highest values in S6 (23 μg m−3) also supports the above comments. Strong dust outbreaks were on average responsible for high PM10 mass concentrations at the study site (Perrone et al., 2016). Moreover, Table 1 shows that the PM2.5/PM10 mass ratio was equal to 0.41 and 0.16 in S3 and S6, respectively, likely because of the significant contribution of coarse particles due to desert dust advection. OTU, phylum, and genus numbers, which reached rather high values, and the Shannon and Simpson indices (Table 2) suggest a relationship between richness, high microbiome diversity, and dust events, since dust events providing terrestrial particles to the atmosphere may increase the biodiversity of airborne microbial communities. The results of this study are in satisfactory accordance with the findings by Mazar et al. (2016), Gat et al. (2017), and Polymenakou et al. (2008). In fact, they also found that the samples collected during dust events had a significantly more diverse microbiome than samples collected in dust-free days. Calothrix, Bacillus, Streptomyces and the obligate intracellular Rickettsia were the most abundant genera contributing to samples S3 and S6. Pseudomonas (8.13%) also contributed to S6. The “Other” genera RA was of about 39% and 23% in S3 and S6, respectively, because of the high biodiversity of both samples. The low percentage contribution of Cyanobacteria in the African dust samples collected at Rehovot, Israel (Mazar et al., 2016; Gat et al., 2017) with respect to the samples S3 and S6 may be likely due to the different pathways of the African dust air masses, which spent most of their time over land before reaching Rehovot. In contrast, African dust particles must cross the Mediterranean Sea before reaching the study site. Polymenakou et al. (2008) found, during an intense African dust event in the Eastern Mediterranean, that the spore-forming bacteria, such as Firmicutes, dominated the large-size particles, in accordance with the findings of our study. Moreover, Maki et al. (2017), investigating the airborne bacteria dynamics in the Gobi desert area, found that, after the dust events, members of Firmicutes (Bacilli) and Bacteroidetes, which form endospore

and attach with coarse particles, respectively, increased their relative abundance in the air samples. Consequently, the bacterial community's structures were different between dust and dust-free events. Proteobacteria, which represented the dominant phylum at the study site (Fig. 2a), reached a RA in S3 and S6 (22 and 32%, respectively) significantly smaller than the corresponding mean value (45.56%), likely because of the contribution of the bacteria associated with the coarse particles transported from Northern Africa. Sample S12 is also quite detached from the other samples in the PCA plot (Fig. 7). The OTU number (756), and the genus-Shannon (2.07) and Simpson index (0.20) indicate that the sample bacterial community was characterized by high richness and biodiversity. Bacillus reached the highest RA (32.57%) in S12. The other most abundant genera (RA N 2%) were Calothrix (4.6%), Sphingomonas (3.47%), Escherichia (2.44%), and Gluconacetobacter (2.19%). The 4-day backtrajectory pathways crossed the Mediterranean Sea before reaching the study site during the S12 sampling time (Fig. SI15) and results from the NMMB/BSCDust model reveal a significant African dust load over the central-east Mediterranean Sea on May 15–16, 2018 (Fig. SI16). Therefore, dust particles were likely advected at the study site during the S12 sampling time. In fact, the highest Bacillus RA found in S12 was likely due to the ability of Firmicutes to attach with coarse particles and maintain in the atmosphere for longer time after dust events, according to Maki et al. (2017). The PM2.5/PM10 mass ratio that was equal to 0.34 in S12, because of the significant contribution of coarse mode particles, as dust particles, may support the above comment. The S12 “Other” genera RA (about 39%) was nearly equal to that of S3. However, sample S12 is detached from S3 in both the NMDS (Fig. 6) and the PCA (Fig. 7) plot likely because of the impact of meteorological parameters (Fig. 1): S3 and S12 were sampled in February and May, respectively. 4.3. Marine air samples: S4, S7, and S8 Let us characterize the cluster identified by the samples S4, S7, and S8. Samples S7 and S8 share a similar bacterial community (Fig. 6 and Table SI2). The OTU number and the genus-level Shannon and Simpson indices, which varied within the 26–250, 0.66–1.68, and 0.31–0.64 range, respectively, indicate that all the three samples and in particular S7 and S8 were characterized by a low richness and biodiversity of the bacterial community (Table 2). Calothrix parietina, a microcystin (cyanotoxin)-producing species (Mohamed et al., 2006) generally found in aquatic environments, reached the largest RA in the three samples: 51.07%, 68.24%, and 77.71% in S4, S7, and S8, respectively (Figs. 5b and SI7). Rickettsia was the second most abundant genus contributing by 7.77, 2.07, and 0.55% in S4, S7, and S8, respectively. The genus-level Simpson index (Table 2), which was equal to 0.31, 0.52, and 0.64 in S4, S7, and S8, respectively, showed that the sample biodiversity decreased with the increase of the Calothrix RA, as observed in Section 3.4.1. Note also that the rather small Pseudomonas RA in the three samples (1.34, 1.16, and 0.18% in S4, S7, and S8, respectively) was associated with high Calothrix RA (Section 3.4.1). The pathway of the 4-day analytical backtrajectories in Figs. SI17, SI18, and SI19 shows that air masses that crossed the Atlantic and the Mediterranean Sea were advected to the study site throughout the sampling time of S4, S7, and S8, respectively. Previous studies (e.g., Perrone et al., 2014) have shown that “clean” air masses were on average advected to the study site from the west, in accordance with the results of this study. In fact, low values of PM10 mass concentrations (18, 17, and 18 μg m−3 in S4, S7, and S8, respectively) were found. Likely, these results suggest that Calothrix was mainly advected from the sea to the study site. 4.4. Continental air samples: S1, S2, and S10 The S1, S2, and S10 samples, whose Simpson index at the genus level was 0.13, 0.15, and 0.14, respectively, represent in the PCA diagram

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(Fig. 7) a cluster consisting of high biodiversity samples. S1 and S2 are expected to share a similar bacterial community according to the NMDS plot (Fig. 6 and Table SI2). In contrast, a dissimilar bacterial community characterizes S10, which occupies a detached position with respect to S1 and S2 in the NMDS plot. The quite small Cyanobacteria RA (4.1, 3.0, and 10.67% in S1, S2, and S10, respectively) and the rather high Proteobacteria RA (65.37, 62.61, and 59.52% in S1, S2, and S10, respectively) is a peculiarity of the three samples (Fig. 2b). Actinobacteria, which can be found in soil, was the second most abundant phylum in the three samples (7.2, 16.86, 12.85% in S1, S2, and S10, respectively). Figs. SI20, SI21, and SI22 show the pathways of the 4-day analytical backtrajectories that reached the study site during the S1, S2, and S10 sampling time. The backtrajectory pathways varied with the sampling time, but all the air masses spent several hours over land or close to the land affected by anthropogenic activities before reaching the study site. In fact, fine anthropogenic particles were likely responsible for the high PM2.5/PM10 mass ratios that characterize S1, S2, and S10. Previous studies (Perrone et al., 2013, 2014, 2015) demonstrated that fine mode particles were associated with NNW and NE airflows, since the air masses crossed anthropogenic polluted north and eastern European countries before reaching the study site. These last comments also suggest that Proteobacteria were mainly associated with fine mode particles, as observed by Polymenakou et al. (2008). Main Proteobacteria, Cyanobacteria and Bacteroidetes genera were similar in S1 and S2, although characterized by different RA. In contrast, Geodermatophilus (2.8%) and Rubrobacter (1.8%) were the most abundant Actinobacteria of S1, while Streptomyces (12.61%) and Propionibacterium (0.82%) were the most abundant Actinobacteria in S2. Vibrio (29.41%), which reached one of the highest RA in S10, was not found in S1 and S2. 4.5. Late spring samples: S14, S15, S16, S17, S18, S19, and S20 The samples S14–S20 occupy a detached position in the PCA plot (Fig. 7) with respect to the other investigated samples. The daily means of T and RH, which varied within the 21.7–24.7 °C and 70–77% range, respectively, during their sampling time likely contributed to their clustering. Moreover, the OTU number and the Shannon and Simpson indices indicate that all these samples were characterized by a high richness and biodiversity of the bacterial community. The NMDS plot shows that the S14–S19 samples are characterized by a low dissimilarity, in contrast to S20, which occupies a detached position with respect to the other late spring samples (Fig. 6). A striking result of Fig. 5b is that the RA of the 12 main genera listed in Fig. 5a varied within the 20–31% range in S14–S19, in contrast to the “Other” genera RA that varied within the 54–65% range. We believe that seasonal changes of the meteorological conditions all over the Mediterranean basin and at the study site have likely contributed to this last result. In particular, besides favoring the atmospheric air mass aging, the decrease of stormy, windy, and rainy days and the increase of sunny and warm days in spring likely has also contributed to the decrease of atmospheric stressors helping to maintain for longer time the bacterial community in the atmosphere. Overall, the presented results have suggested that desert dust events and, in general, the advection of long-range-transported air masses as well as meteorological conditions and seasons have contributed to the shaping of the sampled airborne bacterial communities with distinct contributions that could be clearly identified in several samples. As mentioned, the study site that is on a flat and narrow peninsular area of the central Mediterranean is quite affected by the airflows from the surrounding countries and the Mediterranean Sea itself. Long-range transported air masses contribute to the worldwide bacterial community homogenization and the atmospheric transport of bacteria may play a major role in the dispersal of surface marine bacteria as in the intercontinental transport of their terrestrial counterparts, as outlined by

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Mayol et al. (2017). There was evidence that the ocean mainly acted as a sink for airborne bacteria, largely originated from terrestrial sources as He et al. (2017) also showed. 5. Human (opportunistic) pathogenic bacteria characterization The introduction of the next-generation sequencing (NGS) techniques has led to novel applications of the metabarcoding methods. However, NGS techniques have some limitations, mainly due to the shorter sequencing reads that often limit the microbial composition analysis to the species level because of the high similarity of 16S rRNA amplicon sequences (Bukin et al., 2019). Nevertheless, many efforts are in progress to overcome these limitations, mainly in clinical field (e.g., Miao et al., 2017; Castelino et al., 2017; Graspeuntner et al., 2018; Roura et al., 2017; Di Salvo et al., 2019). The species-level OTUs screening reported in the present study was meant to provide some indications on the presumptive presence of potential (opportunistic) pathogenic bacteria in the sampled air masses. The classification confidence at the different taxonomic levels has been reported in Section 2.2. Screening of human (opportunistic) bacteria was carried out by using the ABSA (American Biological Safety Association) International database (https://my.absa.org/), as a reference (Ghaju Shrestha et al., 2017; Hugon et al., 2015). The database was integrated with the opportunistic Acinetobacter species and consisted of 640 species. Overall, 79 (out of 640 screened) human (opportunistic) pathogenic species were detected in our samples by the 16S rRNA gene metabarcoding analysis and have been listed in Table SI4 of the SI file with the corresponding within-sample RA. The relative abundance of the human pathogenic species, calculated as the percentage ratio of the abundance of all detected human pathogenic species to the abundance of all detected bacterial species in each sample, denoted as RATOT, is shown in Fig. 8a for the 20 analyzed samples. The mean human pathogen RATOT appeared to be highly inhomogeneous between samples without any significant difference between winter and spring samples (Fig. 8a). S1, S2, S5, S11, S14, S16, S19, and S20 were highly enriched in human (opportunistic) pathogenic species with within-sample mean RATOT N5% (Fig. 8a). It is noteworthy from Fig. 8a that the samples likely affected by desert dust (S3, S6, and S12) and clean marine (S4, S7, and S8) airflows, according to Sections 4.2 and 4.3, respectively, were poorly enriched in human pathogenic species. Gat et al. (2017) also found that dust storms did not seem to be an important vector for the transport of antibiotic resistant genera, even if they can be responsible for the enrichment of the ambient airborne microbiome, in accordance with the results of this study (Section 4.2). Fig. 8b shows the mean RA of the human pathogenic species, calculated as the percentage ratio of the abundance of a single pathogenic species to the total detected pathogenic species, for the 18 most abundant human pathogens (N0.45% within-sample RA). The percentage contributions of the remaining 61 human pathogenic species (with b0.45% within-sample RA) have been grouped in “Other”, whose mean RA was equal to 18.39% (Fig. 8b). Ten of the 18 most abundant pathogen species were Proteobacteria (Fig. 8b), which was the dominant phylum in the analyzed samples (Fig. 2a). With reference to Proteobacteria pathogen species, it is noteworthy that many ESKAPE (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species) pathogens (Table SI4) are the leading cause of nosocomial infections throughout the world. Most of them are multidrug resistant isolates, which is one of the greatest challenges in clinical practice (Rice, 2008; Boucher et al., 2009). The within-sample RA of the human pathogenic species is shown in Fig. 8c. The NMDS ordination plot based on the within-sample RA of the human pathogenic species is depicted in Fig. 9 for the 20 analyzed samples. Most of the spring and winter samples are located in the upper (NMDS2 N 0.0) and lower (NMDS2 b 0.0) section of the NMDS plot,

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Fig. 8. (a) Relative abundance of the human pathogenic species, calculated as the percentage ratio of the abundance of each detected human pathogenic species to the abundance of all detected bacterial species in the PM10 samples. (b) Mean percentage contribution (on a logarithmic scale) for each of the 18 most abundant (N0.45% mean within-sample relative abundance) human pathogenic species out of the total pathogenic species and (c) their relative percentage contribution in each sample. The error bars in (b) represent the standard error of the mean. The b0.45% mean within-sample relative abundance (denoted as “Other”) is also represented in (b) and in (c). The corresponding phyla related to each human pathogenic species are also reported in (b).

respectively, likely because of the season impact on the species RA in each sample. Sample S5, which is the most enriched in human (opportunistic) pathogenic species (Enterobacter hormaechei RA = 84.87%) is detached from all the other samples. E. hormaechei belongs to the Enterobacter cloacae complex and may be responsible for nosocomial infections, including sepsis. This species is often associated with extended-spectrum beta-lactamase production, which makes the treatment of infections caused by this bacterium a difficult challenge by limiting therapeutic options (Townsend et al., 2008). The pathway of the 4day backtrajectories (Fig. SI9) highlighted that the long-range transported airflows spent most of the time over the central and western Mediterranean Sea throughout the S5 sampling time. Therefore,

the advection of marine air masses likely contributed to the increase of the Enterobacter hormaechei species in S5 being Proteobacteria a major group of bacteria widely distributed in both terrestrial and aquatic environments. Sample S8, where Propionibacterium acnes is the most abundant pathogenic species (RA = 51.43%), is also detached (Fig. 9) from all the other samples and from S7, where Enterobacter hormaechei is the most abundant pathogenic species (RA = 71.71%), in contrast to the results depicted by Figs. 7 and 2b, 4b, and 5b. A cluster made by the spring-samples S12, S13, S15, S17, and S18 can be identified in Fig. 9. The same pathogenic species have mostly contributed to the above samples although the RA of each species varied from sample to sample.

S. Romano et al. / Science of the Total Environment 697 (2019) 134020

Fig. 9. NMDS (Non-metric MultiDimensional Scaling) ordination plot based on the withinsample RA of the human pathogenic species for the 20 analyzed samples. Winter and spring samples are reported by black and grey circles, respectively.

Sample S20 is detached from all samples with the exception of samples S2, S1, and S11. The Enterobacter hormaechei and the Acinetorbacter johnsonii were the most abundant pathogenic species in S20, S1, and S11 (Fig. 8c). Fig. SI23 shows the pathways of the 4-day backtrajectories that reached the study site during the S20 sampling time. Then, the comparison of Fig. SI23 with Figs. SI11 and SI20 shows that on average air masses from northeastern Europe were advected to the study site during the S20, S11, and S1 sampling time. Fig. SI21 shows that, during the S2 sampling time, the air masses spent most of the time over Italy before reaching the study site. Consequently, we can hypothesize that airflows that crossed areas characterized by anthropogenic activities before reaching the study site have likely contributed to the increase of Proteobacteria pathogen species in S1, S2, S11, and S20. It is noteworthy from Fig. 8a that the samples likely affected by desert dust (S3, S6, and S12) and clean marine (S4, S7, and S8) airflows, according to Sections 4.2 and 4.3, respectively, were poorly enriched in human pathogenic species. Gat et al. (2017) also found that dust storms did not seem to be an important vector for the transport of antibiotic resistant genera, although they can be responsible for the enrichment of the ambient airborne microbiome, in accordance with the results of this study (Section 4.2). The reasons for the co-occurrence of specific microorganisms in the samples are largely unknown to date. However, co-occurrence data in PM10 samples may help to understand more about the infection biology, epidemiology, and transmission route of the clinically relevant species. At the same time, they can provide us a useful tool to predict the effects of advection patterns, dust events, and/or particular local meteorological conditions on human health. In conclusion, we believe that the advection pattern analysis has also allowed a better understanding of the variability of the human (opportunistic) pathogenic species RA in PM10 samples, although we are aware that further and more accurate studies are required to support the hypotheses and results of the study.

6. Conclusion PM10 samples collected from January to June 2018 at a Central Mediterranean coastal site (Lecce, in southeastern Italy) have been analyzed by the 16S rRNA gene metabarcoding approach to characterize the structure of the airborne bacterial community and investigate their dependence on meteorology, seasons, and long-range transported air masses.

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• The PCA of the normalized meteorological factors allowed identifying by 95% confidence ellipses two separate clusters representative of samples collected in winter and spring. T and VP, which significantly varied from winter to spring, likely contributed to the PCA results. • The OTU number, richness and biodiversity of the spring (May, June) collected samples were on average greater than that of winter samples (January, February, and March), likely showing the season impact on the airborne bacterial community structure. The sample dissimilarity on average decreased from winter to spring. The spring decrease of meteorological stressors due to stormy days and the increase of sunny days and mean air temperature all over the Mediterranean, which favored the air mass aging, have likely contributed to the above results. This represented a striking result of this study. • 17 predominant phyla (with N0.01% within-sample RA) were identified in the analyzed PM10 samples. Proteobacteria (45.6%), Cyanobacteria (18.8%), Actinobacteria (11.4%), Firmicutes (10.5%), and Bacteroidetes (5.4%) were the most abundant bacterial phyla with a highly variable percentage contribution from sample to sample. • 79 predominant genera (with N0.01% within-sample RA) with a variable sample to sample RA were identified. Calothrix (16.84%), Pseudomonas (9.23%), Bacillus (4.27%), Enterobacter (3.67%), Vibrio (3.09%), and Streptomyces (3.05%) were the most abundant genera. • The PCA applied to the normalized meteorological parameters (with the exception of VP), OTU, and genus RA has been used to identify samples and/or sample clusters with particular features. It has been shown that the main identified clusters were on average made by samples collected during similar advection patterns. • The samples collected during the advection of African dust particles suggested that dust events increased the diversity of the airborne bacterial communities. • The advection of air masses that crossed the Atlantic and the Mediterranean Sea before reaching the study site showed that the sample biodiversity was on average low and decreased with the increase of the Calothrix RA. • In contrast, the samples collected during the advection of air masses that spent several hours over or near areas affected by anthropogenic activities before reaching the study site were characterized by high biodiversity. • High biodiversity and dissimilarity was also found in the samples collected during heavy rainy days. • Results on the presumptive presence of potential (opportunistic) pathogenic bacteria in the sampled air masses have shown that 79 (out of 640 screened) human (opportunistic) pathogenic species were detected in the analyzed samples. The NMDS ordination plot based on the within-sample RA of the human pathogenic species also showed the likely season impact. Samples collected during the advection of desert dust and Atlantic air masses were on average the less enriched in human (opportunistic) pathogenic species.

In conclusion, this work has suggested that changes of meteorological conditions, seasons, and long-range-transported air masses have likely been responsible for the shape of the bacterial community of PM10 samples, highlighting the fundamental role of the atmosphere in the airborne bacteria transport. Although the study's results refer to a single site, we believe that they can be considered representative of coastal sites of the Central Mediterranean, being the study site away from large sources of local pollution and affected by meteorological conditions and air flows typical of the Central Mediterranean. Consequently, the study can be considered of general interest although further studies are required to better support the main reported results.

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Author contributions P.A., M.R.P., A.T., and S.R. conceived and designed the study. G.R. collected the PM10 samples, M.D.S and A.T. processed the PTFE filters and performed DNA extraction. Data analysis was conducted by S.R., M.D.S., P.A., and A.T. The manuscript was written by P.A., M.R.P., A.T., and S.R. All authors critically revised and approved the final version of the manuscript. Declaration of competing interest The authors declare no competing financial interest. Acknowledgments S. Romano has carried out this work with the support of a postdoctoral fellowship financed by EARLINET as part of the ACTRIS Research Infrastructure Project by the European Union's Horizon 2020 research and innovation programme under grant agreement no. 654169 (previously under grant agreement no. 262254) in the 7th Framework Programme (FP7/2007–2013). The financial support from INFN (Istituto Nazionale Fisica Nucleare) is kindly acknowledged. The NOAA Air Resources Laboratory is kindly acknowledged for the provision of the HYSPLIT backtrajectories, in addition to the BSC-CNS for the provision of the NMMB/BSC-Dust model data. This research was also supported by grants from the Italian Ministry of Education, Universities and Research to P. Alifano (grant numbers: PON ARS01_01220, PRIN 2017SFBFER). Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.scitotenv.2019.134020. References Amato, P., Joly, M., Besaury, L., Oudart, A., Taib, N., Moné, A.I., Deguillaume, L., Delort, A.M., Debroas, D., 2017. Active microorganisms thrive among extremely diverse communities in cloud water. PLoS One 8 (12(8)), e0182869. https://doi.org/10.1371/journal. pone.0182869. Barberán, A., Ladau, J., Leff, J.W., Pollard, K.S., Menninger, H.L., Dunn, R.R., Fierer, N., 2015. Continental-scale distributions of dust-associated bacteria and fungi. Proc. Natl. Acad. Sci. U. S. A. 112 (18), 5756–5761. https://doi.org/10.1073/pnas.1420815112. Basart, S., Pérez, C., Cuevas, E., Baldasano, J.M., Gobbi, G.P., 2009. Aerosol characterization in Northern Africa, Northeastern Atlantic, Mediterranean Basin and Middle East from direct-sun AERONET observations. Atmos. Chem. Phys. 9, 8265–8282. https://doi.org/ 10.5194/acp-9-8265-2009. Beckers, B., Op De Beeck, M., Thijs, S., Truyens, S., Weyens, N., Boerjan, W., Vangronsveld, J., 2016. Performance of 16S rDNA primer pairs in the study of rhizosphere and endosphere bacterial microbiomes in metabarcoding studies. Front. Microbiol. 13 (7), 650. https://doi.org/10.3389/fmicb.2016.00650. Bertolini, V., Gandolfi, I., Ambrosini, R., Bestetti, G., Innocente, E., Rampazzo, G., Franzetti, A., 2013. Temporal variability and effect of environmental variables on airborne bacterial communities in an urban area of Northern Italy. Appl. Microbiol. Biotechnol. 97, 6561–6570. https://doi.org/10.1007/s00253-012-4450-0. Blais-Lecours, P., Perrott, P., Duchaine, C., 2015. Non-culturable bioaerosols in indoor settings: impact on health and molecular approaches for detection. Atmos. Environ. 110, 45–53. https://doi.org/10.1016/j.atmosenv.2015.03.039. Boucher, H.W., Talbot, G.H., Bradley, J.S., Edwards, J.E., Gilbert, D., Rice, L.B., Scheld, M., Spellberg, B., Bartlett, J., 2009. Bad bugs, no drugs: no ESKAPE! An update from the Infectious Diseases Society of America. Clin. Infect. Dis. 48 (1), 1–12. https://doi.org/ 10.1086/595011. 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, 299–304. https://doi.org/10.1073/pnas.0608255104. Bukin, Y., Galachyants, Y.P., Morozov, I.V., Bukin, S.V., Zakharenko, A.S., Zemskaya, T.I., 2019. The effect of 16S rRNA region choice on bacterial community metabarcoding results. Sci. Data. 6, 190007. https://doi.org/10.1038/sdata.2019.7. Burton, N.C., Grinshpun, S.A., Reponen, T., 2007. Physical collection efficiency of filter materials for bacteria and viruses. Ann. Occup. Hyg. 51 (2), 143–151. https://doi.org/ 10.1093/annhyg/mel073. Cáliz, J., Triadó-Margarit, X., Camarero, L., Casamayor, E.O., 2018. A long-term survey unveils strong seasonal patterns in the airborne microbiome coupled to general and regional atmospheric circulations. Proc. Natl. Acad. Sci. U. S. A. 115 (48), 12229–12234. https://doi.org/10.1073/pnas.1812826115 27.

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