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Environment International 183 (2024) 108370
Available online 6 December 2023
0160-4120/© 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-
nc-nd/4.0/).
Full length article
Anthropogenic impact on airborne bacteria of the Tibetan Plateau
Zhihao Zhang
a
,
1
, Jing Qi
b
,
c
,
1
, Yongqin Liu
a
,
b
,
*
, Mukan Ji
b
, Wenqiang Wang
b
,
c
, Wenjie Wu
a
,
Keshao Liu
a
, Zhongwei Huang
d
a
State Key Laboratory of Tibetan Plateau Earth System, Resources and Environment (TPESRE), Institute of Tibetan Plateau Research, Chinese Academy of Sciences,
Beijing 100101, China
b
Center for the Pan-third Pole Environment, Lanzhou University, Lanzhou 730000, China
c
College of Ecology, Lanzhou University, Lanzhou 730000, China
d
Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, China
ARTICLE INFO
Keywords:
Airborne bacteria
Anthropogenic impact
Monsoon
Westerly
The Tibetan Plateau
ABSTRACT
The Tibetan Plateau is a pristine environment with limited human disturbance, with its aerosol microbiome
being primarily inuenced by the monsoon and westerly circulations. Additionally, the diversity and abundance
of airborne microorganisms are also affected by anthropogenic activities, such as animal farming, agriculture,
and tourism, which can lead to increased risks to the ecosystem and human health. However, the impact of
anthropogenic activities on airborne microbes on the Tibetan Plateau has been rarely studied. In this work, we
investigated the airborne bacteria of areas with weak (rural glacier) and strong human disturbance (urban
building), and found that anthropogenic activities increased the diversity of airborne bacteria, and the con-
centration of potential airborne pathogens. Moreover, airborne bacteria in rural aerosols demonstrated signi-
cant differences in their community structure during monsoon- and westerly-affected seasons, while this pattern
was weakened in urban aerosols. Additionally, urban aerosols enriched Lactobacillus sp. (member of genus
Lactobacillus), which are potential pathogens from anthropogenic sources, whereas rural aerosols enriched
A. calcoaceticus (member of genus Acinetobacter) and E. thailandicus (member of genus Enterococcus), which are
both speculated to be sourced from surrounding animal farming. This study evaluated the impact of human
activities on airborne bacteria in the Tibetan Plateau and contributed to understanding the enrichment of
airborne pathogens in natural and anthropogenic background.
1. Introduction
Airborne bacteria are key components of bioaerosols, which can be
emitted from terrestrial and marine environments as well as from human
activities (Burrows et al., 2009; Zhao et al., 2022). Some of the emitted
airborne bacteria can withstand extreme conditions and transfer freely
across ecosystems (Jiang et al., 2022), spreading globally through the
atmosphere, biosphere, and anthroposphere (Fr¨
ohlich-Nowoisky et al.,
2016). Several studies demonstrated that airborne bacteria have a pro-
found impact on nutrient cycling, plant and animal health, and global
climate (Jones and Harrison 2004; Morris et al., 2004; Humbal et al.,
2018), thereby inuencing ecosystem dynamics and public health
(Pearce et al., 2016; Xie et al., 2021). In recent years, many studies have
reported that aeoline dust and microorganism could cause plant (Brown
and Hovmøller, 2002), animal (Ichinose et al., 2005), and human
disease, such as the respiratory (Maki et al., 2022; Rod´
o et al., 2011; Ren
et al., 2014) and skin diseases (Ma et al., 2017) In another hand,
airborne bacterial composition and biodiversity are also sensitive to
anthropogenic impacts (Archer and Pointing 2020). Human activities
weaken the compositional disparity between surface and high-elevation
airborne bacteria in agricultural elds (Spring et al., 2021). In cities, the
diversity of airborne bacteria and fungi near residential areas and roads
is higher than that in less populated zones (Pollegioni et al., 2022).
Moreover, densely populated urban environments have higher airborne
bacterial diversity than sparsely populated areas (Cuthbertson et al.,
2017).
The Tibetan Plateau is typically considered a pristine environment
affected by the westerly and monsoonal circulation systems (Yao et al.,
2013). Previous studies have revealed increased airborne bacterial and
antibiotic resistance genes diversity over the glaciers under the effect of
* Corresponding author at Center for the Pan-third Pole Environment, Lanzhou University, Lanzhou 730000, China.
E-mail address: yql@lzu.edu.cn (Y. Liu).
1
Both authors contributed equally.
Contents lists available at ScienceDirect
Environment International
journal homepage: www.elsevier.com/locate/envint
https://doi.org/10.1016/j.envint.2023.108370
Received 15 September 2023; Received in revised form 28 November 2023; Accepted 4 December 2023
Environment International 183 (2024) 108370
2
the Indian monsoon (Qi et al., 2022; Mao et al., 2023). However, the
inuence of anthropogenic activities on airborne bacteria has received
little attention, which is mainly through increased local resident and
tourist populations due to social and economic activities such as animal
farming, agriculture, and tourism. These airborne microbe dispersed
into local downwind areas or ascend to high altitudes through the heat
pump effect, resulting in a wide range of eco-environmental effects (Xu
et al., 2018; Qi et al., 2021), altering the development and succession of
microbial communities in downwind ecosystems (Mayol et al., 2017;
Maki et al., 2018; Chen et al., 2021). Previous studies have revealed that
long distance transport of Asian dust form urban and industry area is
possible and this may alter the microbial composition of bioaerosols.
Maki et al. revealed that selective alteration of the composition of
aerosol bacteria during transportation over different underlying surface
(Maki et al., 2019) and vertically transported atmospheric bacteria to
the ground surface (Maki et al., 2010). Tang et al. also revealed that dust
clouds can carry bacteria of various types into downwind regions (Tang
et al., 2018). Moreover, airborne pathogens can be transported through
the atmospheric circulation, thus further extending their health risks
(Triado-Margarit et al., 2021). Yet, the understanding of how human
activities impact the spread and health hazards of atmospheric microbes
in the Tibetan Plateau is still very limited.
Overall, understanding the relationships between anthropogenic
activities and airborne bacteria in Tibetan Plateau requires an in-depth
comparison of the dynamics of airborne bacteria community under
natural and anthropogenic background. Here, we collected 60 aerosols
samples from rural glacier (natural background) and urban building
(anthropogenic background) during 2019–2021, then compared the
diversity, taxonomic composition, and community dynamic of airborne
bacteria between westerly and monsoon period under natural and
anthropogenic background. We hypothesize that human activity would
increase the diversity of airborne bacteria, increase the concentration of
potential airborne pathogens, and decrease community heterogeneity of
airborne bacteria from different origins.
2. Material and methods
2.1. Sampling sites and sample collection
The Tibetan Plateau (TP) has the highest average elevation in the
world (an average of 4000 m above sea level) and it is the core area of
inuence of the monsoon and westerly circulation (Yao et al., 2012).
Also, the southern part of the TP is known as the monsoon domain,
which is mainly inuenced by the Indian monsoon during the summer,
while in other seasons it is mainly affected by the south branch of the
westerly (Yao et al., 2013; Wang et al., 2016) (Fig. S1).
In this study, aerosol samples were collected from Qiangyong Glacier
(rural area with weak human activity) and Lhasa City (urban area with
strong human activity) in the southern part of the TP (Fig. S1). Qian-
gyong Glacier (QY, 28.89◦N, 90.23◦E; 4884 m a.s.l) is located in the
southern part of the TP between the Himalayas and the Brahmaputra
River (Tian and Tian 2019). The glacier is 4.9 km in length, the
maximum width is 2.8 km, and covers an area of 7.7 km
2
(Luo et al.,
2003). In addition, the QY glacier is far from the inuence of human
activities, making it an ideal site for studying atmospheric background
values in a natural environment. Lhasa City (LS, 29.65◦N, 91.03◦E;
3,650 m a.s.l) is the capital of Tibet and located at the north of QY
glacier 115 km away (Shen et al., 2022). LS is one of the highest urban in
the world and has a large resident population with 550,00 (Zhang,
1997). Moreover, LS receives 26 million tourists every year, and the
number of tourists shows a trend of rapid growth recent years (Chen
et al., 2018; Wang et al., 2021).
A total of 60 aerosol samples were collected from 2019 to 2021
(Table S1). Of these, 17 samples were collected at the terminal parts on
the QY glacier from February to October, other 43 samples were
collected on the roof of a building at the Institute of Tibetan Plateau
Research in LS city during January to December. Samples were collected
at 1.5 m above the ground using pre-sterilized polycarbonate lters and
lter holders. The sampling method has been described in a previous
study (Qi et al., 2021). Samples were stored in a freezer at −20 ◦C
immediately after collection. The samples were then transported to the
laboratory and promptly frozen at −20 ◦C until analyses.
2.2. Back-Trajectory analysis and meteorological data
A ten-day backward trajectory was calculated using the HYSPLIT
model and Global Data Assimilation System meteorological data at 10 m
above ground level to analyze the origin of aerosols (Choufany et al.,
2021). Back trajectories show the air mass history (origins and transport
paths) and thus can provide information on the geographical location of
potentially advected emissions at large geographical scales. The samples
were classied into monsoon- or westerly-inuenced groups based on
the back trajectory and sample collection date. Specically, air mass that
passed over Indian ocean could be inuenced by monsoon, in contrast,
air mass predominately originated from Central Asia or Europe could be
inuenced by westerly. For westerly group, air mass that passed over the
Indian continent (Bangladesh, India, and Nepal) was inuenced by
south branch of westerly, and air mass that passed over Xinjiang
Autonomous Region of China was inuenced by north branch of west-
erly. We integrated south and north branch of westerly into westerly
group. However, for several samples (e.g., QY6, QY8 and QY9), their
backward trajectory showed air mass originated at local regions. Hence
their sampling seasons were used to determine whether the samples
were inuenced by monsoon or westerly. In general, samples from the
summer season were considered being under the inuence of monsoon,
whereas those non-summer were considered under the inuence of
westerly (Table S1; Fig. S2).
Meteorological data of QY glacier and LS city used in this study were
obtained from the National Centers for Environmental Prediction
(https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.surface.html,
last access: 3 March 2022). We download meteorological data for the
sampling periods including air temperature (Temp, ℃), relative hu-
midity (RH, %), and wind speed (WS, m/s).
2.3. High throughput sequencing of 16S rRNA genes
In this study, the lter was opened on a clean bench and the poly-
carbonate lm with a pore size of 0.2
μ
m was taken out (Qi et al., 2022).
The DNA of microorganism on the membrane was extracted using the
UltraClean Soil DNA kit with minor modication on the cell disruption
step. Specically, in addition to the C1 solution included in the kit, we
have also added 4
μ
l of Proteinase K Solution (20 mg/ml), and 25
μ
l of
DL-Dithiothreitol (DTT) (1 M) and incubated overnight at 55 ℃ for
better cell lysis effects. Then, total DNA was subsequently extracted from
polycarbonate lms using the UltraClean Soil DNA kit (MoBio, San
Diego, CA) according to the manufacturer’s instructions (Qi et al.,
2021). The V4-V5 region of the bacterial 16S rRNA genes was amplied
with the primer set 515F/907R (515F: 5
′
-GTGCCAGCMGCCGCGG-3
′
;
907R: 5
′
-CCGTCAATTCMTTTRAGTTT-3
′
) (Fang et al., 2017).
PCR reactions were performed in a volume of 50 µl containing 25
μ
l
2x Premix Taq (Takara Biotechnology, Dalian Co. Ltd., China), 1
μ
l of
each primer (10 mM), and 3
μ
l DNA (20 ng/
μ
l) template in a volume of
50 µl (Qi et al., 2022). At last, sequencing of the bacterial 16S rRNA gene
clone libraries was performed using an Illumina MiSeq Sequencer
(Illumina, San Diego, CA) with a paired-end strategy (2 ×250 bp)
provided by Guangdong Magigene Biotechnology Co. Ltd. (Guangzhou,
China) (Qi et al., 2021). Reads created in this study have been uploaded
to the NCBI SRA database (BioProject accession number
PRJNA1013017).
Z. Zhang et al.
Environment International 183 (2024) 108370
3
2.4. Bioinformatic analysis
Taxonomic analysis of raw sequences was processed using the
Quantitative Insights into Microbial Ecology pipeline (QIIME2; version
2021.04) (Caporaso et al., 2010). In a word, for all sequenced samples,
raw paired-end sequences were imported into Qiime2, the demulti-
plexed, and quality ltered by q2-demux and DADA2 denoise plugin
(Callahan et al., 2016; Almeida et al., 2018). The result of high-quality
sequences was clustered as amplicon sequence variants (ASVs) at 99
% identity for downstream diversity and taxonomic analysis (Qi et al.,
2022). The taxonomic assignment of the ASVs was performed against
the SILVA v132 as the database (Quast et al., 2012). To normalize the
uneven sequencing depth, all samples were arbitrarily subsampled to the
smallest library sizes, which is 11,114. After taxonomy had been
assigned, non-bacterial ASVs (like the chloroplast, mitochondria,
archaea, and unclassied) sequences were eliminated from the subse-
quent analysis.
The presence of potential pathogens was identied by comparing the
16S rRNA gene sequences against the bacterial pathogens database
using Blastn (v2.5.0) and 100 % identities hits were kept. Bacterial
pathogens database was built using BLAST+(Boratyn et al. 2013) based
on several previous studies about the opportunistic pathogenic bacteria
(Li et al., 2019; Delgado-Baquerizo et al., 2020; Taylor et al., 2001;
Wardeh et al., 2015). Total 174,378 full-length 16S rRNA gene se-
quences were picked out from SILVA 16S rRNA database based on the
taxonomy of pathogens.
2.5. Microscopic analysis of particle concentrations
The black carbon particles, yellow organic particles, and bacterial
cells concentrations in the bioaerosols were determined by a micro-
scopic counting method that was previously described by Tang et al.
(Tang et al. 2018). Briey, the samples were stained with 10
μ
g/mL 4,6-
diamidino-2-phenylindole (DAPI, D9542, Sigma) for 15 min after being
xed in a 4 % paraformaldehyde solution for 1 h. Next, the prepared
slides were observed using an epiuorescence microscope (BX53 and.
DP72, Olympus, Japan) equipped with an ultraviolet excitation system.
Fluorescent particles including black carbon particles, yellow organic
particles, and bacterial cells were counted in 10 randomly selected elds
and converted to concentrations based on following formula:
C=S1 ×N0
S0 ×V(1)
where C is the concentration of uorescent particles, S1 is ltration area
on the membrane, S0 is each microscopic eld area, N0 is the mean
number of uorescent particles in the microscopic eld, and V is the
ltered sample volume. Additionally, the potential airborne pathogens
were quantied using a combination of total bacterial concentrations
determined by Fluorescence microscope and relative abundances of
pathogens determined by sequencing (Liu et al., 2016).
2.6. Statistical analyses
In this study, to compared with concentration data, the alpha-
diversity index (Chao1 index) was calculated in an R environment
(v4.0.3). The correlation matrices between bacteria and factors were
constructed by calculating Spearman’s rank correlation coefcients with
“psych” (v2.2.5) package. Kruskal-Wallis one-way analysis was used for
multiple comparisons of two or more sample populations. It was used to
test the signicance of differences between different groups. Distance-
based community analyses were calculated based on Bray-Curtis dis-
similarities. The beta-diversity statistical analyses were tested using
PERMANOVA (permutational multivariate analysis of variance) based
on Bray-Curtis dissimilarities and 999 permutations. All analyses and
visualizations were performed in R using the “vegan” (v2.6–2),
“ggplot2” (v3.3.6) and “pheatmap” (v1.0.12) packages (Oksanen et al.,
2019; Kolde, 2010).
3. Results
3.1. Sources of air masses for rural and urban aerosols
The ten-day backward trajectory plots of the 60 aerosol samples
demonstrated that all samples were grouped according to atmospheric
circulations into two categories, i.e., monsoon- and westerly-inuenced
(Table S1; Fig. S2; Material and methods). Samples collected from the
rural and urban areas in the same season were typically associated with
the similar atmospheric circulation type, due to their geographical
proximity. The monsoon-inuenced air masses mostly originated from
the Indian Ocean and passed the Indian continent, being transported at
low altitudes (0–1000 m) before arriving at the sampling sites. Westerly-
inuenced air masses mostly came from northwestern China or Europe,
being mostly transported at high altitudes (1,000–6,000 m) before
reaching the sampling sites.
3.2. Concentration and diversity of airborne bacteria in rural and urban
aerosols
The airborne bacterial concentration ranged from 5.2 ×10
2
to 4.3 ×
10
3
cell/m
3
in rural aerosols and from 1.3 ×10
3
to 4.1 ×10
4
cell/m
3
in
urban samples (Fig. 1). The bacterial concentrations in rural aerosols
were signicantly lower than those in urban aerosols under both
monsoon and westerly inuences (Kruskal-Wallis, P =0.007 and P <
0.001, respectively, Fig. 1A). However, there were no signicant con-
centrations difference between monsoon and westerly groups of urban
and rural aerosols (Fig. S3). Across all aerosol samples, we identied
48,489 ASVs, with a Chao1 index ranging from 513 to 3,832 (mean =
1,184) in rural aerosols and from 619 to 4,542 (mean =2,010) in urban
aerosols. The Chao1 index in rural aerosols was signicantly lower than
that in urban aerosols under monsoon inuence (P <0.001, Fig. 1B).
However, the Chao1 indices between the two groups were not signi-
cantly different under westerly inuence (P =0.33, Fig. 1B).
3.3. Community structure of airborne bacteria in rural and urban aerosols
The non-metric multidimensional scaling (NMDS) ordination plot
indicated that aerosol samples clustered by sampling location (Fig. 2A
and C; PERMANOVA, P <0.001). Moreover, rural aerosol samples were
further separated into two groups, i.e., those under the inuence of
monsoon and those under the inuence of westerly, while this pattern
was much weaker in urban aerosol samples (Fig. 2A and C). This was
evidenced by the rural aerosol samples exhibiting a signicantly higher
within-group Bray-Curtis dissimilarity compared with urban aerosols
(Kruskal-Wallis, P <0.001, Fig. 2B and D).
3.4. Compositional differences in airborne bacteria of rural and urban
aerosols
We identied 8,468 (17.5 % of the total ASVs identied) and 36,281
(74.8 %) unique ASVs in rural and urban aerosol samples, respectively,
with 3,740 (7.7 %) shared between the two. These shared ASVs
accounted for 73.2 % of the rural aerosol and 51.8 % of the urban
aerosols (Fig. 3B).
The dominant taxa in rural and urban airborne bacteria under
westerly or monsoon was similar (Fig. S4). Proteobacteria (29 %),
Actinobacteriota (28 %), Firmicutes (19 %), and Bacteroidota (9 %)
were dominant across all samples. At the order level, Pseudomonadales
and Lactobacillales were signicantly enriched in rural aerosols, while
Micrococcales, Rhodobacterales, and Frankiales were enriched in urban
aerosols (Fig. 3C, P <0.05). Correlation analyses revealed different re-
lationships between these dominant taxa and meteorological factors. For
Z. Zhang et al.
Environment International 183 (2024) 108370
4
Fig. 1. Airborne bacterial concentration (A) and the Chao1 index (B) in rural and urban under monsoon and westerly circulation. The blue and grey box represent the
samples in rural and urban, respectively. The box stretches from the 25th percentile to the 75th percentile with medium values marked. Signicance is tested using
the Kruskal-Wallis test, with P <0.05 considered signicant.
Fig. 2. Distribution of airborne bacterial community structure in rural and urban under monsoon and westerly circulation. Non-metric multidimensional scaling
(NMDS) ordination plots based on the Bray-Curtis dissimilarity in rural and urban using the relative abundance of ASVs (A) and binary transformed (absent or
present) ASVs (C). Comparison of the community dissimilarity comparison between rural and urban groups, the community dissimilarity was calculated using the
weighted Bray-Curtis dissimilarity (based on the relative abundance of ASVs, C) and unweighted Bray-Curtis dissimilarity (binary transformed ASVs, D). The blue and
grey color represent the samples in rural and urban, respectively. The hollow triangle and square represent the samples in rural and urban, respectively. Signicance
is tested using the Kruskal-Wallis test, with P <0.05 considered signicant.
Z. Zhang et al.
Environment International 183 (2024) 108370
5
rural enriched taxa, the relative abundances of Pseudomonadales and
Lactobacillales were negatively correlated with black carbon, while that
of Pseudomonadales was positively correlated with wind speed, tem-
perature, and relative humidity (Spearman’s rank correlation, all P <
0.05, Fig. 3C). For urban aerosol-enriched taxa, the relative abundances
of Micrococcales and Rhodobacterales negatively correlated with wind
speed but positively correlated with black carbon, temperature, and
relative humidity (Spearman’s rank correlation, all P <0.05, Fig. 3C).
Additionally, the relative abundances of Frankiales showed a positive
correlation with black carbon (Spearman’s rank correlation, P <0.05;
Fig. 3C).
3.5. Potential pathogens identied in the aerosol samples
A total of 92 bacterial ASVs, belonging to Proteobacteria, Actino-
bacteria, Firmicutes, Bacteroidota, and Fusobacteriota, were identied
as potential pathogens by sequence comparison with the infectious
disease database. The potential pathogen concentrations in rural aerosol
samples ranged from 3.9 to 45.5 cells/m
3
, while those in urban aerosol
samples ranged from 3.6 to 234.1 cells/m
3
(Fig. 4B). Thus, the con-
centration of potential pathogens was signicantly lower in rural aero-
sols (mean =8.83 cell/m
3
) than in urban aerosols (mean =60.66 cell/
m
3
) in both monsoon- and westerly-inuenced periods. Furthermore,
the difference between the concentrations in rural aerosols (mean =
19.00 cell/m
3
) and urban aerosols (mean =50.24 cell/m
3
) was smaller
during the westerly-inuenced period (Kruskal-Wallis, P =0.004 and
0.043, respectively, Fig. 4B).
For the top 20 most abundant ASVs classied as pathogen, two ASVs
were enriched in rural aerosols, belonging to Acinetobacter calcoaceticus
and Enterococcus thailandicus, respectively. In comparison, one ASV was
enriched in urban aerosols, belonging to Lactobacillus sp. Oral clone
HT002 (Fig 4B). Correlation analyses showed different relationships
between these ASVs and meteorological factors (Fig. 4C). Only the
concentration of E. thailandicus, which enriched in rural aerosols, was
negatively correlated with black carbon but positively correlated with
wind speeds with signicance at a threshold of P < 0.05 (Spearman’s
rank correlation, wind speed: P =0.04; black carbon: P =0.00003; Fig
4C). Other enriched pathogens had no signicant correlation with
meteorological factors. Correlation analysis also revealed that nearly
half of pathogens (9/20) were signicant correlated with temperature.
Of these pathogens, only the concentration of Citrobacter freundii was
negatively correlated with temperature (Spearman’s rank correlation, P
=0.002; Fig 4C).
4. Discussion
4.1. Airborne bacterial concentration and diversity in rural and urban
aerosols
The bacterial concentrations and diversity of urban aerosols were
higher than that of rural aerosols during the monsoon period (Fig. 1).
Although the number of samples was not equal between the two groups,
there was a statistically signicant difference. Conversely, such patterns
were not observed for bacterial diversity during the westerly-inuencing
period. This difference could be attributed to tourism-related activities,
as the monsoon season (July to September) is also the peak tourist
season. According to Lhasa Statistic Yearbook 2021, approximately
26,43 million people entered or exited Lhasa. Thus, tourists may bring in
microorganisms from the outside of the Tibetan Plateau. This is evi-
denced by an early study that the number of people and the intensity of
human activities positively correlated with the concentration of bacteria
(Heo et al., 2017). Moreover, urban aerosols had a higher number of
unique ASVs than rural aerosols, further demonstrating the effect of
anthropogenic contributions on airborne bacterial diversity (Fig. 3B).
Airborne bacterial concentrations and diversity can be inuenced by
local environments (Zhao et al., 2022) and large-scale atmospheric cir-
culation (Qi et al., 2022). As the Qiangyong glacier and Lhasa city are
geographically close and are both inuenced by the monsoon, the bac-
terial diversity and community structural differences observed were
most likely due to local environmental variations. Urban aerosols are
Fig. 3. (A), Venn diagram showing the number of shared ASVs between rural and urban samples, and their respective relative abundances. The blue and grey color
represent the ASVs special in rural and urban samples, respectively. The orange color represents the shared ASVs between rural and urban samples. (B). Spearman’s
rank correlation between shared ASVs and environmental factors. RH, Temp, Black, WS and Yellow are relative humidity, air temperature, black carbon particles,
wind speed and yellow organic particles, respectively. Signicance level: *** (p ≤0.001); ** (p ≤0.01); * (p <0.05)
Z. Zhang et al.
Environment International 183 (2024) 108370
6
heavily impacted by local anthropogenic activities, such as biomass
burning and vehicle exhausts (Woo et al., 2013). These activities can
increase the emissions of black carbon and dust particles (Archer and
Pointing 2020), which provide a shelter for microbial attachment (Zhu
et al., 2017; Hu et al., 2020). Therefore, explaining the higher bacterial
diversity and concentrations in urban aerosols. By contrast, glacial en-
vironments mainly consist of bare ground, grassland, ice, and snow,
which typically have fewer bacteria than urban areas (Brinkmeyer et al.,
2003; Liu et al., 2009), explaining the lower bacterial diversity and
concentration of rural aerosols.
4.2. Airborne bacterial community structure and taxonomy composition
in rural and urban environments
Airborne bacterial community structure differed signicantly be-
tween the rural and urban environments. Rural aerosols clustered into
two groups, which were inuenced by the monsoon and westerly,
respectively (Fig. 2). This is likely due to the different bacterial pop-
ulations that monsoon and westerly carry (Qi et al., 2022). Specically,
monsoon and westerly passes over different ecosystems, which can emit
distinct microbes, this ultimately causes the bacterial community
structure variations in rural aerosols. However, this pattern was much
weaker in urban aerosols (Fig. 2). We propose that the community
Fig. 4. Enrichment, concentrations, and relationship with environmental factors of potential pathogen ASVs in rural or urban sample. (A), The concentrations of top
20 potential airborne pathogens ASVs across rural and urban samples. The blue and grey color represent the samples in rural glacier and urban building, respectively.
Signicance is tested using the Kruskal-Wallis test, with P <0.05 considered signicant, it is denoted by a and b. The ns represents no signicant difference. (B),
Potential airborne pathogen concentration in rural and urban samples under monsoon and westerly circulation. (C), Spearman’s rank correlation between shared
ASVs and environmental factors. RH, Temp, Black, WS and Yellow are relative humidity, air temperature, black carbon particles, wind speed and yellow organic
particles, respectively. Signicance level: *** (p ≤0.001); ** (p ≤0.01); * (p <0.05)
Z. Zhang et al.
Environment International 183 (2024) 108370
7
homogeneity of airborne bacteria in urban areas could be attributed to
human activities, which weakens the inuence of monsoon and westerly
circulations. Similar results have been reported in aerosol studies of
other urban areas, where anthropogenic activities increased the com-
munity similarity of airborne bacteria across different seasons (Liu et al.,
2019; Hu et al., 2020). Despite the sampling time was different for rural
and urban samples (Table S1), the bacterial communities in the aerosol
were clustered by the inuence of atmospheric circulation and anthro-
pogenic activities, but not by sampling time (Fig. S5).
Taxonomy composition in rural and urban environments were
different as well. The relative abundances of Pseudomonadales and
Lactobacillales were higher in rural aerosols, positively correlating with
wind speed and negatively with black carbon (Fig 3C). Pseudomona-
dales are dominant in soil and plant-associated microbiota (Banerjee
et al., 2018), while Lactobacillales are dominant in dairy products
(Bahreini-Esfahani and Moravejolahkami 2020). This is consistent with
the environmental background of rural aerosols, which are dominated
by bare ground and grassland with occasional animal farming. Previous
studies have reported similar results that wind speed and direction alter
the composition of airborne microbial communities by mediating the
inputs from surrounding landscapes (Tignat-Perrier et al., 2019).
Conversely, higher relative abundances of Micrococcales, Frankiales,
and Rhodobacterales occurred in urban aerosols, positively correlating
with black carbon but negatively with wind speed (Fig. 3C). Indeed,
comparison in environment factor between rural and urban samples
showed that urban wind speed was signicantly lower than rural, but
the concentration of black carbon was signicantly higher (Kruskal-
Wallis, P <0.05, Fig. S6). Thus, it is possible that urban environment
decreased the wind speeds and enriched black carbon, which subse-
quently caused enrichment of certain microbial groups.
4.3. Potential airborne pathogens in rural and urban
Our study revealed a high concentration of potential airborne
pathogens in urban aerosols than in rural aerosols, with a larger dif-
ference during the monsoon period. This nding is coincident with the
more intense human disturbance during the monsoon season, such as
tourism and agricultural activities (Singh et al., 2019). Furthermore, the
increased pathogen concentration due to human activities is consistent
with a meta-analysis showed that human are the major source of
airborne pathogens compared to the natural environment (Jiang et al.,
2022).
The pathogenic bacteria enriched in rural aerosols were
A. calcoaceticus (member of genus Acinetobacter) and E. thailandicus
(member of genus Enterococcus), while Lactobacillus sp. oral clone HT002
was enriched in urban aerosols. Acinetobacter are found in a wide range
of natural environments (Choi et al., 2012; Yang et al., 2015), harboring
diverse causative agents of nosocomial infections including pneumonia
(Hartzell et al., 2007). Furthermore, it is one of the most challenging
pathogens that carries multiple antibiotic resistance (Wenzler et al.,
2017). A. calcoaceticus is an important species of the genus -
Acinetobacter and harboring bla
NDM-1
resistant gene which limiting the
effectiveness of antimicrobial therapy (Li et al., 2015). Globalization and
international travel accelerate the dissemination of bla
NDM-1
-harbing
bacteria between different countries and continents (Muir and Weinb-
ren, 2010). Our results indicate the potential of the transport of resistant
bacteria between natural and anthropogenic environment. Enterococcus
genus contains many opportunistic pathogens, such as E. faecalis and
E. faecium, which have been isolated from milk (de Garnica et al., 2013),
milk products, and poultry faeces (Chandra and Garg 2006; Tejedor-
Junco et al.,2005).This is consistent with the intense animal farming
activities near the QY glacier (Yang et al., 2019). E. thailandicus has been
reported to cause intra-abdominal infection in human (Mbouche et al.,
2023). The concentration of E. thailandicus showed signicantly positive
correlation with wind speed and negative correlation with black carbon.
This result could be expiation by that wind could accelerate
E. thailandicus aerosolization from the faeces of animal near the glacier.
As for enriched taxa in urban aerosols, Lactobacillus sp. oral clone HT002
has been commonly found in oral microbiome and is linked to oral
disease (Duran-Pinedo and Frias-Lopez, 2015) This result is in line with
the strong human activity in urban environment. Our results also
showed the concentration of nine pathogens signicantly correlated
with temperature (Fig. 4C). A pervious study on Citrobacter freundii have
showed that biolm formation decreased in increasing temperature
(Ramos-Vivas et al., 2020), which is consist with our result. Eight
pathogens were positively correlated with temperature, which suggests
that further global warming may increase the health risk.
5. Conclusion
We compared the airborne bacteria diversity and community struc-
ture in rural and urban regions. The results demonstrated that anthro-
pogenic impact increases the diversity of airborne bacteria and weakens
their differences between monsoon- and westerly-affected periods,
which was observed in glacial settings. Thus, glaciers provide a useful
background for studying the natural dynamics of airborne bacteria.
Moreover, we found that glacial and urban environments have their own
dominant species and potential pathogens derived from surrounding
environments. Notably, anthropogenic inuence substantially elevates
the concentration of potential airborne pathogens, thus posing a health
risk. This investigation provides a research background to forecast the
effect of increased human activity on atmospheric microorganisms in
the Tibetan Plateau.
CRediT authorship contribution statement
Zhihao Zhang: Data curation, Formal analysis, Visualization,
Writing – review & editing. Jing Qi: . Yongqin Liu: Conceptualization,
Funding acquisition, Project administration, Writing – review & editing.
Mukan Ji: Conceptualization, Writing – review & editing. Wenqiang
Wang: Investigation. Wenjie Wu: Investigation. Keshao Liu: Writing –
review & editing. Zhongwei Huang: Writing – review & editing.
Declaration of competing interest
The authors declare that they have no known competing nancial
interests or personal relationships that could have appeared to inuence
the work reported in this paper.
Data availability
Data will be made available on request.
Acknowledgments
This work was supported by National Natural Science Foundation of
China (Grant Nos. U21A20176 and 92251304), National Key Research
and Development Plans (Grant Nos. 2021YFC2300904), Second Tibetan
Plateau Scientic Expedition and Research Program (STEP) (Grant Nos.
2019QZKK0503), Key research and development plan of Tibet Auton-
omous Region (Grant Nos. XZ202301ZY0008G), Gansu Province Basic
Research Program for Excellent Phd Student (Grant Nos. 23JRRA1131).
Appendix A. Supplementary material
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.envint.2023.108370.
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