Association between ambient particulate matter exposure and semen quality in fertile men

Abstract

Background
Several studies have suggested adverse effects of particulate matter (PM) exposure on male reproductive health; few have investigated the association between PM exposure and semen quality in a large population of fertile men.

Methods
We evaluated 14 parameters of semen quality in 1554 fertile men in Nanjing from 2014 to 2016. Individual exposure to particular matter ≤10 μm in diameter (PM 10 ) and ≤ 2.5 μm in diameter (PM 2.5 ) during key periods of sperm development (0-90, 0-9, 10-14, 15-69, and 70-90 days before semen collection) were estimated by inverse distance weighting interpolation. Associations between PM exposure and semen quality were estimated using multivariable linear regression.

Results
Higher 90-days average PM 2.5 was in association with decreased sperm motility (2.21% for total motility, 1.93% for progressive motility per 10 μg/m ³ increase, P < 0.001) and four quantitative aspects of sperm motion (curvilinear velocity (VCL), straight line velocity (VSL), average path velocity (VAP), and amplitude of lateral head displacement (ALH), P < 0.01). The association between PM 2.5 exposure and semen quality were generally stronger for the earlier exposure window (70-90 days prior to ejaculation) than for recent exposure (0-9, 10-14, or 15-69 days). In the subgroup of men who had normal sperm parameters ( n = 1019), similar results were obtained. Ninety-days PM 10 exposure was associated only with decreased VCL and VAP and was not related to sperm concentration.

Conclusions
Exposure to PM 2.5 adversely affects semen quality, specifically lower sperm motility, in fertile men.

Graphical abstract

Full text

Wuetal. Environmental Health (2022) 21:16
https://doi.org/10.1186/s12940-022-00831-5
RESEARCH
Association betweenambient particulate
matter exposure andsemen quality infertile
men
Wei Wu1,2,3*† , Yiqiu Chen1,2†, Yuting Cheng1,2, Qiuqin Tang4, Feng Pan5, Naijun Tang6, Zhiwei Sun7,
Xinru Wang1,2, Stephanie J. London3 and Yankai Xia1,2*
Abstract
Background: Several studies have suggested adverse effects of particulate matter (PM) exposure on male reproduc-
tive health; few have investigated the association between PM exposure and semen quality in a large population of
fertile men.
Methods: We evaluated 14 parameters of semen quality in 1554 fertile men in Nanjing from 2014 to 2016. Individual
exposure to particular matter ≤10 μm in diameter (PM10) and ≤ 2.5 μm in diameter (PM2.5) during key periods of
sperm development (0-90, 0-9, 10-14, 15-69, and 70-90 days before semen collection) were estimated by inverse dis-
tance weighting interpolation. Associations between PM exposure and semen quality were estimated using multivari-
able linear regression.
Results: Higher 90-days average PM2.5 was in association with decreased sperm motility (2.21% for total motil-
ity, 1.93% for progressive motility per 10 μg/m3 increase, P < 0.001) and four quantitative aspects of sperm motion
(curvilinear velocity (VCL), straight line velocity (VSL), average path velocity (VAP), and amplitude of lateral head
displacement (ALH), P < 0.01). The association between PM2.5 exposure and semen quality were generally stronger for
the earlier exposure window (70-90 days prior to ejaculation) than for recent exposure (0-9, 10-14, or 15-69 days). In
the subgroup of men who had normal sperm parameters (n = 1019), similar results were obtained. Ninety-days PM10
exposure was associated only with decreased VCL and VAP and was not related to sperm concentration.
Conclusions: Exposure to PM2.5 adversely affects semen quality, specifically lower sperm motility, in fertile men.
Keywords: Ambient air pollution, PM2.5, PM10, Fertility, Semen quality, Sperm motility
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Background
Male factors are responsible for about 50% of infertility in
couples and male infertility is an important public health
issue worldwide [1]. A significant decline in human
semen quality has been observed over the past 70 years,
even in fertile men [2, 3]. Exposures that have been asso-
ciated with reduced semen quality include air pollutants,
smoking, and heavy metals [4–6]. e World Health
Organization (WHO) reports that 1.4 billion urban resi-
dents live in areas with air quality that does not meet
WHO air quality guidelines [7]. China has experienced
deterioration of the air quality due to its rapid socioeco-
nomic development. e situation is especially notable in
the Yangtze River Delta, one of the areas in China under-
going most rapid urbanization [8].
Open Access
*Correspondence: wwu@njmu.edu.cn; yankaixia@njmu.edu.cn
†Wei Wu and Yiqiu Chen contributed equally to this work.
2 Key Laboratory of Modern Toxicology of Ministry of Education, School
of Public Health, Nanjing Medical University, Nanjing, China
3 Department of Health and Human Services, National Institute
of Environmental Health Sciences, National Institutes of Health, Research
Triangle Park, Durham, USA
Full list of author information is available at the end of the article
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A large body of literature documents the association
between ambient air pollution and a range of important
health conditions including cardiovascular and respira-
tory diseases [9–12] and cancers [13]. A growing body
of literature suggests that exposure to ambient air pol-
lutants during pregnancy increases the risk of adverse
birth outcomes [14, 15]. Sentinel animal studies provide
cogent evidence that ambient air pollution exposure can
damage male germ cells [16]. Particulate matter (PM) is
a key component of air pollution and various diseases
are associated with it [17]. Pires etal. showed that fine
particulate matter (PM2.5) levels in Sao Paulo adversely
affects spermatogenesis in mice, but they didn’t investi-
gate the effects of PM10 exposure [18]. During the past
few years, there has been increasing interest in the effects
of air pollution on male reproductive health [19, 20]. In
humans, several studies have reported changes in sperm
parameters, such as sperm mobility and movement, in
relation to exposure to air pollution, providing evidence
for exposure-related reductions in sperm quality [4, 21–
23]. However, the reported effects of PM exposure on the
male reproductive system are inconsistent [19, 24, 25]. A
number of studies have focused on men being evaluated
for infertility, but studies in men known to be fertile are
few. Furthermore, most of these studies focused only on a
few semen parameters. ese limitations may impair the
identification of true associations between PM exposure
and semen quality.
Because development of sperm takes approximately
3 months [26], most studies have analyzed PM exposure
in the 90 days before semen examination [25, 27, 28]. It
is known that the sperm development covers four key
stages before semen ejaculation: 0-9 (epididymal storage),
10-14 (development of sperm motility), 15-69 (spermato-
genesis stage II), and 70-90 days (spermatogenesis stage
I). However, few researchers have considered these differ-
ent exposure stages within the 90-days window [29–31],
and the conclusions are inconsistent.
We therefore investigated the association between
ambient PM (PM2.5 and PM10) exposure, in the 90 days
prior to semen collection and semen quality in a large
population of men in Nanjing, China known to be fertile
and attempted to clarify which stage of sperm develop-
ment is most impacted by PM exposure. Our large cohort
with participants of known fertility would make the
results more representative.
Methods
Study population
e study population initially consisted of 1607 fer-
tile men from Nanjing Medical University Longitudinal
Investigation of Fertility and the Environment (NMU-
LIFE) study from January 1st, 2014 to December 31st,
2016. e NMU-LIFE was established in September
2010 to examine the effects of environmental and life-
style factors on reproductive health and birth outcomes
in the offspring. e study area of NMU-LIFE locates
in Yangtze River Delta Region, China. Pregnant women
that went for registration at the hospital were identified
as candidates for the study. Maternity care doctors deter-
mined the eligible individuals. Exclusion criteria included
maternal age < 20 or > 45 years , non-per manent residents
and intention of delivering in other cities. After learn-
ing about the study in details, the woman that agreed
to participate would represent herself and her family
members to sign an informed consent, which meant the
whole family was recruited. e information collected
in NMU-LIFE include the basic information and disease
information of fertile males, pregnant women, and their
children, as well as biospecimen including blood, urine,
semen, placenta and follicular fluid (more detailed infor-
mation about the cohort can be found in supplementary
material). We excluded 21 men without complete semen
reports, 17 with missing examination dates, and 15 with-
out exact addresses, leaving 1554 men for analysis. All of
the 1554 ferile men had fathered at least one healthy child
within the previous year. All male participants in our pre-
sent study were without a history of treatment. e study
was approved by the Institutional Ethics Committee of
Nanjing Medical University. All study participants gave
written informed consent. All activities involving human
subjects were conducted under full compliance with gov-
ernment policies and the Declaration of Helsinki.
Data sources
We obtained daily average PM air quality indices (AQIs)
and concentrations published daily by the Nanjing Envi-
ronmental Protection Bureau. To make our results
comparable with those from other studies, we chose
concentrations (μg/m3) in our analysis. PM2.5 and PM10
concentrations were continuously measured at nine fixed
state-controlled air quality monitoring stations located
in Nanjing city (Fig.1). Data on daily ambient average
temperature (in Celsius) in Nanjing over the same period
were obtained from the Nanjing Regional Climate Center.
Each participant was interviewed to collect information
including residence address, age, height, weight, ethnic-
ity, education, family income, cigarette smoking and alco-
hol consumption. Body mass index (BMI, in kg/m2) was
calculated as weight in kilograms (kg) divided by height
in meters squared (m2). Participants were asked about
the number of days of abstinence from ejaculation. After
an interview, each participant donated a semen sample
for semen quality analysis.
All semen samples were collected during the second
trimester of pregnancy of the participants’ spouses.
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Subjects were instructed to collect semen samples by
masturbation into sterile plastic specimen containers
in a semen collection room. Semen specimens were
allowed to liquefy at 37 °C, and aliquots were analyzed
at approximately 30 min after ejaculation using com-
puter-assisted semen analysis (CASA) in accordance
with guidelines of the WHO 5th Laboratory Manual for
the Examination of Human Semen [32]. Semen volume
was measured using a sterile serological pipette. Sperm
outcomes include semen volume, sperm concentration,
total sperm number, total motility, progressive motility.
Additionally, motility measures are curvilinear veloc-
ity (VCL), straight-line velocity (VSL), linearity (LIN),
average path velocity (VAP), wobble (WOB), straight-
ness (STR), mean angular displacement (MAD), beat
cross frequency (BCF), and amplitude of lateral head
displacement (ALH). VCL indicates the average veloc-
ity of the sperm head along its curved path. VSL indi-
cates the average straight-line velocity of the sperm
head from the initial location to the last location. VAP
indicates the time-averaged velocity of the sperm head
moving along its average trajectory. MAD indicates
the average angle of the sperm head turning along the
curved path. BCF indicates the time-averaged velocity
of the sperm curve trajectory across its average path.
ALH indicates the swaying amplitude of the sperm
head along its average trajectory (Fig.S1).
Exposure assessment
First, we used XGeocoding software to convert the par-
ticipants’ specific home address information and the
locations of nine atmospheric monitoring stations in
Nanjing into longitude and latitude values. en we
imported the longitude and latitude data and the aver-
age exposure values of pollutants at different stages
into ArcGIS software. e location of nine atmospheric
monitoring stations in Nanjing enables them to effec-
tively monitor air pollution throughout the adminis-
trative area. On that basis, we used an inverse distance
weighting (IDW) modeling method to assign PM2.5 and
PM10 exposure levels for each residence address on each
day using daily pollutant concentrations from air quality
monitoring data between January 1st, 2014 and Decem-
ber 31st, 2016 [33]. IDW interpolation was used as a spa-
tial interpolation method to model the distribution of air
pollutants using data from the fixed monitoring stations.
e process of spermatogenesis includes a series of com-
plex steps including stem cell replication, meiosis, and
spermiogenesis that occur over approximately 74 days
in humans [34]. With several days of epididymal transit
time (3-12 days) and an abstinence interval (controlled
in the analysis), an exposure period of approximately
90 days (a spermatogenic cycle) is regarded as being of
sufficient duration to detect effects on any stage of sper-
matogenesis [25]. erefore, concentrations of PM2.5 and
Fig. 1 Spatial distribution of nine fixed ambient air quality monitoring stations and residence addresses of the 1554 participants in Nanjing, China
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PM10 were calculated accordingly for the entire 90-day
period and four key periods (0-9, 10-14, 15-69, 70-90 days
before semen collection) preceding sampling [30].
Statistical analysis
Basic descriptive statistics were calculated to character-
ize the demographic information, PM exposure, and
semen quality parameters of the study population. We
analyzed the associations between the air pollution vari-
ables and semen parameters using adjusted multivariable
linear regression models and obtained coefficients and
95% confidence interval (CI). Given the non-normal dis-
tribution for semen volume, total sperm number, MAD,
VSL, STR, ALH, and BCF, these data were converted to
base-10 logarithms to meet the normality assumptions of
the statistical analysis.
Selection of covariates was based on biological plausi-
bility and their importance in the literature. We adjusted
all models for age, BMI, ethnicity, education, family
income, smoking status, alcohol consumption, season
of sperm collection, average ambient temperature, and
abstinence period. e average temperature was calcu-
lated as the mean of daily average temperature during
each exposure period.
Participants were further divided into ‘normal’ and
‘abnormal’ semen quality groups based on their semen
volume, sperm concentration, total sperm number and
total motility. e abnormal group (n= 535) was defined
by having at least one of the following abnormal semen
parameters as defined by reference levels from WHO
guidelines: semen volume < 1.5 ml, sperm concentra-
tion < 15 × 106/ml, total sperm number < 39 × 10,6 or
total motility < 40% [35]. Removing this abnormal semen
parameter group left 1019 individuals as the normal
group. e effect estimates were calculated for an incre-
ment of every 10 μg/m3 in average PM concentrations.
In addition, to better characterize exposure-response
associations, we divided PM2.5 exposures into quintiles
based on the distribution among all participants and esti-
mated regression coefficients with the first quintile as the
reference level. Effect modification by age, BMI, family
income, smoking status, and drinking status was assessed
by calculating product terms. A test for linear trend was
conducted with the use of quintiles of the PM2.5 expo-
sure variables as an ordinal variable.We also performed
stratified analyses of the association between PM2.5
and semen quality by age (< 35 and ≥ 35 ye ars), BMI
(< 24 and ≥ 24 kg/m2), family income (< 100,000 yuan
and ≥ 100,000 yuan), smoking status (never and former
or current) and alcohol drinking status (never and for-
mer or current). R software version 4.0.2 (R Core Team
R, 2020) was used to perform all statistical analyses. P
values < 0.05 were considered significant. To address
multiple testing, we also calculated the false discovery
rate (FDR) using the Benjamini & Hochberg (BH) proce-
dure and the total number of hypotheses tested was 14.
All P values reported were two-sided.
Role ofthefunding source
e funder of the study did not play any role in study
design, data collection, data analysis, data interpreta-
tion, or writing of the report. e corresponding author
had full access to all the data in the study and was finally
responsible for the decision to submit for publication.
Results
A flowchart of participants in the study is shown in
Fig.S2. Characteristics of the entire group of 1554 fertile
men and the normal (n = 1019) and abnormal (n = 535)
semen quality groups are shown in Table1. e mean
age of men participating in this study was 30.9 (stand-
ard deviation [SD]: 4.2) years and two-thirds had college
or higher education (66.8%). e majority of men were
never smokers (62.3%) and less than half of the subjects
were ever drinkers (44.5%) (Table1).
Table 2 shows the distributions of the semen param-
eters for all participants. e Pearson correlations (r)
between the 14 semen parameters are shown in TableS1.
Total motility and progressive motility were very highly
correlated (r = 0.94) as were straight-line velocity (VSL)
and average path velocity (VAP) (r = 0.96), linearity (LIN)
and wobble (WOB) (r = 0.94) (Table S1). Fig. S3 shows
average daily temperatures in Nanjing between 2014
and 2016. e daily average temperature was as high as
34.1 °C in summer and as low as − 6.6 °C in winter. Aver-
age daily concentrations of PM2.5 and PM10 are plotted in
Fig.2 and Fig.S4 respectively. e daily concentrations
averaged 59.61 μg/m3 for PM2.5 and 101.77 μg/m3 for
PM10 over the study period (January 1st, 2014 - Decem-
ber 31st, 2016, 1098 days). PM2.5 and PM10 were posi-
tively correlated (r = 0.92, P <  0.05). e concentrations
of PM2.5 and PM10 showed clear seasonal variation with
higher levels in winter (maximums of 83.44 μg/m3 for
PM2.5 and 134.82 μg/m3 for PM10) than summer (mini-
mums of 45.19 μg/m3 for PM2.5 and 72.93 μg/m3 for
PM10). During the study period, PM2.5 was above the Chi-
nese 24-h standard (75 μg/m3, Grade II) on over 26% of
days; the rate of exceedance was lower for PM10 (16.7%
of days). TableS2 gives descriptive statistics for estimated
90-days participant exposures to the two pollutants. For
PM2.5, the average 90-days concentration was 60.80 μg/
m3 (SD = 13.30; interquartile range, IQR = 17.9). For
PM10, the average 90-days concentrations was 103.10 μg/
m3 (SD = 20.50; interquartile range, IQR = 34.10).
e results of the regression models for PM2.5 dur-
ing 0-90, 0-9, 10-14, 15-69, 70-90 days before the date of
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Table 1 Characteristics of all fertile men participating in the study and the normal and abnormal semen quality groups
Note: SD standard deviation, BMI body mass index
a Group dened by semen volume ≥ 1.5 ml, sperm concentration ≥ 15 × 106/ml, total sperm number ≥ 39 × 10,6 and total motility ≥40%
b Group dened by at least one abnormal semen parameter (semen volume, sperm concentration, total sperm number or total motility)
Characteristic All participants (n= 1554) Normal semen quality group
(n= 1019)aAbnormal semen
quality group
(n= 535)b
Age (years), n (%)
< 35 1279 (82.3) 867 (85.1) 412 (77.0)
≥ 35 275 (17.7) 152 (14.9) 123 (23.0)
Range 17-52 21-50 17-52
Mean (SD) 30.9 (4.2) 30.7 (4.0) 31.4 (4.4)
BMI (kg/m2), n (%)
< 18.5 25 (1.6) 18 (1.8) 7 (1.3)
18.5 - 24.0 643 (41.4) 420 (41.2) 223 (41.7)
24.0 - 28.0 652 (42.0) 426 (41.8) 226 (42.2)
≥ 28.0 234 (15.0) 155 (15.2) 79 (14.8)
Mean (SD) 24.7 (3.3) 24.7 (3.3) 24.6 (3.2)
Ethnicity, n (%)
Han 1507 (97.0) 986 (96.8) 521 (97.4)
Other 47 (3.0) 33 (3.2) 14 (2.6)
Education, n (%)
Middle school and below 19 (1.2) 10 (1.0) 9 (1.7)
High school and secondary school 497 (32.0) 321 (31.5) 176 (32.9)
College degree and above 1038 (66.8) 688 (67.5) 350 (65.4)
Family income, n (%)
< 100,000 558 (35.9) 359 (35.2) 199 (37.3)
100,000 - 200,000 706 (45.4) 480 (47.0) 226 (42.3)
≥ 200,000 290 (18.7) 181 (17.8) 109 (20.4)
Smoking status, n (%)
Never smoker 968 (62.3) 629 (61.7) 339 (63.4)
Ever smoker 586 (37.7) 390 (38.3) 196 (36.6)
Current smoker 500 (32.2) 334 (32.8) 166 (31.0)
Former smoker 86 (5.5) 56 (5.5) 30 (5.6)
Drinking status, n (%)
Never drinker 862 (55.5) 575 (56.5) 287 (53.6)
Ever drinker 692 (44.5) 444 (43.5) 248 (46.4)
Current drinker 597 (38.4) 388 (38.0) 209 (39.1)
Former drinker 95 (6.1) 56 (5.5) 39 (7.3)
Season of sperm collection, n (%)
Spring 457 (29.4) 336 (33.0) 121 (22.6)
Summer 358 (23.0) 225 (22.1) 133 (24.9)
Autumn 371 (23.9) 225 (22.1) 146 (27.3)
Winter 368 (23.7) 233 (22.8) 135 (25.2)
Days of abstinence, mean (SD)
< 3 375 (24.1) 176 (17.3) 199 (37.2)
3 - 5 699 (45.0) 505 (49.5) 194 (36.3)*
≥ 5 480 (30.9) 338 (33.2) 142 (26.5)*
Mean (SD) 3.9 (2.6) 4.1 (2.7) 3.6 (2.4)
Semen volume (ml), mean (SD) 2.7 (1.3) 2.9 (1.2) 2.3 (1.5)*
Sperm concentration (106/ml)c58.3 (37.1-84.4) 63.6 (43.0-87.7) 47.5 (25.6-76.6)*
Total sperm number (106)c142.9 (79.8-231.8) 168.0 (111.9-248.6) 81.9 (34.2-169.6)*
Total motility (%)c56.3 (42.0-69.2) 61.8 (52.2-73.2) 36.0 (26.6-52.6)*
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c Values are given as median (P25 - P75)
* P < 0.05 when compared with normal semen quality group
Table 1 (continued)
Table 2 Distribution of semen parameters for the participants (n = 1554)
a ALH amplitude of lateral head displacement, BCF beat cross frequency, LIN linearity, MAD mean angular displacement, SD standard deviation, STR straightness, VAP
average path velocity, VCL curvilinear velocity, VSL straight line velocity, WOB cur vilinear path wobble
Semen parameteraMean (SD) Percentile
10th 25th 50th 75th 90th
Semen volume (ml) 2.7 (1.3) 1.0 2.0 2.0 3.0 5.0
Sperm concentration (106/ml) 62.0 (32.0) 23.3 37.1 58.3 84.4 108.0
Total sperm number (106)172.2 (129.9) 39.6 79.2 142.6 231.3 341.7
Total motility (%) 55.2 (19.3) 28.5 42.0 56.2 69.3 80.0
Progressive motility (%) 43.8 (16.9) 21.9 31.8 44.1 55.9 65.4
VCL (μm/s) 47.7 (8.9) 37.3 42.1 47.2 53.3 59.2
VSL (μm/s) 29.8 (6.1) 23.0 25.8 29.6 33.6 37.6
VAP (μm/s) 33.5 (6.2) 26.4 29.4 33.4 37.5 41.3
BCF (Hz) 5.1 (0.7) 4.3 4.6 5.1 5.5 6.0
ALH (μm/s) 3.6 (1.1) 2.4 2.9 3.6 4.3 4.9
LIN (%) 61.3 (7.5) 52.1 56.2 61.2 66.4 70.8
STR (%) 85.2 (4.1) 80.2 83.0 85.6 88.0 89.8
WOB (%) 70.3 (6.4) 62.6 65.7 70.2 74.6 78.3
MAD (°) 56.6 (7.9) 46.8 52.0 57.2 62.1 65.9
Fig. 2 Distribution of daily PM2.5 in Nanjing between 2014 and 2016. The points in top and bottom graphs indicate daily PM2.5. The straight black
line indicates Chinese 24-h standard (Grade II) for PM2.5 (75 μg/m3)
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semen examination in all participants are summarized
in Table 3. For the 0-90 days exposure period, an incre-
ment of 10 μg/m3 in PM2.5 was associated with a 2.21%
decrease in total motility and a 1.93% decrease in pro-
gressive motility. Inverse associations were also seen by
PM2.5 for the sperm motion parameters VCL, VSL, VAP
and ALH (all P < 0.05). No statistically significant associ-
ation was observed for semen volume, sperm concentra-
tion, or total sperm number. For the four key periods of
sperm development (0-9, 10-14, 15-69, and 70-90 days),
the associations between PM2.5 exposure and progressive
motility as well as for the motion parameters BCF were
only related to the 70-90 days exposure period.
Table S3 presents the results of regression models of
PM10 exposure and semen quality in all subjects. For
the entire 0-90 days exposure period, PM10 exposure
were significantly associated with the velocity param-
eters VCL and VAP, both inverse. Divided into four key
periods of sperm development (0-9, 10-14, 15-69, and
70-90 days), these statistically significant inverse associa-
tions were limited to 70-90 days prior to semen collection
(Table S3). e analyses of PM2.5 and PM10 in normal
semen quality males during the entire 0-90 days exposure
period generally yielded similar findings to the entire
dataset (TablesS4 and S5). As for PM2.5, the coefficients
of total motility and progressive motility were − 1.444
and − 1.370 respectively in the normal group. But the
associations became no statistically significant after the
FDR adjustment in the abnormal group.
Table4 shows the results of exposure-response analy-
ses using quintiles of PM2.5 in the 0-90 days before semen
collection and semen quality in all participants. For all
participants, PM2.5 exposure was inversely associated
with total motility and progressive motility. e coef-
ficient of total motility of the highest quintile of PM2.5
concentration compared with the lowest was − 8.42
(95%CI: − 11.98, − 4.86; P-trend < 0.001) for PM2.5 expo-
sure (Table4). In normal semen quality group, the coef-
ficient of total motility of the highest quintile of PM2.5
exposure compared with the lowest was − 5.56 (95%CI:
− 8.79, − 2.33; P-trend = 0.006) (Table S6). It seemed
that the associations were linear as suggested by mono-
tonic trends across quintiles of PM2.5 exposure (P-trend
< 0.05). In abnormal semen quality group, the coefficient
of total motility of the highest quintile of PM2.5 exposure
compared with the lowest was of − 7.35 (95%CI: − 13.16,
− 1.55; P-trend = 0.546) ( Table S7). It indicated that the
association between total motility and PM2.5 exposure
was nonlinear for abnormal semen quality group.
e results of the analyses of PM2.5 in relation to total
sperm motility stratified by age, BMI, family income,
smoking, and alcohol drinking are summarized in
Table S8. e effect estimates were calculated for an
increment of every 10 μg/m3 in average PM2.5 concentra-
tions. Inverse associations were found in all subgroups
of BMI, family income (P < 0.05). For participants whose
age were over 35 years, who were former or current
smokers and who were former or current drinkers, there
were no obvious correlation between PM2.5 exposure and
total motility (P = 0.180, P = 0.053 and P = 0.195).
Discussion
As far as we know, there are few studies investigating the
effects of PM exposure on semen quality in a such large
population of men known to be fertile (TableS9). Tak-
ing into account potential confounding factors including
season and temperature, we found that PM2.5 exposure
was negatively associated with sperm motility, both total
and progressive. e inverse associations with PM2.5
exposure were generally stronger during 70-90 lag days
than those associations in the other three periods exam-
ined, which indicated that PM2.5 exposure might reduce
human semen quality by generally influencing the early
stage of sperm development and sperm motility. PM2.5
carries various environmental pollutants such as heavy
metals which interfere with germ cell function and affect
gene expression [36]. Spermatogenesis goes through
three stages: proliferative phase, meiotic phase, and sper-
matogenic phase. During the proliferative phase, the gene
expression caused by the contaminant is transmitted.
We identified statistically significant findings but we do
not know if these have clinical significance. We note that
although there are men in this population with sperm
parameters that meet a WHO definition of “abnormal”, all
of these men are fertile. Although we do not have data to
address this question, it is possible that these differences
in sperm parameters could have subtle effects on fertil-
ity, such as time to pregnancy. e associations between
PM10 and sperm motility were weaker compared with
those for PM2.5, which further implicates PM2.5 specifi-
cally as a reproductive toxicant. No significant associa-
tion was found between PM and sperm concentration.
Motility parameters have been reported to be sensi-
tive biomarkers of human reproductive toxicity [37].
Fertility assessment of men is generally dependent on
the quality assessment of semen by conventional param-
eters such as motility, concentration, and morphology
of spermatozoa. Sperm motility is considered one of
the most important sperm functions that affect natural
conception. Reduced sperm motility causes about 18%
of male infertility and infertility cases [38]. In our study,
we found that PM2.5 was consistently associated with
decreased sperm total motility and progressive motil-
ity, but not sperm concentration. And the coefficients in
total population were higher than in the normal group.
It indicated that the general population was much more
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Wuetal. Environmental Health (2022) 21:16
Table 3 Coefficients from linear regression for PM2.5 exposure in relation to semen parameters by exposure period (0 - 90, 0 - 9, 10 - 14, 15 - 69, 70 - 90 days) prior to semen
collection in all participants (n = 1554) expressed as change in the parameter for a 10 μg/m3 increase in exposure
a ALH amplitude of lateral head displacement, BCF beat cross frequency, LIN linearity, MAD mean angular displacement, PM2.5 particulate matter with aerodynamic less than 2.5 μm, CI condence interval, STR straightness,
VAP average path velocity, VCL curvilinear velocity, VSL straight line velocity, WOB curvilinear path wobble
b Results were adjusted for age, BMI, ethnicity, education, smoking status, drinking status, family income, abstinence period, season, and temperature. P value for adjusting FDR using the Benjamini & Hochberg procedure
c Ambient particulate matter exposure of 10-14 days, 15-69 days, 70-90 days were also adjusted
d Ambient particulate matter exposure of 0-9 days, 15-69 days, 79-90 days were also adjusted
e Ambient particulate matter exposure of 0-9 days, 10-14 days, 70-90 days was also adjusted
f Ambient particulate matter exposure of 0-9 days, 10-14 days, 15-69 days was also adjusted
Semen parametera0-90 days 0-9 days 10-14 days 15-69 days 70-90 days
Coecient (×10; 95%
CI)
PbCoecient (×10; 95%
CI)
Pb, c Coecient (×10; 95%
CI)
Pb, d Coecient (× 10;
95% CI)
Pb, e Coecient (×10; 95%
CI)
Pb, f
Semen volume (ml) −0.003 (−0.025, 0.018) 0.965 −0.004 (−0.018, 0.010) 0.980 0.002 (−0.013, 0.010) 0.975 −0.002 (−0.023, 0.019) 0.851 0.009 (−0.007, 0.025) 0.324
Concentration (106/ml) 0.178 (−1.342, 1.698) 0.965 −0.127 (−1.105, 0.802) 0.980 −0.333 (−1.136, 0.471) 0.975 1.363 (−0.142, 2.868) 0.152 −0.496 (−1.625, 0.633) 0.419
Total sperm number
(106)0.001 (−0.039, 0.041) 0.965 −0.004 (−0.030, 0.022) 0.980 −0.011 (−0.032, 0.011) 0.975 0.025 (−0.015, 0.065) 0.372 0.003 (−0.033, 0.027) 0.834
Total motility (%) −2.211 (−3.121,
−1.301) < 0.001 −0.194 (−0.782, 0.394) 0.980 −0.216 (−0.698, 0.267) 0.975 −0.981 (−1.886,
−0.077) 0.094 −0.770 (−1.451,
−0.090) 0.075
Progressive motility (%) −1.925 (−2.720,
−1.131) < 0.001 −0.135 (−0.650, 0.380) 0.980 −0.090 (−0.512, 0.332) 0.975 −0.893 (−1.685,
−0.102) 0.094 −0.828 (−1.421,
−0.234) 0.029
VCL (μm/s) −1.054 (−1.467,
−0.640) < 0.001 −0.202 (−0.470, 0.066) 0.841 −0.011 (−0.208, 0.231) 0.989 −0.714 (−1.125,
−0.302) 0.009 −0.306 (−0.613,
−0.001) 0.089
VSL (μm/s) −0.022 (−0.032,
−0.011) < 0.001 −0.005 (−0.011, −0.002) 0.841 −0.001 (−0.006, 0.005) 0.975 −0.014 (−0.025,
−0.004) 0.038 −0.013 (−0.021,
−0.006) 0.004
VAP (μm/s) −0.714 (−1.000,
−0.428) < 0.001 −0.165 (−0.350, 0.020) 0.841 −0.016 (−0.135, 0.168) 0.975 −0.469 (−0.752,
−0.185) 0.009 −0.389 (−0.598,
−0.371) 0.004
BCF (Hz) 0.0004 (−0.003, 0.010) 0.510 −0.0004 (−0.004, 0.004) 0.980 −0.001 (−0.004, 0.002) 0.975 0.003 (−0.004, 0.009) 0.620 0.006(0.002, 0.011) 0.031
ALH (μm/s) −0.023 (−0.037,
−0.008) 0.005 0.004 (−0.005, 0.014) 0.980 0.0001 (−0.008, 0.008) 0.989 −0.014 (−0.029, 0.0001) 0.112 −0.006 (−0.017, 0.004) 0.312
LIN (%) −0.018 (−0.377, 0.340) 0.965 −0.003 (−0.235, 0.229) 0.980 0.071 (−0.119, 0.262) 0.975 −0.071 (−0.428, 0.285) 0.851 −0.264 (−0.528,
−0.0002) 0.089
STR (%) −0.001 (−0.003, 0.002) 0.965 0.00002 (−0.002, 0.002) 0.980 0.001 (−0.0004, 0.002) 0.975 0.0003 (−0.003, 0.002) 0.851 −0.002 (−0.004,
−0.0001) 0.086
WOB (%) −0.005 (−0.301, 0.310) 0.965 −0.009 (−0.207, 0.189) 0.980 0.019 (−0.143, 0.181) 0.975 −0.035 (−0.338, 0.268) 0.851 −0.217 (−0.443,
−0.008) 0.091
MAD (°) 0.004 (−0.009, 0.010) 0.965 −0.002 (−0.008, 0.005) 0.980 −0.002 (−0.007, 0.003) 0.975 0.001 (−0.008, 0.011) 0.851 0.006 (−0.002, 0.013) 0.174
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Wuetal. Environmental Health (2022) 21:16
sensitive to PM2.5 exposure to some extent. e inverse
associations prompted reduced motility caused by PM2.5
exposure may result in infertility but not absolutely.
Reduced motility would also affect the time to pregnancy
[39], which needs to be collected and analyzed in further
study. Similar results, a negative correlation between
PM2.5 and sperm motility, were reported by Hammoud
etal. [40]. Lao et al. [28] did not find significant asso-
ciations between PM2.5 exposure and sperm motility in
reproductive-age men. Selevan et al. [25] examined the
relationship between air pollution levels and VSL, VCL,
and LIN, and found that medium levels of air pollution
were negatively associated with VCL, but positively asso-
ciated with LIN. Wu etal. [31], in a study of 1759 infer-
tile men, reported that decreased sperm concentration
and count, but not sperm motility, were associated with
PM2.5. A recent study explored the association between
PM exposure and semen quality in a cohort of under-
graduate students and suggested that PM10 but not PM2.5
is associated with semen quality [41]. ese conflicting
research results may be due to geographic or racial differ-
ences and the differences in study designs and methods.
In particular, exposure concentrations of pollutants vary
from region to region (TableS8). us, although they
are the same contaminant, their effect on semen quality
varies depending on the amount of individual exposure.
e participants of most previous studies were infertile
men [31, 42, 43], which may cause selection bias. While
in our study, participants were fertile men which could
better represent the general population. Besides, different
measures of individual exposure also contribute to the
discrepancy. Lao etal. [28] used a spatiotemporal model
with high resolution (1 × 1 km) to estimate individual
exposure of PM2.5. Some other studies estimated air pol-
lution exposure at the community level [19, 25]. It may
mask exposure variation and cause misclassification.
Smoking has been reported to be detrimental to semen
quality. Sokol et al. found that an increase in O3 lev-
els was associated with a decline in sperm quality, but
they did not look at the effect modifications of cigarette
smoking on pollution and semen quality [29]. Consider-
ing that the smoking effect may operate concurrently in
the similar pathways [39], we further analyzed the asso-
ciations between PM2.5 exposure and semen quality by
smoking status. We found significant effect modifications
by smoking in the association between PM2.5 and semen
quality. Furthermore, we noted that our findings were
seen across age subgroups and drinking status subgroups.
Table 4 Coefficients (95% CIs) from linear regression of PM2.5 exposure during 0 - 90 days before semen collection in relation to semen
parameters in all participants (n = 1554)
The coecients and 95% CIs were estimated using a linear model, adjusting for age, BMI, ethnicity, education, family income, smoking status, drinking status,
abstinence period, season, and temperature. Natural log transformation was applied for some sperm parameters
a ALH amplitude of lateral head displacement, BCF beat cross frequency, CI condence interval, LIN linearity, MAD mean angular displacement, PM2.5 particulate matter
with aerodynamic less than 2.5 μm, STR straightness, VAP average path velocity, VCL curvilinear velocity, VSL straight line velocity, WOB curvilinear path wobble
b P trend value for adjusting FDR using the Benjamini & Hochberg procedure
Semen parameteraQuintile of PM2.5 exposure (range) P trendb
Q1 (28.7-49.6) Q2 (49.7-57.0) Q3 (57.1-64.8) Q4 (64.9-74.0) Q5 (74.1-92.1)
Semen volume (ml) 0 (reference) 0.005 (−0.07, 0.08) −0.04 (−0.13, 0.04) −0.07 (−0.17, 0.01) −0.03 (−0.11, 0.06) 0.583
Sperm concentration
(106/ml) 0 (reference) 0.12 (−5.11, 5.36)) 2.53 (−3.33, 8.39) −2.18 (−8.18, 3.82) −0.96 (−6.92, 5.00) 0.862
Total sperm number
(106)0 (reference) 0.03 (−0.11, 0.17) 0.06 (−0.10, 0.22) −0.07 (−0.24, 0.09) −0.04 (−0.21, 0.12) 0.583
Total motility (%) 0 (reference) −6.21 (−9.33, −3.08) −4.98 (−8.48, −1.48) −7.00 (−10.58,
−3.41)
−8.42 (−11.98,
−4.86) < 0.001
Progressive motility
(%) 0 (reference) −5.04 (−7.76, −2.31) −3.74 (−6.79, −0.69) −4.69 (−7.82, −1.57) −7.41 (−10.52,
−4.30) < 0.001
VCL (μm/s) 0 (reference) −0.03 (−1.46, 1.40) −1.38 (−2.98, 0.22) −1.18 (−2.82, 0.46) −3.24 (−4.82, −1.61) < 0.001
VSL (μm/s) 0 (reference) −0.005 (−0.04, 0.03) −0.01 (−0.05, 0.03) −0.01 (−0.05, 0.03) −0.07 (−0.11, −0.03) < 0.001
VAP (μm/s) 0 (reference) −0.21 (−1.20, 0.77) −0.37 (−1.47, 0.73) −0.47 (−1.60, 0.66) −2.27 (−3.39, −1.15) < 0.001
BCF (Hz) 0 (reference) −0.002 (−0.02, 0.02) −0.02 (−0.04, 0.003) −0.03 (−0.06, −0.01) 0.01 (−0.01, 0.04) 0.611
ALH (μm/s) 0 (reference) 0.01 (−0.04, 0.06) −0.02 (−0.07, 0.03) −0.03 (−0.09, 0.02) −0.06 (−0.11,
−0.003) 0.028
LIN (%) 0 (reference) −0.46 (−1.69, 0.76) 0.78 (−0.59, 2.16) 0.83 (−0.58, 2.24) −0.37 (−1.77, 1.03) 0.971
STR (%) 0 (reference) 0.001 (−0.01, 0.01) 0.005 (−0.004, 0.01) 0.01 (−0.01, 0.02) −0.003 (−0.01, 0.01) 0.862
WOB (%) 0 (reference) −0.42 (−1.46, 0.63) 0.56 (−0.61, 1.73) 0.61 (−0.59, 1.81) −0.22 (−1.42, 0.97) 0.986
MAD (°) 0 (reference) −0.0005 (−0.03, 0.03) −0.01 (−0.05, 0.02) −0.01 (−0.03, 0.04) 0.002 (−0.03, 0.04) 0.971
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As one of the most significant characteristics related
to the fertilizing ability of spermatozoa, motility reflects
their viability and structural integrity [44]. However, the
biological mechanisms behind the association between
PM exposure and decreased sperm motility have yet to
be determined. Previous investigations have suggested
that PM exposure is probably associated with increased
oxidative stress due to decreased antioxidant defenses
or excess reactive oxygen species (ROS) production.
Oxidative stress performs an essential role to trigger
cellular pathological process including proliferation,
inflammation, and apoptosis [45]. PM2.5 component spe-
cies include elemental carbon, organic compounds like
polycyclic aromatic hydrocarbons (PAHs), and heavy
metals [46]. PM2.5 is mainly deposited within the distal
alveoli after inhalation [42]. Such PM2.5 exposure will
result in ROS generation which then leads to system-
atical oxidative stress and cell impairment. An animal
study has reported that Sertoli cells (SCs) would pro-
duce a large amount of ROS after exposure to PM2.5. e
oxidative stress damage in cells resulted in activation of
the mitogen-activated protein kinases (MAPK) pathway,
increasing SCs apoptosis, then destroying the integrity
of the blood-testis barrier, finally causing the quality of
semen [47]. Moreover, PAHs and multiple trace elements
in PM2.5 might contribute to poor sperm quality. Human
and animal studies have suggested possible associations
between PAH exposure and male reproductive function
[48, 49]. Izawa etal. [50] demonstrated inverse associa-
tions between PAH exposures and sperm production,
sperm abnormalities and sperm motility in an animal
study. Our previous study suggested that PAH exposure
contributed to decreased semen quality in a Chinese
population [51]. Adverse influences of metals such as
cadmium and lead on spermatogenesis have been dem-
onstrated [52]. Further studies are warranted to elucidate
the underlying mechanism as well as the specific compo-
nents of PM2.5 that may be driving associations.
Some limitations need to be addressed. As in most
studies of health effects of air pollution, we did not meas-
ure exposure directly at the individual level. It is not
accurate to substitute the average exposure of a region
for that of an individual. And we didn’t rule out the
influence of individual time-activities. However, sam-
pling air pollution at the individual level is not realistic.
Moreover, there is no clear exposure marker to charac-
terize individual exposure levels. Secondly, we estimated
ambient PM2.5 and PM10 using outdoor air monitors,
but indoor environments and time-activity patterns can
also influence individual and population level exposures.
us, individual exposure is underestimated. In part, it
may weaken the link between ambient particulate mat-
ter exposure and semen quality. irdly, the fertile men
delivered only one semen sample each. However, a pre-
vious study has shown that while a single semen sample
may not be adequate for clinical diagnosis of infertility,
that it should suffice for studies aimed at identifying aver-
age differences in semen quality between individuals [53].
e associations between PM2.5 and sperm motility both
in men with all normal sperm parameters as well as in
men with at least one abnormal sperm parameter provide
additional evidence that PM2.5 influences sperm motil-
ity across the population as a whole. Besides, we did not
have the urine or blood sample to detect other exposure
metabolities like endocrine disruptors.
is study has many strengths. One unique feature is
that we examined representative samples from a popula-
tion known to be fertile rather than the infertile popula-
tions from infertility clinics or men of unknown fertility
as in most previous studies [31, 54]. When evaluating
the effects of air pollution on semen quality properly, it
is important to control for confounding factors [28]. We
had information on various potential confounders in
our study. In addition, we used the IDW interpolation to
estimate individual air pollution exposures. Some previ-
ous studies estimated air pollution exposure at the com-
munity level, which could cause misclassification and
mask exposure variation [55]. Further, we assessed a wide
range of 14 semen parameters to investigate the associa-
tions between PM exposure and semen quality in a more
comprehensive manner than in most studies.
In conclusion, a robust association was found between
exposure to PM2.5 in specific window (70-90 days prior to
ejaculation) and reduced sperm motility measures in fer-
tile men. We did not see significant associations between
PM2.5 and sperm concentration or count. ese results
indicate that PM2.5 exposure does not reduce the num-
ber of sperm produced but does impact its functional-
ity which could reduce the ability to fertilize the ovum.
Meanwhile, PM10 plays a less important role than PM2.5
in the relationship between PM exposure and sperm
motility. is suggests that PM2.5 is a more meaningful
reproductive toxicant.
Supplementary Information
The online version contains supplementary material available at https:// doi.
org/ 10. 1186/ s12940- 022- 00831-5.
Additional le1: Figure S1. Sperm kinematic parameters measured by
computer assisted semen analysis (CASA). ALH, amplitude of lateral head
displacement; BCF, beat cross frequency; LIN, linearity; MAD, mean angular
displacement; STR, straightness; VAP, average path velocity; VCL, curvilinear
velocity; VSL, straight line velocity; WOB, curvilinear path wobble. Figure
S2. Flowchart of participants in the study. Normal semen quality group
defined by semen volume ≥ 1.5 ml, sperm concentration ≥ 15 × 106/ml,
total sperm number ≥ 39 × 10,6 and total motility ≥40%. Abnormal semen
quality group defined by at least one abnormal semen parameters (semen
volume, sperm concentration, total sperm number or sperm motility).
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Page 11 of 12
Wuetal. Environmental Health (2022) 21:16
Figure S3. Distribution of daily temperatures in Nanjing between 2014
and 2016. The points in top and bottom graphs indicate daily tempera-
tures. Figure S4. Distribution of daily PM10 in Nanjing between 2014
and 2016. The points in top and bottom graphs indicate daily PM10. The
straight black line indicates Chinese 24-h standard (Grade II) for PM10
(150 μg/m3). TableS1. Coefficient of correlation between the semen
parameters. TableS2. Distribution of air pollutant exposure for study
subjects. TableS3. Coefficients from linear regression for PM10 exposure
in relation to semen parameters by exposure period (0-90, 0-9, 10-14,
15-69, 70-90 days) prior to semen collection in all participants (n = 1554)
expressed as change in the parameter for a 10 μg/m3 increase in exposure.
TableS4. Coefficients from linear regression for PM2.5 exposure in rela-
tion to semen parameters by exposure period (0-90, 0-9, 10-14, 15-69,
70-90 days) prior to semen collection in normal and abnormal semen
parameters groups expressed as change in the parameter for a 10 μg/
m3 increase in exposure. TableS5. Coefficients from linear regression for
PM10 exposure in relation to semen parameters by exposure period (0-90,
0-9, 10-14, 15-69, 70-90 days) prior to semen collection in normal and
abnormal semen parameters groups expressed as change in the param-
eter for a 10 μg/m3 increase in exposure. TableS6. Coefficients (95% CIs)
from linear regression of PM2.5 exposure during 0-90 days before semen
collection in relation to sperm parameters in normal semen parameters
group expressed as change in the parameter for a 10 μg/m3 increase in
exposure. TableS7. Coefficients (95% CIs) from linear regression of PM2.5
exposure during 0-90 days before semen collection in relation to sperm
parameters in abnormal semen parameters group expressed as change in
the parameter for a 10 μg/m3 increase in exposure. TableS8. Coefficients
from linear regression for PM2.5 exposure in relation to total sperm motility
by categories of age, BMI, income, cigarette smoking and alcohol drinking
expressed as change in the parameter for a 10 μg/m3 increase in exposure.
TableS9. Characteristics and main results of previous studies examined
the association between PM and semen quality.
Acknowledgements
Not applicable.
Authors’ contributions
Wei Wu: Conceptualization, methodology, formal analysis, resources, writing -
original draft preparation, funding acquisition, writing - review & editing. Yiqiu
Chen: Methodology, formal analysis, writing - original draft preparation. Yuting
Cheng: Validation, data curation, formal analysis, visualization. Qiuqin Tang:
Validation, funding acquisition, writing - review & editing. Feng Pan: Resources,
writing - review & editing. Naijun Tang, Zhiwei Sun and Xinru Wang: Resources.
Stephanie J. London: Writing - reviewing and editing, funding acquisition.
Yankai Xia: Resources, supervision, funding acquisition, writing - review & edit-
ing. The author(s) read and approved the final manuscript.
Funding
This work was supported by the National Key R&D Program of China
(2017YFC0211605), National Natural Science Foundation of China (81673217,
81971405), Major Research Projects in Natural Science of Jiangsu University
(20KJA330001), Medical Research Project of Jiangsu Health and Health Com-
mission (Z2019010), Jiangsu Overseas Visiting Scholar Program for University
Prominent Young & Middle-aged Teachers and Presidents, and the Prior-
ity Academic Program for the Development of Jiangsu Higher Education
Institutions (Public Health and Preventive Medicine). Supported in part by the
Intramural Research Program of the NIH, National Institute of Environmental
Health Sciences (ZO1 ES49019).
Availability of data and materials
The datasets used and/or analysed during the current study are available from
the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
This study was approved by the Institutional Review Board of Nanjing Medical
University, China NJMUIRB (2010) 0028.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1 State Key Laboratory of Reproductive Medicine, Institute of Applied Toxicol-
ogy, School of Public Health, Nanjing Medical University, 101 Longmian
Avenue, Nanjing 211166, China. 2 Key Laboratory of Modern Toxicology
of Ministry of Education, School of Public Health, Nanjing Medical University,
Nanjing, China. 3 Department of Health and Human Services, National Institute
of Environmental Health Sciences, National Institutes of Health, Research Tri-
angle Park, Durham, USA. 4 Department of Obstetrics, The Affiliated Obstetrics
and Gynecology Hospital of Nanjing Medical University, Nanjing Maternity
and Child Health Care Hospital, Nanjing, China. 5 Department of Urology, The
Affiliated Obstetrics and Gynecology Hospital of Nanjing Medical University,
Nanjing Maternity and Child Health Care Hospital, Nanjing, China. 6 Depart-
ment of Occupational and Environmental Health, School of Public Health,
Tianjin Medical University, Tianjin, China. 7 Beijing Key Laboratory of Environ-
mental Toxicology, School of Public Health, Capital Medical University, Beijing,
China.
Received: 4 August 2021 Accepted: 7 January 2022
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