A mountain of evidence has shown that people’s physical and mental health can be affected by various air pollutions. Poor pregnancy outcomes are associated with exposure to air pollution. Therefore, this study aims to investigate the association between air pollutions (PM2.5, PM10, SO2, NO2, CO, and O3) and preterm birth/low birth weight in Guangdong province, China.
Method
All maternal data and birth data from January 1, 2014 to December 31, 2015 were selected from a National Free Pre-pregnancy Check-ups system, and the daily air quality data of Guangdong Province was collected from China National Environmental Monitoring Center. 1784 women with either preterm birth information (n = 687) or low birth weight information (n = 1097) were used as experimental group. Control group included 1766 women with healthy birth information. Logistic regression models were employed to evaluate the effects of air pollutants on the risk of preterm birth and low birth weight.
Results
The pollution levels of PM2.5, PM10, SO2, NO2, CO, and O3 in Guangdong province were all lower than the national air pollution concentrations. The concentrations of PM2.5, PM10, SO2, NO2 and CO had obvious seasonal trends with the highest in winter and the lowest in summer. O3 concentrations in September (65.72 μg/m3) and October (84.18 μg/m3) were relatively higher. After controlling for the impact of confounding factors, the increases in the risk of preterm birth were associated with each 10 μg/m3 increase in PM2.5 (OR 1.043, 95% CI 1.01–1.09) and PM10 (OR 1.039, 95% CI 1.01~1.14) during the first trimester and in PM2.5 (OR 1.038, 95% CI 1.01~1.12), PM10 (OR 1.024, 95% CI 1.02~1.09), SO2 (OR 1.081, 95% CI 1.01~1.29), and O3 (OR 1.016, 95% CI 1.004~1.35) during the third trimester. The increase in the risk of low birth weight was associated with PM2.5, PM10, NO2, and O3 in the first month and the last month.
Conclusion
This study provides further evidence for the relationships between air pollutions and preterm birth/low birth weight. Pregnant women are recommended to reduce or avoid exposure to air pollutions during pregnancy, especially in the early and late stages of pregnancy.
Hinweise
Ying Liu and Jihong Xu contributed equally to this work.
Abkürzungen
95% CIs
95% confidence intervals
AIC
Akaike Information Criterion
CO
Carbon monoxide
df
degree of freedom
NO2
Nitrogen dioxide
O3
Ozone
ORs
odds ratios
PM10
Particulate matter of less than 10 μm in aerodynamic diameter
PM2
Particulate matter of less than 2.5 μm in aerodynamic diameter
PTD
Percutaneous Transluminal Dilatation
SO2
Sulfur dioxide
TSP
Total suspended particle
Background
An enormous body of evidence has shown that people’s physical and mental health can be affected by various air pollutions [1‐3]. More recently, an increasing number of researches have shown that there is a potential association between the exposure to air pollution and poor pregnancy outcomes, such as preterm birth, low birth weight, and mortality [4‐6]. A review from Stieb et al. (2012) examined the association between air pollution and low birth, change in birth weight and preterm birth for pollutants including particulate matter < 10 and 2.5 μm in aerodynamic diameter (PM10 and PM2.5), nitrogen dioxide (NO2), sulphur dioxide (SO2), and carbon monoxide (CO) [7]. Xu et al. (1995) found that in the third trimester, the duration of gestation was significantly reduced with the increase in levels of sulfur dioxide (SO2) and total suspended particle (TSP) [8]. The risk of low birth weight increased as the mothers were exposed to higher levels of pollutants in the first trimester in Seoul [9]. Another recent meta-analysis study has also showed that maternal exposure to fine particulate air pollution increases the risk of preterm birth and term low birth weight [10]. The researchers indicated that exposure to high concentrations of PM2.5 in the second trimester [11] and exposure to PM10 in the late pregnancy [12] had a strong effect on preterm birth, while birth weight was more consistently correlated to maternal exposure to PM2.5 than preterm birth [13]. Although some studies found that air pollutants significantly impacted birth outcomes, others failed to find such associations, leading to inconsistent and controversial conclusions [4].
In China, the air quality in urban and rural areas has deteriorated in recent years. The Chinese government has paid great attention to the environmental protection issues, such as the average concentration limits of PM and ozone (O3) are included in the “National Ambient Air Quality Standards” implemented by the Chinese Ministry of Environmental Protection in 2016, and the concentration limits of PM and NO2 are also adjusted. Data from the National Monitoring Center shown that the annual average concentrations of PM10, PM2.5, and SO2 have decreased, whereas the pollution levels of NO2 and O3 have increased. [14] It is worth noting that the concentration of O3 is increasing year by year, and O3 pollution has gradually replaced PM2.5 as the primary air pollutant in major Chinese cities [14]. However, most previous studies have only focused on PM. Not all previous studies have found that O3 is a risk factor for preterm birth [11], and few studies have focused on the association between O3 and low birth weight.
Anzeige
Therefore, the objective of this study is to respectively investigate the relationships between atmospheric pollutants (i.e., PM2.5, PM10, SO2, NO2, CO, O3) and preterm birth/low birth weight in Guangdong province, a main province located in the southern China. The expected results are that there are significant correlations between the incidence of preterm birth/low birth weight and maternal exposure to air pollutions, respectively.
Methods
Data
All maternal data and birth data from January 1, 2014 to December 31, 2015 were selected from a National Free Pre-pregnancy Check-ups (NFPC) system. The NFPC has been supported by the National Health and Family Planning Commission of the People’s Republic of China since 2010, a population-based health survey for couples of reproductive-aged who wish to conceive. In this study, maternal demographic information (e.g. maternal age, education level, occupation, registered residence, pregnancy time and gestational age), pregnancy outcomes (e.g. preterm birth weight and low birth weight) and infant information (e.g. infant sex, birth weight, childbirth time, and parity) were collected. A total of 86,139 reproductive-aged women with fetal information were selected. Of these women, 1784 had either preterm birth information (n = 687) or low birth weight information (n = 1097), and 84,095 had healthy birth information excluding the data of duplicate records. The control group was selected from the 84,095 reproductive-aged women with healthy birth information using the simple random sampling method. To match the number of women in the experimental group, 2.1% of the data with healthy birth information were randomly selected as the control group (N = 1766). The participants were from 20 to 49 years old with the average age of 28.45 ± 4.53 years. The majority of the participants belonged to Han ethnic group (96.19%). There were no significant differences in demographic variables between the experimental group and the control group (p > 0.05). The descriptive statistics regarding the participants were showed in Table 1. It should be noted that preterm birth here is defined as the live birth of a baby between 28 and 37 weeks of gestational age, and low birth weight is defined as the live birth weight of baby less than 2500 g [15].
Table 1
Descriptive statistics of the participants
Experimental group
Control group
χ2
p
N
%
N
%
Agea
912.29
0.38
20~25
350
25.38
368
26.82
26~30
690
50.04
674
49.13
31~35
237
17.19
239
17.42
36~40
73
5.29
60
4.37
41~49
29
2.10
31
2.26
Educationa
13.67
0.85
Primary school and below
20
1.71
18
1.59
Junior high school
271
23.20
319
28.21
Senior high school
286
24.49
283
25.02
College
526
45.03
473
41.82
Postgraduate and above
65
5.57
38
3.36
Occupationa
39.95
0.52
Farmer
249
22.02
266
24.01
Worker
223
19.72
259
23.38
Service industry
111
9.81
102
9.21
Business
41
3.63
47
4.24
Housework
33
2.92
23
2.08
Teacher/Civil servant
395
34.92
323
29.18
Others
79
6.98
88
7.94
Registered residencea
1.19
0.28
Rural
845
60.97
912
66.23
Urban
541
39.03
465
33.77
Note: a Variables with missing data
On the other hand, the air pollution data was collected from China National Environmental Monitoring Center which provides daily concentrations of pollutants from 111 monitoring site stations in Guangdong province, including 102 National Ambient Air Quality Monitoring Sites in 21 prefecture-level cities and Shunde District, 8 regional stations and 1 superstation. There are typically multiple monitors located within a city, some of which provide integrated daily measurements. Therefore, city-specific exposure analysis can be used to reduce exposure misclassification. The routine detections of pollutants mainly include the detections of PM2.5, PM10, SO2, NO2, CO, and O3. The 24-h average concentrations of PM2.5, PM10, SO2, NO2, CO, and 8-h (from 10 AM to 6 AM) average concentration of O3 were collected. There must be at least 75% of the one-hour values on a particular day in order to calculate the 24-h average concentration of PM2.5, PM10, SO2, NO2, and CO. It is required to have at least six hourly values from 10 AM to 6 PM to calculate the 8-h average of O3 [16].
Statistical analyses
Excel 2010, SPSS 20.0, and some packages in R 3.5.1 (i.e., ‘rms’, ‘Hmisc’, ‘lrm’, and ‘mgcv’) were used for data analysis. The mean values of the concentrations of air pollutants measured at all monitors in each city were used as the daily air pollution levels. Logistic regression models were employed to evaluate the effects of air pollutants on the risks of preterm birth and low birth weight, which effectively controlled for the impact of other variables such as maternal age, education level, occupation, registered residence, gestational age, infant sex, childbirth time, month of conception and parity. According to the division of seasons by meteorological department, this study divided the whole year into four seasons: spring (from March to May), summer (from June to August), autumn (from September to November) and winter (from December to February).
Anzeige
Natural cubic splines were employed for air pollutants in single-pollutant model to check whether the associations between air pollutants and preterm birth/low birth weight were linear or nonlinear. The degree of freedom (df) was selected by assessing the model fitting on the basis of the Akaike Information Criterion (AIC). If the relationships between air pollution and preterm birth/low birth weight were linear, then the odds ratios (ORs) and the corresponding 95% confidence intervals (CIs) of preterm birth/low birth weight for a 10 μg/m3 increase in PM2.5, PM10, SO2, NO2, O3 and for a 100 μg/m3 increase in CO were calculated; Each air pollutant was added into the single-pollutant model separately. Otherwise, the ORs and the 95% CIs of preterm birth/low birth weight comparing the 75th and 95th percentiles of air pollution versus the minimum preterm birth/low birth weight concentration of air pollution (threshold) were computed. To determine the threshold of air pollutant, we plotted the relationships between air pollutants and preterm birth/low birth weight, and then visually examined the possible range of the threshold. The concentrations of air pollutants corresponding to the lowest AIC values were selected as the thresholds (minimum preterm birth/low birth weight concentrations of air pollutants) [17]. In addition, the pregnancy period was divided into three stages, called trimesters: first trimester (from the first month to the third month), second trimester (from the fourth month to the seventh month), and third trimester (from the eighth month to birth). Statistical inferences were based on the significance level of 0.05 (i.e., p < 0.05).
Results
Table 2 provides the descriptive statistics for the daily number of air pollution concentrations. The mean concentrations of PM2.5, PM10, SO2, NO2, CO, and O3 were 36.45 μg/m3, 55.45 μg/m3, 14.90 μg/m3, 26.37 μg/m3, 1.02 mg/m3, and 56.40 μg/m3, respectively. According to the reference values of National Ambient Air Quality Standard (GB 3095–2012), the pollution levels of PM2.5, PM10, SO2, NO2, CO, and O3 in Guangdong province were all lower than the national air pollution concentrations.
Table 2
Descriptive statistics for the daily number of air pollution concentrations
Pollutant (unit)
\( \overline{x} \)±s
P (25)
Median
P (75)
Range
Concentration limits
PM2.5 (μg/m3)
36.45 ± 18.54
22.00
33.00
47.00
11.00~126.00
75
PM10 (μg/m3)
55.45 ± 24.56
36.00
50.00
69.00
16.00~171.00
150
SO2 (μg/m3)
14.90 ± 5.35
11.00
14.00
18.00
6.00~42.00
150
NO2 (μg/m3)
26.37 ± 10.09
19.00
24.00
31.00
10.00~72.00
80
CO (mg/m3)
1.02 ± 0.21
0.87
0.97
1.16
0.66~1.85
4
O3 (μg/m3)
56.40 ± 19.11
42.00
52.00
68.00
17.00~123.00
160
Note: Concentration limits of PM2.5, PM10, SO2, NO2 and CO: the maximum allowable value of the average concentration within any 24 h. Concentration limit of O3: the maximum allowable value of the average concentration within any 8 h
Table 3 and Fig. 1 present the seasonal distributions of air pollution concentrations. The results showed that the concentrations of PM2.5, PM10, SO2, NO2 and CO had obvious seasonal trends with the highest in winter and the lowest in summer. However, the concentration of O3 mainly concentrated in autumn, especially on October (84.18 μg/m3) and September (65.72 μg/m3).
Table 3
Seasonal distribution of air pollution
Seasons
Month
PM2.5 (μg/m3)
PM10 (μg/m3)
SO2 (μg/m3)
NO2 (μg/m3)
CO (mg/m3)
O3 (μg/m3)
Spring
3
39.07
57.47
15.51
31.28
1.14
43.17
4
35.49
54.48
14.77
26.80
1.05
59.98
5
24.13
39.66
12.15
23.18
0.96
48.45
Summer
6
20.63
36.97
11.32
18.50
0.84
50.32
7
23.15
39.42
11.58
17.13
0.85
56.71
8
23.55
39.78
13.21
18.59
0.84
54.88
Autumn
9
29.63
46.25
12.90
19.92
0.91
65.72
10
47.82
72.16
17.18
26.44
1.01
84.18
11
40.27
60.60
15.87
28.73
1.06
54.30
Winter
12
41.15
62.21
17.85
33.73
1.08
44.50
1
66.24
93.57
22.40
43.53
1.34
61.65
2
46.38
62.55
13.80
28.46
1.18
52.80
Fig. 1
Graphical illustration of the seasonal distribution of air pollution
×
Figures 2 and 3 present the approximate linear relationships between air pollutants and preterm birth/low birth weight. Basic on the AIC statistics, 3 degrees of freedom were selected to represent the models. It was found from Figs. 2 and 3 that, as the concentrations of PM2.5, PM10, NO2, CO, and O3 above the knot locations increased, the risks of preterm birth/low birth weight increased as a whole. As shown in Table 4, after controlling for the impact of maternal age, education level, occupation, registered residence, gestational age, infant sex, childbirth time, month of conception, and parity, the increases in the risk of preterm birth were associated with each 10 μg/m3 increment in the exposure to PM2.5 (OR 1.043, 95% CI 1.01–1.09) and PM10 (OR 1.039, 95% CI 1.01~1.14) during the first trimester, indicating 4.3 and 3.9% increased risk of preterm birth, respectively. In addition, significant associations were found for preterm birth with PM2.5 (OR 1.038, 95% CI 1.01~1.12), PM10 (OR 1.024, 95% CI 1.02~1.09), SO2 (OR 1.081, 95% CI 1.01~1.29), and O3 (OR 1.016, 95% CI 1.004~1.35) during the third trimester. Moreover, the increase in the risks of low birth weight was associated with each 10 μg/m3 increment in NO2 (OR 1.124, 95% CI 1.02–1.24) during the second trimester and with each 100 μg/m3 increment in CO (OR 1.063, 95% CI 1.00~1.14) during the first trimester. For the entire pregnancy, the odds ratios of preterm birth for a 10 μg/m3 increase in PM2.5 and PM10 were 1.007 (95% CI 1.01–1.08) and 1.038 (95% CI 1.01–1.07), respectively. And the odds ratios of low birth weight for a 10 μg/m3 increase in PM2.5 and PM10 were 1.028 (95% CI 1.00–1.06), 1.018 (95% CI 1.01–1.04) and for a 100 μg/m3 increase in CO was 1.340 (95% CI 1.04–1.73), respectively.
Fig. 2
Concentration-response relationships between air pollutants and preterm birth
Fig. 3
Concentration-response relationships between air pollutants and low birth weight
Table 4
Associations between pregnancy exposure to air pollutions and preterm birth/low birth weight
Preterm birth
Low birth weight
First trimester
Second trimester
Third trimester
Whole pregnancy
First trimester
Second trimester
Third trimester
Whole pregnancy
PM2.5
OR
1.043a
1.056
1.038a
1.007a
1.063
1.061
0.925
1.028a
95% CI
1.01~1.09
0.98~1.14
1.01~1.12
1.01~1.08
0.98~1.15
0.99~1.13
0.86~0.99
1.00~1.06
PM10
OR
1.039a
1.031
1.024a
1.038a
0.967a
1.046
0.937a
1.018a
95% CI
1.01~1.14
.96~1.10
1.02~1.09
1.01~1.07
0.91~1.03
0.99~1.11
0.88~0.99
1.01~1.04
SO2
OR
0.990
1.112
1.081a
1.047
0.907
0.927a
1.019
1.007
95% CI
0.82~1.19
0.89~1.38
1.01~1.29
0.95~1.15
0.77~1.07
0.89~0.96
0.98~1.05
0.99~1.02
NO2
OR
1.130
1.078
0.970
1.051
0.916
1.124a
0.896
1.039
95% CI
0.98~1.30
0.96~1.21
0.88~1.06
0.99~1.11
0.84~1.00
1.02~1.24
0.83~1.10
0.99~1.09
CO
OR
1.059
0.839a
0.991
1.276
1.063a
0.845a
0.974
1.340a
95% CI
0.98~1.14
0.76~0.92
0.92~1.06
0.97~1.68
1.00~1.14
0.77~0.92
0.91~1.04
1.04~1.73
O3
OR
0.891
0.932
1.016a
1.000
0.895
0.979
1.023
0.999
95% CI
0.76~1.04
0.89~1.06
1.004~1.35
0.99~1.01
0.778~1.03
0.87~1.10
0.889~1.18
0.99~1.00
Note: ap<0.05
×
×
More specifically, the associations between PM2.5, PM10, NO2, CO and preterm birth in the second month were found, and the ORs were 1.038 (95% CI 1.01–1.07), 1.021 (95% CI 1.01–1.04), 1.043 (95% CI 1.01–1.08) and 1.069 (95% CI 1.001–1.14), respectively (see Table 5). The associations were also observed with exposure to PM2.5, PM10 and O3 in the eighth month (p < 0.05). The ORs of premature birth for a 10 μg/m3 increase in NO2 in the last month was 1.034 (95% CI 1.00–1.07). Additionally, for each 10 μg/m3 increase, the resulting ORs of low birth weight were 1.059 (95% CI 1.02–1.10) for PM2.5, 1.090 (95% CI 1.03–1.15) for PM10, 1.328 (95% CI 1.01–1.75) for SO2, 1.185 (95% CI 1.06–1.32) for NO2, and 1.108 (95% CI 1.03–1.19) for O3 in the first month; and for each 100 μg/m3 increase, the ORs was 1.117 (95% CI 1.05–1.19) for CO. And the associations between PM2.5, PM10, NO2, O3 and low birth weight in the last month were also found (OR 1.082, 95% CI 1.01~1.17; OR 1.063, 95% CI 1.01~1.13; OR 1.030, 95% CI 1.01~1.15; OR 1.106, 95% CI 1.03~1.12) (see Table 6).
Table 5
Associations between preterm birth and pregnancy exposure to air pollutions during each month
The first month
The second month
The third month
The fourth month
The fifth month
The sixth month
The seventh month
The eighth month
The ninth month
The tenth month
PM2.5
OR
1.006
1.038a
1.001
0.991
1.002
0.995
1.010
1.028a
0.992
1.021
95% CI
0.99~1.01
1.01~1.07
0.98~1.02
0.97~1.01
0.98~1.02
0.98~1.01
0.99~1.03
1.01~1.05
0.97~1.01
0.99~1.04
PM10
OR
1.006
1.021a
0.993
0.989
1.000
0.999
1.007
1.012a
0.990
1.008
95% CI
0.98~1.02
1.01~1.04
0.99~1.01
0.97~1.00
0.99~1.01
0.99~1.01
0.99~1.02
1.00~1.02
0.98~1.00
0.99~1.02
SO2
OR
1.052
1.018
.920a
1.002
1.035
0.998
0.987
1.019
0.995
1.033
95% CI
0.99~1.12
0.97~1.07
0.88~0.96
0.95~1.05
0.98~1.09
0.95~1.05
0.94~1.03
0.98~1.06
0.96~1.04
0.99~1.07
NO2
OR
1.032
1.043a
0.962a
1.010
1.013
1.016
0.986
1.021
1.000
1.034a
95% CI
0.99~1.08
1.01~1.08
0.93~0.99
0.97~1.05
0.98~1.06
0.98~1.05
0.95~1.02
0.98~1.06
0.97~1.03
1.00~1.07
CO
OR
1.905a
1.069a
0.970
0.906a
0.887
0.902a
0.924
0.971
0.990
1.001
95% CI
1.02~1.17
1.001~1.14
0.91~1.04
0.84~0.98
0.81~0.97
0.84~0.97
0.87~0.98
0.91~1.03
0.931~1.05
0.93~1.08
O3
OR
1.004
1.000
0.999
0.934
1.006
0.958
1.023
1.103a
0.987
1.007
95% CI
0.93~1.09
0.99~1.00
0.99~1.00
0.86~1.01
0.94~1.08
0.90~1.01
0.96~1.08
1.03~1.18
0.89~1.04
0.99~1.02
Note: ap<0.05
Table 6
Associations between low birth weight and pregnancy exposure to air pollutions during each month
The first month
The second month
The third month
The fourth month
The fifth month
The sixth month
The seventh month
The eighth month
The ninth month
The tenth month
PM2.5
OR
1.059a
1.046
0.982
0.998
1.004
0.899
0.951
0.947
0.990
1.082a
95% CI
1.02~1.10
0.98~1.11
0.91~1.06
0.99~1.01
0.99~1.02
0.84~0.96
0.89~1.01
0.89~1.01
0.96~1.01
1.01~1.17
PM10
OR
1.090a
1.035
0.988
0.989
1.002
0.921
0.964
0.965
1.063a
1.063a
95% CI
1.03~1.15
0.98~1.08
0.98~1.00
0.98~1.00
0.99~1.01
0.87~1.00
0.92~1.01
0.92~1.01
1.00~1.13
1.01~1.13
SO2
OR
1.328a
0.957
0.980
1.026
0.968
0.951a
0.981
0.992
1.124
1.124
95% CI
1.01~1.75
0.92~1.07
0.95~1.02
0.98~1.07
0.93~1.01
0.91~0.99
0.94~1.02
0.81~1.21
0.88~1.44
0.88~1.43
NO2
OR
1.185a
1.137a
1.036
0.999
0.978
0.995
0.968a
0.918
1.010
1.030a
95% CI
1.06~1.32
1.02~1.27
0.91~1.17
0.87~1.15
0.95~1.01
0.97~1.02
0.94~0.99
0.83~1.02
0.99~1.03
1.01~1.15
CO
OR
1.117a
1.038
0.978
0.930a
0.862a
0.886a
0.924a
0.942a
0.960
1.018
95% CI
1.05~1.19
0.97~1.11
0.92~1.04
0.87~0.99
0.79~0.94
0.83~0.95
0.87~0.98
0.89~0.99
0.90~1.02
0.95~1.09
O3
OR
1.108a
0.999
0.999
0.885
0.984
0.993
1.084a
1.000
0.907
1.106a
95% CI
1.03~1.19
0.98~1.00
0.99~1.00
0.82~0.96
0.92~1.05
0.94~1.05
1.02~1.15
0.93~1.07
0.84~1.00
1.03~1.12
Note: ap<0.05
Discussion
This study investigated the association between air pollutions (PM2.5, PM10, SO2, NO2, CO, O3) and preterm birth/low birth weight. The results showed that after controlling for the impact of confounding factors, there were significant associations between preterm birth and PM2.5, PM10, SO2, NO2, and O3, especially during the first trimester and the third trimester, which were consistent with the previous studies [18‐20]. Olsson et al. (2013) indicated that the risk of preterm birth could be increased with rising O3 concentration during the early pregnancy [18]. Cheng et al. (2016) found that exposure to high concentrations of PM2.5 in the third trimester might increase the risk of preterm birth, especially in the half a month before delivery [20]. Exposure to PM10 also affected on preterm birth in the late pregnancy, especially in the seventh and ninth month of pregnancy [21]. Additionally, Leem et al. (2006) also found that exposure to SO2 in the late pregnancy was statistically significant for Percutaneous Transluminal Dilatation (PTD) patients [22]. Moreover, the increased concentration of SO2 during the third trimester increased the risk of preterm birth, and this relationship was statistically significant [23].
On the other hand, the significant associations were found for low birth weight with PM2.5, PM10, SO2, NO2, O3, CO in the first month and with PM2.5, PM10, NO2, O3 in the last month. The effects of air pollution on low birth weight also were found from the previous studies [24]. For example, Chen et al. (2000) indicated that exposure to PM10 in the late pregnancy could predict the neonatal weight after controlling for baby gender, the pregnant women’s age, living area, ethnic, education, drugs and alcohol use. For every 10 μg/m3 increase in PM10 concentration of 24 h during the late trimester, the weight of newborn was reduced by 11 g [24]. Dugandzic et al. (2006) collected the pregnant women data within 25 km from the air monitoring station at the Ministry of Health of Nova Scotia in Canada from 1988 to 2000, and found the higher risk effect of exposure to higher SO2 and PM10 concentrations during early pregnancy on low birth weight using the multiple regression models [25]. Gouveia et al. (2004) found that if pregnant women were exposed to CO in the early pregnancy, an increase in the mean concentration of 1 μm would reduce the weight of newborn by 23 g [26]. An interquartile of exposure to NO2, CO, PM10 and PM2.5 during pregnancy increased, and birth weight decreased by 8.9 g, 16.2 g, 8.2 g and 14.7 g, respectively [27]. Additionally, for each 50 μg/m3 increase in concentration, the OR value of the effect of exposure to SO2 at early pregnancy on low birth weight was 1.20, and the corresponding 95% CI was from 1.11 to 1.30 [28].
Therefore, the present study demonstrated that the early and late pregnancy might be the critical period of preterm birth and low birth weight caused by PM2.5, PM10, NO2, SO2, CO, O3 pollutions, and further confirmed the previous reports on the adverse effect of air pollution on preterm birth and low birth weight. These results suggested that pregnant women should reduce or avoid exposure to air pollutants during pregnancy, especially in the early and late stages of pregnancy.
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This study has some limitations that merit future improvements. First, the number of monitoring sites in each city of Guangdong province is different and individual cities has only four or five monitoring sites, which might lead to incomplete monitoring data. Second, we assumed that the pollution levels were homogeneous for every resident in the present study. People in some areas inevitably expose to high pollution, while others are relatively low. Therefore, it is valuable to investigate the differences in adverse pregnancy outcomes between the areas with the highest levels and the lowest levels of air pollution in the future. Third, some other important factors associate with pregnancy outcomes are not considered in this study, such as social economic status, smoking, altitude, etc. Last, but not least, another line of research worth considering is to explore the interactions of various pollutants and other influencing factors, and the influence of two or more pollutants on preterm birth and low birth weight.
Conclusions
This study provides further evidence for the relationships between air pollutions and preterm birth/low birth weight. The risks of preterm birth increase for each 10 μg/m3 increase in PM2.5, PM10 during the first trimester and in PM2.5, PM10, SO2, O3 during the third trimester. The increase in the risk of low birth weight is associated with PM2.5, PM10, NO2, and O3 in the first month and the last month. Additionally, the current study has found that the concentrations of O3 in September and October are relatively high, thus it is strongly recommended that pregnant women in Guangdong should avoid pregnancy during the two months with high O3 concentrations. Finally, public policies and guidelines for maternal health should be improved to protect women from the risks of preterm birth and low birth weight due to air pollution.
Acknowledgments
The authors thank the China National Environmental Monitoring Center for sharing useful the air quality daily data of Guangdong Province.
Funding
The study was supported by the Special Fund of the Chinese Central Government for Basic Scientific Research Operations (2016GJM03). The funding sources had no role in the study design, data collection, analysis and interpretation, and in the writing of the manuscript or in the decision to submit the manuscript for publication.
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Availability of data and materials
Please contact author for data requests.
Ethics approval and consent to participate
A written informed consent form was obtained from each participant before enrolment. The study was approved by Institutional Review Board of Chinese Association of National Research Institution for family planning.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
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