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The relationship between air pollutants and preterm birth and blood routine changes in typical river valley city

BMC Public Health volume 24, Article number: 1677 (2024) Cite this article

Abstract

Objective

To collect maternal maternity information on preterm births in two tertiary hospitals in the urban area of Baota District, Yan'an City, from January 2018 to December 2020, to explore the long-term and short-term effects of air pollutants (PM2.5, PM10, SO2, NO2, CO and O3) and preterm births, and to explore changes in blood cell counts due to air pollutants.

Methods

Daily average mass concentration data of six air pollutants in the urban area of Yan'an City from January 1, 2017 to December 31, 2020 were collected from the monitoring station in Baota District, Yan'an City. Meteorological information was obtained from the Meteorological Bureau of Yan'an City, including temperature,relative humidity and wind speed for the time period. The mass concentration of air pollutants in each exposure window of pregnant women was assessed by the nearest monitoring station method, and conditional logistic regression was used to analyze the relationship between air pollutants and preterm births, as well as the lagged and cumulative effects of air pollutants. Multiple linear regression was used to explore the relationship between air pollutants and blood tests after stepwise linear regression was used to determine confounders for each blood test.

Results

The long-term effects of pollutants showed that PM2.5, PM10, SO2, NO2and CO were risk factors for preterm birth. In the two-pollutant model, PM2.5, PM10, SO2 and NO2 mixed with other pollutants were associated with preterm birth. The lagged effect showed that PM2.5, PM10, SO2, NO, and CO were associated with preterm birth; the cumulative effect showed that other air pollutants except O3 were associated with preterm birth. The correlation study between air pollutants and blood indicators showed that air pollutants were correlated with leukocytes, monocytes, basophils, erythrocytes, hs-CRPand not with CRP.

Conclusion

Exposure to air pollutants is a risk factor for preterm birth. Exposure to air pollutants was associated with changes in leukocytes, monocytes, basophils and erythrocytes and hs-CRP.

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Introduction

While cities in China have been accelerating the progress of urbanization in recent years, a large amount of industrial emissions and vehicle exhausts have polluted the air. Air pollution mainly includes particulate matter (PM2.5, PM10), ozone (O3), nitrogen dioxide (NO2) and sulphur dioxide (SO2). A large number of studies in various countries have shown that air pollutants are associated with a variety of systemic diseases in the human body [1, 2].such as the nervous system [3],the immune system [4],the endocrine system [5]and the reproductive system [6]. Air particulate matter can enter the body and cause direct damage to the respiratory tract [7]. Long-term exposure to ozone pollution may lead to airway inflammation and decreased lung function [8],and eye irritation [9]. In addition, ozone has been associated with an increased incidence of cardiovascular disease [10], which has also been associated with increased mortality from diseases of the cardiovascular system [11]. It has also been shown that prolonged exposure to high mass concentrations of nitrogen dioxide and sulfur dioxide can also cause respiratory irritation [12]. Exposure to nitrogen dioxide and sulfur dioxide is also associated with cardiovascular disease [13]. In addition to the long-term exposure effects of pollutants, short-term exposure also has an impact on the human body. Studies have shown that short-term exposure to air pollutants is not only associated with ischemic stroke [14], but also associated with childhood respiratory diseases [15]. Short-term exposure has also been documented to cause changes in male reproduction-related hormones [16]. Even anxiety, depression, mental illnesses [17], mortality rate [18] have also been shown to be associated with short-term exposure.

Air pollutants have also been shown to be associated with adverse pregnancy outcomes [19]. Preterm birth is one of the adverse pregnancy outcomes and it is also one of the most common perinatal complications in pregnant women, and according to statistics, 15 million preterm babies are born worldwide every year [20]. With the increasing social pressure, environmental and climate changes and the full opening of the two-child policy, China's preterm birth rate is increasing year by year [21]. Preterm babies are often born with preterm complications and are at increased risk for other diseases as they grow. Preterm birth and its complications are the leading cause of neonatal deaths. Pregnancy is a long and multifactorial process, so there are many factors that can lead to preterm birth in pregnant women. Common factors that lead to preterm birth are the pregnant woman's own factors, genetic factors, and infections during pregnancy, environmental factors, psycho-behavioral factors  and ethnic factors.Although the majority of studies on air pollutants and preterm birth have shown a correlation, there are inconsistencies in the major exposure windows, such as a strong correlation between exposure to PM2.5 and preterm birth throughout the entire pregnancy [22], and some studies suggesting that PM2.5 has its strongest effect in late pregnancy [23] or a correlation with exposure in the week prior to delivery [24].

Some studies have also shown a correlation between blood routine and preterm birth [25, 26]. Blood routine examination is a test to judge blood conditions and diseases by observing the changes in the number and morphological distribution of blood cells. Changes in blood cell counts reflect subtle changes in the body.

Existing studies have also shown that air pollutants have a certain effect on blood. It has been shown that exposure to air pollutants decreases the number of red blood cells and increases the ratio of white blood cells, neutrophils and lymphocytes, with no effect on monocytes [27]. It has also been shown that increased PM2.5 mass concentrations are associated with lower erythrocyte [28]. C-reactive protein(CRP) is a nonspecific marker of inflammation and tissue damage in the human body, hypersensitive C-reactive protein(hs-CRP) is synthesized by the liver and is a nonspecific marker of the acute phase of the systemic inflammatory response. Changes in the concentrations of both have been shown to be associated with a variety of human systemic diseases [29, 30]. However, there are still some discrepancies in the studies on blood markers [31, 32], Therefore, further research is needed on air pollutants and blood markers.

The city of Yan'an is located in the hilly and gully area of the Loess Plateau in northern Shaanxi Province, which is a typical hilly and gully landscape. The urban area of Yan'an is located in the middle of a "Y" shaped valley, and the narrow geographic environment facing the mountains on both sides creates a mountain screen effect, which restricts the horizontal diffusion of pollutants in the near-surface layer, and then creates a buildup of pollutants in the air above the city. Although air quality in Yan'an has improved year by year in recent years, there are still periods of time when air pollutant mass concentrations are high.

In this study, we collected data on pregnant women and air pollutants in the urban area of Yan'an City from 2018 to 2020 to assess the exposure dose of air pollutants received by pregnant women during pregnancy. The relationship between air pollutants and preterm birth and the relationship between air pollutants and blood indicators were analyzed to provide a basis for the impact of air pollution on preterm birth. Based on the existing literature we predict that atmospheric pollutants may be a risk factor for the occurrence of preterm birth in pregnant women. At the same time, air pollutants may also cause certain changes in the blood counts of pregnant women.

Methods

Research population

In this study, the data of pregnant women in the Department of Obstetrics and Gynecology of two local hospitals in Bota District, Yan'an City, were collected from 2018 to 2020. After collecting data information, we confirmed the inclusion and exclusion criteria. Inclusion criteria: residents of Baota District, Yan'an City, who have lived in the district for one year or more; normal mental status; no communication barriers; no assisted conception; no missing information; no major diseases.Exclusion criteria: people with various missing information; people with abnormal mental status who cannot communicate; people with assisted conception or multiple pregnancies; people with hereditary diseases.All pregnant women have signed an informed consent form, and the study has been approved by the ethics committee of Medical School of Yan'an University (approval number: 2018051).

After screening for inclusion criteria, 460 cases of preterm birth(PTB) with complete maternal data were collected as the case group. In order to reduce the influence of confounding factors on the results of the study, we took term births(TB) of the same age and the same gestation as preterm births as the control group and selected 1,840 cases of term births as the control group in a ratio of 1 to 4. The data collected from pregnant women included the following: general data of pregnant women (name, age, gestational address, occupation), pregnancy data (gestational age, Pregnancy times, number of births, number of caesarean sections, regularity of menstrual cycle, season of the last menstrual period, Complications diseases, Comorbidity diseases, and hypertension in pregnancy), neonatal data (birth weight, date of birth) and routine blood data:leukocyte count (WBC), percentage of neutrophils (NEUT), percentage of lymphocytes (LYM), percentage of monocytes (MONO), percentage of eosinophils (EOS), percentage of basophils (BAS), erythrocyte count (RBC), hs-CRP,CRP.

Complications diseases: severe vomiting of pregnancy, ectopic pregnancy, placenta previa, placental abruption, excessive or low amniotic fluid, premature rupture of membranes, hyperemesis gravidarum, acute chorioamnionitis.

Comorbidity diseases: combined cardiovascular diseases (congenital heart disease, rheumatic heart disease,etc.), combined hematological diseases (chronic aplastic anemia, idiopathic thrombocytopenic purpura,etc.), combined respiratory diseases (tuberculosis, bronchial asthma), combined gastrointestinal system diseases (viral hepatitis, acute appendicitis,etc.), combined urinary system diseases (acute pyelonephritis, chronic glomerulonephritis,etc.), combined endocrine system diseases (hyperthyroidism, hypothyroidism,etc.), combined dermatological disorders (scleroderma, hives, herpes), combined infectious diseases (cytomegalovirus infections, genital herpes,etc.), combined tumors (uterine fibroids, cervical cancer,etc.).

Pollutant exposure assessment

Data sources for air pollutants: All pollutants information was obtained from the Qingyue Open Environmental Data Centre (http://data.epmap.org/), and data were obtained from the daily average mass values of six pollutants (PM2.5, PM10, SO2, NO2, CO and O3) at four monitoring stations in Baota District, Yan'an City (the mass concentration unit of CO is mg/m3, and the mass concentration of the remaining air pollutants is μg/m3). Meteorological data were obtained from Yan'an Meteorological Monitoring Station. The time span of pollutant data and meteorological data is from 1 January 2017 to 31 December 2020.

Exposure assessment method: Based on the address of the pregnant woman's place of residence during pregnancy, the latitude and longitude of her place of residence were obtained from Gaode Map. The distance from the latitude and longitude of the place of residence to the latitude and longitude of the monitoring station was calculated, and the pollutant mass concentration of the nearest monitoring station to the place of residence was selected as the individual's exposure mass concentration. After dividing each exposure window according to the date of the last menstrual period of pregnant women, the mass concentration of pollutants in each exposure window was calculated (Fig. 1).

Fig. 1
figure 1

Location of Yan'an city in the surrounding area

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Calculation of pollutants

The exposure window is divided according to the date of the mother's last menstrual period into:E: the entire pregnancy (last menstrual period to the date of birth of the births), T1:early pregnancy (last menstrual period to the twelfth week of gestation), T2:mid-pregnancy (the thirteenth week of gestation to the twenty-seventh week of gestation), and T3:late pregnancy (the twenty-eighth week of gestation to the date of birth of the births). Considering that air pollutants have short-term effects in addition to long-term effects on birth, lagged and cumulative effects were calculated based on the date of admission to hospital. Matching of same-day and lag-day mass concentrations: Short-term same-day lagged mass concentrations of each pollutant on the day of the admission date and before the admission date are matched based on the maternal admission date. This exposure mass concentration is the daily average of the day, where the short-term lagged exposure dates are the day of the admission date (Lag0) and the days 1 (Lag1), 2 (Lag2), 3 (Lag3), 4 (Lag4), 5 (Lag5), 6 (Lag6), and 7 (Lag7) before the admission date. Calculation of mass concentration values for cumulative exposure: Matching the mass concentration of each pollutant prior to the date of admission to the date of maternal admission, the cumulative exposure mass concentration value is the average of all daily averages over the exposure period. The cumulative exposure period is 1 day (Lag-1), 2 days (Lag-2), 3 days (Lag-3), 4 days (Lag-4), 5 days (Lag-5), 6 days (Lag-6), and 7 days (Lag-7) before the day of admission.

Statistical methods

A database was created by EXCEL and data were analyzed by IBM SPSS 20. Normality test for pollutants was performed and pollutants were described using mean, median, standard deviation(SD), and interquartile range(IQR). The categorical variable information in the general information of pregnant women was statistically described using frequency (n) and composition ratio (%), and the difference between the case and control groups was compared using the chi-square test to determine confounders in turn. The correlation between air pollutants and temperature and relative humidity was analyzed, and pollutant mass concentrations were calculated for each exposure window using the R language. The long-term effects and lagged and cumulative effects of air pollutants and preterm birth were analyzed using conditional logisitic models, and after adjusting for each confounding factor, pollutants were introduced into the conditional logistic model with as a continuous variable with in, and ORs and 95% confidence intervals were calculated. After identifying the association between air pollutants and preterm birth, we adjusted for confounders and investigated the relationship between air pollutants and blood counts using multiple linear regression. Logistic regression was then used to explore the relationship between air pollutants and C-reactive protein at a test level of 0.05 (two-sided test).

Correlation analysis

Spearman's correlation analysis was used to analyze the correlation between air pollutants, blood markers, and meteorological factors (mean temperature throughout the pregnancy, relative humidity throughout the pregnancy, and mean wind speed throughout the pregnancy).

Sensitivity analysis

In order to verify the stability of the main model, the effect of each pollutant on preterm birth was analyzed separately. Then other confounding factors were added one by one to analyze the relationship between each pollutant and preterm delivery. Three sensitivity analyses were performed with meteorological factors as confounding factors: (1) Model 1: correlation analysis between a single pollutant and each blood index; (2) Model 2: add occupation, birth order and last menstrual season to analyze the correlation between each pollutant and preterm delivery in Model 1; and (3) Model 3: add menstrual cycle and complications diseases to Model 2.

Results

General situation of pollutants in urban areas of Baota District

Mass concentration data of six pollutants in Yan'an City were collected from 2017 to 2020.PM2.5, SO2, NO2 and CO showed a U-shaped trend, while O3 an inverted U-shaped pattern.The mass concentration of SO2 was basically flat from March to October each year, and then increased sharply from November to February of the following year.From April to October, there was little change in the mass concentration of CO, and then increased and decreased from November to March of the next year. CO mass concentrations also varied slightly from April to October, then increased and decreased from November to March, and O3 mass concentrations increased gradually from January to April, reached a maximum in May–June, and then decreased over time. Mass concentrations of all six pollutants showed seasonal variations, with O3 increasing in the spring and summer compared to fall and winter,peaking between May and June (Fig. 2).

Fig. 2
figure 2

General Air Quality in the Baota District, 2017–2020

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PM2.5, PM10, SO2 and NO2 were all above the national secondary standard at some time during each year. The median and maximum mass concentration of PM2.5 in winter are higher than the secondary standard, indicating that the pollution is mainly concentrated in winter. The PM2.5 mass concentration is gradually decreasing with the change of time. Compared with other air pollutants, Mass concentrations of PM10 are concentrated near the secondary standard. The mass concentration of PM10 also shows a decreasing trend from year to year. SO2 has been below the state's secondary standard since 2019. Mass concentrations of NO2 were above the secondary standard at times in each year and again showed higher concentrations in winter and spring than in summer and fall. CO and O3 are generally within the state's secondary standard mass concentrations (Fig. 3).

Fig. 3
figure 3

Air Quality Statistical Indicators for Baota District, 2017–2020

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General demographic characteristics of the study population

Based on Table 1, the chi-square analysis of the study population showed statistically relevant differences between the case and control groups in terms of maternal occupation, number of births, season of last menstruation, menstrual cycle, fetal birth weight, complications diseases, and hypertension in pregnancy. In the collected study population, pregnant women who experienced preterm birth relative to term births had a higher proportion of those who were 25–30 years of age, unemployed, > 1 times pregnancy, ≤ 1 times births, ≤ 1 times caesarean sections, last menstrual season of winter, regular menstrual cycle, fetal birth weight < 2500, and suffered from complications diseases, Comorbidity diseases, no hypertension in pregnancy. The greater proportion of preterm births occurring in winter is also consistent with the seasonal variation of air pollutants (Table 1).

Table 1 Chi-Square test for data on pregnant women
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Comparison of pollutants by exposure window for pregnant women

According to Table 2, throughout the pregnancy, the mean PM2.5, PM10, SO2, NO2 and CO exposure mass concentrations in the preterm pregnant women were 33.732 μg/m3, 77.506 μg/m3, 12.136 μg/m3, 38.084 μg/m3 and 0.993 mg/m3, all of which were higher than those in the term group. And the O3 mass concentration was lower than that in the term group. In early pregnancy, the mass concentrations of PM2.5, PM10, SO2, NO2 and CO were also higher in the preterm group than in the term group, with mass concentrations of 34.791 μg/m3, 79.017 μg/m3, 11.990 μg/m3, 38.701 μg/m3, and 1.023 mg/m3, and O3 mass concentrations were also lower than in the term group. In mid-pregnancy, the mass concentrations of PM2.5, PM10, SO2, NO2 and CO were 33.150 μg/m3, 77.283 μg/m3, 12.180 μg/m3, 37.830 μg/m3, and 0.978 mg/m3, which were also higher than those in the term birth group. And the mass concentration of O3 was lower than that in the term birth group. In late pregnancy, the mean values of SO2 and O3 exposure mass concentrations were higher than those of the full-term group, with mass concentrations of 12.165 μg/m3 and 90.232 μg/m3, while PM2.5, PM10, NO2 and CO were lower than those of the full-term group (Table 2).

Table 2 Comparison of statistics of the mass concentrations of pollutants by exposure window
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Air pollutants and meteorological factors analysis for the Baota District

Based on Table 3 and table S3,We found that among the six pollutants, Only O3 is High positive correlation with temperature.The remaining five pollutants are negative correlation with meteorological factors, where SO2 and NO2 have no correlation with wind speed.SO2, CO and temperature are Moderate negative correlation, and PM10 is negligible correlation. PM10 and NO2 are Low negative correlation with relative humidity, and PM2.5, SO2, CO, O3 and relative humidity are negligible correlation. All air pollutants have a negligible correlation with wind speed.The mean value of temperature is 10.11 °C with a SD of 10.05 °C. The mean value of wind speed is 2.03 m/s with a SD of 0.81m/s. The mean value of relative humidity is 59.33% with a SD of 20.49%.

Table 3 Correlation analysis between atmospheric pollutants and meteorological factors
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Logistic regression model and sensitivity analysis

Based on Table 4, before adjustment, all air pollutants except O3 were shown to be risk factors for preterm birth throughout pregnancy, early pregnancy and mid-pregnancy. After adjusting for maternal occupation, maternal parturition, last menstrual season, regularity of menstrual cycle, fetal birth weight, pregnancy complications, hypertension in pregnancy and meteorological factors, PM2.5 (OR: 1.098, 95% CI: 1.054–1.145), PM10 (OR: 1.031, 95% CI: 1.017–1.045), SO2 (OR: 1.107, 95% CI: 1.075–1.139), and NO2 (OR: 1.107, 95% CI: 1.060–1.156) exhibited risk factors for preterm birth throughout pregnancy. It means that for every 10 μg/m3 increase in pollutants, the risk of preterm birth increased by 9.8%, 3.1%, 10.7%, and 10.7%, respectively. In early pregnancy PM2.5 (OR: 1.049, 95% CI: 1.021–1.077), SO2 (OR: 1.053, 95% CI: 1.026–1.080), and NO2 (OR: 1.054, 95% CI: 1.015–1.094) behaved as a risk factor for preterm birth, and for every 10 μg/m3 increase in pollutants, the risk increased by 4.9%, 5.3% and 5.4%, respectively. In mid-pregnancy PM2.5 (OR: 1.045, 95% CI: 1.022–1.069), PM10 (OR: 1.018, 95% CI: 1.010–1.025), SO2 (OR: 1.088, 95% CI: 1.061–1.117) and NO2 (OR: 1.075, 95% CI: 1.039- 1.112) were risk factors for preterm birth, and the risk of preterm birth increased by 4.5%, 1.8%, 8.8% and 7.5%, respectively, when each 10 μg/m3 increase in pollutants was observed. In late pregnancy, PM2.5 (OR: 1.028, 95% CI: 1.005–1.052), PM10 (OR: 1.016, 95% CI: 1.009–1.023), SO2 (OR: 1.060, 95% CI: 1.040–1.080), NO2 (OR: 1.069, 95% CI: 1.037- 1.103) were all risk factors for preterm birth. When the pollutant mass concentration increased by every 10 μg/m3, the risk of preterm birth occurrence increased by 2.8%, 1.6%, 6%, and 6.9%. Exposure to CO was a risk factor for preterm birth throughout pregnancy, early, mid and late pregnancy (Table 4).

Table 4 Analysis of the correlation between air pollutants and preterm birth
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Sensitivity analysis

From Table 5,after adding each confounding factor one by one, the correlation between each pollutant and preterm birth did not change significantly, proving that the main model was stable (Table 5).

Table 5 Sensitivity analysis
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Analysis of the correlation between mixed pollutants and preterm birth

According to Table 6, thro0ughout pregnancy, mixing of PM10 with NO2 was associated with preterm birth (OR: 1.025, 95% CI: 1.009–1.040), and mixing of PM10 with O3 was also associated with the occurrence of preterm birth (OR: 1.027, 95% CI: 1.012–1.042).Mixing of SO2 with NO2, CO, and O3, respectively, was also correlated with preterm birth, with ORs and confidence intervals of (OR: 1.187, 95% CI: 1.125–1.252), (OR: 1.287, 95% CI: 1.172–1.413), and (OR: 1.098, 95% CI: 1.062–1.135), respectively. Mixing of NO2 with O3 was also correlated with preterm birth (OR: 1.061, 95% CI: 1.005–1.119) (Table 6).

Table 6 Analysis of the correlation between the two-pollutant model and preterm birth for E
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From Table 7, in early pregnancy, mixing of PM2.5 with SO2 was associated with preterm birth (OR: 1.032, 95% CI: 1.005–1.060). PM2.5 was also associated with preterm birth by mixing with NO2 (OR: 1.040, 95% CI: 1.009–1.072). PM2.5 was also correlated with preterm birth by mixing with O3 (OR: 1.043, 95% CI: 1.017–1.070). Mixing of SO2 with NO2 was correlated with preterm birth (OR: 1.055, 95% CI: 1.019–1.093). Mixing of SO2 with O3 was also correlated with preterm birth (OR: 1.049, 95% CI: 1.023–1.075). Mixing of NO2 with O3 was correlated with preterm birth (OR: 1.044, 95% CI: 1.005–1.085) (Table 7).

Table 7 Analysis of the correlation between the two-pollutant model and preterm birth for T1
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We explain below for the Table 8,at mid-pregnancy, PM10 mixed with NO2, CO, and O3, respectively, was correlated with preterm birth with ORs and confidence intervals of (OR: 1.014, 95% CI: 1.006–1.022), (OR: 1.011, 95% CI: 1.003–1.020), and (OR: 1.015, 95% CI: 1.007–1.023).The mixing of SO2 with NO2 (OR: 1.096, 95% CI: 1.059–1.133), CO (OR: 1.123, 95% CI: 1.071–1.177), and O3 (OR: 1.074, 95% CI: 1.045–1.103) was associated with preterm birth (Table 8).

Table 8 Analysis of the correlation between the two-pollutant model and preterm birth for T2
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Based on Table 9, in late pregnancy, PM10 mixed with NO2 (OR: 1.009, 95% CI: 1.002–1.016) and with CO (OR: 1.009, 95% CI: 1.002–1.016) was associated with preterm birth. SO2 mixed with NO2 (OR: 1.044, 95% CI: 1.020–1.069) and with CO (OR: 1.053, 95% CI: 1.021–1.087) was associated with preterm birth (Table 9).

Table 9 Analysis of the correlation between the two-pollutant model and preterm birth for T3
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Lagging and cumulative effects of air pollutants

Across exposure windows, we observed a correlation between Lag1 and Lag2 in the lag window and Lag-2 in the cumulative window with preterm birth. In the lag window, for every 10 ug/m3 increase in PM2.5 mass concentration, the risk of preterm birth occurrence increased by 0.7% (95% CI: 1.001–1.014), 0.6% (1.001–1.012), respectively. In the Lag-2 cumulative window, the risk of preterm birth increased by 0.8% (1.001–1.015) for every 10 ug/m3 increase in PM2.5 mass concentration. No correlation between the other exposure windows and preterm birth was observed.

In the lag and cumulative study of PM10 and preterm birth, we found that Lag2, Lag5, Lag6, and Lag7 in the lag window were all correlated with preterm birth, and the magnitude of the change was not consistent across days. The strongest correlation was found on day 7 of the lag, with a 0.5% increase in the risk of preterm birth for every 10 ug/m3 increase in PM10 exposure (95% CI: 1.001–1.008). And the smallest correlation was found on day 2 of the lag, with a 0.2% increase in the risk of preterm birth for every 10 ug/m3 increase in PM10 exposure (95% CI: 1.001–1.004). The cumulative effect showed that the correlation between PM10 exposure mass concentration and preterm birth increased progressively with increasing number of days. The cumulative effect was strongest on cumulative day 7, with a 0.5% (95% CI: 1.002–1.008) increase in the risk of preterm birth occurring for each 10 ug/m3 increase in PM10 exposure.

In the lag and cumulative studies of SO2 and preterm birth, we found that both the lag and cumulative windows were correlated with the occurrence of preterm birth. The correlation increased gradually from day 0 to day 4 of the lag period. The strongest correlation was found on day 6, with a 2.2% (95% CI: 1.011–1.033) increase in the risk of preterm birth for every 10 ug/m3 increase in SO2 exposure. The lowest correlation was found on day 0 of the lag, with a 1.1% (95% CI: 1.003–1.019) increase in the risk of preterm birth for every 10 ug/m3 increase in SO2 exposure. In the cumulative effect, the correlation increased progressively as the cumulative number of days increased. The strongest correlation was found on cumulative day 7, with a 2.1% (95% CI: 1.010–1.033) increase in the risk of preterm birth for every 10 ug/m3 increase in SO2 exposure.

In the study of lag and cumulative effects of NO2 and preterm birth, we observed a correlation between lag day 1 to lag day 7 and an increased risk of preterm birth. The correlation gradually increased from lag day 1 to lag day 3 but weakened on lag day 4 and lag day 6. The strongest correlation was seen at lag day 5, with a 2.1% (95% CI: 1.009–1.032) increase in the risk of preterm birth for every 10 ug/m3 increase in NO2 exposure. The lowest correlation was seen at lag day 1, with a 1.2% (95% CI: 1.001–1.024) increase in the risk of preterm birth for every 10 ug/m3 increase in NO2 exposure. Cumulative effects showed that all windows were associated with preterm birth and increased with the number of cumulative days. The strongest effect was seen on cumulative day 7, with a 2.8% (95% CI: 1.012–1.043) increase in risk of preterm birth for every 10 ug/m3 increase in NO2 exposure.

In the study of lag and cumulative effects of CO and preterm birth, we observed that CO was correlated with preterm birth in both the lag window and the cumulative window. The risk of preterm birth was enhanced after maternal exposure to CO, but the correlation had different trends with lag days. The correlation was gradually increasing from day 0 to day 3 of the lag. The correlation was weakening from day 3 to day 6 of the lag. The strongest correlation was found on day 3, with a 59.2% (95% CI: 1.218–2.081) increase in the risk of preterm birth for every 10 mg/m3 increase in CO exposure. The weakest correlation was found on day 0 of the lag, with a 39.1% (95% CI: 1.070–1.809) increase in the risk of preterm birth for each 10 mg/m3 increase in CO exposure. The cumulative effect showed that the correlation was gradually increasing as the number of cumulative days increased. The strongest correlation was found on cumulative day 7, when the risk of preterm birth increased by 76.3% (95% CI: 1.288–2.413) for every 10 mg/m3 increase in CO exposure.

In the study of lagged and cumulative effects of O3 and preterm birth, we did not find a correlation between pollutants and preterm birth in the lagged window. Pollutants were also not correlated with the occurrence of preterm birth in the cumulative window (Fig. 4).

Fig. 4
figure 4

Lagged response and cumulative effects of air pollutants and preterm birth. Note: Adjusted confounders are maternal occupation, parity, season of last menstrual period, menstrual cycle, birth weight,pregnancy complications, pregnancy hypertension

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Relationship between contaminants and routine blood indicators

Relationship between PM2.5 and human blood routine

We did not observe any correlation of PM2.5 with leukocytes, neutrophils, lymphocytes, monocytes, eosinophils, basophils, and erythrocytes in the lag and accumulation windows (Fig. 5).

Fig. 5
figure 5

Correlation analysis between PM2.5 and blood routine

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Relationship between PM10 and human blood routine

We found a correlation between pollutants and leukocytes on the day of Lag7 in our correlation analysis between PM10 and leukocytes. For every 10 ug/m3 increase in PM10 mass concentration, leukocytes decreased by 0.002 percentage points(95%CI:-0.004 ~ -0.0001). The rest of the window had no correlation with leukocytes. We also did not observe any correlation between PM10 and neutrophils, lymphocytes, monocytes, eosinophils, basophils, and erythrocytes in the lag and accumulation windows (Fig. 6).

Fig. 6
figure 6

Analysis of the correlation between PM10 and blood routine

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Relationship between SO2 and human blood routine counts

We did not observe a correlation between SO2 exposure and blood routine (Fig. 7).

Fig. 7
figure 7

Analysis of the correlation between SO2 and blood routine

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Relationship between NO2 and human blood routine

In the correlation analysis of NO2 with blood counts, we observed a correlation between NO2 and monocytes in the Lag4 exposure window, with monocytes decreasing by 0.009 percentage points (95% CI: -0.019 ~ -0.00018) for every 10 ug/m3 increase in NO2 mass concentration.NO2 correlates with basophils in Lag0 versus Lag-1. At both Lag0 and Lag-1, basophils were elevated by 0.001% for every 10 µg/m3 increase in NO2 mass concentration (Fig. 8).

Fig. 8
figure 8

Analysis of the correlation between NO2 and blood routine

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Relationship between CO and human blood routine

In the correlation analysis between CO and blood counts, we found that CO correlated with leukocytes in the Lag6 exposure window, with leukocytes rising by 0.184 × 109/L (95%CI:0.006–0.362) for every 10 mg/m3 increase in CO mass concentration. Also,CO correlated with monocytes, with only Lag7 not observing a correlation throughout the exposure window. Monocytes decreased by 0.253%(95%CI:-0.467 ~ -0.039),0.303%(95%CI:-0.517 ~ -0.088), 0.23%(95%CI:-0.439 ~ -0.02),0.21%(95%CI:-0.416 ~ -0.004),0.25%(95%CI:-0.453 ~ -0.047),0.274%(95%CI:-0.476 ~ -0.071),and0.253%(95%CI:-0.460 ~ -0.047) for each 10 mg/m3 increase in CO mass concentration, respectively. The cumulative effect showed that monocytes decreased by 0.3%(95%CI:-0.522 ~ -0.078), 0.297%(95%CI:-0.524 ~ -0.07),0.293%(95%CI:-0.523 ~ -0.064), 0.301%(95%CI:-0.531 ~ -0.07),0.311%(95%CI:-0.542 ~ -0.081),0.318%(95%CI:-0.551 ~ -0.085),and0.316%(95%CI:-0.551 ~ -0.081), respectively, for every 10 mg/m3 increase in CO mass concentration.CO also correlated with erythrocytes. In the Lag-3 window, for every 10 mg/m3 increase in CO mass concentration, the erythrocytes increased by 0.033 × 1012/L(95%CI:0.001–0.066) (Fig. 9).

Fig. 9
figure 9

Analysis of the correlation between CO and blood routine

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Relationship between O3 and human blood routine

In the O3 with blood correlation analysis, we only observed a correlation between exposure to O3 and monocytes. Every 10 mg/m3 increase in O3 mass concentration, monocytes increased by 0.008%(95%CI:0.004–0.012), 0.007%(95%CI:0.003–0.011), 0.005%(95%CI:0.001–0.009), 0.005%(95%CI:0.001–0.009), 0.006%(95%CI:0.002–0.01), 0.004%(95%CI:0.0004–0.009),0.005%(95%CI:0.001–0.009),0.007%(95%CI:0.002–0.011), respectively, and the cumulative effect indicated that for every 10 mg/m3 increase in O3 mass concentration, monocytes increased by 0.008% (95% CI: 0.004–0.012) at Lag-1 and by 0.007% (95% CI: 0.003–0.012) in each of the remaining cumulative windows (Fig. 10).

Fig. 10
figure 10

Analysis of the correlation between O3 and blood routine

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C-reactive protein and ultrasensitive C-reactive protein and air pollutants

In the study of the correlation between air pollutants and hs-CRP, we found that SO2, NO2, and CO were correlated with hs-CRP in certain windows (Fig. 10). SO2 was correlated with hs-CRP in the lag windows Lag0, Lag6, and Lag7, and the strongest lag window was the 7th day of lag (OR: 1.028, 95% CI: 1.005–1.051). NO2 correlated with hs-CRP in the lag window Lag0, Lag3, Lag5, Lag6, Lag7, with the strongest lag window being lag day 7 (OR: 1.019, 95% CI: 1.006–1.033). NO2 also correlated with hs-CRP in the cumulative window Lag-4, Lag-5, Lag-6, Lag-7, and the strongest correlation window was on cumulative day 7 (OR: 1.027, 95% CI: 1.008–1.047).CO correlated with hs-CRP in most of the exposure windows of lag and cumulative, and the correlation was increasing with the increase in the number of lag days in the lag window. The strongest correlation was reached at lag day 6 (OR: 2.181, 95% CI: 1.436–3.311). In the cumulative window, the correlation was gradually increasing as the cumulative days increased. The strongest correlation was reached on cumulative day 7 (OR: 2.028, 95% CI: 1.262–3.260) (Fig. 11).

Fig. 11
figure 11

Correlation analysis between air pollutants and hs-CRP

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We did not observe a correlation between air pollutants and CRP across exposure windows (Fig. 12).

Fig. 12
figure 12

Correlation analysis between air pollutants and CRP

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Discussion

In this study, the data of pregnant women with preterm birth in two tertiary hospitals in Baota District, Yan'an City from January 2018 to December 2020 were collected. Conditional logistic regression model was used to investigate the relationship between air pollutants and preterm birth and it was found that PM2.5 was associated with preterm birth during the whole pregnancy period, early pregnancy period, mid-pregnancy period and late pregnancy period and PM10 was associated with preterm birth during the whole pregnancy period, mid-pregnancy period and late pregnancy period and SO2 was associated with the increased risk of preterm birth during the four windows. NO2 was associated with preterm birth throughout pregnancy. Exposure to CO during pregnancy was associated with preterm birth. O3 was not observed to be associated with preterm birth in this study. In short-term lagged and cumulative effects, PM2.5, PM10, SO2, NO2 and CO were correlated with increased risk of preterm birth in lagged effects; in cumulative effects, PM2.5, PM10, SO2, NO2 and CO were also correlated with increased risk of preterm birth. We also observed that SO2, NO2 and CO were correlated with hs-CRP in some windows, and no correlation between air pollutants and CRP was observed.

Xiaotong et al. study on PM2.5 and preterm birth in Wuhan found that exposure to PM2.5 during pregnancy was associated with the occurrence of preterm birth in all four windows as well [33], Which is consistent with our findings. Ju et al. found a correlation between PM10 and preterm birth in late pregnancy and throughout pregnancy, which is similar to our findings [34]. He et al. showed a correlation between SO2 and preterm birth in mid- and late-pregnancy, which is similar to our results [35]. A study of short-term exposure to air pollutants in preterm birth in Xi'an, China, demonstrated a correlation between SO2 and preterm birth [36]. Some studies have found that exposure to high levels of CO in early pregnancy and throughout pregnancy leads to an increased risk of very preterm birth [37], this is similar to our findings. Su et al. showed that PM10 in the first 3 months of pregnancy was associated with preterm birth, and the study also demonstrated a lag between PM2.5 and PM10 on preterm birth [38].

There are also some studies that differ from our results, Sheridan et al. noted that PM2.5 was associated with preterm birth throughout pregnancy, weeks 17–24 of gestation, and week 36 of gestation PM2.5, which is inconsistent with our results [39]. Chen et al. showed a correlation between SO2 in late pregnancy and preterm birth [40], no association was found between SO2 and preterm birth in early and mid-pregnancy. Zhou et al. showed that SO2 and NO2 was not associated with preterm birth [41], which is inconsistent with our findings.A study of air pollutants and preterm birth in California showed that exposure to O3 was associated with an increased incidence of preterm birth, and also found that the hysteresis effect of O3 could also increase the incidence of preterm birth [42], which is inconsistent with our results.

Regarding the study of air pollutants and blood routines, Pilz et al. showed similar results to ours in that they found that PM10, PM2.5, NO2, and NOX one-pollutant models showed a positive, but not significant, correlation with hs-CRP [43]. Kim et al. showed that hs-CRP was correlated with PM2.5, PM10, SO2, and NO2, which is similar to our findings, but the exposure windows in that study were all long-term exposures [44]. Tang et al. showed a correlation between exposure to air pollutants and increased CRP levels in COPD patients the day before hospitalization, which is inconsistent with our results [45]. Liu et al. similarly showed that exposure to air pollutants was associated with increased circulating CRP levels, which is inconsistent with our results [46]. Gogna et al. showed that exposure to air pollution was associated with abnormal CRP levels, which is different from our results [47].

Regarding these inconsistent results, we found that the possible reasons are as follows. Firstly, the variability of the study areas: different areas have their own unique geomorphology and pollutant sources. Secondly, different experimental designs: different exposure windows and different exposure assessment methods may cause variations in the results. Finally, there are differences in climatic characteristics: climate is the main factor affecting air pollutants, and each study area has its own unique climatic characteristics, which leads to differences in the distribution of air pollutants.

While the data from the four pollutant monitoring stations used in this study allow for a relatively accurate assessment of exposure to individual pollutants. However, the pollutant exposure levels for each window period in the study were based on the residential address during pregnancy, a method that only considers outdoor exposure and ignores the daily mobility of pregnant women, which may introduce bias and measurement error into the study. In addition, although the study populations we chose were all located in the Baota District, there will still be some study populations living farther away from the monitoring stations, and therefore the assessment of pollutant mass concentrations in these populations may not be accurate. Finally, some other unknown confounders may not have been collected in this study, so the results may be biased to some extent.

Conclusion

In this study, we investigated the correlation between air pollutants and the occurrence of preterm birth in pregnant women in Baota District and the relationship between air pollutants and blood cell counts of pregnant women. The results of the study showed that in the long-term effect of air pollutants, pollutants were associated with preterm birth in different exposure windows. In lagged effects, PM2.5, PM10, SO2, NO2, and CO were associated with an increased risk of preterm birth; in cumulative effects, PM2.5, PM10, SO2, NO2, and CO were also associated with an increased risk of preterm birth. Correlation analysis between air pollutants and blood cell counts showed that exposure to PM10 was associated with changes in leukocyte counts, exposure to NO2 was associated with changes in monocyte and basophil counts, respectively, and exposure to CO was associated with changes in leukocyte, monocyte, and erythrocyte counts, respectively. Air pollutants were associated with hs-CRP in the lag and cumulative windows.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

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Funding

This work was supported by the National Natural Science Foundation of China under grant number 41761100; the Natural Science Basic Research Program of Shaanxi under grant number 2018JQ4013; and the 2022 National Innovation and Entrepreneurship Training Program for College Students of Yan'an University under grant number 202210719043.

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  1. Medical School of Yan’an University, Yan’an, Shaanxi, China

    Jimin Li, Jiajia Gu, Lang Liu, Meiying Cao, Zeqi Wang, Xi Tian & Jinwei He

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Jimin Li collected, visualized and analyzed the data and wrote the manuscript; Jiajia Gu collected the data and revised the manuscript; Lang Liu, Meiying Cao, Zeqi Wang, Xi Tian collected the data; Jinwei He provided research direction and guidance, and maintained the data.

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Li, J., Gu, J., Liu, L. et al. The relationship between air pollutants and preterm birth and blood routine changes in typical river valley city. BMC Public Health 24, 1677 (2024). https://doi.org/10.1186/s12889-024-19140-2

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