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Article

Associations of Long-Term Exposure to PM2.5 and Its Constituents with Erythrocytosis and Thrombocytosis in Rural Populations

by
Yiquan Zheng
1,
Yaling He
2,
Ning Kang
1,
Caiyun Zhang
1,
Wei Liao
1,
Yinghao Yuchi
1,
Xiaotian Liu
1,
Jian Hou
1,
Zhenxing Mao
1,
Wenqian Huo
1,
Kai Zhang
3,
Hezhong Tian
4,
Hualiang Lin
1,5 and
Chongjian Wang
1,*
1
Department of Epidemiology and Biostatistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, China
2
Department of Occupational and Environmental Health, Ministry of Education, Key Laboratory of Environment and Health, and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430074, China
3
Department of Environmental Health Sciences, School of Public Health, University at Albany, State University of New York, Albany, NY 12222, USA
4
State Key Joint Laboratory of Environmental Simulation & Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China
5
Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou 510275, China
*
Author to whom correspondence should be addressed.
Toxics 2023, 11(11), 885; https://doi.org/10.3390/toxics11110885
Submission received: 19 September 2023 / Revised: 21 October 2023 / Accepted: 24 October 2023 / Published: 27 October 2023
(This article belongs to the Section Air Pollution and Health)
Browse Figures
Versions Notes

Abstract

:
Evidence on the effect of long-term exposure to fine particulate matter (PM2.5) on erythrocytosis and thrombocytosis prevalence was limited. We aimed to investigate the association of PM2.5 and its constituents with the risks of erythrocytosis and thrombocytosis. The present study included a total of 33,585 participants from the Henan Rural Cohort at baseline between 2015 and 2017. A hybrid satellite-based model was employed to estimate the concentrations of PM2.5 mass and its constituents (including black carbon [BC], nitrate [NO3−], ammonium [NH4+], inorganic sulfate [SO42−], organic matter [OM], and soil particles [SOIL]). The logistic regression model was used to assess the associations of single exposure to PM2.5 and its constituents with the risks of erythrocytosis and thrombocytosis, and the quantile G-computation method was applied to evaluate their joint exposure risk. For the independent association, the odds ratios for erythrocytosis/thrombocytosis with 1 μg/m3 increase was 1.049/1.043 for PM2.5 mass, 1.596/1.610 for BC, 1.410/1.231 for NH4+, 1.205/1.139 for NO3−, 1.221/1.359 for OM, 1.300/1.143 for SO42−, and 1.197/1.313 for SOIL. Joint exposure to PM2.5 and its components was also positively associated with erythrocytosis and thrombocytosis. The estimated weight of NH4+ was found to be the largest for erythrocytosis, while OM had the largest weight for thrombocytosis. PM2.5 mass and its constituents were positively linked to prevalent erythrocytosis and thrombocytosis, both in single-exposure and joint-exposure models. Additionally, NH4+/OM was identified as a potentially responsible component for the association between PM2.5 and erythrocytosis/thrombocytosis.

Graphical Abstract

1. Introduction

Erythrocytosis and thrombocytosis are medical conditions characterized by an abnormal increase in red blood cell count (RBC) or platelet count (PLT), respectively, beyond the specific normal medical reference range [1,2]. In clinical practice, erythrocytosis is diagnosed by an elevated concentration of hemoglobin (HGB) or hematocrit (HCT) [3]. Both erythrocytosis and thrombocytosis could be categorized as primary or secondary conditions. Primary erythrocytosis and thrombocytosis occur due to an autonomous increase in the production of RBCs or platelets, generally stemming from myeloproliferative disorders [2,4]. In contrast, secondary erythrocytosis and thrombocytosis are caused by increased levels of circulating erythropoietin or thrombopoietin in response to various factors, such as cancer, chronic inflammation, infection, as well as lung or heart disease [2,5]. Recent evidence has indicated that erythrocytosis was associated with high clonal hematopoiesis prevalence, as well as cardiovascular morbidity and mortality, and all-cause mortality [4]. Additionally, thrombocytosis has been linked to increased risk of cancer morbidity, cause-specific mortality, and all-cause mortality [6,7].
Fine particulate matter (PM2.5) is a major environmental concern that significantly contributes to the global burden of mortality and morbidity burden [8]. PM2.5 has the ability to penetrate the respiratory barrier and enter the bloodstream, thereby impacting hematological and immune functions through various pathways [9]. Previous studies have established a link between exposure to PM2.5 and diseases such as asthma, anemia, thrombosis, and other blood-related disorders [10,11,12]. However, there is a lack of investigations exploring the association of PM2.5 with erythrocytosis or thrombocytosis to date. Previous reports have examined the impact of PM2.5 on erythrocyte and platelet parameters, including RBC, HGB, HCT, and PLT. Nevertheless, the findings from these studies have been inconsistent and even contradictory [13,14,15,16]. These discrepancies may be attributed to differences in study design, population characteristics, and methods used for exposure assessment [13,17]. Additionally, the composition of PM2.5 mixtures is another significant contributing factor, as it is the primary determinant of overall impact [18,19]. Therefore, it is crucial to identify the specific components of PM2.5 that contribute to its detrimental effects on erythrocytosis and thrombocytosis. However, there is a lack of evidence regarding the effects of PM2.5 constituents on erythrocyte and platelet parameters. A previous cross-sectional study conducted among pregnant women during the third trimester observed inverse relationships of PM2.5, black carbon (BC), nitrate (NO3−), and organic matter (OM) with HGB levels [20]. Additionally, another study reported positive associations of PM2.5 with RBC and PLT; while no significant positive correlation was found between the constituents of PM2.5 and various erythrocyte parameters [21].
To our knowledge, there is currently insufficient evidence to determine the overall effects of PM2.5 and the specific toxic effect of PM2.5 constituents on erythrocytosis and thrombocytosis. Therefore, the purpose of the current study was to investigate the associations of long-term exposure to PM2.5 mass and its constituents with the risk of prevalent erythrocytosis and thrombocytosis and to identify the vital deleterious PM2.5 constituents.

2. Materials and Methods

2.1. Study Population

The study participants were drawn from the baseline of the Henan Rural cohort, which was previously described in detail [22]. Briefly, between 2015 and 2017, a total of 39,259 individuals aged 18 to 79 years were selected to participate in the baseline survey using multistage stratified cluster sampling from five districts in Henan Province, China. In the present study, we excluded 5287 subjects with missing relevant hematologic data, 326 subjects with severe medical conditions (including malignancies and kidney failure), and 61 pregnant women. Finally, 33,585 participants met the inclusion criteria. The Zhengzhou University Life Science Ethics Committee approved this study (reference code: [2015] MEC (S128)), which was conducted in accordance with the Declaration of Helsinki. Additionally, all participants provided informed consent prior to taking part.

2.2. Data Collection and Definition of Outcomes

Information on demographic characteristics, and lifestyle habits was collected using face-to-face questionnaires, while anthropometric data were obtained through measurement. In brief, marital status was categorized into two groups: married/cohabiting and unmarried/divorced/widowed. Education level was classified as primary school or below, junior high school, and senior high school or above, while per capita monthly income was categorized as <500 RMB, 500–999 RMB, and ≥1000 RMB. Moreover, smoking and drinking status were categorized as never, ever, and current. Among them, current smokers were defined as individuals smoking at least one cigarette per day for six sequential or cumulative months, and current drinkers were defined as those consuming alcohol at least 12 times per year. Physical activity was classified into three categories: low, moderate, and high. Furthermore, participants with a high-fat diet were defined as having an average daily intake of ≥75 g of poultry and/or livestock meat, while those with adequate vegetable and fruit intake were defined as having an average daily consumption of ≥ 500 g of vegetables and fruits [23]. Body mass index (BMI) was calculated according to the weight (kg) and height (m) of the individual.
Blood samples were obtained from all participants who had fasted for at least eight hours overnight and were detected by an automatic biochemical analyzer (XS-500i, SYSMEX, Tokyo, Japan) to derive hematological parameters (including HGB, HCT, PLT). Based on the 2016 criteria from the World Health Organization (WHO) guideline, erythrocytosis was defined as HGB concentration > 16.5 g/dL or HCT > 49% in men and HGB concentration > 16.0 g/dL or HCT > 48% in women [3]. According to local laboratory reference intervals and a previous study, thrombocytosis was defined as PLT > 400 × 109/L [24].

2.3. Exposure Assessment

This study employed a hybrid model that combined output from satellite and ground observations with results from a chemical transport model simulation (GEOS-Chem; version 11–01; http://www.geos-chem.org, accessed on 20 March 2022). The model was used to estimate the concentrations of PM2.5 mass and its six constituents (BC, ammonium (NH4+), NO3−, OM, inorganic sulfate (SO42−), and soil particles (SOIL)) at a resolution of 1 km. [25,26,27,28,29,30] In brief, the model estimated the monthly PM2.5 mass concentration by employing satellite-based aerosol optical depth (AOD) data and geographically weighted regression (GWR) at a spatial resolution of 1 km × 1 km, which was then calibrated using ground-based PM2.5 mass concentration data. [31] In addition, the GEOS-Chem CTM was used to evaluate the concentration of each PM2.5 constituent, by incorporating updated emission inventories and meteorological data obtained from a consistent reanalysis (MERRA-2). To determine the monthly exposure concentrations of PM2.5 mass and its constituents, this study used the geocoded home address and investigation date of participants with the GEOS-Chem CTM. The 3-year average concentrations, as a long-term exposure indicator in this study, were derived by aggregating the monthly concentrations before the participants were surveyed. The details regarding the simulation of GEOS-Chem are presented in Supplementary Materials.

2.4. Statistical Analysis

Frequencies (percentages) and means (standard deviations (SD)) were used to present statistical descriptions for categorical and continuous variables, respectively. Correspondently, differences between groups were compared for categorical variables using the Chi-square test and, for continuous variables, using Student’s t-test. Pearson correction analysis was used to evaluate the correlations among PM2.5 constituents, and the logistic regression model was utilized to evaluate their independent associations with erythrocytosis and thrombocytosis. Moreover, restricted cubic splines (with three knots at 25th, 50th, and 75th percentiles) were employed to investigate the dose–response associations of PM2.5 mass and its constituents with erythrocytosis and thrombocytosis. To specify more responsible constituents, the constituent-PM2.5 model and constituent residual model were further applied in addition to the single-pollutant model [32,33]. The constituent-PM2.5 model was utilized in order to control the potential confounding effects, by introducing PM2.5 mass as a surrogate of all other constituents in the single-pollutant model. The constituent residual model replaced the concentration of constituents within the single-pollutant model with the residuals of regression between each constituent and PM2.5. Furthermore, to estimate the associations of joint exposure to PM2.5 and its components with erythrocytosis and thrombocytosis, the quantile G-computation (QGC) method was employed additionally [34]. Briefly, the results of the QGC method include the effects of mixtures and the weight (importance) of individual exposures, and more details of these analyses are presented in Supplementary Materials. Since the effect of PM2.5 might be modified by population characteristics such as age, gender, and BMI, [13] stratified analyses were further conducted by age groups (<65 years and ≥65 years), gender, education levels, per capita monthly income, and BMI groups (<24 kg/m2 and ≥24 kg/m2) in the present study, and the potential effect modifications were evaluated by multiplicative interactions. In addition, according to previous studies [18,19], biological relevance, and findings of the univariate analyses, the multivariable adjustment models adjusted age, gender, marital status, education level, per capita monthly income, physical activity, smoking and drinking status, high-fat diet, adequate vegetable and fruit intake, and BMI. All data were analyzed by SPSS version 21.0 and R software version 4.2.3. The statistical significance was set at a two-tailed p < 0.05.

3. Results

3.1. Characteristics of the Participants

As presented in Table 1, the prevalence of erythrocytosis and thrombocytosis was 10.42% and 0.99%, respectively. Participants with both erythrocytosis and thrombocytosis tended to be younger, have inadequate vegetable and fruit intake, lower physical activity, and a higher BMI. However, the descriptions of other characteristics differed between erythrocytosis and thrombocytosis. Specifically, subjects with erythrocytosis had the following characteristics: men, married/cohabiting, high education and income levels, ever/current smoking or drinking, high-fat diet, and low PLT; while subjects suffered from thrombocytosis were more likely to be women, never smoking or drinking, and had low-fat diet, low levels of HGB and HCT. Importantly, the concentrations of PM2.5 and its constituents averaged over a three-year period were higher in the erythrocytosis group than the non-erythrocytosis group, as well as in the thrombocytosis group than the non-thrombocytosis group (all p < 0.001).
Table 2 displayed the mean, SD, median, and interquartile range (IQR) of the three-year averaged concentrations of PM2.5 and each constituent. The mean (SD) of PM2.5, BC, NH4+, NO3−, OM, SO42−, and SOIL were 73.93 (9.67), 5.09 (0.98), 10.42 (1.35), 17.54 (2.51), 15.61 (1.88), 14.26 (1.72) and 9.75 (1.70) μg/m3, respectively. The median (IQR) of PM2.5, BC, NH4+, NO3−, OM, SO42−, and SOIL were 77.87 (17.61), 5.49 (2.00), 10.68 (2.21), 18.89 (3.82), 15.38 (3.00), 13.86 (3.07) and 8.96 (3.56) μg/m3, respectively. The findings of the high positive correlations (r range from 0.63 to 0.99) between them are shown in Supplementary Figure S1.

3.2. Associations of PM2.5 and Its Constituents with Erythrocytosis and Thrombocytosis

Table 3 exhibits that long-term exposure to PM2.5 and its six constituents were positively associated with prevalent erythrocytosis and thrombocytosis. The single-pollutant model estimated the odds ratio (OR) and 95% confidence interval (CI) of 1.049 (1.044, 1.054) for erythrocytosis and 1.040 (1.027, 1.054) for thrombocytosis, associated with 1 μg/m3 increase in PM2.5 concentrations. Moreover, the elevated risk of erythrocytosis prevalence was significantly related to all six PM2.5 constituents. The ORs and 95% CIs were 1.598 (1.528, 1.671) for BC, 1.407 (1.363, 1.452) for NH4+, 1.206 (1.185, 1.227) for NO3−, 1.230 (1.199, 1.261) for OM, 1.296 (1.265, 1.328) for SO42−, and 1.196 (1.168, 1.224) for SOIL. Thrombocytosis showed similar associations with PM2.5 constituents, with the ORs and 95% CIs for BC, NH4+, NO3−, OM, SO42−, and SOIL were 1.568 (1.372, 1.792), 1.220 (1.118, 1.332), 1.134 (1.080, 1.191), 1.346 (1.245, 1.456), 1.135 (1.061, 1.214), and 1.292 (1.205, 1.385), respectively. Figure 1 illustrates that most of the dose–response relationships of PM2.5 and its constituents with the risk of erythrocytosis and thrombocytosis were nonlinear (P for nonlinear associations < 0.05), which showed no signs of threshold.
The results of the constituent-PM2.5 model and the constituent residual model were consistent. NH4+, NO3−, and SO42− showed consistent and stronger associations with erythrocytosis. The ORs and 95% CIs were 1.317 (1.149, 1.508) and 1.152 (1.059, 1.254), 1.137 (1.058, 1.220) and 1.125 (1.044, 1.211), and 1.152 (1.059, 1.254) and 1.310 (1.202, 1.428) in the constituent-PM2.5 model and the constituent residual model, respectively. Additionally, each 1 μg/m3 increase in exposure to BC, OM, and SOIL corresponded to the elevated risk of thrombocytosis. The ORs and 95% CIs were 4.316 (2.259, 8.246), 1.680 (1.416, 1.992), and 1.232 (1.114, 1.364) in the constituent-PM2.5 model, and 4.053 (2.216, 7.412), 1.547 (1.316, 1.819), and 1.257 (1.134, 1.392) in the constituent residual model.
Figure 2 shows the results of the QGC method. Among PM2.5 and its constituents, NH4+ had the largest estimated weight for erythrocytosis, indicating its significant contribution to this disease. On the other hand, OM had the largest estimated weight for thrombocytosis, suggesting its importance in the development of this condition. The joint exposure to PM2.5 and its constituents was positively associated with erythrocytosis and thrombocytosis, and the ORs and 95% CIs were 1.542 (1.352, 1.761) for erythrocytosis and 1.522 (1.458, 1.590) for thrombocytosis.

3.3. Stratified Analyses

Supplementary Figure S2 shows the finding of stratified analyses examining the relationships of PM2.5 and its constituents with erythrocytosis and thrombocytosis. The results showed that the magnitude of positive associations of PM2.5 and its constituents with erythrocytosis were stronger among women, individuals with a low education level, and those with a low BMI. These characteristics had a significant modifying effect on the relationships of PM2.5 and its constituents with erythrocytosis (P for interaction < 0.05). However, no such significant modifying effect of the above population characteristic was observed on the correlations of PM2.5 and its constituents with thrombocytosis.

4. Discussion

To the best of our knowledge, this study is the first to examine the associations of PM2.5 with prevalent erythrocytosis and thrombocytosis. The results of this study indicate positive associations of PM2.5 and its six constituents with prevalent erythrocytosis and thrombocytosis. Importantly, this study suggests that NH4+ may be responsible for the association between PM2.5 and erythrocytosis, and thrombocytosis may be attributed to OM as the responsible component.
While evidence on the associations of PM2.5 with prevalent erythrocytosis and thrombocytosis is lacking, several investigations have investigated the effects of PM2.5 on erythrocyte and platelet parameters, yielding different and even contradictory results. For instance, in a study conducted in Denmark, the research found that women who experienced a 10 μg/m3 increase in personal PM2.5 exposure showed a 2.6% increase in HGB and a 2.3% increase in RBC based on repeated assessments of PM2.5 exposure [16]. Another study conducted in Chinese cities, using both panel and cross-sectional designs, found that PM2.5 had a positive association with PLT but a negative association with RBC [13]. Researchers in the Korean Children’s Environmental Health Study observed a negative correlation between indoor PM2.5 and HGB and HCT levels [14]. Furthermore, a recent investigation in Beijing reported that elevated PM2.5 levels were associated with a decrease in PLT [15]. These inconsistent associations may be partly attributed to differences in study design and population characteristics, such as age, gender, BMI, and psychosocial stress [13].
Numerous studies have suggested that the toxicity of PM2.5 depends on its chemical composition, with each constituent having distinct impacts [18,32,35,36]. For example, previous research has shown that BC is strongly correlated with glucose metabolism [18], while OC, EC, NH4+, NO3−, and SO42− are more strongly associated with inflammatory or coagulation markers [32]. Consistent with these findings, both the constituent-PM2.5 model and the constituent-residual model in the current study demonstrated that NH4+, NO3−, and SO42− were strongly associated with erythrocytosis, while BC, OM, and SOIL had robust associations with thrombocytosis. Moreover, the prevalence of erythrocytosis in this study was much higher than in previous research, which may be attributed to significantly higher PM2.5 concentrations in rural areas of China compared to European countries [37].
Interestingly, in the current study, NH4+ (a component of secondary inorganic aerosols including NH4+, NO3−, and SO42−) was found to be responsible for the association between PM2.5 and erythrocytosis. These aerosols are predominantly formed through chemical reactions of gaseous precursors emitted into the atmosphere, such as SO2, NOx, and NH3+ [38]. Recent research has indicated a more significant decrease in primary PM2.5 pollutants compared to secondary PM2.5 pollutants as a result of China’s clean air plan [39]. As the proportion of secondary PM2.5 increases, it may have implications for various diseases, including erythrocytosis. On the other hand, this study revealed that OM was responsible for the association between PM2.5 and thrombocytosis. These findings underscore the importance of addressing the energy structure in rural areas, where polluting fuels, the primary source of OM, are commonly used by rural residents for cooking and heating purposes [40,41,42].
The underlying mechanisms linking long-term exposure to PM2.5 with prevalent erythrocytosis and thrombocytosis are unclear. However, there is evidence to suggest that PM2.5 exposure can increase the level of reticulocytes in peripheral blood. This, in turn, may stimulate the release of erythropoietin from the bone marrow, a hormone that promotes the production of RBCs [43]. Additionally, exposure to transition metals, such as cobalt and manganese, bound to PM2.5 has been found to directly stimulate erythropoiesis. These metals have also been associated with the induction effect of hypoxia-inducible factor 1 on erythropoietin [5,43]. The biological mechanism underlying the relationship between PM2.5 and thrombocytosis may involve systemic inflammation, oxidative stress, and impaired endothelial cells. PM2.5 exposure can trigger systemic inflammation through the Toll-like receptor (TLR) 4 pathway, which has been shown to modulate the production and activation of platelets [44,45]. In addition, air pollutants have the ability to induce platelet aggregation in injured vascular endothelium and stimulate megakaryocytes to produce platelets [46]. However, the exact reason why certain constituents are more strongly associated with erythrocytosis or thrombocytosis remains unclear. Further investigations are required to determine the potentially harmful effects of various components implicated in erythrocytosis or thrombocytosis.
This study provides significant implications for public health strategies. Firstly, it highlights the positive associations of PM2.5 and its constituents with prevalent erythrocytosis and thrombocytosis, with each constituent being strongly associated with one of the outcomes. Indeed, rural residents are more vulnerable to PM2.5 exposure due to limited access to medical resources and higher levels of pollution compared to urban areas [47,48]. Therefore, reducing emissions of PM2.5 from all sources in rural regions is crucial and can lead to significant health benefits. Second, considering the variations in the sources and relative contributions of PM2.5 constituents, along with the limited financial and health resources in rural areas, targeted interventions focusing on specific pollutant sources can be more effective in reducing adverse health outcomes. By identifying the key contributors to PM2.5 pollution in specific rural regions, resources can be allocated more efficiently to address those specific sources. This approach can lead to better air quality and improved health outcomes for the residents. To sum up, this study highlights the urgent need to address PM2.5 pollution, particularly in rural areas, and provides insights into the potential strategies for reducing its adverse health effects. By implementing comprehensive measures to reduce PM2.5 emissions and adopting targeted interventions, public health can be significantly improved, benefiting the overall well-being of the population.
This study has several limitations that should be acknowledged. Firstly, the cross-section design used in this study limits the ability to establish causation, highlighting the need for further prospective studies to confirm the findings. Meanwhile, this study employed an ecological epidemiological design, which estimated community-level exposure using models and then extrapolated it to individuals based on their residential locations. Individual exposure levels can vary based on factors such as wind speed, humidity, and the accuracy of the models used for estimation. Secondly, due to the lack of a perfectly comprehensive monitoring system for PM2.5 components in China, the individual exposures evaluated using the Geos-Chem model were not calibrated in this study. However, a previous study has confirmed the accuracy of the estimated PM2.5 concentrations with an R2 value of 0.81 [31], and these estimates have been successfully applied in the Chinese population [18,49]. Finally, while many important confounding factors were adjusted for, there may still be unmeasured and unknown confounders that could influence the results.

5. Conclusions

In summary, this study found a positive association between long-term exposure to PM2.5 and its constituents with prevalent erythrocytosis and thrombocytosis. The study suggests that NH4+ may be responsible for the association between PM2.5 and erythrocytosis, while the association between PM2.5 and thrombocytosis may be attributed to OM as the responsible component. These findings highlight the differences in the relative contributions of constituents and underscore the urgent need for PM2.5 pollution control in rural areas.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/toxics11110885/s1, Figure S1: Correlation between 3-year averaged concentrations of ambient PM2.5 and its constituents; Figure S2: The associations of 3-year averaged concentrations of PM2.5 and its constituents with erythrocytosis and thrombocytosis across general characteristics.

Author Contributions

Conceptualization, K.Z., H.T., H.L. and C.W.; Data curation, Y.Z. and Y.H.; Formal analysis, Y.Z.; Investigation, Y.Z., Y.H., N.K., C.Z., W.L., Y.Y., X.L., J.H., W.H. and C.W.; Methodology, Y.Z., Z.M., K.Z., H.T., H.L. and C.W.; Project administration, C.W.; Supervision, K.Z., H.T., H.L. and C.W.; Validation, N.K., K.Z., H.T., H.L. and C.W.; Visualization, Y.Z.; Writing—original draft, Y.Z. and C.W.; Writing—review and editing, Y.H., N.K., C.Z., W.L., Y.Y., X.L., J.H., Z.M., W.H., K.Z., H.T. and H.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Science and Technology Innovation Team Support Plan of Colleges and Universities in Henan Province (Grant NO:21IRTSTHN029), Cultivation Program of Elite, Niche, and Extinct Disciplines of Zhengzhou University (Grant NO: XKLMJX202212), Foundation of National Key Program of Research and Development of China (Grant NO: 2016YFC0900803). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Institutional Review Board Statement

Ethics approval was obtained from the “Zhengzhou University Life Science Ethics Committee”, and written informed consent was obtained for all participants. Ethic approval code: [2015] MEC (S128).

Informed Consent Statement

Not applicable.

Data Availability Statement

The data analyzed during the current study are available from the corresponding author upon reasonable request.

Acknowledgments

The authors would like to express great gratitude to all the participants, coordinators, and administrators for their support during the research.

Conflicts of Interest

The authors declared no conflict of interest.

References

  1. Mithoowani, S.; Laureano, M.; Crowther, M.A.; Hillis, C.M. Investigation and management of erythrocytosis. CMAJ 2020, 192, E913–E918. [Google Scholar] [CrossRef] [PubMed]
  2. Schafer, A.I. Thrombocytosis. N. Engl. J. Med. 2004, 350, 1211–1219. [Google Scholar] [CrossRef]
  3. Arber, D.A.; Orazi, A.; Hasserjian, R.; Thiele, J.; Borowitz, M.J.; Le Beau, M.M.; Bloomfield, C.D.; Cazzola, M.; Vardiman, J.W. The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia. Blood 2016, 127, 2391–2405. [Google Scholar] [CrossRef] [PubMed]
  4. Wouters, H.; Mulder, R.; van Zeventer, I.A.; Schuringa, J.J.; van der Klauw, M.M.; van der Harst, P.; Diepstra, A.; Mulder, A.B.; Huls, G. Erythrocytosis in the general population: Clinical characteristics and association with clonal hematopoiesis. Blood Adv. 2020, 4, 6353–6363. [Google Scholar] [CrossRef] [PubMed]
  5. Gordeuk, V.R.; Key, N.S.; Prchal, J.T. Re-evaluation of hematocrit as a determinant of thrombotic risk in erythrocytosis. Haematologica 2019, 104, 653–658. [Google Scholar] [CrossRef] [PubMed]
  6. Giannakeas, V.; Narod, S.A. Incidence of Cancer Among Adults with Thrombocytosis in Ontario, Canada. JAMA Netw. Open 2021, 4, e2120633. [Google Scholar] [CrossRef]
  7. Tsai, M.T.; Chen, Y.T.; Lin, C.H.; Huang, T.P.; Tarng, D.C. U-shaped mortality curve associated with platelet count among older people: A community-based cohort study. Blood 2015, 126, 1633–1635. [Google Scholar] [CrossRef]
  8. Cohen, A.J.; Brauer, M.; Burnett, R.; Anderson, H.R.; Frostad, J.; Estep, K.; Balakrishnan, K.; Brunekreef, B.; Dandona, L.; Dandona, R.; et al. Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: An analysis of data from the Global Burden of Diseases Study 2015. Lancet 2017, 389, 1907–1918. [Google Scholar] [CrossRef]
  9. Jin, X.; Ma, Q.; Sun, Z.; Yang, X.; Zhou, Q.; Qu, G.; Liu, Q.; Liao, C.; Li, Z.; Jiang, G. Airborne Fine Particles Induce Hematological Effects through Regulating the Crosstalk of the Kallikrein-Kinin, Complement, and Coagulation Systems. Environ. Sci. Technol. 2019, 53, 2840–2851. [Google Scholar] [CrossRef]
  10. Mirabelli, M.C.; Vaidyanathan, A.; Flanders, W.D.; Qin, X.; Garbe, P. Outdoor PM2.5, Ambient Air Temperature, and Asthma Symptoms in the Past 14 Days among Adults with Active Asthma. Environ. Health Perspect. 2016, 124, 1882–1890. [Google Scholar] [CrossRef]
  11. Honda, T.; Pun, V.C.; Manjourides, J.; Suh, H. Anemia prevalence and hemoglobin levels are associated with long-term exposure to air pollution in an older population. Environ. Int. 2017, 101, 125–132. [Google Scholar] [CrossRef] [PubMed]
  12. Robertson, S.; Miller, M.R. Ambient air pollution and thrombosis. Part Fibre Toxicol. 2018, 15, 1–16. [Google Scholar] [CrossRef] [PubMed]
  13. Wang, W.; Guo, T.; Guo, H.; Chen, X.; Ma, Y.; Deng, H.; Yu, H.; Chen, Q.; Li, H.; Liu, Q.; et al. Ambient particulate air pollution, blood cell parameters, and effect modification by psychosocial stress: Findings from two studies in three major Chinese cities. Environ. Res. 2022, 210, 112932. [Google Scholar] [CrossRef] [PubMed]
  14. Kwag, Y.; Ye, S.; Oh, J.; Lee, D.W.; Yang, W.; Kim, Y.; Ha, E. Direct and Indirect Effects of Indoor Particulate Matter on Blood Indicators Related to Anemia. Int. J. Environ. Res. Public Health 2021, 18, 12890. [Google Scholar] [CrossRef] [PubMed]
  15. Hu, D.; Jia, X.; Cui, L.; Liu, J.; Chen, J.; Wang, Y.; Niu, W.; Xu, J.; Miller, M.R.; Loh, M.; et al. Exposure to fine particulate matter promotes platelet activation and thrombosis via obesity-related inflammation. J. Hazard Mater. 2021, 413, 125341. [Google Scholar] [CrossRef]
  16. Sørensen, M.; Daneshvar, B.; Hansen, M.; Dragsted, L.O.; Hertel, O.; Knudsen, L.; Loft, S. Personal PM2.5 exposure and markers of oxidative stress in blood. Environ. Health Perspect. 2003, 111, 161–166. [Google Scholar] [CrossRef]
  17. Liu, M.; Guo, W.; Zhao, L.; Yang, H.; Fang, Q.; Li, M.; Shu, J.; Chen, S.; Lai, X.; Yang, L.; et al. Association of personal fine particulate matter and its respiratory tract depositions with blood pressure in children: From two panel studies. J. Hazard Mater. 2021, 416, 126120. [Google Scholar] [CrossRef]
  18. Kang, N.; Wu, R.; Liao, W.; Zhang, C.; Liu, X.; Mao, Z.; Huo, W.; Hou, J.; Zhang, K.; Tian, H.; et al. Association of long-term exposure to PM(2.5) constituents with glucose metabolism in Chinese rural population. Sci. Total Environ. 2023, 859 Pt 2, 160364. [Google Scholar] [CrossRef]
  19. Wang, J.; Li, T.; Fang, J.; Tang, S.; Zhang, Y.; Deng, F.; Shen, C.; Shi, W.; Liu, Y.; Chen, C.; et al. Associations between Individual Exposure to Fine Particulate Matter Elemental Constituent Mixtures and Blood Lipid Profiles: A Panel Study in Chinese People Aged 60–69 Years. Environ. Sci. Technol. 2022, 56, 13160–13168. [Google Scholar] [CrossRef]
  20. Xie, G.; Yue, J.; Yang, W.; Yang, L.; Xu, M.; Sun, L.; Zhang, B.; Guo, L.; Chung, M.C. Effects of PM(2.5) and its constituents on hemoglobin during the third trimester in pregnant women. Environ. Sci. Pollut. Res. Int. 2022, 29, 35193–35203. [Google Scholar] [CrossRef]
  21. Wang, S.; Kaur, M.; Li, T.; Pan, F. Effect of Different Pollution Parameters and Chemical Components of PM(2.5) on Health of Residents of Xinxiang City, China. Int. J. Environ. Res. Public Health 2021, 18, 6821. [Google Scholar] [CrossRef] [PubMed]
  22. Liu, X.; Mao, Z.; Li, Y.; Wu, W.; Zhang, X.; Huo, W.; Yu, S.; Shen, L.; Li, L.; Tu, R. Cohort Profile: The Henan Rural Cohort: A prospective study of chronic non-communicable diseases. Int. J. Epidemiol. 2019, 48, 1756-1756j. [Google Scholar] [CrossRef] [PubMed]
  23. Chinese Nutrition Society. The Dietary Guidelines for Chinese Residents; The Tibet people’s Publishing House: Lhasa, China, 2011; pp. 97, 197, 198. [Google Scholar]
  24. McMullin, M.F.; Bareford, D.; Campbell, P.; Green, A.R.; Harrison, C.; Hunt, B.; Oscier, D.; Polkey, M.I.; Reilly, J.T.; Rosenthal, E.; et al. Guidelines for the diagnosis, investigation and management of polycythaemia/erythrocytosis. Br. J. Haematol. 2005, 130, 174–195. [Google Scholar] [CrossRef] [PubMed]
  25. Li, C.; Martin, R.V.; van Donkelaar, A.; Boys, B.L.; Hammer, M.S.; Xu, J.W.; Marais, E.A.; Reff, A.; Strum, M.; Ridley, D.A.; et al. Trends in Chemical Composition of Global and Regional Population-Weighted Fine Particulate Matter Estimated for 25 Years. Environ. Sci. Technol. 2017, 51, 11185–11195. [Google Scholar] [CrossRef]
  26. Available online: http://wiki.seas.harvard.edu/geos-chem/index.php/GEOS-Chem_overview (accessed on 23 March 2023).
  27. Available online: http://wiki.seas.harvard.edu/geos-chem/index.php/Carbonaceous_aerosols (accessed on 24 March 2023).
  28. Available online: http://wiki.seas.harvard.edu/geos-chem/index.php/Mineral_dust_aerosols (accessed on 24 March 2023).
  29. Available online: http://wiki.seas.harvard.edu/geos-chem/index.php/Sea_salt_aerosols (accessed on 24 March 2023).
  30. Zhang, L.; Kok, J.F.; Henze, D.; Li, Q.; Zhao, C. Improving simulations of fine dust surface concentrations over the western United States by optimizing the particle size distribution. Geophys. Res. Letts. 2013, 40, 3270–3275. [Google Scholar] [CrossRef]
  31. Hammer, M.S.; van Donkelaar, A.; Li, C.; Lyapustin, A.; Sayer, A.M.; Hsu, N.C.; Levy, R.C.; Garay, M.J.; Kalashnikova, O.V.; Kahn, R.A.; et al. Global Estimates and Long-Term Trends of Fine Particulate Matter Concentrations (1998–2018). Environ. Sci. Technol. 2020, 54, 7879–7890. [Google Scholar] [CrossRef]
  32. Liu, C.; Cai, J.; Qiao, L.; Wang, H.; Xu, W.; Li, H.; Zhao, Z.; Chen, R.; Kan, H. The Acute Effects of Fine Particulate Matter Constituents on Blood Inflammation and Coagulation. Environ. Sci. Technol. 2017, 51, 8128–8137. [Google Scholar] [CrossRef]
  33. Mostofsky, E.; Schwartz, J.; Coull, B.A.; Koutrakis, P.; Wellenius, G.A.; Suh, H.H.; Gold, D.R.; Mittleman, M.A. Modeling the association between particle constituents of air pollution and health outcomes. Am. J. Epidemiol. 2012, 176, 317–326. [Google Scholar] [CrossRef]
  34. Keil, A.P.; Buckley, J.P.; O’Brien, K.M.; Ferguson, K.K.; Zhao, S.; White, A.J. A Quantile-Based g-Computation Approach to Addressing the Effects of Exposure Mixtures. Environ. Health Perspect. 2020, 128, 47004. [Google Scholar] [CrossRef]
  35. Yang, M.; Cheng, H.; Shen, C.; Liu, J.; Zhang, H.; Cao, J.; Ding, R. Effects of long-term exposure to air pollution on the incidence of type 2 diabetes mellitus: A meta-analysis of cohort studies. Environ. Sci. Pollut. Res. Int. 2020, 27, 798–811. [Google Scholar] [CrossRef]
  36. Yang, T.; Chen, R.; Gu, X.; Xu, J.; Yang, L.; Zhao, J.; Zhang, X.; Bai, C.; Kang, J.; Ran, P.; et al. Association of fine particulate matter air pollution and its constituents with lung function: The China Pulmonary Health study. Environ. Int. 2021, 156, 106707. [Google Scholar] [CrossRef]
  37. Beelen, R.; Raaschou-Nielsen, O.; Stafoggia, M.; Andersen, Z.J.; Weinmayr, G.; Hoffmann, B.; Wolf, K.; Samoli, E.; Fischer, P.; Nieuwenhuijsen, M.; et al. Effects of long-term exposure to air pollution on natural-cause mortality: An analysis of 22 European cohorts within the multicentre ESCAPE project. Lancet 2014, 383, 785–795. [Google Scholar] [CrossRef]
  38. Wang, S.; Wang, L.; Fan, X.; Wang, N.; Ma, S.; Zhang, R. Formation pathway of secondary inorganic aerosol and its influencing factors in Northern China: Comparison between urban and rural sites. Sci. Total Environ. 2022, 840, 156404. [Google Scholar] [CrossRef]
  39. Li, N.; Zhang, H.; Zhu, S.; Liao, H.; Hu, J.; Tang, K.; Feng, W.; Zhang, R.; Shi, C.; Xu, H.; et al. Secondary PM(2.5) dominates aerosol pollution in the Yangtze River Delta region: Environmental and health effects of the Clean air Plan. Environ. Int. 2023, 171, 107725. [Google Scholar] [CrossRef]
  40. Anenberg, S.C.; Schwartz, J.; Shindell, D.; Amann, M.; Faluvegi, G.; Klimont, Z.; Janssens-Maenhout, G.; Pozzoli, L.; Van Dingenen, R.; Vignati, E.; et al. Global air quality and health co-benefits of mitigating near-term climate change through methane and black carbon emission controls. Environ. Health Perspect. 2012, 120, 831–839. [Google Scholar] [CrossRef] [PubMed]
  41. Liu, L.; Zhang, Y.; Yang, Z.; Luo, S.; Zhang, Y. Long-term exposure to fine particulate constituents and cardiovascular diseases in Chinese adults. J. Hazard Mater. 2021, 416, 126051. [Google Scholar] [CrossRef] [PubMed]
  42. World Health Organization. Who Publishes New Global Data on the Use of Clean and Polluting Fuels for Cooking by Fuel Type. 2022. Available online: https://www.who.int/news/item/20-01-2022-who-publishes-new-global-data-on-the-use-of-clean-and-polluting-fuels-for-cooking-by-fuel-type (accessed on 11 April 2023).
  43. Liao, J.; Li, Y.; Wang, X.; Zhang, B.; Xia, W.; Peng, Y.; Zhang, W.; Cao, Z.; Zhang, Y.; Liang, S.; et al. Prenatal exposure to fine particulate matter, maternal hemoglobin concentration, and fetal growth during early pregnancy: Associations and mediation effects analysis. Environ. Res. 2019, 173, 366–372. [Google Scholar] [CrossRef] [PubMed]
  44. Kampfrath, T.; Maiseyeu, A.; Ying, Z.; Shah, Z.; Deiuliis, J.A.; Xu, X.; Kherada, N.; Brook, R.D.; Reddy, K.M.; Padture, N.P.; et al. Chronic fine particulate matter exposure induces systemic vascular dysfunction via NADPH oxidase and TLR4 pathways. Circ. Res. 2011, 108, 716–726. [Google Scholar] [CrossRef]
  45. Beaulieu, L.M.; Freedman, J.E. The role of inflammation in regulating platelet production and function: Toll-like receptors in platelets and megakaryocytes. Thromb. Res. 2010, 125, 205–209. [Google Scholar] [CrossRef]
  46. Hou, J.; Duan, Y.; Liu, X.; Li, R.; Tu, R.; Pan, M.; Dong, X.; Mao, Z.; Huo, W.; Chen, G.; et al. Associations of long-term exposure to air pollutants, physical activity and platelet traits of cardiovascular risk in a rural Chinese population. Sci. Total Environ. 2020, 738, 140182. [Google Scholar] [CrossRef]
  47. Lim, S.; Bassey, E.; Bos, B.; Makacha, L.; Varaden, D.; Arku, R.E.; Baumgartner, J.; Brauer, M.; Ezzati, M.; Kelly, F.J.; et al. Comparing human exposure to fine particulate matter in low and high-income countries: A systematic review of studies measuring personal PM(2.5) exposure. Sci. Total Environ. 2022, 833, 155207. [Google Scholar] [CrossRef] [PubMed]
  48. Han, C.; Xu, R.; Gao, C.X.; Yu, W.; Zhang, Y.; Han, K.; Yu, P.; Guo, Y.; Li, S. Socioeconomic disparity in the association between long-term exposure to PM(2.5) and mortality in 2640 Chinese counties. Environ. Int. 2021, 146, 106241. [Google Scholar] [CrossRef] [PubMed]
  49. Wang, X.; Guo, Y.; Cai, M.; Qian, Z.M.; Zhang, S.; Zhang, Z.; Yang, Y.; Vaughn, M.G.; Aaron, H.E.; Wu, F.; et al. Constituents of fine particulate matter and asthma in 6 low- and middle-income countries. J. Allergy Clin. Immunol. 2022, 150, 214–222.e5. [Google Scholar] [CrossRef] [PubMed]
Toxics 11 00885 g001
Figure 1. The exposure–response associations of 3-year averaged concentrations of PM2.5, and its constituents with erythrocytosis and thrombocytosis. Abbreviation: PM2.5: fine particulate matter; BC: black carbon; NH4+: ammonium; NO3−: nitrate; OM: organic matter; SO42−: inorganic sulfate; SOIL: soil particles. Model adjusted age, gender, marital status, education level, per capita monthly income, physical activity, smoking and drinking status, high-fat diet, adequate vegetable and fruit intake, and BMI. Panel (A–G) showed the exposure–response associations of PM2.5, BC, NH4+, NO3−, OM, SO42−, SOIL with erythrocytosis and thrombocytosis, respectively. Dots and triangles represented the 25th, 50th, and 75th percentiles of pollutants.
Figure 1. The exposure–response associations of 3-year averaged concentrations of PM2.5, and its constituents with erythrocytosis and thrombocytosis. Abbreviation: PM2.5: fine particulate matter; BC: black carbon; NH4+: ammonium; NO3−: nitrate; OM: organic matter; SO42−: inorganic sulfate; SOIL: soil particles. Model adjusted age, gender, marital status, education level, per capita monthly income, physical activity, smoking and drinking status, high-fat diet, adequate vegetable and fruit intake, and BMI. Panel (A–G) showed the exposure–response associations of PM2.5, BC, NH4+, NO3−, OM, SO42−, SOIL with erythrocytosis and thrombocytosis, respectively. Dots and triangles represented the 25th, 50th, and 75th percentiles of pollutants.
Toxics 11 00885 g001
Toxics 11 00885 g002
Figure 2. Weight estimations of joint exposure to PM2.5 and its components with erythrocytosis and thrombocytosis. Abbreviation: PM2.5: fine particulate matter; BC: black carbon; NH4+: ammonium; NO3−: nitrate; OM: organic matter; SO42−: inorganic sulfate; SOIL: soil particles. Model adjusted age, gender, marital status, education level, per capita monthly income, physical activity, smoking and drinking status, high-fat diet, adequate vegetable and fruit intake, and BMI.
Figure 2. Weight estimations of joint exposure to PM2.5 and its components with erythrocytosis and thrombocytosis. Abbreviation: PM2.5: fine particulate matter; BC: black carbon; NH4+: ammonium; NO3−: nitrate; OM: organic matter; SO42−: inorganic sulfate; SOIL: soil particles. Model adjusted age, gender, marital status, education level, per capita monthly income, physical activity, smoking and drinking status, high-fat diet, adequate vegetable and fruit intake, and BMI.
Toxics 11 00885 g002
Table 1. Basic characteristics of study participants.
Table 1. Basic characteristics of study participants.
VariablesErythrocytosisNon-Erythrocytosisp-ValueThrombocytosisNon-Thrombocytosisp-Value
(n = 3499)(n = 30,086)(n = 333)(n = 33,252)
Age (year), mean ± SD52.68 ± 13.0655.58 ± 12.15<0.00152.54 ± 12.5455.31 ± 12.28<0.001
Gender, n (%) <0.001 <0.001
 Men2926 (83.62)10,648 (35.39) 56 (16.82)13,518 (40.65)
 Women573 (16.38)19,438 (64.61) 277 (83.18)19,734 (59.35)
Marital status, n (%) 0.021 0.897
 Married/cohabiting3194 (91.28)27,093 (90.05) 301 (90.39)29,986 (90.18)
 Unmarried/divorced/widowed305 (8.72)2993 (9.95) 32 (9.61)3266 (9.82)
Education levels, n (%) <0.001 0.172
 Primary school or below1048 (29.95)13,634 (45.32) 149 (44.74)14,533 (43.71)
 Junior high school1623 (46.38)11,798 (39.21) 142 (42.64)13,279 (39.93)
 Senior high school or above828 (23.67)4654 (15.47) 42 (12.62)5440 (16.36)
Per capita monthly income, n (%) <0.001 0.467
 <500 RMB1111 (31.75)10,772 (35.80) 122 (36.64)11,761 (35.37)
 500–999 RMB1143 (32.67)9789 (32.54) 98 (29.43)10,834 (32.58)
 ≥1000 RMB1245 (35.58)9525 (31.66) 113 (33.93)10,657 (32.05)
Smoking status, n (%) <0.001 <0.001
 Never1384 (39.56)22,851 (75.95) 287 (86.19)23,948 (72.02)
 Ever513 (14.66)2151 (7.15) 20 (6.01)2644 (7.95)
 Current1602 (45.78)5084 (16.90) 26 (7.80)6660 (20.03)
Drinking status, n (%) <0.001 <0.001
 Never1756 (50.18)24,183 (80.38) 301 (90.39)25,638 (77.1)
 Ever276 (7.89)1310 (4.35) 5 (1.50)1581 (4.75)
 Current1467 (41.93)4593 (15.27) 27 (8.11)6033 (18.14)
Physical activity, n (%) <0.001 0.013
 Low1353 (38.67)9368 (31.14) 129 (38.74)10,592 (31.85)
 Moderate1065 (30.44)11,611 (38.59) 104 (31.23)12,572 (37.81)
 High1081 (30.89)9107 (30.27) 100 (30.03)10,088 (30.34)
High fat diet, n (%)898 (25.66)5388 (17.91)<0.00146 (13.81)6240 (18.77)0.021
Adequate vegetable and fruit intake, n (%)1363 (38.95)13,716 (45.59)<0.001129 (38.74)14,950 (44.96)0.023
BMI (kg/m2, mean ± SD) 25.44 ± 3.4724.71 ± 3.56<0.00125.79 ± 3.4324.78 ± 3.56<0.001
HGB (g/L, mean ± SD) 166.86 ± 16.92135.17 ± 14.62<0.001119.90 ± 24.77138.66 ± 17.57<0.001
HCT (%, mean ± SD) 52.00 ± 4.9442.10 ± 3.89<0.00139.03 ± 6.4743.17 ± 4.99<0.001
PLT (109/L, mean ± SD) 206.62 ± 64.60216.15 ± 71.75<0.001437.78 ± 28.40212.92 ± 67.80<0.001
PM2.5 (μg/m3, mean ± SD)77.69 ± 8.9973.50 ± 9.66<0.00177.75 ± 8.7773.90 ± 9.68<0.001
Constituents
 BC (μg/m3, mean ± SD)5.48 ± 0.925.05 ± 0.98<0.0015.52 ± 0.895.09 ± 0.98<0.001
 NH4+ (μg/m3, mean ± SD)10.95 ± 1.3110.36 ± 1.35<0.00110.85 ± 1.2210.42 ± 1.36<0.001
 NO3− (μg/m3, mean ± SD)18.49 ± 2.3717.43 ± 2.50<0.00118.40 ± 2.2817.54 ± 2.51<0.001
 OM (μg/m3, mean ± SD)16.22 ± 1.6215.55 ± 1.90<0.00116.49 ± 1.8115.61 ± 1.88<0.001
 SO42− (μg/m3, mean ± SD)14.95 ± 1.7014.19 ± 1.71<0.00114.74 ± 1.5714.26 ± 1.73<0.001
 SOIL (μg/m3, mean ± SD)10.31 ± 1.679.69 ± 1.69<0.00110.57 ± 1.639.75 ± 1.70<0.001
Student’s t-test and chi-square test were performed to compare the differences of continuous variables and categorical variables, respectively. Abbreviation: SD, standard deviation; RMB, Renminbi; BMI, body mass index; PM2.5: fine particulate matter; BC: black carbon; NH4+: ammonium; NO3−: nitrate; OM: organic matter; SO42−: inorganic sulfate; SOIL: soil particles.
Table 2. Summary distributions of PM2.5 and its constituents during the study.
Table 2. Summary distributions of PM2.5 and its constituents during the study.
VariablesMeanSDMinimumMedianMaximumIQR
PM2.5 (μg/m3)73.939.6748.4977.8794.0917.61
Constituents
  BC (μg/m3)5.090.983.115.497.112.00
  NH4+ (μg/m3)10.421.356.7410.6813.652.21
  NO3− (μg/m3)17.542.519.9718.8923.413.82
  OM (μg/m3)15.611.888.7415.3819.743.00
  SO42− (μg/m3)14.261.7210.3313.8618.363.07
  SOIL (μg/m3)9.751.706.948.9614.033.56
Abbreviation: SD, standard deviation; IQR, interquartile range; PM2.5: fine particulate matter; BC: black carbon; NH4+: ammonium; NO3−: nitrate; OM: organic matter; SO42−: inorganic sulfate; SOIL: soil particles.
Table 3. The associations of single ambient PM2.5 and its constituents (μg/m3) with erythrocytosis and thrombocytosis.
Table 3. The associations of single ambient PM2.5 and its constituents (μg/m3) with erythrocytosis and thrombocytosis.
VariablesSingle-Pollutant Model aConstituent-PM2.5 Model bConstituent Residual Model c
Erythrocytosis
  PM2.51.049 (1.044, 1.054)
  Constituents
 BC1.598 (1.528, 1.671)1.072 (0.852, 1.348)1.054 (0.839, 1.325)
 NH4+1.407 (1.363, 1.452)1.317 (1.149, 1.508)1.521 (1.318, 1.756)
 NO3−1.206 (1.185, 1.227)1.137 (1.058, 1.220)1.125 (1.044, 1.211)
 OM1.230 (1.199, 1.261)0.818 (0.771, 0.869)0.777 (0.736, 0.821)
 SO42−1.296 (1.265, 1.328)1.152 (1.059, 1.254)1.310 (1.202, 1.428)
 SOIL1.196 (1.168, 1.224)0.979 (0.946, 1.013)0.980 (0.947, 1.015)
Thrombocytosis
  PM2.51.040 (1.027, 1.054)
  Constituents
 BC1.568 (1.372, 1.792)4.316 (2.259, 8.246)4.053 (2.216, 7.412)
 NH4+1.220 (1.118, 1.332)0.228 (0.141, 0.369)0.158 (0.092, 0.272)
 NO3−1.134 (1.080, 1.191)0.729 (0.594, 0.895)0.692 (0.559, 0.856)
 OM1.346 (1.245, 1.456)1.680 (1.416, 1.992)1.547 (1.316, 1.819)
 SO42−1.135 (1.061, 1.214)0.433 (0.346, 0.542)0.434 (0.347, 0.541)
 SOIL1.292 (1.205, 1.385)1.232 (1.114, 1.364)1.257 (1.134, 1.392)
Abbreviation: PM2.5: fine particulate matter; BC: black carbon; NH4+: ammonium; NO3−: nitrate; OM: organic matter; SO42−: inorganic sulfate; SOIL: soil particles. All models adjusted age, gender, marital status, education level, per capita monthly income, physical activity, smoking and drinking status, high-fat diet, adequate vegetable and fruit intake, and BMI. a Single-pollutant model regarded the concentration of PM2.5 mass and constituents as independent variable and with adjustment of covariates. b Constituent-PM2.5 model further adjusted PM2.5 on the basis of constituent concentration model. c Constituent-residual model replaced the concentration of constituents in the single-pollutant model with the residuals of regression between each constituent and PM2.5.
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Zheng, Y.; He, Y.; Kang, N.; Zhang, C.; Liao, W.; Yuchi, Y.; Liu, X.; Hou, J.; Mao, Z.; Huo, W.; et al. Associations of Long-Term Exposure to PM2.5 and Its Constituents with Erythrocytosis and Thrombocytosis in Rural Populations. Toxics 2023, 11, 885. https://doi.org/10.3390/toxics11110885

AMA Style

Zheng Y, He Y, Kang N, Zhang C, Liao W, Yuchi Y, Liu X, Hou J, Mao Z, Huo W, et al. Associations of Long-Term Exposure to PM2.5 and Its Constituents with Erythrocytosis and Thrombocytosis in Rural Populations. Toxics. 2023; 11(11):885. https://doi.org/10.3390/toxics11110885

Chicago/Turabian Style

Zheng, Yiquan, Yaling He, Ning Kang, Caiyun Zhang, Wei Liao, Yinghao Yuchi, Xiaotian Liu, Jian Hou, Zhenxing Mao, Wenqian Huo, and et al. 2023. "Associations of Long-Term Exposure to PM2.5 and Its Constituents with Erythrocytosis and Thrombocytosis in Rural Populations" Toxics 11, no. 11: 885. https://doi.org/10.3390/toxics11110885

APA Style

Zheng, Y., He, Y., Kang, N., Zhang, C., Liao, W., Yuchi, Y., Liu, X., Hou, J., Mao, Z., Huo, W., Zhang, K., Tian, H., Lin, H., & Wang, C. (2023). Associations of Long-Term Exposure to PM2.5 and Its Constituents with Erythrocytosis and Thrombocytosis in Rural Populations. Toxics, 11(11), 885. https://doi.org/10.3390/toxics11110885

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