The acute effects of fine particulate matter constituents on circulating inflammatory biomarkers in healthy adults

Science of the Total Environment 707 (2020) 135989

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The acute effects of fine particulate matter constituents on circulating inflammatory biomarkers in healthy adults Qingli Zhang a,1, Yue Niu a,1, Yongjie Xia a, Xiaoning Lei a, Weidong Wang a, Juntao Huo b, Qianbiao Zhao b, Yihua Zhang b, Yusen Duan b, Jing Cai a, Zhekang Ying a, Shanqun Li c, Renjie Chen a,⁎, Qingyan Fu b,⁎⁎, Haidong Kan a a b c

School of Public Health, Key Lab of Public Health Safety of the Ministry of Education and NHC Key Lab of Health Technology Assessment, Fudan University, Shanghai 200032, China Shanghai Environmental Monitoring Center, Shanghai 200235, China Department of Pulmonary Medicine, Zhongshan Hospital, Fudan University, China

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• It remains unclear how PM2.5 constituents affect systemic inflammation differently. • 27 constituents and 7 inflammatory biomarkers were evaluated in Shanghai, China. − + • SO2− 4 , Cl , K and some elements may be responsible for inflammation.

a r t i c l e

i n f o

Article history: Received 9 July 2019 Received in revised form 3 December 2019 Accepted 6 December 2019 Available online 14 December 2019 Editor: Lidia Morawska Keywords: Fine particulate matter Chemical constituents Systemic inflammation Panel study

a b s t r a c t Background: Systemic inflammation is considered one of the key mechanisms in the development of cardiovascular diseases induced by fine particulate matter (PM2.5) air pollution. However, evidence concerning the effects of various PM2.5 constituents on circulating inflammatory biomarkers were limited and inconsistent. Objectives: To evaluate the associations of short-term exposure to a variety of PM2.5 constituents with circulating inflammatory biomarkers. Methods: We conducted a panel study from May to October 2016 among 40 healthy adults in Shanghai, China. We monitored the concentrations of 27 constituents of PM2.5. We applied linear mixed-effect models to analyze the associations of PM2.5 and its constituents with 7 inflammatory biomarkers, and further assessed the robustness of the associations by fitting models adjusting for PM2.5 mass and/or their collinearity. Benjamini-Hochberg false discovery rate was used to correct for multiple comparisons. Results: The associations of PM2.5 were strongest at lag 0 d with tumor necrosis factor-α (TNF-α), at lag 1 d with interleukin-6, interleukin-8, and interleukin-17A, at lag 02 d with monocyte chemoattractant protein-1 (MCP-1) and intercellular adhesion molecule-1 (ICAM-1). After correcting for multiple comparisons in all models, Cl−, K+, and Se were marginally significantly Si, K, As, and Pb were significantly associated with interleukin-8; SO2− 4

⁎ Correspondence to: R. Chen, Department of Environmental Health, School of Public Health, Fudan University, P.O. Box 249, 130 Dong-An Road, Shanghai 200032, China. ⁎⁎ Correspondence to: Q. Fu, Shanghai Environmental Monitoring Center, 55 Sanjiang Road, Shanghai 200235, China. E-mail addresses: [email protected] (R. Chen), [email protected] (Q. Fu). 1 Qingli Zhang and Yue Niu contributed equally to this work.

https://doi.org/10.1016/j.scitotenv.2019.135989 0048-9697/© 2019 Elsevier B.V. All rights reserved.

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Q. Zhang et al. / Science of the Total Environment 707 (2020) 135989

associated with interleukin-8; SO2− 4 , As, and Se were marginally significantly associated with TNF-α; and Si, K, Zn, As, Se, and Pb were marginally significantly associated with MCP-1. − + Conclusions: Our results suggested that some constituents (SO2− 4 , Cl , K , and some elements) might be mainly responsible for systemic inflammation triggered by short-term PM2.5 exposure. © 2019 Elsevier B.V. All rights reserved.

1. Introduction

dietary supplements were allowed. We also asked the participants to record information on medication use and whether were suffering from a disease. The study protocol (NO. 2016-TYSQ-07-3) was approved by the Institutional Review Board of the School of Public Health. All participants signed informed consents at enrollment.

Cardiovascular system is one of the most important target organs of fine particulate matter (≤2.5 μm in aerodynamic diameter, PM2.5) air pollution, accounting for about 57% of premature deaths attributable to PM2.5 (Cohen et al., 2017). Numerous epidemiological studies have linked PM2.5 exposure to higher risks of cardiovascular morbidity and mortality (Brook et al., 2010; Chen et al., 2017). Although the exact mechanism was not completely understood, blood inflammation has been proposed to play a critical role in the development and exacerbation of cardiovascular diseases induced by PM2.5 (Langrish et al., 2012; Sun et al., 2005). PM2.5 is a complex mixture composed of various constituents originating from distinct pollution sources. A full understanding on the effects of various PM2.5 constituents is helpful to interpret the heterogeneity in the epidemiological findings of PM2.5, and to determine the most important sources that need particular attention in formulating measures of air pollution control. Accumulating epidemiological studies have explored associations of PM2.5 constituents with adverse cardiovascular events (Krall et al., 2013; Ostro et al., 2007; Peng et al., 2009). Mechanistic evidence in this regard was valuable to provide biological plausibility in differentiating the potentially “more important” constituents from the others. Nevertheless, investigations on the associations of PM2.5 constituents with cardiovascular biomarkers tended to simultaneously assess only a small number of constituents and the results were largely inconsistent (Chuang et al., 2007; Liu et al., 2017; Neophytou et al., 2013). For example, in our previous panel study among chronic obstructive pulmonary disease (COPD) patients, we observed blood inflammation was robustly associated with NO− 3 , while the association with SO2− 4 turned to be insignificant after adjusting for the confounding effects and collinearity of other constituents (Liu et al., 2017). On the contrary, in another panel study, inflammatory biomarkers were found to be significantly associated with SO2− but not 4 NO− 3 among young adults (Chuang et al., 2007). Therefore, in order to compare the acute effects of a wide range of PM2.5 constituents on circulating inflammatory biomarkers, we designed a panel study in Shanghai, China. We evaluated 27 constituents, including 2 carbonaceous fractions, 8 inorganic ions and 17 elements.

We measured levels of PM2.5 and various constituents at a supersite, which was located on the rooftop of a 15 m-high building, and approximately 9 km away from the campus where the subjects resided. The supersite was not in the direct vicinity of main roads, industry, and other apparent sources of air pollution. Thus, the measurements of this monitor may be representative of general background exposure levels in Shanghai. Similarly, the campus is also not in the direct vicinity of apparent emission sources, probably avoiding substantial differences of PM2.5 exposures between the two sites. The concentration of PM2.5 total mass was measured using the Tapered Element Oscillating Microbalance (TEOM) approach adjusting by a Filter Dynamic Measurement System (FDMS) (Thermo Scientific, Waltham, MA, USA). The concentrations of carbonaceous fractions, including elemental carbon (EC) and organic carbon (OC), were analyzed by a semi-continuous OC/EC analyzer (Model 4G, Sunset Laboratory, Tigard, OR, USA). Eight water-soluble inorganic ions [i.e. sulfate (SO2− 4 ), ammo− + − nium (NH+ 4 ), chlorine (Cl ), nitrate (NO3 ), sodium (Na ), potassium (K+), magnesium (Mg2+), and calcium (Ca2+)] were measured by an online aerosols and gases monitor (MARGA, model ADI 2080, Applikon Analytical B.V., the Netherlands). There are 17 elements, including silicon (Si), potassium (K), zinc (Zn), iron (Fe), calcium (Ca), manganese (Mn), copper (Cu), barium (Ba), arsenic (As), selenium (Se), lead (Pb), chromium (Cr), vanadium (V), nickel (Ni), gold (Au), gallium (Ga), and cobalt (Co). Their concentrations were measured by a continuous multi-metal monitor with a nondestructive analysis using X-ray fluorescence spectrometry (Xact® 625i ambient air monitor, Cooper Environmental, Beaverton, OR, USA). The detailed principle and operation of the instrument have been described elsewhere (Wang et al., 2016). In addition, we obtained daily mean temperature and mean relative humidity from the Shanghai Meteorological Bureau during the study period.

2. Materials and methods

2.3. Biomarker measurements

2.1. Study design and participants

In each round of visit, samples of venous peripheral blood (3 ml) were drawn using coagulant vacuum tubes on the same time of day (4:00 PM–5:00 PM) to control for the potential influences of circadian rhythms. Serum samples were immediately separated and stored at −80 °C until lab analyses. We tested 7 inflammatory biomarkers:: interleukin-8 (IL-8), monocyte chemoattractant protein-1 (MCP-1), interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), intercellular adhesion molecule-1 (ICAM1), interleukin-17A (IL-17A), and vascular cell adhesion molecule-1 (sVCAM-1). These cytokines were well-established inflammatory biomarkers that are involved in the development of cardiovascular diseases (Niu and Kolattukudy, 2009; Ridker et al., 1998; Sarzi-Puttini et al., 2005; Zhang et al., 2018). Increases of these biomarkers had been linked to a short-term exposure to particulate air pollution in multiple panel studies (Chen et al., 2015; Dadvand et al., 2014; Liu et al., 2017; Wu et al., 2012). Biomarker tests were performed using a commercial

This panel study was conducted in the medical campus of Fudan University, Shanghai, China among 43 students. We included healthy young college students who were 18 years of age or older, studied and lived in this campus for at least 3 consecutive months before the beginning of this study. We excluded those who were drinkers, active or passive smokers, suffered from chronic diseases and those who used any medication or dietary supplements during one month before the start of this study. We conducted four rounds of repeated physical examinations between May 29 and October 12 in 2016. For each subject, the interval between two visits were about two to four weeks. In order to expand the inter-individual variations of exposure to ambient PM2.5, we divided subjects into 6 subgroups (6–8 subjects per group) randomly and arranged physical examinations at different days in each round of follow-up. During follow-ups, no alcoholic beverages and

2.2. Exposure measurements

Q. Zhang et al. / Science of the Total Environment 707 (2020) 135989

human cytokine/chemokine kit, (Millipore MILLIPLEX MAP Kits, Millipore Corp., Billerica, MA, USA). A MAGPIX system was used to quantify the level of each biomarker simultaneously. All biomarker tests were performed following standard methods under the same conditions. In order to assess the assay quality, we randomly selected 10% of samples to measure repeatedly both in in-plate and between-plate. There was good consistency between assays with all results within the quality control ranges. 2.4. Statistical analyses We merged data of exposure and health indicators by the date of blood collection. Before statistical analyses, biomarker levels were natural log-transformed to improve the normality. Linear mixed-effect models were used to investigate the associations of PM2.5 and its constituents with inflammatory biomarkers. Three models were applied to investigate and compare the effects of PM2.5 constituents on inflammatory biomarkers. Firstly, we established the single-constituent model with a biomarker as a dependent variable and a constituent as a fixed-effect independent variable. Secondly, for the constituent-PM2.5 model, PM2.5 total mass was introduced into the single-constituent model as a surrogate of all other constituents to control for their potential confounding effects (Liu et al., 2017; Wu et al., 2012). Thirdly, for the constituent−residual model, we first created the residual of each constituent by establishing a linear regression model between the constituent and PM2.5 total mass, and then replaced the constituent in the single-constituent model by its residual. As the residual was uncorrelated with PM2.5 total mass concentration, therefore it could be considered as a crude measure of the “independent” contribution of each constituent (Cavallari et al., 2008). We assessed the effects of PM2.5 total mass at different single-day lags from 0 d to 4 d and averageday lags of 0–1 d, 0–2 d, 0–3 d and 0–4 d in order to determine an appropriate lag to investigate the short-term effects of various constituents. For the above models, an identity number for each subject was introduced as a random-effect intercept to account for correlations among repeated measurements on the same person. The covariates in these models included: (1) a natural cubic smooth function of the date of physical examinations with 3 degrees of freedom (df) for adjusting the influences of unmeasured time trends; (2) “day of the week” as an indicator variable; (3) natural cubic smooth functions of temperature and humidity during the previous day before blood collection with 3 df to control for their potentially lagged and nonlinear confounding effects; and (4) personal information, including gender, age, body mass index (BMI), and whether suffering an infectious disease (e.g., cold) during the previous week before blood collection. As a supplementary analysis, we conducted principal component (PCA) analysis for constituents and then used PCA factors to correlate with pro-inflammatory markers using the above Linear mixed-effect model. A p-value of b0.05 (two-sided) was considered statistically significant. R software (Version 3.1.1, R Foundation for Statistical Computing, Vienna, Austria) with the “lme4” package was used to perform all models. Effect estimates were expressed as percentage changes (geometric means) coupled with their 95% confidence intervals (CIs) in biomarkers associated with an interquartile range (IQR) increase in PM2.5 or a constituent. In order to avoid false positive findings, we used the Benjamini-Hochberg false discovery rate (FDR) to correct multiple comparisons with FDR b 0.05 considered as statistically significant and FDR between 0.05 and 0.10 as “marginally significant”. 3. Results 3.1. Descriptive statistics We excluded three subjects with asthma to avoid the uncertain influences of asthmatic attacks on our results. Finally, a total of 30

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females and 10 males were included in current analyses. The mean (standard deviation, SD) age of the subjects was 24.5 (1.5) years, and their mean (SD) BMI was 20.7 (2.8) kg/m2. According to selfadministered questionnaires, none of them had been exposed to second-hand smoke, used alcoholic beverages, medications and dietary supplements. We totally obtained 145 blood samples. There were 11 samples collected when the subjects have a cold during the study period. The distributions for the levels of 7 inflammatory biomarkers were summarized in Table 1. On average, the concentrations of IL-8, MCP-1, IL-6, TNF-α, ICAM-1, sVCAM-1 and IL-17A were 9.51 pg/ml, 392.32 pg/ml, 1.40 pg/ ml, 7.27 pg/ml, 133.18 ng/ml 519.97 ng/ml, and 9.33 pg/ml, respectively. There were large intra- and inter- individual variations for these biomarkers. Table 1 also presents the daily concentrations of PM2.5 and its constituents during the study period. The average of 24-h mean PM2.5 concentrations was 41.09 μg/m3, ranging between 2.22 μg/m3 and 80.46 μg/ m3. Three elements (Ga, Au, and Co) were deleted because of high proportions of missing data (N30%). There was a small fraction (about 5%) of missing data for the remaining constituents. Among various constituents, inorganic ions and carbonaceous components contributed to the − + largest proportions of PM2.5 total mass: SO2− 4 , NO3 , NH4 , OC and EC accounted for 16.5%, 13.6%, 11.9%, 11.5%, and 4.5%, respectively. All the 14 elements accounted for only 4.2% of PM2.5 total mass. The correlations of the constituents and PM2.5 mass were shown in Table S1 in Supplementary material. The correlation coefficients among PM2.5 total + − + mass and some constituents (OC, EC, SO2− 4 , NO3 , NH4 , K , Si, K, Fe, Zn, Mn, Ba, Cu, As, Se, and Pb) were relatively high, whereas the coefficients among Cl−, Na+, Ca2+, Mg2+, Ca, V, Ni, and Cr were low. The average of daily mean temperature was 27.10 °C (range: 20–33 °C) and the average of daily mean relative humidity was 74.05% (range: 53%– 87%) during the study period. In PCA analysis, we identified 4 main PCA factors that explained 80% of the variance (Factor 1: 35%, Factor 2: 27%, Factor 3: 9%, Factor 4: 9%). Using a factor loading cutoff of 0.65, Factor 1 was mainly loaded on OC, − EC, K+, Si, K, Zn, Ca, Ba, Se, and Pb; Factor 2 was loaded on SO2− 4 , NO3 , − + NH4 , Cl , Fe, Mn, and Cr; Factor 3 was loaded on V and Ni; and Factor 4 was loaded on Na+. Loading factors for all constituents on each of the PCA factors are presented in Table S2. 3.2. Regression results The lag patterns for the associations between PM2.5 total mass and inflammatory biomarkers were shown in Fig. 1 and Fig. S1. The effect was strongest at lag 0 d for TNF-α, at lag 1 d for IL-6, IL-8, and IL-17A, at lag 02 d for MCP-1 and ICAM-1, so these lags were selected in the analyses of PM2.5 constituents and corresponding biomarkers. sVCAM1 was not significantly associated with PM2.5 exposure. An IQR increase in PM2.5 was associated with an increase of 13.00% (95%CI: 2.13%– 25.00%) in TNF-α at lag 0 d, increments of 18.78% (95%CI: 6.60%– 32.35%), 16.66% (95%CI: 1.34%–34.30%) and 20.04% (95%CI: 3.22%– 39.59%) in IL-8, IL-6 and IL-17A at lag 1 d, and increments of 7.23% (95%CI: 0.53%–14.37%) and 9.22% (95%CI: 0.58%–18.61%) in MCP-1 and ICAM-1 at lag 02 d, respectively. Figs. 2(A)–6(A) illustrates the associations of inflammatory biomarkers with PM2.5 constituents by an IQR increase in singleconstituent models. For IL-6, we observed significant increments fol− + + lowing exposure to EC, NO− 3 , NH4 , Cl , K , K, Zn, As, Se, and Pb. Most constituents except for Na+, Ca2+, Mg2+, Cu, V, Ni, and Cr were significantly related to IL-8. For TNF-α, significant associations were present + + with OC, EC, SO2+ 4 , NH4 , K , Si, K, Fe, Zn, Ca, Mn, Ba, Cu, Cr, As, Se, and − Pb. There were significant associations of MCP-1 with OC, SO2− 4 , Cl , + K , Si, K, Mn, Ba, Fe, Zn, As, Se, Pb and significant associations of − ICAM-1 with NO− 3 , Cl , K, Ba, Fe, Zn, Se, Pb. The associations of sVCAM-1 with PM2.5 constituents were not significant in all models we examined (Fig. S2). For IL-17A, we observed some increments

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Table 1 Descriptive statistics on biomarkers and 24-h average levels of PM2.5 constituents and weather conditions on the day of health examinations. Variables

Mean

SD

Min

P25

Median

P75

Max

IQR

Biomarkers IL-6 (pg/ml) IL-8 (pg/ml) TNF-α (pg/ml) MCP-1 (pg/ml) ICAM-1 (ng/ml) sVCAM (ng/ml) IL-17A (pg/ml)

1.40 9.51 7.27 392.32 133.18 519.97 9.33

1.35 7.18 4.21 156.15 69.43 131.90 16.50

0.41 2.74 1.70 160.24 25.79 34.15 0.82

0.60 4.15 4.48 293.13 94.37 434.28 1.85

0.91 6.93 6.65 361.94 112.08 504.76 2.96

1.52 12.82 8.81 474.19 139.81 612.55 8.71

10.01 33.22 24.57 1005.00 440.74 907.56 122.67

0.92 8.67 4.33 181.07 45.43 178.26 6.86

PM2.5 Total mass (μg/m3) OC (μg/m3) EC (μg/m3) 3 SO2− 4 (μg/m ) 3 NO− 3 (μg/m ) 3 NH+ 4 (μg/m ) Cl− (μg/m3) K+ (μg/m3) Na+(μg/m3) Ca2+ (μg/m3) Mg2+ (μg/m3) Si (ng/m3) K (ng/m3) Fe (ng/m3) Zn (ng/m3) Ca (ng/m3) Mn (ng/m3) Ba (ng/m3) Cu (ng/m3) V (ng/m3) Ni (ng/m3) As (ng/m3) Cr (ng/m3) Se (ng/m3) Pb (ng/m3)

41.09 4.72 1.84 6.78 5.57 4.91 0.40 0.24 0.23 0.09 0.02 525.50 410.88 352.66 165.62 151.00 29.08 27.04 16.95 9.59 5.20 5.06 4.69 3.16 29.90

21.13 2.14 0.61 3.91 4.57 2.85 0.32 0.16 0.09 0.05 0.01 158.42 220.29 182.64 100.68 71.65 16.07 11.29 18.08 7.34 2.76 4.65 3.49 2.51 21.41

12.22 1.79 0.84 2.17 1.09 1.28 0.04 0.07 0.12 0.02 0.00 323.18 123.76 114.45 19.95 48.14 7.21 9.72 3.09 0.45 2.04 0.02 0.66 0.35 4.40

24.00 3.06 1.40 4.04 2.15 2.45 0.07 0.11 0.16 0.04 0.01 391.68 238.12 235.26 64.92 107.78 16.79 19.47 7.74 5.76 3.10 1.19 2.11 1.18 14.19

32.63 4.52 1.81 5.43 3.29 4.12 0.29 0.19 0.22 0.09 0.02 491.89 357.18 280.01 186.59 142.18 25.30 24.70 10.34 7.49 4.85 4.82 3.68 2.33 21.68

63.67 5.61 2.15 9.42 8.60 6.75 0.68 0.37 0.29 0.12 0.02 610.88 602.42 450.46 248.71 175.75 42.06 34.94 19.03 13.92 7.21 7.33 5.76 3.98 40.86

80.46 10.05 3.01 19.16 17.10 10.86 1.08 0.65 0.57 0.18 0.04 924.24 977.29 952.81 340.21 408.23 78.49 59.33 92.61 30.58 12.62 21.73 14.57 9.65 87.03

39.67 2.55 0.75 5.38 6.45 4.30 0.61 0.26 0.13 0.08 0.01 219.20 364.30 215.20 183.79 67.97 25.27 15.47 11.29 8.16 4.11 6.14 3.65 2.80 26.68

27.10 74.05

3.20 8.20

20.00 53.00

25.00 70.00

27.55 74.00

30.00 79.75

33.00 87.00

5.00 9.75

Weather Temperature (°C) Humidity (%)

Abbreviations: SD, standard deviation; P25, 25th percentile; P75, 75th percentile; IQR, interquartile range; IL-6, interleukin-6; IL-8, interleukin-8; TNF-α, tumor necrosis factor-α; MCP-1, monocyte chemoattractant protein-1; ICAM-1, intercellular adhesion molecule-1.

+ + following exposure of EC, NO − 3 , NH 4 , K , K, Ba, As, Se, and Pb, but these associations turned to be insignificant after correcting multiple − + comparisons (Fig. S3). To sum up, we found EC, OC, SO2− 4 , NO3 , NH4 , Cl−, K+ , Si, K, Fe, Zn, Ca, Mn, Ba, As, Se, and Pb were consistently associated with at least 2 inflammatory biomarkers in singleconstituent models. After correcting for multiple comparisons, EC, + + OC, SO2− 4 , NH4 , K , Si, K, Fe, Zn, Ca, Ba, As, Se, and Pb were still significantly or marginally significantly associated with at least two inflammatory biomarkers. We further evaluated the robustness for the effect estimates found from single-constituent models. Results from the constituent-PM2.5 models [Figs. 2(B)–6(B), Figs. S2(B)–S3(B)] and the constituent−residual models [Figs. 2(C)–6(C), Figs. S2(C)–S3(C)] were similar, which indicated some positive associations attenuated appreciably or lost statistical significances after adjusting for PM2.5 and/or collinearity. It − + is notable that the estimated effects of EC, SO2− 4 , Cl , K , Si, K, Fe, Zn, Ba, As, Se, and Pb were not sensitive to such adjustment on at least two inflammatory biomarkers. After correcting for multiple comparisons, Cl−, K+, Si, K, As, and Pb were significantly associated with IL-8; 2− SO2− 4 and Se were marginally significantly associated with IL-8; SO4 , As, and Se were marginally significantly associated with TNF-α; and Si, K, Zn, As, Se, and Pb were marginally significantly associated with MCP-1.PCA Factor 1 was significantly or marginally significantly associated with increment of TNF-α, IL-8, and MCP-1; Factor 2 was significantly or marginally significantly associated with increase of IL-6, IL-8, ICAM-1, and MCP-1; while no associations were found for Factor 3 nor Factor 4 (Table S3).

4. Discussion This study made a relatively comprehensive comparison of the effects of PM2.5 constituents on inflammatory biomarkers. After correcting for multiple testing, Cl−, K+, Si, K, As, and Pb were significantly associated with at least one inflammatory biomarker. SO2− 4 , Zn, and Se were marginally significantly associated with at least one inflammatory biomarker in all models. Inflammation is one of the key mechanisms involved in the development of cardiovascular diseases (Hansson, 2005; Libby et al., 2002). Many molecular epidemiological studies have suggested a steady link between short-term PM2.5 exposure and elevated blood levels of inflammatory biomarkers (Chuang et al., 2007; Sun et al., 2005). We found the effects of PM2.5 on inflammatory biomarkers occurred on the same day or on the second or on the third day, and then attenuated at longer lag days. An IQR (39.67 μg/m3) increase in PM2.5 was associated with increments of 18.78%, 7.23%,16.66%, 13.00%, and 9.22% in IL8, MCP-1, IL-6, TNF-α, and ICAM-1, respectively. The effect estimates and lag patterns were comparable with previous studies. For example, we found that MCP-1, TNF-a, and ICAM-1 increased by 6.6%, 4.5%, and 12%, separately with an IQR (27.4 μg/m3) increase in PM2.5 total mass among patients with COPD in our previous panel study, and these associations were restricted within 24 h (Liu et al., 2017). Wu et al. observed an IQR (63.4 μg/m3) increase in PM2.5 at the preceding one day before blood collection led to a 7.1% increase in TNF-α among healthy adults (Wu et al., 2012). The varying specific lag hours may reflect the influences by a number of measured or unmeasured factors, including

Q. Zhang et al. / Science of the Total Environment 707 (2020) 135989

Fig. 1. Percent changes (geometric means and 95% confidence intervals) in inflammatory biomarkers associated with an interquartile range increase in PM2.5 total mass concentration at different lag days. Abbreviations as in Table 1.

study populations, study duration, sample size, PM2.5 levels and characteristics, measurement errors, and model specifications. The identification of PM2.5 components that play key roles in inducing biological effects could provide the plausibility that is critically important for causal inferences. However, only a limited number of studies have evaluated the effects of PM2.5 constituents on inflammatory biomarkers and the results remain inconsistent. Carbonaceous components (EC and OC) were mainly originated from fossil combustion (Kinney et al., 2000; Park et al., 2008). Most previous epidemiological studies have indicated independent effects of EC and OC on systemic inflammation (Delfino et al., 2009; Liu et al., 2017), but the results are not always consistent for the same biomarker. We found significant associations between EC and most inflammatory biomarkers examined (IL-6, IL-8 and TNF-α), which were consistent with previous studies of inflammatory biomarkers (IL-6, TNF-α, and IL-β) (Delfino et al., 2009; Liu et al., 2017). Nevertheless, the associations of EC lost statistical significance when correcting for multiple testing. The present study found the associations of OC with IL-8 and TNF-α disappeared after adjusting for the confounding effects and collinearity of other constituents. Likewise, Delfino et al., also did not observe robust effects of total OC on IL-6 and sTNF-RII. However, Neophytou et al., found significant association between OC and IL-6 (Neophytou et al., 2013). Liu et al., observed a robust effect of OC on TNF-α (Liu et al., 2017). Further investigations are needed to confirm whether there are robust effects of OC and EC on circulating proinflammatory biomarkers. Inorganic ions typically account for the largest proportion of PM2.5 total mass and have been widely investigated in epidemiological stud− + ies. We found SO2− showed robust associations with 4 , Cl , and K blood inflammation. Previous findings about inorganic ions and inflammatory biomarkers were quite inconsistent. For example, we observed

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+ that NO− 3 and NH4 were robustly associated with inflammatory biomarkers, but the estimated effects of Cl− and SO2− 4 attenuated to be insignificant after adjusting for PM2.5 or collinearity in our previous panel study (Liu et al., 2017). Wu et al. reported that Cl− was consistently as− sociated with TNF-α, but the associations with SO2− 4 and NO3 turned to be insignificant after adjusting for the influences of other constituents (Wu et al., 2012). Therefore, investigations with more controlled design are warranted to verify the potential effects of various ions on circulating biomarkers. Although metal elements account for only a small fraction of PM2.5 total mass, their associations with adverse cardiovascular outcomes were also frequently reported (Basagana et al., 2015; Valdes et al., 2012). For example, Ostro et al. found the acute effects of K, Zn and Fe in PM2.5 on increased cardiovascular mortality (Ostro et al., 2007). Rohr et al. concluded in a literature review that elemental Ni, V, Zn, Cu, Si, and K were most frequently reported to be significantly related to adverse health effects in epidemiological studies (Rohr and Wyzga, 2012). Nevertheless, evidence on the effects of metallic elements on inflammatory biomarkers was quite limited in humans. The present study revealed that some elements (Si, K, Zn, As, Se, and Pb) were consistently associated with inflammatory biomarkers and these associations were relatively robust after adjusting for PM2.5 total mass and collinearity. Similarly, Wu et al. found consistently positive associations of TNF-α with Zn in a panel study among healthy adults (Wu et al., 2012). Toxicological studies also supported the pro-inflammatory effect of some metals in PM2.5. For example, An in vitro study in murine alveolar macrophages, Shang et al., explored the biological effects of PM2.5 collected before and during the 2008 Beijing Summer Olympics suggesting that As was correlated with six inflammatory biomarkers; Cr correlated with three, Fe, and Ba correlated with two; Ni, Mg, K and Ca correlated with one (Shang et al., 2013). Considering the high correlation among these metallic elements in the present study, it is difficult to separate the effect of one metal from another. Future studies with larger sample size are needed to identify the effect of specific metallic elements using multipollutant modelling approaches (Bobb et al., 2015). The identification of “biologically important” components of PM2.5 is also helpful for formulating control strategies for air pollution according to their sources. In this study, we found independent associations of − + SO2− 4 , Cl , K , and some metallic elements with blood inflammatory biomarkers. For example, Cl− is generally contained in emission of waste incineration and coal combustion (Sun et al., 2004; Xie et al., 2006). SO2− 4 and As are mainly generated from coal consumption (Xie et al., 2006). These metals identified in the present study mainly originate from industrial and traffic emissions (Kinoshita et al., 2005; Reff et al., 2009; Sun et al., 2004). K may serve as a surrogate of biomass combustion (Maykut et al., 2003). Si is generally considered as a marker of toxic constituents in mineral dust and is also related with traffic (Reff et al., 2009). In PCA analyses, Factor 1 represented the combined sources of industry, traffic, and biomass combustion related emissions. Factor 2 was likely to be linked to coal combustion and waste incineration. Factor 3 was mainly derived from metallurgical processes. Factor 4 was likely to represent mineral aerosols. Factor 1 and Factor 2 were positively associated with inflammatory biomarkers. Therefore, both analyses of specific constituents and PCA indicated that coal dust, industry, motor vehicles, biomass combustion, and incineration should be of particular concern. Our study has the advantage of using nonsmoking college students as subjects, avoiding the potentially confounding effects in relation to socioeconomic status, indoor air pollution, medication use, the prevalence of comorbidity and behavior risk factors. Another advantage is the relatively complete coverage of common constituents of PM2.5. Nevertheless, several limitations should be noted. First, as we obtained concentrations of PM2.5 and its constituents from a fixed-site monitor instead of personal measurements, exposure misclassification was inevitable. However, we may assume this kind of exposure misclassification to be generally non-differential in short-term effect studies in which the association between personal and ambient PM2.5 concentrations did not

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Fig. 2. Percentage changes (geometric means and 95% confidence intervals) in IL-6 associated with an interquartile range increase in concentrations of PM2.5 constituents at lag 1 d in the single-constituent model (A), the constituent-PM2.5 adjustment model (B) and the constituent-residual model (C). Abbreviation as in Table 1.

Fig. 3. Percentage changes (geometric means and 95% confidence intervals) in IL-8 associated with an interquartile range increase in concentrations of PM2.5 constituents at lag 1 d in in the single-constituent model (A), the constituent-PM2.5 adjustment model (B) and the constituent-residual model (C). Abbreviation as in Table 1.

Q. Zhang et al. / Science of the Total Environment 707 (2020) 135989

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Fig. 4. Percentage changes (geometric means and 95% confidence intervals) in TNF-α associated with an interquartile range increase in concentrations of PM2.5 constituents at lag 0 d in in the single-constituent model (A), the constituent-PM2.5 adjustment model (B) and the constituent-residual model (C). Abbreviation as in Table 1.

Fig. 5. Percentage changes (geometric means and 95% confidence intervals) in MCP-1 associated with an interquartile range increase in concentrations of PM2.5 constituents at lag 02 d in in the single-constituent model (A), the constituent-PM2.5 adjustment model (B) and the constituent-residual model (C). Abbreviation as in Table 1.

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Fig. 6. Percentage changes (geometric means and 95% confidence intervals) in ICAM-1 associated with an interquartile range increase in concentrations of PM2.5 constituents at lag 02 d in in the single-constituent model (A), the constituent-PM2.5 adjustment model (B) and the constituent-residual model (C). Abbreviation as in Table 1.

change much when the distance between monitoring sites and the population center was short (Sarnat et al., 2010); and further the exposure misclassification may lead to underestimation of the effects to some extent (Richmond-Bryant and Long, 2019; Zeger et al., 2000). Second, our sample size is relatively small, potentially adding uncertainty to our estimations. Furthermore, the small sample size restricted the use of multi-pollutant models like BKMR and others, which require larger sample sizes for a robust analysis (Zhang et al., 2019). Third, some other toxic components like endotoxin might also be important contributor for pro-inflammatory response of PM2.5 (Osornio-Vargas et al., 2003), but were not evaluated in this study limited by the inherent properties of our instruments. 5. Conclusions In summary, this study suggested that some PM2.5 constituents − + (SO2− 4 , Cl , K , Si, K, Zn, As, Se, and Pb) might be mainly responsible for the observed systemic inflammation induced by short-term PM2.5 exposure. Further studies with larger sample size and personal exposure measurements on PM2.5 constituents are warranted to confirm our findings. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgement This work was funded by the National Key Research and Development Program of China (2016YFC0206504, 2016YFC0206402 and

2018YFC1313600) and National Natural Science Foundation of China (91843302, 91643205 and 91743111). Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.scitotenv.2019.135989.

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