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Ecotoxicology and Human Environmental Health

Hydrophobic organic components of ambient fine particulate matter (PM2.5) are associated with inflammatory cellular response Xing Jiang, Fanfan Xu, Xinghua Qiu, Xiaodi Shi, Michal Pardo, Yu Shang, Junxia Wang, Yinon Rudich, and Tong Zhu Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.9b02902 • Publication Date (Web): 09 Aug 2019 Downloaded from pubs.acs.org on August 10, 2019

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Environmental Science & Technology

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Hydrophobic Organic Components of Ambient Fine Particulate Matter (PM2.5)

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are Associated with Inflammatory Cellular Response

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Xing Jiang1, Fanfan Xu1, Xinghua Qiu1*, Xiaodi Shi1, Michal Pardo2, Yu

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Shang3, Junxia Wang1, Yinon Rudich2, Tong Zhu1

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1.

State Key Joint Laboratory for Environmental Simulation and Pollution Control,

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College of Environmental Sciences and Engineering, and Center for

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Environment and Health, Peking University, Beijing 100871, P.R. China

9

2.

Rehovot 76100, Israel

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Department of Earth and Planetary Sciences, Weizmann Institute of Science,

3.

Institute of Environmental Pollution and Health, Shanghai University, Shanghai 200444, P.R. China

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*Address correspondence to Xinghua Qiu: College of Environmental Sciences and

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Engineering, Peking University, Beijing 100871, P.R. China. Telephone: 86-10-6275

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1285. E-mail: [email protected]; ORCID: 0000-0001-9874-8030.

1

ACS Paragon Plus Environment


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Graphical abstract

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ABSTRACT

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Nowadays knowledge regarding component-specific inflammatory effect of fine

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particulate matter (PM2.5) is limited. In this study, an omics approach based on

24

time-of-flight mass spectrometry was established to identify the key hydrophobic

25

components of PM2.5 associated with pro-inflammatory cytokines released by

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macrophages after in vitro exposure. Of 768 compounds, 62 components were

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robustly screened with firmly identified 37 specific chemicals. In addition to

28

polycyclic aromatic hydrocarbons (PAHs) and their methylated congeners, novel

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oxygen- and nitrogen-containing PAHs and especially oxygenated PAHs (Oxy-PAHs)

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were identified. Interleukin (IL)-6 was associated with Oxy-PAHs of 1,8-naphthalic

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anhydride, xanthone and benzo[h]quinolone, especially, whereas IL-1β and tumor

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necrosis factor (TNF)-α were associated with most species. Most species were related

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to IL-1β which was significantly higher in the heating season, with a monotonic

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dose-response pattern mainly for Oxy-PAHs, and a U-shaped dose-response pattern

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for primary species. Based on the identified components, four sources of pollution

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(coal combustion, traffic emissions, biomass burning, and secondary formation, traced

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by Oxy-PAHs such as 1,8-naphthalic anhydride and quinones) were resolved by

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positive matrix factorization model. TNF-α was associated with primary sources,

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whereas IL-1β and IL-6 were associated with both primary and secondary sources,

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suggesting different inflammatory effects between primary and secondary sources

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when assessing the toxicity-driven disparities of known and unknown PM2.5

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components.

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INTRODUCTION

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Ambient fine particulate matter (PM2.5, composed of particles with

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aerodynamic diameters below 2.5 μm) pollution poses a significant public health risk

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and results in over 8.9 million premature deaths annually worldwide, the major causes

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of which are various noncommunicable diseases and lower respiratory infections.1

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Inhaled PM2.5 can trigger a pulmonary inflammatory response (i.e., secretion of

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pro-inflammatory cytokines, mainly from macrophages), typically accompanied by

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oxidative stress, which is thought to be an essential process,

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released into the circulatory system can further mediate a series of systemic health

52

effects.3

2

and the cytokines

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In addition to mass concentration, several other key factors can influence the

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adverse effects of particulate matter (PM) including size, shape, and especially

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composition. The chemical components of PM2.5 comprise complicated composition

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that is likely unequal in toxicity. Some major components of PM by mass, such as

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inorganic sulfates and nitrates have been shown to have little biological potency on

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their own at environmental levels;4 whereas some organic species, mainly

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hydrophobic species such as polycyclic aromatic hydrocarbons (PAHs, which

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typically refer to 16 priority species), have demonstrated to be carcinogenicity,

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teratogenicity, and mutagenicity, mainly through the induction of reactive oxygen

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species (ROS) and pro-inflammatory cytokines (e.g., IL-6). 5, 6, 7 However, targeted

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PAHs analysis usually only identifies a small portion of the total organic mass in

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ambient PM.6, 8 Many other organics are unaccounted for, despite the fact that some 4


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other species may be even more biologically active (e.g., quinones, a group of

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oxygenated PAHs with dione structure).6 To date, only a few studies have

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systematically evaluated the toxicity of “all” of the organic components of PM2.5 in

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real atmospheric samples.

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Given the complex composition of PM2.5, it is a challenge to identify the

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potential toxic constituents. Due to the rapid development of high throughput

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screening techniques, mainly based on high-resolution mass spectrometry, it is

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possible to use an omics approach to screen unknown organic contaminants at

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environmentally relevant levels. This approach allows for a more efficient and

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comprehensive assessment of components associated with the mechanisms of health

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hazards at the individual chemical level,9,

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identifying characteristic signals from massive data and apportion to specific

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molecules .9

10

although challenges still exist in

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The chemical characteristics of PM2.5 are determined by a diversity of sources

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and atmospheric processes. For example, in China, PAHs are mostly emitted from

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coal combustion sources, particularly during the cold winter season,11,

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quinones mostly arise from secondary oxidative transformations in the warm season.13

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Therefore, airborne PM2.5 with certain components, which are determined by

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combinations of source categories and the prevailing chemistry, would likely have

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different

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biologically-effective constituents in PM2.5 and to apportion them to certain sources is

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essential for pollution control policies for public health protection.

toxicological

properties.14

In

this

sense,

5


to

identify

12

whereas

the

most


87

In this study, our main objective was to set up an omics approach for screening

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the key hydrophobic components of PM2.5 at the molecular level based on their ability

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to induce inflammation. To achieve this goal, one-year PM2.5 samples, with temporal

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variations in chemical composition, were collected in Beijing, China. After extraction

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with an organic solvent, synchronous in vitro exposure experiments and omics

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measurements on chemical composition were performed, and the molecular species

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associated with inflammatory cellular responses were further filtered and identified.

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In addition, source apportionment was performed on the identified components, and

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associations between the sources and their pro-inflammatory effects were explored.

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This study presents a novel approach in quantitative assessment of the relative

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contributions of the known and unknown molecular components of PM2.5 to its

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biological effects. We aimed to further elucidate disparities in the source-toxicity

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relationships between air pollution and human health, which may enable the

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formulation of more effective strategies for controlling air pollution in China.

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MATERIALS AND METHODS

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Sample collection and extraction. High-volume PM2.5 samples were collected at

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quartz fiber filters (20.3 × 25.4 cm2, prebaked at 450°C for 4 h; Pall Life Science, Port

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Washington, NY, USA) at the Peking University Urban Atmosphere Environmental

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Monitoring Station (PKUERS) for 24 h every sixth day from March 3, 2012 to March

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1, 2013 in Beijing, China. Additional samples were collected on heavily polluted days

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as previously described

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37 were collected in the nonheating season and 28 were sampled in the heating season.

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A 5×3.7-cm portion of each sample filter was extracted with dichloromethane

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according to a previously reported protocol to get the dichloromethane extracted

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semi-volatile organic compounds.8 Each extract was rotary-evaporated until dry and

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weighed. The residue was re-dissolved in a dimethyl sulfoxide (DMSO) solution

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prepared with sterile ultrapure water (with final DMSO concentration of 85% of the field samples.

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NPRs were then subjected to logarithmic transformation and unit variance (UV)

149

scaling correction. We obtained a matrix with 764 compounds with data containing

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RT, m/z, and NPRs.

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Screening of key components and chemical identification. The clustering of the

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QC samples with the data from the 764 compounds was first checked using principal

153

components analysis (PCA; Figure S1 in the Supporting Information). To extract the

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key components from high dimensional data with a high degree of noise collinearity,

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orthogonal partial least squares discriminant analysis (OPLS-DA; a supervised pattern

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recognition approach as described in the Supporting Information) was performed on

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the first and fourth quartile samples on the RFCs of cytokines (Figure S2 in the

158

Supporting Information). The model was further validated using a seven-fold

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cross-validation process, and overfitting was examined using a 999-time permutation

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test (Table S2 in the Supporting Information). Using IL-1β, IL-6, and TNF-α as the

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classifiers, the key compounds were determined using two criteria: variable

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importance in the projection (VIP) scores >1, and jack-knifing confidence interval >0.

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Finally, 62 components were filtered from the 764 compounds, of which 49, 17, and

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33 were significantly associated with IL-1β, IL-6, and TNF-α, respectively (Figure S3

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in the Supporting Information).

166 167

Identification from the screened compounds data was conducted as follows: (1) The unit m/z fragments were matched with those in the NIST library (version 14.L) 9



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with a score >60 using the Agilent Unknowns Analysis software; (2) for the selected

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tentative candidates, the accurate m/z of the molecular ion or characteristic fragments

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were within 25 ppm of the theoretical values, and also possessed an appropriate

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isotopic distribution;17 and (3) the accurate mass of lost fragments in the spectra were

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used to interpret the key substructures: 15.0235 for CH3 in methylated PAHs

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(MePAHs),18,

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(Oxy-PAHs),20 28.0187 for HC-NH in nitrogen-containing PAHs (PANHs)19, 20, and

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15.9949 for oxygen or 29.0027 for CHO in oxygen-containing PAHs (PAOHs).21

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Finally, 37 components were assigned actual molecular formulas, 32 of which were

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confirmed using commercially-available reference standards (Table S3 in the

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Supporting Information).

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Source

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receptor-based positive matrix factorization (PMF; US EPA PMF 5.0) model for the

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identified components. A sample size of 65 met the requirement for a statistically

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reliable result.22 An uncertainty of 0.4 of the mean NPRs was assigned for the data

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set.23 The details regarding the PMF model are provided in the Supporting

184

Information.

19

27.9949 for CO or 55.9898 for C2O2 in oxygenated PAHs

apportionment.

Source

apportionment

was

performed

using

the

185

Given that the source profiles resolved from the PMF model were linearly

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independent from each other,22 we assessed the relationship between the sources and

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cytokines using multiple linear regression (MLR) based on the PMF result:14

188 189

yi = α + βc xc + βb xb + βt xt + βs xs + ε where yi is the log-transformed RFC of cytokine i; x is the source factor of the NPRs 10


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after log-transformation (subscripts of c, b, t, and s indicate the resolved sources of

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coal combustion, biomass burning, traffic emission, and secondary formation by

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oxidation, respectively, which will be discussed below); α and β are the fixed

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intercept and slopes of the sources, respectively; and ε is the residual. All

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statistical analyses were performed using the SPSS package version 21.0 (IBM Corp.

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Armonk, NY, USA), and a p-value 70%,

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and >62%, respectively, and was significantly enhanced during the heating season

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(Figure S9 in the Supporting Information), overlapping with coal combustion in

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Beijing.11 The profile of Factor #2 was similar to that of residential straw burning.12

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Factor #3 was dominated by the hopanes, suggesting a traffic-related source.23 Factor

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#4 had high loading of certain Oxy-PAHs, particularly 1,8-naphthalic anhydride, and

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hence it is attributed to the secondary aerosols formed due to oxidation in the

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atmosphere.13, 39

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[Insert Figure 3 here]

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Seasonal variation is also reflected in the distribution of the major chemical

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families as a function of the different sources (Figure S10 in the Supporting

335

Information). Traffic emissions are identified through the hopanes in both seasons,

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thus providing high reliability in the apportionment of this source. PAHs, MePAHs,

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and PAOHs have similar primary sources, principally coal combustion in the heating

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season and traffic emissions in the non-heating season. This agrees with a previous

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study,15 although few studies have reported the source of the PAOHs (i.e.,

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benzonaphthofuran isomers). For Oxy-PAHs, secondary formation, followed by

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traffic emissions, were the major sources in the non-heating season, whereas coal

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combustion, secondary formation, and biomass burning were dominant in the heating

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season. As markers of SOA, Oxy-PAHs are primarily formed through reactions 17



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initiated by OH and NO3 radicals and O3 with high concentrations occurring in the

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summer, suggesting a high loading of the secondary transformation of oxidation in the

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non-heating season.40 The high levels in the heating season could be due to the high

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abundance of precursors (e.g., gaseous acenaphthene and acenaphthylene) and their

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preferential presence in the particulate phase in the low temperature, incoming solar

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radiation, boundary layer height, and more frequent stable weather conditions .41

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Pro-inflammatory signatures of the sources. Based on the above source

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apportionment using fingerprint characteristics of the identified components, the

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pro-inflammatory effect of various sources was assessed with multiple linear

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regression, and the results are shown in Figure 4.

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[Insert Figure 4 here]

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All of the four sources, particularly coal combustion were significantly

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associated with IL-1β secretion, which suggests that PM components from coal

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combustion play a major role in activating the IL-1β pathway, particularly in the

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winter due to intense domestic heating activity. IL-6 secretion is correlated with

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traffic emissions and secondary formation. This result is in agreement with previous

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studies reporting that PM2.5 from secondary formation was significantly associated

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with IL-6, presumably due to hydroxyl and carbonyl functional groups or peroxides

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and ROS in the SOA formed through atmospheric chemical processes.

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IL-1β and IL-6, TNF-α was associated with primary sources, especially biomass

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burning. In fact, we found similar temporal variations between TNF-α and biomass

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burning, which usually increased in the heating season. It was found that TNF-α 18


29-31

Unlike

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activity can be induced by combustion-related particles instead of secondary aerosols,

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suggesting that components associated with different sources could lead to the release

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of different cytokines via diverse molecular machanisms.36

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In summary, we utilized an innovative approach to the exhaustive assessment

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of the relationships between the inflammatory response and various PM2.5

371

components. We combined a high-resolution MS-based omics approach and the

372

corresponding health-oriented source apportionment. This approach may help to

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overcome the limitations of studies on PM2.5 that fail to either break through the

374

paradigm of existing toxic organic components awareness (e.g., the study of PAHs) or

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resolve the contributions of the individual chemicals in a complex mixture. Ultimately,

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the goal is to develop an approach that will help reduce PM2.5 emissions from

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health-impacting sources, with the toxicity-driving components as priority control

378

targets.

379

Although we uncovered types of unknown and key chemical molecules (i.e.,

380

MePAHs, PAOHs, PANHs, and Oxy-PAHs) of differential PM2.5 toxicity in a cell

381

line model, it is plausible that some unmeasured substances contributed to the cellular

382

effects of PM2.5 extract. Therefore, other methods especially the highly sensitive

383

targeted method are essential to complement the current approach. In addition,

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rigorous toxicity testing and determination of the biological mechanisms of the

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differential toxicological responses is still very limited for most of the novel

386

components identified in this study. Further, extrapolation to the impacts on human

387

health requires additional exploration. Thus, future studies confirming the 19



388

chemical-induced pro-inflammatory responses of the identified new species using

389

pure chemicals are warranted, particularly the components sourced from secondary

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formation of oxidation, 42 given the increased atmospheric oxidative capacity in recent

391

years in China. 43

392 393

SUPPORTING INFORMATION

394

The additional information noted in the text is available:

395

Texts on GC-TOF-MS analysis, OPLS-DA, and PMF model; tables on IC50,

396

OPLS-DA models, information on identified components, and the heating to

397

non-heating season ration of identified components; and figures on PCA score plot,

398

score plots of OPLS-DA model, Venn diagram, relative fold change of cytokines,

399

temporal variation of components, dose-response relationships of TNF- and IL-6,

400

correlations among identified components, temporal trend of identified components

401

from PMF model, and source contributions for identified components.

402 403

ACKNOWLEDGMENTS

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This research was supported by the National Natural Science Foundation of China

405

grant (NSFC; 21876002), the Israel Science Foundation (ISF)-NSFC joint project

406

(#2229/15 and 41561144007), the National Research Program for Key Issues in Air

407

Pollution Control (DQGG0401-03), and the Ministry of Science and Technology of

408

China Grant (973 program; 2015CB553401). YR acknowledges support by a research

409

grant from the Herbert L. Janowsky Lung Cancer Research Fund, Adam Glickman,

410

Eric Gordon, Alex Rotzang, the David M. Polen Charitable Trust, the Benoziyo

411

Endowment Fund for the Advancement of Science, and the Midwest Electron

412

Microscope Project. This work is part of AeroHEALTH Helmholtz International Lab. 20


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oxygenated-PAHs in PM2.5 in the Beijing-Tianjin-Hebei region. Environ. Pollut. 2017, 231,

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25



556 557

Figure 1. Heat map of identified organic species associated with the pro-inflammatory cytokines

558

IL-1β, TNF-α, and IL-6 with variable importance in the projection (VIP) value >1. All species

559

were validated using authentic standards, except those marked with an asterisk (see Table S3 in the

560

Supporting Information).

26


Page 26 of 29

Page 27 of 29


561 562

Figure 2. Relative fold changes (RFCs) of IL-1β release after exposure to identified organic

563

components at four quartiles of peak areas with a suspected U-shaped dose-response mainly for

564

the primarily emitted species (Panel A), and a monotonic dose-response mainly for the

565

Oxy-PAH species (Panel B). Columns represent geometric means and the bars indicate standard error.

27



100 80

Page 28 of 29

Source #1: Coal combustion

60 40 20 0 100

Source #2: Biomass burning

80 60

Percentage (%)

40 20 0 100 80

Source #3: Traffic emission

60 40 20 0 100 80

Source #4: Secondary formation

60 40 20 0

566

ne ne ne ne ne ne ne ne ne ne ne ne ne ne ne ne ne an an an ne ne ne de ne ne ne ne ne ne ne ne ne ne re re re e re e re re re re re re re re re e te ur ur ur -o o o ri -o -o -o -o -o o o a a a th nth py nth luo hrys nth ]py nth nth nth nth lpy lpy lpy nth re -d]f -d]f -d]f n-1 ren nth hyd n-4 n-5 -11 -11 -11 uin uin hop hop hop n q q a f y y y a a a ,2 ,1 ,3 le uo xa an hre yra ren ren ren ra hra nor nor nor or zo c ena zo[e ena ena ena ena eth eth eth uor en en [1 [2 [2 na -fl th t ri - l ic t p flu en al an cd] ]fluo ]fluo ]fluo -an lan 0-t -30 (H) ph f]ph ph ben ylph ylph ylph ylph 1-m 4-m m hylf ho tho tho he 9 b ] t h c t n b a b c 0 y ,3 ) h h h -p 2 e h h h h et o[ 1, ph he ,5- o[ o[ o[ ,1 eth 29 (H 1β [d ap ap ap H et et et et nz -m ta ]n b]n b]n 1 m -m -m im na ef]p o[4 enz enz enz 9 -m 22, 1β )-2 e 2 n b [ [ [ d b 1 2 3 e 8 d r 2 )- )-2 (H o o o 71, ta[ nth H-b H-b H-b op nz enz enz (H (H 7α 1, n cl 1 1 1 e a 1 y e 1 b b b n 1 1 7α 17α -c op he 1 l H c -p 4 y -c 5H 4H

PAHs

MePAHs

PAOHs

Oxy-PAHs

Hopanes

567

Figure 3. Source profiles of identified organic species resolved by the positive matrix

568

factorization (PMF) model.

28


Page 29 of 29


0.12

*

-value with 95% CI

C. IL-6

B. TNF-α

A. IL-1β

***

*

0.08

** **

*

*

0.04

*

*

0.00

om

as s

bu

tra rn ffi in se c g em co nd is si ar on y fo rm co at io al n co m bu bi om st io as n s bu tra rn ffi in se c g em co nd is si ar on y fo rm co at al io co n m bu bi om st io as n s bu tra rn in ffi se g c em co nd is si ar on y fo rm at io n

569

bi

co a

lc om

bu

st io

n

-0.04

570

Figure 4. Multiple linear regression analyses between IL-1β (Panel A), TNF-α (Panel B), and IL-6

571

(Panel C) and the resolved sources. The error bars represent 95% confidence intervals (CI), and *, **,

572

and *** indicate P-values

Hydrophobic organic components of ambient fine ... - ACS Publications

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