Characteristics and potential inhalation exposure risks of PM2.5–bound environmental persistent free radicals in Nanjing, a mega–city in China

Atmospheric Environment 224 (2020) 117355

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Characteristics and potential inhalation exposure risks of PM2.5–bound environmental persistent free radicals in Nanjing, a mega–city in China Xuewen Guo a, Nan Zhang b, Xin Hu a, *, Yue Huang b, Zhuhong Ding b, **, Yijun Chen a, Hongzhen Lian a, *** a

State Key Laboratory of Analytical Chemistry for Life Science, Centre of Materials Analysis and School of Chemistry & Chemical Engineering, Nanjing University, Nanjing, 210023, PR China School of Environmental Science & Engineering, Nanjing Tech University, 30 Puzhu Southern Road, Nanjing, 211816, PR China

b

H I G H L I G H T S

� Levels and types of PM2.5-bound environmental persistent free radicals were analyzed. � Average 83.5 days of half-life of PM2.5-bound EPFRs under room temperature. � Significant correlations between EPFRs and SO2/PM2.5–bound Fe. � Weak or no correlations between EPFRs and PM2.5/Cu/Zn/Cr/Mn/V/Cd/Ni. � Potential oxidative stress from ROS induced by EPFRs via inhalation exposure. A R T I C L E I N F O

A B S T R A C T

Keywords: Temporal variation Half–life Inhalation exposure Reactive oxygen species Oxidative stress

PM2.5–bound toxic elements and organic pollutants have been extensively investigated, while limited informa­ tion is available for environmental persistent free radicals (EPFRs) associated with PM2.5, which may lead to oxidative stress in the human lung when exposed to PM2.5. In this study, the levels and types of PM2.5–bound EPFRs present in Nanjing, a mega–city in China, were analyzed. PM2.5–bound EPFRs were found to mainly be a mixture of carbon– and oxygen–centered radicals. The concentration of PM2.5–bound EPFRs ranged from 2.78 � 1012 to 1.72 � 1013 spins m 3, with an average value of 7.61 � 1012 spins m 3. The half–life of the PM2.5–bound EPFRs was calculated to be an average of 83.5 days when stored at room temperature, with only weak corre­ lations observed between EPFRs and conventional air pollutants (NO2, O3, CO and PM2.5)/PM2.5–bound tran­ sition metals (Cu, Zn, Cr, Mn, V, Cd, and Ni) and significant correlations between EPFRs and SO2/PM2.5–bound Fe. PM2.5–bound EPFRs can induce the formation of reactive oxygen species (ROS) in both water and a H2O2 solution, which are used to simulate lung solution of a healthy person and patient, respectively. Therefore, PM2.5–bound EPFRs can lead to potential oxidative stress in humans. Overall, PM2.5–bound EPFRs show an obvious temporal variation and can pose potential health risks to humans via the induction of ROS in the lung solution.

1. Introduction Epidemiological and toxicological investigations show that contam­ inated atmospheric particulate matter (APMs), particularly fine particles (PM2.5), may result in airway damage, cardiovascular impairment, in­ duction/exacerbation of diabetes mellitus and adverse effects in infancy

when entering the alveoli and penetrating the blood–air barrier in the cardiovascular system (Mukherjee and Agrawal, 2017; Yang et al., 2019). Therefore, contaminated APMs have been classified as Group 1 carcinogens to humans by the International Agency for Research on Cancer (IARC) of the World Health Organization (IARC, 2019). Induced oxidative stress in the lung has been considered to be one of the most

* Corresponding author. ** Corresponding author. *** Corresponding author. E-mail addresses: [email protected] (X. Hu), [email protected] (Z. Ding), [email protected] (H.-z. Lian). https://doi.org/10.1016/j.atmosenv.2020.117355 Received 28 November 2019; Received in revised form 17 January 2020; Accepted 15 February 2020 Available online 17 February 2020 1352-2310/© 2020 Elsevier Ltd. All rights reserved.

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popular toxic mechanisms of exposure of humans to APMs (Fiordelisi et al., 2017; Gawda et al., 2017; Kelly and Fussell, 2015). Toxic elements and organic pollutants together with the environmental persistent free radicals (EPFRs) in APMs play important roles in the toxic mechanisms underlying oxidative stress and carcinogenicity (Saravia et al., 2013; Valavanidis et al., 2010, 2013). APM–bound toxic elements and organic pollutants, such as polycyclic aromatic hydrocarbons (PAHs), have been considered to be the main risk sources for APM exposure, and their potential sources, the spatial–temporal variability of their concentration and the health risk they pose have been well documented (Duan and Tan, 2013; Yan et al., 2019). Although APM–bound EPFRs have been gradually reported in recent years (Chen et al., 2019; Vejerano et al., 2018; Wang et al., 2019), some factors remain unclear, such as the species, formation, spatial–temporal variability, sources, and potential health risk. Therefore, more investigations should be carried out. EPFRs are found in coal (Ingram et al., 1954), biochar (Ruan et al., 2019), soil (dela Cruz et al., 2011), cigarettes (Lyons et al., 1958; Pryor et al., 1983) and APMs (Vejerano et al., 2018), which are considered to be primary emissions from the combustion and thermal processing of organic materials (Vejerano et al., 2018). Differing greatly from tran­ sient free radicals, such as short–lived reactive oxygen species (ROS), EPFRs are highly stable, with lifetimes of minutes and even up to several months, and have long–distance transmission potential (Gehling and Dellinger, 2013; Pan et al., 2019a). APM–bound EPFRs are generally distributed in airborne particles emitted from the incineration of municipal solid wastes, combustion of coal, biomass burning, and so on (Gautam et al., 2019; Liang et al., 2016; Lin et al., 2018; Pan et al., 2019b). It is reported that the EPFR concentration in atmospheric par­ ticle matter ranges from 1016 to 1020 spin g 1 and greatly differs among different sampling regions or seasons (Arangio et al., 2016; Gehling and Dellinger, 2013; Shaltout et al., 2015; Yang et al., 2017). EPFRs can directly generate ROS, such as hydrogen peroxide, superoxide, and hy­ droxyl radicals, in aqueous solution, which can induce DNA damage (Dellinger et al., 2001; Khachatryan et al., 2011). For example, biochar-bound EPFRs can activate hydrogen peroxide or oxygen to generate hydroxyl radicals (�OH) and superoxide radical (O2 � ) in water (Fang et al., 2014a, 2015b, 2017; Luo et al., 2019). Therefore, APM–­ bound EPFRs may directly result in oxidative stress in the lung when exposed to APMs. In recent years, different in vitro procedures have been used to simulate lung physiological solution to evaluate the potential health risks of human to APM–bound heavy metals and PAHs (Boisa et al., 2014; Hu et al., 2018; Kastury et al., 2017). Water, the basic component of lung physiological solution, was used to represent the extracellular lung fluid to estimate in vitro lung bioaccessibility of APM–bound heavy metals (Julien et al., 2011) and is thus used to evaluate the potential risks of a healthy person and patient to PM2.5–bound EPFRs. PM2.5 is a key indicator of APMs in the Air Quality Guidelines advocated by the WHO in 2005. PM2.5 is also the premier pollutant in atmospheric pollution in most Chinese cities (Gautam et al., 2019; Lin et al., 2018). PM2.5 can penetrate the lung barrier and enter the blood system; therefore, PM2.5–bound EPFRs together with PM2.5–bound contaminants can lead to potential health risks to humans (Lin et al., 2018; Yang et al., 2019). In this study, PM2.5 samples were collected, and the concentrations of PM2.5–bound EPFRs and transitional metals were analyzed. The main ambient atmospheric pollutants, including SO2, NO2, CO and O3, were also recorded at the same time. ROS induced by PM2.5–bound EPFRs in an aqueous environment used to simulate the lung solution were investigated to discovery the potential oxidative stress posed by ROS induced by PM2.5–bound EPFRs in the lung. These results may lead to a better evaluation of the integrated health risks of exposure to PM2.5.

2. Materials and methods 2.1. Collection of PM2.5 Nanjing (118� 220 and 119� 140 E, 31� 140 and 32� 370 N) is a hub and mega–city with an urban permanent population of approximately 8.34 million in 2019. Nanjing is located in the Yangtze Delta and is one of the fastest–growing districts in China. PM2.5 was sampled using Quartz fil­ ters (20.3 � 25.4 cm2) and a high–volume air sampler (HY–1000, Qingdao Henyuan Instruments Co. Ltd., Qingdao, China) with a flow rate of 1.05 m3 min 1. The sampling site was located at the Gulou campus of Nanjing University, located in the central part of Nanjing (118� 220 and 119� 140 E, 31� 140 and 32� 370 N), China. Each sample was collected with a sampling time of 24 h from March 25 to April 3 and May 5 to 18, 2019. The concentrations of conventional atmospheric pollut­ ants, including SO2, NO2, CO and O3, were recorded by local environ­ mental monitoring stations, and the specific sampling dates and pollutant concentrations are shown in Fig. 1. After sampling, a portion of the Quartz filter membrane was used to immediately analyze PM2.5–bound EPFRs by using an X–band EPR spectrometer (EMX 10/12; Bruker) and other Quartz filter membranes were kept at 20 � C for elemental analyses. To identify the decay behavior of PM2.5–bound EPFRs under room temperature, the filter membranes after initial analysis were stored at room temperature at a relative humidity of 60 � 5%. 2.2. Radical detection in filter membranes and the simulating solution An X–band EPR spectrometer (EMX 10/12; Bruker) was used for the identification and quantification of EPFRs and ROS. To detect PM2.5–bound EPFRs, filter membrane was cut into strips of 7 � 30 mm and introduced into a quartz tube with an I.D. of 10 mm for direct detection. The dual cavity mode of the EPR was used, and stable radicals were quantified using a self–made coal sample as an external standard with 2.64 � 1014 spins (Sui, 1996). The EPR detection was carried out at room temperature (23 � 2 � C) and specific parameters were 9.77 GHz for resonance frequency, 5 mW for microwave power, 100 kHz for modulation frequency, 2.0 � 103 for receiver gain, 1.0 G for modulation amplitude, 150 G and 6600 G for sweep width, 40.96 ms for time con­ stant, and 83.89 s for sweep time. The EPR spectra for both standard and filter samples were integrated twice. A blank quartz filter membrane was used as a control. The spins of the sampled filters could be obtained since the ratio of the spin number is equal to the ratio of the quadratic integral value. To evaluate the oxidative stress due to exposure to PM2.5–bound EPFRs via inhalation exposure, quartz filter membranes were immersed in water and a dilute H2O2 solution to simulate lung solutions of a

Fig. 1. Concentrations of conventional atmospheric pollutants during the study period. 2

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healthy person and patient, respectively. To identify the ROS induced by PM2.5–bound EPFRs, sampled filters with a size of 75 � 75 mm were extracted in 6 mL water with 50 mmol L 1 DMPO as a spin–trapping agent for 20 min. Moreover, filters of the same size were also extracted in 6 mL water with 5 mmol L 1 H2O2 and 50 mmol L 1 DMPO to identify whether PM2.5 can induce changes in ROS under H2O2 that result in oxidative stress to cells. For the control group, a blank filter without PM2.5 was extracted in 6 mL water with the 50 mmol L 1 DMPO spin–trapping agent and in 6 mL water with 5 mmol L 1 H2O2 and 50 mmol L 1 DMPO for 20 min. Parameters of the EPR for ROS determi­ nation were set as mentioned above. For better quantification and determination of the ROS, EPR spectra were fitted and simulated using a MATLAB–based computational package EasySpin. H2O2 of chemical grade (30 wt%) was bought from Nanjing Chemical Reagent Co., Ltd and DMPO of chemical grade (97%) was bought from Shanghai Aladdin Biochemical Technology Co., Ltd. Ultrapure water was prepared by an ultrapure water machine (TM–D24UV pure water/ultrapure water in­ tegrated system, Millipore, USA).

3. Results and discussion 3.1. Temporal variation of PM2.5–bound EPFRs The concentration, g factor and ΔHp-p (peak to peak distance) for the PM2.5–bound EPFRs are listed in Table 1, and the EPR spectra for all the samples as a single and unstructured peak are shown in Figs. S1 and 2, with a g factor of 2.0029~2.0045 and with ΔHp-p ranging from 5.27 G to 7.43 G. Generally, an unpaired electron in a different chemical envi­ ronment has a unique g factor. The closer the unpaired electron is to an oxygen atom, the greater the g factor. g factors of <2.0030 are classified as carbon–centered radicals, g factors in the range of 2.0030–2.0040 indicate a mixture of carbon– and oxygen–centered radicals, and g fac­ tors >2.0040 indicate oxygen–centered radicals (Dellinger et al., 2007; Ruan et al., 2019). The PM2.5–bound EPFRs in this study were found to mainly belong to a mixture of carbon– and oxygen–centered radicals (Table 1). The relatively wide peak to peak distance indicates the presence of multiple radicals or strong matrix interactions. In addition to an organic peak, the presence of paramagnetic metals was detected, with ΔHp-p ranging from 400 G to 500 G (Fig. S2). The most common peak was Fe3þ, at an approximate g factor of 2.1 (Gehling and Dellinger, 2013). The concentration of PM2.5–bound EPFRs ranged from 2.78 � 1012 to 1.72 � 1013 spins m 3, with an average value of 7.61 � 1012 spins m 3, and showed an obviously temporal variation (Table 1). For example, the lowest EPFR concentration level was found to be 2.78 � 1012 spins m 3 (1.16 � 1016 spins g 1) on May 16, 2019, while the highest level was 1.72 � 1013 spins m 3 (1.08 � 1017 spins g 1) on March 30, 2019. The concentrations of PM2.5–bound EPFRs in Xi’an in 2017 were found to be 1.79 � 1014 spins m 3 in winter, 1.65 � 1014 spins m 3 in spring, 1.04 � 1014 spins m 3 in autumn, and 9.52 � 1013 spins m 3 in summer (Wang et al., 2019), all of which were higher than the levels found in this study. The concentration of PM2.5–bound EPFRs ranged from 8.9 � 1015 to 3.9 � 1016 spins m 3 (PM2.5: 83.0–112.4 μg m 3), 3.4 � 1015 to 1.5 � 1016 spins m 3 (PM2.5: 173.1–229.7 μg m 3), and 1.6 � 1016 4.5 � 1016 spins m 3 (PM2.5: 235.1–280.6 μg m 3) in Beijing, China from November to December 2016 (Yang et al., 2017). The concentration of PM2.5–bound EPFRs in Xi’an, China was found to be 1.7 � 1014 spins m 3 from March to May 2016 (Chen et al., 2019). The EPFR concentration in total sus­ pended particles (TSP) in Xuanwei, China was 4.74 � 1018 spins g 1 (Wang et al., 2018). The EPFR concentrations in PM2.5 in Nanjing were significantly lower than those in Beijing, Xi’an and Xuanwei. In this study, the mass concentration of PM2.5 during the sampling periods ranged from 12 to 68 μg m 3. The mass concentrations of PM2.5 during sampling ranged from 21 to 365 μg m 3, 22–179 μg m 3 and 21–50 μg m 3 in Beijing (Yang et al., 2017), Xi’an (Chen et al., 2019) and Xuanwei (Wang et al., 2018), respectively. Therefore, the PM2.5 concentrations in Nanjing are lower than those in Beijing and Xi’an and similar to those in Xuanwei. To better understand the environmental behavior of EPFRs, the relationship between the pollutant concentration and the EPFR concentration was analyzed by Pearson’s correlation. The correlation coefficients for EPFRs with SO2, O3, NO2, CO and

2.3. EPFR decay under room temperature To identify the decay behavior of PM2.5–bound EPFRs under room temperature, the sampled filters were placed at room temperature (from 14 to 25 � C day/night in our laboratory) at a relative humidity of 60 � 5% after initial analysis, and repeated analyses were carried out at in­ tervals of 7 days, 30 days, 60 days, and 120 days. A first–order decay – C0 � e–k t) was used to fit the decay data to calculate the 1/e model (C– lifetimes (t1/e) for the EPFRs and the half–life (t1/2) of the radicals as in a previous report (Kiruri et al., 2014). The linear equation of the model was: ln(C/C0) ¼ –k t. The rate constant (k) is the slope of the logarithm of the EPFR concentration ratio (C/C0) versus time (t). 2.4. Analyses for the transitional metals To identify the potential relationship between the EPFRs and tran­ sition metals, the sampled Quartz filter was digested with ultrapure HNO3, HF and HClO4 on an electric hot plate. The concentrations of the transition metals in the digestion solutions were detected by using an inductively coupled plasma atomic emission spectrometer (ICP‒OES, Optima 5300, PerkinElmer SCIEX). 2.5. Correlation analysis Pearson’s correlation coefficient (ρ) was used to identify the corre­ lation between EPFRs and atmospheric pollutants and the transition metal, where coefficient values closer to 1.0 indicate a strong positive relationship; coefficient values approaching 0.0 indicate a weak or no relationship; and coefficient values near 1.0 indicate a strong negative relationship (Gehling and Dellinger, 2013). Pearson’s correlation coef­ ficient was calculated using IBM SSPA Statistics 25. Table 1 Concentrations (spins m 3), g factor and ΔHp-p (G) of PM2.5–bound EPFRs. Date

g

ΔHp-p

Concentration

Date

g

ΔHp-p

Concentration

2019.3.25 2019.3.26 2019.3.27 2019.3.28 2019.3.29 2019.3.30 2019.3.31 2019.4.01 2019.4.02 2019.4.03

2.0029 2.0030 2.0035 2.0031 2.0030 2.0029 2.0031 2.0031 2.0033 2.0030

5.70 5.27 5.89 5.89 4.66 5.27 5.40 5.64 6.50 6.26

6.17 � 1012 7.77 � 1012 5.08 � 1012 6.84 � 1012 7.02 � 1012 1.72 � 1013 1.06 � 1013 1.28 � 1013 7.60 � 1012 7.80 � 1012

2019.5.5 2019.5.06 2019.5.07 2019.5.08 2019.5.09 2019.5.10 2019.5.12 2019.5.15 2019.5.16 2019.5.17

2.0033 2.0033 2.0032 2.0032 2.0033 2.0029 2.0032 2.0042 2.0038 2.0045

6.13 6.91 6.48 7.26 6.57 6.66 6.05 7.43 6.83 7.00

8.78 � 1012 8.31 � 1012 7.65 � 1012 1.20 � 1013 7.79 � 1012 5.79 � 1012 3.47 � 1012 3.29 � 1012 2.78 � 1012 3.38 � 1012

3

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PM2.5 were determined to be 0.46, 0.15, 0.27, 0.25 and 0.40, respectively (Table S1). There was a positive significant correlation between the EPFR concentration and SO2 (p < 0.05) and a weak nega­ tive correlation between the EPFRs and CO, NO2, and PM2.5. Moreover, PM2.5 showed a positive correction with CO, NO2, and SO2, with Pear­ son’s correlation coefficients of 0.64, 0.63 and 0.29, respectively, and there was a weakly negative correction between PM2.5 and O3 (r ¼ 0.28). It has been reported that PM2.5–bound EPFR concentrations have significant positive correlations with PM2.5, SO2, NO2 and O3 (Chen et al., 2019). A positive correlation with PM2.5 is not consistent with our results. Generally, PM2.5 involves atmospheric primary particles directly emitted from specific sources, such as power plants, metallurgical plants, and vehicles, and secondary particles formed from physical and chemical reactions among atmospheric components. Vejerano et al. believe that AMP–bound EPFRs primarily form from combustion and thermal processing of organic materials (Vejerano et al., 2018). Particles emitted from coal/biomass combustion and vehicle exhaust contain an abundance of EPFRs (Yang et al., 2017). Wang et al. believe that EPFRs originate from primary emissions rather than secondary inorganic re­ actions (Wang et al., 2019). Therefore, primary particles emitted from fossil fuel (coal/petroleum) combustion and biomass combustion significantly contribute to the EPFR concentration, while particles originating from industry emissions and secondary particles may contain no EPFRs. Therefore, the variation of sources and their contri­ bution to PM2.5 have a defining influence on the correlation between PM2.5 and PM2.5–bound EPFRs, suggesting that EPFRs might be able to be used as a tool for determining the source of PM2.5 to identify particles emitted from coal/biomass combustion and vehicle exhaust.

elements. The source of PM2.5 plays a key role in the correlation between EPFRs and main transition metals, which is consistent with the results obtained from the correlation analyses between EPFRs and PM2.5 dis­ cussed above in that the sources and their contribution to the primary particles of PM2.5 have a significant influence on the correlation between PM2.5–bound EPFRs and PM2.5 (PM2.5–bound main transition metals). 3.3. Decay of PM2.5–bound EPFRs The sampled filters were placed at room temperature after the initial analysis and were analyzed again after 7 days, 30 days, 60 days, and 120 days. The decay curve for the average PM2.5–bound EPFRs is shown in Fig. 2. The data were fitted to the following equation: ln(Ct/C0) ¼ 0.0083t (R2 ¼ 0.74). The half–life (t1/2) of the PM2.5–bound EPFRs was calculated to be an average of 83.5 days. Fig. 2 also shows the long lifetime of t1/e, which greatly differs from that of the transient free radicals. Gehling and Dellinger reported that there are three types of EPFR decay in PM2.5, including a fast decay type with a t1/e of 1–21 days (attributed to the decomposition of a phenoxyl–type radical), a slow decay type with t1/e of 21–417 days (attributed to the decomposition of a semiquinone–type radical), and a no decay type, where radicals are entrapped in the bulk of PM2.5 or restricted inside a solid matrix (Geh­ ling and Dellinger, 2013). The decay of PM2.5–bound EPFRs here may be attributed to the decomposition of semiquinone–type radicals. In this study, the lifespan of the PM2.5–bound EPFRs is very long (>100 days); therefore, once PM2.5 enters the alveoli and is deposited or even pene­ trates the physiological barrier of the alveolar wall to enter the cardio­ vascular system, it may cause continuous harm to the human body. Therefore, PM2.5–bound EPFRs are an important factor that cannot be ignored when considering the potential health risks of PM2.5.

3.2. Correlation between transition metals and EPFRs

3.4. Potential health risks posed by ROS induced by PM2.5–bound EPFRs

To better understand the relationship between PM2.5–bound transi­ tion metals and EPFRs, correlation analyses were carried out (Table 2, the detailed data are presented in Table S2). Table 2 shows that Zn and Fe were the dominant metal elements, which existed in significantly higher amounts than other transition metals. The Mn and Cu contents basically ranged from 20 to 100 ng m 3. V, Ni and Cd were present with the lowest contents, with values of less than 10 ng m 3 for most sam­ pling days. Pearson’s correlation between EPFRs and the main transition metals was analyzed (Table S3). A significantly positive correlation between PM2.5–bound EPFRs and Fe was observed, with a Pearson’s correlation coefficient of 0.72 (p < 0.01) (Table S3), indicating that Fe might play an important role in the formation of PM2.5–bound EPFRs in Nanjing. Cr (0.062), Mn (0.281), Cu (0.015), and Zn (0.064) had weakly positive correlations with PM2.5–bound EPFRs, while weakly negative correla­ tions were found between EPFRs and V(-0.23), Ni ( 0.030), and Cd ( 0.152) (Table S3). Gehling et al. found a weakly significant or no correlation between EPFRs and metals in PM2.5 in Baton Rouge, LA, United States (Gehling and Dellinger, 2013). Chen et al. reported that EPFR concentrations were poorly correlated with the content of 24 metal elements in PM2.5 in Xi’an, China (Chen et al., 2018). Primary particles originating from industry emissions enrich abundant metallic elements, while those from vehicle exhaust and biomass combustion that contain an abundance of EPFRs have only small amounts of metallic

Inhalation exposure is the main pathway for PM2.5 to enter the human body. When PM2.5 enters into the lung, it first reacts with the solution in the pulmonary alveoli. Although several in vitro procedures were developed to simulate lung physiological solution (Boisa et al., 2014), water, the main component of lung physiological solution, is

Fig. 2. Decay curve of average PM2.5–bound EPFRs.

Table 2 Concentrations (ng m 3) of transition metals in PM2.5 samples. Min Max Median Average SD

V

Cr

Mn

Fe

Ni

Cu

Zn

Cd

3.19 16.2 5.80 7.06 3.73

0.480 32.4 15.9 15.6 6.6

39.4 116 79.6 76.0 19.1

720 2752 1708 1765 595

1.74 17.1 5.22 6.64 4.24

14.2 73.9 35.4 36.3 14.6

966 19952 8257 8324 4616

0.970 7.34 2.85 3.19 1.65

Ave: average value; SD: standard deviation; CV: coefficient of variation. 4

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used as a simplified in vitro procedure to evaluate the potential risks posed by PM2.5–bound EPFRs to a healthy person. To further explore the oxidative stress due to ROS in the lung induced by PM2.5–bound EPFRs, the sampling filters were extracted with water containing a spin–trap­ ping agent, DMPO. The extract solution was then detected by an X–band EPR spectrometer to explore whether ROS were produced. Compared with blank filters without PM2.5, ROS was detected when immerging filters with PM2.5 in water (Fig. S3). Fig. 3 shows that hydroxyl radicals (aH ¼ 14.9 G, aN ¼ 14.9 G) and carbon–centered free radicals (aH ¼ 22.8 G, aN ¼ 15.5 G) were the main radicals produced in the water extractant when immerging the filters with PM2.5. The trend of the intensity of the ROS with extract time is shown in Fig. 4. With increasing extraction time, the intensity of the ROS first increased and then slowly decreased (Fig. 4). Therefore, EPR detection of subsequent samples was carried out at 20 min after extraction. The normalized free radical intensity for all samples extracted for 20 min is shown in Fig. 5; the PM2.5–induced ROS were also found to have a large temporal variation (Fig. 5). Furthermore, hydrogen peroxide is one of the markers of oxidative stress in lung diseases, such as asthma, chronic obstructive pulmonary disease (COPD), and adult respiratory distress syndrome (Dennis et al., 2015; Stolarek et al., 2010). PM2.5–bound EPFRs entering the lungs of patients may react with hydrogen peroxide and then induce the pro­ duction of more ROS, which may aggravate the patients’ condition. Therefore, the ROS present in the extraction solution of the sampling filters in water and hydrogen peroxide were studied (Fig. 4). The extract time showed similar effects on the intensity of the ROS in both water and a water/H2O2 solution (Fig. 4). Fig. 5 shows that the intensity of the hydroxyl radicals generally increased compared to that in the water extraction. The ROS concentrations extracted with water/H2O2 on all sampling days were higher than those extracted with water, suggesting increased potential risks for patients with lung disease. The trends for ROS extracted with water and water/H2O2 were similar, with a Pear­ son’s correlation coefficient of 0.71. According to the related literatures (Fang et al., 2014b, 2015b; Pan et al., 2019a; Vejerano et al., 2018), the induction of �OH by PM2.5–bound EPFRs may be speculated to occur as follows: e

H2 O

e

also found that �OH could be formed without H2O2, while the addition of H2O2 promoted the formation of �OH (Gehling et al., 2014). Vala­ vanidis et al. (2005) and Khachatryan and Dellinger (2011) concluded that �OH generation is a catalytic cycle process. Moreover, transition metals or other analogous systems present in APMs can be considered to be the main contributors to �OH formation because they might catalyze the generation of �OH via a Fenton–like reaction (Gehling et al., 2014; Laurent et al., 2005; Vidrio et al., 2008). For example, Gehling consid­ ered that the promotion of �OH after adding H2O2 was the result of a Fenton–like reaction between transition metals and H2O2, while �OH was produced without H2O2 because of the contribution of EPFRs (Gehling et al., 2014). Fang et al. reported the effects of metal loaded onto biomass on EPFRs (Fang et al., 2015a). Their results showed that the formation of ROS in solution might be impacted by both transition metals and EPFRs. 4. Conclusion This study shows that PM2.5–bound EPFRs found in Nanjing, a hub and mega–city in China, in the late spring and early summer of 2019 mainly belong to a mixture of carbon– and oxygen–centered radicals. The concentration of PM2.5–bound EPFRs ranged from 2.78 � 1012 to 1.72 � 1013 spins m 3, with an average of 7.61 � 1012 spins m 3. Weak correlations were observed between EPFRs and conventional air pol­ lutants (NO2, O3, CO and PM2.5)/PM2.5–bound transition metals (Cu, Zn, Cr, Mn, V, Cd, and Ni) while significant correlations between EPFRs and SO2/PM2.5–bound Fe, suggesting a characteristic indicator of fine par­ ticles for EPFRs emitted from coal/biomass combustion and vehicle exhaust but not for EPFRs emitted from industry emission due to mining, metallurgical engineering or construction dust. The temporal variation in the concentration of PM2.5–bound EPFRs was confirmed. The half–life of PM2.5–bound EPFRs was calculated to be an average of 83.5 days when stored at room temperature. Moreover, ROS were observed in both water and a H2O2 solution, which were used to simulate lung solutions of a healthy person and patient respectively, when exposed to PM2.5–bound EPFRs, suggesting their potential to induce oxidative stress in humans via inhalation exposure. Therefore, the potential risks posed by ROS induced by PM2.5–bound EPFRs in the lung solution of humans can’t be ignored when subjected to exposure by inhalation.

EPFRs

EPFRs→O2 → O2 � →H2 O2 → � OH e

EPFRs→H2 O2 → � OH

Declaration of competing interest

PM2.5–bound EPFRs may act as electron donors and transfer elec­ trons to dissolved oxygen to generate O2 � , then form H2O2 and, finally, produce �OH via the transfer of electrons from the EPFRs to H2O2. The addition of H2O2 enhances the transfer of electrons to produce �OH, which is consistent with previous reports. For example, Gehling et al.

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.

Fig. 3. ROS induced by immerging filters with PM2.5 in water and H2O2 solution. 5

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Fig. 4. Trend of normalized intensity of ROS with the extraction time.

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Fig. 5. Normalized free radical intensity of all samples extracted.

CRediT authorship contribution statement Xuewen Guo: Investigation, Data curation, Visualization, Writing original draft. Nan Zhang: Investigation. Xin Hu: Conceptualization, Writing - review & editing, Visualization, Resources, Funding acquisi­ tion. Yue Huang: Investigation. Zhuhong Ding: Writing - review & editing, Resources, Supervision. Yijun Chen: Investigation. Hong-zhen Lian: Writing - review & editing, Resources. Acknowledgments This work was financially supported by the National Key R&D Pro­ gram of China (No. 2018YFC1800603) and the Natural Science Foun­ dation of Jiangsu Province, China (BK20181261). Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.atmosenv.2020.117355.

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