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. Author manuscript; available in PMC: 2022 Aug 1.
Published in final edited form as: Environ Sci Pollut Res Int. 2021 Mar 23;28(29):39310–39321. doi: 10.1007/s11356-021-13292-z

Phytosampling—a supplementary tool for particulate matter (PM) speciation characterization

Chuqi Guo 1, Farhana Hasan 1, Dean Lay 1, Albert Leo N Dela Cruz 1, Ajit Ghimire 1, Slawo M Lomnicki 1
PMCID: PMC8713460  NIHMSID: NIHMS1764785  PMID: 33755885

Abstract

Ambient air particulate matter (PM) and PM-associated environmentally persistent free radicals (EPFRs) have been documented to contribute to pollution-related health effects. Studies of ambient air PM potentially bear artifacts stemming from the collection methods. We have investigated the applicability of PM phytosampling (PHS) as a supplementary tool to a classic PM sampler in respect of achieving better PM chemical composition assessment (primarily organic fraction). Phytosampling is a static PM collection method relying on the particle entrapment by the plant’s leaf through electrostatic forces and surface trichomes. We have investigated the differences in the EPFR and polycyclic aromatic hydrocarbon (PAH) speciation and concentration on ambient air PM for PHS and high-volume PM sampler (HVS). The advantages of PHS are easy particle recovery from the matrix, collection under natural environmental conditions, and the ability to apply a dense collection network to accurately represent spatial pollutant distribution. The experimental results show that the PHS can provide valuable speciation information, sometimes different from that observed for HVS. For PM collected by PHS, we detected the larger contribution of oxygen-centered EPFRs, different decay behavior, and more consistent PAH distribution between different PM sizes compared to the PM from HVS. These results indicate that the isolation of samples from the ambient during HVS sampling and exposure to high-volume airflow may alter the chemical composition of the samples, while the PHS method could provide details on the original speciation and concentration and be more representative of the PM surface. However, PHS cannot evaluate an absolute air concentration of PM, so it serves as an excellent supplementary tool to work in conjunction with the standard PM collection method.

Keywords: Particulate matter, PM2.5, PM0.1, Phytosampling, High-volume PM sampler, Environmentally persistent free radicals, Polycyclic aromatic hydrocarbons

Introduction

Ambient air particulate matter (PM) is one of the major contributors to pollution-related health effects. The data from the World Health Organization shows that about three million deaths were attributed to the ambient air particulate matter globally in 2012, and around 6% of these deaths were children under five years with acute lower respiratory diseases (World Health Organization 2016). The same model showed that 92% of the world population was experiencing a higher PM2.5 exposure level than WHO Air Quality Guidelines (annual mean of 10 μg/m3) (World Health Organization 2016). Numerous studies have concluded that the exposure to particulate matter, especially fine (da ≤ 2.5 μm) and ultrafine (da ≤ 0.1 μm) particles, is responsible for increased morbidity and mortality of respiratory and cardiovascular diseases including asthma, pneumonia, chronic obstructive pulmonary disease (COPD), lung cancer, and cardiac arrhythmias (Brook et al. 2010; Katanoda et al. 2011; Kim et al. 2015; Chen et al. 2016; Xing et al. 2016; Du et al. 2016; Li et al. 2018).

PM is a heterogeneous material with complex chemical compositions (or pollutants load), often determined by the origins of PM: emit by natural sources such as volcanic eruptions, wildfire, sea spray, and soil dust or result from condensation/reaction of atmospheric pollutants (Dzierżanowski et al. 2011). Studies have shown that > 70% of PM2.5 and > 90% of PM0.1 are combustion-borne (Cass et al. 2000; Kennedy 2007), and with the decreasing size of PM, the proportion of anthropogenic particles is increasing.

Anthropogenic PM is characterized by high-content carbonaceous materials, including polycyclic aromatic hydrocarbons (PAHs). PAHs are a common constituent of fine and ultrafine particulates. Due to the ubiquity of PAHs and carcinogenic and mutagenic characters of certain PAHs, the chronic effect of long-term PAH exposure has been proved to be associated with cancer, cardiovascular diseases, and adverse effects on fetal growth (Armstrong et al. 2004; Nethery et al. 2012; Clark et al. 2012; Xia et al. 2013; Alshaarawy et al. 2016; Huo et al. 2019).

In recent years, a new type of PM-associated pollutant has been identified and studied—environmentally persistent free radicals (EPFRs) (Balakrishna et al. 2011; Yang et al. 2017; Guo et al. 2020; Qian et al. 2020), with potentially significant health implications. EPFRs are long-lived surface-stabilized radicals (Dellinger et al. 2007; Lomnicki et al. 2008; Gehling and Dellinger 2013) formed in the cool zone of combustion processes through the interaction of transition metal oxide with substituted aromatic compounds on the surface of particles (Lomnicki and Dellinger 2003; Cormier et al. 2006; Lomnicki et al. 2008). New studies have emerged, focusing on EPFR formation under other conditions, including at the presence of metals other than transition metals (Wu et al. 2020), through heterogeneous oxidation mechanisms (Borrowman et al. 2016), or on the microplastics under the light irradiation (Zhu et al. 2019). Due to the common anthropogenic origin, EPFRs and PAHs share the same transport vehicle, i.e., PM (Wang et al. 2008; Wu et al. 2014; Yang et al. 2018). The toxicity of EPFRs stems from their ability to generate reactive oxygen species (ROS) and exacerbation of oxidative stress in biological systems (Dellinger et al. 2001; Nel et al. 2006; Ayres et al. 2008; Khachatryan et al. 2011; Kelley et al. 2013). Recent studies of combustion-borne PM have implicated cardiac and pulmonary dysfunctions to the inhalation exposure of EPFRs carried by PM (Fahmy et al. 2010; Balakrishna et al. 2011; Lord et al. 2011; Wang et al. 2011; Mahne et al. 2012; Thevenot et al. 2013; Burn and Varner 2015; Filep et al. 2016; Chuang et al. 2017).

For the regulatory and research purposes, PM is collected from ambient air using sampling devices. Most commonly, the concentration of PM and associated pollutants in the air is determined through the gravimetric analysis of the collection matrix. Major drawbacks of such method include the difficulty of particle retrieval from the collection matrix for further analysis if needed, altered environmental conditions during the sampling process (i.e., excessive airflow, changed solar irradiation conditions, or prolonged time lapse during collection (Fonseca et al. 2016)), and the collection matrix itself could absorb gas-phase semivolatile organic compounds, impacting the compositions of the PM sample (Galarneau et al. 2017). The network density of the sampling devices is also a factor to accurately represent spatial distribution of PM-associated pollutants.

It has been shown that the urban forest has the function to remove air pollutants, including particulate matter (Yang et al. 2005; Nowak et al. 2006; McDonald et al. 2007; Dzierżanowski et al. 2011; Yin et al. 2011; Terzaghi et al. 2013). PM removal from ambient air by plants is based on the “enhanced” deposition mechanism on leaves, i.e., electrostatic interaction and entrapment by trichomes (Fig. 1). This phenomenon creates a potential for a “phytosampler” development for ambient air PM collections. Though such method will not be able to define the PM concentration in ambient air, it can be an excellent tool for PM chemical characterization, and further translate to human exposure to PM components through referencing with PM concertation in the air (evaluated by other methods). Most importantly, PHS samples are exposed to the same solar irradiation and atmospheric conditions as those particles suspended in the air.

Fig. 1.

Fig. 1

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PM entrapment by trichomes on the leaf surface

PHS offers more flexibility for PM collection spatial resolution (such as creating a dense sampling network (Oyana et al. 2017; Wang et al. 2020)), reduced cost, no instrument set-up, and easier particle recovery from the collection matrix. This requires a detailed evaluation of such PM collection method and the understanding of the differences in the samples compared to those collected by the established methods. This study focused on the comparison between the PHS and the HVS methods in respect of the characterization of selected pollutants on the PM to provide further information and establish the feasibility of PHS as a supplementary tool for PM pollution studies.

Materials and methods

PM sample collection methods

Phytosampling

PM samples in this study were retrieved from the plant leaves collected in the Baton Rouge, LA, area during the years of 2015 to 2018. Three common to Louisiana plant species were selected, and fresh leaves of those plants were collected around the campus of Louisiana State University, Baton Rouge: Ligustrum japonicum (Japanese privet), Callicarpa americana (American beautyberry), and Camellia japonica (Japanese camellia). The sampling site features and locations are provided in Table 1 and Fig. 2. The collections were repeated at different times of the year, in spring, summer, and fall. The average PM retrieving yield (μg/cm2 leaf surface) from each plant species is presented in Table S1 (Supplementary material).

Table 1.

Sampling sites features

Sampling site GPS coordinates Plant species Site characteristics
SP #1 30.396734, −91.177729 Ligustrum japonicum (Japanese privet) The corner of an intersection where traffic was an important source for PM.
SP #2 30.413446, −91.182333 Callicarpa americana (American beautyberry) A busy bus stop on LSU campus.
SP #3 30.409424, −91.177341 Camellia japonica (Japanese camellia) Next to a department building on LSU campus.
HV #1 30.410441, −91.176964 High-volume PM sampler (HVS) On the roof of a seven-story building on LSU campus.
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Fig. 2.

Fig. 2

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Sampling site locations in Google Earth

Only healthy-looking leaves from 1.5 to 2 m above the ground were collected and stored in plastic zipper storage bags. After collection, samples were immediately transferred to the laboratory for PM retrieval, and the retrieving process for all samples was completed within 6 h after collection. The total leaf surface area per collection was measured using a grid paper. Next, leaves were transferred to beakers containing deionized water in the amount sufficient to cover all leaves except for petioles (usually around 100 mL). The zipper storage bags were rinsed with water, which would then be transferred to the beakers with leaves. All leaves were sonicated in a beaker filled with DI water using an ultrasonic bath (40 kHz, M3800H, Bransonic) for 30 s followed by transfer of water containing retrieved particles to vacuum filtration assemblies. This study is focusing on PM freshly trapped on the leaf surface—no PM embedded in the leaves wax is collected. The retrieving process is shown in Fig. 3.

Fig. 3.

Fig. 3

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PM retrieving procedure

Three sizes of PM were collected from water suspensions. Larger size particles were removed by a 3.0-μm pore size filter (Pall Corporation), and filtrate containing particles < 3 μm was collected (P3). Filtration using 0.7-μm pore size A/D glass fiber filter (PALL Corporation) or a 0.2-μm polyethersulfone membrane sterile syringe filter (VWR brand) produced filtrate suspension containing particles < 0.7 μm (P07) and < 0.2 μm (P02), respectively. Filtrates were next subjected to freeze drying (lyophilizer, Labconco Co., model #7740020), and after dried, corresponding particle size fractions based on the filter pore size were collected (Fig. S1, Supplementary material). Considering the site characteristics that vehicle emission provides sufficient PM and availability of plants, site SP #1 with P3 and P02 had been chosen for more extensive studies. The filtration assembly used in this study could separate the PM based on their physical size fraction. However, this setup concentrates the water-soluble components from all sizes of particles to the collected size range. This is an experimental artifact that overestimates the mass of each size fraction which translates to an underestimation of mass concentration of analytes in each fraction. The water-soluble components, however, are not the subject studied in this research.

High-volume PM sampler

PM2.5 and PM0.1 were collected using a high-volume PM sampler (BGI 900 High Volume Cascade Impactor, working flow rate of 900 L/min) fitted with three collection stages (PM10, PM2.5, and PM0.1). Polyurethane foam (PUF) filter was used as impacting matrix while glass microfiber filter (Emfab™, PALL) was placed as a residual stage. The sampler was set up on the roof of Choppin Hall, Louisiana State University (marked as HV#1 in Table 1 and Fig. 2), which is a seven-story building. Based on other studies, the PM concentration in the air exhibit a small monotonic decreasing vertical gradient with elevated building height (Chan et al. 2005; Shen et al. 2011; Quang et al. 2012; Azimi et al. 2018; Zauli Sajani et al. 2018). For example, a 4% PM2.5 concentration difference (not significant) has been observed in a 2- to 65-m elevated building height in warm period, while the variance is 11% in cold period (Zauli Sajani et al. 2018). An 18.4% and 10.4% concentration drop for PM1 and PM2.5 have been observed from the 1st floor to the 16th floor, respectively (Azimi et al. 2018). In another study concerning a pollution episode, no dramatic differences in PM concentration and chemical composition between the ground and 100 m were observed (Shen et al. 2011). In current studies, we are comparing exclusively the composition of PM collected by different methods; thus, the differences in the collection heights have no impact on the data. The average monthly meteorological parameters in Baton Rouge are given in Table S2 (Supplementary material).

Before collection, PUF filters were prepared by sequential washing with Milli-Q water, hexane (≥ 95%, MilliporeSigma), methanol (≥ 99.8%, BDH), and dichloromethane (99.9%, MilliporeSigma) under 1-h sonication each to remove residual contaminants. After cleaning, PUF filters were covered and left overnight to dry, and cleaned PUF filters were stored at 4 °C in plastic zipper storage bags before use. The air sampling rate of 900 L/min was checked by a BGI High Volume Calibrator before each sampling and every other day (adjusted if needed), and PM was collected continuously for 2 weeks. After collection, PUF filters were cut into 1-cm2 pieces and tapped to retrieve free falling particles (PM0.1 TAP and PM2.5 TAP) that were weighed and saved for further analysis. The remaining particles in the matrix were retrieved by sonication in water: filter pieces were put into a beaker with 50 mL Milli-Q water and sonicated for 10 min, filters were removed and the water suspension was immediately frozen using dry ice and acetone mixture and freeze dried using lyophilizer, and particles corresponding to each stage size were gathered (PM0.1 FD and PM2.5 FD) and weighed. To preserve EPFRs on PM, only water sonication was used to retrieve PM (no organic solvent or digestion and extraction was used).

Although there are scientific reports indicating the formation of hydroxyl radicals and potentially EPFRs and slight oxidation of PM components during the ultrasonic treatment to the particles, these studies refer to a high-power sonication over an extended period of time (10–60 min). On the contrary, studies of PAH type EPFR formation pointed out that the 30-min ultrasonic bath extraction showed no influence on the EPFR formation and persistence (Jia et al. 2018). In the present study, we applied a very short sonication of 30 s in a low frequency ultrasonic bath, with no significant difference observed for EPFRs between tapped samples and freeze-dried samples and their particle sizes are comparable based on our preliminary data.

Sample analysis

Scanning electron microscopy

Collected leaf surface microstructure was analyzed using a JEM 6610LV scanning electron microscope (SEM) at an accelerating voltage of 5–10 kV before and after PM retrieval. A 10 × 10-mm piece was cut from the center of the leaf and fixed on the stub with double-sided adhesive tape. PM collected by PHS and HVS were analyzed by the Quanta™ 3D DualBeam™ FEG FIB-SEM at the accelerating voltage of 20 kV, and energy dispersive X-ray spectroscopy provided information of the elemental analysis.

EPR characterization

All PM samples were characterized for the EPFRs using a Bruker EMX 10/2.7 EPR spectrometer (X-band). The parameters for EPR measurements were as follows: 2.03 mW for power; 4.0 G modulation amplitude; 100 G sweep width and 167.77 s sweep time; 40.96 ms time constant (corresponding to a 163.84-ms conversion time); 3.56 × 104 receiver gain; and three scans. EPFR concentration was calculated by comparing the peak area to a 2,2-diphenyl-1-picrylhydrazyl (DPPH) standard. The EPFR speciation and concentration were averaged for P02 (16 collections), P3 (9 collections), PM0.1 TAP (8 retrievable collections), PM0.1 FD (11 collections), PM2.5 TAP (7 retrievable collections), and PM2.5 FD (10 collections).

Simulated PM aging

After the particles had been retrieved from the medium, an EPFR aging study was conducted. We aged particles at room temperature under two conditions for 3 weeks: passive and active. The timeframe was chosen based on the results from previous environmental PM sample decay studies that the PM-associated EPFRs would tend to experience a fast decay in the first 3 weeks and followed by a slow decay (Gehling and Dellinger 2013; Chen et al. 2019a, 2019b). This change in decay time is related to the change of radical types (most likely) that the slow decaying radicals are associated with typical, carbon-centered radicals of less environmental and health concerns. Thus, our study focused on the initial decay stage. The same sample was divided into two groups, and particles were left in the EPR quartz tube for easier measurement. The first group of samples was subject to forced airflow at a constant rate of 0.048 L/min and labeled as active aging, and this group was set up to simulate the collection conditions for HVS. The second group was exposed to the air and subject only to air diffusion and called passive aging, simulating the PHS air conditions (Fig. S2, Supplementary material). Although the flow rate in active aging is far lower than in the real scenario, this achievable level is enough to separate the condition from the passive aging group. We compared the EPFR concentration and EPFR speciation changes in different treatment groups.

Polycyclic aromatic hydrocarbon analysis

Selected PM samples retrieved from the matrix were subject to PAH analysis. Samples were vortexed for 2 h 45 min by Vortex Genie 2 (Daigger brand) and sonicated for 15 min (FS-20 ultrasonic, Fisher Sci.) in 3 mL of dichloromethane (≥ 99.9%, MilliporeSigma). Suspensions were then centrifuged for 2 h (Drucker Diagnostics), and the supernatant was collected, dried under a gentle nitrogen flow, and redissolved in 200 μL of dichloromethane. Samples were then analyzed by GC-MS/MS (Agilent 7980B/7000C Triple Quad system) with a capillary GC column HP-5ms (30 m × 250 μm × 0.25 μm). The list of the analyzed PAHs and detail of the method used for analysis are given in Table S3 (Supplementary material).

Results and discussion

Leaf surface microstructure

The PM retrieval process from the leaf surface includes sonication of collected leaves. Compromising leaf integrity may affect the outcome of the process and introduce the experimental artifacts. SEM pictures of leaf surface before and after the PM retrieving (cf. Fig. 4) show that the process reduced the number of particles captured on the leaf surface with no damage to the stomata and trichomes.

Fig. 4.

Fig. 4

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SEM results on leaf surface before (a1, b1, c1, d1, and e1) and after (a2, b2, c2, d2, and e2) the PM retrieving from two species. a and b are from the leaves of Hedera helix (the common ivy), which were collected from Memphis, TN, for previous study (Oyana et al. 2017). c–e are from the leaves of Ligustrum japonicum (Japanese privet)

The energy dispersive X-ray spectroscopy results of retrieved PM from both HVS (PM0.1 TAP, PM0.1 FD, PM2.5 TAP, and PM2.5 FD) and PHS methods (P02 and P3) are shown in Fig. S3 (Supplementary material). Elemental carbon and oxygen accounted for the majority of elements in the samples from both collection methods. However, C, O, and Pt are also background elements due to the utilization of carbon tape and platinum coating for sample fixing. Thus, the actual content of those elements cannot be established with this method. We normalized remaining element data and presented as inset graph with the average from several samples in analysis areas. The results show nitrogen and sulfur (for PM0.1) and sodium, silicon, and chlorine (for PM2.5) dominated elemental distribution for HVS samples while potassium and calcium dominated in PHS samples. It is, however, worth to note that nitrogen and sulfur were also prominent in P02 samples from PHS. These elements are commonly detected in ambient PM, and nitrogen and sulfur are mainly from nitrates and sulfates, which are the most abundant inorganic components of secondary PM in the ambient air (Fine et al. 2008).

EPFR characterization

We compared the EPFR speciation and concentration on PM samples collected by both methods. g-factors and the peak width ΔHp-p were obtained from the EPR spectra, and they are indicators for the radical types in the PM sample. The EPFR concentration based on the PM mass was calculated and reported as spins concentration (spins/g), which represent the number of radicals in each gram of the PM sample. The average g-factor values of all samples are in the range of 2.0030–2.0050. The values closer to 2.003 are indicative of more carbon-centered radicals, while higher g-factors (2.004 and above) are typical for oxygen-centered EPFRs (Lomnicki et al. 2008). The g-factor of EPFRs on phytosampled PM (>2.0045) is statistically significantly higher (p<0.01, by ANOVA) compared to those collected by HVS (<2.0040), while the g-factors of EPFRs from same collection method but different PM sizes are not statistically different from each other (Fig. 5a). Therefore, EPFRs on phytosampled PM contained a larger contribution of oxygen-centered EPFRs compared to PM from HVS. This supports the hypothesis that the collection method affects EPFR speciation (and potentially other chemical changes). We posit that the variation of g-factors in samples collected by two methods is due to difference of ambient conditions during sampling. The two main environmental factors affecting the EPFR characteristics are solar irradiation and the volume of air flowed over the sample. Studies have shown the generation of secondary EPFRs by visible-light illumination with secondary EPFRs characterized by higher g-factors compare to the primary EPFRs (Chen et al. 2019c). For the samples collected by air sampler (particularly for an extended collection period), PM samples were not exposed to sunlight and exposed to the excess volume of air during the collection process. Such condition prevents the generation of secondary EPFRs and accelerates the decay of primary EPFRs. On the contrary, PM deposited on the leaves is exposed to ambient conditions which resembles the PM suspended in the air in respect of the elements, solar irradiation, and surface air diffusion rather than forced airflow.

Fig. 5.

Fig. 5

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Average g-factor (a), ΔHp-p (b), and spins concentration (c) of EPFRs on PM0.1, PM2.5, P02, and P3. * indicates a statistically significant difference (p<0.01, by ANOVA)

To investigate the impact of sample treatment during PM recovery from the matrix, we compared a fraction of PM simply tapped out of the collection matrix with that removed by sonication followed by freeze-drying (TAP versus FD). We found that applied treatment does not significantly impact EPFRs and elemental composition (the observed differences in the elemental composition can account for heterogenicity of PM samples).

For both types of samples, ΔHp-p is similar, between 6.9 and 7.7 (Fig. 5b), indicating a presence of carbon- and oxygen-centered EPFR mixture. Figure 5c compares the average EPFR spin concentration on PM samples, and variations were observed in all sample sizes. To further investigate the EPFR concentration on different PM sizes, Fig. 6 compares EPFR concentrations between different PM sizes within each collection.

Fig. 6.

Fig. 6

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Comparison of spins concentration of EPFRs on PM0.1, PM2.5, P02, and P3 within the same collection. a is the comparison of EPFR spins concentration between PM0.1 TAP and PM2.5 TAP collected from HVS. b is the comparison of EPFR spins concentration between PM0.1 FD and PM2.5 FD collected from HVS. And c is the comparison of EPFR spins concentration between P02 and P3 collected from PHS

No correlation between EPFR concentration with PM size was detected for samples collected by the PHS (Fig. 6c). However, for HVS samples, PM0.1 always contains higher EPFR concentration than PM2.5 (Fig. 6a–b). We believe that the difference in EPFR concentration depending on PM size is an inherent artifact of the impaction collection method. Different PM size fractions originate from various sources. EPFRs are typically associated with PM from combustion origin and are smaller than 2.5 μm. In cascade impactor, such as the HVS used in the current study, particles collected on each stage are normally distributed in size, with a median aerodynamic diameter of the stage’s nominal size. Thus, each collection stage will also collect larger and heavier particles, originating from erosion, dust, and sand, which typically do not contain EPFRs. With the decreasing nominal size of the collection stage, the contribution of those large particles is also decreasing. This is in line with other observations that related increased EPFR concentration to smaller PM with higher carbon content (Tian et al. 2009; Yang et al. 2017). The PHS method, in contrast, is a filter size cutoff method which will only collect particles with the size below nominal filter size, thus avoiding the bias of larger particle fractions.

Simulated aging study

A significant difference in EPFRs between HVS samples and PHS samples was observed during the aging studies (Fig. 7). In general, HVS samples have shown an overtime concentration decay behavior, described by others before (Dellinger et al. 2007; Chen et al. 2018), with EPFR lifetime ranging from 63 to 167 days depending on the model of aging (forced airflow—active aging or diffusion air contact—passive aging). On the contrary, PHS samples have shown an increasing EPFR concentration with time to reach a steady state level (with trending concentration downwards after prolonged aging). Interestingly, g-factor remains relatively constant for HVS samples, while it decreased with aging for PHS samples. We have concluded, based on this observation, that EPFRs in the natural environment are constantly formed leading to oxygen-centered EPFRs (higher g-factor) and at the same time decayed and transformed to carbon-centered EPFRs: this is why during aging, though the g-factor on phytosampled EPFRs is declining (increasing contribution of carbon-centered EPFRs), overall radical concentration is increasing. The solar light irradiation had been reported to facilitate the formation of EPFRs, especially oxygen-centered radicals at room temperature (Li et al. 2014; Chen et al. 2019c; Jia et al. 2019). On the contrary, HVS samples provide a snapshot of PM further in time, where a decay starts to dominate (due to extended exposure to high airflow in the sampler and lack of solar light irradiation) where g-factor is already much lower, and no formation of new EPFRs is observed.

Fig. 7.

Fig. 7

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Comparison of average spins concentration (blue line) and average g-factors (red line) of EPFRs on PM from both collection methods under active and passive aging conditions. The blue shade and the red shade represent one standard deviation range of EPFR concentrations and g-factors, respectively. C0 is the initial EPFR concentration and C is the EPFR concentration after certain decaying time. PM0.1 and PM2.5 were collected by HVS, while P02 and P3 were collected by PHS

This result provides a valuable direction for understanding the dynamic change of EPFRs in the ambient air, and further supports more accurate EPFR representation on samples from PHS compared to HVS.

PAH analysis by GC-MS/MS

Three collections of PM from HVS and two PM collections from leaves were extracted and analyzed for PAHs content. For PHS samples, larger particle size (P3) in each case contained more PAHs compared to ultrafine samples (P02) (Fig. 8b). This is in contrast to HVS samples, where smaller PM contained typically more PAHs (Fig. 8a). The PAH distribution difference depending on the sampling method is most likely due to the collection mechanism differences—PHS delivers particles with a diameter below filter cutoff size; thus, P3 includes all PAHs associated with smaller particles. However, this is not the case for HVS samples, which represent the normal distribution of particle sizes with a shifting median. However, both methods suffer from artifacts—in the case of PHS samples, a larger content of water-soluble PM components will effectively decrease observed PAHs expressed in mass/mass units. This phenomenon is best observed in the sample collected on 06/29/17 (Fig. 8b).

Fig. 8.

Fig. 8

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Concentration of total extracted PAHs on PM from HVS (a) and PHS (b)

The normalized concentration percentage of individual PAH to the total PAHs is shown in Fig. 9. The specific PAH profile for both sizes (P02 and P3) of PHS samples were very similar and these samples contain more lighter PAHs (five or fewer rings) (Fig. 9a), dominated by the presence of cyclopenta[c,d]pyrene, phenanthrene, pyrene, fluoranthene, and to a lesser extent, benzo[c]fluorene and benzo[a]pyrene. For HVS samples (Fig. 9b), a distinct difference was noticed in PAH profiles with the changing size: PM2.5 samples contained a relatively higher amount of lighter PAHs (four or fewer rings, profile dominated by pyrene, fluoranthene, and phenanthrene); PM0.1 contain more heavier PAHs (more than five rings, profile dominated by indeno[1,2,3-c,d]pyrene and benzo[g,h,i]perylene). The sample collected on 05/21/18 appears to show a different pattern in both PM0.1 and PM2.5 sizes and indicated a different source of PM. In fact, this sample resembles more phytosamples in respect of specific PAH profile. Though no sufficient data is available, it is possible that this resemblance is associated with the seasonal change in PAH profile (this specific HVS sample and both PHS samples were all collected in the summer months, while other HVS samples were collected in cooler months).

Fig. 9.

Fig. 9

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Comparisons of PAH concentration percentage on PM from both collection methods within similar particle size. a compares the PAH profiles between P02 and P3 from two collections by PHS. b compares the PAH profiles between PM0.1 FD and PM2.5 FD from three collections by HVS

It is not possible at this point to explain the difference in the PAH profile between PM0.1 and PM2.5, considering that such difference is not observed for phytosamples. One can speculate that it might be associated with the combination of the collection method and the vaporization of lighter PAHs from smaller particles (high surface-volume ratio). This finding, however, underscores the more consistent data for phytosampling over impactors in respect of chemical speciation characterization associated with PM in ambient air.

Conclusions

There is no perfect method for collecting ambient air PM samples. The proposed in here phytosampling method is particularly useful in the analysis and evaluation of chemical speciation associated with PM as well as their spatial distribution pattern. The PM deposited on leaves is to a more degree exposed to similar elements as air suspended particles, compared to those collected on the sampler collection matrix. In particular, this method is beneficial to evaluate the EPFR speciation and content on PM, which are very sensitive to changing conditions and elapsed time from the moment they are removed from ambient. Also, other chemicals (for example, PAHs) present a potentially more representative description of both their speciation and content in respirable particles. The major challenge is the translation of thus found results into the ambient air concentration, as phytosamples cannot be used to evaluate an absolute air concentration of PM and their components in the air, but rather the relative content of chemicals on PM. This can be overcame by supplementing phytosampling data with other monitoring methods that can provide a local concentration of PM in the air. However, one needs to remember that phytosampling-collected samples are also not free of artifacts, mostly due to the enrichment of the retrieved particles in water-soluble fraction and thus mass-based dilution of analytes.

One of the biggest advantages of phytosampling is the ability to sample at almost any location and to create a dense network of sampling sites to achieve a high-resolution data on chemical distribution with no high cost of HV PM samplers.

Supplementary Material

Supplementary Material

Funding

This work was supported by the National Institute of Environmental Health Sciences (2P42ES013648 - 08A1). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Footnotes

Supplementary Information The online version contains supplementary material available at https://doi.org/10.1007/s11356-021-13292-z.

Competing interests The authors declare no competing interests.

Data Availability

All data generated or analyzed during this study are included in this published article and the supplementary material.

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Supplementary Materials

Supplementary Material
NIHMS1764785-supplement-Supplementary_Material.pdf (1.3MB, pdf)

Data Availability Statement

All data generated or analyzed during this study are included in this published article and the supplementary material.

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