The capability of ambient particles to generate in vivo reactive
oxygen species (ROS), called oxidative potential (OP), is a potential metric
for evaluating the health effects of particulate matter (PM) and is
supported by several recent epidemiological investigations. Studies using
various types of OP assays differ in their sensitivities to varying PM
chemical components. In this study, we systematically compared two
health-relevant acellular OP assays that track the depletion of antioxidants
or reductant surrogates: (i) the synthetic respiratory-tract lining fluid (RTLF)
assay that tracks the depletion of ascorbic acid (AA) and glutathione (GSH)
and (ii) the dithiothreitol (DTT) assay that tracks the depletion of DTT.
Yearlong daily samples were collected at an urban site in Atlanta, GA
(Jefferson Street), during 2017, and both DTT and RTLF assays were performed
to measure the OP of water-soluble PM2.5 components. PM2.5 mass
and major chemical components, including metals, ions, and organic and
elemental carbon were also analyzed. Correlation analysis found that OP as
measured by the DTT and AA depletion (OPDTT and OPAA,
respectively) were correlated with both organics and some water-soluble
metal species, whereas that from the GSH depletion (OPGSH) was
exclusively sensitive to water-soluble Cu. These OP assays were moderately
correlated with each other due to the common contribution from metal ions.
OPDTT and OPAA were moderately correlated with PM2.5 mass
with Pearson's r=0.55 and 0.56, respectively, whereas OPGSH
exhibited a lower correlation (r=0.24). There was little seasonal
variation in the OP levels for all assays due to the weak seasonality of
OP-associated species. Multivariate linear regression models were developed
to predict OP measures from the particle composition data. Variability in
OPDTT and OPAA were not only attributed to the concentrations of
metal ions (mainly Fe and Cu) and organic compounds but also to antagonistic
metal–organic and metal–metal interactions. OPGSH was sensitive to
the change in water-soluble Cu and brown carbon (BrC), a proxy for ambient
humic-like substances.
Introduction
Epidemiological studies have consistently reported associations between
particulate matter (PM) and increased morbidity and mortality (Brunekreef
and Holgate, 2002; Cohen et al., 2017; Lippmann, 2014; Norris et al., 1999;
Pope et al., 2004; Samet et al., 2000; Sun et al., 2010; Thurston et al.,
2017). The capacity of inhaled PM to elicit oxidative stress has emerged as
a hypothesis to explain PM-induced adverse health effects. Inhaled PM can
directly introduce PM-bound reactive oxygen species (ROS) to the surface of
the lung, where they react with and deplete lung-lining fluid antioxidants,
or introduce redox-active PM species, which can react with biological
reductants and generate ROS in vivo (Lakey et al., 2016). The latter can occur in
organs beyond the lungs by particles or chemical species being translocated
from the lungs throughout the body. Oxidative stress arises when the
presence and production of ROS overwhelms the antioxidant's defenses, and can
lead to cell and tissue damage and induction of chronic and degenerative
diseases (Das, 2016; Halliwell, 1994; Pizzino et al., 2017). The ability of
PM to generate ROS in vivo, referred to as the oxidative potential (OP) of
particles, has gained increasing attention as a possibly more integrative
health-relevant measure of ambient PM toxicity than PM mass concentration,
which may contain a mix of highly toxic (e.g., polycyclic aromatic
hydrocarbons (PAHs), quinones, environmentally persistent free radicals,
highly oxygenated organic molecules, and transition metals) to relatively
benign (e.g., sulfate and ammonium nitrate) PM components (Frampton et al.,
1999; Khachatryan et al., 2011; Lippmann, 2014; Tong et al., 2018, 2019).
A variety of acellular assays have been developed to assess PM OP (Ayres et
al., 2008; Bates et al., 2019). In general, these assays involve the
incubation of PM extracts or suspension with chemical reagents and probes, and
the response is recorded over time or after incubation. The responses
recorded include the depletion of reductant surrogate, such as the
dithiothreitol (DTT) assay (Cho et al., 2005), and depletion of specific
antioxidants in a composite solution, such as a synthetic respiratory-tract lining fluid (RTLF) model (Godri et al., 2011; Mudway et al., 2004;
Zielinski et al., 1999). In contrast, other assays measure ROS generation
(e.g., the dichlorofluorescein assay; Huang et al., 2016; Venkatachari et
al., 2005) or hydroxyl radical formation in the presence of H2O2
(e.g., electron paramagnetic, EPR, or electron spin resonance, ESR; Shi et al., 2003a, b). The assays based on exposing PM species to antioxidants
are currently more commonly used. The DTT assay (Cho et al., 2005) is a
chemical system that mimics the in vivo PM-catalyzed electron transfer process. In
this assay, DTT acts as a surrogate of the cellular reductant (NADH or
NADPH), donating electrons to oxygen and producing ROS with the catalytic
assistance of PM redox-active species. PM OP (i.e., OPDTT in this case)
is determined by measuring the depletion of DTT over time, which is assumed
to be proportional to the concentration of redox-active compounds in PM. For
the RTLF assay, RTLF is constructed to simulate the aqueous environment that
particles first encounter when inhaled into the lungs and deposited. The
antioxidants in RTLF, specifically the major low-molecular-weight
antioxidants, ascorbic acid (AA), uric acid (UA), and reduced glutathione
(GSH), provide protective defenses against PM-induced oxidative damage. The
extent to which they are depleted by PM over time reflects a direct measure
of PM oxidative activity by this assay, expressed as OPAA, OPUA,
and OPGSH (Kelly et al., 1996; Mudway et al., 2004; Zielinski et al.,
1999). Some studies (DiStefano et al., 2009; Fang et al., 2016; Janssen et
al., 2015; Mudway et al., 2005) have used a simplified alternative approach
to AA analysis in RTLF, where only the single antioxidant AA is contained in
the solution. If not explicitly stated, OPAA in this paper represents
OPAA obtained from the RTLF model.
Recently, a growing number of epidemiological studies have used these OP
assays to examine the link between particle OP and adverse health outcomes.
OPDTT of ambient fine particles has been found to be more strongly
associated with multiple cardiorespiratory outcomes, such as airway
inflammation (Delfino et al., 2013; Janssen et al., 2015; Yang et al.,
2016), asthma (Abrams et al., 2017; Bates et al., 2015; Fang et al., 2016;
Yang et al., 2016), and congestive heart failure (Bates et al., 2015; Fang et
al., 2016), than PM mass. Multiple population-scale studies employed the
RTLF assay to assess OP of PM2.5 and found that OPGSH was
associated with lung cancer, cardiometabolic mortality (Weichenthal et al.,
2016a), emergency room visits for respiratory illness (Weichenthal et al.,
2016c), myocardial infarction (Weichenthal et al., 2016b), and adverse birth
outcomes (Lavigne et al., 2018). The association between airway inflammation
in asthmatic children and OPGSH of PM2.5 personal exposure was
also reported (Maikawa et al., 2016). In the study of Strak et al. (2012),
no association was found between OPGSH and acute airway inflammation in
healthy volunteers after 5 h of exposure. A population-scale study in
London, UK, found no association between OPGSH and mortality and
hospital admission (Atkinson et al., 2016). For the AA depletion, either in
the composite RTLF model or in a simplified AA-only model, no association
with adverse health end points has been found, including asthma,
cause-specific mortality, and cardiorespiratory emergency department visits
(Fang et al., 2016; Maikawa et al., 2016; Weichenthal et al., 2016a;
Weichenthal et al., 2016b; Weichenthal et al., 2016c). Bates et al. (2019)
provide a review of the relationships of various OP assays with adverse
health effects.
The differences in observed health effects may be due to the different
sensitivity of OP assays to various PM species. Past studies have shown that
specific assays are correlated with different PM components. OPDTT has
been found to be sensitive to transition metals (Charrier and Anastasio,
2012; Fang et al., 2016; Verma et al., 2015a) and organic species,
especially more oxygenated aromatic organics, such as quinones and
hydroxyquinones (Cho et al., 2005; Kumagai et al., 2002; McWhinney et al.,
2011; Verma et al., 2015b). OPAA obtained from the simplified AA-only
model are mostly responsive to the metal content of PM (Fang et al., 2016;
Maikawa et al., 2016; Yang et al., 2014). Antioxidants (AA and GSH) within
the synthetic RTLF are responsive to a slightly different group of metals.
For example, OPAA responds to iron and OPGSH is related to
aluminum (Godri et al., 2010). However, both OPAA and OPGSH are
sensitive to copper (Ayres et al., 2008). Studies performed on real PM
samples or standard solutions indicate that quinones also drive the
oxidative losses of both antioxidants (Ayres et al., 2008; Calas et al.,
2018; Kelly et al., 2011; Pietrogrande et al., 2019).
Since different assays capture different chemical fractions of the oxidative
activity of PM, it is challenging to synthesize the findings from OP health
studies. There remains a need to compare different assays on identical
particle samples to advance our understanding of the effects of PM species
on OP measures and in turn assess the results of the health studies that
use OP. In this study, we used two acellular OP assays, DTT and RTLF, to
measure the water-soluble OP of ambient PM2.5 collected from urban
Atlanta, GA, over a 1-year period. These two assays were chosen since they
are currently most commonly used and have shown significant associations
with adverse health outcomes in some studies. A suite of chemical components
was also measured on these samples and univariate and multivariate linear
regression analyses were performed to identify and evaluate the contribution
of major chemical components to each of these OP metrics.
MethodsSampling
Yearlong sampling was conducted in 2017 from 1 January to 30 December at
the Jefferson Street SEARCH (Southeastern Aerosol Research and
Characterization) site (Edgerton et al., 2006, 2005). Jefferson Street is
situated roughly 4.2 km northwest of downtown Atlanta and 2.3 km from a
major interstate highway and is representative of the urban Atlanta region. The
site has been extensively used in past studies characterizing urban Atlanta
air quality (Hansen et al., 2006) and the data used in OP and
epidemiological studies (Abrams et al., 2017; Bates et al., 2015; Fang et
al., 2016; Sarnat, 2008; Verma et al., 2014).
Ambient PM2.5 was collected daily (from midnight to midnight, 24 h
integrated samples) onto pre-baked 203×254 mm quartz filters (Pallflex
Tissuquartz, Pall Life Sciences) using high-volume samplers (HiVol, Thermo
Anderson, nominal flow rate 1.13 m3 min-1, PM2.5 impactor).
A total of 349 filter samples were collected for analysis; missing days were
due to instrumentation issues. The HiVol quartz filters were wrapped in
pre-baked aluminum foil after collection and stored at -18∘C
until analysis. PM2.5 mass concentration was monitored continuously
by a tapered element oscillating microbalance (TEOM, Thermo Scientific TEOM
1400a), the sample stream dried at 30 ∘C using a Sample
Equilibration System (Meyer et al., 2000). A Sunset semicontinuous OCEC
analyzer (Sunset Laboratory) was used to provide in situ measurements of
organic and elemental carbon (OC / EC) content of fine PM. The data were
obtained hourly by using 1 h cycles in which the instrument sampled ambient
air through an activated carbon denuder for 45 min and analyzed the
particles collected on the quartz filter for 15 min using the NIOSH 5040
analysis protocol (Birch and Cary, 1996).
Oxidative potential measurements
Two acellular assays, DTT and RTLF assays, were performed to measure the
oxidative potential of water-soluble PM2.5. The DTT analysis was
completed at Georgia Institute of Technology, and all filters were analyzed
within 1 month after collection. The RTLF assay was conducted at Yale
University during October 2018. Prior to the OP analyses, a fraction of each
HiVol filter (5.1 cm2 for DTT and 4.5 cm2 for RTLF) was punched
out, placed in a sterile polypropylene centrifuge vial (VWR International
LLC, Suwanee, GA, USA), and then extracted in 5 mL of deionized water (DI, >18MΩ) via 30 min sonication. The water
extract was filtered through a 0.45 µm PTFE syringe filter
(Fisherbrand, Fisher Scientific) and then used for OP analysis.
DTT assay
The DTT assay was performed with a semiautomated system developed by Fang
et al. (2015b), following the protocol described by Cho et al. (2005). In
brief, the PM extract (3.5 mL; 40±15µg mL-1 of PM) was
incubated with DTT solution (0.5 mL; 1 mM) and potassium phosphate buffer (1 mL; pH ∼7.4, Chelex resin treated) at 37 ∘C. A
small aliquot (100 µL) of the mixture was withdrawn at designated
times (0, 4, 13, 23, 31 and 41 min) and mixed with trichloroacetic acid
(TCA, 1 % w/v) to quench the DTT reactions. After addition of Tris buffer
(pH ∼8.9), the remaining DTT was reacted with
5,5′-dithiobis-(2-nitrobenzoic acid) (DTNB) to form a colored product that
absorbs light at 412 nm. The final mixture was pushed through a 10 cm path
length liquid waveguide capillary cell (LWCC; World Precision Instruments,
Inc., FL, USA), and the light absorption was recorded by an online
spectrometer, which included a UV-Vis light source (DT-mini-2, Ocean Optics,
Inc., Dunedin, FL, USA) and a multiwavelength light detector (USB4000
Miniature Fiber Optic Spectrometer). The DTT consumption rate, used as a
measure of OP, was determined from the slope of the linear regression of DTT
residual vs. time. Good linearity was found for all samples with correlation
coefficients (R2) larger than 0.98. In parallel with all sample
batches, at least one field blank and one positive control
(9,10-phenanthraquinone) was analyzed, and their OP values remained constant
throughout the analysis. The limit of detection (LOD), defined as 3
times the standard deviation of OPDTT for blanks, was 0.31 nmol min-1. The PM OP measured by this assay (i.e., OPDTT) was
blank-corrected and normalized by the air volume that passed through the
extracted filter fraction, expressed as nmol DTT min-1 m-3.
This approach did not involve the use of PM samples with constant mass,
which is sometimes employed to limit nonlinear DTT response to certain metal
ions (Charrier et al., 2016).
RTLF assay
The RTLF assay is based on the protocol adopted by Maikawa et al. (2016). PM
water extracts (35±13µg of PM mL-1) were transferred into a
96-well microplate with 180 µL of sample liquid in each well. A total of 20 µL of synthetic RTLF (pH ∼7.0) containing equimolar
concentrations (2 mM) of AA, UA, and GSH was added into each well, resulting
in a final starting concentration of 200 µM of antioxidants. The
PM-RTLF mixture was incubated in a plate reader (SpectraMax190, Molecular
Devices, LLC, San Jose, CA, USA) for 4 h at 37 ∘C with gentle
mixing. Following incubation, the concentrations of AA and GSH were analyzed
immediately. UA concentration was not measured since studies have
consistently suggested that no depletion of UA was observed in the presence
of PM (Kunzli et al., 2006; Mudway et al., 2004; Zielinski et al., 1999).
AA concentration was determined with the plate reader by measuring the light
absorbance at 260 nm. The GSH concentration was indirectly quantified by
measuring total glutathione (GSx) and oxidized glutathione (GSSG)
concentrations, both compounds determined using the enzymatic recycling
method (Baker et al., 1990). The incubated PM-RTLF mixture was diluted
49-fold with 100 mM sodium phosphate buffer (pH ∼7.5)
containing ethylenediaminetetraacetic acid (EDTA). To measure the GSx
concentration, 50 µL of each diluted sample was dispensed onto a
microplate. A total of 100 µL of reaction mixture (0.15 mM DTNB, 0.2 mM NADPH,
and 1 U glutathione reductase, GR) was added to each well. In the mixture,
GSH reacted with DTNB, forming a yellow-colored product
5-thio-2-nitrobenzoic acid (TNB) and the mixed disulfide GS-TNB. In the
presence of NAPDH and GR, GSSG and GS-TNB were reduced back to GSH, leading
to more TNB production. The plate was analyzed on the plate reader for 2 min under constant mixing to continuously monitor the formation of TNB.
The TNB formation rate, which is proportional to the GSH concentration, was
measured at an absorbance of 405 nm. For GSSG measurement, 5 µL of
2-vinylpyridine was added to 130 µL of the diluted sample to
conjugate GSH. The solution was incubated at room temperature for 1 h,
followed by similar procedures performed for the GSx measurement. The GSH
concentration was calculated by subtracting 2 times the GSSG concentration
from the measured GSx concentration.
Field blanks and known controls (e.g., for positive controls H2O2 and
Cu were used and Zn was used as a negative control) were run in parallel with all sample batches. All
samples and controls were measured in triplicate. The percentage of AA and
GSH depletion after 4 h incubation for each PM sample was calculated
relative to the field blank. The LOD for AA and GSH depletion after 4 h
incubation was 4.0 % and 4.5 %, respectively. PM OP obtained from this
assay, i.e., OPAA and OPGSH, was determined by normalizing the
percentage loss with the sampled air volume, in unit of percentage depletion per cubic meter.
Chemical analysis on PM filtersElemental analysis
Both total and water-soluble trace metals were determined by inductively
coupled plasma mass spectrometry (ICP-MS) (Agilent 7500a series, Agilent
Technologies, Inc., CA, USA), including magnesium (Mg), aluminum (Al),
potassium (K), calcium (Ca), chromium (Cr), manganese (Mn), iron (Fe),
copper (Cu), and zinc (Zn). For the determination of concentrations of total
metals, a 1.5 cm2 filter punch from the HiVol quartz filter was
acid-digested for 20 min using aqua regia (HNO3+3HCl). The
acid-digested sample was then diluted in DI water to 10 mL and filtered with a
0.45 µm PTFE syringe filter. For the analysis of water-soluble
metals, no digestion was performed. In this case, one circular punch (5.1 cm2) was extracted in 5 mL of DI via 30 min sonication. The extract was
filtered using a 0.45 µm PTFE syringe filter and then acid-preserved
by adding concentrated nitric acid (70 %) to a final concentration of 2 % (v/v).
Water-soluble organic carbon (WSOC) and brown carbon (BrC)
Two 1.5 cm2 filter punches from the HiVol filter were extracted in 15 mL DI in a pre-baked glass centrifuge vial (DWK Life Sciences, Rockwood, TN,
USA) by 30 min sonication. The extracts filtered with 0.45 µm PTFE
syringe filters were used to measure water-soluble organic carbon (WSOC) and
its light absorption properties (BrC, used as a source tracer). A fraction
(∼6 mL) of filter extract was injected by a syringe pump
(Kloehn, Inc., NV, USA) into a 2.5 m path length LWCC (World Precision
Instruments, Inc., FL, USA), with an internal volume of 500 µL . The
absorbance at 365 nm wavelength (BrC) was measured by an online
spectrophotometer. The remaining liquid extract was drawn into Sievers total
organic carbon (TOC) analyzer (Model 900, GE Analytical Instruments,
Boulder, CO, USA) for determination of WSOC concentration. The TOC was
calibrated with a series of prepared sucrose standards.
Water-soluble ionic species
One 1.5 cm2 filter punch from the HiVol filter was extracted in 10 mL
DI via sonication. The inorganic ions (SO42-, NO3-, and
NH4+) in the filtered water extracts were measured by ion exchange
chromatography (IC) with conductivity detection. For anion measurements, eluent containing 3.2 mM Na2CO3 and 1.0 mM NaHCO3 was passed through an anion separation column (Metrosep A Supp
5-150/4.0) at a flow rate of 0.78 mL min-1. For cation measurements, eluent containing 1.7 mM HNO3 and 0.7 mM dipicolinic acid was passed through a cation separation column (Metrosep C4-150/4.0) at a flow rate of 0.9 mL min-1. An automated sampler (Dionex AS40, Thermo
Fisher Scientific, Waltham, MA) was used to inject samples into IC.
Multivariate regression models
Multivariate linear regression models were developed to predict OPs based on
PM speciation data and investigate the relative importance of species on
different OPs. Prior to the regression analysis, boxplots were used to
identify the outliers and test the normality of data. Extreme values (a
total of ∼3 % of the OP measurements) were removed from
the data set. Linear regression was performed between PM components and the
various OPs. To simplify the analysis, PM components correlated with OP
(r>0.4, p<0.05) were selected as the independent
variables of the models. A stepwise regression was applied to the data set
using MATLAB R2016a to form the multivariate regression models. To evaluate
the performance of the final models, 5-fold cross-validation was employed
and repeated 50 times. For each OP measure, the average mean-squared error
over 50 iterations was within 25 % of the mean OP value (23.4 %, 17.9 %, and 12.2 % for OPDTT, OPAA, and OPGSH, respectively).
Time series of PM2.5 mass
concentration. The pie charts show the average aerosol composition based on
PM2.5 mass measured by the tapered element
oscillating microbalance (TEOM) during the whole sampling year,
summer, and winter. WSOM is water-soluble organic matter
(=WSOC×1.6); WIOM is water-insoluble organic matter
(=OM-WSOM); WS-metals is the sum of water-soluble metals, including Al,
Mg, Ca, K, Fe, Cu, Mn, Zn; and WI-metals are water-insoluble metals
(= total_metals–WS_metals). Summer is
June–August, and winter is January–February and November–December.
∗pvalue<0.05. ∗∗pvalue<0.01. Correlations that are not
statistically significant (p>0.05) are in italics; r>0.65 are bold. All metals listed are water-soluble metals.
Results and discussionAmbient PM composition
Figure 1 shows the time series of PM2.5 mass concentration and the
averaged chemical composition of ambient particles collected at the site. A
factor of 1.6 was applied to convert organic carbon to organic matter
(Turpin and Lim, 2001; Weber, 2003). Reconstituted mass from measured
chemical species agreed well with PM2.5 mass measured by the TEOM
with Pearson's r=0.84, and accounted for more than 80 % of the
PM2.5 mass. The missing mass may result from other species not
measured, semi-volatile material lost from the filter, and the uncertainty
in converting measured carbon mass to organic matter (factor of 1.6 used).
Fractions of various chemical components in PM2.5 are consistent with
previous observations (Edgerton et al., 2005; Verma et al., 2014). In
general, PM mass was dominated by organic compounds
(WSOM + WIOM 50 %), followed by inorganic ions (10 %
SO42-, 4 %–7 % NH4+, and 1 %–8 % NO3-).
Metals constituted 6 %–13 % of the PM mass, among which water-soluble
metals were at trace amounts (1 %–2 %). EC accounted for a small fraction
of the PM mass (5 %–6 %). NH4+, and NO3-, which are
semi-volatile, accounted for a larger fraction of fine particle mass during
the cold season. The metal fraction also increased in winter, whereas the
fractions of other PM components did not vary significantly during the
sampling period.
Although insoluble PM components also play an important role in OP (Gao et
al., 2017; Verma et al., 2012), this study solely focuses on water-soluble
OP measurements, and thus the water-soluble PM components are the primary
focus in this study. The OP (DTT assay only) contribution from
water-insoluble components was discussed in detail in another paper from
this study (Gao et al., 2020).
Association of OP with PM components
Pearson's correlation coefficients for the linear regression between OP and
select chemical components are shown in Table 1. The detailed correlation
matrices for different seasonal periods are given in Tables S1–S3 in the Supplement. We
defined the strength of the absolute correlation coefficient value as strong
for values ≥0.65, moderate from 0.40 to 0.65, and weak for values
<0.4. The OP assays were moderately intercorrelated over the
sampling year. In all cases (whole year and summer or winter), OPAA and
OPGSH had the highest correlations, which may in part be due to these
measurements being conducted on the same sample extracts. As for OPDTT,
the correlations with OPAA and OPGSH varied but were largely
similar. The correlations between the OP measures and various PM components
varied, highlighting the different sensitivities of OP assays to various PM
components. Note these correlations do not imply that the compounds are
responsible for the OP as some of them are not redox active compounds.
However, the correlations may indicate the emission sources (as source
tracers, e.g., vehicular emissions for EC, secondary processing for WSOC and
SO42-, and biomass burning for BrC and K), which also probably
emit the water-soluble species contributing to the measured OP.
As shown in Table 1, OPDTT was correlated with OC and WSOC, indicating
a contribution from PM organic compounds. The correlations between
OPDTT and certain water-soluble metals, such as Fe, Cu, and Mn, were
also observed. A moderate correlation of EC with OPDTT (r=0.51) and
somewhat with metals and OC (r=0.55, 0.43, and 0.83 for Fe, Mn, and OC,
respectively; Table S1) suggests that incomplete combustion could be one of
their common sources. The associations found in this study between
OPDTT and PM composition are consistent with a number of previous
studies (Fang et al., 2016, 2015b; Verma et al., 2014; Yang et
al., 2014), though the correlations in our work were weaker (r>0.5, compared with r>0.65 in other studies).
Similar to OPDTT, OPAA was moderately correlated with OC, WSOC, and
water-soluble metals, mainly Fe and Cu (r=0.47–0.55). The results are
compared with a previous study conducted in the same Atlanta region by Fang
et al. (2016), wherein a simplified AA assay was applied to assess
water-soluble OP of PM2.5. The AA depletion in the AA-only model was
found to be strongly correlated with water-soluble Cu with Pearson's r>0.65, and associations with WSOC (or BrC) and metals were also
observed. The weaker sensitivity of AA to water-soluble Cu observed in our
study is possibly due to the reactivity hierarchy existing within the
antioxidant model with GSH > AA (Zielinski et al., 1999). This is
further supported by other studies. In the study of Charrier et al. (2011),
ligand speciation modeling indicated that GSH was a stronger ligand compared
to AA and caused a dramatic shift in Cu speciation by forming Cu–GSH
complexes. The experimental results in the study of Pietrogrande et al. (2019) showed that the response of the acellular AA assay was strongly
dependent on the composition of synthetic RTLF used and the presence of GSH
and UA would lower the sensitivity of AA response to Cu. The correlations of
organics and EC, which comprised a large fraction of PM mass (Fig. 1), with
OPDTT and OPAA, likely account for the OPDTT and OPAA
correlations with PM mass.
In contrast to OPDTT and OPAA, OPGSH was found to be
exclusively correlated with water-soluble Cu with Pearson's r>0.7. The consistently lower correlation of OPAA with water-soluble Cu
than OPGSH with Cu, is consistent with GSH outcompeting AA in the RTLF
in forming Cu complexes. The results are also consistent with other studies
(Aliaga et al., 2010; Ayres et al., 2008; Godri et al., 2011).
The correlations differed by seasons. In winter, OPAA and OPDTT
were more correlated with organic species, with stronger associations with
WSOC, BrC, and K, indicating biomass burning as a common source of OPAA
and OPDTT. In summer, all OP assays tended to be metal-driven.
OPAA and OPDTT were more correlated with Cu, along with
SO42-, suggesting possible influence of secondary processing on
metal mobilization (Fang et al., 2017; Ghio et al., 1999) and resulting in a
strong intercorrelation between different OP metrics.
Temporal variation for (a) OPDTT m-3 (nmol min-1 m-3), (b) OPAA m-3 (% depletion of AA m-3), and (c) OPGSH m-3 (% depletion of GSH m-3). The warm period is May–August, and the cold period is January–February
and November–December.
Temporal variation
The time series of the monthly and seasonal averages of different OP
measures are shown in Fig. 2. Significant seasonal variability in these OP
measures was not evident; only a subtle seasonal variation observed for
OPDTT and no variations for OPAA and OPGSH. OPDTT was
slightly higher during the cold period (January–February and November–December) with an
average level of 0.24±0.08 nmol min-1 m-3 compared to
0.20±0.04 nmol min-1 m-3 in the warm period (May–August) and
a median OPDTT ratio between two periods of 1.20. However, OPAA
and OPGSH had more similar levels across seasons, with median ratios
between cold and warm periods of 1.10 and 0.97, respectively.
Temporal variation for select PM components.
The seasonality in OP measures should result from the temporal variations in
PM species driving the various OP. From the temporal variation in the
OP-associated species shown in Fig. 3 (the seasonal averaged concentrations
were given in Fig. S1 in the Supplement), BrC had an obvious seasonality, i.e., higher in winter and
lower in summer, which is due to the stronger influence of biomass burning
in winter. The variation in BrC may lead to the small variation in
OPDTT, considering the good correlation between OPDTT and BrC in
winter. Water-soluble Cu is slightly higher in midsummer (August) and
water-soluble Fe is slightly higher in fall (September), but these trends are not
seen in the various measures of OP.
Multivariate model
Given that one or more PM components contributed to these measures of OP,
multivariate linear regression analysis was conducted to identify the main
PM components that drive the variability in OP and provide a contrast
between the assays. Water-soluble organic species (WSOC or BrC) and metals,
mainly Fe, Cu, Mn, were selected as the independent variables to form
multivariate linear regression models for OPDTT and OPAA, based on
their high correlations, as noted above. EC, though also correlated with
OPDTT and OPAA, was not chosen as one of the predictors due to its
correlations with selected water-soluble species (e.g., WSOC, water-soluble
Mn and Fe). For OPGSH, WSOC and BrC were used as input in addition to
water-soluble Cu to include the possible influence of organic species on
OPGSH. The resulting linear relationships between different OP measures
and PM components are shown in Table 2. The time series of measured and
predicted OPs and the contributions of model variables to each OP measure
are given in Fig. S2. Overall, the multivariate models explained variability
in OP measures reasonably well with the coefficients of determination
between modeled and measured OPs (R2) greater than 0.4, with the models
better capturing the OPAA and OPGSH variability. In the regression
results for OPDTT and OPAA, components including water-soluble Fe,
Cu, and BrC (or WSOC) and interaction terms between metal and organics and
metal and metal were included, suggesting that the variability of OPDTT or
OPAA is dependent upon not only bulk concentrations of PM components
but also interactions between species. The regression model for OPGSH
captured the contributions from Cu and BrC but had no interaction terms. The
intercept in each regression model, though large and accounting for over 50 % of the mean of OP measures (Fig. S2), is practically meaningless
because the regression models are applicable only when the PM components are
at ambient concentrations.
Multivariate linear regression models for OP metrics.
All metals are water-soluble metals. The values represent the
coefficients for variables. Cells are left blank when the corresponding
variable is not included in the equation. As an example, the linear equation
of OPDTT is as follows: OPDTT=2.28×10-3×Fe+2.69×10-3×Cu+5.75×10-2×BrC-1.36×10-3×(Cu×BrC)-4.09×10-4×(Fe×Cu)+0.13. The concentrations
of water-soluble metals are in units of ng m-3, whereas the units for
WSOC and BrC are given in µg m-3 and Mm-1, respectively.
All models captured the contributions from organic species; however, the
organic contributions in different models were represented by different
measures of organics. In the OPDTT and OPGSH models, the organic
contribution was denoted by BrC, whereas WSOC was used in the OPAA
model. Although WSOC and BrC were correlated with each other (Tables S1–S3),
there is a difference between these two parameters. The OP contribution from
BrC, an optically defined measure, may not be straightforward; however, the
optical properties of BrC are related to chemical properties. There is
evidence showing that BrC components may directly contribute to OP (Chen et
al., 2019). It has been found that BrC predominantly represents the
hydrophobic organic fraction (i.e., the humic-like substances (HULIS)
fraction) in PM (Verma et al., 2012) and is largely from incomplete
combustion (mainly biomass burning) (Hecobian et al., 2010). For example,
quinones, as a subset of the HULIS fraction (Verma et al., 2015b), can be
estimated better with BrC than with WSOC. WSOC also includes organic
compounds present in the hydrophilic fraction, e.g., levoglucosan (Lin and
Yu, 2011), and low-molecular-weight organic acids (Sullivan and Weber, 2006)
and thus is a more integrative measure of organic compounds compared to BrC.
The difference between BrC and WSOC is also supported by the different
seasonal variation observed in BrC and WSOC (Fig. 3).
For OPDTT results, the presence of Cu and BrC in the equation is as
expected, since Cu and organic species have been found to be active in DTT
oxidation (Charrier and Anastasio, 2012; Cho et al., 2005). However, Fe,
which has a low intrinsic DTT activity (Charrier and Anastasio, 2012), was
found to be predictive of OPDTT, likely suggesting that Fe represents
surrogate measures of constituents with intrinsic redox active properties
which were not quantified. This is also supported by the evidence that Fe
had correlations with other PM constituents such as OC and EC (Tables S1–S3),
which may suggest that Fe in the PM water extracts is solubilized by forming
complexes with combustion-derived organic species. The interaction terms,
along with their negative coefficients, suggest antagonistic interactions
between Cu and organic compounds and between Cu and Fe. The interaction
between metal and organics, though not taken into account when applying
multivariate regression analysis in previous studies (Calas et al., 2018;
Verma et al., 2015a), is consistent with experimental results (Yu et al.,
2018), wherein antagonistic interactions between Cu and ambient HULIS were
observed in the DTT consumption. However, the interaction between metals
contrasts with experiments which showed additive effects for metal mixtures
(Yu et al., 2018). However, it should be noted that the interactions among metals
were usually tested with mixtures of individual species, which can only poorly
represent the complex chemistry of ambient PM.
For RTLF assay, the variability of OPAA was attributed to the
concentrations of Fe, Cu, and WSOC; antagonistic metal–organic interaction
between Fe and organic compounds; and metal–metal competition between Fe and
Cu. Even though the RTLF assay in previous studies was generally used to
measure the OP of methanol-extracted PM suspension, the contributions from
metals and organics observed in our water-soluble OP agree with previous
results (Ayres et al., 2008; Kelly et al., 2011; Pietrogrande et al., 2019).
The presence of interaction terms is novel and is supported by little
empirical evidence. The antagonistic interaction between Fe and WSOC is
reasonable. Fe in the water extracts has been found largely complexed with
organic compounds (Wei et al., 2019). Since AA is not a strong ligand for Fe
(Charrier and Anastasio, 2011), the complexation between Fe and organic
compounds can prevent Fe from reacting with AA. For OPGSH, despite weak
correlation of OPGSH with BrC, BrC still accounted for the variability
in OPGSH, consistent with previous findings that OPGSH is
responsive to quinones (Ayres et al., 2008; Calas et al., 2018).
It is noteworthy that the multivariate regression models do not account for
the possible nonlinear behavior of species with OP responses. For example,
nonlinear concentration–response curves have been found for DTT oxidation
by dissolved Cu and Mn (Charrier and Anastasio, 2012), which may not be
characterized in the multivariate regression model, potentially affecting
the accuracy of the OPDTT model. We note that the variables in the
models not only represent the contribution from individual species but also
show a possible influence from co-emitted unquantified components. There are
also interactions that exist among PM species, affecting the relationships
between PM compounds and OP metrics.
To further investigate the sensitivity of different OP assays to PM species,
standardized regression was applied to rescale the variables measured in
different units and make the coefficients in the regression equations
comparable. It is also an effective way to reduce collinearity induced by
the intercorrelated nature of PM species and the existence of interaction
terms. The standardized coefficient of a specific component indicates the
estimated change in an OP measure for every one-unit increase in components.
The higher the absolute value of the standardized coefficient, the stronger
the effect of the PM species on the OP measure.
Standardized regression coefficients for different OP
measures with selected PM components.
Figure 4 shows the relative importance of each PM component to OP metrics
based on the calculation of standardized coefficients. As shown in Fig. 4,
water soluble Fe was the most important variable in the model of OPDTT,
followed by BrC, Cu, and antagonistic interactions. Even though OPDTT is
not responsive to water-soluble Fe, Fe may be a surrogate measure of
compounds co-emitted with Fe from brake and tire wear and secondary formation,
which have been identified as two major sources of Fe in the southeastern US
(Fang et al., 2015a). For OPAA, the strength of the effects of Fe, Cu,
and WSOC on OPAA was similar. OPGSH was approximately 4 times
more sensitive to Cu than to BrC, with standardized coefficients of 0.70 and
0.18 for Cu and BrC, respectively. These results show clear contrasts
between the assays, where OPDTT and OPAA are more similar and both
have significant contrasts to OPGSH.
In all of these cases, it must also be kept in mind that the measurements were
performed on the PM water extracts, which are not the conditions that are
found in the ambient aerosol. Thus, inferring associations between species
in the extracts and applying to ambient conditions is not straightforward;
however, this analysis is useful for interpreting and contrasting the
possible causes for associations between these types of assays and any
health effects.
Water-soluble Fe, as the most important determinant of OPDTT, has been
estimated to have the strongest effect on cardiovascular outcomes in the
Atlanta metropolitan region (Ye et al., 2018), which may account for
associations between OPDTT and health outcomes observed in this region.
OPGSH is strongly dependent on a limited number of PM components, and
thus associations between OPGSH and health outcomes may vary more
significantly by region than other assays do and associations could be
expected if the PM toxicity in a region is mainly driven by specific
species, such as water-soluble Cu. OPAA is affected by the composition
of synthetic lung fluid, and thus the AA responses obtained from RTLF and
AA-only model are not comparable and should be distinguished from each
other. In the RTLF model, the response in the OPAA metric by PM is
diminished due to the presence of GSH, possibly leading to weaker
associations between OPAA and health endpoints.
Conclusions
In this study, a comparison was made between two of the most common
techniques used for the assessment of PM oxidative potential based on
antioxidant depletion from a complex synthetic RTLF (OPAA and
OPGSH) and DTT oxidation (OPDTT). These two assays were used to
quantify the water-soluble OP of ambient PM2.5 collected in urban
Atlanta over a 1-year period based on daily filter samples. We observed
moderate correlations among the OP assays, suggesting different
sensitivities of OP measures to PM species. Univariate and multivariate
regression analyses indicated that OPDTT and OPAA were correlated
to organic species and water-soluble metals (Fe and Cu) and were negatively
affected by the interactions among species. At a more detailed level, for
organic components, OPDTT was associated specifically with HULIS and
incomplete combustion products identified by BrC, whereas OPAA was
associated to a more general measure of organic components, WSOC.
OPGSH, though also affected by organic species, was predominantly
sensitive to water-soluble Cu. Subtle temporal variation in OPDTT and
no seasonal variations in OPAA and OPGSH were observed, which
appears to be due to little seasonality in the combined PM constituents
affecting each assay. A small OPDTT variation was associated with
variation in BrC that was higher in the cold seasons.
This study suggests that all three OP metrics, OPAA and OPGSH in
the RTLF assay, as well as OPDTT in the DTT assay, are associated with
transition metal ions. However, OPDTT and OPAA are more chemically
integrative OP measures compared to OPGSH and thus may be more
informative and helpful in linking OP with health end points. The
multivariate regression models for different OP measures indicate the degree
to which OP variability in the PM water extracts is predicted by PM
constituents.
We should note that the filter samples used in this study were averaged over
24 h, which may dampen variability in the emission sources that
contribute to OP and obscure the impact of specific species (e.g.,
traffic-related metals) on redox activity of PM. Furthermore, all of these
results were obtained from a specific location in Atlanta, GA, and should be
interpreted and generalized with caution, as the chemical composition or
sources of PM varies by region. There are marked differences in RTLF
composition in different levels of the respiratory tract. The synthetic RTLF
reflects select antioxidants in the lung and other key constituents are not
represented in this simplified chemical model. The DTT assay is also subject
to similar limitations, as DTT cannot fully represent biological
complexities. However, these assays can be used as PM screening tools and
provide rapid health-relevant assessment of PM.
Data availability
All data are uploaded and available at: https://doi.pangaea.de/10.1594/PANGAEA.908043 (last access: 20 April 2020, Gao et al., 2019).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-20-5197-2020-supplement.
Author contributions
DG collected and analyzed the data and drafted the manuscript. KJGP
assisted with RTLF measurements. JAM and AGR helped with model development.
The data were interpreted by DG and RJW. RJW conceived, designed, and oversaw
the study. All authors discussed the results and contributed to the final
manuscript.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This publication was made possible by Georgia Tech EAS Jefferson St Fund,
which was a generous gift from Georgia Power/Southern Company. We would like
to thank Linghan Zeng, Gigi Pavur, Joseph Caggiano, and Allison Weber for
assisting with the sampling campaigns and the lab work.
Financial support
This research has been supported by Georgia Tech EAS Jefferson St Fund (GT project No. GTF 350000064).
Review statement
This paper was edited by Alex Lee and reviewed by two anonymous referees.
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