Characterization and comparison of PM 2.5 oxidative potential assessed by two acellular assays

. 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 15 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: the synthetic respiratory tract lining fluid (RTLF) assay that tracks the depletion of ascorbic acid (AA) and glutathione (GSH), and the dithiothreitol (DTT) assay that tracks the depletion of DTT. Year-long daily samples were collected at an urban site in Atlanta, GA 20 (Jefferson Street) during 2017 and both DTT and RTLF assays were performed to measure the OP of water-soluble PM 2.5 components. PM 2.5 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 (OP DTT and OP AA , respectively) were correlated with both organics and some water-soluble metal species, whereas that from the GSH depletion (OP GSH ) was exclusively sensitive to water-soluble Cu. These OP assays were moderately 25 correlated with each other due to the common contribution from metal ions. OP DTT and OP AA were moderately correlated with PM 2.5 mass, with Pearson’s r = 0.55 and 0.56, respectively, whereas OP GSH 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 OP DTT and OP AA were attributed to not only the concentrations of metal 30 ions (mainly Fe and Cu) and organic compounds, but also antagonistic metal–organic and metal–metal interactions. OP GSH was sensitive to the change in water-soluble Cu and brown carbon (BrC), a proxy for ambient humic-like substances. 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 (OP AA and OP GSH ) and DTT oxidation (OP DTT ). These two assays were used to quantify the water-soluble OP of ambient PM 2.5 collected in urban 450 Atlanta over a one-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 OP DTT and OP AA 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, OP DTT was associated specifically with HULIS and incomplete combustion products identified by BrC, whereas 455 OP AA was associated to a more general measure of organic components, WSOC. OP GSH , though also affected by organic species, was predominantly sensitive to water-soluble Cu. Subtle temporal variation in OP DTT and no seasonal variations in OP AA and OP GSH were observed, which appears to be due to little seasonality in the combined PM constituents affecting each assay. A small OP DTT variation was associated with variation in BrC that was higher in the cold seasons. linking OP with health end points. multivariate regression models for different OP measures degree OP variability in the PM water extracts predicted PM

Thank you for this suggestion. The figures were numbered according to their sequence in the text. The primary aim of this work was to compare the OP measures so we presented the temporal variation of OP first, and the PM species provided an explanation for the observed differences in OP measures. Figure 1) with probe-based aerosol OP?

What is the association of water insoluble organic matter and metals in PM2.5 (in
In another paper from this study (Gao et al., 2020, submitted to Atmos Environ), in which we focused more on the OP (DTT assay only) contributions from water-insoluble PM components, the association of total or water-insoluble PM species with total PM OP was studied in detail. However, the manuscript is focused only on the water-soluble OP fraction. Compared to the association between water-soluble species and OP measures, the association of water-insoluble PM species with water-soluble OP was less informative about determinants of water-soluble OP, and thus was not discussed in this paper.
To clarify, the manuscript has been modified.
Line 276-277: "The OP (DTT assay only) contribution from water-insoluble components were discussed in detail in another paper from this study (Gao et al., accepted)." 6. L158-172: whether the efficiency of NAPDH and GR to reduce GS-TNB to GSH can be interfered by the co-existence of ascorbic acid? Similarly, to which extent the covariation of ascorbic acid and GSH concentrations will influence the OPAA and OPGSH?
The reviewer is thanked for raising this concern. The presence of AA is not expected to interfere with the reduction of GSSG or GS-TNB to GSH. This reduction reaction is the key reaction involved in both total and oxidized glutathione measurements. We can check the reduction efficiency by examining the amount of total glutathione (GSx). Within error, the GSx concentration we measured in each plate well is consistent with the expected initial concentration (~200 µM per well), and the amount remains constant during the whole incubation, suggesting that all oxidized form glutathione can be reduced efficiently in GSx determination.
This study only indicated the antioxidants depletion in RTLF was affected by the RTLF composition. To what extent OP AA and OP GSH are influenced by RTLF composition still needs further investigation.
7. L243-244: The sentence of 'However, they could be considered as indicators of other compounds simultaneously produced by the same source' is a vague statement, which needs further clarification.
To improve clarity, the manuscript has been modified.
Line 290-293: "However, the correlations may indicate the emission sources (e.g., as source tracers -vehicular emissions for EC, secondary processing for WSOC and SO 4 2-, and biomass burning for BrC and K), which also probably emit the water-soluble species contributing to the measured OP."

L249: What does the "PM species" exactly refer to?
To clarify, we modified the manuscript.
Line 298: "The associations found in this study between OP DTT and PM composition are consistent with a number of previous studies (Fang et al., 2016;Verma et al., 2014;Yang et al., 2014), though the correlations in our work were weaker…" 9. L274 (3.3 Temporal variation): to discuss the seasonal distribution of OP clearly, the averaged PM2.5 OP of different seasons should be presented in Figures 2, 3 or SI, similar like the seasonal distribution of different PM components in Figure 1.
Thank you for this suggestion. Figure 2 has been modified to include the averaged OP levels during warm and cold periods. The seasonal averaged PM species concentrations have been added into SI (Fig. S1). We have also modified the manuscript accordingly. Based on the reference cited by the reviewer, the redox active substances the reviewer mentioned are more likely bounded on particles in atmospheric conditions rather than WSOC. Given that we were only measuring water-soluble OP of long-time integrated PM samples, we may fail to capture the effects of these short-lived species or radicals. Therefore, including these substances as part of WSOC-related OP contribution may not be appropriate.
We have included some of these substances in the introduction to provide a more comprehensive description about OP contributors.
Line 58-64: "The ability of PM to generate ROS in vivo, referred to as the oxidative potential (OP) of particles, has gained increasing attention as possibly a 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;Tong et al., 2019)." Fig. 3 should be corrected.

The y-axis title of the upper left panel (for BrC) in
Thank you for pointing this out! The y-axis title for BrC in Fig. 3 has been corrected from "Mm -1 " to "1/Mm" so that it remains consistent with the format of other species' units.

Reference:
Gao, D., Mulholland, J. A., Russell, A. G., and Weber, R. J.: Characterization of water-insoluble oxidative potential of PM2.5 using the dithiothreitol assay, Atmos Environ (accepted) Response to anonymous referee #2 comments: In this work, the authors compared the results from 3 different acellular assays of oxidative potential in 2 different media. OP has recently become a popular topic of research due to its potential to represent PM's ability to drive oxidative stress and explain PM health effects. Understanding the assays used to measure OP is an important topic for atmospheric chemists, because they will provide insights into sources and/or compounds that may be particularly toxic. The authors found different level of sensitivities of these assays to different components, such as copper, iron, and organic compounds. These relationships were investigated by association, using multilinear regression models. Overall the results are a valuable contribution and are complementary to those currently in the literature. I just one major point of concern, and I hope the authors will consider it while revising the manuscript. I recommend publication in ACP My major issue with this work is the use of a per-air volume measure of OP (extrinsic OP) rather than a per-PM mass measure. All the comparisons made here are chemical, with the attempt to associate a particular fraction of PM to its contribution to OP. In that case, I would argue that the OP should be an intrinsic measure (i.e. oxidant depletion rate per PM mass). Otherwise the variability could be driven by total PM mass. I understand that the assays were performed on a per filter basis (which is equivalent to a per-volume basis), and it might be difficult to fix the amount of PM mass used to analyze OP. At the very least, there needs to be a discussion examining whether or not the variability in OP (and therefore the reported associations shown here) is driven by the PM mass, rather than its composition. Line 469-473: "There are marked differences in RTLF composition in different levels of 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 that DTT cannot fully represent the biological complexity. However, these assays can be used as PM screening tools and provide rapid health-relevant assessment of PM." 2. Samples are collected on a daily basis. Would that bias against sources that vary on shorter timescales (i.e. traffic-related emissions of metals)? If so, that should be stated as a limitation of this study.
Thanks for this suggestion. This limitation has been added in the manuscript.
Line 465-467: "We should note that the filter samples analyzed in this study were averaged over 24 hours, 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." 3. Should we really expect a difference between summer and winter, given that the climate in Atlanta is similar between the seasons? What are the known differences in the sources between summer and winter this area? This type of comparison can be somewhat misleading and is likely not generalizable to other regions, because every city might have its own characteristic summer/winter sources. Just seeing "summer" and "winter" in analysis, one could jump to the wrong conclusions.
We thank the reviewer for the comment. While temperature change across seasons in Atlanta (typically varies from 35 °F to 89 °F) is not as extreme as other cities, there are notable differences. Consequentially, the seasonal difference in OP are expected. In previous studies conducted in the same region, obvious seasonal patterns were observed for water-soluble OP DTT and its related sources Verma et al., 2014). The results suggested that OP DTT levels in Atlanta were generally higher in the cold months, driven mostly by biomass burning emissions, than in summer when secondary oxidation processes dominated OP DTT . The temporal variation in our BrC data (Fig. 3) also supported the seasonally varying emission sources. Moreover, based on our correlation analysis, the association of OP measures with PM species differed by seasons, suggesting possible seasonal differences in OP metrics. Even though the temporal variation in OP from this study was not evident, we believe it is worthwhile to do such seasonality analysis as it may provide a better understanding of the impact of seasonally varying sources or species upon OP metrics.
To avoid misleading our readers, we have added a reminder in the revised manuscript.
Line 467-469: "Furthermore, all these results were obtained from a specific location in Atlanta and should be interpreted and generalized with caution as the chemical composition or sources of PM varies by region." 4. It would be useful to state in the Methods section the concentration of PM during these assays. PM concentrations should be much lower than those of the antioxidants to ensure one is looking at the catalytic redox cycling.
Thanks for the suggestion. The PM concentration in the water extracts has been specified in the manuscript.
Line 168: "In brief, the PM extract (3.5 mL; 40±15 µg mL -1 of PM) was incubated with DTT solution…" Line 189: "PM water extracts (35±13 µg of PM per mL) were transferred into a 96-well microplate with 180 µL of sample liquid in each well."

Limits of detection and quantification for all of the assays should be reported.
The manuscript has been modified accordingly.
In section 2.2.1 DTT assay, Line 183-184: "The limit of detection (LOD), defined as three times of the standard deviation of OP DTT for blanks, is 0.31 nmol min -1 ...." In section 2.2.2 RTLF assay, Line 216-217: "The LOD for AA and GSH depletion after 4 h incubation was 4.0 % and 4.5 %, respectively..." 6. The discussion around BrC comparison needs to be better motivated. It is not clear why that comparison was made in the first place, other than that measurement was available and it was convenient to make that comparison. BrC from biomass burning, for example, can be derived from nitrophenols, and is not exclusively HULIS. Unlike the other chemical species, BrC is not chemically defined, but rather an optically defined group of compounds, so their contribution to OP might not be straightforward.
To better motivate the BrC vs. WSOC comparison, we have modified the manuscript.
Line 362-365: "All models captured the contributions from organic species, however, the organic contributions in different models were represented by different measures of organics. In the OP DTT and OP GSH models, the organic contribution was denoted by BrC, whereas WSOC was used in the OP AA model. Although WSOC and BrC were correlated with each other (Table S1-S3), there is a difference between these two parameters." Regarding the reviewer's 2 nd point, the reviewer is correct that BrC likely covers a wide range of aerosol components, which include but are not limited to HULIS. HULIS has been recognized as important components of BrC and has been found to be strongly linked to BrC (Hoffer et al., 2006;Laskin et al., 2015). As mentioned in the manuscript, Verma et al. (2012)  To clarify, we have added more explanation of the BrC-related OP in the revised manuscript.
Line 366-374: "…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 . It has been found that BrC predominantly represents the hydrophobic organic fraction…" 7. Why is EC not included in the multilinear regression analysis? It seems to have a reasonable Pearson's r from Table 1.
This manuscript was focused on water-soluble OP which we assume is more driven by watersoluble PM components. Therefore, in the multivariate regression analysis, we used watersoluble species as input to construct the models. Furthermore, EC was also correlated with some of the selected water-soluble species, such as WSOC and water-soluble Mn and Fe (Table S1). In this instance, even if EC was chosen as one of the predictors, the stepwise regression procedure would identify it as a redundant predictor and exclude it from the model. To clarify, the manuscript has been modified.
Line 344-349: "Given that one or more PM components contributed to these measures of OP, multivariate linear regression analysis was conducted to identify the main water-soluble PM components that drive the variability in OP and provide a contrast between the assays. Watersoluble organic species (WSOC or BrC) and metals, mainly Fe, Cu, Mn, were selected as the independent variables to form multivariate linear regression models for OP DTT and OP AA , based on their high correlations, as noted above. EC, though also correlated with OP DTT and OP AA , 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)." Technical/formatting comments:

Line 164: typo after GR
We made a double-check and found no typo here.

Line 169: typo in 2-vinylpyridine; not sure if the abbreviation 2-VP is needed if it is not used again
Typo has been corrected, and the abbreviation was deleted.

Corrected.
Line 227: "For the analysis of water-soluble metals, no digestion was performed." Line 257: If UA is not studied here, it might be better not to include UA in this comparison UA has been deleted in the comparison.
Line 315: "The consistently lower correlation…" Line 365: "shown the strongest estimated effect" is a strange word choice. Perhaps "estimated to have the strongest effect"?
The sentence has been modified.
Line 435: "Water-soluble Fe, as the most important determinant of OP DTT , has been estimated to have the strongest effect on cardiovascular outcomes in the Atlanta metropolitan region…" Line 388-390: The sentence here is stylistically awkward and grammatically incorrect.

The sentence has been modified.
Line 462-463: "However, OP DTT and OP AA are more chemically integrative OP measures compared to OP GSH , and thus may be more informative and helpful in linking OP with health end points..." Table 2: the number of digits in the exponent are not consistent (some are E-3, and some are E-05) Corrected.
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: the synthetic respiratory tract lining fluid (RTLF) assay that tracks the depletion of ascorbic acid (AA) and glutathione (GSH), and the dithiothreitol (DTT) assay that tracks the depletion of DTT.
Year-long 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 PM 2.5 components. PM 2.5 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 (OP DTT and OP AA , respectively) were correlated with both organics and some water-soluble metal species, whereas that from the GSH depletion (OP GSH ) was exclusively sensitive to water-soluble Cu. These OP assays were moderately 25 correlated with each other due to the common contribution from metal ions. OP DTT and OP AA were moderately correlated with PM 2.5 mass, with Pearson's r = 0.55 and 0.56, respectively, whereas OP GSH 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 OP DTT and OP AA were attributed to not only the concentrations of metal ions (mainly Fe and Cu) and organic compounds, but also antagonistic metal-organic and metal-metal interactions.
OP GSH 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 35 morbidity and mortality (Brunekreef and Holgate, 2002;Cohen et al., 2017;Lippmann, 2014;Norris et al., 1999 (Lakey et al., 2016). The latter can occur in organs beyond the lungs by particles or chemical 55 species being translocated from the lungs throughout the body. Oxidative stress arises when the presence and production of ROS overwhelms the antioxidant 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 possibly a more integrative health-relevant measure of ambient PM toxicity than PM mass concentration which may contain a 60 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;Tong et al., 2019).
A variety of acellular assays have been developed to assess PM OP (Ayres et al., 2008;Bates et al., 2019). In 65 general, these assays involve the incubation of PM extracts or suspension with chemical reagents/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 70 assay (Huang et al., 2016;Venkatachari et al., 2005)), or hydroxyl radical formation in the presence of H 2 O 2 (e.g., electron paramagnetic/spin resonance (EPR/ESR) (Shi et al., 2003a;Shi et al., 2003b)). 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 75 catalytic assistance of PM redox-active species. PM OP (i.e., OP DTT 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 80 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 OP AA , OP UA , and OP GSH (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, OP AA in this paper represents OP AA obtained 85 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. OP DTT 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;Fang et al., 2016;Yang et al., 2016) and congestive heart failure Fang et al., 2016), than PM mass. Multiple population-scale studies employed the RTLF assay to assess OP of PM 2.5 , and found that OP GSH 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 OP GSH of PM 2.5 personal exposure was also reported (Maikawa et al., 2016). In the study of Strak et al. (2012), no association was found between OP GSH and 100 acute airway inflammation in healthy volunteers after 5 h of exposure. A population-scale study in London, UK found no association between OP GSH 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 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. OP DTT 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., 110 2005;Kumagai et al., 2002;McWhinney et al., 2011;Verma et al., 2015b). OP AA obtained from the simplified AAonly 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, OP AA responds to iron and OP GSH is related to aluminum (Godri et al., 2010). But both OP AA and OP GSH are sensitive to copper (Ayres et al., 2008). Studies performed on real PM samples or standard solutions 115 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 120 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 PM 2.5 collected from urban Atlanta, GA over one-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 125 major chemical components to each of these OP metrics. Year-long 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(Edgerton et al., , 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 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;145 Fang et al., 2016;Sarnat, 2008;Verma et al., 2014).
Ambient PM 2.5 were collected daily (from midnight to midnight, 24 h integrated samples) onto pre-baked 8×10 in.
quartz filters (Pallflex Tissuquartz, Pall Life Sciences) using high-volume samplers (HiVol, Thermo Anderson, nominal flow rate 1.13 m 3 min -1 , PM 2.5 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 150 collection and stored at -18 °C until analysis. PM 2.5 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 semi-continuous 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 155 denuder for 45 min and analyzed the particles collected on the quartz filter for 15 min using 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 PM 2.5 . The DTT analysis was completed at Georgia Institute of Technology, and all filters were analyzed within one

DTT assay
The DTT assay was performed with a semi-automated system developed by , following the protocol described by Cho et al. (2005). In brief, the PM extract ( was found for all samples with correlation coefficients (R 2 ) 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 three times of the standard deviation of OP DTT for blanks, was 0.31 nmol min -1 . The PM OP measured by this assay (i.e., OP DTT ) was blank-corrected and normalized by the air volume that passed through the extracted filter fraction, expressed as nmol DTT min -1 per m 3 .

185
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). since studies have consistently suggested that no depletion of UA was observed in the presence of PM (Kunzli et al., 195 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 200 ethylenediaminetetraacetic acid (EDTA). To measure the GSx concentration, 50 µL of each diluted sample was dispensed onto a microplate. 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 205 for two minutes 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 hour, followed by similar procedures performed for the GSx measurement.
The GSH concentration was calculated by subtracting two times the GSSG concentration from the measured GSx 210 concentration.
Field blanks and known controls (e.g. positive controls: H 2 O 2 and Cu; negative control: Zn) 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., OP AA and OP GSH , was determined by normalizing the percentage loss with the sampled air volume, in unit of % depletion per m 3 .

Elemental 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 cm 2 filter punch from the HiVol quartz filter was acid-digested 225 for 20 min using aqua regia (HNO 3 +3HCl). The acid-digested sample was then diluted in DI water to 10 mL, 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 (1 in. diameter) 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).

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

Results and discussion
260 3.1 Ambient PM composition Figure 1 shows the time series of PM 2.5 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 PM 2.5 mass measured by the TEOM with Pearson's r=0.84, and accounted for more than 80 % of the PM 2.5 mass. The missing 265 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 PM 2.5 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 % SO 4 2-, 4-7 % NH 4 + , and 1-8 % NO 3 -). Metals constituted 6-13 % of the PM mass, among 270 which water-soluble metals were at trace amounts (1-2 %). EC accounted for a small fraction of the PM mass (5-6 %). NH 4 + , and NO 3 -, 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 275 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 were discussed in detail in another paper from this study (Gao et al., accepted).

Association of OP with PM components
Pearson's correlation coefficients for the linear regression between OP and select chemical components are shown in  Table S1) suggests that incomplete combustion could be one of their common sources. The associations found in this study between OP DTT and PM composition are consistent with a number of previous studies (Fang et al., 2016;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).

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Similar to OP DTT , OP AA 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 PM 2.5 . 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 305 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 310 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 OP DTT and OP AA , likely account for the OP DTT and OP AA correlations with PM mass.
In contrast to OP DTT and OP AA , OP GSH was found to be exclusively correlated with water-soluble Cu with Pearson's r >0.7. The consistently lower correlation of OP AA with water-soluble Cu than OP GSH with Cu, is consistent with 315 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, OP AA and OP DTT were more correlated with organic species, with stronger associations with WSOC, BrC and K, indicating biomass burning as a common source of OP AA and OP DTT .
In summer, all OP assays tended to be metal-driven. OP AA and OP DTT were more correlated with Cu, along with 320 SO 4 2-, suggesting possible influence of secondary processing on metal mobilization Ghio et al., 1999) and resulting in a strong inter-correlation between different OP metrics.

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 OP DTT and no variations for OP AA and OP GSH . OP DTT was slightly higher during the cold period (Jan-Feb and Nov-Dec) 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-Aug) and a median OP DTT ratio between two periods of 1.20. However, OP AA and OP GSH had more similar levels across seasons, with median ratios between cold and warm periods of 1.10 and 0.97, respectively.

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The seasonality in OP measures should result from the temporal variations in PM species driving the various OP.
From the temporal variation of the OP-associated species shown in Fig. 3 (the seasonal averaged concentrations were given in Fig. S1), BrC had an obvious seasonality, 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 OP DTT , considering the good correlation between OP DTT and BrC in winter. Water-soluble Cu is slightly higher in midsummer (Aug) and water-soluble Fe is slightly higher in fall (Sep), 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 345 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 OP DTT and OP AA , based on their high correlations, as noted above. EC, though also correlated with OP DTT and OP AA , 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 OP GSH , WSOC and BrC were used as input in addition to water-soluble Cu to include the possible influence of 350 organic species on OP GSH . 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 (R 2 ) greater than 0.4, with the models better capturing the OP AA and OP GSH variability. In the regression results for OP DTT and OP AA , components 355 including water-soluble Fe, Cu and BrC (or WSOC) and interaction terms between metal-organic and metal-metal were included, suggesting that the variability of OP DTT or OP AA is dependent upon not only bulk concentrations of PM components but also interactions between species. The regression model for OP GSH 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.
All models captured the contributions from organic species, however, the organic contributions in different models were represented by different measures of organics. In the OP DTT and OP GSH models, the organic contribution was denoted by BrC, whereas WSOC was used in the OP AA model. Although WSOC and BrC were correlated with each  (Table 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 . 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 375 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).

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For OP DTT results, the presence of Cu and BrC in the equation is as expected, since Cu and organic species have been found 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 OP DTT , 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 (Table S1-385 S3) which may suggest that Fe in the PM water extracts is solubilized by forming complexes with combustionderived 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 390 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). But it should be noted that the interactions among metals were usually tested with mixtures of individual species, which can poorly represent the complex chemistry of ambient PM.
For RTLF assay, the variability of OP AA was attributed to the concentrations of Fe, Cu and WSOC, antagonistic 395 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 400 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 OP GSH , despite weak correlation of OP GSH with BrC, BrC still accounted for the variability in OP GSH , consistent with previous findings that OP GSH is responsive to quinones (Ayres et al., 2008;Calas et al., 2018).

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Deleted: and both represent the contribution from organic species, Deleted: are in agreement dissolved Cu and Mn (Charrier and Anastasio, 2012), which may not be characterized in the multivariate regression model, potentially affecting the accuracy of the OP DTT 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 existing among PM species, affecting the relationships between PM compounds and OP metrics.

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To further investigate the sensitivity of different OP assays to PM species, standardized regression was applied to  In all 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 OP DTT , has been estimated to have the strongest effect on 435 cardiovascular outcomes in the Atlanta metropolitan region (Ye et al., 2018), which may account for associations between OP DTT and health outcomes observed in this region. OP GSH is strongly dependent on a limited number of PM components, and thus associations between OP GSH and health outcomes may vary more significantly by regions 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. OP AA is affected by the composition of synthetic lung fluid, and thus the AA 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 (OP AA and OP GSH ) and DTT oxidation (OP DTT ). These two assays were used to quantify the water-soluble OP of ambient PM 2.5 collected in urban

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Atlanta over a one-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 OP DTT and OP AA 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, OP DTT was associated specifically with HULIS and incomplete combustion products identified by BrC, whereas

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OP AA was associated to a more general measure of organic components, WSOC. OP GSH , though also affected by organic species, was predominantly sensitive to water-soluble Cu. Subtle temporal variation in OP DTT and no seasonal variations in OP AA and OP GSH were observed, which appears to be due to little seasonality in the combined PM constituents affecting each assay. A small OP DTT variation was associated with variation in BrC that was higher in the cold seasons.

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This study suggests that all three OP metrics are associated with transition metal ions. However, OP DTT