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Can we achieve atmospheric chemical environments in the laboratory? An integrated model-measurement approach to chamber SOA studies
Atmospheric chemical environments in the lab
https://orcid.org/0000-0002-0096-530X
Kenagy Hannah S. Conceptualization Formal analysis Funding acquisition Investigation Methodology Software Validation Visualization Writing - original draft Writing - review & editing 1 *
https://orcid.org/0000-0003-2894-5738
Heald Colette L. Conceptualization Funding acquisition Methodology Project administration Writing - review & editing 1 †
https://orcid.org/0000-0001-7831-3216
Tahsini Nadia Investigation 2
https://orcid.org/0000-0002-2688-5463
Goss Matthew B. Formal analysis Investigation 1
https://orcid.org/0000-0002-6275-521X
Kroll Jesse H. Conceptualization Funding acquisition Project administration Supervision Visualization Writing - review & editing 1 2
1 Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
2 Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
* Corresponding author. Email: hskenagy@mit.edu
† Present address: Institute for Atmospheric and Climate Science, ETH Zürich, Zürich, Switzerland.

13 9 2024
13 9 2024
10 37 eado148222 1 2024
08 8 2024
Copyright © 2024 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).
2024
The Authors
https://creativecommons.org/licenses/by-nc/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.

Secondary organic aerosol (SOA), atmospheric particulate matter formed from low-volatility products of volatile organic compound (VOC) oxidation, affects both air quality and climate. Current 3D models, however, cannot reproduce the observed variability in atmospheric organic aerosol. Because many SOA model descriptions are derived from environmental chamber experiments, our ability to represent atmospheric conditions in chambers directly affects our ability to assess the air quality and climate impacts of SOA. Here, we develop an approach that leverages global modeling and detailed mechanisms to design chamber experiments that mimic the atmospheric chemistry of organic peroxy radicals (RO2), a key intermediate in VOC oxidation. Drawing on decades of laboratory experiments, we develop a framework for quantitatively describing RO2 chemistry and show that no previous experimental approaches to studying SOA formation have accessed the relevant atmospheric RO2 fate distribution. We show proof-of-concept experiments that demonstrate how SOA experiments can access a range of atmospheric chemical environments and propose several directions for future studies.

Multiscale modeling provides a framework for simulating atmospheric peroxy radical chemistry in laboratory experiments.

http://dx.doi.org/10.13039/100000001 National Science Foundation 2137238 http://dx.doi.org/10.13039/100000015 U.S. Department of Energy DE-SC0022017 http://dx.doi.org/10.13039/100000015 U.S. Department of Energy DE-SC0022017 http://dx.doi.org/10.13039/100000015 U.S. Department of Energy DE-SC0022017 http://dx.doi.org/10.13039/100000015 U.S. Department of Energy DE-SC0022017
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pmcINTRODUCTION

Atmospheric aerosols are integral to two of today’s most important environmental concerns: air pollution and climate. Exposure to aerosol pollution is associated with more than 8 million premature deaths each year, making it the leading environmental risk factor for premature mortality (1, 2). Atmospheric aerosols also affect the global climate by absorbing and scattering sunlight, as well as by altering cloud properties, with these impacts representing the largest source of uncertainty in our understanding of global radiative forcing (3). Organic aerosol (OA) constitutes a large, and sometimes dominant, fraction of fine aerosol mass (4). Much of this OA is secondary [secondary organic aerosol (SOA)] (4, 5), produced from volatile organic compounds (VOCs) that are oxidized in the atmosphere to form lower-volatility species. Despite the importance of SOA in the atmosphere and the decades of SOA-focused laboratory experiments, field measurements, and modeling studies, the complex chemical processing that leads to SOA formation is still not well constrained (6).

The first laboratory experiments examining SOA formation were recorded by Tyndall (7) in the late 19th century; he observed the formation of a “blue cloud” when irradiating organic vapors in a glass tube and attributed his observations to the formation of particles. Haagen-Smit’s (8) “smog chamber” experiments in the 1950s demonstrated that a mixture of vehicular hydrocarbon pollutants (e.g., alkenes), O3, and NO2 formed particles upon irradiation by sunlight, helping to explain the chemistry of urban smog. Later, Went (9) extended these experiments to include the oxidation of natural hydrocarbons and postulated that photochemistry involving biogenic VOCs contributed “blue hazes” in the atmosphere. Smog chamber experiments throughout the 1980s continued to show evidence for SOA production from photochemistry involving biogenic and anthropogenic VOCs with O3 and NOx (10, 11).

By 1990, researchers began to quantitatively determine SOA yields for oxidation of individual VOCs (11, 12), which would eventually enable yield-based parameterizations in atmospheric models. A dependence of SOA yields on NOx concentrations emerged from these chamber experiments, suggesting that atmospheric SOA formation depends on the chemical environment (13–16). Because of this observed NOx dependence, studies began to characterize experiments by their initial hydrocarbon-to-NOx ratio as is common in descriptions of tropospheric ozone formation (13, 14, 16). The mechanism for the dependence of SOA yield on this ratio was initially uncertain, but by 1999, it was generally assumed to solely result from the changes in the relative importance of different oxidants (e.g., OH, O3, and NO3) with varying NOx concentrations (17, 18). The first regional and global models of SOA also reflected this understanding: NOx influences on O3 and OH were included, but SOA yields for a given oxidant were assumed not to vary with NOx concentration (19–21).

In the early- and mid-2000s, chamber studies demonstrated that changes in SOA yields with NOx were a result of both changes in oxidant ratios and changes in the fate of RO2, organic peroxy radicals produced in most VOC oxidation processes (22). For example, chamber experiments demonstrated that the relative importance of hydroperoxides (products of the reaction of RO2 radicals with HO2) to the oxidation product distribution could help explain the dependence of the SOA yield on NOx (23–25). With the understanding that RO2 chemistry is influenced by NOx concentrations (26, 27), chamber experiments were designed to access “limiting conditions” in which the fate of RO2 was dominated entirely by reaction with a single coreactant: “Low-NO” experiments were designed to mimic “clean” atmospheric conditions with RO2 + HO2 as the dominant RO2 fate, whereas “high-NO” experiments were designed to mimic “polluted” atmospheric conditions with RO2 + NO dominating RO2 reactivity (27–32). The development of a chemical coordinate to track the competition between NO and HO2 for reaction with RO2 followed; this coordinate was gradually refined and, by 2010, was defined by Pye et al. (33) asβ=kRO2+NONOkRO2+NONO+kRO2+HO2HO2(1)

Using SOA yields measured under limiting RO2 fate conditions, β could be interpreted as a “mixing parameter” to estimate SOA yields at intermediate-NOx conditions (34, 35). By the late 2000s, SOA parameterizations in global models began to reflect the newly understood importance of NOx for RO2 fate by incorporating β-based SOA parameterizations (33, 36–38).

Recent chamber experiments, however, have demonstrated that SOA yields may also depend on three additional RO2 fates: RO2 isomerization, RO2 + NO2, and RO2 + RO2 reactions (Fig. 1). Laboratory and computational studies over the past decade have demonstrated that for some RO2 species, unimolecular isomerization pathways have sufficiently short lifetimes (τuni) that they can be competitive with bimolecular reactions (i.e., τuni ≤ τbi, where τbi is the RO2 lifetime to bimolecular reactions) (39, 40). Such isomerization reactions and subsequent rapid molecular oxygen additions can lead to the production of highly oxidized products, which can contribute to the formation of low-volatility SOA (41). Recent chamber studies have also demonstrated that the NO/NO2 ratio during an experiment, which controls what fraction of acyl-RO2 [RC(=O)O2] react with NO versus NO2, has a measurable effect on the oxidative product distribution and/or amount of SOA formed. At lower NO/NO2 ratios, RO2 + NO2 reactions can dominate the chemistry of acyl-RO2 radicals, thereby altering the oxidative product distribution and increasing the formation of peroxy acyl nitrates (PANs) (42, 43) which can have an influence on SOA formation (44). Other chamber studies have demonstrated that SOA yields are affected by the RO2/HO2 ratio, which controls what fraction of RO2 react with HO2 versus with other RO2 radicals (23, 45–47). The direction and magnitude of this effect on SOA yields are determined by the volatility of RO2 + RO2 products, which include alkoxy radicals (RO + RO), alcohol and carbonyl products (ROH + R′CHO), and organic peroxides (ROOR). Numerous studies have highlighted ROOR products in particular as potential contributors to SOA formation (48–50). As such, current evidence indicates that four bimolecular RO2 fates (reactions with NO, HO2, NO2, and RO2), as well as unimolecular RO2 reactions, may all affect SOA formation.

Fig. 1. Schematic of atmospheric RO2 chemistry and its role in SOA production.

RO2 are formed during VOC oxidation after initial oxidant attack and subsequent molecular oxygen addition. Atmospheric RO2 can react bimolecularly with NO, HO2, RO2, and NO2, or they can undergo unimolecular isomerization. These RO2 reaction products can then undergo further chemistry, including later-generation RO2 formation, and/or gas-particle partitioning which can lead to the formation of SOA.

Current model parameterizations of SOA that include NOx dependence, however, are still based on chamber experiments run under high-NO and low-NO extremes, with the untested assumption that SOA yields can be parameterized as a linear combination of yields measured at the two limiting cases (β = 0 and β = 1). By design, these “limiting condition” experiments include high radical abundances that shorten the RO2 bimolecular lifetime, thereby limiting RO2 isomerization, and do not control for the role of RO2 + NO2 or RO2 + RO2 reactions. Moreover, experiments run at limiting high-NO and low-NO conditions that underlie today’s model parameterizations are likely not fully relevant for SOA produced over multiple generations in the atmosphere, since the RO2 fate might differ from one generation of oxidation to the next. In addition, radical balances, and therefore RO2 fate, during these experiments are affected nonlinearly by the large initial VOC concentrations necessary to quantitatively measure SOA yields. As a result, current model parameterizations of SOA are mimicking SOA produced under idealized chamber conditions rather than representing realistic atmospheric chemical environments. This likely contributes to the inability of current large-scale models to reproduce the observed variability in atmospheric OA (51, 52).

Because model parameterizations are informed by laboratory experiments, model accuracy hinges on the atmospheric relevance of both the physical and chemical conditions of laboratory experiments. Porter et al. (53) identified that chamber studies of SOA are often run under dry, room temperature conditions, which are not representative of much of the atmosphere, and began to quantify what chemical space chamber experiments of SOA need to match to represent the atmosphere. Further quantification of atmospheric and chamber chemical environments and development of strategies for maximizing overlap between the two is the focus of this work.

Given the importance of bimolecular and unimolecular RO2 chemistry for SOA production, here, we define a four-parameter chemical space framework that allows us to track the fate of RO2 in both the atmosphere and in laboratory studies. We use these parameters in conjunction with GEOS-Chem global modeling to define the chemical environments in which SOA is formed in the atmosphere, and we use mechanistic box modeling of environmental chambers with the Master Chemical Mechanism (MCM) to assess what parts of this chemical space are accessible in the laboratory. We discuss the challenges associated with mimicking the atmospheric chemical environment in chamber studies, show proof-of-concept experiments that demonstrate how SOA experiments can access a range of atmospheric chemical environments, and present pathways forward for furthering our understanding of SOA production in the atmosphere in light of the challenges.

RESULTS

Atmospheric RO2 fate distribution

A complete, quantitative description of RO2 fates is needed to fully characterize an atmospheric chemical environment (54). Although β describes the competition between RO2 + HO2 and RO2 + NO reactions, the contributions of RO2 isomerization, RO2 + RO2 reactions, and RO2 + NO2 reactions to atmospheric RO2 reactivity necessitate additional chemical coordinates to fully describe the RO2 fate distribution. Here, we combine four parameters (β, τbi, RO2/HO2, and NO/NO2) which have been used independently to characterize subsets of RO2 reaction pathways (23, 33, 44, 53, 55, 56) to complete a parameter space that fully describes atmospheric RO2 fates as outlined in Fig. 1. This study considers these parameters together, and considers more than one at a time, in a framework to describe RO2 chemistry in the atmosphere and within chambers.

We adopt the bimolecular RO2 lifetime against reaction with NO and HO2, as originally defined by Teng et al. (55)τbi=1kRO2+NONO+kRO2+HO2HO2(2)

A comparison between τbi and τuni, the RO2 lifetime to unimolecular isomerization, indicates the relative contribution of bimolecular and unimolecular reactions. We also adopt the RO2/HO2 ratio to quantify the relative importance of RO2 + RO2 chemistry and thus ROOR formation (23), as well as the NO/NO2 ratio to denote the relative importance of PANs to the RO2 fate (43, 44). Ideally, a ratio of rates (analogous to β) instead of a ratio of radical concentrations (RO2/HO2 and NO/NO2) would be used to define the relative importance of RO2 + RO2 and RO2 + NO2 reactions to the RO2 fate distribution, and a complete τbi definition would also include the lifetime of RO2 against reaction with other RO2 and with NO2. However, because RO2 + RO2 rate constants are both highly variable and uncertain (48, 49), here, we use the RO2/HO2 ratio to indicate the relative importance of RO2 + RO2 reactions and omit RO2 + RO2 reactions from the τbi determination (Eq. 2). Similarly, because RO2 + NO2 reactions only produce stable products for acyl-RO2, we use the NO/NO2 ratio instead of a ratio of rates to simplify accounting and omit RO2 + NO2 reactions from the τbi calculation, noting that τbi will be shorter for acyl-RO2 than for other RO2 (Eq. 2). Note that although we omit RO2 + RO2 and RO2 + NO2 reactions for simplicity from our τbi definition, such chemistry is included in the chemical mechanisms (MCM v3.3.1 and GEOS-Chem v13.4.0) used in this work.

To examine the fate of RO2 in the atmosphere, we focus our analysis here on the oxidation of isoprene. Multigeneration isoprene oxidation is an important contributor to global SOA production (36), and the gas-phase oxidation chemistry of isoprene (including its RO2 chemistry) has been studied extensively over the past decade (57). We also include a parallel set of analyses for monoterpene-derived SOA (figs. S1 and S2); the results for monoterpenes are similar to those presented here for isoprene.

Understanding atmospheric RO2 chemistry requires identifying which parts of the four-dimensional (4D) chemical space defined here are populated during VOC oxidation in the atmosphere. Figure 2 shows the atmospheric distribution of β, τbi, RO2/HO2, and NO/NO2 for isoprene-derived RO2, calculated from hourly output from the GEOS-Chem chemical transport model (version 13.4.0, https://doi.org/10.5281/zenodo.7254268) for January and July 2016. We show the entire calculated global distribution of these four parameters, weighted by the rate of isoprene + OH oxidation; as such, any references to the “atmospheric distribution” refer to the atmospheric distribution in regions where isoprene is undergoing OH-initiated oxidation (and any references to isoprene oxidation refer to OH-initiated oxidation), unless otherwise specified.

Fig. 2. Global atmospheric distribution of RO2 fates.

Shaded regions are 2D histograms of the global distribution of (A) β and τbi, (B) β and RO2/HO2, and (C) β and NO/NO2, as predicted by GEOS-Chem in hourly output from January and July and weighted by the rate of isoprene + OH oxidation. Surrounding the 2D histograms are projections (1D histograms) of τbi (A, top), RO2/HO2 (B, top), NO/NO2 (C, top), and β (C, right). β and τbi are calculated for RO2 specific to OH-initiated oxidation of isoprene; RO2/HO2 is calculated with the sum of all RO2 radicals (including CH3O2).

We note that the use of a global chemical transport model means our analysis is inherently at coarse spatial resolution (here 2° × 2.5°), a scale relevant for considering global SOA production where most isoprene oxidation occurs. However, the model does not capture the distribution of RO2 fates at fine spatial scales. For example, highly polluted environments where most anthropogenic VOC oxidation occurs would likely tend toward higher β and lower τbi than seen in the global distribution, resulting in conditions where RO2 + NO reactions are more likely to dominate the RO2 reactivity. A global model will also likely underestimate the NO/NO2 and RO2/HO2 ratios in highly polluted environments. Even so, as NOx emissions decrease in cities as a result of emissions controls, values of τbi in urban areas are increasing (58), meaning that future urban chemical environments may be better reflected in the global distributions shown here.

As shown in Fig. 2, β spans the full possible range but, in most regions of the global atmosphere, does not lie at either extreme (median = 0.44, 25th percentile = 0.27, 75th percentile = 0.61). Rather, the “intermediate-β” values that dominate the distribution indicate that RO2 bimolecular reactions with NO and HO2 are in competition with each other throughout most of the global atmosphere.

Moreover, values of τbi mostly lie between 20 and 300 s throughout the atmosphere (Fig. 2A). Within this distribution, τbi is generally longer in regions with lower β (where reaction with HO2 dominates) and shorter in higher-β environments (where reaction with NO dominates). The relative importance of RO2 isomerization is determined by the relative rates of each process (i.e., τbi versus τuni). However, values of τuni are highly variable, as RO2 isomerization rates are highly structure dependent and vary over orders of magnitude for different RO2. In some cases, isomerization is fast (τuni ≪ 1 s), meaning that it will dominate throughout virtually the entire atmosphere (55, 59). In contrast, the atmospheric fate of any RO2 with very slow isomerization rates (τuni > 1000 s) will be dominated by bimolecular reactions. RO2 radicals with intermediate τuni that are within an order of magnitude of atmospheric τbi (i.e., 1 s < τuni < 1000 s), per contra, may react by both bimolecular and unimolecular pathways in the atmosphere. In addition, we note that the temperature dependence of unimolecular isomerization rates is much steeper than the temperature dependence of most bimolecular RO2 reactions (39, 55); as such, the relative importance of unimolecular and bimolecular reactions for a given RO2 in the atmosphere can change with temperature. However, the temperature variability for regions of the atmosphere with substantial isoprene oxidation is relatively narrow (10th percentile = 285 K, median = 295 K, 90th percentile = 302 K).

Figure 2B also shows that atmospheric RO2/HO2 ratios are largely below 1 and generally increase as β decreases. Determining the relative importance of RO2 + HO2 and RO2 + RO2 reactions relies critically on uncertain and variable RO2 + RO2 rate constants. Assuming an RO2 + RO2 rate constant of 10−12 cm3 molec−1 s−1 at 298 K (49), an average atmospheric RO2/HO2 ratio of ~0.5 indicates that 5% of RO2 + peroxy radical reactions in the atmosphere are RO2 + RO2. Faster RO2 + RO2 rate constants of 10−11 and 10−10 cm3 molec−1 s−1 (48) would suggest that 33 and 83%, respectively, of RO2 + peroxy radical reactions are RO2 + RO2.

The atmospheric NO/NO2 ratio is centered around 0.4 (Fig. 2C) and is largely controlled by O3 abundance and NO2 photolysis rates. In the GEOS-Chem mechanism, this average value corresponds to net formation (production minus loss) of PANs in ~90% of atmospheric acyl-RO2 + NOx reactions at atmospheric temperatures. Note that, in the GEOS-Chem mechanism, ~10% of global RO2 are acyl-RO2.

RO2 fate distributions during SOA chamber experiments: Previous experiments and challenges

The global distributions of RO2 fates in the atmosphere shown in Fig. 2 can be viewed as providing “targets” for RO2 chemistry in laboratory studies. However, as shown in Fig. 3, previous SOA chamber experiments have generally simulated RO2 reactivities that differ substantially from those in the atmosphere. The classic smog photochemistry chamber experiments of the 1990s (18), involving the irradiation of hydrocarbon-NOx mixtures, achieved the high-β conditions typical of highly polluted urban areas, but at much lower values of τbi and NO/NO2 than are typical in the atmosphere. The limiting condition SOA experiments of the mid-2000s (27, 29, 30) achieved the extreme-β conditions they were designed for, but they did not capture all possible atmospheric RO2 isomerization products, nor did they fully match atmospheric distributions of RO2/HO2 and NO/NO2 ratios (60, 61). Traditional dark ozonolysis experiments (62) did access atmospheric τbi but achieved RO2/HO2 ratios much higher than those found in the atmosphere. Oxidation flow reactors (OFRs), which can be considered as a limiting case for small chambers, have become increasingly popular for studying SOA formation over longer aging timescales. Although OFRs can access the full range of β (63, 64), most OFR experiments have accessed τbi for RO2 that are shorter than those in the atmosphere (65, 66). Experiments measuring exclusively gas-phase products that can be initialized with lower VOC concentrations have accessed a range of β at longer RO2 lifetimes but have largely not necessarily achieved atmospheric RO2/HO2 and NO/NO2 ratios (67, 68). Further details about the chemical environments achieved in these previous approaches are included in section S1.

Fig. 3. Comparison of the RO2 fate distribution achieved during previous approaches to chamber experiments with atmospheric RO2 fate distributions.

Numbered ovals show the average values achieved with previous approaches to chamber experiments for (A) β and τbi, (B) β and RO2/HO2, and (C) β and NO/NO2. Blue ovals represent parameter values for SOA experiments, whereas green ovals correspond to parameter values for exclusively gas-phase experiments with lower initial VOC concentration. In purple are the corresponding 2D histograms showing the global distribution of each parameter as predicted by GEOS-Chem (as in Fig. 2). NO/NO2 ratios are not shown for experiments with β = 0 (no NOx). Note that RO2 + RO2 reactions are included in the τbi calculation only for experiment types 5 and 6; in all other instances RO2 + RO2 reactions contribute <10% to τbi and are thus omitted for simplicity. All experiment parameters are based on modeled HOx concentrations apart from experiment type 7 for which HOx measurements were reported.

Although the aforementioned previous approaches to SOA studies span a wide range of RO2 fates, no previous approach for measuring SOA yields has overlapped with the atmospheric distribution of RO2 reactivity. The longstanding challenges in matching atmospheric conditions in SOA chamber experiments derive from two fundamental laboratory constraints (Fig. 4): (i) A sufficiently high quantity of products is needed to surpass instrumental limits of detection (LODs), and (ii) oxidation timescales must be fast enough to outcompete lab-specific loss processes, namely, wall loss and dilution. To produce enough OA to surpass instrument LODs (~1 μg m−3 for comprehensive, qualitative OA mass measurements), experiments must start with large quantities of the VOC precursor (typically hundreds of ppb carbon). Although some new techniques (69–71) are available for real-time, ultrasensitive measurements of aerosol components, these methods are not yet fully comprehensive nor quantitative. Together, the need for large precursor concentrations and the requirement for fast oxidation timescales mean that experiments must be run at relatively high oxidant levels, resulting in the production of high HO2 and RO2 concentrations and/or requiring the addition of large amounts of NO. When only gas-phase products, and not aerosol yields, are examined, these constraints are relaxed, and experiments can achieve RO2 reactivity that better matches that of the atmosphere (e.g., experiment types 6 and 7 in Fig. 3). Ultimately, the chemical conditions of the chamber are driven by the VOC oxidation itself, in contrast to the atmosphere where the ambient chemical environment controls VOC oxidation conditions. When VOC oxidation controls the chemical environment, which is the case not only in standard laboratory experiments but also in outdoor perturbation SOA experiments where a VOC is added to a chamber filled with ambient air, control of RO2 reaction conditions can be challenging.

Fig. 4. Challenges of matching atmospheric conditions in chamber experiments.

(Left) Fundamental constraints of SOA studies and implications for photochemical conditions within chambers. (Right) Simplified radical chemistry of the VOC oxidation chemistry that underlies such challenges.

Possible RO2 fates in chamber experiments of SOA formation

A key question is what chemical conditions can be accessed in laboratory chamber experiments measuring SOA yields and to what extent chambers can span the range of RO2 reaction conditions found in the global atmosphere (as shown in Fig. 2). Using a box model with near-explicit chemistry [Framework for 0D Atmospheric Modeling (F0AM) (72) with MCM v3.3.1 (73, 74)], we assess the RO2 fate distribution accessible during photochemical isoprene oxidation in a typical environmental chamber at 298 K, as shown in Fig. 5. We span combinations of initial concentrations of H2O2, HONO, and NO with 100–parts per billion (ppb) initial isoprene, a mixing ratio sufficiently high to allow for SOA yield measurements. For each simulated experiment, we determine whether the initial conditions allow for two generations of oxidation during an 8-hour experiment, and for those experiments that satisfy this criterion, we assess the corresponding distribution of RO2 fates. The results we present here use experimental parameters (dilution rates and light intensities) from a single chamber [the 7.5-m3 Massachusetts Institute of Technology environmental chamber (75)], but the overall conclusions are applicable to most indoor environmental chambers. The challenges presented here would be exacerbated in smaller-volume chambers (including OFRs) because the timescales for dilution and wall loss are shorter, necessitating even higher oxidant concentrations.

Fig. 5. Comparison of RO2 fate distributions achievable in chambers and in the atmosphere.

Points correspond to values of (A) β and τbi, (B) β and RO2/HO2, and (C) β and NO/NO2 achievable during theoretical chamber experiments calculated with F0AM initiated with 100 ppb isoprene and different combinations of initial HONO, H2O2, and NO. Solid points correspond to 8-hour experiments that achieve second-generation chemistry (defined as final MVK concentration less than half of the maximum MVK concentration); lines correspond to 8-hour experiments that do not achieve sufficient second-generation chemistry. Points are overlaid on 2D histogram of the global distribution of β, τbi, RO2/HO2, and NO/NO2 as predicted by GEOS-Chem (as in Fig. 2). Note that β and τbi from GEOS-Chem are calculated with the corresponding atmospheric temperature and pressure, whereas the chamber points are all calculated for experiments at 298 K. The unimolecular lifetimes for the two isoprene-derived RO2 isomers that isomerize in the atmosphere are indicated in pink and orange (55). Shaded pink and orange regions indicate the range of unimolecular lifetimes that exist over the 10th to 90th percentile temperature range in the atmosphere during isoprene + OH oxidation.

Figure 5 shows results from these box modeling simulations. The entire range of β between 0 and 1 can be achieved by careful selection of precursor concentrations. Achieving atmospheric RO2/HO2 ratios is possible at intermediate and high β but is more difficult at low β, when very high quantities of H2O2 are required to achieve sufficiently fast oxidation (76). However, RO2 speciation also differs between chambers and the atmosphere: CH3O2 is ≈50% of atmospheric RO2, whereas it only makes up ≈1 to 10% of chamber RO2 (fig. S3). As such, chamber RO2 + RO2 product distributions likely differ from those in the atmosphere. NO/NO2 ratios are generally lower in chamber conditions than in the atmosphere as a result of high HO2 concentrations (see fig. S4) and low NO2 photolysis rates (jNO2). While increasing the chamber light intensity would result in increased NO/NO2 ratios, the high chamber HO2 concentrations preclude reaching atmospheric NO/NO2 ratios even at atmospheric jNO2.

With various initial conditions, bimolecular lifetimes (τbi) between 2 and 20 s can be achieved at low β but are orders of magnitude lower at high β. At moderate β, experimental conditions can access τbi in the tens of seconds, but the lower oxidant levels at these longer τbi mean it becomes increasingly difficult to achieve two generations of oxidation in an 8-hour experiment. However, to capture the atmospheric distribution of bimolecular and unimolecular RO2 products in a chamber experiment, it is not always necessary to have values of τbi in the chamber directly overlap with those in the atmosphere. Instead, experiments need only to access a regime where the relative importance of bimolecular and unimolecular reactions is largely the same as it is in the atmosphere. Accessing atmospherically relevant RO2 fates for RO2 with fast or slow isomerization (τuni < 1 s or τuni > 1000 s) is achievable with many combinations of initial conditions in the chamber. Careful selection of initial conditions is required, however, for RO2 with intermediate isomerization rates (1 s < τuni < 1000 s) to ensure that competition between bimolecular and unimolecular fates in the chamber matches that of the atmosphere.

In the case of isoprene, eight different first-generation RO2 radicals are formed, but only two (the Z-δ isomers) undergo isomerization reactions at atmospheric conditions. Both Z-δ isomers isomerize rapidly enough [4-OH Z-δ τuni = 0.3 s, 1-OH Z-δ τuni = 3 s at 297 K (55)] that isomerization almost always outcompetes their bimolecular reactions in the atmosphere. SOA chamber experiments can mimic atmospheric conditions where the fates of these RO2 are dominated by isomerization across the entire β range for the 4-OH Z-δ isomer and at high- and intermediate-β for the 1-OH Z-δ isomer. Low-β experiments can achieve conditions in which isomerization outcompetes bimolecular reactions of the 1-OH Z-δ RO2 but cannot access atmospheric conditions where isomerization entirely dominates the 1-OH Z-δ RO2 fate. We note that β and τbi derived from GEOS-Chem shown in Fig. 5 are calculated at the corresponding atmospheric temperatures and pressures, whereas the β and τbi that describe chamber chemistry are all calculated at 298 K. Over the range of atmospheric isoprene oxidation temperatures, however, variation in τuni for isoprene-derived RO2 is constrained to within an order of magnitude (shaded rectangles in Fig. 5) and therefore does not significantly alter the atmospheric competition between bimolecular and unimolecular fates for isoprene-derived RO2.

Ratios of oxidants also vary between extreme-β experiments and intermediate-β experiments. As shown in fig. S5, extreme-β experiments can access conditions in which nearly all the isoprene is oxidized by OH, without contributions from other oxidants (O3 and NO3). At β = 0, the lack of NO prevents production of O3 or NO3, while at β = 1, there is sufficient excess of NO to titrate away any O3 and NO3 produced. However, at intermediate-β conditions, photochemical cycling will produce O3 and NO3, which will persist because the levels of NO are too low to titrate them. Intermediate-β experiments involve OH/O3 ratios that are similar to those in regions of the atmosphere characterized by intermediate-β conditions. NO3 concentrations at intermediate-β, however, will be higher in most indoor chambers compared to daytime tropospheric conditions because of the lower visible light intensities. The presence of multiple oxidants does complicate experimental interpretation, but the SOA produced during intermediate-β experiments is arguably more representative of atmospheric photochemical SOA production than single-oxidant studies.

Changes in chemical conditions during an experiment resulting from the oxidation of both NOx and VOCs are another consideration during intermediate-β experiments (77). The decreases in NO concentrations over time lead to reductions in β and increases in τbi. As shown in fig. S6, changes in τbi over a typical experiment are on the order of seconds to tens of seconds. Changes in β vary and are minimized at the extremes, but intermediate-β experiments can transverse nearly the full range of β values over time, and these time-dependent changes in chemical conditions make it more challenging to locate these experiments in chemical space. Addition of a small amount of NO (or NO precursor) throughout an experiment (28, 63, 78) may reduce the time-dependent changes in chemical conditions.

Proof-of-concept chamber experiments with a range of RO2 fates

Using the global model- and box model–informed approach to experimental design described here, we carried out a series of isoprene + OH oxidation chamber experiments. In Fig. 6, we focus on three experiments that span the chamber-accessible β-τbi parameter space. These proof-of-concept experiments are, to our knowledge, the first that explicitly and systematically attempt to span the atmospheric chemical environment during SOA production, despite the aforementioned challenges. Initial conditions for these experiments (shown in table S1) are used as box modeling inputs to predict RO2 fate parameters (β and τbi) for each experiment. To assess whether we achieved the predicted ranges of β and τbi in these experiments, we examine the first-generation gas-phase oxidative product distribution, shown in Fig. 6 and fig. S9. At low-β conditions (β = 0.02), the first-generation product distribution is dominated, as expected, by C5H10O3; this corresponds to ISOPOOH, the main product formed from RO2 + HO2 chemistry. At intermediate- and high-β conditions (β = 0.61 and 1.0, respectively), isoprene hydroxy nitrate (C5H9O4N, the RO2 + NO termination product) becomes an increasingly larger contributor to the product distribution. The relative importance of the isomerization product (HPALD, C5H8O3) to the product distribution is highest at intermediate-β conditions in which τbi is expected to be highest. These first-generation gas-phase product distribution trends confirm that our experiments did indeed span ranges of β and τbi, demonstrating that chamber experiments can be run under a wider range of atmospheric RO2 fates than have been accessed previously.

Fig. 6. Demonstration of the range of RO2 fate distributions achievable in chamber experiments of isoprene oxidation.

First-generation (uncalibrated) gas-phase product yields from isoprene oxidation at a variety of β and τbi are overlaid on the atmospheric distribution of β and τbi as shown in Fig. 2. The entire product distribution is not represented; rather, three masses measured by NH4+-CIMS representative of the three first-generation product channels are shown. Product yields are calculated as the ratio of product growth (measured by NH4+-CIMS) to the isoprene decay (measured by Vocus PTR) and then normalized to give the relative yields shown in the pie charts here (see fig. S9). Because the data are uncalibrated, fractions shown here should be used only to assess relative differences and are not representative of absolute product distributions. β and τbi for each experiment are calculated from modeled experiments (F0AM with MCM v3.3.1 chemistry) initialized with measured initial isoprene, HONO, NO, and NO2 concentrations and with estimated initial H2O2 concentrations. Structures shown in the legend are isomers derived from the Z-Δ 4-OH, 1-OO isoprene RO2. The unimolecular lifetimes for the two isoprene-derived RO2 isomers that can isomerize are indicated in pink and orange (55).

The limitations of our experimental instrumentation preclude measurements of unique RO2 + RO2 and RO2 + NO2 products. However, modeling of our experiments indicates that the relative contribution of these pathways to RO2 fate in our experiments is in line with what is shown in Fig. 5: RO2/HO2 ratios fall within the atmospheric RO2/HO2 distribution whereas NO/NO2 ratios are far below the atmospheric NO/NO2 distribution. As a result of the high HOx concentrations in typical chamber SOA experiments, faster photolysis rates or higher NO concentrations than those under typical atmospheric photochemical conditions are required to achieve atmospheric NO/NO2 ratios.

DISCUSSION

Pathways forward

The results of the proof-of-concept experiments described above confirm the possibility of designing and executing experiments to mimic many of the key features of the atmospheric chemical environment in laboratory chambers, including chemical environments where multiple RO2 fates compete (e.g., the intermediate-β, longer-τbi experiment in Fig. 6). As such, this model-informed approach to experimental design represents a useful approach for future chamber experiments of SOA production. The next generation of SOA chamber studies and model treatments of SOA production, however, must find ways to tackle the remaining challenges. Here, we suggest some directions for future research aimed at enabling progress in our understanding of SOA formation chemistry.

As discussed earlier, one of the key limitations of SOA chamber experiments is the need to produce enough products such that they surpass instrument LODs. This is particularly challenging for VOCs with low SOA yields, such as isoprene. The ability to measure SOA mass concentrations with nanograms per cubic meter–level sensitivity would relax this constraint, allowing experiments to be run with lower initial VOC concentrations. For example, as shown in fig. S7, longer τbi are accessible during experiments with lower initial isoprene concentrations, including τbi up to 200 s for high-β experiments initialized with 0.1-ppb isoprene, because of lower HO2 and RO2 concentrations. Such improvements to analytical instrumentation for measuring OA mass, especially for experiments with added seed particles, could involve increased sensitivity for hard-ionization quantitative aerosol mass measurements (e.g., the aerosol mass spectrometer or aerosol chemical speciation monitor) as well as the development of sensitive (non-mass spectrometric) approaches for measuring OA mass. Better quantification methods for highly sensitive soft ionization techniques (e.g., chemical ionization mass spectrometry and extractive electrospray ionization mass spectrometry) could also help relax this constraint.

Quantifying SOA yields through new particle formation (NPF) could alleviate some of the LOD constraint by improving signal-to-noise ratios. However, ultrafine particles produced during nucleation events are lost to chamber walls more quickly than the accumulation mode particles typically present during seeded experiments. Therefore, NPF experiments that accommodate multigeneration chemistry require large chambers and fast oxidation, resulting in increased HOx concentrations and thus altering the radical balance.

In addition, inclusion of the role of all RO2 reactions involved in SOA formation may require more complex SOA parameterizations in models. The untested linear mixing assumption between RO2 + NO and RO2 + HO2 yields should at least be tested, and SOA parameterizations may need to increase in complexity to account for more than two RO2 fates. For example, existing RO2 fate–based SOA parameterizations that include SOA yields for RO2 + HO2 and RO2 + NO (37) may need to include yields for other RO2 reaction pathways. Future experimental work is required, however, to evaluate how much a given RO2 reaction pathway actually affects atmospheric SOA production and therefore how many dimensions are required to accurately model SOA formation chemistry. If SOA yields do not vary significantly over the atmospheric ranges of an RO2 fate, then that reaction pathway does not need to be carefully studied or included in models. For example, variation within the atmospheric distribution of τbi are likely to matter only for systems with isomerization reactions that have lifetimes within an order of magnitude of atmospheric τbi (i.e., 1 s < τuni < 1000 s). Recent work has demonstrated that SOA yields from some monoterpene reactions are sensitive to RO2/HO2 only when this ratio is changed markedly (47, 79), suggesting that the atmospheric variation of RO2/HO2 between 0 and 2 has an insignificant effect on biogenic SOA yields. Other laboratory studies, however, have suggested that this narrow atmospheric range may affect SOA yields from anthropogenic VOCs (46). Additional experiments have shown that isoprene SOA yields more than halve between NO/NO2 ≈ 0.1 and 0.3 (44), suggesting that changes in NO/NO2 across the range found in the atmosphere (≈ 0.2 to 0.7) can affect isoprene SOA yields. Similar tests are required across these atmospheric ranges for an assortment of VOCs. Future experimental work is also needed to address the validity of species lumping in SOA formation mechanisms, particularly for monoterpenes.

An alternative to the parameterized approach for modeling SOA production is a semimechanistic approach, which describes SOA formation in terms of key condensable species generated from gas-phase chemical mechanisms. This approach combines laboratory measurements of gas-phase products with estimates of their volatilities to predict particle-phase products and aerosol yields and was recently implemented in the Community Regional Atmospheric Chemistry Multiphase Mechanism (80). Such an approach has the advantage of inherently including the effects of changes in atmospheric variables (e.g., RO2 chemistry and temperature) on SOA production, in contrast to a parameterized approach where such effects require explicit consideration. However, a semimechanistic approach generally requires uncertain estimates of oxidation product volatilities as well as thoughtful consideration of what species are included given resource constraints, and predicted SOA formation has generally not been validated by yields measured in laboratory studies. More model species will also increase computational demand and so may not be appropriate for large-scale models, including global chemistry-climate and/or Earth system models.

Regardless of the model approach used to describe SOA formation, a crucial component is continual interfacing between laboratory measurements, models, and field observations. Updated laboratory-based model predictions of SOA should be compared to ambient observations, and further experiments should then be targeted to chemical environments in which the model-measurement difference is greatest. Validation of model SOA descriptions against SOA yields measured in chamber experiments that match the atmospheric chemical environment will also aid model parameterization and further development.

Final remarks

The mechanistic advances in our understanding of RO2 chemistry over the past two decades have enabled us to replace the high- and low-NOx binary used in the past to describe RO2 fates with a more nuanced description of the complex, multidimensional space that controls the product distribution of atmospheric VOC oxidation. Leveraging 3D and box modeling tools with our understanding of the complexity of RO2 chemistry along with careful control of chamber inputs will help ensure that experimental resources are dedicated to experiments that span chemical parameter spaces that best match those of the atmosphere.

Because laboratory studies are the foundation for model parameterizations of SOA, their limitations inherently become the limitations of models. Modeling SOA based on experiments run at extreme-β conditions has major shortcomings: Extreme-β conditions do not describe the chemical environment in most of the atmosphere, the assumption of linearity between extremes has never been tested, and extreme-β chamber conditions generally do not mimic the role of other RO2 reactions (RO2 isomerization, RO2 + RO2, and RO2 + NO2) that may occur in the atmosphere. However, improvements are possible, via the use of modeling to explicitly design the chemical environment for SOA chamber experiments. The fact that models are fundamentally limited by laboratory constraints also means that model results should be interpreted with the limitations of chamber experiments in mind. The work presented here indicates that some atmospheric RO2 fate distributions can be better represented in chambers than others. For example, achieving τbi that allows for the same competition between bimolecular and unimolecular RO2 reactions as what occurs in the atmosphere is most challenging for low-β environments but can be reasonably well-captured under more polluted high-β conditions. As such, interpretation of model results and their ability to reproduce field observations should be done with awareness of the relevant RO2 chemical environment.

Experimental chamber work over the past several decades has advanced our understanding of the complexity of the multigeneration oxidative chemistry involved in VOC oxidation and SOA production. To achieve the ultimate goal of fully understanding the processes that control the spatiotemporal distribution of pollutants in the atmosphere, the next generation of chamber studies must be more tightly connected to true atmospheric reaction conditions of RO2 radicals. Such studies are the essential foundation for interpreting past and future field observations and for building the accurate model parameterizations that are required to fully understand and predict atmospheric SOA and ultimately its impacts on air quality and climate.

MATERIALS AND METHODS

Modeling

We use the GEOS-Chem chemical transport model (version 13.4.0) at a horizontal grid resolution of 2° × 2.5° and with 47 vertical layers. Results shown are from simulations of January and July 2016 after a 1-year spin-up with output saved every hour. The model is driven by assimilated meteorology from the Modern-Era Retrospective Analysis for Research and Applications, version 2, from the NASA Global Modeling and Assimilation Office. Our simulations use the full gas-phase chemistry available in GEOS-Chem, including a coupled HOx-NOx-VOC-O3-halogen chemical mechanism (81–84). Biogenic emissions are calculated online using the Model of Emissions of Gases and Aerosols from Nature (MEGANv2.1) framework (85), and anthropogenic emissions are from the global Community Emissions Data System (CEDS) inventory (86).

Box model simulations were run using the F0AM (72) with near-explicit chemistry described by the MCM (v3.3.1) (73, 74). Our simulations use a measured light spectrum scaled by a measured jNO2 rate of 0.12 min−1. To span possible chamber initial conditions, we run a series of 8-hour simulations where we logarithmically permute initial isoprene (4 values between 0.1 and 100 ppb, inclusive, shown in fig. S7), HONO (18 values between 0 and 1000 ppb, inclusive), H2O2 (12 values between 10 and 10,000 ppb, inclusive), and NO (18 values between 0 and 1000 ppb, inclusive) concentrations that vary over orders of magnitude (Fig. 5 and fig. S8). We run a separate series of simulations where we permute initial CH3ONO and NO concentrations (fig. S8). We consider a simulation in which the final methyl vinyl ketone (MVK) concentration is less than half of the maximum MVK concentration as an experiment that achieved second-generation oxidation chemistry. For each simulation, we calculate β, τbi, RO2/HO2, and NO/NO2. For experiments that achieve second-generation chemistry, the experiment end is defined as the time at which the MVK concentration decreases to half of its maximum. Other experiments are assumed to end at 8 hours. β is calculated from the cumulative integrated rates of RO2 loss over the course of the experiment; τbi, RO2/HO2, and NO/NO2 are calculated from the mean value during the simulated experiment.

Chamber experiments

Experiments were conducted in a 7.5-m3 temperature-controlled environmental chamber (75) held at 20°C and relative humidity < 10%. The chamber is constructed of perfluoroalkoxy (PFA) Teflon and is surrounded by 48-ultraviolet lamps whose emission is centered at 340 nm and whose intensity drives NO2 photolysis at a rate of 0.12 min−1. The chamber is operated in “semi-batch” mode in which clean air is continuously added to make up for instrument sample flow to maintain a constant volume. Between experiments, the chamber is flushed by zero air for at least 12 hours to ensure a clean background. Before the start of each experiment, ammonium sulfate seed particles are injected to serve as condensation nuclei, followed by injection of acetonitrile (dilution tracer), isoprene, and the oxidant precursors (HONO and/or H2O2). Seed particles are atomized from a solution of ammonium sulfate (2 g/liter) in water. Isoprene and H2O2 are added through a silicone septum into a 10–liter per minute flow. HONO is generated by adding sulfuric acid via a syringe to a constantly stirred solution of sodium nitrite; the HONO produced, as well as any NO and NO2 produced as coproducts, is carried into the chamber via a 1-lpm air stream. Experiments are initiated by turning on the chamber lights.

The gas-phase composition of the reaction mixture was monitored with a proton-transfer-reaction time-of-flight mass spectrometer (Vocus PTR-MS, Aerodyne Research Inc.) (87) and an ammonium time-of-flight chemical ionization mass spectrometer (NH4+ CIMS, Ionicon Analytic) (88). Although not discussed here, particle-phase measurements were also made with an aerosol mass spectrometer (Aerodyne Research Inc.) and a scanning mobility particle sizer (TSI). Additional measurements include those from a NO-NO2-NOx analyzer (Thermo Fisher Scientific), a Cavity Attenuated Phase Shift Spectroscopy NO2 monitor (CAPS NO2) (Aerodyne Research Inc.), and an ozone monitor (2B Technologies).

Parameters that describe RO2 fate (β, τbi, RO2/HO2, and NO/NO2) for these experiments are calculated using MCM v3.3.1 box model simulations in F0AM as described above. Simulations are initialized with initial measured NO (from the NO channel of the Thermo Fisher Scientific NO-NO2-NOx analyzer), NO2 (from the CAPS NO2 monitor), HONO (estimated by subtracting the CAPS NO2 signal from the NOx analyzer NO2 signal), and isoprene concentrations (from the Vocus PTR-MS). Initial H2O2 concentrations were estimated on the basis of injection amount.

Acknowledgments

We thank M. Canagaratna of Aerodyne Research as well as F. Keutsch and Y. Li of Harvard University for providing the CIMS instruments used in this study.

Funding: This work was supported by National Science Foundation Atmospheric and Geospace Science Postdoctoral Research Fellowship 2137238 (H.S.K.) and Department of Energy grant DE-SC0022017 (M.B.G., C.L.H., J.H.K., and N.T.).

Author contributions: H.S.K., C.L.H., and J.H.K. designed the research and acquired the funding. H.S.K. performed the model simulations and created the visualizations. H.S.K., N.T., and M.B.G. performed the experiments. H.S.K., C.L.H., and J.H.K. wrote the paper with input from all authors.

Competing interests: The authors declare that they have no competing interests.

Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Model and experimental data shown can be found on Zenodo archive 10.5281/zenodo.10986395.

Supplementary Materials

This PDF file includes:

Supplementary Text

Figs. S1 to S9

Tables S1 to S4
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