We implemented GC13 and the other mechanisms outlined in Sect. 3 into box model simulations for comparisons to environmental chamber data and for mechanism intercomparisons under a range of boundary layer conditions. The simulations use a fourth-order Rosenbrock kinetic solver implemented with the Kinetic PreProcessor tool (KPP; ). We standardize the inorganic and C1–C3 chemistry to that of the MCM in all mechanisms so that the only differences between the mechanisms are in their BTX oxidation chemistry. For mechanisms with speciated xylenes, we assume equal contributions from the three isomers, and comparisons of xylene product yields with literature values are only conducted for experimental studies that targeted all three isomers.

Environmental chamber simulations. To quantify product yields and compare them to environmental chamber data, we simulate BTX chemistry in a box model representative of chamber experimental conditions. For each mechanism, we initialize the box model with fixed mixing ratios of one aromatic precursor, NO, and H2O2 as a photolytic OH source; we then run the simulation forward in time with a fixed light intensity and temperature until the aromatic precursor is 99 % depleted. Next, we vary all initial settings (temperature, light intensity, and each reactant concentration) individually and rerun the simulation to sample the full range of possible experimental conditions. Result shown in Sect. 4.2 and 4.3 are for simulations with initial [NO]0=5ppt–2.5ppm, P=1atm, T=298 K, [VOC]0=100 ppb, [H2O2]0=2.5 ppm, and an NO2 photolysis rate (jNO2) of 8×10-3 s-1 (other photolysis rates, given in Sect. S1 in the Supplement, are scaled to jNO2). “initial” yields shown in Figs. 4–6 are after 10 min of photooxidation; additional results showing long-term yields after 24 h of oxidation are provided in Sect. S2 in the Supplement. Sensitivities to temperature and initial VOC concentrations are generally small, but additional results showing the effects of these parameters, as well as the effects of light intensity and oxidant source, can be found in the Supplement. Wall losses of semi-volatile gases are not represented in these simulations, as we do not seek to model SOA formation or the yields of direct SOA precursors. An improved representation of SOA from aromatics in GEOS-Chem will be the subject of future work. While it is possible that wall losses of semi-volatile intermediates affect experimental yields of oxidized VOCs (OVOCs), this multigenerational chemistry is expected to play only a minor role at the short timescales isolated here.

Continental boundary layer simulations. To examine longer-term product yields and effects of BTX oxidation on the ambient atmosphere, the same box model described above is also run under conditions meant to simulate a continental boundary layer like that of the heavily studied Seoul metropolitan area with constant NO and aromatic emissions. The well-mixed boundary layer exchanges with the background free troposphere with a fixed ventilation timescale of 1 d for all species. Simulations are initialized with 75 ppb O3, 1.8 ppm CH4, 200 ppb CO, 300 ppt CH2O, and 1 % H2O, and these species are also present in the same concentrations in the background free troposphere with which the boundary layer box exchanges. Photolysis rates follow a clear-sky diurnal profile at 45∘ latitude at the summer solstice with an ozone column of 350 DU, while temperature varies sinusoidally with an amplitude of 4 ∘C, centered at 25 ∘C, peaking at 13:00 solar time, and a period of 1 d. Results shown in Sect. 4.2 and 4.3 are for a total aromatic VOC emission rate of 120 ppth-1, distributed between benzene, toluene, and xylene in a 2:2:1 molar ratio or for a single precursor, and for fixed NO emission rates between 1 ppth-1 and 10 ppbh-1. Additional results showing sensitivities to VOC emission rates can be found in the Supplement. The model does not represent deposition or aerosol uptake processes, except to impose a 1 h loss rate on N2O5 for conversion to HNO3. We apply 7 d of initialization to reach diurnal steady state; the results shown in Sect. 4.3 are from the eighth simulated day.

We use environmental chamber simulations to determine prompt product yields from each mechanism and compare them to experimental data; we also use both chamber and boundary layer simulations to investigate the differences in long-term yields of later-generation and multigenerational products between mechanisms. In both cases, we present our results as a function of initial NOx mixing ratio ([NOx]0) imposed in the model either directly (chamber simulations) or through emissions (boundary layer simulations). In some cases, yields from organic peroxy radical reactions may depend on the branching between bimolecular and unimolecular reactions in addition to the branching between reaction with NO and other reactions, and comparisons would be more appropriately made as a function of peroxy radical lifetime against all bimolecular reactions rather than just NO (see, e.g., ); however, it is difficult to approximate these bimolecular lifetime conditions for many past experimental results, so we opt for the more straightforward comparisons as a function of initial NOx.

In comparisons with experimental yields (Figs. 4–6), simulated yields are shown after 20 min of oxidation, whereas experimental yields are the earliest reported value. Because the experimental yields shown here come from a range of chamber studies with differing setups (e.g., chamber size, oxidant source, light spectrum and intensity, duration, and initial concentrations), care should be taken when comparing observed yields as a function of [NOx]0 to each other or to simulated yields. Experiments conducted with no added NOx are shown at [NOx]0=10 ppt (below which modeled yields are invariant with [NOx]0) regardless of whether NOx concentrations were monitored during the experiment. In some cases, NOx may off-gas from chamber walls . When reported, instrumental uncertainties on measured NOx mixing ratios are shown as horizontal error bars on experimental points.

Ring-retaining products. Figure 4 shows environmental chamber molar yields of ring-retaining alcohols and aldehydes from BTX oxidation in the mechanisms and in the experimental literature as functions of initial NOx mixing ratio. For the ring-retaining alcohols formed via OH addition followed by H abstraction (Fig. 1iii), all mechanisms implement fixed direct yields from the BTX precursors, which therefore do not vary with NO. GC13 uses a phenol yield from benzene of 54 %, derived from an error-weighted average of the yields measured by (53±7 %), (61±7 %), and (49±13 %) at atmospherically relevant NOx levels. Among the other mechanisms implemented here, SAPRC uses a fixed phenol yield of 57 % from benzene, whereas all others use 53 %. Fixed cresol and xylenol yields of 19 % and 15 % in GC13, from toluene and xylene, respectively, are taken from MECCA and are consistent with observations.

For phenol, the mechanisms' fixed yields of 53 %–57 % fit most data under low-to-moderate NO conditions; however, when [NOx]0 exceeds 100 ppb, the observed yield declines, as bimolecular reactions become too fast to allow equilibration to occur. This behavior is not exhibited by any of the mechanisms studied here. Such high NO concentrations are also rarely seen in ambient environments; therefore, the fixed yields are suitable for atmospheric simulations. Experimental yields of cresol (from toluene) and xylenol (from xylene) exhibit little correlation with NOx and are, therefore, adequately represented by fixed branching ratios, although the ratios implemented vary widely between mechanisms, with low yields in RACM2 (6 % from toluene and xylene) and no representation of xylenols in CRI v2-R5.

The ring-retaining aldehydes, benzaldehyde and tolualdehyde, are formed following H abstraction from the methyl groups of toluene and xylene, respectively (Fig. 1i). Most mechanisms that include benzaldehyde chemistry (CRI and MOZART-GC do not) explicitly treat the benzyl peroxy intermediate in this reaction pathway, resulting in peak aldehyde yields of ∼6 % when [NOx]0 exceeds 100 ppb, dropping to 1 %–3 % at (NOx]0<1 ppb due to the competing formation of benzyl hydroperoxide. However, this results in lower benzaldehyde yields than observed under NOx-free conditions. The lack of observed NOx dependence in the benzaldehyde yield may reflect a short lifetime of benzyl hydroperoxide, a high incidence of RO2+RO2 reactions , a non-hydroperoxide-forming channel in the benzyl peroxy + HO2 reaction , or another pathway altogether . Regardless, to fit observations and further simplify the mechanism, we bypass the benzyl peroxy intermediate and form benzaldehyde directly from the reaction of toluene and xylene with OH with a fixed yield of 6 %, consistent with most experimental results at both high and low NOx.

C1–C3 carbonyl products. Figure 5 shows prompt environmental chamber yields of glyoxal from BTX oxidation in both the mechanisms and the experimental literature as functions of initial NOx mixing ratio (initialized as NO in simulations and in most chamber experiments, with some NO2 in chamber experiments). Initial glyoxal yields from BTX oxidation generally range between 10 % and 40 %, with the highest yields from benzene and the lowest from xylenes. With a fixed NOx-independent first-generation glyoxal yield and secondary yields from the representative C4–C5 intermediates (Fig. 3), GC13 accurately simulates experimentally measured glyoxal yields across a wide range of [NOx]0 for all three aromatic precursors, whereas most other mechanisms exhibit excessive glyoxal formation at [NOx]0>100 ppb and insufficient production at [NOx]0<1 ppb. Due to the long lifetime of benzene relative to its intermediate oxidation products, it is difficult to isolate prompt vs. multigenerational glyoxal yields, which partially explains the range of experimental results; the prompt yield that we implement in GC13 (18 %) is able to match the lower limit of observed yields at both high and low NOx. We also find in GC13 simulations that the glyoxal yield from benzene is sensitive to the initial benzene concentration in chamber experiments (see Sect. S4 in the Supplement), which may further explain the spread of measured yields.

Figure 6 shows simulated and observed environmental chamber yields of formaldehyde and methylglyoxal from toluene and xylene oxidation as functions of initial NOx mixing ratio. Because it lacks alkyl substituents, benzene is unable to produce either formaldehyde or methylglyoxal. As with glyoxal, GC13 is consistent with experimental yields across the full spectrum of [NOx]0 conditions, although some observed methylglyoxal yields from toluene deviate considerably from the general trend. Most other mechanisms exhibit lower prompt formaldehyde and methylglyoxal yields at [NOx]0<1 ppb and higher yields at [NOx]0>100 ppb than GC13 or observed values, although observational evidence is sparse for xylene. In particular, SAPRC and the MCM exhibit high prompt formaldehyde and methylglyoxal yields at [NOx]0>100 ppb, respectively.

In addition to these prompt yields, later-generation formation of C1–C3 carbonyl species can be important for HOx radical and carbon budgets. Data on long-term yields are sparse, so here we rely mostly on model intercomparisons of boundary layer simulations with the MCM taken as a reference. Results are shown in Fig. 7 for mixed aromatic emissions and in Fig. S6 in the Supplement for individual aromatic precursors. The maxima in yields at intermediate NOx, particularly evident for formaldehyde, reflect the corresponding maxima in OH concentrations. Generally, the MCM is able to produce higher late-generation yields of the C1–C3 carbonyls than more reduced mechanisms, but GC13 exhibits high yields similar to the MCM. GC13 also simulates similar midday glyoxal-to-formaldehyde concentration ratios from aromatic oxidation to the MCM, ranging from 0.3 when NOx exceeds 1 ppb to >0.7 at NOx<10 ppt (Fig. S8 in the Supplement). Results speciated by BTX precursor reveal a similar trend. MOZART-GC and MOZART-T1, the most reduced of the previous mechanisms, tend to simulate the lowest long-term carbonyl yields. MECCA is generally able to reproduce the MCM's high long-term carbonyl yields except from xylene, which MECCA lumps as toluene; this results in an overprediction of glyoxal yields and underprediction of formaldehyde and methylglyoxal yields relative to the MCM. Long-term yields from simulated chamber experiments (Figs. S3 and S4 in the Supplement) show results similar to the boundary layer simulations.

Other VOC products. Some experimental and theoretical evidence exists for formation pathways of fumaraldehydic, formic, and acetic acids from BTX oxidation , but this is generally not included in mechanisms. Global models tend to underestimate ambient concentrations of these compounds, and additional formation pathways could help alleviate this discrepancy . Here, we include the formation of formic and acetic acids as described in and via the ketene-enols, represented as part of the lumped C4 and C5 products. Few chamber data are available for comparison; measured a 13 % yield of formic acid from benzene under nominally NOx-free conditions, while measured a 6 % yield of acetic acid from toluene under high-NO (940 ppb) conditions. Both are consistent with prompt yields in GC13. Long-term yields of formic acid in the mechanism can reach 32 % from benzene, 28 % from toluene, and 16 % from xylene, while acetic acid yields can reach 12 %–13 % from toluene and xylene (See Figs. S2 and S5 in the Supplement).

Most mechanisms also represent the formation of larger (C4+) dicarbonyls, including biacetyl, photolabile conjugated dialdehydes, and less-reactive ketones, with varying degrees of complexity. In GC13, these compounds are not treated explicitly, and are instead grouped into the two representative C4–C5 reactive intermediates. For this reason, we do not optimize any branching ratios in GC13 with environmental chamber yields of C4+ dicarbonyls, but Fig. S1 in the Supplement provides a comparison between measured and simulated yields of photolabile dicarbonyls in each mechanism.

Finally, as described in Sect. 2, most mechanisms include the direct formation of a ring-opened C6+ epoxide from BTX oxidation. These yields are NOx-independent and span a wide range, from 0 % from all precursors in GC13 to >70 % from xylene in RACM. While little evidence for this pathway exists under atmospherically relevant conditions, it may be useful as an intermediate, particularly given that other known pathways cannot achieve carbon closure in many experimental studies (e.g., ). Although these epoxides are not included in GC13, Fig. S1 provides a comparison between measured yields in environmental chambers and simulated yields in other mechanisms.

Figure 8 shows the effects of BTX oxidation on daytime abundances of ozone, OH, and HO2 in the continental boundary layer simulations. Results speciated by aromatic precursor are shown in Fig. S7. GC13 and MECCA have less ozone production than the MCM, RACM, and SAPRC, all of which are known to overestimate peak ozone from chamber experiments . This is primarily due to phenoxy–phenylperoxy radical cycling chemistry, represented only in GC13 and MECCA . The dominant phenoxy–phenylperoxy cycle converts NO2 to NO3 and consumes one ozone molecule; during the day, this cycle largely balances via NO3 photolysis (although the minor channel to NO + O2 represents an odd oxygen sink), but at night, conversion of NO3 to N2O5 and on to HNO3 amplifies ozone loss. MECCA and GC13 both exhibit a surge in NO3 (and corresponding reduction in NO2) at sunset, which propagates to lower NO2 and ozone levels throughout the following day (Fig. S30 in the Supplement). Flux through this phenoxy–phenylperoxy system is highest from benzene because of the high phenol yields (Fig. 1), so the ozone differences between mechanisms are strongest for benzene (see Fig. S7). The reduced ozone formation in GC13 and MECCA relative to other mechanisms may improve model biases relative to chamber experiments and would likely reduce the high simulated contribution of aromatics to ambient ozone formation in box model analyses of polluted environments .

GC13 also simulates higher HOx concentrations than other mechanisms, especially under low-NOx (<1 ppb) conditions. This effect is due primarily to increased radical propagation from the bimolecular reactions of the bridged bicyclic peroxy radicals (Fig. 1c), which do not form radical-terminating hydroperoxides or organonitrates in GC13. Higher radical recycling from subsequent reactions of the representative C4 and C5 intermediates also contributes. It has been reported that other mechanisms tend to underpredict HOx concentrations in simulations of chamber experiments , and both and comment on the need to increase HOx recycling from aromatic oxidation; thus, GC13 brings HOx concentrations into improved alignment with chamber results. These effects are stronger for toluene and xylene because of their higher bridged bicyclic peroxy radical and C4–C5 intermediate yields relative to benzene (see Fig. S7).

To test the sensitivities of these outcomes to specific aspects of the GC13 mechanism, we conduct additional simulations with individually perturbed reaction rate constants and yields. Results from the sensitivity simulations with the most prominent changes are shown in Fig. 9. We find that ozone is most sensitive to changes in the rates of the key reactions in the phenoxy–phenylperoxy system. These rates remain uncertain; measured a rate constant of 2.86 (±0.35) ×10-13 cm3molec.-1s-1 at 298 K for the C6H5O+O3 reaction, which we use in GC13, but noted that it might be a lower limit, whereas did not set uncertainty bounds on their best fit rate constant of 7×10-12 cm3molec.-1s-1 for C6H5O2+NO2 (used in GC13), instead only specifying a minimum of 1×10-12 cm3molec.-1s-1 consistent with their results. Increasing either rate by a factor of 10 substantially increases ozone loss due to phenoxy–phenylperoxy cycling, highlighting the importance of better constraints on these rates.

Ozone and OH are both sensitive to changes in the fates of catechols and methylcatechols. While most mechanisms assume that the reactions of catechols and methylcatechols with OH proceed by abstraction to form functionalized phenoxy radicals, showed that addition pathways dominate, leading to heavily substituted low-volatility products that may contribute to SOA formation. In a sensitivity simulation with the product channels from catechols + OH turned off (representing complete loss of products to aerosols), the effects of aromatic oxidation on ozone production and OH are strongly diminished, as this represents a major loss of later-generation gas-phase products such as the C1–C3 carbonyls and C4–C5 intermediates. For both catechol and phenoxy–phenylperoxy perturbations, benzene is the most sensitive of the primary aromatics, due to its higher yields of the phenolic pathway than toluene or xylene (Fig. S25 in the Supplement).

HOx concentrations are also highly sensitive to the relative contributions of the radical propagation and termination pathways from the reactions of HO2 with the initial bridged bicyclic peroxy radicals from BTX + OH under low-NOx (<1 ppb) conditions. A perturbation simulation with 100 % radical termination (i.e., hydroperoxide formation), as in most mechanisms, reduces HO2 in GC13 to levels similar to the other mechanisms, and causes a smaller reduction in OH, highlighting the importance of this branching ratio. However, showed that hydroperoxide formation is minimal from the benzene-derived bridged bicyclic peroxy radical, motivating our treatment in GC13.

Results from additional sensitivity simulations are shown in Sect. S5 in the Supplement (Figs. S25–S29 in the Supplement). Changes to other reactions in the phenoxy–phenylperoxy system can be important – perturbations to the C6H5O+NO2 rate have similar effects to those of C6H5O+O3 because the two reactions are in direct competition, whereas increasing the C6H5O2+NO3 rate increases ozone loss in a manner similar to increasing the C6H5O2+NO2 rate, although peaking at slightly lower ambient NOx concentrations. Mechanism outcomes are only mildly sensitive to changes in the reactions of nitrophenols, a pathway implemented in GC13 and MECCA. Other novel aspects of GC13, including the 0 % yield of organonitrates from the reactions of NO with the initial bridged bicyclic peroxy radicals (following ) and increased HOx recycling from the benzoylperoxy radical + HO2 reaction, have minimal effects on ambient ozone and HOx.

To more deeply investigate the effects of BTX oxidation on atmospheric chemistry, we implement the GC13 mechanism into the GEOS-Chem CTM and compare it to other selected mechanisms in the GEOS-Chem environment. GEOS-Chem is driven by meteorology from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) assimilation product of the NASA Global Modeling and Assimilation Office (GMAO). We use GEOS-Chem version 12.3 (DOI 10.5281/zenodo.2658178) with added C2H4 and C2H2 chemistry as a base, which includes 196 species in its chemical mechanism (not including aromatic chemistry), of which 149 are transported. We run global simulations at a 2∘×2.5∘ horizontal resolution with 47 vertical layers. For each simulation, we perform an initial 8 month spin-up (1 March–1 December 2015), followed by 1 year of simulation from which seasonal and annual averages are output. We conduct one simulation with no aromatic emissions as a base case as well as one simulation with GC13 for comparison. For simulations with GC13 chemistry, Henry's law coefficients of newly included species (Table S1 in the Supplement) are taken from and for use in GEOS-Chem dry and wet deposition modules. The added aromatic chemistry in the GC13 simulation increases overall CPU time by an average of 1.7 % relative to the base simulation, attributable predominantly to gas-phase chemistry (64 %) and transport (26 %).

Anthropogenic VOC emissions in our GEOS-Chem simulations are from the Community Emissions Data System (CEDS) , overwritten with the Multi-resolution Emission Inventory for China (MEIC; ) and with the KORUS v5 inventory for the rest of East Asia . Biogenic emissions in GEOS-Chem are from the Model of Emissions of Gases and Aerosols from Nature (MEGAN) version 2.1 , and open-fire emissions are from the Global Fire Emissions Database (GFED) version 4 . Global annual emissions are 7.23, 10.42, and 7.30 Tg for benzene, toluene, and xylene from anthropogenic sources, respectively, and the corresponding values from open fires are 1.67, 0.88, and 0.26 Tg, respectively. No emissions of other gas-phase aromatics are included. Total BTX emissions are 60 % higher than in the global model simulation of but only 4 % higher (in carbon mass) than their total emissions of C6–C9 aromatics (including phenol, benzaldehyde, ethyl benzene, and lumped C9 aromatics).

We also implement the two simplest alternative mechanisms, RACM2 and MOZART-T1, in the GEOS-Chem environment. MOZART-GC and SAPRC-11 were previously implemented in GEOS-Chem by and , respectively, but neither were incorporated into the standard version of GEOS-Chem. Instead, aromatic chemistry previously implemented in the standard version of GEOS-Chem was simply parameterized to achieve reasonable glyoxal and methylglyoxal yields with fixed branching ratios for SOA and peroxyacetyl nitrate (PAN) formation . Comparison of GC13 to this parameterized GEOS-Chem aromatic chemistry and to MOZART-GC is shown in the Supplement (Sects. S7 and S8) for reference to past GEOS-Chem studies.

Figure 10 shows the impact of GC13 aromatic chemistry on concentrations of glyoxal, methylglyoxal, and formic acid in the lowest 1 km of the atmosphere. Aromatic oxidation increases the tropospheric production of these three oxygenated VOCs by 30 %, 5 %, and 9 %, respectively. Although absolute changes are strongest in source regions, the relative contribution of aromatic chemistry to these concentrations extends globally, due both to later-generation production and to the longer lifetimes of aromatics relative to other precursors (e.g., anthropogenic alkenes and isoprene). Changes to gas-phase acetic acid are similar but smaller in magnitude than those of formic acid, with aromatic chemistry increasing production by 5 %. Because these oxygenated VOCs are also formed in isoprene oxidation, the relative contribution of aromatics is much lower in high-isoprene regions, especially the tropics. In the Middle East, aromatics are responsible for >80 % of glyoxal because anthropogenic VOC emissions are high while biogenic emissions are low.

Our simulated contributions of aromatic oxidation to the budgets of glyoxal and methylglyoxal are substantially larger than in previous GEOS-Chem studies using simplified mechanisms with lower yields , but they are more consistent with results from the detailed mechanisms in and . The increased glyoxal yields in GC13 align with general model findings of negative biases relative to satellite observations of glyoxal columns in regions with strong anthropogenic influence . simulated even higher aromatic contributions to the global glyoxal budget, due in part to differences in the mechanisms (higher long-term yields of glyoxal and methylglyoxal from MECCA; see Figs. S3 and S4 in the Supplement) and potentially to differences in non-aromatic glyoxal and methylglyoxal sources between the models. Neither nor discuss the contribution of aromatic chemistry to gas-phase formic or acetic acid budgets.

Additional effects from aromatic chemistry on the global distribution of oxygenated VOCs are shown in Fig. 11. PAN is subject to competing influences: methylglyoxal formation from aromatic oxidation increases the source strength of the acyl peroxy radical, the organic precursor to PAN, whereas lower NO2 due to phenoxy–phenylperoxy cycling tends to decrease PAN production. The former effect dominates in source regions; aromatic oxidation with the GC13 mechanism increases PAN mixing ratios over Northern Hemisphere continents by up to 40 %. Downwind, particularly over oceans, PAN decreases due to phenoxy–phenylperoxy consumption of NO2.

Formaldehyde exhibits a similar spatial pattern to PAN, with competing effects from its direct secondary production via aromatic oxidation, leading to locally increased mixing ratios of up to 12 % from aromatic oxidation, and indirect decreases due to reduced OH, which dominates downwind. Global formaldehyde production changes by just -0.1 % from aromatic oxidation. GC13 increases the tropospheric CO burden relative to the simulation without aromatic chemistry by 3 %, due to both direct production and the decreased OH sink, with less spatial heterogeneity than other effects. The changes to both formaldehyde and CO are spatially consistent with the findings of using MECCA but are smaller in magnitude, driven primarily by the smaller change in tropospheric OH in GC13.

Figure 12 compares the effects of aromatic chemistry on oxygenated VOC concentrations in GEOS-Chem with GC13 to simulations with the MOZART-T1 and RACM2 mechanisms. The most prominent difference between the simulations is the higher overall yield of glyoxal from aromatic oxidation in GC13, especially in later-generation chemistry. This results in increases of up to 60 % in surface glyoxal when switching from either MOZART-T1 or RACM2 to GC13, with the strongest effects over the Middle East (where a lack of biogenic emissions renders aromatics the dominant glyoxal source) and in remote areas where later-generation chemistry dominates and decreases in OH increase VOC lifetimes. Overall tropospheric glyoxal loadings are 10 % lower in MOZART-T1 and 13 % lower in RACM2 than in GC13, whereas tropospheric glyoxal production from aromatics is 38 % lower in MOZART-T1 and 61 % lower in RACM2 than in GC13.

Methylglyoxal exhibits similar, although less pronounced, differences between the mechanisms, confined mostly to the Northern Hemisphere where its production is greatest. The strongest differences are seen for the MOZART-T1 mechanism, which produces only 62 % of the methylglyoxal from BTX that GC13 produces in global simulations, resulting in decreases in the surface methylglyoxal mixing ratio of up to 30 % (2 % globally). The RACM2 mechanism produces more methylglyoxal than MOZART-T1 (74 % of the amount produced by GC13 globally) and, therefore, exhibits smaller changes (up to 10 % decreases locally and 1 % decreases globally) and even some local increases. Differences in formaldehyde between the mechanisms are minor; global tropospheric formaldehyde is 0.4 % higher with MOZART-T1 and 0.8 % higher with RACM2 relative to GC13. The most prominent change is an increase of 5 % in boundary layer formaldehyde over Northeast China with GC13 relative to MOZART-T1.

The effects of aromatic chemistry on radical and ozone budgets are shown in Fig. 11. Impacts on HOx and ozone are consistent with the results of the continental boundary layer simulations in Sect. 4.3. HO2 is increased by aromatic chemistry by up to 20 % annually averaged in high-NOx aromatic source regions, but it exhibits little change (<0.1 %) globally. OH and ozone both increase in high-NOx aromatic source regions, by up to 6 % and 5 ppb, respectively, on annual averages but decrease elsewhere, largely due to phenoxy–phenylperoxy radical cycling. On a global scale, these decreases slightly dominate; aromatic chemistry reduces tropospheric OH and ozone by 2.2 % and <1 % (0.37 ppb) on annual average. The effect on OH has a strong seasonal variation (Fig. 13), with increases in source regions in the Northern Hemisphere winter – up to 24 % over Northeast China – but small effects and even slight decreases in the summer. This is due to the importance of carbonyl photolysis as a wintertime OH source .

NOx concentrations decrease everywhere as a result of aromatic chemistry, most notably in regions downwind of aromatic emissions in the Northern Hemisphere (Fig. 11). Although PAN and peroxybenzoyl nitrate can act as NOx reservoirs, releasing NOx in remote air, there are additional NOx sinks from phenoxy–phenylperoxy cycling and nitrophenol formation. The impacts of NO3 production from phenylperoxy + NO2 are particularly pronounced in downwind regions with very low NOx concentrations, where this pathway can increase annual average NO3 concentrations by up to 200 %.

Generally, these effects of aromatic chemistry on oxidants in GC13 are consistent with those from MECCA in , who also showed global decreases in NOx, OH, and ozone from aromatic chemistry, along with local increases and seasonal cycles for OH and ozone in areas of strong aromatic emissions, and strong increases in NO3. Global average changes tend to be stronger in , especially for OH and ozone, consistent with the sharper decreases in these compounds due to aromatic chemistry in continental boundary layer simulations (Fig. 8). By contrast, SAPRC implemented in GEOS-Chem by showed increases in ozone, OH, and NOx concentrations as well as decreases in NO3. We attribute this primarily to the absence of phenoxy–phenylperoxy cycling in SAPRC.

Differences in oxidant concentrations between global simulations with GC13 and with the MOZART-T1 and RACM2 mechanisms are shown in Fig. 14. Changes to ozone, HOx, and NOx between the mechanisms can be attributed primarily to the inclusion of phenoxy–phenylperoxy cycling and increased OH recycling in GC13. Surface ozone and OH are reduced in GC13 relative to MOZART-T1 and RACM2, consistent with findings in box model simulations (Sect. 4.3). Both MOZART-T1 and RACM2 cause increases in the tropospheric ozone burden relative to a simulation without aromatic chemistry, as also showed for SAPRC, whereas aromatic chemistry in GC13, like MECCA , causes a global ozone decrease. As an additional consequence of their lack of phenoxy–phenylperoxy cycling, the MOZART-T1 and RACM2 mechanisms simulate much lower tropospheric burdens of NO3 (by 18 % and 19 %, respectively) and slightly higher NOx burdens (by 3 % and 2 %, respectively) than GC13. Finally, higher OH recycling in GC13 leads to local increases in OH relative to MOZART-T1 (up to 4 %) in source regions, but globally, differences in tropospheric and surface OH are <2 %.