CMAQ-VBS CIOA concentrations averaged 0.65 µgm-3 (28 % of modeled OA) at Pasadena during the modeling period with a diurnal profile that was generally flat throughout the day and peaked at night. This is compared to an average AMS CIOA concentration of 1.22 µgm-3 (17 % of measured OA) and a diurnal profile that peaked in the afternoon and at night, slightly later than typical mealtimes and likely due to transport time . The AMS diurnal profile at Pasadena is consistent with AMS measurements from several major urban areas, including Barcelona, Beijing, London, Manchester, New York City, and Paris . CMAQ-VBS generally compared well to AMS measurements in the morning but underpredicted the afternoon peak by 3.8 × and evening peak by 2.8 ×.

To determine whether partitioning alone explained the underprediction in modeled midday CIOA concentrations, we considered two potential scenarios. In the first scenario, we removed model OA bias by replacing modeled OA with AMS-measured OA and then calculated the theoretical partitioning of modeled semivolatile CIOA vapors. Using the higher AMS OA concentrations, more semivolatile CIOA vapors partitioned to the particle phase and increased modeled CIOA concentrations by approximately 10 % in the afternoon. In the second scenario, we treated the modeled CIOA as nonvolatile (100 % of emissions in the particle phase). Model concentrations increased by 30–40 % and generally improved model performance (-32 % normalized median bias compared to -51 % in the semivolatile treatment). However, the modeled CIOA still underpredicted the afternoon and evening peaks by 2.9 × and 2 ×, respectively.

Even a nonvolatile treatment was unable to reproduce the measured peaks, suggesting the model CIOA emissions were low, particularly during afternoon and evening hours. This is expected since the 2011 emission inventory excludes residential meat cooking and the inclusion of these emissions would ameliorate some of the underprediction bias in evening hours and on weekends. To account for missing residential meat cooking emissions and potential underestimates in commercial meat cooking emissions, a doubling of CIOA emissions did well to reproduce the averaged measured value (1.28 µgm-3 modeled vs. 1.22 µgm-3 measured). However, the diurnal profile applied in SMOKE to the majority of CIOA emissions (profile 26) is a low-arcing profile that peaks at 15:00 LST (see Fig. S1 in the Supplement). Additional mass of emissions applied to this profile helped to capture average CIOA and improved underpredictions of the evening peak (lowered from 2.8 × to 1.3 ×) but overpredicted measurements in the morning and maintained the underprediction of afternoon AMS-measured CIOA (Fig. S2), suggesting some morning emissions should be reallocated to occur in the afternoon. It is also possible that some of the measured CIOA peak was due to photochemistry, as the afternoon peak coincides with the peak in AMS SV-OOA.

The highest observed CIOA value (8.9 µgm-3) occurred on 30 May at 20:00 LST, which corresponds to the Saturday of the Memorial Day weekend. Results from , who reported a high non-fossil fraction of OA at the CalNex site on 30 May, corroborate the AMS data. SMOKE/CMAQ emission processing does not allocate more emissions to holidays like Memorial Day for meat cooking, when a larger number of people grill meat and emissions are likely to be higher than normal. Therefore, we would not expect CMAQ to reproduce such events. When the CIOA measurements during the Memorial Day weekend were excluded, the midday and evening AMS peaks were reduced by 0.2 and 0.5 µgm-3, respectively, which corresponds to CMAQ-VBS underpredictions of 3.3 × and 2.2 × (or 2.5 × and 1.6 × for nonvolatile emissions and 1.4 × and 1 × for a doubling of CIOA emissions).

Given that the majority of both modeled and measured POA at Pasadena was attributable to cooking sources, further evaluation of the total CIOA emissions as well as the diurnal profile and volatility distribution applied to those emissions may help to improve POA model performance in urban areas.

Generally, CMAQ-VBS ncPOA results compared reasonably well against AMS HOA measurements (Fig. ) in total magnitude, particularly during morning and evening hours. However, modeled ncPOA was biased low, more so in the afternoon. The modeled ncPOA underpredicted the AMS HOA peak (which occurred at 14:00 LST) by a factor of 3 compared to underpredictions of 7–55 % (average of 31 %) during morning (00:00–10:00 LST) and evening (19:00–23:00 LST) hours. The modeled ncPOA peak instead occurred at night (likely due to the collapse of the planetary boundary layer), which did correspond to a measured evening peak offset by ∼1 h.

We calculated that when modeled OA concentrations were increased to match measured OA, partitioning of SVOCs increased ncPOA concentrations by 20 %. The 20 % increase in modeled ncPOA corresponded to a 2.3 × underprediction of afternoon ncPOA, with little change to morning and evening performance. When ncPOA was instead treated as nonvolatile, the model overpredicted AMS measurements in the morning and evening (by 1.5 to 1.8 ×) and underpredicted measurements in the afternoon (by 1.5 to 1.6 ×). The resulting diurnal pattern (Fig. S3) was higher in the morning and evening, with a minimum in the afternoon, similar to the more muted diurnal pattern of the semivolatile treatment (Fig. a) but opposite the AMS measurements (lower in the morning and evening, peaked in the afternoon).

While neither ncPOA volatility treatment captured the afternoon peak in measured ncPOA, the semivolatile treatment predictions during morning and evening hours suggest it to be the more appropriate model representation of the two. However, further considerations are needed to better account for the AMS-measured midday peak in ncPOA. The measured HOA peak followed a similar pattern to OOA both in the diurnal profile (Fig. ) and on an hourly basis (Fig. ), which may suggest that photochemistry served a role in the measured HOA peak as additional OA mass attributed to photochemistry could promote partitioning of semivolatile HOA to the particle phase. However, photochemical age and CO are correlated at this location due to the arrival of downtown LA plume in the early afternoon, so the observed correlation should not be over-interpreted. Alternative aging schemes to the approach used in CMAQ-VBS, such as those proposed by and , generally produce more OA mass than the scheme and if applied to primary SVOCs could better represent the ncPOA midday peak assuming the majority of aged primary SVOCs (i.e., oxidized POA) remains as primary SVOCs/POA. These alternative aging schemes may also degrade the morning and evening performance, though found the scheme performed reasonably well throughout the day.

Average CMAQ-VBS ncPOA concentrations were approximately a factor of 1.6 lower than AMS-measured HOA values at Pasadena (0.51 vs. 0.83 µgm-3). Increasing the CMAQ-VBS ncPOA emissions by a factor of 1.5 produced average modeled ncPOA concentrations (0.78 µgm-3) comparable to the AMS-measured HOA, though the model overpredicted HOA in the morning and evening and underpredicted HOA in the afternoon (Fig. S4). The factor of 1.5–2 underprediction in ncPOA and CIOA, respectively, is similar to the 1.6–2.3 underprediction attributed to low emissions or excessive dispersion for SOA at the Pasadena site (Sect. ). If the underprediction were entirely attributable to emissions, these results would suggest that the 2011 NEI underestimates non-cooking-related SVOCs by ∼1.5 × and cooking-related SVOCs by ∼2 ×, and therefore our SVOCs emissions are approximately 1.5 to 2 × lower than those estimated using measured HOA at Pasadena in . However, further work is needed to quantify the role of emissions vs. transport in CMAQ at the Pasadena site.

A source of uncertainty in the ncPOA results is the volatility distribution used for industrial, point, and area sources (i.e., “other” sources) which is based on measurements made from diesel generator exhaust . However, we assume that this has less impact on ncPOA predictions than missing emissions since the nonvolatile ncPOA treatment underpredicted the measurements and ncPOA from these sources only comprises 13 % of total modeled POA (or 25 % of ncPOA (Sect. )).

Similar to the routine measurement comparisons of total OC, CMAQ-VBS underpredicted AMS OOA (SV-OOA + LV-OOA) (Fig. b). This is consistent with many regional air quality studies , including CMAQ , which often underpredict urban SOA. Although those studies are not specific to LA, the similarity of tracer-normalized SOA concentrations across urban areas e.g., supports the occurrence of a general urban SOA underprediction with models. Other regional models that use high S/IVOC emissions to approximately match the observed POA do match or even exceed the urban observations (e.g., ), though we found this not to be the case in CMAQ-VBS. The diurnal pattern of CMAQ-VBS SOA is generally more consistent with measurements of SV-OOA compared to LV-OOA (Fig. ). The fact that LV-OOA is heavily oxidized and has relatively constant concentrations suggests that it is a background source, comprised of OA formed elsewhere and transported to Pasadena . Note, the diurnal profile of CMAQ-AE6 SOA (Fig. ) formed from particle oligomerization (a process not included in CMAQ-VBS) did follow a similar pattern to AMS LV-OOA (relatively flat throughout the day with a midday/afternoon bimodal peak) but model concentrations were significantly (∼40 ×) lower (Fig. S5).

CMAQ-VBS predicted considerably more SOA mass than CMAQ-AE6 (∼0.9 µgm-3 at Pasadena in CMAQ-VBS compared to ∼0.2 µgm-3 for CMAQ-AE6). Overall, CMAQ-VBS SOA diurnal concentrations were approximately 4 to 5.4 × lower than the AMS OOA, with the largest underestimate corresponding to the peak AMS measurement (13:00 LST). The underprediction could be attributed to low emissions, low photochemical age, excessive dispersion or too little transport of emissions to the Pasadena site in the model, or low intrinsic SOA production efficiency.

Comparisons of modeled and measured CO normalized for background CO (△CO, where △CO = CO - CObackground and modeled CObackground = 75 ppb; see for CO background measurements) show 300 ppb measured △CO vs. 150 ppb modeled △CO. This observation suggests CMAQ anthropogenic CO emissions, which are often used as a proxy for anthropogenic emissions, may be a factor of 2 too low, or alternatively that excessive dispersion and/or too low transport of emissions to Pasadena in the model results in the lower modeled CO (see Fig. S6 of the Supplement for CMAQ-VBS CO model performance). , who also used the 2011 NEI, reported a similar model underprediction (approximately a factor of 2) for total VOCs at Pasadena. However, reported the 2011 NEI-based SOA precursor concentrations were in relatively good agreement with measured values, though xylene and toluene were generally overpredicted, which could be attributed to underpredictions in photochemical age leading to insufficient xylene oxidation (e.g., at 0.1 day photochemical age, ∼75 % of emitted xylene would remain, but at actual ambient photochemical age a larger fraction would have reacted). CMAQ SOA precursor concentrations were a factor of 1.2 too low compared to 3 h measurements and a factor of 1.1 too high compared to 1 h measurements . The slight overprediction of SOA precursor concentrations, along with the factor of 2 underprediction of CO, suggests the SOA precursor to CO emission ratio was incorrect by a factor of 2. Comparisons of the ratio of xylene and toluene emissions to CO emissions in LA and Orange counties against observed xylene and toluene extrapolated to zero photochemical age (to account for photochemistry) to observed CO support this, as the emissions ratio (0.030) is approximately twice the observed ratio (0.014) and consistent with △CO being low by a factor of 2 in the model.

The role of photochemical age in underpredictions was explored at Pasadena by examining SOA formed (plotted as SOA / ΔCO to approximately correct for differences in emissions and dilution between times) in CMAQ-VBS vs. photochemical age (estimated using -log⁡(NOx / NOy); ) (Fig. ). The slope of the best fit line (66 µgm-3ppm-1) was low by ∼1.6 × compared to the measured value of 108 µgm-3ppm-1 . However, when the lower ΔCO in CMAQ is accounted for, the best estimate for the underprediction is 3.2 ×. Compared to the measured photochemical age (estimated by -log⁡(NOx / NOy)), the photochemical age component of CMAQ-VBS SOA was low by ∼1.5 ×, which helps explain part of the underprediction in SOA concentrations (Figs.  and ) but not underpredictions of SOA production efficiency (Fig. ) (i.e., the efficiency per unit precursor at a given age). For reference, Fig.  also includes the slope for CMAQ-AE6 predictions (8 µgm-3ppm-1), which was much lower than the slope for CMAQ-VBS and also much lower than observations for multiple urban areas .

Examining modeled SOA vs. odd oxygen (Ox ≡ O3 + NO2) , which leverages high measured correlations of SOA and Ox with generally good model performance of Ox (true for Pasadena during CalNex; ), the slope for CMAQ-VBS was 72 µgm-3 ppbV-1 (Fig. ). This is approximately a factor of 2 lower than observations at Pasadena (146 µgm-3 ppbV-1) , where measurements were comparable to other urban areas . In comparison, CMAQ-AE6 (which has identical Ox concentrations to CMAQ-VBS) underpredicted the metric by a factor of 16, again suggesting that while CMAQ-VBS underpredicts SOA, it does considerably better than the traditional CMAQ-AE6 SOA treatment. Note, in CMAQ-VBS sensitivity simulations without aging reactions (Sect. ) the slope of SOA vs. Ox (11 µgm-3 ppbV-1) was nearly equivalent to the slope of CMAQ-AE6 (9 µgm-3 ppbV-1). This indicates most of the CMAQ-VBS SOA mass was produced as a result of aging of SVOCs and is further discussed in Sect. .

Thus our analysis suggests that the SOA production efficiency in CMAQ-VBS is too low by 1.6 to 2 ×, photochemical age is too low by a factor of 1.5 ×, and the remaining underprediction (1.6 to 2.3 ×) is attributed to other factors (emissions, transport, etc.). Combining both underestimates of the SOA / ΔCO (1.5 × and 3.2 ×) implies that SOA concentrations should be too low by 4.8 ×, which agrees with the 5.2 × underprediction of SOA compared to AMS OOA.

One possible reason for the underestimation of SOA production efficiency in CMAQ-VBS (and CMAQ-AE6) is that CMAQ SOA yields do not account for SVOC wall loss, which indicated can reduce SOA production by 2 to 4 × in chambers. However, the factor of 4 is for alkane systems (speciated long alkanes are not considered SOA precursors in CB05) and toluene and is specific to the smog chamber used in . Other studies have generally reported lower values, ranging from 1.2 to 4.1 for low-NOx conditions and 1.1 to 2.2 for high-NOx conditions . Therefore, the 2–4 factor likely represents an upper bound and SVOC wall loss does not likely account for the entire underestimate of SOA production efficiency.

Another possibility for the underprediction of SOA in CMAQ-VBS is SOA formed from missing or mischaracterized (as unspeciated VOCs) IVOC emissions. There is significant uncertainty currently associated with IVOC emissions and their SOA yields. Current CMAQ-VBS IVOC emissions are scaled to primary SVOC emissions (1.5 ×) based on the results of a diesel generator and could potentially be updated to utilize more recent results, such as those reported by , who indicated unspeciated organics (S/IVOCs) dominated SOA mass formed from combustion emissions. Future work is needed to explore whether better constraining IVOC emissions and yields in CMAQ would help improve model performance, but it would likely not account for the entire missing SOA mass based on sensitivity simulations using upper-bound S/IVOC emissions. In these simulations, S/IVOC emissions were increased by 3.75 × (SVOC = 1.5 × POA to match HOA; IVOC = 1.5 × SVOC), 5 × (SVOC = 2 × POA to match CIOA; IVOC = 1.5 × SVOC), and 7.5 × (SVOC = 3 × POA; IVOC = 1.5 × SVOC) but CMAQ continued to underpredict both average (by factors of 4.4 ×, 3.7 ×, and 2.9 ×) and daily peak (by factors of 4.6 ×, 3.9 ×, and 2.8 ×) measured OOA. When the factor of 7.5 × is used, the model is in approximate agreement with the observations once the lower model photochemical age and low emissions/excessive dispersion are taken into account, which is consistent with previous modeling efforts for CalNex and elsewhere . However, the approximate agreement may be for the wrong reasons as increased S/IVOC emissions may account for SOA from other missing (or underrepresented) formation pathways and should not be over-interpreted as direct evidence of the presence of SOA formation efficiency of S/IVOCs.

Note that CMAQ-VBS does not include an oligomerization formation pathway in which heterogeneous/multiphase reactions form SOA . The lack of this pathway could account for underpredictions in production efficiency though it is plausible the SVOC aging parameterization already accounts for some of the mass formed through oligomerization. CMAQ-AE6, which does include an oligomerization formation pathway , estimates that approximately 20–25 % of SOA at Pasadena is comprised of oligomers (Fig. S5); however, because CMAQ-AE6 significantly underpredictions SOA, this equates to only a small amount of total SOA mass (0.06 µgm-3 on average).

Higher CMAQ-VBS predictions of POA from gasoline vehicles compared to diesel vehicles was true throughout southern California (Fig. ). Most POA was comprised of meat cooking POA, followed by POA from gasoline vehicles, “other” sources, and finally diesel vehicles. Note that the diesel vehicle panel in Fig.  required a scale an order of magnitude lower than the other sources. At Pasadena, POA was comprised of 48 % meat cooking, 18 % gasoline vehicles, 13 % biomass burning (in the form of residential wood combustion), 13 % “other”, and 8 % diesel vehicles. This further emphasizes the relative importance of meat cooking activities relative to mobile sources as well as gasoline vehicle emissions compared to diesel vehicle emissions. We note that the predicted urban POA has larger non-fossil than fossil fraction.

Of note was the limited contributions of gasoline and diesel vehicle POC emissions to total carbon at Pasadena, where fossil OC was dominated by ASOC (Fig. ). This result, coupled with the fact that the majority of ASOC precursor emissions originated from gasoline vehicles and point sources, suggests that gasoline vehicles dominated mobile source OC contributions .

As a sensitivity study, the aging of secondary biogenic SVOCs was turned on using the same oxidation pathways used for the aging of secondary anthropogenic SVOCs in CMAQ-VBS. That is, secondary biogenic SVOCs were aged by reactions with OH in the gas phase using a rate constant of 2×10-11 cm3molec-1s-1 and each aging step reduced the volatility by an order of magnitude. In the simulation with aging of secondary biogenic SVOCs, SOA concentrations were ∼0.5 µgm-3 higher throughout the day at Pasadena compared to simulations that did not age secondary biogenic SVOCs (Fig. ). The diurnal profile indicates aged biogenic SOA concentrations were essentially constant throughout the day, which is the same pattern as AMS LV-OOA. A scenario where LA basin biogenic SOA precursor emissions were zeroed out indicated almost all (95 %) of the predicted biogenic SOA originated from outside the basin, which is consistent with .

The additional non-fossil carbon mass from biogenic SOA would help to close the gap in the modeled vs. measured non-fossil carbon at Pasadena. Furthermore, the additional SOA mass improved overall OC model performance at routine monitoring network sites (Table ) comparable to, if not better, than CMAQ-AE6 model performance. Monoterpene concentrations were underestimated at Pasadena , although biogenic VOCs emitted in the LA basin make a very small contribution to SOA in Pasadena. Rather, biogenic VOCs emitted in the Central Valley and surrounding mountains are thought to be the major source of biogenic SOA observed in the basin . CMAQ-VBS could potentially overestimate biogenic SOA if the underprediction of monoterpene emissions applies to other areas of California. Further evaluation of the impacts of biogenic SOA aging are needed, particularly in areas dominated by biogenic SOA, such as in the southeastern USA.

Figure  also provides the contribution of the three standard SOA formation pathways in CMAQ-VBS (VOCs, IVOCs, and aging) to predicted SOA concentrations at Pasadena. These were estimated using sensitivity simulations without IVOCs, aging, or both and then taking the difference between results from the various scenarios. The results indicate the majority of SOA was formed from aging, representing a technique to increase model SOA yields. Although via a different process, the resulting outcome is similar to that obtained whether SOA yields are increased to account for SVOC losses to chamber walls, as proposed by  and used with CMAQ-AE6 in . Also, although the inclusion of aging reactions leads to an increase in SOA concentrations, the model parameterization may overemphasize the contribution from aging as recent model-to-measurement comparisons with chamber experiments suggested the addition of aging reactions on top of existing parameterizations can lead to overpredictions of SOA concentrations . CMAQ-VBS predicted comparable SOA (considering first generation only) from VOCs to CMAQ-AE6, which one would expect given that they produce comparable SOA yields (see Figs. S9–S15 of the Supplement for SOA yield curves). However, the inclusion of higher-volatility semivolatile products (C∗ of 100 and 1000) provides high yielding points along the yield curve missing in Odum two-product framework of CMAQ-AE6. Thus, CMAQ-VBS transfers more mass from VOC precursor to semivolatile oxidation product but requires the aging process to lower the volatility of the semivolatile product to the point of condensing to form SOA.

Given the limitations in CMAQ-AE6 and CMAQ-VBS to accurately predict SOA at Pasadena and uncertainty about how best to improve predictions, the question is raised as to whether other parameterizations can improve CMAQ performance in the near term. To this end, we have applied a simplified SOA parameterization (SIMPLE) in CMAQ to provide an alternative SOA modeling budget for comparison with AE6 and VBS. SIMPLE was originally developed by and recently shown to perform well in predicting anthropogenic SOA at Pasadena . A key goal of the parameterization is to provide a quick way to estimate the amount of anthropogenic SOA formed from pollution sources, especially for studies in which mechanistic SOA formation description is not the goal, but having the correct amount of aerosol present is important for the results of the simulation. It can also serve as a simple-to-implement benchmark to compare more complex parameterizations across different models. The parameterization uses a single SOA precursor (VOC∗) scaled to CO emissions which reacts with OH. The oxidation product is treated as nonvolatile. In our implementation in CMAQ-VBS, we use an emission rate of 0.069 gVOC∗g-1CO and a kOH = 1.25 × 10-11 cm3molec-1s-1, based on the optimum values for Pasadena reported in . found that the SIMPLE parameterization compared favorably to measurements and VBS box model results at Pasadena.

SIMPLE predicted more anthropogenic SOA mass than CMAQ-VBS (2.5 × more at the afternoon peak) following a similar diurnal cycle (Fig. ). However, it still underpredicted the AMS-measured SV-OOA by a factor of 2.3 × at the afternoon peak. The slope of SOA / ΔCO vs. -log⁡(NOx / NOy) for SIMPLE was 113 µgm-3ppm-1, which was slightly more than the measured 108 µgm-3ppm-1 and suggests that the SIMPLE parameterization is performing as expected and has an intrinsic SOA formation efficiency consistent with the observations. Underpredictions of photochemical age and low emissions/excessive dispersion most likely explain the observed difference, as CO was underpredicted both in this study (see the Supplement) and in . The factor of 2 difference in modeled vs. measured CO indicated in Sect.  are similar to the 2.3 × underprediction in SIMPLE. This shows that the use of SIMPLE in a model can help diagnose model problems that are unrelated to the model intrinsic SOA formation efficiency. CO inventories can also be estimated from ambient data (e.g., ), providing an alternative to bottom-up inventories.