## 4 Discussion  
The dynamic model evaluation component of this study showed that, throughout much of the SoCAB, CMAQ can replicate the observed ozone response to the COVID-19 emissions reductions and changes in meteorology between 2019 and 2020 using the EPA's ozone projection procedures. The modeled ozone response was, however, generally weaker than observations. This is similar to the finding of [PERSON] et al. (2015), who found the CMAQ model to be \"stiff\" (i.e., underpredicted the ozone response). Our results of the evaluation of the COVID-19 impacts with CMAQ showed variable ozone response to NOx reductions across the SoCAB, with an approximately \(50\) km \(\times\)\(50\) km region in Los Angeles County that had average MDA8 ozone increases, and elsewhere throughout the SoCAB there were ozone decreases. This result agrees with regional modeling studies that use different air quality models and grid resolutions (e.g., [PERSON] et al. (2016), [PERSON] et al. (2017), and [PERSON] et al. (2018)), which found similar variable ozone responses with localized increases in urban areas. This suggests that this phenomenon is robust to different air quality models at differing spatial scales. This study is limited by a few factors: (1) relatively small COVID-19 emissions reductions (i.e., \(\sim\)13% NOx); (2) limited spatial and temporal resolution for the emissions adjustments (i.e., no day-of-the-week effect for on-road adjustments); (3) non-COVID adjusted boundary conditions (COVID adjusted boundary conditions would account for regional and global background changes in ozone and ozone precursors transported into the SoCAB via boundary conditions); and (4) uncertainties in emissions, in particular, VOCs and biogenics. Regarding the third limitation, [PERSON] et al. (2019) investigated the response of chemical species in the free troposphere during the COVID-19 pandemic using the CAM-chem model and found zonally averaged ozone to be 5-15% lower than 19-year climatological values. Their modeling successfully reproduced the observed ozone anomalies during the 6 months that followed the COVID-19 outbreak that were presented in [PERSON] et al. (2019). The 2020 anomaly compared to climatology was found to be primarily due to reduced air traffic, reduced surface emissions, and anomalous 2020 meteorology. It is unclear how potential longer-term trends were considered and accounted \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **Period-Average** & **Period-Maximum** & **Period-Minimum** \\
**AQS Number** & **Monitor Name** & **MDA8 Difference** & **MDA8 Difference** & **MDA8 Difference** \\  & & **(ppb)** & **(ppb)** & **(ppb)** \\ \hline
60371602 & Pico Rivera \#2 & 0.56 & 1.95 & \(-\)0.31 \\
60371701 & Pomona & 0.20 & 2.08 & \(-\)1.06 \\
60372005 & Pasadena & 0.55 & 1.98 & \(-\)0.21 \\
60375005 & LAX Hastings & 0.23 & 0.93 & \(-\)0.28 \\
60376012 & Santa Clarita & \(-\)0.67 & 1.53 & \(-\)1.70 \\
60590007 & Anaheim & 0.03 & 1.03 & \(-\)0.75 \\
60592022 & Mission Viejo & \(-\)0.26 & 0.74 & \(-\)1.00 \\
60595001 & La Habra & 0.29 & 1.15 & \(-\)0.32 \\
60650009 & Pechanga & \(-\)0.59 & 0.10 & \(-\)1.30 \\
60650012 & Banning Airport & \(-\)1.09 & 0.27 & \(-\)2.30 \\
60650016 & Temecula & \(-\)0.69 & 0.07 & \(-\)1.57 \\
60651016 & Morongo & \(-\)1.13 & 0.48 & \(-\)2.19 \\
60656001 & Perris & \(-\)1.16 & 0.10 & \(-\)1.98 \\
60658001 & Rubidoux & \(-\)0.50 & 0.72 & \(-\)1.38 \\
60658005 & Mira Loma & \(-\)0.42 & 0.90 & \(-\)1.31 \\
60659001 & Lake Elismore & \(-\)1.04 & 0.01 & \(-\)2.21 \\
60710005 & Crestline & \(-\)0.65 & 2.01 & \(-\)2.15 \\
60711004 & Upland & 0.10 & 1.88 & \(-\)1.29 \\
60712002 & Fontana & 0.05 & 1.72 & \(-\)1.18 \\
60714003 & Redlands & \(-\)0.76 & 0.75 & \(-\)1.74 \\
60719004 & San Bernardino & \(-\)0.58 & 1.27 & \(-\)1.69 \\ \hline \hline \end{tabular} Red indicates increases and blue indicates decreases in period-average MDA8 ozone  
\end{table} 
Table 3: _Cont._for over the climatological period.