###### Abstract
[8] performed a global modeling study of the Global Impact of COVID-19 restrictions on the surface concentrations of nitrogen dioxide and ozone using a machine learning algorithm that is trained to predict and correct for the systematic (recurring) model bias between hourly observations and the collocated model predictions. These biases can be due to errors in the model, such as emission estimates, sub-grid scale local influences, or meteorology and chemistry. They found the COVID-19 ozone response to be complicated by competing influences of non-linear atmospheric chemistry and to be dependent on season, time scale, and environment. Studies that focused on ozone in the SoCAB include [PERSON] et al. [9], who studied the impacts of the 2020 COVID-19 shutdown measures on ozone production in the Los Angeles basin. Their analysis was restricted to March and April of 2020 when daily activities were most restricted, and emissions reductions were greatest. This period generally has lower ozone concentrations compared to mid-summer and are of lesser importance as regards defining ozone attainment control plans. They found mixed ozone results, with overall increases in the upwind site at Pasadena and decreases in the downwind site at Crestline. [PERSON] et al. [10] studied the impacts of traffic reductions associated with COVID-19 on Southern California Air Quality and found an overall reduction in NOx across the basin and moderate levels of mixed changes in O\({}_{3}\) concentrations, which suggests to them that additional mitigation approaches besides on-road NOx emissions reductions will be necessary to comply with air quality standards. The common thread in these studies is that NOx decreased in response to COVID-19 restrictions and O\({}_{3}\) showed mixed results. The O\({}_{3}\) response was generally location dependent and increased within urban regions, which is consistent with the expected ozone response to reductions in NOx within VOC-limited regions. In our study, we use the COVID-19 emissions reductions for two purposes:To perform a dynamic model evaluation of the Community Multiscale Air Quality (CMAQ) [11] modeling system to evaluate whether CMAQ correctly predicts the ozone air quality response to the COVID-19 emissions reductions for the SoCAB;
2. To assess the impact of the COVID-19 emissions reductions on ozone in the SoCAB with the CMAQ model. Dynamic model evaluations are recommended by [PERSON] et al. [12] as one of four types of evaluations within their proposed model evaluation framework. The other three types of evaluations are: operational, diagnostic, and probabilistic. The operational evaluation has been standardized, and there is an Environmental Protection Agency (USEPA) Atmospheric Model Evaluation Tool (AMET) [13] that aids with operational model evaluations. The EPA also states that a dynamic evaluation is always recommended but acknowledges that the needed measurements and resources may not be available and that there are additional challenges, such as identifying appropriate case studies [14]. The COVID-19 emissions reduction scenario is such a case study because there was an abrupt emissions change over a short time scale and the requisite meteorological and air quality observational data are readily available. Thus, this real-world experiment can provide insight into whether the CMAQ model--which is used to predict future-year ozone air quality in regulatory planning--can correctly simulate the ozone response to the emissions reductions. A correct response is critical to ensure that appropriate control measures are applied to attain the National Ambient Air Quality Standards (NAAQS). [PERSON] et al. [15] recently performed a dynamic model evaluation of the CMAQ model for the SoCAB region. They used two recent South Coast Air Quality Management District (SCAQMD) databases, used to define ozone control plans for the SoCAB, to evaluate the ability of CMAQ to reproduce the observed changes in ozone from 1990 to 2014/2015.