We applied the MLR model to CAMx source apportionment NO2 sectoral output and GCAS NO2 data, as shown in Equations 1 and 2, to estimate scaling factors for the sectoral NO2. All median scaling factors were between a value of 0.5 and 2.5 as shown in Figure . A median value below 1 indicates the sector needs a NOx decrease, while a median value greater than 1 indicates the sector needs a NOx increase. The box-andwhisker plots represent the uncertainties of the scaling factors quantified by bootstrapping on two different levels. The most important level for bootstrapping was randomly selecting, with replacement, the GCAS rasters included in the optimization. For the full time series, there were 27 rasters over 10 days. In addition to performing the simulations for these 27 rasters, we performed 100 simulations with random selections of the 27 rasters. The second level for bootstrapping was to randomly select grid blocks within each raster for use in the analysis. We randomly select 7 x 7 blocks of cells within the CAMx grid cells and include them until we have the same number of points as in the initial grid. We did this 100 times for each selection of rasters, leading to a total of 10,000 simulations.