## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**City** & \multicolumn{2}{c}{**PM2.5 Model**} & \multicolumn{2}{c}{**NO2 Model**} \\ \hline  & \
\begin{tabular}{c} **RMSE** \\ **(\(\mathrm{\SIUnitSymbolMicro g/m^{3}}\))** \\ \end{tabular} & **NRMSE** &
\begin{tabular}{c} **RMSE** \\ **(\(\mathrm{\SIUnitSymbolMicro g/m^{3}}\))** \\ \end{tabular} & **NRMSE** \\ \hline Los Angeles (LA) & 1.495 & 0.743 & 4.605 & 0.480 \\ \hline Vancouver & 1.967 & 0.592 & 4.234 & 0.987 \\ \hline London & 1.709 & 1.192 & 6.647 & 0.551 \\ \hline New York City (NYC) & 1.902 & 1.499 & 20.199 & 1.776 \\ \hline Combined (just training cities) & 1.64 & 0.321 & 4.925 & 0.165 \\ \hline Combined (all cities) & 1.706 & 0.484 & 11.107 & 0.682 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: PM2.5 and NO2 model performance. Figure 3: NO2 target data, model estimates and errors. NO2 target data (**a**), based on 2010 LUR data in Los Angeles, Vancouver, and London. Column (**b**) shows the 200 m model estimates of NO2 annual concentrations using the testing dataset (10% of the available data for all cities). Column (**c**) shows the model residuals calculated by the difference between the predicted concentrations and the target data, and the residuals’ distributions are shown in column (**d**). Here, we used the same imagery input from all study regions with an adjusted patch size of 200 m \(\times\) 200 m, derived from the target data resolution, to estimate NO\({}_{2}\) annual concentrations. We obtained results with a reasonably strong Pearson correlation between the predicted concentrations and target data (LUR) NO\({}_{2}\) concentrations for all training locations of R = 0.95, and a low RMSE \(<\) 6.7 \(\mu\)g/m\({}^{3}\) (Figure 3; Table 2). These results demonstrate a robust model. The model explained 91% of the variance in the data using solely the satellite imagery as input to the model. The model clearly captured patterns in the spatial distribution of NO\({}_{2}\), which are largely affected by local sources, such as traffic, as seen in the results (Figure 3). However, the model underestimates highly polluted roads, and overestimates green areas with very low pollution--similar to the PM\({}_{2.5}\) model results. Although London city center is well captured by the model, its surrounding road networks are underestimated to a level of 15 \(\mu\)g/m\({}^{3}\). This may reflect the greater proportion of diesel vehicles and higher levels of congestion in London compared to the other training cities [41]. LA and Vancouver show better results with more evenly distributed model residuals. * Validation Stage 1: The model performed well (see Figure 4 and Table 2) but lacks some of the feature specificity the trained cities have. The model clearly underestimates pollution over Central Park, which is an atypically large 'green lung' located in the middle of Manhattan--a bustling and densely populated borough of NYC. In the satellite images, these 'green lungs', which are large, green areas surrounded by high levels of human activity, look virtually indistinguishable from green areas outside the city, especially for small patch sizes. Stage 2: Figure 5 shows the correlations between the estimated annual PM\({}_{2.5}\) and NO\({}_{2}\) concentrations and the observed annual mean concentrations at co-located ground monitoring sites with a total R\({}^{2}\) of 0.86, 0.43 and RMSE of 1.78, 16.68 \(\mu\)g/m\({}^{3}\), respectively. We obtained data from 33 sites for PM\({}_{2.5}\) and 31 sites for NO\({}_{2}\). Figure 4: PM\({}_{2.5}\) and NO\({}_{2}\) target data, model estimates and errors for NYC. Target data (**a**), based on 2010 LUR data in New York City. Column (**b**) shows the 100 m, and 200 m model estimates of PM\({}_{2.5}\) and NO\({}_{2}\) annual concentrations, respectively. Column (**c**) shows the model residuals calculated by the difference between the predicted concentrations and the target data, and the residuals’ distributions are shown in column (**d**).