## 3 Results
Figure 5: Correlations between observed and predicted PM\({}_{2.5}\) and NO\({}_{2}\) concentrations. Correlations between observed PM\({}_{2.5}\) (**upper panel**) and NO\({}_{2}\) (**lower panel**) concentrations at ground monitoring sites and predicted values based on our model; the dashed line is the 1:1 line and the black line is the regression line with 95% confidence intervals (R\({}^{2}\) = 0.86, 0.43, and RMSE = 1.78, 16.68 \(\mathrm{\SIUnitSymbolMicro m}^{3}\), respectively). Only sites that had co-located model-estimated values within a distance less than 500 m were included. For London, daily station data was used to calculate annual means of measured pollution over ground stations that had at least one measured value every month. For the other cities, annual means of measured data were used for the stations that had collected data for over 50% of the year. * Model Interpretability  
To further examine the main hypothesis that urban features visible from high-spatial-resolution satellite images directly correlate with pollution levels, we analyzed the effect of different urban features on the value of the prediction. We wanted to investigate whether inputs (e.g., roads, trees) to the LUR models (whose outputs were used as labels) are being captured by the deep learning models. Neural networks are known to often be black-box models with learned features that cannot be interpreted easily [42; 43; 44]. However, recent advances in machine learning have made it possible to gain insights into some types of neural networks, such as CNNs. We know that certain urban features, such as roads and industrial objects, are correlated with high pollution levels. Conversely, green areas and residential blocks are associated with lower pollution levels [45]. To test the effect these urban features have on the pollution estimate, we created synthesized images by adding urban features on top of existing imagery, and we used these synthesized images to examine how these added features affected the model estimate (Figure 6b). We started by randomly selecting different images that have urban features consisting of greenery, road, industrial and residential objects. Then, we synthesized satellite images with urban features associated with low and high pollution levels (i.e., artificially adding image features over existing images) with examples in Figure 6b. Each row/column in Figure 6b represents an urban feature category and the intersection of each row and column shows a synthesized image combining those features. The differences between pollution concentrations of original and synthesized images predicted by the model are indicated on the right hand of each subplot in Figure 6b. We fed the original and synthesized images into the trained model and analyzed the difference in pollution levels predicted by the model. In Figure 6a, we hypothesize the expected changes in PM\({}_{2.5}\) concentrations between the original imagery and the synthesized imagery. For example, we expect that, for all categories of features, adding greenery features will lower the concentration. The correspondence between the expected changes (Figure 6a) and the interpretability of the results (Figure 6b) supports the hypothesis that our model learns the non-linear relations between visual urban features and their corresponding pollution levels. The overall results indicate the trained model successfully learns correlations between visual urban features and pollution levels and are in line with our physics-backed understanding of urban feature compositions.