## 4 Discussion  
The goal of our study is to estimate hundred-meter-scale PM\({}_{2.5}\) and NO\({}_{2}\) concentrations in different urban environments. Our approach advances the science by (i) introducing a novel approach to the earth science community by applying deep learning to solely very high-spatial-resolution satellite imagery for AQ estimation, (ii) developing fine-scale continuous AQ maps, and (iii) creating an efficient approach for high-spatial-resolution AQ monitoring independent of ground monitoring and ancillary data. Although we have  
Figure 6: Feature contribution analysis (**a**) Hypothesis of expected changes in PM\({}_{2.5}\) concentrations (ug/m\({}^{3}\)) when adding different urban features to original imagery; (**b**) Confusion table following the hypothesize in subfigure (**a**) using satellite images with and without synthesized residential, greenery, and road features. The columns represent the original satellite images of greenery, road, industrial, and residential from left to right. From top to bottom, the rows are partial residential, greenery and road images to be overlayed on original images. We demonstrate the differences of PM\({}_{2.5}\) levels between the original and synthesized images on the right hand of each subplot. The results in subfigure (**b**) are in line with our hypothesis demonstrated in subfigure (**a**) and support the conclusion that our model is learning the non-linear relations between urban objects and air pollution concentrations. compelling results, we recognize some of the limitations of our model and discuss methods to address them. The existing literature on satellite-based AQ estimation incorporates many variables in the modeling scheme, including meteorology, traffic, topography, and distance from emission source [15; 16; 18]. The model by [46] reported 36% explained variation in PM\({}_{2.5}\) in NYC, with a coarser (kilometer-scale) spatial resolution. Other models reported a PM\({}_{2.5}\) prediction RMSE of 3.1 \(\mu\)g/m\({}^{3}\) in the LA region [47], and PM\({}_{2.5}\) (NO\({}_{2}\)) hourly prediction RMSE of 6.7 (43.5) \(\mu\)g/m\({}^{3}\) in London [48]. Our model results, trained and validated on modeled LUR data, improve current modeling efforts in all study areas (Table 3). The models exhibit reduced error and produce high-spatial-resolution predictions, while significantly decreasing the amount of input datasets required. Certain features of the urban environment demonstrated a clear effect on the air quality prediction (e.g., greenery, roads). Nevertheless, changes in air pollution can be related to urban features that cannot be recognized by the image-analysis model (e.g., the use of electric cars, winds, and temperature). These features may also affect the physical relationship between the measured air quality variables and the image reflectance values. Furthermore, the model underestimates PM\({}_{2.5}\) and NO\({}_{2}\) concentrations in certain urban areas that lack context--the aforementioned \"Central Park problem'. The model perceives Central Park only as a green area, without any context of the human activity in the areas surrounding it. This causes it to under-predict air pollution over that area. Incorporating the \"bigger picture\" context into the model is expected to resolve this issue. We examined the use of satellite-based night-time light data retrieved from the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument onboard the Suomi-NPP satellite [49] as a means for incorporating context-based on human activity into the model. The VIIRS night-time light product over NYC was analyzed as a potential additional input to the model. This dataset can detect the large, bright areas around Central Park that represent its location with context (i.e., in the heart of urban activity) (Figure S2). Apart from adding larger-scale context to the model, it was also observed that the VIIRS night-time light data demonstrated a high pixel-to-pixel Pearson correlation coefficient of R = 0.64 with PM\({}_{2.5}\) over NYC.