## 4 Discussion
This shows promise for night-time light data to also be used as a feature co-located with the satellite images. Moreover, the VIIRS data are available globally, which could help facilitate the process of transferring the model to different urban regions around the globe. Although we have demonstrated generalizability to a completely unseen city, we need to explore how well the model generalizes to a wide range of global urban environments in developed and developing locations with different distributions of both urban features and AQ. This study sets the standard for future work applying this model to other developed urban environments as well as examining the transferability of the model to developing cities that lack ground measurements. The ability to exclusively use satellite images to infer AQ on a local scale is novel to the AQ-modeling research community and can address some of the shortcomings of current modeling approaches, particularly in areas where historical ground data do not exist. The reason we were able to \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Study**} & \multicolumn{1}{c}{**Variable**} & \multicolumn{1}{c}{**Study Region**} & \multicolumn{1}{c}{**R\({}^{2}\)**} & \multicolumn{1}{c}{**RMSE**} \\  & & & & **(\(\mu\)g/m\({}^{3}\))** \\ \hline [PERSON] et al., 2006 [46] & PM\({}_{2.5}\) & NYC & 0.36 & Not reported \\ \hline [PERSON] et al., 2016 [47] & PM\({}_{2.5}\) & LA & 0.80 & 3.10 \\ \hline [PERSON] et al. 2013 [48] & PM\({}_{2.5}\) & \multirow{2}{*}{London} & 0.46 & 6.70 \\  & NO2 & & 0.50 & 43.50 \\ \hline \multirow{2}{*}{Current model} & PM\({}_{2.5}\) & \multirow{2}{*}{All cities *} & 0.86 & 1.78 \\  & NO2 & & 0.43 & 16.68 \\ \hline \hline \end{tabular} \({}^{*}\)Calculation performed between model estimates and ground monitoring data.  
\end{table} 
Table 3: Baseline model performance. use an image taken on a single day to predict the annual mean is because larger scale objects (e.g., buildings, roads, cars) do not change drastically from day to day or even over the course of months. Changes in larger features would generally happen on the scale of years. Therefore, contributions such as urban density and buildings correlated with industry, or infrastructure correlated with driving, would not change substantially over the imagery taken within a year. We recognize the need to train our model on additional years to be able to pick up changes from year to year and test the sensitivity of the model to annual changes. The scope of our project and funding is primarily to demonstrate a proof of concept that a CNN model can infer some types of air pollutants solely from satellite imagery. This paper presents a strong and feasible methodology to work with for future AQ-modeling efforts. Although satellite images are a promising source of data for generating estimates at high spatial resolution on a local scale, as they do capture some spatial variability, they are limited to generalize in certain areas. Future work should focus on combining additional datasets readily available globally (e.g., additional bands of satellite data, Normalized difference vegetation index (NDVI)/ Enhanced Vegetation Index (EVI), meteorology, Digital Elevation Model (DEM)) that can be combined with meter-scale satellite images to generate better estimates. In addition, future efforts will need to assess the sensitivity of image-based models to images collected with different temporal aspects such as time of day and season. The following are available online at [[https://www.mdpi.com/article/10.3390/atmos13050696/s1](https://www.mdpi.com/article/10.3390/atmos13050696/s1)]([https://www.mdpi.com/article/10.3390/atmos13050696/s1](https://www.mdpi.com/article/10.3390/atmos13050696/s1)), Figure S1: Deep Learning Model Architecture, Table S1: Common variables used in LUR models, Figure S2: VIIRS Night-Time Light over Manhattan [50,51]. Conceptualization, M.S.-H., M.V.P., A.S., A.A.A. and K.D. ; Data curation, M.S.-H. and M.V.P. ; Formal analysis, M.V.P., A.S. and A.A.A.