Please see the attached document for the comments.

The authors develop a machine learning algorithm to infer surface NO2. The manuscript is well-written, and the results appear robust. I recommend the paper for publication after minor revisions.

General comments:

1. The authors state, “Currently, to our knowledge, there is little machine learning done to derive surface concentrations in less populated areas such as Canada.” While this points out the motivation of the work, it would be helpful to expand on why this is significant. What differences between more populated regions (e.g., China, Germany) and less populated regions (e.g., Canada) would justify the need for this study? This additional context could better motivate the work.
2. Section 2.4.2. I found it surprising that, in addition to NOₓ emissions, SO₂ and NH₃ emissions were included as predictors. Have you tested the sensitivity of your model to these additional species? It would be worthwhile to elaborate on why these variables are expected to play a role in predicting surface NO₂. In Figure 3, I observe that NO₂ emissions are far more significant compared to other parameters, including TROPOMI NO₂. This observation seems inconsistent with the conclusion that “This is a feature that we explicitly wanted to see in the random forest prediction, as this means the random forest function is primarily driven by the satellite measurements of NO₂.” Clarifying this potential discrepancy would strengthen the argument.
3. The authors mention that NO2 surface concentrations from the GEM-MACH model were used to augment the training dataset in remote areas. Would assigning greater weight to rural monitoring stations achieve a similar effect? If this has not been tested, it would be worthwhile to evaluate whether such an approach could improve the model's predictions for less populated regions.

Specific comments:

1. line 170. 1e14 shall be superscript.