The authors present a long time series of SO₂ observations using active DOAS instruments at two measurement sites in Shanghai. The first measurement site is located at a river, while the second one is an urban background site. During the observation period, the SO₂ emissions of ships were restricted twice and the changes on the ambient SO₂ levels as a result of these changes were evaluated and interpreted. In order to interpret the measurements, two machine learning models were used to first interpolate data gaps and then to eliminate the influence of different weather conditions on the measured SO₂ levels. The manuscript is generally well written and of high interest for scientists and policymakers, but I would suggest some improvements before publication in ACP.

General comments:

1. I would highly recommend adding some more explicit information how ship traffic changed and evolved at the measurement site during the years, e.g. average number of ship passages per year and the composition of ship types throughout the years. Changes in ship traffic density or fleet composition are often mentioned and used for interpretation of results, but never explicitly shown to the reader. Figure 8 somewhat reflects this, but only for ships where the plumes were captured with the DOAS instrument.
2. I would suggest adding Figure S6 of the Supplement to Figure 6 because it’s an important piece of information.
3. How are ship emissions treated in the machine learning gap-filling algorithm? Does the gap-filling only reproduce the baseline SO₂ signal from other sources than ships? Can you provide a comparison of the result of the gap-filling algorithm with measured data?
4. Also, in the supplement it looks like, there were almost no measurements at WSW in 2020 and from July 2022 to July 2023, how does this influence the results?
5. What is the main wind direction at FDU and WSW? Even though FDU is a background station I would assume ship traffic could influence the SO₂ signal at this station, when the wind blows somewhat from the direction of the river.
6. Could you elaborate a little bit on what measures the ships can use to reduce SO₂ emissions in this control area (e.g., change of fuel to lower sulphur fuels, scrubbers, …)?

Specific comments:

L159: If these differences are caused by irregular ship traffic, this should be assessable in the AIS data and should be shown (as already mentioned in general comment 1).

L173: Was there a strong reduction in ship traffic in 2020 due to COVID19 compared to the other years? Is this decrease in WSW data maybe influenced by the lack of observational data in 2020?

L196 to L199: FDU shows a decrease and stabilization at a lower level, while WSW shows a decrease and then increases again in 2022 and 2023. Please clarify.

Add Figure S6 to Figure 6, because it is an important piece of information for your reasoning.

Technical corrections:

L12: Zhou should be capitalized.

L125: please add a reference for the ERA5 dataset.

Figure 4: here CDECA is mentioned, but this is not mentioned or explained anywhere else, please clarify. Also, there is a typo in “low-sulfur fuel oil” right before “CDECA” in this Figure.

L250: Please verify 2023, I think it should be 2021.

Supplement:

"𝑚𝐿𝐹/𝑎𝐸𝐹: Main engine/auxiliary engine emission factor, g/kWh", I think mLF  needs to  be changed to mEF.