This manuscript describes analysis of wildfire smoke events over the Baltimore-Washington region using multiple observations and trajectory model simulations. It brings together these multiple lines of evidence for four different events, and shows how there can be different transport regimes even for similar satellite signals.  The paper is generally well-written and presents interesting results; I think it will be acceptable for publication with relatively minor revisions.

Major Comment:  The manuscript focuses on 4 episodes, but contains no discussion of how these were chosen or how these 4 episodes fit into the variations for 2023 (or any other year).  Line 91 states “analysing four representative case studies for different plume-PBL interaction scenarios”, but I see no discussion of why representative.  The Results section starts straight away with the first case study.   There needs to be a discussion of how the 4 events were chosen. There also needs to be a discussion of how these episodes compare with the rest of the summer.  E.g., Are these the only 4 smoke events (using some criteria)?  How does the PM2.5 compare with the rest of the summer?  The think plots show a few quantities for each day of the summer would help, both introducing the events and also showing if other events.

Minor Comments.

1. I agree with comments in CC1, especially the length of trajectories shown and the PBL height.

2. Line 196. I am not sure “End of May 2023, the …” is correct grammar. 

The paper deals with the analyses of different smoke events transported to the Washington – Baltimore area. Intensive fires happened in Canada in late spring 2023 and therefore they are the main study cases, where discussion and analyses try to show the impact of these fires in air-quality for the measurement site in the Howard University Beltsville Campus. Most of the analyses are based on measurements by a Vaisala CL61 Ceilometer capable of providing lidar depolarization and attenuated backscattered at one wavelength. Additional measurements of nitrogen oxides (NOₓ), carbon monoxide (CO), volatile organic compounds (VOCs), and particulate matter (PM2.5) are presented to support the impact of transported smoke in air-quality. Main analyses are focused on identifying different transport patterns associated with different meteorological conditions and how they ultimately impact particle types in planetary boundary layer (PBL). This objective is of great importance and has potential for its publication in Atmospheric Measurement Techniques. But although the paper is generally well-written, I have some major concerns:

My first major concern is that authors do not identify appropriately the novelty of this study compared to previous studies in the same region. The Washington – Baltimore area is home of AERONET/Pandora networks, being the Howard University Beltsville Campus less than 15 km from NASA Goddard Space Flight Center where reference instruments of these networks are deployed with long datasets. There are many reference studies using these data (for example, look at Eck et al., 2013). Moreover, the station has been referenced for many multiwavelength lidar Raman developments and there are many studies. For example, Veselovskii et al., (2013) demonstrated how multiwavelength Raman lidar can retrieve aerosol optical and microphysical properties vertically resolved. Fire transport is not new – Veselovskii et al., (2015) study fire transport from California and how it impacts with the PBL. The DISCOVER-AQ field campaign served to study air-quality and how meteorological conditions and hygroscopic growth ultimately affect aerosol optical and microphysical properties (see Perez-Ramirez et al., 2021). I am discouraged because authors ignore all these studies, and I believe that it must be addressed.

My second major concern is related to the comments from the public discussions, which I support. I did not see clearly how the synoptic conditions changed during the entire study period. Backward-trajectories can give an overview of where air-masses come from, but they alone can not serve to interpret meteorological conditions. Authors need to give extensive explanations of the meteorological conditions supported by meteorological maps/charts. I encourage homogenizing the explanations as in the current manuscript there are maps for the first event (although only for 500 hPa with is not enough) while there is nothing for the other events.

My other major concern is about the use of lidar depolarization as the key parameter to study the transport of smoke particles. Apart from that authors do not specify if it is aerosol depolarization or total depolarization, this parameter only gives information of particle non-sphericity. Lidar community has largely used this parameter only for identifying aerosol non-sphericity (see articles from High Spectral Resolution Lidar developed in NASA Langley). I cannot accept references to Vaisala CL61 White paper (page 15, lines 336 and 339). Authors must acknowledge this limitation and base their study on aerosol attenuated backscatter that is related to aerosol load.

My last major concern is that discussion of smoke events is not well-addressed, taking into account the large databases publicly available. Indeed, it is based just in a satellite image. I strongly support the use of satellite aerosol retrievals that can give a quick overview of fires intensity. Also, if authors claim limitations in models they should compare the events with models such as MERRA-2 or CAMS. This has been done in previous studies for the study region (see Veselovskii et al., 2019). The large number of AERONET stations in North America can also serve to have a better picture of these extreme smoke events.

All these major concerns should encourage authors to review their conclusions. Particularly, statements such as ‘These findings advocate for the wider inclusion of

depolarization lidar in regional air‑quality networks and the assimilation of near‑real‑time LDR fields into smoke‑forecast systems (Lines 439-440)’ or ‘Depolarisation lidar proved indispensable for diagnosing smoke layers ' vertical and microphysical character (Lines 448-449).’ must be re-thought because LDR only serve for aerosol typing. I insist that for appropriate vertical aerosol optical and microphysical properties characterization multiwavelength lidar Raman are needed, and even these techniques require of case-dependent optimized-constraints (see Perez-Ramirez et al., 2019).

Minor Concerns

• Lines 44 – 45: Reference needed
• Line 48: Reference needed
• Line 65: Reference needed for the statement of uncertainties of smoke impact on PBL.
• Line 67: Please, specify which past studies.
• 1.1 – Study region: Give some coordinates for the region
• Line 110: Give reference.
• Line 131: LDR can not say anything about aerosol phase state. Please correct.
• Line 140: How temperature and relative humidity profiles were obtained ?
• Figure 1: Please, correct axis. It is difficult to read. Same happens in Figures 4, 6 and 8.
• Figure 1: I observe sub-figures with wind vectors. How were they obtained? Do you have wind profiles? Same happens in Figures 4, 6 and 8.
• Line 168: Referenced HYSPLIT appropriately.
• 2.5: Description of AERONET is vague. Which version of data are you using? What is data quality level used? Also, reference AERONET appropriately.
• Figure 3: Why did you use trajectory ensemble? If it provides clear added value. Why did you not use trajectory ensemble for the rest of cases?
• Line 238: Figure #? It looks like a error. Same for line 248
• Line 240: I insist that LDR alone only serves aerosol typing. Be careful.
• Line 245: Why is stabilization of the PBL typical in the evening?
• Lines 257 – 258: Angström exponent can only give information about possible predominance of fine or coarse mode. See studies from AERONET.
• Line 264: Give reference for NAQQS limits.
• Line 281: I do not understand the mention to GOES. Is it a typo?
• Line 301: With your lidar data alone you can not identify particle as fine mode predominance. See my major comments.
• Lines 309 – 310: The statement of the impact of clouds in PBL is vague. From your data, is there differences in PBL when these cirrus clouds are present?
• Case study 3: I can not understand the increase of air-quality related parameters If smoke is transported at altitudes above PBL and is decoupled.
• Lines 354 – 355: I can not understand your statement of pre-frontal winds role in facilitating vertical mixture.
• Lines 358 – 359: AOD is related to aerosol load. For better characterization of smoke properties you need to use AERONET inversion data.
• Lines 360 – 362: I can not see the impact of smoke on ozone suppression
• Lines 391: Please, provide reference.
• Line 401 – 402: You have not showed attenuated backscatter that is related to total aerosol load. LDR serves only for aerosol typing. This is related to my previous major comment
• Discussions and Conclusions: I recommend using a table that summarizes the main findings – i.e. statistics of different air-quality related parameters

References

• Eck et al., 2014: Observations of rapid aerosol optical depth enhancements in the vicinity of polluted cumulus clouds, Chem. Phys., 14, 11633–11656
• Perez-Ramirez et al., 2019: Retrievals of aerosol single scattering albedo by multiwavelength lidar measurements: Evaluations with NASA Langley HSRL-2 during discover-AQ field campaigns, Remote Sensing of Environment, 222, 144-164.
• Perez-Ramirez et al. 2021: Spatiotemporal changes in aerosol properties by hygroscopic growth and impacts on radiative forcing and heating rates during DISCOVER-AQ 2011, Chem. Phys., 21, 12021–12048, 2021
• Veselovskii et al., 2013: Retrieval of spatio-temporal distributions of particle parameters from multiwavelength lidar measurements using the linear estimation technique and comparison with AERONET, Meas. Tech., 6, 2671–2682,
• Veslovskii et al., 2015: Characterization of forest fire smoke event nearWashington, DC in summer 2013 with multi-wavelength lidar, Chem. Phys., 15, 1647–1660.