The submitted manuscript by Pikmann et al introduces a new, automated offline sampler for both aerosols and gases called the AERTRACC, which is part of the mobile laboratory platform MoLA. The stated aim in its design is to allow for more efficient source separation when sampling complex source environments, specifically for the analysis of organic aerosol (OA) and their precursors. The manuscript describes the hardware and control software in detail, and then demonstrates its operation with a (static) single source experiment. That includes an in-depth analysis of gas and particulate phase tracers detected by their analyzer of choice (an TD-CIMS) and the source-specific contrast ratios observed. The authors conclude with a detailed discussion of the optimal choice of input parameters to control the sampler, in order to increase source contrasts for this particular case. So while there is no data shown from actual mobile sampling, the reader is reasonably reassured that it will in fact work for that scenario.

The overwhelming majority of offline samplers operate on some type of predefined time-grid, which leads to different airmasses and hence sources and chemistries being integrated into one sample. For mobiles sources where air mass variability is higher this can limit the usefulness of offline analysis significantly. Hence, on airborne platforms, most offline samplers (such as whole air samplers) are operated based on some type of expert assessment of airmass change, which in (some, rare) cases is automated. While aircraft payloads are highly variable from mission to mission, that is obviously not the case for the MoLA platform, which has a well-defined instrument payload. Therefore, it makes sense to not only build a “smart” offline sampler for MoLA, but to automate it based on all the possible ancilliary measurements available. So while this type of sampling has been done before, this paper shows a technically proficient, well-executed implementation of it for a platform that with certainly make good use of it in the future. So this is well within the scope of and of general interest to the AMT audience. However, I have some concerns about the way the work is framed and about the details of the analysis, which are described in the following:

• Unless I am completely misunderstanding the work presented, the key aspect of this sampler is that it switches between “source” and “background” conditions many, many times over the course of the experiment. This is quite different from most offline samplers were only one continuous sample is taken. I would recommend making this much clearer in the description, and possibly illustrating this by e.g. showing the actual sample times on top of Figure S5. It would also be good if the authors discussed what the minimum sample time (per valve switch) actually is, and how this could possibly impact the overall performance once MoLA is actually moving and hence the source sector segments become smaller.
• Related to the last item, can the authors please discuss what the limitations are on leaving the “source” channel idle for long periods between source interceptions? My specific concern is that any type of filter substrate has issues with loss of volatiles (e.g. Heim et al, 2020), and while during active sampling these “losses” will likely end up in the TDTs, once the line is idle they will simply evaporate and possibly diffuse/deposit to the “background” channel. And there is also, specifically for CIMS measurements, obviously the oligomerization problem (e.g. Lopez-Hilfiker et al, 2015), which should at least be acknowledged.
• I do not think that the current introduction does the best job in framing the significance and applicability of AERTRACC. First of all, I think the sampler needs to be separated more clearly from the specific application (in this cases, molecular OA composition). So by way of example, the current AERTRACC, with a simple change in filter media would surely be of high interest to single-particle TEM folks, a field where optimizing contrast for the source-specific aerosol types of interest (and not necessarily organic ones) can be extremely challenging. So framing AERTRAC as an efficient way of solving the contrast issue for any type of off-line analyzers will better illustrate its potential.
• More importantly, this statement (L79-80) “Currently, no instrument offers detailed chemical analysis of aerosols in real-time for the analysis of individual sources (Parshintsev and Hyötyläinen, 2015)” which is the linchpin of the current introduction, is simply incorrect. There are real-time, 1 Hz capable molecular OA detectors, such as the EESI-ToF (Lopez-Hilfiker et al, 2019, Pagonis et al, 2021) or the CHARON-PTR-MS (Eichler et al, 2015, Piel et al, 2019). None of them are perfect (no instrument is), but the point is that molecular identification of OA from mobile sources can be accomplished in many different ways, and AERTRACC+TD-CIMS is just one of many, and not really the main reason (in my view) for AERTRACC.
• What both the introduction and the rest of the paper do not discuss are detection limits, which are typically one of the main design criteria for samplers. This needs to be addressed, since currently there is not context whatsoever on what a reasonable total sample time would be (and hence how practical the use of AERTRACC is for typical urban source applications). Based on the description, under typical polluted urban conditions (~10 ug m-3 OA), it would take about 2 hours to sample 1 ug of material, which given typical DLs for organic compounds in the FIGAREO (~0.5 ng sm3 from filter blanks) seems sensible. But that seems like a long sampling time for a local source, so a discussion of the possible tradeoffs would be appreciated.
• The (nicely done) PMF analysis of the AMS measurements seems a bit underutilized at the moment. Its sole purpose currently is to show the wind dependence for the OA sources, which could certainly be showcased with less work. Obviously it is an ancillary measurement, and not part of AERTRACC, but Figure S1 shows a lot of high-time resolution, high contrast data that could be certainly utilized to assess what ideally AERTRACC can achieve. So I would encourage the authors to add the PMF factors to Figure 5 (and Tables S3-S5), and the sampling periods to Figure S1, which should make current trends clearer, at the very least. In that context (assuming the AMS is a regular instrument on MoLA), the authors could also consider adding some discussion of to what extent the ancillary variables used in the last section could be augmented by using the f43, f55, f57, f60 from the AMS real time feed.

Minor comments:

• The manuscript often uses “aerosol” when it really means “aerosol type”, suggest rephrasing
• L29: “Within multiphase processes aerosol interacts with atmospheric gases forming new substances”. While this is certainly true, in many (most) cases the chemistry happens in the gas phase and the products end up in the particle phase by simple partitioning, so consider rephrasing.
• L36: Seasalt (as well as dust) is by far the largest primary aerosol type by mass, consider revising
• It’s a minor point, but I find the distinction between the FIGAREO-CIMS and the “TD-HR-ToF-CIMS” not helpful and mostly distracting. It’s the same instrument, and per the AERTRACC description the authors are even using the FIGAREO inlet for their filter desorption. So it is the same instrument, and the authors are just using it in offline mode, no need to suggest that there is a difference beyond that.
• L105: “non-refractory chemical composition of submicron particles”. This sounds as if the AMS is part of the standard MoLA suite, but that’s not what the Table reflects. Please make it consistent.
• Just a general comment: it seems that the flows are simply checked before startup and then left to their own devices. Given that a typical sample day is probably 4-6 h before the TDTs and filter holders are taken out, relying on the needle valves alone seems fine, but there could still be some variations that could potentially lead to turbulence while valve switching. So some monitoring equipment might be wise…
• Section 2.3: It should be stated in what language the software was developed (Igor Pro, I assume) and also what type of license is used for it.
• Section S1: What organic density is used? 1.4 g/cc or the variable one based on Kuwata et al (2012). For source studies the latter seems like the better choice

References

Heim, E. W., Dibb, J., Scheuer, E., Jost, P. C., Nault, B. A., Jimenez, J. L., Peterson, D., Knote, C., Fenn, M., Hair, J., Beyersdorf, A. J., Corr, C., and Anderson, B. E.: Asian dust observed during KORUS-AQ facilitates the uptake and incorporation of soluble pollutants during transport to South Korea, Atmos. Environ., 224, 117305, https://doi.org/10.1016/j.atmosenv.2020.117305, 2020.

Lopez-Hilfiker, F. D., Mohr, C., Ehn, M., Rubach, F., Kleist, E., Wildt, J., Mentel, T. F., Carrasquillo, A. J., Daumit, K. E., Hunter, J. F., Kroll, J. H., Worsnop, D. R., and Thornton, J. a.: Phase partitioning and volatility of secondary organic aerosol components formed from α-pinene ozonolysis and OH oxidation: the importance of accretion products and other low volatility compounds, Atmos. Chem. Phys., 15, 7765–7776, https://doi.org/10.5194/acp-15-7765-2015, 2015.

Lopez-Hilfiker, F. D., Pospisilova, V., Huang, W., Kalberer, M., Mohr, C., Stefenelli, G., Thornton, J. A., Baltensperger, U., Prevot, A. S. H., and Slowik, J. G.: An extractive electrospray ionization time-of-flight mass spectrometer (EESI-TOF) for online measurement of atmospheric aerosol particles, Atmos Meas Tech, 12, 4867–4886, https://doi.org/10.5194/amt-12-4867-2019, 2019.

Pagonis, D., Campuzano-Jost, P., Guo, H., Day, D. A., Schueneman, M. K., Brown, W. L., Nault, B. A., Stark, H., Siemens, K., Laskin, A., Piel, F., Tomsche, L., Wisthaler, A., Coggon, M. M., Gkatzelis, G. I., Halliday, H. S., Krechmer, J. E., Moore, R. H., Thomson, D. S., Warneke, C., Wiggins, E. B., and Jimenez, J. L.: Airborne extractive electrospray mass spectrometry measurements of the chemical composition of organic aerosol, Atmos. Meas. Tech., 14, 1545–1559, https://doi.org/10.5194/amt-14-1545-2021, 2021.

Eichler, P., Müller, M., D’Anna, B., and Wisthaler, A.: A novel inlet system for on-line chemical analysis of semi-volatile submicron particulate matter, Atmos Meas Tech, 8, 1353–1360, https://doi.org/10.5194/amt-8-1353-2015, 2015.

Piel, F., Müller, M., Mikoviny, T., Pusede, S. E., and Wisthaler, A.: Airborne measurements of particulate organic matter by proton-transfer-reaction mass spectrometry (PTR-MS): a pilot study, Atmospheric Measurement Techniques, 12, 5947–5958, https://doi.org/10.5194/amt-12-5947-2019, 2019.

Kuwata, M., Zorn, S. R., and Martin, S. T.: Using elemental ratios to predict the density of organic material composed of carbon, hydrogen, and oxygen, Environ. Sci. Technol., 46, 787–794, https://doi.org/10.1021/es202525q, 2012.

Review of “The AERosol and TRACe gas Collector (AERTRACC): an online measurement controlled sampler for source-resolved emission analysis” by Julia Pikmann et al.

This manuscript by Pikmann et al., describes a novel automated batch sampler for aerosols and trace gases integrated into a mobile laboratory platform. The intended use of this instrument is to predefine continuously monitored trace gas measurements, aerosol measurements, and metrological data to control the sampling scheme for targeted airmasses (i.e. urban, biomass burning, cooking emissions, etc). The authors describe the hardware, software and a single source experiment to demonstrate the operation and data product this instrument can provide.

The AERTRACC system is a novel addition to the batch sampling measurement system community.  This ‘smart’ sampler, allows for unattended sampling of different airmasses, without the input of an expert user. The authors describe the implementation of this system clearly and I believe this is well in the scope and interest of the AMT audience. However, I have concerns on the clarity of how this work is presented and the validity of assumptions made for this analysis:

• The filters were analyzed with Iodide FIAGAREO-CIMS and the thermal desorption tubes (TDTs) were analyzed with a custom desorption unit coupled to the Iodide CIMS, however little information is given on analyte sensitivities used in this analysis. It is well known that Iodide CIMS sensitivities vary many orders of magnitude by analyte (Iyer et al., 2016; Lee et al., 2014; Bi et al., 2021) and are a function of Iodide:H₂O ratio, ion optics tuning, and ion molecule reactor (IMR) temperature (Lee et al., 2014; Lopez-Hilfiker et al., 2016; Robinson et al., 2022). Nowhere in this manuscript or SI can the reader find information on what sensitivities are applied to the detected molecular ions in the mass spectra. If the authors did not calibrate, at minimum the assumed sensitivities and dependencies to water and IMR temperature should be described or assumptions stated and the impact to uncertainty. For example, the IMR temperature dependence of sensitivity may explain the large uncertainties found for the TDT calibration samples (62%). I recommend adding sensitivities used for identified compounds from filter analysis in Table 2 and for TDT analysis in Table 3. Additional information on the temperature programed ramp for the FIAGERO-CIMS analysis should also be included (including the IMR temperature time series). Even if sensitivities are not applied to these data, it should be made clear to the reader that reporting ion signal intensities can falsely report molecular abundance in a sample due to the widely varying sensitivities.
• While the discussion of sampling time delay (3.2) is interesting, I believe the magnitude of the correction to be minimal, and the discussion detracts for the clarity of the methods. I believe most of this section could be moved to the SI.
• On a similar note as above, I found the introduction of the AMS PMF analysis to be abrupt and confusing. I believe a more thorough description of the AMS would be useful to introducing these data. It is not clear to the reader if the AMS is a part of the MoLa or was deployed at a different location. I recommend a separate section describing the AMS measurements to make this clear.
• A discussion of the detection limits for several important compounds (i.e. Levoglucosan, IPN1, IPN2) which appear in large abundance or are close to the I_source/I_background = 1 would be helpful to the reader, as it appears the 10% or 62% error bars are applied to all compounds detected (either by filter or TDT analysis). Furthermore, a more rigorous discussion of how these errors are determined would be useful.
• Do the sample media (filters and TDTs) have to be manually changed and at what frequency? It is unclear to the reader if this system can sample for several hours/days unattended or only short timescales (hours/minutes).
• Why does AERTRACC not measure sample flows in real time with MFCs or use a more robust flow control system than needle valves such as critical flow orifices in the sample paths? It seems as though flow drift could be a significant uncertainty in this measurement.
• Adding the exact masses, chemical formula, and sensitivities used in this analysis to Table 2 and Table 3 would be useful, rather than putting most of that information in the SI.
• Could the authors not have driven the MoLa setup to an appropriate background airmass sampling location and manually sampled in order to confirm the automated determination of background airmasses?
• A source/site map and MoLa sampling location, with Hysplit trajectories would improve the clarity of how these airmasses were sampled. It could be put in the SI if the authors don’t believe it adds clarity to the main text.

Minor comments:

• The manuscript often begins a sentence with a conjunction or preposition:

Line 51 : “For chemical analysis, this approach…”

Line 64: “To obtain data with high time …”

Stylistically, these sentences read better without beginning them with a conjunction or preposition.

• Line 79: “Currently, no instrument offers detailed chemical analysis…”, I believe there a many instruments which provide this capability, one of which you are using (FIAGERO-CIMS), EESI, CHARON, VIA, all come to mind, so I believe this sentence needs to be reworded to more accurately describe the work you are presenting (Lopez-Hilfiker et al., 2019).
• Line 84: “Since offline methods or highly species-resolving semi-online methods…”, this should be reworded, as written it is confusing.
• Line 110: “Table 1…”, The AMS should be included in this table.
• Line 135: “Downstream of the cyclone…”
• Line 175: “The two available sampling modes for AERTRACC are either all four sampling paths collecting PM1 aerosol or two…”, I believe this has been stated clearly already and is a somewhat redundant sentence.
• Line 182: “…EDM”, has this been defined somewhere already?
• Line 226: “…CIMS”, has this been defined already?
• Line 364: “..is only based on a literature review.” The literature review methodology referenced here is not clear to the reader. Please provide references and more detail in the SI.
• Line 376: “… the ratio is expected to be on the order of one.”
• Line 384: “In absolute concentrations…”, please provide more information on how absolute concentrations are determined in this work or remove the sentence.
• Line 394: “Some of those species, associated with cooking and biomass burning, can also originate from various other emissions sources and were assigned to the mixed group.” Couldn’t this also be due to improperly assigning sampling criteria? This assumes the authors perfectly setup the sampling criteria, does it not?
• Line 404: “…have partially ratios on the order of one…”

References:

Bi, C., Krechmer, J. E., Frazier, G. O., Xu, W., Lambe, A. T., Claflin, M. S., Lerner, B. M., Jayne, J. T., Worsnop, D. R., Canagaratna, M. R., and Isaacman-VanWertz, G.: Quantification of isomer-resolved iodide chemical ionization mass spectrometry sensitivity and uncertainty using a voltage-scanning approach, Atmospheric Meas. Tech., 14, 6835–6850, https://doi.org/10.5194/amt-14-6835-2021, 2021.

Iyer, S., Lopez-Hilfiker, F., Lee, B. H., Thornton, J. A., and Kurtén, T.: Modeling the Detection of Organic and Inorganic Compounds Using Iodide-Based Chemical Ionization, J. Phys. Chem. A, 120, 576–587, https://doi.org/10.1021/acs.jpca.5b09837, 2016.

Lee, B. H., Lopez-Hilfiker, F. D., Mohr, C., Kurtén, T., Worsnop, D. R., and Thornton, J. A.: An Iodide-Adduct High-Resolution Time-of-Flight Chemical-Ionization Mass Spectrometer: Application to Atmospheric Inorganic and Organic Compounds, Environ. Sci. Technol., 48, 6309–6317, https://doi.org/10.1021/es500362a, 2014.

Lopez-Hilfiker, F. D., Iyer, S., Mohr, C., Lee, B. H., D’Ambro, E. L., Kurtén, T., and Thornton, J. A.: Constraining the sensitivity of iodide adduct chemical ionization mass spectrometry to multifunctional organic molecules using the collision limit and thermodynamic stability of iodide ion adducts, Atmospheric Meas. Tech., 9, 1505–1512, https://doi.org/10.5194/amt-9-1505-2016, 2016.

Lopez-Hilfiker, F. D., Pospisilova, V., Huang, W., Kalberer, M., Mohr, C., Stefenelli, G., Thornton, J. A., Baltensperger, U., Prevot, A. S. H., and Slowik, J. G.: An extractive electrospray ionization time-of-flight mass spectrometer (EESI-TOF) for online measurement of atmospheric aerosol particles, Atmospheric Meas. Tech., 12, 4867–4886, https://doi.org/10.5194/amt-12-4867-2019, 2019.

Robinson, M. A., Neuman, J. A., Huey, L. G., Roberts, J. M., Brown, S. S., and Veres, P. R.: Temperature-dependent sensitivity of iodide chemical ionization mass spectrometers, Atmospheric Meas. Tech., 15, 4295–4305, https://doi.org/10.5194/amt-15-4295-2022, 2022.