General comments:

This manuscript showcases an important method that can be applied to measurements from low-cost sensors for source apportionment. I recommend the following major revisions:

Specific comments

1) Some of the sentences in the Introduction are very long and should be shortened to make the manuscript clear

2) In the methods, it might be useful to have a table detailing the different instruments, their method of operation, pollutants measured as well as if they were low-cost/ reference, and location.  This was not clear for some of the instruments mentioned, for example, the Box of Clustered Sensors. This section was a little hard to follow with the number of instruments mentioned but not described in detail.  It also wasn't clear why indicators such as LDSA were mentioned in this section and what that had to do with source apportionment. I think including a few more details about the method and the pollutants used in the Introduction would be helpful to readers.

3) The last paragraph in section 2.1 was not about the instruments at all. I suggest moving this paragraph to the next sub-section. 

3) When explaining the PMF method I suggest that the authors actually include equations to describe the two-step PMF process used in this analysis. The authors do not explain the limitations of using a combination of PNSD and particle composition, and the need to use the two-step PMF method. I think this is a critical point and needs to be elaborated on. How did this method differ from that used by Hagan et al.- the study the authors cited in the Introduction?

4) More details of the PMF method were included in the Results instead of the Methods section (eg section 3.2). This again makes it hard for the reader to follow with the authors did.

5) It appeared that without data from reference monitors, the four factors identified from the OPC data alone were hard to interpret. If so- why bother conducting a source apportionment analysis with low-cost sensors?

6) Given that the OPCs do not measure particles < 0.3 micrometers, how useful is this technique in areas dominated by vehicle emissions?

The authors present a new methodology for using LCS for source apportionment. This is an important topic as being able to extract source information from LCS AQ data would immensely improve the utility and power of LCS. Overall I think the paper is adequate for publication subject to minor revisions. 

Specific comments: 

1. I think the paper overall, but especially the abstract, could be a little more quantitative in its description. The abstract contains several instances of describing results qualitatively (e.g., "provide results that were consistent with a previous study" line 28; "good consistency between results", line 35, etc). It would be better to provide the numbers/statistics that show this rather than just telling the reader that the results were consitent. 

2. There is no discussion or citation of the performance of the Alphasense OPC-N3, which is critical in interpreting the source apportionment results. Have the authors compared the PNSDs from the Alphasense to any reference field monitors or lab instruments? The performance of these optical particle counters through publicly available resources such as AQ-SPEC is fairly mixed. 

3. Line 203 mentions separate NO/NO2 LCS data. Is this from the "Box of Clustered Sensors"? It's a little unclear what devices are being used here. I have a similar concern with the quality of the data here as well, as several studies have shown that the NO2 from alphasense gas sensors are not very reliable

4. The data showing the source apportionment from the LCS alone (particles and gases) seems to be of weaker utility than when the ACSM is brought in. In particular LC4 does not really have any source condition associated with it, as the authors mention. I find the statement on line 461-462, saying that hyperlocal source apportionment is now possible with only LCS, to be exaggerating a little bit. I'd recommend softening that or at least adding in the caveats that some sources can't be well characterized. The way it is written now somewhat oversells the results, I think.