the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Realtime measurement of phase partitioning of organic compounds using a Proton-Transfer-Reaction Time-of-Flight Mass Spectrometer coupled to a CHARON inlet
- 1Department of Environmental Science and Engineering, Fudan University, Shanghai, 200438, China
- 2State Environmental Protection Key Laboratory of Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai, 200233, China
- 3School of Natural Sciences, University of California, Merced, 95343, USA
- 4Department of Atmospheric and Oceanic Sciences, Fudan University, Shanghai, 200438, China
- 5Big Data Institute for Carbon Emission and Environmental Pollution, Fudan University, Shanghai, 200438, China
- 1Department of Environmental Science and Engineering, Fudan University, Shanghai, 200438, China
- 2State Environmental Protection Key Laboratory of Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy of Environmental Sciences, Shanghai, 200233, China
- 3School of Natural Sciences, University of California, Merced, 95343, USA
- 4Department of Atmospheric and Oceanic Sciences, Fudan University, Shanghai, 200438, China
- 5Big Data Institute for Carbon Emission and Environmental Pollution, Fudan University, Shanghai, 200438, China
Abstract. Understanding the gas-particle partitioning of semivolatile organic compounds (SVOCs) is of crucial importance in the accurate representation of the global budget of atmospheric organic aerosols. In this study, we quantified the gas- vs. particle-phase fractions of a large number of SVOCs in real time in an urban area of East China with the use of a CHemical Analysis of aeRosols ONline (CHARON) inlet coupled to a high resolution Proton Transfer Reaction Time-of-Flight Mass Spectrometer (PTR-ToF-MS). We demonstrated the use of the CHARON inlet for highly efficient collection of particulate SVOCs while maintaining the intact molecular structures of these compounds. The collected month-long dataset with hourly resolution allows us to examine the gas-particle partitioning behaviors of a variety of SVOCs under different ambient conditions. By comparing the measurements with model predictions using the instantaneous equilibrium partitioning theory, we found that the dissociation of large parent molecules during the PTR ionization process likely introduces large uncertainties to the measured gas- vs. particle-phase fractions of less oxidized SVOCs, and therefore, caution should be taken when linking the molecular composition to the particle volatility when interpreting the PTR-ToF-MS data. Our analysis suggests that understanding the fragmentation mechanism of oxidized SVOCs and accounting for the neutral losses of small moieties during the molecular feature extraction from the raw mass spectra could reduce, to a large extent, the uncertainties associated with the gas-particle partitioning measurement of SVOCs in the ambient atmosphere.
- Preprint
(2390 KB) -
Supplement
(2060 KB) - BibTeX
- EndNote
Yarong Peng et al.
Status: final response (author comments only)
-
RC1: 'Comment on amt-2022-147', Anonymous Referee #2, 22 Jul 2022
Review
Realtime measurement of phase partitioning of organic compounds using a Proton-Transfer-Reaction Time-of-Flight Mass Spectrometer coupled to a CHARON inlet
by Yarong Peng et al.
General:
The authors conducted month-long ambient SVOC measurements in East China using a CHARON-PTR-TOF-MS. This instrument measures the gas-particle partitioning of organic molecules and is a still relatively new technique that is not widely used. Peng et al. find that fragmentation inside the PTR instrument is a major challenge in the correct representation of gas-particle partitioning of SVOCs. I think the validation of the data obtained by CHARON-PTR-MS by a model that the authors provide here, and the lessons learnt on the challenges in interpreting the ion signals, are very useful for the community. The paper is written in a concise and clear language.
I recommend publication of the manuscript in AMT after the following comments have been addressed.
l. 112: Can you specify the typical wind direction and what kind of sources (broadly) are in the fetch?
l. 173 ff: The authors used a standard gas mixture where the component with the highest mass is a-pinene (136 amu), although later on, they report compounds with masses over 200 amu and even 342 amu. It would be good practice to calibrate the PTR transmission and sensitivity for the whole mass range you measure. Nowadays, gas standards of siloxanes e.g. with mass 370 amu or 444 amu are available. Please comment on the added uncertainty due to the lower mass range covered in calibrations.
Equation 2: what does “a” stand for?
l. 187 ff & Fig. S2a: The ToF transmission efficiency is generally expected to follow a root function if not corrected for duty cycle. The reason that it doesn’t look like a root function here is probably that the authors used duty cycle correction in TofWare (please specify in the methods description if you did). What I do not fully understand is why there still seems to be a reduced transmission in low masses – is this instrument equipped with a low-mass filter (e.g. a quadrupole) that lowers the transmission of lower mass molecules? If not, the duty cycle correction should, in my understanding, have brought all masses on the line where relative mass discrimination = 1. The data in Fig. S2a look like the sigmoidal fit makes sense for your transmission function, I am just asking why, because the method description isn’t clear on that. Please specify the instrument model that you used and explain why your transmission curve looks like it does. And please plot the sigmoidal fit function in the figure, too.
l. 267 ff: Can you give an estimate of the total uncertainty of the gas/particle partitioning propagated from the uncertainties for both gas and particle concentration and the assumption on saturation vapor pressures? Mentioning somewhere in the methods the separate uncertainty of gas and particle phase concentrations would be interesting as well (or are they both the same?).
l. 137: Why did you run the TDU at 140°C, even though your desorption temperature variation experiments showed that even at 70°C-80°C all the SVOCs are transferred to the gas phase?
l. 327: I wonder if this plot might be more instructive if you showed the ratios of parent masses vs their fragments and clusters? From the way the data is presented, I find it hard to see whether the fragment ions really are stable or if they just look stable because of the log scale. Please deliberate more on what can be seen in Fig. 2. For example, it looks like some fragments or parent masses have a dip in the middle temperatures, while others decrease and others increase with temperature. Is this just scatter or is there a physical/chemical explanation? (If it is just scatter: error bars would help.)
l. 353: I wouldn’t describe a correlation of r=0.6-0.8 (r² = 0.36-0.64) as “well”. Maybe “reasonable”?
l. 354 ff: How can loss of H2O cause a lower total carbon mass? (There is no carbon in H2O.)
l. 363: Why the focus on compounds below 250 Th? Please add a half sentence. (Instrument limitation?)
l. 381: Since Fig. S7 is so deliberately discussed here, I think it should be part of the main manuscript instead of the supplement.
Fig. S7: The y-axes should be more correctly labeled as CxHy, CyHyO4, etc., instead of “CH”, “CHO4” which is chemically incomplete.
l. 442: It is not true that 140 Td is very common. The Yuan et al. review that you cite here states “The drift tube in PTR-MS has been commonly run with an E/N of 100−120 Td.”
Section 3.3:
- What is the uncertainty of the modeled gas-particle partitioning? (How much of the difference between measurements and model could be due to model uncertainty?)
- Is the discrepancy in the gas-particle partitioning between the model and the TAG data smaller? (Thereby you could show that the reason for the mismatch is indeed a fragmentation issue in the PTR.)
- Would there be a way to optimize a combination of the four loss mechanisms to find an optimal correction?
- Gkatzelis et al (https://doi.org/10.1029/2020GL091351) reported that small aldehydes, acids and alcohols significantly contributed to the particle phase during a campaign in Beijing. Is it possible that part of the low mass particle phase contribution that you see is because of aqueous-phase uptake of such small OVOCs?
-
RC2: 'Comment on amt-2022-147', Anonymous Referee #1, 10 Aug 2022
This paper describes the use of an online CHARON inlet coupled to PTR-MS to measure the gas to particle fractions of a range of semi-volatile organic compounds. It combines experiments using a broad range of standard compounds and then expands this to look at real world data collected in Beijing. The main finding is that fragmentation of large molecules during the ionization process occurs and that this can have important implications when estimating gas-particle partitioning. This is a well-written paper and is well within scope AMT. The authors have provided the community with useful knowledge and some suggestions for how these difficulties could be overcome. I recommend publication after the following comments have been addressed.
Figures: My main issue with this paper is a lack of clarity in the figures. Figure 1b is impossible to read with so many overlapping points. I think Figure 2 is meant to show the fragmentation pattern and how they change (or not in this case) at different temperatures. But the use of a log scale and a bar chart makes this very difficult to see. Also, each mass spectrum should have a labeled x-axis as these are all different.
I also think the use of a log scale in Figure 3c) and d) makes this very hard to interpret. It is difficult to see the relative percentages of the different groups using a log scale, where the pink colour is large but actually the % is relatively small. I would convert this to a linear scale or provide a scaled 100 % plot in addition to the one here.
Minor comments
Line 241: Why have the nitrophenols been excluded from the analsysis?
Figure 1b: There are no blue or purple points at 70 C. Is there a reason for this? The figure legend needs more details. Is this normalized to the CPC counts at 300 nm diameter? Does the “PTR-TOF-MS” signal include the fragment ions?
Line 330: Sucrose has a lower EF and this is attributed to dehydration. However, the light blue point in figure 1C (2,7-dihydroxynaphtalene) also looks to have a lower value but this compounds doesn’t fragment.
Figure 2: I would have liked to have seen more discussion of the fragmentation data. What are the red labels on some peaks? They are not the molecular ion so I’m not sure why they are a different colour.
Line 348: I think you need to make clear at the start of this section that you have moved to discussing the field data.
Line 350-352: What does “agree very well mean”? In figure S5 you have R values. Can these be added to figure S4?
Line 355: Figure S5 shows a comparison of specific compounds not total carbon mass.
Line 356: This comparison is interesting – it seems that when the mass is low towards the end of the measurement period, there is a larger discrepancy between the two methods. Do you have any suggestions for why this is?
Line 358: I cannot see a plot of CHARON derived total OA or its comparison to AMS data?
Line 394: Is there any explanation for why the O4 group is closest or is it just by chance? In Figure 4, adding additional functional groups to the O4 species makes the agreement worse. Perhaps these are less prone to fragmentation?
Figure 4: I think you should present the data in Figure 4 in a table in addition to the plots. This would be more useful for readers.
Line 430: I agree with your final comment here but I think you need to be clear that currently this isn’t possible as you can’t predict what the parent molecule was.
Yarong Peng et al.
Yarong Peng et al.
Viewed
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 259 | 71 | 7 | 337 | 39 | 2 | 4 |
- HTML: 259
- PDF: 71
- XML: 7
- Total: 337
- Supplement: 39
- BibTeX: 2
- EndNote: 4
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1