the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Measurement report: Underestimated reactive organic gases from residential combustion: insights from a near-complete speciation
Yaqin Gao
Hongli Wang
Lingling Yuan
Shengao Jing
Guofeng Shen
Liang Zhu
Abigail Koss
Yingjie Li
Qian Wang
Dan Dan Huang
Shuhui Zhu
Shikang Tao
Shengrong Lou
Abstract. Reactive organic gases (ROGs), as important precursors of secondary pollutants, are not well resolved as the chemical complexity challenged its quantification in many studies. Here, a near-complete speciation of ROGs with 125 species was developed and applied to evaluate their emission characteristics from residential solid fuel combustion. ROGs identified by the present method accounted for ~90 % of the “total” as the sum of species by Gas Chromatography equipped with a Mass Spectrometer and a Flame Ionization Detector (GC-MS/FID) and H3O+/NO+ Proton Transfer Reaction Time-of-Flight Mass Spectrometer (Vocus PTR-ToF-MS). The study further revealed that with 55 species, mainly oxygenated species, higher hydrocarbons with > 8 carbon atoms, and nitrogen-containing ones, previously un- and under-characterized, ROG emissions from residential coal and biomass combustion were underestimated by 44.3 % ± 11.8 % and 22.7 % ± 3.9 %, respectively, which further amplified the underestimation of secondary organic aerosols formation potential (SOAP) as high as 70.3 % ± 1.6 % and 89.2 % ± 1.0 %, respectively. The OH reactivity (OHR) of ROG emissions was also undervalued significantly. The study highlighted the importance of acquiring completely speciated measurement of ROGs from residential emissions, as well as other processes.
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Yaqin Gao et al.
Status: final response (author comments only)
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RC1: 'Comment on acp-2022-711', Anonymous Referee #1, 21 Nov 2022
General comments:
This study constructs a near-complete chemical description of ROGs emitted from residential coal and biomass combustion using GC-MS/FID and Vocus PTR-ToF-MS. ROG emissions were underestimated by 44.3 ± 11.8% and 22.7 ± 3.9%, considering the un-characterized species, which resulted in the underestimation of SOA formation potential and OH reactivity. In the current framework, 125 species were quantified, accounting for approximate 90% of the total ROG emission. SOAP and OHR were re-estimated incorporating the newly identified species and the spatial distribution and annual variation of ROG emissions from residential coal and straw combustion in China are reported. The topic is covered by ACP and the data is of interests to relevant readers. However, novelty is lacking with respect to methodology and results and the discussion lacks depth. Also, the manuscript is not well drafted, mistakes and errors are commonly seen.
Specific comments:
Line 113-116. PTR-MS with H3O+ detects the VOC molecules with higher proton affinity than water and PTR-MS with NO+ extents the range of compounds that can be detected. As you mentioned, a total of 1005 peaks were extracted with m/z lower than 200 in H3O+ mode and selected peaks in NO+ mode.
- What are the range of compounds that NO+ mode supplements and the criteria of selecting these compound species
- What are the organic gases that are not sensitively detected by PTR-MS with H3O+ and NO+ modes and how can GC/MS-FID complement the measurement? This information should be included, either in the main text or the SI. Currently, the analysis section is brief.
Figure 1, when conducting the mass closure analysis, how to quantify the organic gases that are not effectively detected by PRT-MS and GC-FID/MS. Given FID is adopted, THC data (if any) may help.
Line 118, 162 ions with a relative high degree of certainty… Again, the introduction of the data treatment is inadequate. What is the standard of selecting these ions and how to determine the certainty threshold. Similar question goes to line 156, 87 out of 162 species were used….please give more details regarding data treatment.
Line 122…. assuming all the signals with the same sensitivity as acetone. Does this assumption stand, since acetone has a higher proton affinity than many VOC compounds.
Line 169 why is formaldehyde not reported in your study since PTR ionize formaldehyde efficiently.
Line 255 figure 2. What is the x-axis label, m/z or numbers of identified species. Add x-axis label.
Technical corrections:
Line 73, 276, 320, 360 space missing before the left bracket.
Line 94 “particles was” should be “particles were”
Line 97 space missing beforeâ.
Line 100 there were 23 samples were collected, grammatical mistake
Line 130 space missing before the left bracket.
Line 135 10 carbonyls are included in Table S2.
Line 137 delete including 68 species.
Line 185 add a space before and after and check throughout the whole manuscript.
Line 211 as → that
Line 227 as Fig. 2(c) shown → as shown in Fig. 2(c) or as Fig. 2(c) showed/shows.
Line 251 253 two types of data format shown, i.e., xxx% ± xxx% or xxx ± xxx%. Check throughout the whole manuscript keep consistency.
Line 276, according SOA yields → respective SOA yields.
Line 351, due to is a preposition and should not a complete sentence.
Line 354, comparing with → compared with
Line 357, 2017 benefiting → 2017, benefiting
Line 397, in rural of China → in rural China
I believe the above technical corrections are not complete, authors should check carefully.
Citation: https://doi.org/10.5194/acp-2022-711-RC1 -
RC2: 'Comment on acp-2022-711', Anonymous Referee #2, 22 Nov 2022
The manuscript by Gao et al. reported the near-complete speciation of reactive organic gases (ROGs) with 125 species identified to evaluate their emission characteristics from residential combustion. The authors used a Gas Chromatography equipped with a Mass Spectrometer and a Flame Ionization Detector (GC-MS/FID) and H3O+/NO+ Proton Transfer Reaction Time-of- Flight Mass Spectrometer (Vocus PTR-ToF-MS) to identify 55 previously un- and under-characterized species. Without considering these “newly identified species”, the ROG emissions from residential coal and biomass combustion would be underestimated by 44.3% ± 11.8% and 22.7% ± 3.9%, respectively, which further highlighted the potential underestimation of secondary organic aerosols formation potential (SOAP) and OH reactivity (OHR) of ROG emissions. Overall, this study would be a useful addition to better understanding the detailed speciation of ROGs from residential combustion. However, the novelty of this study should be clearly addressed, especially given that some previous studies also applied these advanced instruments and have identified these “newly identified species” (Figure 2).
Specific issues:
Line 115-116: why only selected peaks (mainly higher alkanes) under NO+ mode PTR measurements were studied?
Line 151: the loss of acids and alcohols in the canister is larger, and the author attributed this to their functional groups of -COOH and -OH. Would it be more direct to relate this to the volatility of compounds? Are there any criteria to exclude these compounds from the analysis?
Line 174 and 324: why is benzene chosen for normalization purposes?
Figure S2 (b): how many compounds were used here, and why were they chosen for comparison but not all the compounds?
Given this study is based on offline analysis that some dynamic changes in emissions from residential combustion may not be reflected. Could this cause a potential bias?
Section 3.2: the SOA formation potential was estimated by using SOA yields from the literature. Are those values obtained at specific conditions? What would be the uncertainties for the estimation?
Section 3.3: The authors cited literature information to get the EFs of anthracite and straw and then applied these values to estimate the ROG emissions of residential coal and straw combustion in mainland China. Are the quantification of EFs from limited sources representative? Is anthracite representative of residential coal combustion in mainland China?
Citation: https://doi.org/10.5194/acp-2022-711-RC2
Yaqin Gao et al.
Data sets
Data for speciation of ROG emissions from residential combustion Hongli Wang https://data.mendeley.com/datasets/z78zz7mv7h/1
Yaqin Gao et al.
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