the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
PM2.5 Source Apportionment using Organic Marker-based CMB Modeling: Influence of Inorganic Markers and Sensitivity to Source Profiles
- 1State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin, 300071, China
- 2School of Geography Earth and Environmental Science, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
- 3Department of Environmental Sciences/Center of Excellence in Environmental Studies, King Abdulaziz University, PO Box 80203, Jeddah, 21589, Saudi Arabia
- 1State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control, College of Environmental Science and Engineering, Nankai University, Tianjin, 300071, China
- 2School of Geography Earth and Environmental Science, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
- 3Department of Environmental Sciences/Center of Excellence in Environmental Studies, King Abdulaziz University, PO Box 80203, Jeddah, 21589, Saudi Arabia
Abstract. Chemical mass balance (CMB) is one of the most popular methods to apportion the sources of PM2.5. However, the source apportionment results are dependent on the choices of measured chemical species and the source profiles. Here, we explore the sensitivity of CMB results to source profiles by comparing CMB modeling based on organic markers only (OM-CMB) with a combination of organic and inorganic markers (IOM-CMB), using organic and inorganic markers in PM2.5 samples collected in the Chinese megacity of Chengdu. OM-CMB results show that gasoline vehicles, diesel vehicles, industrial coal combustion, resuspended dust, biomass burning, cooking, vegetation detritus, SOA, sulphate, and nitrate contributed to 4 %, 10 %, 15 %, 12 %, 5 %, 3 %, 4 %, 9 %, 10 %, and 20 %, in comparison to 4 %, 11 %, 15 %, 17 %, 6 %, 2 %, 5 %, 10 %, 7 %, and 18 % from IOM-CMB modelling. The temporal variations of PM2.5 contributions from sulphate, nitrate, SOA, gasoline vehicles, and biomass burning, characterized by unique markers and low collinearity, were in good agreement between the OM-CMB and IOM-CMB results. However, resuspended dust estimates from OM-CMB had a poor correlation with that from IOM-CMB, due to the different tracers used. When replacing the source profile for industrial coal combustion with that for from residential sources, the contributions of resuspended dust and residential coal combustion were overestimated because the residential coal combustion profile contained a higher concentration of OC and organic compounds but lower crustal elements. Different source profiles for gasoline vehicles were also evaluated. Our results confirm the superiority of combining inorganic and organic tracers and using up-to-date locally-relevant source profiles in source apportionment of PM.
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Yingze Tian et al.
Status: final response (author comments only)
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RC1: 'Comment on acp-2021-1007', Anonymous Referee #1, 14 Feb 2022
The manuscript investigated the OC and PM2.5 sources with inorganic and organic source profiles, with the dataset of Chengdu of about 64 samples. Considering that the source profiles have been published in other journals, the datasets adopted in this manuscript are only the ambient PM2.5 samples actually. Compared with other similar papers published in ACP, such as the a recent paper of Srivastava et al. (2021, ACP) (two sites, one month for two seasons, with totally about 120 samples, and the filter-based source apportionment results were also compared with those of AMS) of the same group, I feel that this manuscript has a long way to move for possible consideration in ACP, with not only improving the scientific questions answered, the structure, the description, the format, the figures, the references and so on.
Following are the detailed comments.
Title:
how to understand the influence of sensitivity to source profiles? And how to under the the influence of sensitivity to source profiles on PM2.5 source apportionment? What is the meaning of sensitivity? It is quite unclear logically.
Abstract:
Line 20-22, the first and second sentences can be deleted as they are so common. The authors should directly give the main scientific questions existed or unsolved currently. For example, how and why the organic or inorganic source profiles adopted impacting the CMB results is a good question for this study to answer.
Line 27-28, the authors should referred to other high quality journal paper and should be conscious that listing data is a kind of much low description, which is quite inapposite for a scientific paper especially with high quality.
Line 31-38, I found that no quantitative conclusions were given. I can not accept this. Poor correction, give the r value; higher, lower, overestimate, please use detailed data.
Line 37-38, scientists all know. Do not repeat. Please give the individualized suggestion or implication based on the main conclusions of this study.
Keywords:
These words are so common that they can not reflect the key questions the paper wanted to answer.
Introduction:
- Line 43-47, they can be deleted directly. I suggest that the authors can change them to “To design effective PM2.5 reduction strategies in polluted regions currently, more refined and accurate source apportionment results of PM2.5 are urgently needed”.
- Line 57-63, delete them directly. The chemical compositions and formation mechanisms of PM2.5 is not the key problem to be solved of this study. They are so common descriptions which are not suitable for ACP or even a lower quality journal.
- Line 65-74, the organic markers and their adoption in CMB modeling should be better and thoroughly summarized. How and to what extent do they improve the source apportionment results? What are the new findings with organic tracers added compared with no organic tracers? And so on. All these are the base of this study.
- Line 65, many PM2.5 sources do not have a unique composition? If it stands, how can the formers conducting source apportionment studies? Such as cooking? Many sources, Why only cooking was listedï¼There are papers published on the source markers of cooking emission.
- Line 66, some organic compounds? Which?
- Line 70-71, has been widely used, but the author give no references.
- Line 73, why OM-CMB can not estimate contributions of inorganic ions. I think it is the key problem that the authors should answer with the dataset obtained.
- Line 76-77, are all the papers listed here adopting no local source profiles? I can not believe so.
- Line 97-100, they can be deleted. The air quality, energy consumption, vehicle numbers and so on which impact the air quality should be described clearly.
- Line 102, the sampling map should be given.
- Line 145, extracted for 10 min and repeated for 3 times.
- Line 161, delete it.
- Line 182, why 1.8 was selected, not 1.4, 1.6 or others?
- Line 187-191, so common, delete them directly.
- Line 208, the source profile of domestic coal burning is not given?
- Line 227, the MSR method for calculating SOC has been published in recent years.
- Line 247-249, I believe the source profiles of gasoline in China are abundant. The inorganic ions and elements are not given in Cai et al. (2017), why the authors refer it? I can not understand. Showed to shown.
- Line 255-257, line 260-261, line 268-270, line 272-275, line 291-292, line 295-300, line 398-399, I can not believe that it is a paper written by the authors wo have published many papers already. Do you want to increase the length of the paper? Please give the main rules hided behind the data, not repeat the data.
- Line 281-282, why these sources showed no seasonally variation? It is unbelievable. The combustion condition, the rain, the emission conditions of them in summer and winter are quite different. I believe that it may be related with the limited sampling numbers of this study.
- A biggest problem of the results and discussion is that the author give no quantitative results for any comparison. For example, in Line 302-325, obvious higher contributions during the cold period, emitted more PM, were higher during the dry season, a large percentage, higher fractions during autumn and winter, weaker seasonal variation, high wind strength, strong illumination, less precipitation, high temperature, higher contributions, high precursor concentrations, humidity and PM, the high relative humidity during wintertime, etc. All these data can be obtained by the authors, but no were given. Also no statistic test of the comparison was done.
- Line 316-317, the wet cleaning of them is also higher in summer than that in winter.
- Line 320-321, the recent references should be cited.
- Line 345, in agreement with each other, how to judge?
- Line 347, was more consistent with, how to judge?
- Line 358, very different from other source, how to judge? What are the markers of other sources?
- Line 360, moderately consistent, what is moderately? How to judge?
- Line 362-363, it is sure. What is the main finding of this study. Some differences indicate what extent? Difference is difference, how to understand some differences?
- Line 372, I don not believe it is necessary for such kind of comparison with the replacement of noncatalyst vehicle profile. Of course, the scientists will select the source profiles obtained in China and in recent years. Why the authors selected such two source profiles with obvious difference, with one for USA (Schauer et al., 2002) and one for China (Cai et al., 2017).
- Line 389, with the results using our gasoline vehicle profiles, it is not your source profiles, but cited from formers.
- Line 383, little is how many? How to understand the central city of southern China? Is residential coal not low in non-central city of southern China?
- Line 390-391, Line 407-409, nothing is said. Do not repeat what we already know in the conclusion. Please give the main and specific findings and implications of this study.
- Line 404-405, poor descriptions. What is “the OM-CMB resuspended dust”? It should be the contributions of resuspended dust obtained from the OM-CMB modeling. The comparison is for the source contributions, not for the sources.
- Reference: most of the journals are below the levels of ACP? How can this manuscript published on ACP? Most source apportionment studies published on Nature, ACP, EST, JGR, EI, etc are not cited.
- Figures, the authors should refer to the figure styles of papers on ACP.
- Figure 3, the days are not continuous, so column figures should be used.
- Figure 4, it can be separated and detailedly discussed for each source.
- For all the figures, only day variation, pie figures, etc are given, which indicating that the detailed analysis of the results are not done and quite necessary. More abundant types of figures are needed. Please see the papers already published.
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RC2: 'Comment on acp-2021-1007', Anonymous Referee #2, 01 Apr 2022
The comment was uploaded in the form of a supplement: https://acp.copernicus.org/preprints/acp-2021-1007/acp-2021-1007-RC2-supplement.pdf
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RC3: 'Comment on acp-2021-1007', Anonymous Referee #3, 12 Apr 2022
Interative comment on “PM2.5 Source Apportionment using Organic Marker-based CMB Modeling: Influence of Inorganic Markers and Sensitivity to Source Profiles” by Tian et al.
The manuscript investigated the influences of source markers (e.g., with or without inorganic markers) and source profiles (e.g., local or nonlocal/nonnative) adopted in CMB model on the PM2.5 source apportionment results based on a dataset collected in Chengdu City in China. Several papers on similar topics have also been published in recent years (e.g., Srivastava et al., 2021 ACP; Chow et al., 2022 STOTEN). Generally, the statement and discussion in this manuscript are too general and bland. The authors should clearly highlight their scientific questions and contributions, and improve the manuscript to make it meet the standards of high quality of ACP journal.
Major Comments
(1) Title: The meaning of “sensitivity” in the title was not clear.
(2) Abstract: The topic of the first sentence is the CMB model, which was not the focus of this study. The authors should clearly present the scientific questions especially those unsolved questions. Generally, the authors need to tease out their new contributions and findings of this study more clearly and forcefully in the abstract.
(3) Introduction: There are many common and insignificant descriptions which are not favorable for the understanding of current knowledge and research progresses on PM2.5 source apportionment based on receptor models. Furthermore, the authors should also introduce/summarize the research status based on other source apportionment methods, and address the advantage of using CMB compared to other methods.
(4) Methodology: More details should be given in this section. For example, the authors should clearly present the detailed inorganic and inorganic markers they used in the OM-CMB and IOM-CMB models. Besides, it is better to list/present the source profiles used in this study in the Table or Figure. Furthermore, the authors should clarify how they convert the OC source apportionment results to PM2.5 source apportionment results in this section.
(5) Results and discussion: In this section, the authors presented their results more like a technique report by reporting numbers, lacking further interpretation and discussion. The similarity and difference of using OM-CMB versus IOM-CMB, or local source profiles versus nonlocal source profiles, which were the main focuses of this paper, were not clear. Please revise and improve.
(6) Figures: The presentation of the figures in this manuscript should be improved to match the journal figure styles. The figures were not well presented or interpreted, and were not that straightforward. Furthermore, “Sensitivity of source apportionment to source profiles” should be an important section as it has been addressed in the title. However, there was no figure or table related to represent and support the discussion about this section. Please add one.
(7) Conclusion: The conclusion section looks quite similar with the abstract. Please revise it. Conclusions should be drawn and atmospheric implication should be given in the section.
Other Comments
Line 45-46: revise to “exposed to high PM2.5 mass concentrations”.
Line 46: “impacting sources” can be replaced by “PM2.5 sources”
Line 64: Do the authors mean that such studies were conducted based on individual organic and inorganic markers together? or just with bulk OC?
Line 71: Please add references.
Line 88-89: Please clarify what “the other source contributions” mean.
Line 104: “The sampling points” can be replaced by “The sampling sites”.
Line 106: “each sampling lasted for 22h” can be revised to “the sampling duration was 22h.”
Line 117: The first sentence of this paragraph can be revised to “Source specific inorganic and organic markers were analyzed, including OC, EC, ions….”.
Line 120-121: What types of extraction and digestion methods were used in this study?
Line 144: “Fifteen ml” should be replaced by “15 ml”. And please check the number. 15 ml/10 ml was not equal to v/v 1:2.
Line 150-152: Please explain why levoglucosan was not analyzed by GC-MS together with other polar compounds.
Line 154: Please clarify what “the other organic markers” represent here.
Line 164: Do the authors mean that they use desiccators to balance the filters? Please explain why and how to control the RH and temperature conditions.
Line 167-170: This sentence should be revised.
Line 175: What “most organic compounds” include? Please give more information about the recoveries of different compounds.
Line 197-202: It is not quite clear how nitrate and sulfate source contributions to PM2.5 were determined based on OM-CMB methods.
Line 210-211: What about the influences by other sources that might be not included in the current source apportionment results? BTW, do the authors mean that they used 1.8 for the conversion of OC to OM and SOC to SOA? Please add references and explain why this conversion factor (1.8) was used.
Line 212-213: Please explain why “vegetation detritus did not work in this calculation”.
Line 213-215: I cannot understand why there was vegetation influence on soil dust. Please further explain why.
Line 240: Maybe better revise “which were measured by ourselves” to “which were reported in our previous publication (Tian et al., 2021b)”.
Line 305-306: not well presented in the current figures.
Line 317-321: Please give/show the information of meteorological parameter and gases precursors data during the study period in figure or table. Or add references here.
Line 347: It should be unified to use “SOAEC”. Besides, the authors should further explain or present the evidences to support that the SOA estimated by the IOM-CMB were consisted with the SOAmin than that estimated by OM-CMB.
Line 365-366: Since organic markers and inorganic markers were both input in IOM-CMB model, why C31 was not used for dust in IOM-CMB model? Please show what sources markers were used for different source identification.
Line 368-370: Please clarify and detail what “other source categories” might influence the determination of cooking contributions.
Line 382-383: Please add references here or provide related data for evidence.
Yingze Tian et al.
Data sets
Organic components in PM2.5 of a Chinese megacity Roy M. Harrison and Yingze Tian https://doi.org/10.25500/edata.bham.00000745
Yingze Tian et al.
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