the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 04 Oct 2023
Particulate emissions from cooking activities: emission factors, emission dynamics, and mass spectrometric analysis for different preparation methods
Abstract. As most people, especially in developed countries, spend most of their time indoors, they are strongly exposed to indoor aerosol, which potentially can lead to adverse health effects. A major source of indoor aerosols are cooking activities releasing large amounts of particulate emissions, both number and mass wise, with often complex composition. To investigate the characteristics of cooking emissions and parameters, which influence these characteristics, we conducted a comprehensive study in form of a measurement series cooking 19 dishes with different ingredients and preparation methods. The emissions were monitored in real time with multiple online instruments measuring physical and chemical particle properties as well as trace gas concentrations. With the same instrumentation, the influence of cooking emissions on the ambient aerosol load was studied at two German Christmas markets.
For six variables, we observed changes during the cooking: particle number concentration of smaller (particle diameter dp > 5 nm) and larger particles (dp > 250 nm), PM (PM1, PM2.5, PM10), BC, PAH and organics mass concentrations. Generally, similar emission characteristics were observed for dishes with the same preparation method mainly due to similar cooking temperature and use of oil. The emission dynamics of the above-mentioned variables as well as the sizes of emitted particles were mostly influenced by the cooking temperature and activities during cooking. The emissions were quantified via emission factors, with the highest values for grilled dishes, one to two orders of magnitude smaller ones for oil-based cooking (baking, stir-frying, deep-frying) and the smallest for boiled dishes.
For the identification of cooking emissions with the Aerodyne aerosol mass spectrometer (AMS) and generally the identification of new AMS markers, we propose a new diagram type where the variability of the mass spectra of different aerosols is considered. Combining our results and those from previous studies for quantification of cooking-related organic aerosol with the AMS, we recommend using values for the relative ionization efficiency which are larger than the default value for organics (RIEOrg = 1.4): for rapeseed oil-based cooking 2.17 ± 0.48 and for soy oil-based cooking 5.16 ± 0.77.
- Preprint
(1897 KB) - Metadata XML
-
Supplement
(2341 KB) - BibTeX
- EndNote
Status: final response (author comments only)
-
RC1: 'Comment on egusphere-2023-2172', Anonymous Referee #1, 03 Jan 2024
What did I understand this paper to be about:
This paper describes cooking experiments where a handful of different techniques and ingredients are used. The authors quantify total emissions for each dish, as well as emissions for individual phases/methods within the cooking of each dish, which are novel. There is considerable effort given to placing the experimental results in context of both emissions from other common sources as well as ambient PM measurements. The experiments appear to be well-done, with one major possible exception (which I note in detail) of whether or not temperature was measured.
Big picture comments:
-With due respect to the work—which appears to be well done, has novelty, and is largely presented well visually—the writing throughout this paper is poor. It is not publishable in its current form, in my opinion. In the beginning of my reading of the manuscript, I was attempting to do copy-editing, and line-by-line suggestions for grammatical changes, but I lost steam. It’s a pervasive issue throughout the manuscript that needs a lot of revision. My best suggestion to the authors would be to recruit a few scientific colleagues (which would not have to be subject-matter experts) to specifically help with re-writing the manuscript. Improving the grammar of the manuscript and and aiming for clear communication should be the primary goal of this possible collaboration.
-Noted below in detailed comments for “Introduction,” but there needs to be a reframing of what exactly is novel about this study. It is not clear from the abstract nor introduction why this is novel and takes a step further than what others have done.
-There are numerous statements and conclusions made in relation to the temperature of the prepared food/oil and cookware. Temperature has been shown to be an important variable in cooking emissions before, and this study identifies it as being key as well. And yet there is no measurement of it? This is a large issue to my mind. At the very least, it needs to be explicit that temperature was not measured (if indeed it wasnt?) and that the statements made about temperatures are educated guesses. Or, if it was measured (again, not clear to me), then those data should be reported in some way.
-There is a pervasive sense that the AMS PMF “COA” factor is simply volatilized oil. This is suggested by the simple mass spectral comparison using Pearson’s R. However, the PM emissions factors of BBQ (where no rapeseed oil is mentioned with e.g., vegetable skewers, and very unfortunately is used for marinating steak—this would have been a nice opportunity to see what steak emissions are like in the absence of rapeseed oil (in the U.S. at least, few are marinating steak in oil like this…)) are going to largely not be associated with oil, and are orders of magnitude higher. There appears to almost be a “super-emitter” problem when comparing pan-prepared food vs. BBQ, which is shown by the steak vs. brats vs. fries calculations presented near the end. Some discussion of this super-emitter issue, especially when the emissions from BBQ are likely largely not rapeseed oil, deserves some attention, in my opinion.
Detailed comments:
Abstract:
L11: get rid of “both number and mass wise”
L13: get rid of “and parameters” (not clear what this means)
L13: “in THE form of…”
L13: This whole sentence needs to be re-written. Grammatically un-sound.
L13: “The emissions” would refer to everything—condensed and gas phase—so they way this is written is gas” coming at the end, as if that isnt a part of “emissions,” or is auxiliary.
L17: “For six variables, we observed changes during the cooking:” — this is very poorly worded, suggest re-writing this sentence.
L18: “Organics mass concentrations” should be “organic aerosol mass concentration.”
L20: “The emission dynamics of the above-mentioned variables” — this sentence is poorly-worded on a variety of levels.
L24: the last paragraph of the abstract is interesting, but again is very poor grammatically.
General comment: I started to copy-edit this for grammar from the beginning of my reading, but am stopping now because there are too many instances to deal with in my role as a reviewer. I will make a “big picture comment” on this issue above, but this paper needs a lot of help in improving the writing before it can be published.
Introduction:
-General comment: the literature on cooking is summed up fairly well, though I think one major omission that should be briefly mentioned is the extent to which cooking emissions may form secondary PM. This is one of the big open questions about cooking emissions and associated PM, in my opinion. I realize this is not the focus of the manuscript, but here in the introduction when you are trying to sum up the spectrum of what is important with cooing as an emissions source this should be mentioned.
-There is a disproportionate amount of time/space given towards summing up “the state of knowledge” in cooking emissions and their impacts. However, the end of the introduction does not, in my mind, really set up what is novel about this study in particular. I would suggest trying to reframe more specifically what is novel about your work, because it doesn’t shine through in reading your introduction.
Methods:
-section 2.1: is temperature measured? If so, how? For this to really be a “systematic” study of cooking emissions, I expected to see temperature being measured. It’s one of the key variables. I see no mention of what the ambient temperature of the experimental hall/kitchen is, nor, more importantly, what the temperature is of the food being cooked and/or cookware being used. This omission is striking given that temperature is mentioned within this paper as a key variable influencing emissions (e.g. L737: “The relevant parameters influencing the amount of cooking emissions are the cooking temperature, use of oil, ingredients, and activities during the cooking process.” or e.g. L386: “An increase of BC and PAH mass concentrations was observed only for cooking methods operating at high temperatures like grilling or in the final phase of preparing stir-fried dishes.” How is the following statement made in the absence of a temperature measurement? How is Figure 6 even constructed?). There is even mention of “an increase of the food and cookware temperature, as deduced from repeated temperature measurements” (L376), but no details of the measurement or actual quantification of temperature presented as far as I can tell?
-Simply assuming CE=1 “because liquid” does not seem appropriate. You measure plenty of BC in ambient cooking emissions at the market, to illustrate this point, and are doing . I strongly suggest taking the approach of reporting the “response factor,” similar to Katz et al., of RIE x CE. I understand that this equates to RIE when CE=1, but I think it is more honest to be clear that the response factor is the fundamental thing you are able to assess, as opposed to RIE since you aren’t measuring CE itself.
Results:
-general comment: I strongly recommend changing the way that your axes are labeled in all of your plots—you have the format of “variable / units.” This is something I rarely (maybe never?) see. Instead please change to the standard “variable (units).”
-Figure 2: these comparisons are are pinned to the spectra of rapeseed oil. But I don’t see any discussion of why that is the choice of the reference spectra. Please note why this choice, and what you lose/gain by showing only this comparison. Also, you could easily show the correlation with another reference spectra—currently, this figure is duplicating the displayed information by a factor of two. I would suggest refining this figure considerably and/or moving it to SI. If anything, it would be more interesting to show this same figure with some canonical “COA” spectra, as opposed to rapeseed oil.
-L226: “therefore we assume that also during field measurements the detected cooking-related emissions mostly consisted of vaporized and re-condensed oil.” This seems like a really big leap, given that a LOT of cooking is done with oil-temperature combinations where the smoke point is coming into play.
-Section 3.1.2: this reads more like a literature review instead of results. A lot of what you are describing could (and should) be presented visually.
-Table 3: why is HOA not included here? Suggest inclusion. It should be made much more clear what from this table is from the literature vs. this study.
Section 3.1.3: I’m confused why, after reading a page about m/z 60 and 73, why these are now absent or considered in any kind of rectangle plot here?
-Section 3.1.3: Given the abundance of experiments conducted, I am really surprised that this whole section—which is about relative fractions of 55 and 57—does not include a discussion on the variability of the actual molecular ion fragments, as e.g., “55” is a combination of C4H7+ and C3H5O+. Are these ratios always the same? This dataset seems rich is terms of helping us better understand what “55” means for cooking aerosol, perhaps even during different phases of cooking the same dish (speculating).
Section 3.1.
-Figure 7: I applaud this figure and the work that went into it. But for legibility, please consider adding text in the main figure as to what the symbols mean. I dont think it’s appropriate to make the reader have to refer to another figure to decipher this. I imagine you can just move around the images an add extra text boxes, but if limited space is really the issue then consider replacing the quantities (e.g., “1.5-5x”) with the activity (e.g., “Tilting pan”) while keeping the color-coded order of magnitude indicators.
-Section 3.1.4 - Why is there only a single marker on Figs 3 and 4 for rapeseed oil? Were there not replicates? No variation at all? Your assumption that so much of your emissions are essentially rapeseed oil seems to not square with the fact that there is very little overlap of the “RO” marker and any of the boxes from either of these plots.
-Section 3.1.4 - There is very little discussion about how your experiments do not have much overlap with the “COA” box in either of these plots (figs. 3 or 4). Also, it seems like you are presenting the COA box from entries in the AMS MS database. I would strongly suggest adding your own COA spectrum from the market to this as well for completeness.
-figure 9: pretty difficult to read. The background colors are pretty dark, and the light green markers barely stand out. One simple improvement would just be to make the markers black and not light green. I myself am not colorblind, but can imagine this presentation would be extra difficult for those who are. Worth verifying that the combinations of colors used in this plot are ‘colorblind-friendly’
-Figure 10: Strongly recommend moving BC and PAH EF results out of SI and into Figure 10. These figures can be made much smaller, without sacrificing the ability to read them, which will allow for the BC and PAH results to be shown in the main text. That “one unit” of barbecuing contributes a roughly equivalent amount of both BC and PAHs to the atmosphere as does “one unit” of traffic, is notable and worth putting in the main body.
-Figure 10: It becomes clear when sifting through the text what the units of “g” or “#” mean here, but it should be clear (or at least hinted at) within the Figure/figure legend itself. There needs to be some note that these are “Emissions per unit activity” or something along those lines, so that the reader understands that we are comparing e.g., a cooked dish to smoking two cigarettes to driving 100 km.-Section 3.5.2 - Recommendation: take the calculation one step further—tell the reader how many bratwursts/orders of fries/steaks these mass ranges equate to using some e.g., average unit weight. This will ‘make real’ this calculation for the reader in a way that “80 kg bratwurst” does not.
-Section 3.5.2 and Figure 12: the text alludes to the comparison being better after adjusting the response factor for COA, “the PM1 values align reasonably well with the one-to-one line.” There are so many data points on this plot that visual perception of the improvement of the comparison is suspect. The improvement can easily be quantified by e.g., reporting the slope of the fit, and should be.
-Section 3.5.2 - The following statement is made: “These calculated masses of food prepared per hour are all in a realistic order of magnitude (especially for the steak dish),” which implies that the combination of the laboratory emission factors for steak BBQ compares well/realistically to what is observed at the market. And yet, throughout the manuscript, there is a repeated assumption that the COA can largely be understood at volatilized oil (rapeseed oil specifically). However, barbecued steak is not cooked in oil and does not contain “oil;” most of the OA emissions from steak itself are presumably from volatilized fats, though emissions from flame/charcoal will also be a part of the mix from the market cooking. How do we square this?
Citation: https://doi.org/10.5194/egusphere-2023-2172-RC1 -
RC2: 'Comment on egusphere-2023-2172', Anonymous Referee #2, 22 Feb 2024
Summary:
This paper presents a detailed description of particulate matter emissions from cooking in controlled laboratory experiments (e.g., boiling, baking, frying, grilling) and at two German Christmas markets. An AMS, OPCs, Aethalometer, and other instruments were used to describe the physical and chemical properties of the cooking emissions.
Laboratory and ambient observations were connected using a few methods, which I found interesting and novel. (1) The amount of food prepared at the German markets was estimated using laboratory-derived cooking emission factors and estimated ambient emissions of cooking organic aerosol. (2) The chemical composition of ambient cooking organic aerosol was compared to laboratory cooking emissions using introduced rectangle plots. (3) Laboratory-derived quantification parameters for cooking organic aerosol were used to improve agreement between the AMS and collocated instruments.
The laboratory results are presented with great detail – the authors show which cooking actions (e.g., stirring, tilting, flipping, etc.) cause the greatest emissions of PNC, PM1, BC, etc. They also present a few methods for comparing AMS spectra and identifying the signatures of cooking emissions using a few specific tracer m/z. The authors conclude that aerosolized oil is mainly responsible for cooking emissions due to spectral comparisons between heated oil and the cooking emissions.
General comment:
I agree with the other reviewer - the grammar requires improvement before publication. For that reason, I selected major revision. However, I believe the scientific methods of the paper are sound and thorough, and the results are important and novel. The figures and content are of decent quality and require only minor revision.
Specific comments:
Line 160: “Mobile laboratory” and “MoLa” are side by side in this sentence – probably a typo?
Line 157: Did the wind direction correspond with the mobile laboratory being downwind of the Christmas markets? It would be helpful to include this detail in the text.
Line 192: How did you determine which spectra to compare to in the database? Were all spectra of a given type (e.g., OOA) utilized? It would be helpful to mention this briefly in the main text.
Section 3.1.3: Since you are using high-resolution AMS, why not mention the specific ion fragments in m/z 55 and 57 and the relative abundances of each?
Figure 3 and 4: I like these figures and find they provide a nice way to differentiate the source of primary OA. It may be a helpful reference to include oleic acid and/or other oils on the plot if they are available. Additionally, it would be very helpful for the reader to include a brief definition of what the rectangles represent in the figure caption (e.g., they represent the standard deviation of PMF factors from the literature?).
Section 3.1.4: A very nice and thorough summary of the quantification is presented here. One area for improvement is the discussion of CE. I think a reference is needed in line 322 to justify using a CE of 1. Or, as the other reviewer suggested, combining RIE and CE as one response factor may be justified since CE was not measured.
Section 3.1.4: After seeing the scatter in Figure 12 I am curious about the comparison between the AMS and other instruments during laboratory experiments. Did you see a high correlation for those experiments? It would be helpful to see the scatter plots (in the SI maybe?) showing how you derived the response factors. I think it’s important to show quantitatively that there was good agreement between the trends in signal reported by the collocated instruments before utilizing them to calculate a response factor.
Figure 6: I think there should be some explanation as to where the temperature data is coming from. In the methods you write that there were temperature measurements, but do not report them in the results section.
Figure 7: Please denote what the icons mean.
Line 423: You mention that the size distributions for the different methods were “partially significant.” If you aren’t referring to statistically significant, please use different language.
Line 540-545: I personally feel like too much text is used to describe the methods of the other study. Your summary which begins on line 546 suffices.
Line 552: The use of the labels “traffic” and “biomass burning” are somewhat misleading. To me, “traffic” implies multiple cars, but you are only including one car driving 100 km if I’m not mistaken? And again, to me “biomass burning” implies burning on a much larger scale that what you are using. Perhaps consider switching to “car” and “wood home heating” or something more clear, or indicate exactly what you mean in the figure caption.
Figure 12: What do you suspect is causing the scatter in this plot? How was your agreement during normal ambient sampling (not during the cooking market)? Was there a similar amount of scatter?
Line 702: How did you determine 20 to 80%? This seems arbitrary… Consider presenting only the PM1 and organics results since you have the PMF factor for support.
Figures S10: The enhancement in Chl is interesting. Why not report the other inorganic ion families from the AMS? Where do you suspect the Chl is from? It would be really interesting to see if NO3 was also emitted during these time periods.
Figure S11 and S12: During the Christmas markets there is a clear enhancement in concentration relative to the nighttime and mornings. Do you have data from the same time periods on days the market wasn’t happening? How do you know that some of the signal you are attributing to the market isn’t a daily occurrence during those time periods? Some controls would provide more confidence.
Citation: https://doi.org/10.5194/egusphere-2023-2172-RC2
Viewed
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 256 | 119 | 15 | 390 | 37 | 10 | 9 |
- HTML: 256
- PDF: 119
- XML: 15
- Total: 390
- Supplement: 37
- BibTeX: 10
- EndNote: 9
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1