the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 26 Jul 2024
Characterization of Brown Carbon absorption in different European environments through source contribution analysis
Abstract. Brown carbon (BrC) is a fraction of Organic Aerosols (OA) that absorbs radiation in the ultraviolet and short visible wavelengths. Its contribution to radiative forcing is uncertain due to limited knowledge of its imaginary refractive index (k ). This study investigates the variability of k for OA from wildfires, residential, shipping, and traffic emission sources over Europe. The MONARCH atmospheric chemistry model simulated OA concentrations and source contributions, feeding an offline optical tool to constrain k values at 370 nm. The model was evaluated against OA mass concentrations from Aerosol Chemical Speciation Monitors (ACSM) and filter sample measurements, and aerosol light absorption measurements at 370 nm derived from AethalometerTM from 12 sites across Europe. Results show that MONARCH captures the OA temporal variability across environments (regional, suburban and urban background). Residential emissions are a major OA source in colder months, while secondary organic aerosols (SOA) dominate in warmer periods. Traffic is a minor primary OA contributor. Biomass and coal combustion significantly influence OA absorption, with shipping emissions also notable near harbors. Optimizing k values at 370 nm revealed significant variability in OA light absorption, influenced by emission sources and environmental conditions. Derived k values for biomass burning (0.03 to 0.13), residential (0.008 to 0.13), shipping (0.005 to 0.08), and traffic (0.005 to 0.07) sources improved model representation of OA absorption compared to a constant k. Introducing such emission source-specific constraints is an innovative approach to enhance OA absorption in atmospheric models.
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CC1: 'Comment on egusphere-2024-2086', Ye Kuang, 27 Jul 2024
Some latest advancements in derivations of brown carbon absorptions from multiwavelength aerosol absorption measurements that account for the spectral dependence[Li et al., 2019; Luo et al., 2022; Wang et al., 2018] of AAE_BC should be included and discussed. In addition, the first percentile of AAE_BC might suffer from systematic bias from covariance of BrC influences and variations embedded in AAE_BC. The used method is based on the assumption that when influence of BrC is negligible, the AAE_BC is smallest. However, AAE_BC itself varies a lot, the smallest AAE_BC likely does not correspond to situations when influence of BrC is negligible, the representativeness of the first percentile is questionable. Indeed, 0.928 to 1.088 looks reasonable, however, the so-called better consideration is not better than using 1.
Li, Z., et al. (2019), Light absorption properties and potential sources of particulate brown carbon in the Pearl River Delta region of China, Atmos. Chem. Phys., 19(18), 11669-11685.
Luo, B., et al. (2022), Parameterizations of size distribution and refractive index of biomass burning organic aerosol with black carbon content, Atmos. Chem. Phys., 22(18), 12401-12415.
Wang, J., et al. (2018), Light absorption of brown carbon in eastern China based on 3-year multi-wavelength aerosol optical property observations and an improved absorption Ångström exponent segregation method, Atmos. Chem. Phys., 18(12), 9061-9074.
Citation: https://doi.org/10.5194/egusphere-2024-2086-CC1 -
AC1: 'Reply on CC1', Hector Navarro-Barboza, 29 Jul 2024
We thank the comment of Ye Kuang highlighting the uncertainties introduced by the selection of AAE_BC. Indeed, both experimental methods and modeling approach are prone to uncertainties when the AAE_BC is determined. For example, in the papers cited in the comment, the spectral dependence of AAE_BC is considered but under the strong assumption that BrC particles do not absorb at 520 nm. It has been shown that the BrC contribution at 520 nm can reach up to more than 30% (e.g. Cuesta-Mosquera et al., 2024). In order to take into account this, other studies have calculated the AAE_BC using absorption measurements between 880 and 950 nm where the assumption of negligible BrC contribution is very reasonable. However, the AAE_880-950 can have a high uncertainty at some sites, and especially at more remote environments, where the aethalometer signal at 950 can be very low.
We also agree with Dr. Kuang that the smallest AAE_BC could not always correspond to situations when influence of BrC is negligible. This might happen at sites where BrC particles dominate the absorption throughout the year which is not commonly observed at the surface. Moreover, the fact that the values of the 1th percentile presented here lie between 0.9 and 1.1 further confirm a reasonable removal of BrC signal in the determination of AAE_BC.
Due to the general uncertainty associated with the determination of AAE_BC, here we preferred to follow the approach from Tobler et al. (2021). Following this approach, the calculation of 1st percentiles were performed after filtering the AAE values when the fit had R2>0.99. This should somehow also solve the possible spectral dependence of AAE_BC. Moreover, recently, Zhang et al. (2020) have reported an uncertainty of approximately 11% in the estimation of the BrC contribution to total absorption at 370 nm when using different AAE_BC values ranging from 0.9 and 1.1.
This is a reasonable uncertainty considering the overall uncertainty of the AAE method. Moreover, also the modeling part presented in this work is prone to uncertainties and any change of AAE_BC can add uncertainties that however lie well within the overall uncertainty of the approach presented in this manuscript.
Based on the above consideration, we prefer to keep here the use of the 1th percentile to determine the AAE_BC based on aethalometer data only.
In order to consider Dr. Kuang comments, the following sentence (line 152 of the submitted manuscript):
“……where AAE_BC is the Absorption Angstrom Exponent (AAE) of BC, which allows for the calculation of babs,BC(λ) (in units of Mm−1) from the measurements of babs,BC(λ) at 880 nm assuming that BrC does not absorb at 880 nm (e.g., Qin et al., 2018). The main source of uncertainty in equations 1 and 2 is the AAE assumed for BC. In many studies a value of 1 was used (Liakakou et al., 2020; Tian et al., 2023; Cuesta-Mosquera et al., 2023, e.g.,). However, theoretical simulations have shown that the AAE_BC can reasonably vary between 0.9 and 1.1 depending on the size and internal mixing of BC particles (Bond et al., 2013; Lu et al., 2015, e.g.,). Here we estimated the site dependent AAE_BC as the first percentile of the AAE frequency distribution. The AAE can be calculated from multi-wavelengths (370, 470, 520, 590, 660, 880, and 950 nm) total absorption measurements as the linear fit in a log-log of the total absorption versus the measuring wavelengths. The effect of BrC absorption is to increase the AAE and, consequently, the first percentile of AAE represents conditions where the absorption is dominated by BC. In order to reduce the noise, the 1st percentile at each site was calculated from AAE values obtained from fit with R2 > 0.99 (Tobler et al., 2021). For sites included here, the 1st percentile method provide AAE_BC values ranging from 0.928 to 1.088 confirming that this experimental method can provide reasonable estimations of the AAE_BC.”
Will be modified as follow during the review process:
“……where AAEBC is the Absorption Angstrom Exponent (AAE) of BC, which allows for the calculation of babs,BC(λ) (in units of Mm−1) from the measurements of babs,BC(λ) at 880 nm assuming that BrC does not absorb at 880 nm (e.g., Qin et al., 2018). The main source of uncertainty in equations 1 and 2 is the AAE assumed for BC. In many studies a value of 1 was used (Liakakou et al., 2020; Tian et al., 2023; Cuesta-Mosquera et al., 2023, e.g.,). However, theoretical simulations have shown that the AAEBC can reasonably vary between 0.9 and 1.1 depending on the size and internal mixing of BC particles (Bond et al., 2013; Lu et al., 2015, e.g.,). Here we estimated the site dependent AAEBC as the first percentile of the AAE frequency distribution. The AAE can be calculated from multi-wavelengths (370, 470, 520, 590, 660, 880, and 950 nm) total absorption measurements as the linear fit in a log-log of the total absorption versus the measuring wavelengths. The effect of BrC absorption is to increase the AAE and, consequently, the first percentile of AAE represents conditions where the absorption is dominated by BC. In order to reduce the noise, the 1st percentile at each site was calculated from AAE values obtained from fits with R2 > 0.99 (Tobler et al., 2021). The applied R2 filtering also helped reducing the uncertainties associated to the fact that AAEBC can vary as a function of wavelength. For example, Wang et al. (2018) and Li et al. (2019) used a combination of Mie theory and experimental data to explore the wavelength dependence of AAEBC and proposed an estimation of babs,BrC(λ) based on the ratio between the AAE calculated from 370 to 520 nm and from 520 to 880 nm. However, this methodology assumed that BrC particles do not absorb at 520 nm whereas it has been shown that the contribution of BrC to absorption at this wavelength can be high (e.g. Cuesta-Mosquera et al., 2024). As a consequence, other studies (e.g. Zhang et al., 2018; Luo et al., 2022) used the AAE calculated from 880 to 950 nm to calculate the AAE_BC assuming that BrC particles do not absorb in the near_IR. Nevertheless, the latter methodology may suffer from additional uncertainties related to the possible low aethalometer signal at 950 nm, frequently observed especially at more remote sites. Thus, it should be considered that the methodologies proposed to estimate AAE_BC, including the use of the 1st percentile applied here, are prone to uncertainties. On the other hand, Zhang et al. (2020) have reported an uncertainty of approximately 11% in the estimation of the bAbs,BrC370 contribution to bAbs370 when using different AAE values ranging from 0.9 and 1.1. For the sites included here, the 1st percentile method provides AAE_BC values ranging from 0.928 to 1.088 confirming that this experimental method can provide reasonable estimations of the AAE_BC.”
References:
- Cuesta-Mosquera, A., Glojek, K., Močnik, G., Drinovec, L., Gregorič, A., Rigler, M., Ogrin, M., Romshoo, B., Weinhold, K., Merkel, M., et al. (2023). Optical properties and simple forcing efficiency of the organic aerosols and black carbon emitted by residential wood burning in rural Central Europe. EGUsphere, 2023, 1–34.
- Tobler, A. K., Skiba, A., Canonaco, F., Močnik, G., Rai, P., Chen, G., Bartyzel, J., Zimnoch, M., Styszko, K., Nęcki, J., et al. (2021). Characterization of non-refractory (NR) PM1 and source apportionment of organic aerosol in Kraków, Poland. Atmospheric Chemistry and Physics, 21, 14893–14906.
- Zhang, Y., Albinet, A., Petit, J.-E., Jacob, V., Chevrier, F., Gille, G., Pontet, S., Chrétien, E., Dominik-Sègue, M., Levigoureux, G., et al. (2020b). Substantial brown carbon emissions from wintertime residential wood burning over France. Science of the Total Environment, 743, 140752.
- Qin, Y. M., Tan, H. B., Li, Y. J., Li, Z. J., Schurman, M. I., Liu, L., Wu, C., and Chan, C. K.: Chemical characteristics of brown carbon in atmospheric particles at a suburban site near Guangzhou, China, Atmospheric Chemistry and Physics, 18, 16 409–16 418, 2018.
- Liakakou, E., Stavroulas, I., Kaskaoutis, D., Grivas, G., Paraskevopoulou, D., Dumka, U., Tsagkaraki, M., Bougiatioti, A., Oikonomou, K., Sciare, J., et al. (2020). Long-term variability, source apportionment and spectral properties of black carbon at an urban background site in Athens, Greece. Atmospheric Environment, 222, 117137.
- Tian, J., Wang, Q., Ma, Y., Wang, J., Han, Y., and Cao, J.: Impacts of biomass burning and photochemical processing on the light absorption of brown carbon in the southeastern Tibetan Plateau, Atmospheric Chemistry and Physics, 23, 1879–1892, 2023.
- Bond, T. C., Doherty, S. J., Fahey, D. W., Forster, P. M., Berntsen, T., DeAngelo, B. J., Flanner, M. G., Ghan, S., Kärcher, B., Koch, D., et al.: Bounding the role of black carbon in the climate system: A scientific assessment, Journal of geophysical research: Atmospheres, 118, 5380–5552, 2013.
- Lu, Z., Streets, D. G., Winijkul, E., Yan, F., Chen, Y., Bond, T. C., Feng, Y., Dubey, M. K., Liu, S., Pinto, J. P., et al.: Light absorption properties and radiative effects of primary organic aerosol emissions, Environmental science & technology, 49, 4868–4877, 2015.
- Wang, J., et al. (2018), Light absorption of brown carbon in eastern China based on 3-year multi-wavelength aerosol optical property observations and an improved absorption Ångström exponent segregation method, Atmos. Chem. Phys., 18(12), 9061-9074.
- Li, Z., et al. (2019), Light absorption properties and potential sources of particulate brown carbon in the Pearl River Delta region of China, Atmos. Chem. Phys., 19(18), 11669-11685.
- Luo, B., et al. (2022), Parameterizations of size distribution and refractive index of biomass burning organic aerosol with black carbon content, Atmos. Chem. Phys., 22(18), 12401-12415.
Citation: https://doi.org/10.5194/egusphere-2024-2086-AC1 -
CC2: 'Reply on AC1', Ye Kuang, 30 Jul 2024
Thanks for your quick and nice response, we should discuss further, please see the attached file for comments.
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AC2: 'Reply on CC2', Hector Navarro-Barboza, 18 Nov 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2086/egusphere-2024-2086-AC2-supplement.pdf
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AC2: 'Reply on CC2', Hector Navarro-Barboza, 18 Nov 2024
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AC1: 'Reply on CC1', Hector Navarro-Barboza, 29 Jul 2024
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RC1: 'Comment on egusphere-2024-2086', Anonymous Referee #1, 06 Sep 2024
This paper aims to investigate the light absorption properties of OA at different environments in Europe using modeling techniques and experimental approaches to constrain specific k indexes for OA originating from different emission sources such as fires, residential, shipping, traffic, and others.
Major comments:
- This is a very long paper. I would recommend that you go through to confirm that everything is needed in the main text or in the paper in general to make your points. You have an SI, but it’s quite short, especially relative to the long main text.
- Figure 3 could probably be better communicated in words or the figure could go in the SI.
- Similarly Table 3 should be in the SI and then referenced.
- Related to Table and Figure 4, can you help the reader better understand any trends you are attempting to make across sites? You are showing a lot of data, so I wonder if it might be more compelling to focus your main figures on large takeaways and then put these thumbnail plots in the SI. What are the big overarching takeaways that you want the reader to know about modeling emissions/absorption across locations? Across seasons? Etc.?
- Perhaps instead of so many figures with separate thumbnails for each site, think through some interesting summary figures that help people pull out the main takeaways.
- If you are going to keep Figure 8, then it should be better saturated to show differences. It’s all blues and greens and not touching the top of your color bar.
- If Figure 9 is meant to be compared to Figure 6, then perhaps a summary figure that helps the reader see the comparison would be more useful. I am not fully convinced that the current figure is needed. Is your main point that the derived k performs better than an average one? Make that point in one sentence and then cite to a statistic showing that, and if you really desire, put this plot in the SI.
- For Figure 10, similar points to above about Figure 9.
- In the summary and conclusions, you don’t need to go over your major methods in a ton of detail again. Make your main points clearly and efficiently.
Minor comments:
- Wording could also be more concise. For example line 27 (“BrC is originating”) should be BrC originates.
- For Figure 1, I would define BrC and BC with the colors that you’re using in the plot instead of labeling all of them in black to the side of each pie chart.
- In Table 5 and Figure 5, please give the cases descriptive names so that the reader can better follow along.
Citation: https://doi.org/10.5194/egusphere-2024-2086-RC1 -
AC3: 'Reply on RC1', Hector Navarro-Barboza, 18 Nov 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2086/egusphere-2024-2086-AC3-supplement.pdf
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RC2: 'Comment on egusphere-2024-2086', Anonymous Referee #2, 11 Sep 2024
This paper presented a method to characterize Brown carbon light absorption from different sources. I think this manuscript provides useful information for the model simulation. There are some concerns I have.
Major comments:
- The manuscript is too long. Please consider avoiding such long papers in the future.
- For OA mass concentration, how do you account for the collection efficiency?
- For equations 1 and 2, it should be noted that many current studies show BrC can also absorb at NIR ("Optical Properties of Individual Tar Balls in the Free Troposphere", "Shortwave absorption by wildfire smoke dominated by dark brown carbon", and "Brown carbon absorption in the red and near-infrared spectral region"). Thus, assuming only BC absorbs light at 880 nm will underestimate the babs of BrC. I suggest adding some relevant discussions. This might explain why your k is so low.
- It is not clear to me why you chose 370 nm instead of 550 nm for the discussion of light absorption, which is widely used for discussing aerosol optical properties. Could you justify that?
- Your k at and babs at 370 nm seems too low to me. some babs even lower than some literature values reported from Arctic (see "On Aethalometer measurement uncertainties and an instrument correction factor for the Arctic" and "On Aethalometer measurement uncertainties and an instrument correction factor for the Arctic").)
Specific comments
- Line 175-183, The discussion of BC and BrC sources lacks the evidence. Do you make these conclusions only based on the contribution? Or can any references support you? Or other data was involved.
- Line 214-215. It is not obvious to me how you make these assumptions. Why do you assume 50% hydrophobic species with OA/OC = 1.4 and all hydrophilic oxygenated components have OA/OC=2.1? How about other 50% hydrophobic species?
- Line 215-216: It is unclear how you get the conversion lifetime of 1.15 days.
- Do you have references fo these values in table 2, or did you derived them?
Citation: https://doi.org/10.5194/egusphere-2024-2086-RC2 -
AC4: 'Reply on RC2', Hector Navarro-Barboza, 18 Nov 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-2086/egusphere-2024-2086-AC4-supplement.pdf
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