the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 30 Jul 2025
Towards better black carbon emission estimates in Europe: assimilating observations with a Bayesian inversion framework
Abstract. There are large discrepancies between different black carbon (BC) bottom-up emission estimates. This affects our understanding of how BC affects population health and climate. Previous studies have presented top-down estimates in various domains but estimates in Europe for more than a few months are lacking. With the Lund University Modular Inversion Algorithm (LUMIA) and the FLEXible PARTicle dispersion model (FLEXPART), we aim to calculate top-down estimates of anthropogenic BC emissions by assimilating surface observations from 24 background sites in Europe (15° W–35° E, 33–73° N) during 2021. The results show that the bottom-up BC inventory generally underestimates emissions in the domain. Annually, the resulting top-down emissions are 411 ± 10 Gg of BC, 18 % higher than the bottom-up estimate of 349 ± 30 Gg used as a prior. The largest increases in emissions occur in Eastern parts of Europe during spring and summer, while emissions in Poland and Italy are reduced during winter and autumn. The overall posterior emissions are most sensitive to changes in the observational network on the periphery, resulting in a reduced standard deviation compared to the prior emissions in the central domain, where the network density is high. A cross-validation scheme, where one site at the time is removed for validation, show that the posterior for a majority of sites fit independent observations better than the prior emissions.
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Status: final response (author comments only)
- CC1: 'Comment on egusphere-2025-1568', Delong Zhao, 22 Sep 2025
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RC1: 'Comment on egusphere-2025-1568', Anonymous Referee #3, 07 Nov 2025
Summary of paper
This paper presents a top-down approach to estimate anthropogenic black carbon (BC) emissions in Europe in 2021 using a Bayesian inversion framework (LUMIA) coupled with the FLEXPART dispersion model. The paper is well organized and provides a thorough literature review with clear comparisons to previous studies. The authors find that the top-down (posterior) estimates are approximately 18% higher than the bottom-up (prior) inventory. Through detailed spatial and seasonal analyses, they identify regions where bottom-up inventories likely underestimate emissions. The study also includes comprehensive sensitivity and uncertainty analyses, offering valuable insights for improving emission inventories and informing future policy and modeling efforts.
With some minor edits, this manuscript should be published.
Questions
Line 76: You mention the error term but you don’t explain how it was calculated. Adding a brief sentence or two describing how it was calculated would help.
Line 101: “Calculating footprints backwards in time is numerically more efficient in our case, since we have fewer observational sites than grid cells”
Can you elaborate more on this? I’m not exactly understanding why this would be more numerically efficient.
Line 213:
“First, by changing the horizontal correlation length Lx from 500 to 250 and 1000 km (SCx.), and then by changing Lt from 14 to 7 and 21 days 215 (SCt.)”
Can you clarify this sentence? I’m a bit confused by the three numbers for Lx and Lt. What are you changing the numbers to and from?
Grammar
Line 69: should be “detail”
Line 80: there should be “the” before cost function
Line 95: there either needs to a new sentence starting at “Useful” or you need something before it like “thus it is”.
Line 155: you have “the” twice
Line 188: remove comma after “quality controlled”
Line 288: “to winter” should be in “in winter”
Line 333: “averaged” to “average”
Line 449: Black Carbon needs to be lower case
Line 449: change “This have” to “This has”
Line 450: Please clarify or reword this sentence: “less emissions in climate or atmospheric transport models may lead to underestimating of for example radiative forcing or air quality effects.”
Line 451: This sentence also needs to be redone: “In addition, this work is a first step in identifying what (missing or misrepresented) sectors in bottom-up inventories is the driver for this underestimation, information which can help guide future policy changes”
Change “identifying what” to “identifying which”. The last part after the comma can also be improved if you add “which provides information that can help..” or something like that.
Citation: https://doi.org/10.5194/egusphere-2025-1568-RC1 -
RC2: 'Comment on egusphere-2025-1568', Anonymous Referee #4, 23 Nov 2025
Overall Assessment
This study presents a valuable effort to refine estimates of black carbon emissions over Europe by applying a Bayesian inversion framework to surface observations. The topic is of clear importance for emission mitigation policies. However, the reliability of the conclusions requires stronger substantiation, and major revisions are necessary to address several key concerns.
Major Comments
1. A primary concern is the attribution of nearly all discrepancies between observed and simulated concentrations to emission errors, without sufficiently accounting for other potential sources of systematic bias. These include errors in the transport model, uncertainties in the conversion from light absorption to equivalent black carbon (e.g., the mass absorption cross-section), inaccuracies in background concentration estimates, and biases in wet/dry deposition parameterizations. The inversion framework appears to assume these errors are either negligible or adequately represented in the observation error covariance matrix R, yet no validation is provided for this critical assumption. Consequently, the extent and spatiotemporal structure of the reported "emission underestimation" may be significantly overstated.
2. The processing of observational data and site-specific quality control procedures lack sufficient detail, raising concerns about potential systematic biases. The manuscript would benefit from a clearer description of the quality control protocols, outlier handling, the sources and uncertainty propagation of instrument-specific correction factors (such as for the AE33), and the steps taken to ensure comparability across different instruments and sites. Given that measurement errors for black carbon are often systematic, failing to properly quantify and propagate these uncertainties risks misinterpreting observational biases as emission signals.
3. The validation of the inversion results relies heavily on the improved fit to the assimilated observations and the comparison with the prior emissions. There is a lack of validation using truly independent evidence, such as external observational datasets, independent emission inventories, or evaluations with independent model simulations. Without this, features like the reported seasonal emission enhancement in Eastern Europe could arise from model artefacts or sampling biases rather than representing true emission patterns.
4. The conclusions exhibit a strong dependence on several choices—including prior covariance length scales, key optical parameters (MAC, AAE), and data selection criteria (e.g., time-of-day sampling). However, a systematic sensitivity analysis of these factors is absent. Providing such an analysis is crucial for readers to assess the robustness of the findings.
5. The interpretation of identified features, such as emission hotspots and seasonal patterns, remains somewhat qualitative. A more in-depth discussion of the potential physical mechanisms—for instance, linking patterns to boundary layer dynamics, specific source activities (like agricultural burning or residential heating), or regional transport pathways—would greatly strengthen the discussion and provide deeper insight.
6. Notably, the real base inversion reported in the manuscript includes "negative emissions" accounting for 0.6% of the total annual emissions, a result that is physically implausible. The authors have not implemented any constraints to address this issue, which undermines the physical consistency of the inversion outcomes.Citation: https://doi.org/10.5194/egusphere-2025-1568-RC2
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- 1
This study addresses significant discrepancies in European black carbon (BC) emission inventories by employing the LUMIA inversion algorithm and FLEXPART dispersion model to assimilate observational data from 24 background sites during 2021, thereby providing the top-down estimates of anthropogenic BC emissions at an annual scale. The study’s innovative high-resolution inversion of European BC emissions is commendable, but flaws in background concentration treatment undermine its core conclusions. The claim of “18% underestimated European BC emissions” lacks credibility unless critical methodological gaps are resolved.
Line 109: The settings for wet deposition efficiency (rainout/cloud scavenging) significantly impact BC lifetime (4-10 days), yet no tests evaluate how different parameters (e.g., snow scavenging efficiency) affect posterior emissions.
The FLEXible PARTicle (FLEXPART) dispersion model has too many assumptions. How did the authors address this issue?
Lines 128-130: The manuscript claims that “total emissions are irrelevant for background calculations; only the ratio between intra- and extra-domain emissions matters.” While theoretically valid under proportional scaling assumptions (e.g., doubling both intra- and extra-domain emissions preserves their relative contribution ratio to background concentration ybg), this contains serious flaws in practical application. The authors tested the impacts of prior uncertainties and observational errors but did not test the sensitivity of ybg to extra-domain emission errors. The manuscript simultaneously states “all emission sources are assumed to have identical mass absorption cross-sections (MAC)” (Line 169), yet MAC values for extra-domain BC (e.g., dust-influenced regions) versus intra-domain BC (e.g., European vehicular emissions) differ significantly, further invalidating the proportionality assumption. Line 405 indicates: “At most, 52% of the average concentration is attributed to ybg at Jungfraujoch (jfj) and Pic du Midi (pmi).” The poor performance of these sites in cross-validation (Figure 7) directly reflects the unreliability of background concentration calculations. If ybg contains biases, the system will misattribute external errors to European emissions, rendering the results invalid. The reported “18% underestimation of European BC emissions” may partly compensate for extra-domain emission/transport errors rather than reflect actual missing sources. How did the authors address this issue?
Line 184: Correction factors (1.76) for the AE33 aethalometer at five stations relied on manual verification, with methods for other stations unspecified. This risks introducing systematic biases into observational data.
Line 200: The manuscript only selected afternoon data (low-altitude sites) or nighttime data (high-altitude sites), discarding >50% of valid observations despite intending to reduce boundary-layer modeling errors. The authors did not verify whether this operation introduces selection bias.
From Section 3.2, it can be seen that the measurement of BC utilized multiple instruments and assumed that the MAC values from different sources would not change. However, the AAE of BC is not exactly equal to 1. How did the author consider the impact of these errors on the posterior results?
Line 261: Remove the redundant parenthesis after “dashed and dotted lines”.
Line 365: Significant increases in Eastern European emissions during spring/summer (Figure 5) potentially originate from agricultural burning (discussed in Sect. 6), yet no comparison was made with satellite fire detection data or existing fire emission inventories (e.g., GFED) for verification. How did the authors address this issue?