Long-term evolution and effects of primary brown carbon aerosol in China

Chen, Guochao; Wang, Yiheng; Ying, Qi; Wang, Xiaofei; Zhang, Hongliang

doi:10.5194/egusphere-2026-1196

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https://doi.org/10.5194/egusphere-2026-1196

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25 Mar 2026

| 25 Mar 2026

Long-term evolution and effects of primary brown carbon aerosol in China

Guochao Chen, Yiheng Wang, Qi Ying, Xiaofei Wang, and Hongliang Zhang

Abstract. Brown carbon (BrC) is a light-absorbing component of organic aerosols that influences atmospheric environment and climate. Although, biomass burning is recognized as the major source of primary BrC (PBrC) globally anthropogenic sources can contribute comparably or more to PBrC in regions with intensive human activities, yet variations in concentrations and effects of PBrC remain underexplored in China where dramatic emission changes occurred in last two decades.

We apply an internal mixing model to simulate the long-term (2005–2020) variations of PBrC surface and vertical concentrations and their effects across China. The mean surface PBrC concentration is 0.81 μgC m^-3, with anthropogenic emissions dominating: residential, industrial combustion, and agricultural sectors contribute on average 57 %, 22 %, and 18 %, respectively, together accounting for 91 % of column concentrations in 2010. PBrC (-20.8 %) declined more than PM_2.5 (-8.1 %), accompanied by a slight reduction in O₃ and a decrease in direct radiative effect (DRE) from +0.032 W m^-2 in 2005 to +0.023 W m^-2 in 2020 (-25.7 %), with anthropogenic sources contributing 84.2 % of total DRE.

This study provides the first long-term assessment of PBrC trends, sources, and radiative effects in human-dominated regions, demonstrating that emission controls can deliver both environment and climate co-benefits.

Received: 03 Mar 2026 – Discussion started: 25 Mar 2026

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Guochao Chen, Yiheng Wang, Qi Ying, Xiaofei Wang, and Hongliang Zhang

Status: final response (author comments only)

RC1:
'Comment on egusphere-2026-1196', Anonymous Referee #1, 30 May 2026
This manuscript investigates the long-term sources, distributions, and impacts of primary brown carbon (PBrC) aerosol in China using a modeling framework with multiple PBrC sensitivity scenarios. The topic is important because brown carbon remains a major uncertainty in aerosol radiative effects, especially in regions with strong anthropogenic emissions. The study attempts to quantify contributions from different emission sources and evaluate uncertainties associated with optical properties, wildfire emissions, and aging processes. The manuscript has several strengths. The authors consider multiple PBrC scenarios, including a no-PBrC case, a baseline PBrC case, a no-wildfire case, a sensitivity case for the imaginary refractive index, and an OH-aging case. The authors also acknowledge several important uncertainties, including the exclusion of secondary brown carbon, fixed PBrC-to-OC ratios, and possible overestimation of wildfire emissions in FINN.
However, several aspects require clarification or revision before the conclusions can be fully supported. In particular, the scenario definitions and differencing methods need to be made clearer and more internally consistent. The model evaluation remains indirect for PBrC, relying mainly on OC and other aerosol-related constraints, and the implications of this limitation should be discussed more explicitly. The uncertainty treatment should also be strengthened, especially for optical properties, emissions, aging, and the exclusion of secondary brown carbon. Therefore, I recommend “major revision”.

Major Comments

The manuscript defines five scenarios: No_PBrC, PBrC_baseline, PBrC_nowldf, PBrC_k_adj, and PBrC_age. This scenario design is useful, but the explanation of how differences between scenarios are used to quantify effects needs revision. For example, the manuscript states that anthropogenic contributions were obtained from “PBrC_baseline minus PBrC_nowldf”. Since PBrC_nowldf is described as the baseline case excluding wildfire emissions, this difference appears to represent the wildfire contribution, not the anthropogenic contribution. If the authors intend to quantify anthropogenic contributions, the no-wildfire case itself may be closer to the anthropogenic component, depending on the treatment of other natural sources. Please clarify and correct this wording or methodology. Similarly, the manuscript states that the impacts of k variability were estimated from “PBrC_k_adj minus PBrC_nowldf”. This comparison appears to conflate two differences: optical property changes and wildfire emission changes. To isolate the effect of k variability, PBrC_k_adj should presumably be compared with PBrC_baseline, unless there is a specific reason to compare it with the no-wildfire case. Please clarify the rationale and ensure that each scenario difference isolates only the intended factor.

The tracer scaling method requires stronger physical justification. The authors scale PBrC mass by 10^-5and compensate by scaling the imaginary refractive index k by 10⁵ apparently assuming that PBrC optical effects scale linearly with However, AAOD is determined by the vertically integrated absorption coefficient, where , not simply by . Although influences MAC, MAC also depends on particle size, wavelength, density, mixing state, and the optical mixing rule. Since the model calculates aerosol optical properties using an internal-mixing core-shell assumption with shell properties determined by component volume fractions, reducing the PBrC mass by and increasing by may not produce an optically equivalent aerosol. The authors should demonstrate that this scaling preserves absorption coefficient, AAOD, heating rate, and radiative forcing by comparing the scaled-tracer method against an unscaled PBrC simulation. Otherwise, the resulting optical and radiative effects may be affected by numerical or physical artifacts. In addition, multiplying by could introduce physically unrealistic optical properties or numerical artifacts. The authors should justify why these specific scaling factors were selected and provide a validation test showing that the scaled-tracer method produces equivalent absorption, AAOD, heating rates, and radiative effects compared with an unscaled PBrC simulation

The manuscript evaluates carbonaceous aerosol performance using observed OC concentrations from published studies and reports a correlation of R = 0.61 between modeled and observed OC. This is helpful because PBrC is a subset of OC and shares similar emission, transport, and deposition processes. However, OC is still an indirect proxy for PBrC. The authors should more explicitly acknowledge that agreement with OC does not necessarily validate PBrC absorption, source-specific PBrC fractions, or radiative effects. If available, the authors should compare model results with observations of aerosol absorption, absorption Ångström exponent, single scattering albedo, organic aerosol absorption, BrC absorption, EC/OC ratios, or field-campaign measurements in China. If such observations are not available, the manuscript should clearly state this limitation and discuss how it affects confidence in the conclusions.

The manuscript acknowledges that SBrC is excluded and that this may lead to underestimation of BrC effects, especially in China. This is an important limitation. However, because the study discusses the impacts of brown carbon on atmospheric composition and radiative effects, the potential magnitude of this missing contribution should be discussed more quantitatively.

The authors should consider adding a discussion comparing their estimated PBrC effects with previous studies that include both primary and secondary brown carbon. If possible, provide an approximate range of how much total BrC absorption or radiative forcing could be underestimated due to the exclusion of SBrC.

The manuscript notes that PBrC emissions are estimated from OC emissions and fixed PBrC-to-OC ratios, and that this approach is constrained by limited chemical characterization and the lack of correction factors for technological improvements over time. This is a significant uncertainty, especially for a long-term study. The authors should provide more information on the PBrC-to-OC ratios used for different sectors and explain whether these ratios vary by fuel type, technology, region, or year. If fixed ratios are used over the entire period, the manuscript should discuss how changes in combustion technologies, emission controls, and fuel use may bias the temporal trends. A sensitivity analysis using alternative PBrC-to-OC ratios would be valuable.

The authors identify the imaginary part of the complex refractive index, k, as a major source of uncertainty and conduct a sensitivity test multiplying or dividing k by a factor of five. This is useful, but the results should be more clearly connected to the major conclusions. For example, do the main conclusions about trends, source contributions, or radiative impacts remain valid across the full range of k values? Please provide uncertainty ranges in the abstract and conclusions where appropriate.

The manuscript includes an OH-induced bleaching scenario and reports that OH aging reduced nationwide PBrC by approximately 3.9% and O₃ by less than 0.1% in 2010. The authors interpret this as a relatively minor effect under anthropogenically dominated and heavily polluted conditions. This is reasonable, but additional explanation is needed. Please clarify whether the OH-aging rate constant is applied uniformly to all PBrC sources and particle types. In reality, aging and bleaching may vary by source, chemical composition, coating state, and atmospheric conditions. The manuscript should discuss whether a single aging parameter could underestimate or overestimate aging effects for particular source categories.

It is not clear how you used the core-shell model to calculate optical properties. Are you considering core-shell for all types of particles, including organics, inorganics, organic-inorganic mixture, soot, etc.? What is the coating, and what is the core? What is their RI? What is the mixing ratio between core and shell? All of this information is missing. Did you use Mie simulation or T-matrix?

Please clarify which wavelengths were used for k, AAOD, AOD, and DRF.

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Minor Comments

The SI tables and figures should be labeled and numbered according to the order in which they are first cited in the main text.

The manuscript refers to Table S4 when discussing the scaled refractive index used to restore PBrC optical effects. Please ensure that Table S4 is cited in the correct order and contains all necessary optical parameters, including the real and imaginary refractive index values, wavelength dependence, and source assumptions.

Please ensure that all acronyms are defined at first use.

Please be consistent with PBrC and PBrA.

Could you clarify what you mean by carbon and non-carbon fractions/components inside PBrC? Are you referring to different elements? Or organic vs inorganic? But you mentioned “brown non-carbon organic matter” in line 145. What does that mean? How could organic be non-carbon?

Figure captions and axis labels should specify whether the reported quantity is PBrC carbon mass or total PBrA mass including non-carbon components.

The manuscript notes that OC is underestimated under heavily polluted conditions and attributes part of this bias to SOA underestimation [1]. Please discuss how this bias may affect simulated PBrC concentrations and radiative effects in highly polluted regions.

Should it be PBrC_k_adj minus PBrC_baseline in line 129?

Why estimate OA absorption using BC:OA ratio is only apply to biomass and biofuel combustion? Saleh et al (https://pubs.acs.org/doi/full/10.1021/acs.estlett.8b00305) and Cheng et al (https://www.tandfonline.com/doi/full/10.1080/02786826.2019.1566593) show this method can also be potentially applied to any type of combustion of organic fuels. Also, it is not clear to me what patterns and effects you refer to in line 143.

Can you make figures instead of a table for Table S1?

There is no significant difference between with and without PBrC in Fig. 1 b. Also, please justify the improvement you mentioned in lines 211 and 212.

Please explain what the BC-tracer method is.
Citation: https://doi.org/10.5194/egusphere-2026-1196-RC1
RC2: 'Comment on egusphere-2026-1196', Anonymous Referee #2, 17 Aug 2026

Review of Chen et al

Comments from the reviewer
This manuscript investigates the long-term (2005–2020) evolution and atmospheric impacts of primary brown carbon (PBrC) aerosols in China – a region marked by intensive human activity and substantial emission shifts over the study period. Using an internal mixing model, the authors comprehensively simulate PBrC surface and vertical concentrations, source contributions, direct radiative effects (DRE), and impacts on formation across the entire country. A key strength of this work is its nationwide scope rather than a narrow regional focus. While using the PBrC/OC ratio method leads to a recognized underestimation of the PBrC DRE, the authors adequately justify this approach. Overall, despite this limitation, I recommend accepting the manuscript for publication after minor revisions.
Comments are given below:
Major comments:
1) Page 12, Line 300-305: The discussion in this section and in other relevant parts (Lines 345-350) of the manuscript (e.g., the graphical abstract) seems counterintuitive. The manuscript reported that as PBrC decreases, its suppression of weakens – meaning PBrC's "braking effect" on formation is removed. This should actually contribute to the increase in , but that is not the case it seems (look at graphical abstract). Please explain/clarify?
2) Please explain why and levels declined at different percentage rates between 2005 and 2020. While the authors briefly note that levels were dominated by the power and industrial sectors whereas was influenced mainly by agriculture, it remains unclear how these distinct sources caused the differing decline rates. Government clean air actions typically target regulated and organized sectors like industry rather than unorganized sectors like agriculture or residential heating; thus, further clarification on how these policy dynamics affected the respective emission trends would strengthen the discussion.
Minor comments:
Page 1, Lines 14 and 16: comma missing after “globally” and “underexplored in China”.
Page 2, Line 40: “Although the chemical consist of BrC remains some debates,” Confusing, rewrite it.
Page 3, Line 53: “in regional scale” is redundant.
Page 3, Line 56: “absorptions” should be “absorption”.
Page 3, Line 61: Year information is missing for the citation.
Page 7, Line 184: should be “remains poorly constrained…”
Page 7: Line 188-189: The sentence is confusing: “model’s performance in PM2.5 and gaseous O3 concentrations”.
Page 7: Line 188-189: should be “physical and chemical processes”
Fig. S4: AOD wavelength should be same (380 nm) for both simulated and observed values. Pls recheck.’
Fig S8: Type mistake. In think figure ‘b’ is for ‘TRAN’ not for ‘WSTE’.
Line 377: Typo: “stud3y”?
Line 378: Should be Figure S11. Please check.
Line 396: “insensive”?

Citation: https://doi.org/10.5194/egusphere-2026-1196-RC2

Guochao Chen, Yiheng Wang, Qi Ying, Xiaofei Wang, and Hongliang Zhang

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https://doi.org/10.5194/egusphere-2026-1196-supplement

Guochao Chen, Yiheng Wang, Qi Ying, Xiaofei Wang, and Hongliang Zhang

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Short summary

Brown carbon (BrC) aerosols absorb solar shortwave radiation and affect the environment and climate. Using a chemical transport model, we investigated the sources and long-term trends of primary BrC in China. Residential and industrial emissions dominate primary BrC in China, unlike the global pattern. Anthropogenic BrC accounts for most of the direct radiative effects and responds significantly to emission changes, indicating potential climate benefits of emission control measures.


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