Widespread 2013-2020 decreases and reduction challenges of organic aerosol in China

Domain: earth_science
Published: 2024-05-25
Editorial outcome: accept

Abstract
High concentrations of organic aerosol (OA) occur in Asian countries, leading to great health burdens. Clean air actions have resulted in significant emission reductions of air pollutants in China. However, long-term nation-wide trends in OA and their causes remain unknown. Here, we present both observational and model evidence demonstrating widespread decreases with a greater reduction in primary OA than in secondary OA (SOA) in China during the period of 2013 to 2020. Most of the decline is attributed to reduced residential fuel burning while the interannual variability in SOA may have been driven by meteorological variations. We find contrasting effects of reducing NO x and SO 2 on SOA production which may have led to slight overall increases in SOA. Our findings highlight the importance of clean energy replacements in multiple sectors on achieving air-quality targets because of high OA precursor emissions and fluctuating chemical and meteorological conditions.

Introduction
Long-term exposure to ambient particles that have an aerodynamic diameter of 2.5 μm or smaller (PM 2.5) is associated with millions of global premature deaths per year 1. Clean air actions are widely taken to mitigate air pollution from PM 2.5, leading to significant air-quality improvements in North America, Europe, and East Asia 2. Remarkably, the emissions of SO 2 and NO x were reduced by 70% and 28%, respectively, from 2013 to 2020 in China under two key policies, namely the Air Pollution Prevention and Control Action Plan (2013–2017) and the Three-year Blue-sky Action Plan (2018–2020) 3. The mean PM 2.5 concentrations of annual averages for 74 key Chinese cities decreased from 72 μg m−3 in 2013 to 34 μg m−3 in 2020 4. Despite these advances, the PM 2.5 concentrations in most countries still far exceed the World Health Organization (WHO) new guideline of annual mean of 5 μg m−3 (see ref. 2). Further reduction of PM 2.5 is challenged by high fraction of organic aerosol (OA) that has an issue of a great complexity but a limited understanding of its precursors and secondary formation pathways 5,6.

The OA precursors may remain in the particle phase after being emitted to form primary OA (POA) or be oxidized in the atmosphere to form secondary OA (SOA). They consist of thousands of volatile (VOC), intermediate-volatility (IVOC), semivolatile, and low-volatility (S/LVOC) organic compounds 6. Because of a large consumption of residential coal and biofuel and a rapid expansion of industrial activities, anthropogenic emissions of the OA precursors in China and India have become several times higher than those in developed countries 7–9. Correspondingly, SOA campaign-mean concentrations reached 30–40 μg m−3 in Chinese and Indian cities 10–12.

Although China’s 2013–2020 pollution-control efforts led to significant emission reductions, rare studies have examined nationwide long-term trends in OA. Responses of OA concentrations to precursor emissions can be quite complicated due to the nonlinearity of gas-particle partitioning and secondary processes 4. Meteorological variations and changes of biomass burning and biogenic emissions may cause extra interannual variations of OA 13–15. Changes of anthropogenic emissions of SO 2, NO x, and OA precursors not only affect anthropogenic OA but also particle acidity and gas-particle partitioning and consequently biogenic SOA formation 16–18. The lack of understanding of the mitigation effectiveness, meteorological impacts, and anthropogenic–biogenic interactions associated with such high loadings of anthropogenic OA hinders the developments of effective control measures in future.

Here, we combine comprehensive surface measurement analysis and state-of-the-art OA simulations to elucidate the long-term variations of OA and their causes in China. We first used ambient observations to discover the OA trends during 2013–2021. We then simulated the OA concentrations with a revised, nested version of global chemical transport model driven by a newly developed emission inventory of OA precursors over the full-volatility range. Model results were validated by observations. A series of model sensitivity runs were conducted to examine the impacts of various drivers on OA during the action-plan period, such as changes in precursor emissions, variabilities in meteorological conditions, control of other pollutants, and changes in natural emission sources. Our results highlight the challenges of controlling OA pollution to reduce the health burden in developing countries, which may aid the acceleration of policy developments on clean-energy transition and technological innovations in the industry.

Results
Observed decreases of OA in China
Long-term continuous measurements of particulate organic carbon (OC) have been conducted in a recently developed national monitoring network for PM 2.5 chemical composition in North China Plain (NCP) and provincial-level monitoring sites in Yangzi River Delta (YRD) since 2015. Measurements in earlier years are only available at some research sites. As shown in Fig. 1a, c, the annual mean OC concentrations decreased by 46% in NCP from 2017 to 2021 and 36% in YRD from 2015 to 2021. Meanwhile, annual PM 2.5 concentrations decreased by about 30% in the two regions 4. Relatively greater OC concentrations present in 2019, which deviates slightly from the general declines. The NCP data further indicate that annual OC concentrations decreased most in winter among the four seasons (Supplementary Fig. S1).

To explore the changes in POA versus SOA, we examined the OA source apportionment results obtained from aerosol mass spectrometer (AMS) measurements in 162 field campaigns across China (Supplementary Table S1 and Fig. 1b). Each campaign covers a short period of 2 weeks to 3 months. NCP has the highest regional-mean OA concentration of 23.1 μg m−3 in contrast to the region-mean concentrations of 13.5–15.5 μg m−3 in YRD, Northwest (NW), and Pearl River Delta (PRD) (Supplementary Table S2). POA accounts for about 50% of OA in north regions (NCP and NW) and about 40% in south regions (YRD and PRD), reflecting a spatial difference in sources 19. The data in five cities from different regions in China indicate clear OA declines in winter, accompanied with increasing SOA mass fractions in three of the cities (Fig. 1e, f). In Beijing, where the campaign data covers over 50% of the period of 2013–2020, the declines of OA and the increases of SOA fractions present in all seasons, with more significant changes in winter. Moreover, all-campaign-mean OA concentrations decreased from 20.6 to 9.0 μg m−3, and SOA mass fractions increased from 53 to 69% (Supplementary Table S3). To conclude, the observations suggest widespread decreasing OA concentrations with increasing SOA mass fractions in China.

Drivers of interannual variations of POA and SOA
The decline of OA follows the emission reduction of OA precursors. We developed VOC, IVOC, and SVOC emission inventories for China that reflect the changes of fuel uses and end-of-pipe control measures from 2013 to 2020 (Fig. 2a–c). Anthropogenic emissions of S/LVOC are estimated to be 2.6–4.0 Tg y−1, among which about 1.4–2.5 Tg y−1 are emitted as POA. The S/LVOC emissions decreased by 35% from 2013 to 2020, and 70% of the reduction occurred during the 2013–2017 period. The replacement of residential stoves with natural gas and electric stoves contributes 56% of the S/LVOC reduction, especially in winter (Supplementary Fig. S2) 3,20. The estimated anthropogenic IVOC emissions are 6.5–7.2 Tg y−1, showing an 8% decrease from 2013 to 2020. Rapid expansion in solvent use leads to high emissions of volatile chemical products that offset the reductions in residential and transportation emissions 8,21. In addition, about 35% of non-methane VOC emissions (mostly aromatic compounds) may act as OA precursors (i.e., 9.0–10.3 Tg y−1). The high aromatic fraction in China is explained by the rapid increase in solvent use and industrial activities since 2000 8. The economic slowdown during the COVID-19 outbreak led to a significant decrease of anthropogenic emissions in 2020. Overall, our estimated annual emissions of IVOC and S/LVOC are comparable with other nationwide inventory values with relative differences of 11–36% (Supplementary Fig. S3) 7,22–24. Compared to the emissions in the United States 9, the annual emissions of IVOC and S/LVOC in China are about 2 times higher but with much less contributions from open biomass burning (Supplementary Fig. S4).

We applied these emissions in GEOS-Chem and conducted simulations under assimilated meteorology with key model updates that improve the model performance on PM 2.5 chemical components and surface hydroxyl radical (OH) concentrations 19. The model can reproduce the decreasing trends in OC in NCP and YRD, and the simulated OC concentrations agree well with the observations (Supplementary Fig. S5). For all-site comparisons, the normalized mean bias (NMB) is 0.01 and the Pearson correlation coefficient (r) is 0.61 (Fig. 1d). Furthermore, model simulations reasonably capture the observed POA (NMB = −0.05; r = 0.87) and SOA (NMB = −0.26; r = 0.54), and the comparisons do not indicate significant regional or seasonal model biases (Fig. 1g, h and Supplementary Fig. S5). Statistical values are similar for the main regions of NCP, YRD, and PRD (Supplementary Table S2). Note that yearly model comparisons to campaign observations are statistically less meaningful, showing relatively greater NMBs for smaller sample sizes (Supplementary Table S3). On average, S/LVOC and IVOC together contribute to about 75% of the simulated SOA in China. Such a high contribution is consistent with the findings from ambient SOA potential analysis and other modeling work in regions of high anthropogenic emissions 25–28.

Figure 2d, e shows the simulated annual mean concentrations of POA and SOA in China. The population-weighted POA concentrations exhibit a steady decline of 53% from 7.9 μg m−3 in 2013 to 3.7 μg m−3 in 2020, while SOA shows a less reduction of 25% from 7.7 to 5.8 μg m−3. Spatially, the reduction in POA mainly occurs in NCP where the residential emissions are high (Fig. 2f, g), which is consistent with the greater OA decline in Beijing than in other cities (Fig. 1e). By contrast, the decrease of SOA is widespread because of atmospheric processing of diverse precursors despite of the scattered changes in precursor emission (Supplementary Fig. S6). For a particular case in southern Sichuan, intensive forest fires in 2020 cause significant enhancements in POA and SOA (Supplementary Fig. S7). Nationally, POA contributes to two-thirds of the OA decline from 2013 to 2020. The mass fraction of SOA in OA becomes greater in most regions (Supplementary Fig. S8), and the average fraction of SOA increases from 49 to 61%, which are consistent with the AMS observations (Fig. 1e, f and Supplementary Table S3).

Under fixed meteorological inputs or emissions, the model results further indicate that the emission changes in OA precursors is the predominant factor to lower OA concentrations from 2013 to 2020, especially during the first phase of 2013–2017 (Fig. 3a, b). The annual reduction of POA emissions relative to 2013 is about 3–9%, corresponding to 1–15% of annual decline in POA concentrations (Supplementary Fig. S9). Meteorological variations may have led to additional decreases of annual mean POA by 0–6% (Fig. 3c). Unlike POA, both positive and negative annual changes of SOA present when changes in SOA precursor emissions are limited (Supplementary Fig. S9). The modeled meteorology-driven changes of annual mean SOA range from −5 to 6% (Fig. 3d), which explains the majority of the year-to-year variations of SOA on top of the emission-driven decline from 2013 to 2020. The year of 2019 appears to be most meteorologically unfavorable for reducing OA, which agrees with the observed high OC concentrations in NCP and YRD (Fig. 1c). Compared to anthropogenic emissions, natural changes in wildfires and biogenic emissions play negligible roles on OA interannual variability (Supplementary Fig. S10).

To identify key meteorological parameters that affect OA, we examined the correlations between the interannual variations of these parameters and the meteorology-driven annual changes of POA and SOA that are simulated under the fixed-2013-emission scenario (Supplementary Fig. S11). Non-stagnation days (nonStagDay), precipitation, and relative humidity (RH) correlate strongly and negatively (r = −0.7 to −0.9) with POA and SOA, explained by the influences of atmospheric dilution and removal on aerosol concentrations (Fig. 3e). SOA correlates better with cloud fraction (CLD-FRAC) and cloud optical thickness (CLD-OT) (r = −0.6) than POA does (r = −0.3), suggesting that cloud-driven radiation changes may significantly affect the photochemical oxidation of OA precursors to form SOA 29.

To consider the spatial difference of meteorological influences on surface PM 2.5 concentrations 30, we trained a random forest model with assimilated meteorological inputs to predict the meteorology-driven interannual variations of POA and SOA in each grid (Supplementary Note S1 and Supplementary Fig. S12). The feature importance and SHapley Additive exPlanations (SHAP) values confirm that CLD-FRAC and CLD-OT are more relevant and important parameters for explaining the interannual variations in SOA than in POA (Fig. 3f and Supplementary Fig. S13). Two other parameters, specific humidity (SH) and friction velocity (U*), are more important for SOA than for POA. These parameters are used for the calculations of air density and gaseous dry deposition (affecting SOA precursors) in the model, respectively.

Atmospheric reaction conditions also affect the SOA formation. Interestingly, the reductions of SO 2 and NO x during 2013–2020 play opposite roles in affecting SOA production. Supplementary Fig. S14 shows greater modeled concentrations of atmospheric oxidants (i.e., OH and ozone) in 2020 than in 2013 in northern and eastern China and Sichuan Basin. The NO x reduction is one of the main drivers of the elevated oxidant levels 31 and enhances SOA production (Supplementary Fig. S15). By contrast, the SO 2 reduction leads to significantly lower aerosol liquid water content (ALWC) and greater aerosol pH in those regions, affecting aqueous SOA production from small carbonyls (e.g., glyoxal) and isoprene epoxydiols 17,32. These changes offset each other among regions, leading to only 0–3% of net increases in annual mean SOA (Fig. 3a, b and Supplementary Fig. S15). On a regional scale and by monthly evaluation, we found much greater OA changes that can be attributed to the meteorological and chemical drivers (Supplementary Fig. S16). However, the model skills in describing finer temporal and spatial variations of OA might be limited because of large uncertainties in estimated emissions and the lack of observational constraints.

Response of OA on precursor reductions
Figure 4 shows the model results under extreme emission-cut scenarios for anthropogenic OA precursors from different sectors in China. We consider the scenarios of cutting all OA precursors in each sector as clean-energy transition pathways and the scenarios of cutting individual category of OA precursors as end-of-pipe solutions. The population-weighted annual mean concentration of OA in the base year of 2019 is 11.2 μg m−3 in which 38% is POA. Because the majority of S/LVOC is contributed by residential sources (Fig. 2c), a full elimination of residential organic emissions may greatly reduce POA by 63% and SOA by 38%, leading to a total decline of 47% of OA. Less reduction in SOA is expected because residential is the predominant source sector of POA whereas solvent use and industry also contributes substantially to SOA. The 100% cut in residential is not a practical solution because of the large volume and dispersed nature of residential sources. A less aggressive cut of 50% of the residential emissions may lower OA by 23%. By contrast, solvent use contributes greatly to the emissions of IVOC and VOC. A 50%-less solvent use has a potential to lower OA by 9%. In addition, about 11% of OA reduction may be achieved by a 50% cut in industrial emissions of OA precursors (i.e., mainly from S/LVOC reduction). Among the source sectors, residential remains as the most effective sector for controlling OA pollution in China. Our results suggest that a coordinated multi-sector control strategy is necessary to effectively lower OA concentrations. However, the fact that a 50% emission cut on all anthropogenic OA precursors cannot guarantee an OA level below 5 μg m−3 indicates a great challenge for China to achieve the new WHO air-quality guideline.

Methods
Model descriptions
We use the nested GEOS-Chem model (13.3.1; https://doi.org/10.5281/zenodo.5703364) with a revised Complex SOA scheme to simulate POA and SOA at 0.5°×0.625° horizontal resolution over the East Asia 19,39. The model is driven by assimilated meteorology from the NASA Modern-Era Retrospective Analysis for Research and Application, Version 2 (MERRA2; http://gmao.gsfc.nasa.gov/reanalysis/MERRA-2). Anthropogenic emission inventories of OA precursors over the full-volatility range from 2013 to 2020 are developed on the basis of fuel-specified Multi-resolution Emission Inventory for China (MEIC) version 1.4 3. Compared to the previous version of MEIC, residential biofuel and coal use are updated on the basis of nationwide on-site surveys 40. The IVOC emissions are estimated from non-methane VOC by applying specific emission ratio and volatility distribution to individual source sectors (i.e., industry, solvent-use, transportation, power, and residential) and fuel types (i.e., diesel, gasoline, coal, residential biofuel and coal, etc.), which are harmonized from measurement-based literature results (Supplementary Table S4). We then validate the emissions and volatility distributions by comparisons to ambient observations of IVOC (Supplementary Figs. S19–S20). The S/LVOC emissions are estimated by using the common empirical S/LVOC-to-OC method but with updated ratios and measurement-based volatility distributions for individual sectors (Supplementary Table S5). About 54–63% of the emitted S/LVOC readily condense to the particle phase to form POA. The good comparisons of modeled POA and POA volatility distributions to the observations validate our estimated S/LVOC emissions (Supplementary Figs. S5 and S21). Biofuel use is treated as an anthropogenic source while open biomass burning is considered as a natural source in this study. All other emissions, model modifications and configurations are described in Supplementary Note S1. Supplementary Table S6 lists the model modifications for HONO sources, heterogeneous uptake of HO 2, SO 2, and NO 2, and SOA parameterizations etc. and the improvements of model performance. Updated SOA yields are provided in Supplementary Table S7.

Simulations and scenarios
Base simulations are performed in GEOS-Chem from 2013 to 2020 with a spin-up period of one month. Chemical boundary conditions at the edges of the simulation domain are provided by global simulations at 2.5°×2.5° horizontal resolution. Model runs under various scenarios are listed in Supplementary Table S8 and are described in detail in Supplementary Note S1. We conducted the model runs from 2014 to 2020 with fixed-2013 meteorology to test the emission-driven (all sources) interannual variations of OA. Similar model runs with fixed-2013 emissions were conducted to test the meteorology-driven interannual variations of OA relative to 2013 (Fig. 3c, d). We then used these model results as well as assimilated meteorological inputs to train a random forest model with SHAP analysis to predict the interannual variations of OA and to identify the key parameters. For the two phases of 2013–2017 and 2017–2020, we first run the model in 2017 with 2013’s emissions of anthropogenic OA precursors, 2013’s emissions of anthropogenic pollutants other than OA precursors, or 2013’s meteorology, and then run the model in 2020 with 2017’s emissions and meteorology. This set of test is to quantify the impacts of anthropogenic emission control on OA, for which meteorology-driven differences only reflect 2017 vs. 2013 or 2017 vs. 2020 not the four-year period (Fig. 3a, b). We applied the population data from the Gridded Population of the World (https://sedac.ciesin.columbia.edu/data/collection/gpw-v4) dataset for 2015 to derive the population-weighted concentrations.

Observations
Comprehensive datasets of ambient observations are used herein for analyzing OA trends and model evaluations. Annual mean concentrations of particulate OC from 2017 to 2021 in NCP are obtained from continuous measurements in a national network for monitoring the PM 2.5 chemical composition which currently consists of 34 sites in 28 cities. Similar OC data in YRD are obtained from continuous measurements at 6 provincial-level PM 2.5 composition monitoring sites in Zhejiang province, Shanghai, and Jiangsu province. In addition, annual OC data at 9 research sites are added to the observation dataset. The mean POA and SOA concentrations from 162 field campaigns are summarized and listed in Supplementary Table S1. These OA measurements are conducted by Aerodyne aerosol mass spectrometers or aerosol chemical speciation monitors from 2013 to 2020, and the source apportionments are analyzed by using the positive matrix factorization (PMF) method with the PMF2 or ME2 solvers 11. The concentrations of hydrocarbon-like, cooking-related, biomass burning-related, and coal-combustion-related OA factors and those of the oxygenated OA factors are summed up to represent the POA and SOA concentrations, respectively. We also collect a large dataset of campaign-mean concentrations of total IVOC in China to validate the precursor emissions, and derive the volatility distributions of IVOC and particle-phase S/LVOC from ambient gas or particle volatility measurements in different places to validate the volatility-bin settings in the emission inventories. The model results from the same locations and measurement periods and arithmetic-mean concentrations are used for model-observation comparisons (Supplementary Figs. S19–S21). Detailed descriptions about the observations are provided in Supplementary Note S2. The choice of PMF factors analog to the modeled OA categories, the model-observation comparisons, and model consistency within various GEOS-Chem simulations are described in detail in Supplementary Note S3.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Summary of Arficle:\nThis study invesfigates the long-term trends of organic aerosol (OA) in China, mofivated by the Chinese\ngovernment's two-phase program to reduce OA between 2013-2020. The researchers combine modeling\nand observafion methods effecfively. Nafion-wide observafions are gathered from the North China Plain\nand province-level sites in the Yanghzi River Delta. Aerosol mass spectrometers (AMS) and aerosol\nchemical speciafion monitors are used for data collecfion. The authors employ the GEOS-Chem chemical\ntransport model in high-resolufion nested-grid mode. Various configurafions, including simulafions with\n2014-2020 emissions and fixed 2013 meteorology, enable separafion of OA precursor reducfion effects\nfrom meteorological influences on OA concentrafions. Notably, the study explores the implicafions of the\nfixed-emissions model runs using a random forest model, idenfifying meteorological variables most\nimportant to OA variability, an interesfing approach.\nKey findings include:\n1. Declines in OA are mainly due to decreased Primary OA (POA).\n2. Anthropogenic emissions drive OA trends, especially for POA.\n3. Meteorology significantly affects Secondary OA (SOA), with ~±5% variafions due to meteorological\nfactors.\n4. Meteorological features show negafive correlafions of both POA and SOA with precipitafion, humidity,\nand non-stagnant days. Cloud-related factors impact SOA more than POA due to their influence on\nphotochemistry.\n5. Reducfions in SOx and NOx in China offset each other, resulfing in negligible net changes in annual\nmean POA and SOA.\nTo further reduce OA pollufion, the authors recommend:\n1. Priorifize replacing solid fuels with clean fuels or electricity for residenfial fuel burning.\n2. Encourage products with less volafile components and focus on collecfing and replacing high-emission\nproducts to reduce solvent-related SOA.\n3. Confinue the clean energy transifion and invest in new technologies.\nIn sum, the authors have produced an excellent piece. This paper contains discussion of a novel data\nrecord, some elegant modeling techniques, and an invesfigafion of the success of the last decade of\nChinese air quality policy. It is a worthy achievement. The findings themselves are not a surprise: the\nfinding that POA is easier to control than SOA is consistent with the US and European experiences; it is\nsimilarly unsurprising to learn that meteorological variability is important to SOA variability. The\ninvesfigafion of meteorological drivers is also as predicted: OA is anfi-correlated with venfilafion,\nprecipitafion, and relafive humidity; SOA is anfi-correlated with cloud fracfion and opfical thickness due\nto photochemical impacts. The surprise to me was that the SOx and NOx reducfions offset one another,\nbut the authors do not highlight these results. The work is valuable in that it is an examinafion of all of\nthese findings in the Chinese context, but the findings themselves are as expected.\nGeneral issues: Model vs AMS definifions of POA and SOA\nThis paper primarily concerns a comparison between AMS observafions and models of organic aerosol,\nto befter understand the controls on the trends of these pollutants. These authors, like many before\nthem, must confront an issue in making these comparisons: interpretafions of AMS data do not make the\nsame disfincfion between ‘primary’ and ‘secondary’ organic aerosols made by models. AMS\nobservafions are essenfially apporfioned between ‘oxidized’ and ‘not-oxidized’ OA, which is related to\nthe POA/SOA disfincfion used by modelers but not idenfical to it.\nThe choices made by the authors in this comparison are logical: Hydrocarbon-like (HOA), cooking-related\n(COA), biomass-burning-related (BBOA) and coal-combusfion-related OA (CCOA) is apporfioned as POA,\noxygenated OA factors – presumably some combinafion of Less-Oxidized Oxidized Organic Aerosol (LO-\nOOA) and More-Oxidized Oxidized Organic Aerosol (MO-OOA) – are apporfioned as SOA. However, they\ndo not agree with several different recent studies of Chinese OA (Brewer et al., 2023; Wang et al., 2021).\nIn both of those studies, the authors found that their observafions were much more consistent with a\ntreatment of POA which apporfioned GEOS-Chem’s Emifted POA (EPOA) tracers to the\nHOA/COA/BBOA/CCOA and oxidized POA (OPOA) tracers to the OOA alongside SOA. This disagreement\nactually presents an opportunity for the authors here to explain how their model and their finding fit\ninto the exisfing literature.\nIt is not clear to me how the authors differenfiated between what GEOS-Chem Classic labels EPOA and\nOPOA, though an examinafion of the cited paper by Miao et al (2021) [citafion 19] seems to suggest that\nsome porfion of what would have been considered POA in the classic GEOS-Chem ‘simple’ and ‘complex’\nmechanisms might now be considered SOA by the authors’ model. This would be consistent with their\nfinding that the S/LVOC and IVOC together contribute ~75% of the simulated SOA in China (Lines 123-\n124), as I believe older GEOS-Chem mechanisms (Hodzic et al., 2016; Pye et al., 2010) mostly consider\nS/LVOC and IVOC as POA-forming. Regardless, I think the authors ought to address this in the text or\nsupplement.\nBrewer, J. F., Jacob, D. J., Jathar, S. H., He, Y., Akherafi, A., Zhai, S., et al. (2023). A Scheme for\nRepresenfing Aromafic Secondary Organic Aerosols in Chemical Transport Models: Applicafion to Source\nAftribufion of Organic Aerosols Over South Korea During the KORUS-AQ Campaign. Journal of\nGeophysical Research: Atmospheres, 128(8), e2022JD037257. hftps://doi.org/10.1029/2022JD037257\nHodzic, A., Kasibhatla, P. S., Jo, D. S., Cappa, C. D., Jimenez, J. L., Madronich, S., & Park, R. J. (2016).\nRethinking the global secondary organic aerosol (SOA) budget: stronger producfion, faster removal,\nshorter lifefime. Atmos. Chem. Phys., 25.\nMiao, R., Chen, Q., Shrivastava, M., Chen, Y., Zhang, L., Hu, J., et al. (2021). Process-based and\nobservafion-constrained SOA simulafions in China: the role of semivolafile and intermediate-volafility\norganic compounds and OH levels. Atmospheric Chemistry and Physics, 21(21), 16183–16201.\nhftps://doi.org/10.5194/acp-21-16183-2021\nPye, H. O. T., Chan, A. W. H., Barkley, M. P., & Seinfeld, J. H. (2010). Global modeling of organic aerosol:\nthe importance of reacfive nitrogen (NOx and NO3). Atmospheric Chemistry and Physics, 10(22), 11261–\n11276. hftps://doi.org/10.5194/acp-10-11261-2010\nWang, J., Ye, J., Zhang, Q., Zhao, J., Wu, Y., Li, J., et al. (2021). Aqueous producfion of secondary organic\naerosol from fossil-fuel emissions in winter Beijing haze. Proceedings of the Nafional Academy of\nSciences, 118(8), e2022179118. hftps://doi.org/10.1073/pnas.2022179118\nSpecific Edits:\nLine 105 – I was surprised by the ‘35% of NMVOC acfing as an OA precursor’ value, and went and looked\nthrough the cited work by Li et al. (2014). I did not find the source of the claimed percentage. It is\nenfirely possible that I have missed something, and welcome being corrected, but the authors should\ndouble check the citafion.\nLi, M., Zhang, Q., Streets, D. G., He, K. B., Cheng, Y. F., Emmons, L. K., Huo, H., Kang, S. C., Lu, Z., Shao, M.,\nSu, H., Yu, X., and Zhang, Y.: Mapping Asian anthropogenic emissions of non-methane volafile organic\ncompounds to mulfiple chemical mechanisms, Atmos. Chem. Phys., 14, 5617–5638,\nhftps://doi.org/10.5194/acp-14-5617-2014, 2014.\nFigure 3: I really like this figure. It is both informafion-dense and understandable. The presentafion\nchoices in panels A-D in parficular are very well done. My only complaint is with Panel F. This panel\ncontains several confusing references: the “Conc.”, “TROPO-P”, and “P1” labels should be clarified. Two\nof these features (“Conc.” and “TROPO-P”) do not appear to be discussed in the supplemental materials\nLine 172: inclusion of “Besides” here is confusing and out of place\nLine 194: “Except the uncertainty in precursor emissions, the representafion of chemical processes for\nthe SOA formafion and aging is greatly simplified in chemical transport models.” This line is confusing to\nme. It seems to suggest that CTMs don’t simplify the uncertainty in precursor emissions, but this is not\nvery true, especially considering the asserfion in lines 123-124 that S/LVOC and IVOC contribute ~75% of\nSOA in China. Possibly one could argue that the representafion of precursor emissions is only ‘very\nsimplified’ rather than ‘greatly simplified’ but I’m not sure that’s a worthwhile line to draw. I suggest\nrephrasing this line to make the intended statement clearer.\nLine 228-229: authors suggest that the large decrease in OA over China from 2013-2020 is\n“unprecedent” (a typo) and then in the very next sentence suggest a precent: “[a] similar extent of\ndecline … occurred in the United States from 1990-2012”. I think this is the wrong word choice.\nLine 242: “solvent use is the second large contributor” should read “solvent use is the second largest\ncontributor”.\nLine 246-247: “lowering OA mass … is essenfially a thrust on clean energy transifion and technology\ninnovafions.” This phrasing doesn’t make sense – I don’t know what the authors mean by ‘a thrust’.\nLines 249-260: This secfion is included in the ‘Policy Implicafions’ secfion of the paper but does not really\nconsist of any policy implicafions. It instead lays out two (extremely correct) needs for future research,\nwhich I summarize as:\n1. Characterizafion of OA species (or OA precursor emissions class or emissions sector) by health impacts\nis urgently needed to befter tailor control strategies at the most damaging OA sources.\n2. More research is necessary on potenfial future climate impacts on OA, and how that will change\npolicy.\nThese points are excellent but not policy. I assume this inclusion was made because the paper contains\nno ‘Conclusions’ secfion, which is where they really do belong. The authors should re-organize this\nsecfion to make clear exactly what the policy recommendafions are and what the aftendant research is\nthat could make those recommendafions even befter. I suggest a numbered list of the three real policy\nimplicafions in this secfion, followed by this addifional discussion of necessary future direcfions.\nLine 275: “24% of the emifted carbon present in low-volafility bins with saturafion concentrafions (C*) of\n≤ 1 ug m-3”. The authors should clarify what this means for the reader as regards their definifions of\nS/LVOC, IVOC, and VOC.", "reviewer_2": "The paper describes the OA trend in China from 2013 to 2020. They used observafional network data\n(2017-2020) and field campaign data (2013-2020) to suggest that OA decrease in China is probably due\nto the decrease of POA, to a lesser extent by SOA. They further use a chemical transport model and was\nable to reproduce this trend of OA. The work is interesfing and has implicafions on future emission\ncontrol as well as emission control strategies in developing countries. However, I find the paper lack of\nclarity on a number of things. I have several comments:\n1. The paper does not provide a mechanisfic explanafion about why SOA does not decrease as much as\nPOA (Figure 1c-f). This could be a major highlight on this paper. The authors menfion the potenfial role\nof radiafion and atmospheric oxidafion but there is liftle clarity on how exactly this could play a role (Line\n159-188). If their model gets this trend right (Figure 3), they should be able to provide some insights at\nleast from the model simulafions rather than mere speculafion.\nThis also makes me wonder about Figure 4. If the trend during 2013-2020 is mainly driven by residenfial\nsources (as stated in the abstract) and is mainly due to the decrease in POA, why Figure 4 shows similar\nreducfion in POA and SOA from 50% and 100% cut in residenfial. Am I missing anything here?\n2. It is important to understand the OA trend in different seasons. The annual mean, as used in this\nstudy, can be heavily weighted towards winter when PM2.5 is higher, without much insights on other\nseasons (Figure S14). For Figure 1A-B, if annual mean has decreased a lot, it is expected that PM2.5 has\ndecreased more in winter than in summer, if the decrease is driven by residenfial fuel burning (Figure S1\nsuggests that from emissions).\nIt would be great to show the seasons of each campaign in Figure 1c-f, as strong seasonality is expected\nfor field campaign data. The authors could consider divide all those field campaigns into different\nseasons (Figure 1c-f), and examine the trends in each season, which would provide a much clearer\nmessage than altogether.\n3. It should be noted that the main conclusion in this paper is actually drawn from all the field campaign\ndata over the period of 2013-2020. The nafional network only covers the period of 2017-2020. Some\ncareful analysis on the field campaign is indeed necessary.\n4. Is the model validated by observafions? The model does not reproduce the trend in YRD (Figure S4).\nAny reason for this?\n5. Can the author explain how much difference the populafion weighfing can make on OA\nconcentrafions? Do they consider the populafion weighfing on the model trend too?"}, "author_response": "Response to reviews\nWe thank the reviewers for their constructive comments. In the following section, reviewer comments are in black. Point-by-point responses are in blue labeled with [R]. Line numbers in the responses correspond to those in the track-change version of the revised manuscript. Specific modifications to the manuscript are in red.\nReviewer 1\nThis study investigates the long-term trends of organic aerosol (OA) in China, motivated by the Chinese\ngovernment's two-phase program to reduce OA between 2013-2020. The researchers combine modeling\nand observation methods effectively. Nation-wide observations are gathered from the North China Plain\nand province-level sites in the Yangzi River Delta. Aerosol mass spectrometers (AMS) and aerosol\nchemical speciation monitors are used for data collection. The authors employ the GEOS-Chem chemical\ntransport model in high-resolution nested-grid mode. Various configurations, including simulations with\n2014-2020 emissions and fixed 2013 meteorology, enable separation of OA precursor reduction effects\nfrom meteorological influences on OA concentrations. Notably, the study explores the implications of the\nfixed-emissions model runs using a random forest model, identifying meteorological variables most\nimportant to OA variability, an interesting approach.\nKey findings include:\n1. Declines in OA are mainly due to decreased Primary OA (POA).\n2. Anthropogenic emissions drive OA trends, especially for POA.\n3. Meteorology significantly affects Secondary OA (SOA), with ~±5% variations due to meteorological\nfactors.\n4. Meteorological features show negative correlations of both POA and SOA with precipitation, humidity,\nand non-stagnant days. Cloud-related factors impact SOA more than POA due to their influence on\nphotochemistry.\n5. Reductions in SO and NO in China offset each other, resulting in negligible net changes in annual\nx x\nmean POA and SOA.\nTo further reduce OA pollution, the authors recommend:\n1. Prioritize replacing solid fuels with clean fuels or electricity for residential fuel burning.\n2. Encourage products with less volatile components and focus on collecting and replacing high-emission\nproducts to reduce solvent-related SOA.\n3. Continue the clean energy transition and invest in new technologies.\nIn sum, the authors have produced an excellent piece. This paper contains discussion of a novel data\nrecord, some elegant modeling techniques, and an investigation of the success of the last decade of\nChinese air quality policy. It is a worthy achievement. The findings themselves are not a surprise: the\nfinding that POA is easier to control than SOA is consistent with the US and European experiences; it is\nsimilarly unsurprising to learn that meteorological variability is important to SOA variability. The\ninvestigation of meteorological drivers is also as predicted: OA is anti-correlated with ventilation,\nprecipitation, and relative humidity; SOA is anti-correlated with cloud fraction and optical thickness due\nto photochemical impacts. The surprise to me was that the SO and NO reductions offset one another,\nx x\nbut the authors do not highlight these results. The work is valuable in that it is an examination of all of\nthese findings in the Chinese context, but the findings themselves are as expected.\n[R0] We sincerely appreciate the time and effort that Reviewer 1 dedicated to providing such insightful\nfeedbacks on our manuscript. We provide point-by-point responses below and have revised the\nmanuscript and supporting information (SI) accordingly. To quantify the offset effects of chemical drivers\n(in particular, SO vs. NO ), we performed further model analysis (Table S8: CTL_OTR_woSO and\n2 x 2\nCTL_OTR_woNO simulations). The results are presented in the updated text in Line 207-223 and 277-\nx\n281 and Section S1 (Model scenarios) in SI with a new figure of Figure S15. We believe that the revised\nmanuscript is significantly strengthened on the delivery and validation of findings, conclusions, and\nimplications and provides a useful reference for developing countries.\nFigure S15. The changes of SOA concentrations led by chemical drivers during the two action periods. The\npercent values in black represent the changes of population-weighted SOA concentration relative to 2013. The\ncolored percent values in the lower right of each panel represent the contributions of emission controls on SO ,\nx\nrelated to gas-phase oxidation processes to represent the impacts of oxidant levels and the changes of SOA\ntracers related to heterogeneous uptake processes to represent the impacts of ALWC as well as aerosol pH.\nGeneral issues: Model vs AMS definitions of POA and SOA\nThis paper primarily concerns a comparison between AMS observations and models of organic aerosol,\nto better understand the controls on the trends of these pollutants. These authors, like many before them,\nmust confront an issue in making these comparisons: interpretations of AMS data do not make the same\ndistinction between ‘primary’ and ‘secondary’ organic aerosols made by models. AMS observations are\nessentially apportioned between ‘oxidized’ and ‘not-oxidized’ OA, which is related to the POA/SOA\ndistinction used by modelers but not identical to it.\nThe choices made by the authors in this comparison are logical: Hydrocarbon-like (HOA), cooking-\nrelated (COA), biomass-burning-related (BBOA) and coal-combustion-related OA (CCOA) is\napportioned as POA, oxygenated OA factors – presumably some combination of Less-Oxidized Oxidized\nOrganic Aerosol (LO-OOA) and More-Oxidized Oxidized Organic Aerosol (MO-OOA) – are\napportioned as SOA. However, they do not agree with several different recent studies of Chinese OA\n(Brewer et al., 2023; Wang et al., 2021). In both of those studies, the authors found that their observations\nwere much more consistent with a treatment of POA which apportioned GEOS-Chem’s Emitted POA\n(EPOA) tracers to the HOA/COA/BBOA/CCOA and oxidized POA (OPOA) tracers to the OOA\nalongside SOA. This disagreement actually presents an opportunity for the authors here to explain how\ntheir model and their finding fit into the existing literature.\nIt is not clear to me how the authors differentiated between what GEOS-Chem Classic labels EPOA and\nOPOA, though an examination of the cited paper by Miao et al (2021) [citation 19] seems to suggest that\nsome portion of what would have been considered POA in the classic GEOS-Chem ‘simple’ and ‘complex’\nmechanisms might now be considered SOA by the authors’ model. This would be consistent with their\nfinding that the S/LVOC and IVOC together contribute ~75% of the simulated SOA in China (Lines 123-\n124), as I believe older GEOS-Chem mechanisms (Hodzic et al., 2016; Pye et al., 2010) mostly consider\nS/LVOC and IVOC as POA-forming. Regardless, I think the authors ought to address this in the text or\nsupplement.\n[R1] We agree with the reviewer that AMS-derived POA and SOA are not identical to the modeled ones.\nIn this study, we think the comparisons between the sum of HOA, CCOA, COA, and BBOA to the\nmodeled POA and the sum of OOAs to the modeled SOA are meaningful for the following reasons.\n(1) The four primary PMF factors show mass spectra and mass ratios of organic matter to organic carbon\n(OM:OC) similar to freshly emitted OA from transportation, residential or industrial combustion,\ncooking, and biomass burning at ambient loading levels [Aiken et al., 2008; Zheng et al., 2020], which\ncovers the main sources of primary emissions in China.\nThe model in this study considered full-volatility-range organic emissions from all these primary\nsources except cooking and applied volatility-based partitioning to determine the portion of organics\n(S/LVOC) that can remain in the particle phase after being emitted (so-called the modeled POA;\nlabeled as the EPOA tracer). An OM:OC ratio of 1.4 is used for EPOA which is consistent with the\nratios of PMF primary factors (1.2-1.6). In this regard, we think it is reasonable to use the comparison\nbetween the sum of PMF primary factors to the modeled POA as a validation of the model settings of\nthe S/LVOC emissions and their volatility distributions as long as cooking is not a major contributor.\nCooking emissions are lack of good constraints on a national level. The PMF results indicate 8-33%\nof the OA mass as COA with insignificant yearly changes in cities [Zheng et al., 2023]. We expect\n<4% of the populated-weighted OA mass from cooking by assuming a COA mass fraction of 15% in\nmodel grids with population densities of >1000/km2 and little interannual variations. Therefore, the\noverlooked COA should not affect much the modeled POA mass and trends herein. We kept COA in\nthe observation dataset because the PMF analysis has been proven to robustly separate primary factors\nfrom oxidized “secondary” factors while individual primary factors may not be well resolved\ndepending on instrument tuning and analysis skills. Nevertheless, for the model-observation\ncomparison of POA in Fig. S4C, if COA is excluded from the observation data, the comparison gives\na r of 0.85 and a NMB of 0.25, still supporting a good model representation of S/LVOC.\nOther evidence to support the good model representation of S/LVOC include the consistent China’s\nemissions with other inventories (Fig. S3), the consistent estimation of US emissions with the findings\nof Pye et al. (https://doi.org/10.23719/1527956) (Fig. S4), and the roughly reproduced total mass\nfraction of volatility bins below C* of 1 μg m-3 (predominantly in the particle phase) although the\nfraction of individual bin has not been well captured (Fig. S21).\n(2) The PMF OOA factors are identified by mass spectra similar to SOA and much greater OM:OC ratios\n(1.8-2.2) than the ratios of primary factors (e.g., [Zheng et al., 2020]). These OOA factors may\nrepresent SOA from various precursors with different photochemical age as well as aqueous SOA [\nXu et al., 2017; Zheng et al., 2023]. In the model, SOA is the sum of biogenic SOA, aromatic SOA, IVOC\nSOA, S/LVOC SOA (labeled as the OPOA tracer), and key aqueous SOA, covering almost all\nimportant precursors as well as the oxidation and aqueous processes. An OM:OC ratio of 2.1 is applied\nto the modeled SOA, which is again consistent with the PMF findings. We think the comparisons\nbetween the sum of PMF OOAs to the modeled SOA along with the model-observation comparison\nof precursor concentrations validate general model skills on the SOA precursor emissions and\nchemical processes.\nIndeed, we are aware of the confusing model treatments of primary emissions and the attribution of tracers\nwhen comparing to the AMS results among studies. To address that, we added a new table in SI (Table\nS9) to summarize the global burdens of OA reported in different GEOS-Chem studies.\nTable S9. Global burdens of OA under different model schemes.\nGlobal burden [Tg]\nSimple scheme Complex scheme Hodzic schemeb Complex scheme Revised complex\n[Pai et al., 2020] [Pye et al., 2010]a [Hodzic et al., 2016] [Pye et al., 2010] scheme [This study]\nTracer or\nSource Category Non-volatile POA treatment Semivolatile POA treatment\nPrecursor\ntwo-product 5-bin volatility\nOM:OC=1.4 for EPOA and 2.1 for OPOAc\nsurrogates distributions\n2013 2000 2005-2008 mean 2000 2013d\nPOA 0.84 0.92 0.94 0.03 0.32\nEPOA 0.06 -- -- 0.03 0.32\nAnthropo- OPOA 0.78 -- -- Not applicable Not applicable\ngenic and SOA 0.69 -- 0.29 (~1.0)f 1.13\nbiomass S/LVOC --e 0.81g 0.58g\nNot included 0.21\nburning IVOC -- 0.09 0.52\nAromatics -- -- 0.08 Not specified 0.03\nOAanth/bb 1.53 -- 1.23 (~1.0)f 1.45\nAll except\nOA 1.86 1.64 1.82 1.65 1.90\nmarine\nNCP and YRD OC\nIMPROVE (US) and data in China;\nEMEP (Europe) OC AMS PMF (POA\ndata; and OOA) data;\nModel results are evaluated by Aircraft OA data\nAMS OOA data for Precursor\nrural/background; concentrations and\nAircraft OA data volatility\ndistributions\na Pai et al. [2020] also reported 2013 burden for complex scheme. Their burden values are greater than those reported by Pye et al.\n[2010] when the primary OC emissions in the two studies are similar (31.2 vs 29.0 Tg C/yr). We list only the values from Pye et al.\n[2010] in the table.\nb The ND_DPH setting is considered as the Hodzic scheme used by Brew et al. [2023]. Brew et al. [2023] further improved aromatic\nSOA parameterization.\nc The OM:OC ratio is 2.1 for both EPOA and OPOA in [Pye et al., 2010] while the OM:OC ratios are not reported in [Hodzic et al.,\n2016].\nd The burden is calculated on the default configuration of GEOS-Chem (v13.3.1) with the revised complex scheme for OA.\ne Included in the calculation but the values are not specified in the literature.\nf The value is not specified in the literature. The aromatic SOA burden should be less than 0.10 Tg.\ng Labeled as the OPOA tracer.\nAs listed in Table S9, the modeled POA under non-volatile POA treatment are much greater than the\nmodeled POA under semi-volatile treatment. This is because the quartz filter based POA emissions\nrepresent organic emissions over a range of volatilities. Some semi-volatile portion of this emission\nremain in the particle phase during the atmospheric dilution but some are gas precursors that may further\nproduce SOA (S/LVOC-SOA). Advanced emission profile measurements suggest a scaling factor of 1.0\n(1.4 for gasoline) to the filter-based POA emissions to represent the total (gas+particle) emissions of\nS/LVOC of anthropogenic sources [Lu et al., 2020; Lu et al., 2018]. Therefore, the non-volatile POA\ntreatment overestimates the particulate organic emissions. This is confirmed by significant greater\nmodeled POA than the AMS-derived POA in previous studies [Brewer et al., 2023; Jo et al., 2013].\nThe question is what fraction should be positioned as freshly emitted POA. Brew et al. [2023] found\nEPOA under non-volatile POA treatment reproduce better the AMS-derived POA, and the rest (the\nhydrophilic portion of non-volatile POA; also labeled as OPOA), if considered as additional S/LVOC-\nSOA, leads to a match between modeled SOA and AMS-derived OOA. This indicates that a majority of\nthe filter-based POA emissions should be gaseous S/LVOC in the “KORUS-AQ” region [Nault et al.,\n2018] and that the Hodzic representation on S/LVOC- and IVOC-SOA is insufficient.\nIn this study, we developed full-volatility-range organic emissions. The S/LVOC-SOA and IVOC-SOA\nare parameterized on the volatility distributions of their precursor emissions (8 bins total). With the\nrevised model scheme, a high fraction of the filter-based POA emissions remain as EPOA in highly\npolluted anthropogenic source regions as shown in Figure S23, which explains why the model can\nreproduce high concentrations of AMS-derived POA in China. The EPOA/(EPOA+OPOA) fractions\ndecrease to <0.4 in KORUS-AQ-related model grids. Note that EPOA/(EPOA+OPOA) needs to be more\nthan 0.1 [Brew et al. 2023]. Table S9 suggest that S/LVOC-SOA+IVOC-SOA in the revised scheme is\ncomparable to OPOA+SVOC-SOA+IVOC-SOA in the Hodzic scheme with non-volatile POA treatment,\nindicating a consistent SOA budget between our study and Brew et al. [2023].\nFigure S23. The modeled EPOA / (EPOA + OPOA) at (A) surface and (B) at the altitude of 3 km in 2013.\nEPOA and OPOA are the tracers that represent the modeled POA and S/LVOC-SOA, respectively.\nTo sum up, previous studies might have misplaced some categories of OA and the standard complex\nscheme is outdated. On the total amount of OA, our study is consistent with previous model studies that\nwere evaluated extensively by the observations [Hodzic et al., 2016; Pai et al., 2020]. We provide a\nbottom-up volatility-based simulation scheme to capture the underestimated anthropogenic contributions\nwith careful evaluations for China. Our treatment of S/LVOC-SOA (although labeled as OPOA in this\nstudy and IVOC-SOA as the modeled SOA is constrained by the AMS-derived OOA and ambient flow-\ntube observations on SOA potential [Hu et al., 2022]. Globally, well-constrained emission inventories\nover the entire volatility range along with improved representation of their SOA formation are needed to\nreproduce the spatial variabilities of OA.\nWe have added the above discussion and references in Section S3 in SI and revised the text in Line 149,\n284-307, 417.\nSpecific Edits:\nLine 105 – I was surprised by the ‘35% of NMVOC acting as an OA precursor’ value, and went and\nlooked through the cited work by Li et al. (2014). I did not find the source of the claimed percentage. It\nis entirely possible that I have missed something, and welcome being corrected, but the authors should\ndouble check the citation.\n[R2] Thanks for pointing out this confusion. Li et al. (2014) reported 14% of NMVOC emissions in MEIC\nas aromatics for the year of 2006, and Li et al. (2019) reported 33% as aromatics for 2017. The large\nincrease is attributed to a rapid expansion of industrial and solvent use activities since 2000 (Li et al.,\n2019). We also use the MEIC inventory for NMVOC but with updates on the residential biofuel and coal\nuse. The NMVOC emissions in this study are slightly different from those reported by Li et al. (2019) for\nthe same years. To clarify, we have made several updates as follows: ① Line 124-127 “about 35% of\nnon-methane VOC emissions (mostly aromatic compounds) may act as OA precursors (i.e., 9.0-10.3 Tg\ny-1) 22. The high aromatic fraction in China is explained by the rapid increase of solvent use and industrial\nactivities since 2000 (Li et al., 2019)”, ② Fig. 2’s figure caption “(A) Anthropogenic emissions of OA\nprecursors from different source sectors. Among VOC, only aromatic compounds, glyoxal, methylglyoxal,\nisoprene, and terpenes are considered as OA precursors”, ③ Fig. S1’s figure caption “ (B−D) The annual\nmean emission compositions of total NMVOC (include non-OA precursors), IVOC, and S/LVOC\ngrouped by species or C* at 298 K in China. Aromatic compounds, GLY, MGLY, isoprene, and terpenes\nare typical OA precursors, which are about 34-37% of NMVOC and are outlined by light green wedges\nin panel B. In panel C, the light green wedges highlight the volatility bins that have the lowest vapor\npressure and highest SOA formation potential. In panel D, the light green wedges highlight the volatility\nbins that are primarily distributed in the particle phase”, and ④ a few words in Section S1 (NMVOC\nemissions and VOC-type OA precursors) in SI.\nFigure 3: I really like this figure. It is both information-dense and understandable. The presentation\nchoices in panels A-D in particular are very well done. My only complaint is with Panel F. This panel\ncontains several confusing references: the “Conc.”, “TROPO-P”, and “P1” labels should be clarified. Two\nof these features (“Conc.” and “TROPO-P”) do not appear to be discussed in the supplemental materials.\n[R3] We have updated Figure 3E-F with a better description of the parameters.\nE POA-0.9 -0.8 -0.7 -0.3 -0.3 -0.2 0.1 0.4 0.9\nnonStagDay 0.8 0.6 0.0 0.0 0.3 0.2 -0.2 -0.7\nPrecipitation0.8 0.4 0.4 0.5 0.2 -0.1 -0.8\nRH 10.7 0.7 0.3 0.2 -0.4 -0.7\nCLD-OT 11.0 -0.1 -0.2 -0.6 -0.6\nCLD-FRAC 1-0.1 -0.3 -0.6 -0.6\nPressure 10.7 0.7 -0.2\n|r|\nT(2m) 0.5 0.2\nPBLH 0.5\n0 0.5 1.0 SOA\nF CON R C H1\nT(2m)1\nPressure\nPressuret\n1\np\nV(10m)\nU(10m)\nPBLH\nSWR\nPrecipitation\nC C LD LD -F - r O a T c1 1\nU*\nSH1 POA SOA\n30 15 00 15 30\nreplaced by Feature importance (%)\nMoreover, the figure caption for Fig. 3E-F is revised as follows: “CONC stands for the concentrations of\nPOA and SOA used in RF which are the model outputs under the fixed-2013-emission scenario. PBLH,\nSWR, T(2m), U(10m), and V(10m) represent planetary boundary layer height, surface shortwave\nradiation, air temperature at 2 m, the east-west and north-south wind components at 10 m, respectively.\nThe subscripts, 1 and tp, denote the first model layer and tropopause, respectively”. We have also revised\nSect. S1 (Random forest model and SHAP analysis) and Fig. S13 in SI with the same set of labels.\nLine 172: inclusion of “Besides” here is confusing and out of place.\n[R4] This sentence is revised as follows: “Besides,Two other parameters, specific humidity (SH) and\nfriction velocity (U*), are more important for SOA than for POA”.\nLine 194: “Except the uncertainty in precursor emissions, the representation of chemical processes for\nthe SOA formation and aging is greatly simplified in chemical transport models.” This line is confusing\nto me. It seems to suggest that CTMs don’t simplify the uncertainty in precursor emissions, but this is not\nvery true, especially considering the assertion in lines 123-124 that S/LVOC and IVOC contribute ~75%\nof SOA in China. Possibly one could argue that the representation of precursor emissions is only ‘very\nsimplified’ rather than ‘greatly simplified’ but I’m not sure that’s a worthwhile line to draw. I suggest\nrephrasing this line to make the intended statement clearer.\n[R5] We have rephrased this part as follows: “Except the uncertainty in precursor emissions, the The\nrepresentation of chemical processes for SOA formation and aging in chemical transport models can be\nan important source of model bias6. Our model simulations reasonably capture the regional variations of\nOA (Fig. S17 and Table S2), suggesting minor impacts of simplified model representation of\nmultigenerational production and fragmentation pathways on SOA in highly polluted environments in\nChina”.\nLine 228-229: authors suggest that the large decrease in OA over China from 2013-2020 is “unprecedent”\n(a typo) and then in the very next sentence suggest a precent: “[a] similar extent of decline … occurred\nin the United States from 1990-2012”. I think this is the wrong word choice.\n[R6] Agreed. We have revised the statement as follows: “Our study identifies a large and rapid decrease\nin OA from 2013 to 2020 in China from observations and model perspectives, indicating remarkable\nunprecedent achievements of the implementation of clean air actions (especially residential fuel\nreplacement). A similar extent of decline (25%-50% of OC) occurred in the United States from 1990 to\n2012”.\nLine 242: “solvent use is the second large contributor” should read “solvent use is the second largest\ncontributor”.\n[R7] We have revised the text as suggested.\nLine 246-247: “lowering OA mass … is essentially a thrust on clean energy transition and technology\ninnovations.” This phrasing doesn’t make sense – I don’t know what the authors mean by ‘a thrust’.\n[R8] We have deleted this sentence and added three new policy implications (see [R9]).\nLines 249-260: This section is included in the ‘Policy Implications’ section of the paper but does not\nreally consist of any policy implications. It instead lays out two (extremely correct) needs for future\nresearch, which I summarize as:\n1. Characterization of OA species (or OA precursor emissions class or emissions sector) by health impacts\nis urgently needed to better tailor control strategies at the most damaging OA sources.\n2. More research is necessary on potential future climate impacts on OA, and how that will change\npolicy.\nThese points are excellent but not policy. I assume this inclusion was made because the paper contains\nno ‘Conclusions’ section, which is where they really do belong. The authors should re-organize this\nsection to make clear exactly what the policy recommendations are and what the attendant research is that\ncould make those recommendations even better. I suggest a numbered list of the three real policy\nimplications in this section, followed by this additional discussion of necessary future directions.\n[R9] Great point! We have re-organized the entire section and changed the section title as “Discussion”\nso that uncertainties, implications and future research needs all fit in. We followed the reviewer’s\nsuggestion and made a number list of three policy implications and discussed two research directions as\nfollows in Line 308-343: “Our findings have three important policy implications for OA pollution\ncontrol: (1) The residential sector is still the biggest contributor of OA in China. As the residential fuel\nreplacement slows down, the OA pollution likely becomes persistently high in China and thus\nchallenges the consideration of tightening national air quality standards. Future policies on air-quality\nimprovement should prioritize the continuation of residential clean-energy transition; (2) Abatements of\nVOC has been suggested as a near-future priority for ozone-pollution control in China 31. Solvent use is\nnow the largest contributor of NMVOC and VOC-IVOC SOA precursors. Coordinated control on ozone\nand SOA should encourage measures on solvent-use emissions; (3) Our model simulations suggest the\nNO reduction increased SOA during 2013-2020 in China. In longer term, if the NO -suppressed regime\nx x\nis transitioned to a NO -limited regime, the NO reduction would decrease surface OH (lowering\nx x\nphotochemical production) and inorganic nitrate concentrations (lowering ALWC and thus aqueous\nSOA), leading to co-benefits on SOA pollution. Notably, high emissions of OA precursors are expected\nin other emerging economies such as India and Southeast Asian countries (Fig. S18). High\nconcentrations of OA in PM have already been a serious problem there. Similar considerations may\n2.5\nbe taken to develop effective pollution control measures in those countries. To gain maximum co-\nbenefits and ensure the effectiveness of pollution mitigation policies, future research needs to focus on\n(1) characterization of OA species or OA precursor class or emission sectors by health impacts, which\nleads to better tailored control strategies at the most damaging OA sources; and (2) quantification of the\npotential climate impacts on OA under different future emission scenarios.”\nLine 275: “24% of the emitted carbon present in low-volatility bins with saturation concentrations (C*)\nof ≤ 1 ug m-3”. The authors should clarify what this means for the reader as regards their definitions of\nS/LVOC, IVOC, and VOC.\n[R10] We meant to highlight the POA portion would be quite large under conditions of high OA\nconcentrations in China when 24% of the S/LVOC emissions distribute in low-volatility bins. However,\nwe noticed such information has been already delivered in Line 117 and therefore deleted this sentence\nfrom Methods.\nReferences:\nMiao, R., Chen, Q., Shrivastava, M., Chen, Y., Zhang, L., Hu, J., et al. (2021). Process-based and observation-constrained SOA\nsimulations in China: the role of semivolatile and intermediate-volatility organic compounds and OH levels. Atmospheric\nChemistry and Physics, 21(21), 16183–16201. https://doi.org/10.5194/acp-21-16183-2021.\nPye, H. O. T., Chan, A. W. H., Barkley, M. P., & Seinfeld, J. H. (2010). Global modeling of organic aerosol: the importance of\nreactive nitrogen (NOx and NO3). Atmospheric Chemistry and Physics, 10(22), 11261–11276. https://doi.org/10.5194/acp-10-\n11261-2010\nWang, J., Ye, J., Zhang, Q., Zhao, J., Wu, Y., Li, J., et al. (2021). Aqueous production of secondary organic aerosol from fossil-\nfuel emissions in winter Beijing haze. Proceedings of the National Academy of Sciences, 118(8), e2022179118.\nhttps://doi.org/10.1073/pnas.2022179118.\nLi, M., Zhang, Q., Streets, D. G., He, K. B., Cheng, Y. F., Emmons, L. K., Huo, H., Kang, S. C., Lu, Z., Shao, M., Su, H., Yu,\nX., and Zhang, Y.: Mapping Asian anthropogenic emissions of non-methane volatile organic compounds to multiple chemical\nmechanisms, Atmos. Chem. Phys., 14, 5617–5638, https://doi.org/10.5194/acp-14-5617-2014, 2014.\nAiken, A. C., et al. (2008), O/C and OM/OC ratios of primary, secondary, and ambient organic aerosols with high-resolution\ntime-of-flight aerosol mass spectrometry, Environ. Sci. Technol., 42(12), 4478-4485, doi:10.1021/es703009q.\nBrewer, J. F., et al. (2023), A Scheme for Representing Aromatic Secondary Organic Aerosols in Chemical Transport Models:\nApplication to Source Attribution of Organic Aerosols Over South Korea During the KORUS-AQ Campaign, J. Geophys. Res.,\n128(8), doi:10.1029/2022jd037257.\nHodzic, A., P. S. Kasibhatla, D. S. Jo, C. D. Cappa, J. L. Jimenez, S. Madronich, and R. J. Park (2016), Rethinking the global\nsecondary organic aerosol (SOA) budget: stronger production, faster removal, shorter lifetime, Atmos. Chem. Phys., 16(12),\n7917-7941, doi:10.5194/acp-16-7917-2016.\nHu, W., et al. (2022), Oxidation flow reactor results in a Chinese megacity emphasize the important contribution of S/IVOCs\nto ambient SOA formation, Environ. Sci. Technol., 56(11), 6880-6893, doi:10.1021/acs.est.1c03155.\nJo, D. S., R. J. Park, M. J. Kim, and D. V. Spracklen (2013), Effects of chemical aging on global secondary organic aerosol\nusing the volatility basis set approach, Atmos. Environ., 81, 230-244, doi:10.1016/j.atmosenv.2013.08.055.\nLu, Q., B. N. Murphy, M. M. Qin, P. Adams, Y. L. Zhao, H. O. T. Pye, C. Efstathiou, C. Allen, and A. L. Robinson (2020),\nSimulation of organic aerosol formation during the CalNex study: updated mobile emissions and secondary organic aerosol\nparameterization for intermediate-volatility organic compounds, Atmos. Chem. Phys., 20(7), 4313-4332, doi:10.5194/acp-20-\n4313-2020.\nLu, Q., Y. Zhao, and A. L. Robinson (2018), Comprehensive organic emission profiles for gasoline, diesel, and gas-turbine\nengines including intermediate and semi-volatile organic compound emissions, Atmos. Chem. Phys., 18(23), 17637-17654,\ndoi:10.5194/acp-18-17637-2018.\nNault, B. A., et al. (2018), Secondary organic aerosol production from local emissions dominates the organic aerosol budget\nover Seoul, South Korea, during KORUS-AQ, Atmos. Chem. Phys., 18(24), 17769-17800, doi:10.5194/acp-18-17769-2018.\nPai, S. J., et al. (2020), An evaluation of global organic aerosol schemes using airborne observations, Atmos. Chem. Phys.,\n20(5), 2637-2665, doi:10.5194/acp-20-2637-2020.\nXu, W., et al. (2017), Effects of Aqueous-Phase and Photochemical Processing on Secondary Organic Aerosol Formation and\nEvolution in Beijing, China, Environ. Sci. Technol., 51(2), 762-770, doi:10.1021/acs.est.6b04498.\nZheng, Y., et al. (2020), Characterization of anthropogenic organic aerosols by TOF-ACSM with the new capture vaporizer,\nAtmos. Meas. Tech., 13(5), 2457-2472, doi:10.5194/amt-13-2457-2020.\nZheng, Y., et al. (2023), Secondary formation of submicron and supermicron organic and inorganic aerosols in a highly polluted\nurban area, Journal of Geophysical Research: Atmospheres, 128(4), e2022JD037865, doi:10.1029/2022JD037865.\nReviewer 2\nThe paper describes the OA trend in China from 2013 to 2020. They used observational network data\n(2017-2020) and field campaign data (2013-2020) to suggest that OA decrease in China is probably due\nto the decrease of POA, to a lesser extent by SOA. They further use a chemical transport model and was\nable to reproduce this trend of OA. The work is interesting and has implications on future emission control\nas well as emission control strategies in developing countries.\n[R0] We are grateful for the constructive comments provided by Reviewer 2. We provide point-by-point\nresponses below and have revised the manuscript and supporting information (SI) accordingly. In the\nrevised manuscript, we present new observation data to support the OA trends from 2013 to 2020 and the\nvalidation of the model. We have conducted seasonal analysis on the observation data and justified the\nresults by seasonality. We believe that the revised manuscript is significantly strengthened with additional\ndata analysis, providing a thorough investigation on the long-term trends of OA and the causes in China.\nHowever, I find the paper lack of clarity on a number of things. I have several comments:\n1. The paper does not provide a mechanistic explanation about why SOA does not decrease as much as\nPOA (Figure 1c-f). This could be a major highlight on this paper. The authors mention the potential role\nof radiation and atmospheric oxidation but there is little clarity on how exactly this could play a role (Line\n159-188). If their model gets this trend right (Figure 3), they should be able to provide some insights at\nleast from the model simulations rather than mere speculation.\nThis also makes me wonder about Figure 4. If the trend during 2013-2020 is mainly driven by residential\nsources (as stated in the abstract) and is mainly due to the decrease in POA, why Figure 4 shows similar\nreduction in POA and SOA from 50% and 100% cut in residential. Am I missing anything here?\n[R1] We thank the reviewer for the good suggestion. The lesser reduction in SOA than in POA is the result\nof the lack of control on solvent-use emissions of IVOC and VOC and the chemical and meteorological\nimpacts. Residential fuel burning is the predominant source sector of POA. For SOA, residential and\nsolvent use are the major contributors of the SOA precursors. During 2013-2020, the emission reduction\nis mainly from residential sector and solvent use emissions increased by 8%. Except difference in\nprecursor emission reduction, the model results indicate a few percent of net increase in SOA led by\nchemical drivers and meteorology-driven changes of annual-mean SOA from -5% to 6%.\nIn Figure 4, 38% of OA is POA in the base case. Because the majority of S/LVOC is contributed by\nresidential sources, a full elimination of residential organic emissions may greatly reduce POA by 63%\nand SOA by 38%, leading to a total decline of 41% of OA. The less reduction in SOA is expected because\nsolvent use and industry contribute substantially to SOA except the contribution of residential sources.\nWe have revised the text in Line 171, 207-223, 248-255, 270-283 to highlight the mechanistic explanation.\n2. It is important to understand the OA trend in different seasons. The annual mean, as used in this study,\ncan be heavily weighted towards winter when PM is higher, without much insights on other seasons\n2.5\n(Figure S14). For Figure 1A-B, if annual mean has decreased a lot, it is expected that PM has decreased\n2.5\nmore in winter than in summer, if the decrease is driven by residential fuel burning (Figure S1 suggests\nthat from emissions). It would be great to show the seasons of each campaign in Figure 1c-f, as strong\nseasonality is expected for field campaign data. The authors could consider divide all those field\ncampaigns into different seasons (Figure 1c-f), and examine the trends in each season, which would\nprovide a much clearer message than altogether.\n[R2] We agree with the reviewer that seasonality needs to be considered more carefully in the analysis.\nIn the revised manuscript, we have examined the seasonal changes for both of the long-term OC and field-\ncampaign OA data. As shown in the new figure below, seasonal mean OC concentrations in NCP are high\nin winter and low in summer as is consistent with the greater residential emissions of S/LVOC in winter.\nIndeed, the observed annual OC concentrations show a larger decrease by about 40% in winter than in\nother seasons (33%, 30%, and 13% for autumn, spring, and summer, respectively) from 2017 to 2021,\nexplained by the greater wintertime residential emission reduction. Similar seasonal trends have been also\nobserved at the research site in Beijing during 2013-2020.\nFigure S1. The annual-mean OC concentrations in different seasons (DJF: winter; MAM: spring; JJA:\nsummer; SON: fall) (A) in NCP obtained from the long-term continuous offline measurements at 34\nsites of the national network and (B) in Beijing obtained from long-term online measurements at a\nresearch roof site in the campus of Peking University.\nFor field-campaign data, we replotted Figure 1. The revised Fig. 1C-D now shows the results in\ndifferent cities in different seasons. The data in five cities from different regions in China indicate clear\nOA declines in winter, accompanied with increasing SOA mass fractions in three of the cities. In Beijing\nwhere the campaign data covers over 50% of the period of 2013-2020, the declines of OA and the\nincreases of SOA fractions present in all seasons with more significant changes in winter.\n40\n30\n20\n10\nAnnual mean OC concentrations from continuous measurements at 34 sites in a recently-developed\nnational monitoring network for PM chemical composition in North China Plain (NCP), 6 provincial\n2.5\n) 3-m gμ(\nAOS\ndeledoM\n60\n50\n40 30\n20\n10\n0\n0 10 20 30 40 50 60\n-3\nCampaign-mean POA (μg m )\nF NCP (n=66)\nYRD (n=21) PRD (n=13) NW (n=13) Others (n=13)\nNMB = -0.26\nr = 0.54\n0 10 20 30 40\n-3 Campaign-mean SOA (μg m )\n) 3-m\ngμ( AOP\ndeledoM\nE\nNMB = -0.05\nr = 0.87\n25\n20\n15\n10\n5\n0\n) 3-m gμ(\nCO\ndeledoM\n21 18\n15 12\n9\n6\n2013 2014 2015 2016 2017 2018 2019 2020 2021\nB Network + Research Sites\nNCP YRD\nNMB = 0.01\nr = 0.61\n0 5 10 15 20 25\nAnnual mean OC (μg m-3 )\n3- ) m gµ(\nCO\nA N Y C R P D N N e e tw tw o o rk r k ( n (n = = 3 6 4 ) ) 50 Research Sites:\n#1 (YRD) 40 #2 (NCP) #3 (NCP) 30 Simulation\n20\n10\n0\n2013 2014 2015 2016 2017 2018 2019 2020\n3- ) m gμ(\nAO\nC DJF: BJ SH SZ LZ XM MAM: BJ\nJJA: BJ SON: BJ\n100\n80\n60\n40\n20\n2013 2014 2015 2016 2017 2018 2019 2020\n)%( noitcarf\nAOS\nD\nmonitoring sites in Yangzi River Delta (YRD), and 3 research sites in the two regions. Error bars\nrepresent the standard deviations (NCP) and the ranges (YRD) of the annual concentrations across sites.\n(B) Annual mean OC concentrations at each site compared to the modeled OC. (C-D) Campaign-mean\nOA concentrations and SOA mass fractions in OA obtained from individual campaigns in different\nseasons (DJF: winter; MAM: spring; JJA: summer; SON: fall) from 2013 to 2020 in five cities in China\n(BJ: Beijing in NCP; SH: Shanghai in YRD; SZ: Shenzhen in PRD; LZ: Lanzhou in NW; XM:\nXiamen). (E-F) Campaign-mean POA and SOA concentrations compared to the modeled\nconcentrations. Sites are marked in the map.\nTo clarify the seasonal effects, we have revised the results and discussion in Line 85-97, 120, 154 and\nadded the discussion on the new Figure S1 in Section S3 (Comparisons of OC).\n3. It should be noted that the main conclusion in this paper is actually drawn from all the field campaign\ndata over the period of 2013-2020. The national network only covers the period of 2017-2020. Some\ncareful analysis on the field campaign is indeed necessary.\n[R3] As described in the revised Section S2 (Long-term organic carbon measurements), we have added\nextra long-term OC measurement data from 4 more provincial monitoring sites and 9 research sites in the\nrevised manuscript. The expanded datasets provide a better coverage for 2013-2017 as shown in the\nrevised Fig. 1A, supporting the decline trend of OC over the whole action-plan period. For the field\ncampaign data, we followed the reviewer’s suggestion in comment #2 to examine the trends of OA, POA,\nand SOA in each season quantitively in five cities from different regions in China. As shown in the revised\nFig. 1C-D, the analysis is updated with the consideration of the seasonal variations. The text related to\nthe observations (Line 75-107) has been revised accordingly.\n4. Is the model validated by observations? The model does not reproduce the trend in YRD (Figure S4).\nAny reason for this?\n[R4] Thanks for bringing up the important question. Yes, the model is validated extensively in this study\nas described in the revised Section S3. We first compared the modeled OC to the long-term measurements\nin NCP and YRD (Fig. S5C-D). The comparison to NCP network indicates a good model performance.\nFor YRD, the original comparison only included two sites. Both sites are located in Zhejiang province.\nThe model underestimated the OC concentrations. Two sites are too limited to evaluate the model\nperformance. In the revised version, we expanded the long-term datasets to 6 sites (3 from Zhejiang\nprovince, 2 from Jiangsu province, and 1 from Shanghai). As shown in the new Fig. S5B, the model is\nable to reproduce the regional means of OC concentrations when more sites are included in YRD. We\nalso added the scatter plots for model-site comparisons with more data from 9 research sites from the\nliterature. The ones cover multiple years are shown in the revised Fig. 1A and the rest are single-year\nmean concentrations that we added to the comparisons in Fig. S5C-D. The model-observation\ncomparisons show small NMB values for both NCP and YRD, indicating no significant bias of the model\nin a regional scale. The model has some overestimations in Jiangsu province and underestimations in\nZhejiang province, indicating possible local emission biases. The comparisons to POA and SOA\nhowever do not show significant regional (Fig. 1E-F) and seasonal bias (Fig. S5E-F). As we discussed in\nLine 221-223, the model skills in describing finer temporal and spatial variations of OA are limited\nbecause of large uncertainties in emissions and the lack of sufficient observational constraints.\nOverall, we think this study provides the best model simulations for OA in China with most\ncomprehensive model validations on OC, POA, SOA, OA precursors and their volatility distributions as\nwell as OH levels. We have revised Figure 1, Figure S5, main text related to model validation and\nuncertainty (Line 50-53, 134-149, 284-307, 417-419), and Sections S2-S3 for clarification.\nFigure S5 Observations compared to model simulations in China. (A-B) Annual-mean OC concentrations\nderived from continuous measurements at 34 sites in a recently-developed national monitoring network\nfor PM chemical composition in NCP and at 6 provincial monitoring sites other sites in YRD. Only the\n2.5\nsites with annual data coverage of >75% were used to calculate the annual-mean concentrations. In YRD,\n1 or 2 sites did not meet the criteria for some years. The actual sites in each year are listed in gray in panel\nB. Error bars in panels A and B represent the standard deviations of the mean concentrations of sites in\nNCP and the ranges of the mean concentrations of sites in YRD, respectively. (C-D) Yearly averages of\nOC concentrations obtained from the NCP (n=34) and YRD (n=6) network sites (including low data-\ncoverage ones as the model simulations matched with the measurement periods) as well as the literature\ndata from different research sites in the two regions. (E-F) Mean concentrations of POA and SOA for\nfrom individual campaigns from 2013 to 2020 in China colored by different seasons (DJF: winter; MAM:\nspring; JJA: summer; SON: fall).\n5. Can the author explain how much difference the population weighting can make on OA concentrations?\nDo they consider the population weighting on the model trend too?\n[R5] As shown in the figure below, the trends of population-weighted and arithmetic-mean concentrations\nof POA and SOA in China are similar. The population-weighted POA and SOA annual mean\nconcentrations decrease by 54% and 25% from 2013 to 2020, while the arithmetic means decrease by 47%\nand 24%, respectively. The population-weighted concentrations are higher because high anthropogenic\nemissions usually occur in areas with high population density. In Fig. S5, we present the arithmetic\nannual-mean concentrations for OC and campaign-mean concentrations for OA for the purpose of\ncomparisons. In Figs. 2-4, we present the population-weighted annual-mean concentrations to link the\npollution levels to WHO PM guidelines directly. Using either of the results to present won’t affect the\n2.5\nconclusions and the discussion on the decline trends. We have added this figure as Figure S22 and some\ntext in Methods (Line 393-396, 415-416) and Section S3 (Population weighting) in SI for clarification.\nFigure S22. The population-weighted and arithmetic-mean annual concentrations of POA and SOA in\nChina.\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nThe changes made in response to my comments were sufficient and the rebuftals convincing. However,\nthe data is not really available to review at the provided Zenodo link - it contains neither the relevant\nGEOS-Chem code nor the actual dataset and code used to generate the analysis or figures in the paper.\nReviewer #1 (Remarks on code availability):\nThis is not really what I think of when I see someone discussing Code Availability. The authors have\nincluded some of the code of GEOS-Chem 13.3.1, something which is publicly available already. In point\nof fact, the authors do not actually include the source code for GEOS-Chem in their link - src/GEOS-Chem\nin the provided zip file is enfirely empty. The porfion of the code that would be most relevant to review -\nthe author's OA scheme, which they discuss in the supplement and in their response to me - is not\nactually included in the file, and is not part of the standard GC 13.3.1 download available from the GEOS-\nChem website.\nThe data I would most expect to be available here would be the model output datasets that the authors\nused to make their figures and perform their analysis, as well as the code used in that analysis and data\npresentafion. However, it is not here and as such I cannot do any sort of analysis or replicafion on its\ncontents. If code availability is a requirement for this submission, I do not believe it is in any way met by\nthe zenodo link above (record/5703364). I am unable to use the provided code to make any meaningful\nstatement on the quality or believability of the work.\nReviewer #2 (Remarks to the Author):\nThe authors have adequately addressed all my comments. I think the paper is ready to be published.\nResponse to reviews\nWe thank the reviewers for their comments. In the following section, reviewer comments are in black.\nPoint-by-point responses are in blue labeled with [R]. Specific modifications to the manuscript are in red.\nReviewer #1 (Remarks to the Author): The changes made in response to my comments were sufficient\nand the rebuttals convincing. However, the data is not really available to review at the provided Zenodo\nlink - it contains neither the relevant GEOS-Chem code nor the actual dataset and code used to generate\nthe analysis or figures in the paper.\n[R0]. We thank Reviewer #1 for the comments. We have updated the Data availability section as\nfollows: “The monitoring network observation data are available from the corresponding author upon\nrequest. The model outputs used in the analysis are available at\nhttps://disk.pku.edu.cn/link/AA03DF14A7E715450BB628921DD9100516”. Note that all the campaign\nmeans of OA used in the analysis are listed in Table S1. We share the 2013-2020 monthly mean model\noutputs now in the public accessible folder. Those are the main model results that we used to produce\nthe OA trends. Comparisons to site observations were calculated on hourly model outputs and the\nmodel results have been listed in Table S1.\nReviewer #1 (Remarks on code availability): This is not really what I think of when I see someone\ndiscussing Code Availability. The authors have included some of the code of GEOS-Chem 13.3.1,\nsomething which is publicly available already. In point of fact, the authors do not actually include the\nsource code for GEOS-Chem in their link - src/GEOS-Chem in the provided zip file is entirely empty.\nThe portion of the code that would be most relevant to review - the author's OA scheme, which they\ndiscuss in the supplement and in their response to me - is not actually included in the file, and is not part\nof the standard GC 13.3.1 download available from the GEOS-Chem website.\nThe data I would most expect to be available here would be the model output datasets that the authors\nused to make their figures and perform their analysis, as well as the code used in that analysis and data\npresentation. However, it is not here and as such I cannot do any sort of analysis or replication on its\ncontents. If code availability is a requirement for this submission, I do not believe it is in any way met by\nthe zenodo link above (record/5703364). I am unable to use the provided code to make any meaningful\nstatement on the quality or believability of the work.\n[R1] We have updated the Code availability section as follows: “The revised GEOS-Chem code and the\nrun directory are available at https://github.com/rqmiao/OA_simulation”. Model installation and\noperation are well documented on the GEOS-Chem website. With the revised GEOS-Chem code, users\nare able to check our model updates which have been described in Sect. S1 and Tables S4-S8. They can\nalso generate the executable file (“gcclassic”) that we have already put in the uploaded run directory.\nWith the run directory, users are able to reproduce our model outputs with all necessary input files. GC\nusers often use GCPy: GEOS-Chem Python toolkit to analyze the model outputs\n(https://gcpy.readthedocs.io/en/stable/index.html). We have also provided an example of reading the\nmodel outputs in the run directory under the subfolder “example user code for analysis”.\nReviewer #2 (Remarks to the Author): The authors have adequately addressed all my comments. I think\nthe paper is ready to be published.\n[R0] We are grateful for the second-round review provided by Reviewer 2.\nREVIEWERS' COMMENTS:\nReviewer #1 (Remarks to the Author):\nThe authors now provide the code required to analyze their GEOS-Chem setup, which is an improvement\nfrom the previous state. They have also provided the full GEOS-Chem output from their modeling runs.\nHowever, they have not included any of the code required to reproduce their figures. Overall, this\nrepresents an improvement from an 'open data' perspecfive, but sfill falls short of the CodeOcean setups\nthat I have seen in other Nature Communicafions pieces. I have looked into some of their model output,\nbut without the code used to generate the figures in quesfion, it would take an enormous amount of\nfime for me to regenerate their figures from scratch.\nReviewer #1 (Remarks on code availability):\nSee my response to the above. The authors have now provided a useful set of GEOS-Chem code, with\nwhich I can analyze the changes they have made to the GEOS-Chem SOA scheme. However, they have\nnot included the code used to generate their figures and as a result I cannot opine on the decisions\nmade in generafing them without rebuilding the code myself enfirely from scratch.\nResponse to reviews\nReviewer comments are in black. Point-by-point responses are in blue labeled with [R]. Specific\nmodifications to the manuscript are in red.\nReviewer #1 (Remarks to the Author): The authors now provide the code required to analyze their GEOS-\nChem setup, which is an improvement from the previous state. They have also provided the full GEOS-\nChem output from their modeling runs. However, they have not included any of the code required to\nreproduce their figures. Overall, this represents an improvement from an 'open data' perspective, but still\nfalls short of the CodeOcean setups that I have seen in other Nature Communications pieces. I have looked\ninto some of their model output, but without the code used to generate the figures in question, it would\ntake an enormous amount of time for me to regenerate their figures from scratch.\nReviewer #1 (Remarks on code availability): See my response to the above. The authors have now\nprovided a useful set of GEOS-Chem code, with which I can analyze the changes they have made to the\nGEOS-Chem SOA scheme. However, they have not included the code used to generate their figures and\nas a result I cannot opine on the decisions made in generating them without rebuilding the code myself\nentirely from scratch.\n[R0]. We thank Reviewer #1 for further suggestions. We are happy to provide the code to read the\nGEOS-Chem model output files, to produce statistical model results, and to generate the figures. The\ncode are now added to the Github repository (http://doi.org/10.5281/zenodo.11114855) or\n(https://github.com/rqmiao/OA_simulation/tree/main/codeforfigures). Please note that some of the\nfigure panels are plotted in spreadsheet-type software for which there are no code available (e.g., Fig.\n1c-h)."}]

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