Anthropogenic and Natural Causes for the Interannual Variation of PM2.5 in East Asia During Summer Monsoon Periods From 2008 to 2018

Ma, Danyang; Xie, Min; He, Huan; Wang, Tijian; Xi, Mengzhu; Feng, Lingyun; Zhang, Shuxian; Chen, Shitong

doi:https://doi.org/10.5194/egusphere-2025-10

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https://doi.org/10.5194/egusphere-2025-10

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14 Apr 2025

| 14 Apr 2025

Anthropogenic and Natural Causes for the Interannual Variation of PM_2.5 in East Asia During Summer Monsoon Periods From 2008 to 2018

Danyang Ma, Min Xie, Huan He, Tijian Wang, Mengzhu Xi, Lingyun Feng, Shuxian Zhang, and Shitong Chen

Abstract. There was a significant difference in near-surface PM_2.5 changes across China after the implementation of the Clean Air Action Plan in 2013. This study used the regional climate-chemistry-ecosystem coupled model, RegCM-Chem-YIBs, to investigate interannual variations in PM_2.5 across East Asia from 2008 to 2018. The drivers of PM_2.5 variability were examined from Anthropogenic and Natural perspectives. Compared to 2008, PM_2.5 showed little variation during 2009–2013 (the PreG phase). However, during 2014–2018 (the PostG phase), a substantial decline in PM_2.5 was simulated, particularly in the North China Plain (-36.76 μg/m³) and the Sichuan Basin (-33.96 μg/m³). Anthropogenic pollution emissions were the primary drivers of PM_2.5 reductions, contributing -10.39 to -3.82 μg/m³ in the PreG period and -33.86 to -8.45 μg/m³ in the PostG period. The influence of meteorological conditions on PM_2.5 during the PreG phase (-6.31 to 2.32 μg/m³) was comparable to that of anthropogenic pollutant emissions. Additionally, in the vegetation-rich region, the impact of CO₂ changes on PM_2.5 was comparable to that of anthropogenic pollutant emissions. Our study comprehensively examined the drivers of PM_2.5 concentration changes from 2008 to 2018. We highlight a significant intensification in the contribution of anthropogenic pollutant emissions and reveal that, in regions characterized by dense vegetation, changes in CO₂ concentrations exert a pronounced impact on PM_2.5 variations.

Received: 02 Jan 2025 – Discussion started: 14 Apr 2025

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Danyang Ma, Min Xie, Huan He, Tijian Wang, Mengzhu Xi, Lingyun Feng, Shuxian Zhang, and Shitong Chen

Status: final response (author comments only)

RC1: 'Comment on egusphere-2025-10', Anonymous Referee #3, 08 May 2025

The manuscript “Anthropogenic and Natural Causes for the Interannual Variation of PM2.5 in East Asia During Summer Monsoon Periods From 2008 to 2018” by Ma et al. used a regional climate chemistry-ecosystem coupled model to investigate interannual variations in PM2.5 across East Asia from 2008 to 2018 and investigates the drivers. This has been an important topic in the past years, and this work improves over previous studies by exploring the impact of CO2. I feel this point is of interest to the community and falls within the scope of ACP. The manuscript is also well written and easy to follow. I recommend publication after addressing the following points.

My major concern is the boundary between CO2 change and meteorology change in the work. As mentioned in the text, CO2 could influence PM via changing radiation, temperature, and precipitation. Aren’t these already counted in the meteorology change? This needs to be explained clearer.

Another important concern is for Section 3.2 and 3.3: I would suggest present some statistics other than just make the conclusions by spatial distribution plots. e.g., when you say a reduction of PM2.5 is associated with an increase of a certain factor, did you find a correlation? We cannot simply say a decrease of A is due to an increase of B and a decrease of C, they may just not relate to each other with small correlation.

Other comments:

- Section 2.3 and Table 1: Can you introduce a bit more detail of what processes CO2 will influence in your model? Are the meteorological fields used in SIMbase and SIMco2=2008 the same or SIMbase also reflect the meteorology change due to CO2? In Fig 1, it seems that meteorology responses to YIBs that changes with CO2, then how do you apply the fixed meteorological field for SIMmet=2008? ignoring the response of CO2 variation?

- line 148: as the impact of meteorological conditions is calculated by SIMbase- SIMmet=2008, it is likely to also include influences of CO2.

- line 154-158: it would be better to bring some information of model evaluation to the text instead of letting the audience check all the information in other references. e.g., you might also show numbers for measured PM2.5 trend when discussing the simulation results in Section 3.1, or include figures to compare with observations in supplements.

- The wording of “anthropogenic emissions” driver in many places of the manuscript might need to be clearer. One key question is whether it is also the anthropogenic emissions contributing to the CO2 changes. If so, the “anthropogenic emissions” in the text should means non-CO2 emissions.

- Section 3.3: Will CO2 also change temperature and cloud due to its effects on radiation balance? Are those negligible factors comparing to those shown in Fig 5?

Citation: https://doi.org/10.5194/egusphere-2025-10-RC1

AC1: 'Reply on RC1', Ma Danyang, 18 Jun 2025

Response to Reviewers

No.: ACP-2025-10

Title: Anthropogenic and Natural Causes for the Interannual Variation of PM_2.5 in East Asia During Summer Monsoon Periods From 2008 to 2018

Anonymous referee #3:

The manuscript “Anthropogenic and Natural Causes for the Interannual Variation of PM_2.5 in East Asia During Summer Monsoon Periods From 2008 to 2018” by Ma et al. used a regional climate chemistry-ecosystem coupled model to investigate interannual variations in PM_2.5 across East Asia from 2008 to 2018 and investigates the drivers. This has been an important topic in the past years, and this work improves over previous studies by exploring the impact of CO₂. I feel this point is of interest to the community and falls within the scope of ACP. The manuscript is also well written and easy to follow. I recommend publication after addressing the following points.

Response: We thank referee #3 for careful reading and valuable comments. We have responded to each specific comment in blue below. Please note that the line numbers given below refer to the clean version of the manuscript.

My major concern is the boundary between CO₂ change and meteorology change in the work. As mentioned in the text, CO₂ could influence PM via changing radiation, temperature, and precipitation. Aren’t these already counted in the meteorology change? This needs to be explained clearer.

Response: Thanks. As shown in Table 1, the difference between SIM_Base and SIM_MET=2008(SIM_Base- SIM_MET=2008) quantifies the impact of meteorological variability on PM_2.5 concentrations. Here, “meteorological variability” refers to the year-to-year changes in weather relative to the fixed 2008 baseline. In contrast, the difference between SIM_Base and SIM_CO2=2008 (SIM_Base- SIM_CO2=2008) isolates the effect of CO₂ emission changes on PM_2.5. As a principal greenhouse gas, CO₂ modifies meteorological parameters—such as radiation, temperature, and precipitation—which in turn influence PM_2.5 levels. In this comparison, all meteorological changes derive solely from variations in CO₂ concentration, a mechanism fundamentally different from the meteorological influences identified in Experiments SIM_Base and SIM_MET=2008.

We have added some discussions on this aspect.

Table 1. The Numerical experimental in this study.

Experiment	Time	Meteorological fields	CO₂ emissions	Anthropogenic pollutant emissions
SIM₂₀₀₈	2008	2008	2008	2008
SIM_Base	2009-2018	2009-2018	2009-2018	2009-2018

SIM_MET=2008	2009-2018 2009-2018	2008	2009-2018	2009-2018
SIM_CO2=2008	2009-2018	2008	2009-2018

Changes in manuscript:

2.1 Model description

(L141–148): “In the RegCM-Chem-YIBs model, changes in CO₂ concentrations affect PM_2.5 primarily via two mechanisms: first, CO₂-induced radiative forcing alters the atmospheric radiation balance, leading to shifts in temperature, precipitation, and boundary‐layer structure that modulate PM_2.5 formation, transport, and removal(Li and Mölders, 2008; Matthews, 2007); And second, through the YIBs module, changes in CO₂ concentration modulate photosynthetic activity and stomatal behavior, altering BVOCs emissions that undergo atmospheric photochemical oxidation to form secondary organic aerosols, a significant fraction of PM_2.5 (Kergoat et al., 2002; Kellomaki and Wang, 1998).”

2.3 Experiment settings

(L189–193): “It is noteworthy that, as a principal greenhouse gas, CO₂ modifies meteorological parameters—such as radiation, temperature, and precipitation—which in turn influence PM_2.5 levels. In this comparison, all meteorological changes derive solely from variations in CO₂emissions, a mechanism fundamentally different from the meteorological influences identified in experiments SIM_Base and SIM_MET=2008.”

References

Kellomaki, S. and Wang, K. Y.: Growth, respiration and nitrogen content in needles of Scots pine exposed to elevated ozone and carbon dioxide in the field, Environmental pollution (Barking, Essex : 1987), 101, 263-274, 10.1016/s0269-7491(98)00036-0, 1998.

Kergoat, L., Lafont, S., Douville, H., Berthelot, B., Dedieu, G., Planton, S., and Royer, J. F.: Impact of doubled CO2 on global-scale leaf area index and evapotranspiration:: Conflicting stomatal conductance and LAI responses -: art. no. 4808, Journal of Geophysical Research-Atmospheres, 107, 10.1029/2001jd001245, 2002.

Li, Z. and Mölders, N.: Interaction of impacts of doubling CO2 and changing regional land-cover on evaporation, precipitation, and runoff at global and regional scales, International Journal of Climatology, 28, 1653-1679, 10.1002/joc.1666, 2008.

Matthews, H. D.: Implications of CO2 fertilization for future climate change in a coupled climate-carbon model, Global Change Biology, 13, 1068-1078, 10.1111/j.1365-2486.2007.01343.x, 2007.

Another important concern is for Section 3.2 and 3.3: I would suggest present some statistics other than just make the conclusions by spatial distribution plots. e.g., when you say a reduction of PM_2.5 is associated with an increase of a certain factor, did you find a correlation? We cannot simply say a decrease of A is due to an increase of B and a decrease of C, they may just not relate to each other with small correlation.

Response: Thanks. We have provided the relevant statistical data in the supplementary information and have incorporated Tables S1–S4 into the main manuscript (Tables 3–6) to facilitate easier access for readers.

We attribute changes in PM_2.5 concentrations to three primary factors: meteorological variability, CO₂ emission changes, and anthropogenic pollutant emissions changes. In Section 3.2, we assessed the combined effects of meteorological factors—including temperature, precipitation, wind speed, and planetary boundary layer height—on PM_2.5 concentrations, without isolating the individual contributions of each factor. As illustrated in Figure 4 and Table 4, PM_2.5 concentrations exhibit a negative correlation with precipitation and a positive correlation with temperature, elucidating the mechanisms by which meteorological conditions influence PM_2.5 levels.

Similarly, in Section 3.3, we quantified the integrated impact of CO₂ on atmospheric PM_2.5 concentrations through its modulation of biogenic volatile organic compound (BVOC) emissions and alteration of meteorological conditions.

Your insightful suggestion has provided us with a new perspective. In our forthcoming research, we plan to conduct sensitivity experiments by individually fixing specific meteorological variables. This approach will enable us to independently assess the impact of temperature, precipitation, wind speed, and other factors on PM_2.5 concentrations. Additionally, we aim to distinguish the respective impacts of CO₂-induced meteorological changes and CO₂-driven alterations in BVOC emissions on PM_2.5 levels. This line of inquiry represents a deeper exploration of the subject and promises to yield valuable insights.

Figure 4. The PM_2.5 (a–c, μg/m³), precipitation (d–f, mm/day), wind speed (g–i, m/s), temperature (j–l, K), and Planetary Boundary Layer (PBL) height (m–o, m) during the EASM period in 2008 (left), and their mean changes due to meteorological variations in PreG (2009–2013, center) and PostG (2014–2018, right) phase relative to 2008 (SIM_Base - SIM_MET=2008).

Table 4. Impact of meteorological condition changes on PM_2.5 (μg/m³), precipitation (mm/day), wind speed (m/s), near-surface temperature (K), and Planetary Boundary Layer (PBL) height (m) during the EASM period in PreG (2009–2013) and PostG (2014–2018) phase relative to 2008 (SIM_Base - SIM_MET=2008).

Region	Period	PM_2.5 （μg/m³)	Precipitation (mm/day)	Wind Speed (m/s)	Near-Surface Temperature (K)	PBL (m)
NCP	PreG	-4.01	0.58	0.17	0.32	-46.8
PostG	-1.6	0.6	0.26	0.6	-14.5
FWP	PreG	2.32	1.68	-0.06	0.1	-108.5
PostG	1	0.81	0.05	0.46	-15.3
YRD	PreG	-6.31	1.02	0.18	-0.29	-33.9
PostG	-0.43	0.48	-0.08	0.45	21.9
PRD	PreG	1.49	-2.39	-0.02	0.36	29.6
PostG	0.11	-3.24	0.18	1.00	52.2
SCB	PreG	0.29	1.81	0.13	-0.58	-136.5
PostG	-1.14	0.37	-0.03	-0.14	-76

Changes in manuscript:

Table 3. Changes in near-surface PM_2.5 concentrations (μg/m³) during the EASM period from 2009 to 2018 relative to 2008 in the North China Plain (NCP), Fen-Wei Plain (FWP), Yangtze River Delta (YRD), Pearl River Delta (PRD), and Sichuan Basin (SCB) (SIM_Base - SIM₂₀₀₈).

Year	NCP	FWP	YRD	PRD	SCB
2009	-11.24	-1.29	-11.37	1.41	-3.16
2010	-3.87	1.9	-15.2	-3.57	-4.79
2011	-6.27	0.22	-14.76	0.13	-8.65
2012	-7.42	1.69	-17.61	2.35	-15.99
2013	-14.67	-15.49	-14.9	-6.34	-20.37
2014	-24.26	-15.36	-19.95	-6.72	-22.87
2015	-31.41	-16.9	-27.76	-9.91	-31.75
2016	-38.5	-25.23	-32.43	-8.18	-35.58
2017	-40.69	-25.49	-26.21	-5.82	-37.43
2018	-48.96	-27.83	-33.08	-9.53	-42.19
PreG	-8.69	-2.59	-14.77	-1.20	-10.59
PostG	-36.76	-22.16	-27.89	-8.03	-33.96

Region	Period	PM_2.5 （μg/m³)	Precipitation (mm/day)	Wind Speed (m/s)	Near-Surface Temperature (K)	PBL (m)
NCP	PreG	-4.01	0.58	0.17	0.32	-46.8
PostG	-1.6	0.6	0.26	0.6	-14.5
FWP	PreG	2.32	1.68	-0.06	0.1	-108.5
PostG	1	0.81	0.05	0.46	-15.3
YRD	PreG	-6.31	1.02	0.18	-0.29	-33.9
PostG	-0.43	0.48	-0.08	0.45	21.9
PRD	PreG	1.49	-2.39	-0.02	0.36	29.6
PostG	0.11	-3.24	0.18	1.00	52.2
SCB	PreG	0.29	1.81	0.13	-0.58	-136.5
PostG	-1.14	0.37	-0.03	-0.14	-76

Table 5. Impact of CO₂ emission changes on PM_2.5 (μg/m³), CO₂ (ppm), precipitation (mm/day), and isoprene (μg/m³) during the EASM period in PreG (2009–2013) and PostG (2014–2018) phase relative to 2008 (SIM_Base - SIM_CO2=2008).

Region	Period	PM_2.5 （μg/m³)	CO₂ (ppm)	Precipitation (mm/day)	Isoprene (μg/m³)
NCP	PreG	0.6	3.19	0.27	-0.1
PostG	-1.3	4.24	0.13	0.26
FWP	PreG	0.84	1.70	0.21	-0.16
PostG	-0.98	2.05	0.06	0.33
YRD	PreG	-0.02	4.1	0.13	-0.32
PostG	-0.05	6.2	0.09	-0.58
PRD	PreG	1.13	1.97	-1.02	0.31
PostG	0.31	3.20	-0.33	0.92
SCB	PreG	-0.49	2.80	0.64	-0.78
PostG	-0.73	2.78	0.21	0.69

Table 6. Changes in total PM_2.5 concentrations (ALL, SIM_Base - SIM₂₀₀₈) and the impacts of anthropogenic pollutant emissions (Emis, All-Met-CO₂), meteorological conditions (Met, SIM_Base - SIM_MET=2008), and CO₂ emission (CO₂, SIM_Base - SIM_CO2=2008) variations on PM_2.5 concentrations (μg/m³) during the EASM period in PreG (2009–2013) and PostG (2014–2018) phase relative to 2008.

Region	Period	ALL	Emis	Met	CO₂
NCP	PreG	-8.69	-5.28	-4.01	0.6
PostG	-36.76	-33.86	-1.6	-1.3
FWP	PreG	-2.59	-5.75	2.32	0.84
PostG	-22.16	-22.18	1	-0.98
YRD	PreG	-14.77	-8.44	-6.31	-0.02
PostG	-27.89	-27.41	-0.43	-0.05
PRD	PreG	-1.2	-3.82	1.49	1.13
PostG	-8.03	-8.45	0.11	0.31
SCB	PreG	-10.59	-10.39	0.29	-0.49
PostG	-33.96	-32.09	-1.14	-0.73

Other comments:

Section 2.3 and Table 1: Can you introduce a bit more detail of what processes CO₂ will influence in your model? Are the meteorological fields used in SIM_Base and SIM_CO2=2008 the same or SIM_Base also reflect the meteorology change due to CO₂? In Fig 1, it seems that meteorology responses to YIBs that changes with CO₂, then how do you apply the fixed meteorological field for SIM_MET=2008? ignoring the response of CO₂ variation?

Response: Thanks. We added some descriptions of what processes CO₂ will influence in our model.

To clarify the experimental design, we have revised Table 1 and its description in the revised manuscript. The SIM₂₀₀₈ experiment represents the baseline conditions for the year 2008. In the SIM_Base experiment, interannual variations in meteorological fields, CO₂ emissions, and anthropogenic pollutant emissions (excluding CO₂ emissions) were considered for simulations spanning 2009–2018, representing the baseline conditions for 2009–2018. Additionally, the SIM_MET=2008 and SIM_CO2=2008 experiments were designed, where meteorological fields and CO₂ emissions were fixed at their 2008 levels, respectively, while simulations were conducted for 2009–2018.

As shown in Table 1,SIM₂₀₀₈ and SIM_Base serve as baseline experiments that collectively capture the evolution of PM_2.5 concentrations under the combined influences of meteorological variability, CO₂ emission changes, and anthropogenic pollutant emissions changes (SIM_Base - SIM₂₀₀₈).

The SIM_Base and SIM_CO2=2008 experiments share identical meteorological conditions, differing only in their CO₂ emission datasets; by comparing SIM_Base and SIM_CO2=2008 (SIM_Base - SIM_CO2=2008), we isolate the impact of CO₂ emission changes on PM_2.5. Likewise, since SIM_Base and SIM_MET=2008 use the same CO₂ emission inputs and differ only in meteorological fields, their comparison (SIM_Base - SIM_MET=2008) quantifies the effect of meteorological variability on PM_2.5

Table 1. The Numerical experimental in this study.

Experiment	Time	Meteorological fields	CO₂ emissions	Anthropogenic pollutant emissions
SIM₂₀₀₈	2008	2008	2008	2008
SIM_Base	2009-2018	2009-2018	2009-2018	2009-2018

SIM_MET=2008	2009-2018 2009-2018	2008	2009-2018	2009-2018
SIM_CO2=2008	2009-2018	2008	2009-2018

Changes in manuscript:

2.1 Model description

2.3 Experiment settings:

(L169–175): “The numerical experiments are presented in Table 1. The SIM₂₀₀₈ experiment represents the baseline conditions for the year 2008. In the SIM_Base experiment, interannual variations in meteorological fields, CO₂ emissions, and anthropogenic pollutant emissions (excluding CO₂ emissions) were considered for simulations spanning 2009–2018, representing the baseline conditions for 2009–2018. Additionally, the SIM_MET=2008 and SIM_CO2=2008 experiments were designed, where meteorological fields and CO₂ emissions were fixed at their 2008 levels, respectively, while simulations were conducted for 2009–2018.”

(L179–188): “By comparing the simulation results from different years in the SIM_Base experiment to SIM₂₀₀₈ (SIM_Base - SIM₂₀₀₈), we quantified changes in PM_2.5 concentrations relative to 2008 for the period 2009–2018. To evaluate the impact of meteorological conditions on PM_2.5 concentrations, we compared the results of the SIM_Base experiment with those of the SIM_MET=2008 experiment for the same year (SIM_Base - SIM_MET=2008). Similarly, the contribution of CO₂ emission changes to PM_2.5 variations was assessed by comparing the SIM_Base experiment with the SIM_CO2=2008 experiment (SIM_Base - SIM_CO2=2008) in the same year. The contribution of anthropogenic pollutant emissions was then determined by subtracting the effects of meteorological and CO₂ emission changes from the total PM_2.5variation.”

References

line 148: as the impact of meteorological conditions is calculated by SIMbase- SIMmet=2008, it is likely to also include influences of CO₂.

Response: Thanks. SIM_Base and SIM_MET=2008 use the same CO₂ emission inputs and differ only in meteorological fields, their comparison (SIM_Base - SIM_MET=2008) quantifies the effect of meteorological variability on PM_2.5. Please refer to comment 3 for a detailed response.

line 154-158: it would be better to bring some information of model evaluation to the text instead of letting the audience check all the information in other references. e.g., you might also show numbers for measured PM_2.5 trend when discussing the simulation results in Section 3.1, or include figures to compare with observations in supplements.

Response: Thank you for your invaluable suggestions. We have incorporated the model evaluation results into the manuscript and expanded the corresponding descriptions.

Changes in manuscript:

2.4 Model evaluations

(L198–207): “Observed PM_2.5 data were obtained from the China National Environmental Monitoring Center (CNEMC). This study used hourly PM_2.5 concentrations during the summer monsoon period (May 1 to August 31) from 2015 to 2018. A total of 366 monitoring stations across Chinese cities, selected based on data completeness and representativeness, were used for model validation. The locations of these stations are shown in Fig. S5. CO₂ observations were sourced from the World Data Centre for Greenhouse Gases (WDCGG), including all seven sites in East Asia: Waliguan, Korea Tae-ahn Peninsula, Ulaanbaatar in Mongolia, Lulin, Yonagunijima, Cape D'Aguilar (Hong Kong), and King's Park. Detailed station locations are shown in Fig. S6. Reanalysis data for temperature, wind fields, and relative humidity were obtained from the ERA-Interim dataset.”

(L208–21): “As shown in Table 2 and Figures S1–S6, the SIM_Base experiments reproduce 2015–2018 PM_2.5 and CO₂ concentrations with high correlations and low biases relative to observations, while their simulated meteorological fields closely match reanalysis data. Overall, the RegCM-Chem-YIBs model effectively captures the fundamental characteristics and temporal trends of meteorological factors, PM_2.5, and CO₂ concentrations in East Asia.”

Table 2. Evaluations of the near-surface CO₂ and PM_2.5in East Asia.

Species	Year	Observation	Simulation	Bias	RMSE	R
CO₂（ppm）	2015	402.82	406.98	4.16	9.37	0.44
2016	407.12	410.44	3.32	8.22	0.69
2017	408.35	413.62	5.27	11	0.39
2018	409.61	416.68	7.07	11.32	0.41
PM_2.5（ug/m³）	2015	36.6	25.57	-11.03	12.99	0.71
2016	31.03	22.91	-8.12	10.31	0.64
2017	29.61	24.02	-5.59	10.57	0.71
2018	27.18	19.04	-8.14	11.62	0.61

RMSE: root mean square error; R: correlation coefficient.

Figure S1. Comparisons between the simulated (right) and reanalysis (left) mean temperature (shading, units: K), wind (vectors, units: m/s), and relative humidity (contours, units: %) at 500 hPa (a, b), 850 hPa (c, d) and 1000 hPa (e, f) during the EASM period in 2015.

Figure S2. Comparisons between the simulated (right) and reanalysis (left) mean temperature (shading, units: K), wind (vectors, units: m/s), and relative humidity (contours, units: %) at 500 hPa (a, b), 850 hPa (c, d) and 1000 hPa (e, f) during the EASM period in 2016.

Figure S3. Comparisons between the simulated (right) and reanalysis (left) mean temperature (shading, units: K), wind (vectors, units: m/s), and relative humidity (contours, units: %) at 500 hPa (a, b), 850 hPa (c, d) and 1000 hPa (e, f) during the EASM period in 2017.

Figure S4. Comparisons between the simulated (right) and reanalysis (left) mean temperature (shading, units: K), wind (vectors, units: m/s), and relative humidity (contours, units: %) at 500 hPa (a, b), 850 hPa (c, d) and 1000 hPa (e, f) during the EASM period in 2018.

Figure S5. Comparisons between the simulated and observed near-surface PM_2.5 concentrations (units: μg/m³) during the EASM period in (a)2015, (b)2016, (c)2017, (d)2018. Colored circles represent the observations.

Figure S6. Comparisons between the simulated and observed near-surface CO₂ concentrations (units: ppm) during the EASM period in (a)2015, (b)2016, (c)2017, (d)2018. Colored circles represent the observations.

The wording of “anthropogenic emissions” driver in many places of the manuscript might need to be clearer. One key question is whether it is also the anthropogenic emissions contributing to the CO₂ changes. If so, the “anthropogenic emissions” in the text should means non-CO₂ emissions.

Response: Thank you for highlighting this critical issue. In response, we have revised the manuscript to replace all instances of “anthropogenic emissions” with “anthropogenic pollutant emissions”. Additionally, in Section 2.3 Experiment settings, we have clarified that “anthropogenic pollutant emissions” exclude CO₂ emissions.

2.3 Experiment settings:

(L170–173): “In the SIM_Base experiment, interannual variations in meteorological fields, CO₂ emissions, and anthropogenic pollutant emissions (excluding CO₂ emissions) were considered for simulations spanning 2009–2018, representing the baseline conditions for 2009–2018.”

Section 3.3: Will CO₂ also change temperature and cloud due to its effects on radiation balance? Are those negligible factors comparing to those shown in Fig 5?

Response: Thanks. CO₂ alters atmospheric radiative properties, thereby influencing meteorological factors such as temperature, cloud cover, and precipitation. Our analysis of the relationships between temperature, cloud cover, and PM_2.5 concentrations indicates that their direct effects are insignificant. Specifically, cloud cover affects PM_2.5 primarily through indirect mechanisms, including modulation of solar radiation and changes in planetary boundary layer height. Temperature influences PM_2.5 via multiple complex pathways, such as regulating secondary organic aerosol formation, vertical convection, and boundary layer dynamics. In contrast, precipitation directly removes PM_2.5through wet deposition processes. Therefore, the primary pathways through which CO₂ impacts PM_2.5 concentrations are its modulation of precipitation patterns and its influence on biogenic volatile organic compound (BVOC) emissions from vegetation.

Citation: https://doi.org/10.5194/egusphere-2025-10-AC1

RC2: 'Comment on egusphere-2025-10', Anonymous Referee #1, 05 Jun 2025

This study presents the results of simulations performed with the RegCM-Chem-YIBs Model over the period 2008-2018. The study focuses on PM2.5 concentrations over China and explores the drivers of change in simulated concentrations before and after the implementation of a Clean Air Action Plan in 2013. This is an interesting study and certainly within the scope of ACP.

Whilst the paper presents an interesting set of experiments, it would be strengthened further if the authors could comment on the extent to which this modelling framework captures the temporal and spatial variability of observed PM2.5 across China during the specific time period concerned. There are many studies that have analysed and reported measured PM2.5 concentrations, several of which are already cited in your Introduction, and could be used to offer a comparison. Others include:

Silver et al., 2018, Environ. Res. Lett. 13 114012

Ma et al, 2019, Atmos. Chem. Phys., 19, 6861–6877

Kong et al., 2021, Earth Syst. Sci. Data, 13, 529–570

Silver et al., 2025, Environment International, 197, 109318

Specific comments:

Line 11: This sentence doesn’t quite make sense - remove word “changes”

Line 16: You haven’t defined PreG or PostG yet so it’s a bit confusing to mention these here. You could either define them or describe the time periods without referring to them by these names

Graphical abstract: At the moment the diagram is slightly confusing and it ideally needs to be entirely self-explanatory since the graphical abstracts do not come with a caption. It’s also not clear what “~” is being used to represent here: are these ranges?

Lines 31-38: You refer to PM2.5 as if it is a single entity, whereas in reality it’s an aggregation of part of the aerosol size distribution, and could be comprised of many different components (this is most relevant to your description of the impact of PM2.5 on climate) - I suggest rewording this paragraph to reflect this.

Lines 42-46: Reword this slightly to clarify whether these are all annual average values, as written it sounds as though some of them could be the maximum value recorded

Lines 74-81: clarify here that as well as affecting photosynthesis, elevated CO2 concentrations can directly inhibit the emission of isoprene. Temperature is mentioned in the previous section in terms of its impact on chemical reactions, but changes in temperature will also drive changes to BVOC emissions (and the partitioning of BVOC oxidation products from the gas to particle phase) so this could be mentioned in the Introduction too.

Lines 82-85: specify here that you are referring to China as the same may not be true for other regions

Line 168: can you be more specific than “more favourable meteorological conditions”? What are the main differences in meteorology in this region that lead to lower PM2.5 concentrations?

Line 105: Model Description - it would be useful to include some details around how the model calculates BVOC emissions (since this process is important to your results) - specifically, how are these emissions affected by CO2 concentration; I don’t think this is covered in your previous paper (Ma et al 2023a).

Figure 1: it would be better if the words in each box weren’t split across lines, as some of them currently are. This could be solved by making some of the boxes slightly larger

Figure 3: Correct the units on the legend within the figure

Line 207: In Section 3.2 it’s not completely clear which time periods the values you report are referring to, i.e., for the PreG period, are these the changes between 2008 and 2013? Or is it the average of 2008-2013 minus 2008. I think this is confused by Figure 4 where it’s not clear which time period the central and right-hand columns refer to. Some Figure captions specify May to August but add this to the others that don’t.

Figure 4: Correct this caption (currently refers to O3)

Line 237: In Section 3.3 (and same for Section 3.2), it would be useful to reiterate which simulations have been used to generate these results, and include this in the captions for Figure.

Line 267: Do you mean the average over the entire region?

Line 283: Correct the units on the legend in this Figure (should be ug/m3)

In the simulations where meteorology varies with the year and you see an increase in temperature, would the model also have simulated an increase in BVOC emissions? If so that needs to be discussed.

Citation: https://doi.org/10.5194/egusphere-2025-10-RC2

AC2: 'Reply on RC2', Ma Danyang, 18 Jun 2025

Response to Reviewers

No.: ACP-2025-10

Title: Anthropogenic and Natural Causes for the Interannual Variation of PM_2.5 in East Asia During Summer Monsoon Periods From 2008 to 2018

Anonymous referee #1:

This study presents the results of simulations performed with the RegCM-Chem-YIBs Model over the period 2008-2018. The study focuses on PM_2.5 concentrations over China and explores the drivers of change in simulated concentrations before and after the implementation of a Clean Air Action Plan in 2013. This is an interesting study and certainly within the scope of ACP.

Response: We thank Referee #1 for his/her valuable comments, which have greatly improved our manuscript. We have attempted to make a revision addressing each of the points mentioned in his/her review. Please note that the line numbers given below refer to the clean version of the manuscript.

Whilst the paper presents an interesting set of experiments, it would be strengthened further if the authors could comment on the extent to which this modelling framework captures the temporal and spatial variability of observed PM_2.5 across China during the specific time period concerned. There are many studies that have analysed and reported measured PM_2.5 concentrations, several of which are already cited in your Introduction, and could be used to offer a comparison. Others include:

Silver et al., 2018, Environ. Res. Lett. 13 114012

Ma et al, 2019, Atmos. Chem. Phys., 19, 6861–6877

Kong et al., 2021, Earth Syst. Sci. Data, 13, 529–570

Silver et al., 2025, Environment International, 197, 109318

Response: Thank you for your valuable suggestions. We have added a comparative analysis with existing studies on PM_2.5 concentration changes in China in Section 3.1 “PM_2.5 variation”. The results show that the simulated PM_2.5 trends from 2008 to 2018 in this study are highly consistent with most previous findings, further validating the reliability of our simulation results.

Changes in manuscript:

3.1 PM_2.5 variation

(L259–270): “Table S1 shows that the mean PM_2.5 trend over China during the PreG (2009-2013) and PostG (2014-2018) periods was −1.84 μg/m³/yr and −2.90 μg/m³/yr, respectively. These values are consistent with the findings of Silver et al. (2025), who reported a PM_2.5 trend of −2.47 μg/m³/yr for 2014–2017 in China based on ground-based observations. Similarly, Lin et al. (2018) reported PM_2.5 trends of −0.65 and −2.30 μg/m³/yr for 2006–2010 and 2011–2015 in China, respectively. Using satellite remote sensing data, Ma et al. (2019) found declines of 1.03 and 4.27 μg/m³/yr for 2010-2013 and 2013-2017 in China, respectively. The high-resolution Chinese air quality reanalysis (CAQRA), developed by Kong et al. (2021) using data assimilation techniques, indicated a more pronounced decline of −5.80 μg/m³/yr for PM_2.5 from 2013 to 2018 in China. In addition, Silver et al. (2018), based on multi-source data, reported a trend of −3.40 μg/m³/yr for 2015–2017 in China. Therefore, our simulation accurately captures the observed PM_2.5 trends over China from 2008 to 2018, providing a robust foundation for subsequent attribution analyses.”

Table S1. Interannual trends of near-surface PM_2.5 concentrations (μg/m³/year) during the PreG period (2009–2013) and PostG period (2014–2018) relative to 2008 over the NCP, FWP, YRD, PRD, and SCB regions.

Year	NCP	FWP	YRD	PRD	SCB	Average
PreG	-0.69	-2.84	-0.71	-1.55	-3.44	-1.84
PostG	-4.94	-2.49	-2.63	-0.56	-3.86	-2.90

References

Kong, L., Tang, X., Zhu, J., Wang, Z., Li, J., Wu, H., Wu, Q., Chen, H., Zhu, L., Wang, W., Liu, B., Wang, Q., Chen, D., Pan, Y., Song, T., Li, F., Zheng, H., Jia, G., Lu, M., Wu, L., and Carmichael, G. R.: A 6-year-long (2013-2018) high-resolution air quality reanalysis dataset in China based on the assimilation of surface observations from CNEMC, Earth System Science Data, 13, 529-570, 10.5194/essd-13-529-2021, 2021.

Lin, C. Q., Liu, G., Lau, A. K. H., Li, Y., Li, C. C., Fung, J. C. H., and Lao, X. Q.: High-resolution satellite remote sensing of provincial PM2.5 trends in China from 2001 to 2015, Atmos Environ, 180, 110-116, 10.1016/j.atmosenv.2018.02.045, 2018.

Ma, Z., Liu, R., Liu, Y., and Bi, J.: Effects of air pollution control policies on PM2.5 pollution improvement in China from 2005 to 2017: a satellite-based perspective, Atmospheric Chemistry and Physics, 19, 6861-6877, 10.5194/acp-19-6861-2019, 2019.

Silver, B., Reddington, C. L., Arnold, S. R., and Spracklen, D. V.: Substantial changes in air pollution across China during 2015-2017, Environmental Research Letters, 13, 10.1088/1748-9326/aae718, 2018.

Silver, B., Reddington, C. L., Chen, Y., and Arnold, S. R.: A decade of China's air quality monitoring data suggests health impacts are no longer declining, Environment International, 197, 10.1016/j.envint.2025.109318, 2025.

Specific comments:

Line 11: This sentence doesn’t quite make sense - remove word “changes”.

Response: Thanks. We have removed the word “changes” in the revised version.

Changes in manuscript:

Abstract

(L11–12): “There was a significant difference in near-surface PM_2.5 across China after the implementation of the Clean Air Action Plan in 2013.”

Line 16: You haven’t defined PreG or PostG yet so it’s a bit confusing to mention these here. You could either define them or describe the time periods without referring to them by these names.

Response: Thanks. We have revised the expressions and clearly defined PreG and PostG in the updated manuscript.

Changes in manuscript:

Abstract

(L15–18): “Compared to 2008, PM_2.5 showed little variation during the PreG phase (2009–2013). However, during the PostG phase (2014–2018), a substantial decline in PM_2.5 was simulated, particularly in the North China Plain (-36.76 μg/m³) and the Sichuan Basin (-33.96 μg/m³).”

Graphical abstract: At the moment the diagram is slightly confusing and it ideally needs to be entirely self-explanatory since the graphical abstracts do not come with a caption. It’s also not clear what “~” is being used to represent here: are these ranges?

Response: Thanks for pointing that out. We have replaced the tilde (~) with a hyphen (–) to indicate ranges. Additionally, the Graphical abstract has been optimized to enhance clarity and make it more self-explanatory.

Changes in manuscript:

Lines 31-38: You refer to PM_2.5 as if it is a single entity, whereas in reality it’s an aggregation of part of the aerosol size distribution, and could be comprised of many different components (this is most relevant to your description of the impact of PM_2.5 on climate) - I suggest rewording this paragraph to reflect this.

Response: Thanks for pointing out this issue. We have added a more detailed description of PM_2.5 in the revised manuscript.

Changes in manuscript:

Introduction

(L32–36): “PM_2.5 refers to fine particulate matter with an aerodynamic diameter less than or equal to 2.5 micrometers (Chen et al., 2018). Its sources include industrial emissions, vehicular exhaust, biomass burning, and secondary formation from atmospheric gases (Wu et al., 2020). Major chemical components of PM_2.5 include sulfates, nitrates, ammonium salts, organic carbon, elemental carbon, and heavy metals (Van Donkelaar et al., 2019; Li et al., 2017a).”

References

Chen, G. B., Li, S. S., Knibbs, L. D., Hamm, N. A. S., Cao, W., Li, T. T., Guo, J. P., Ren, H. Y., Abramson, M. J., and Guo, Y. M.: A machine learning method to estimate PM2.5 concentrations across China with remote sensing, meteorological and land use information, Sci Total Environ, 636, 52-60, 10.1016/j.scitotenv.2018.04.251, 2018.

Li, G. H., Bei, N. F., Cao, J. J., Huang, R. J., Wu, J. R., Feng, T., Wang, Y. C., Liu, S. X., Zhang, Q., Tie, X. X., and Molina, L. T.: A possible pathway for rapid growth of sulfate during haze days in China, Atmospheric Chemistry and Physics, 17, 3301-3316, 10.5194/acp-17-3301-2017, 2017a.

van Donkelaar, A., Martin, R. V., Li, C., and Burnett, R. T.: Regional Estimates of Chemical Composition of Fine Particulate Matter Using a Combined Geoscience-Statistical Method with Information from Satellites, Models, and Monitors, Environmental Science & Technology, 53, 2595-2611, 10.1021/acs.est.8b06392, 2019.

Wu, K., Yang, X. Y., Chen, D., Gu, S., Lu, Y. Q., Jiang, Q., Wang, K., Ou, Y. H., Qian, Y., Shao, P., and Lu, S. H.: Estimation of biogenic VOC emissions and their corresponding impact on ozone and secondary organic aerosol formation in China, Atmospheric Research, 231, 10.1016/j.atmosres.2019.104656, 2020.

Lines 42-46: Reword this slightly to clarify whether these are all annual average values, as written it sounds as though some of them could be the maximum value recorded.

Response: Thank you for pointing out this issue. We have slightly revised the wording in the manuscript to clarify that these values represent averages.

Changes in manuscript:

Introduction

(L48–52): “From 2000 to 2008, the national average PM_2.5 concentration in China was 49.4 ± 14.2 μg/m³. In eastern China, the average concentration was 55.4 ± 16.1 μg/m³, while the Beijing-Tianjin-Hebei region experienced average levels as high as 62.1 ± 22.5 μg/m³. The Yangtze River Delta saw an average concentration of 63.0 ± 11.1 μg/m³, the Pearl River Delta recorded an average of 52.4 ± 5.8 μg/m³, and the Sichuan Basin averaged 61.6 ± 13.4 μg/m³.

Lines 74-81: clarify here that as well as affecting photosynthesis, elevated CO₂ concentrations can directly inhibit the emission of isoprene. Temperature is mentioned in the previous section in terms of its impact on chemical reactions, but changes in temperature will also drive changes to BVOC emissions (and the partitioning of BVOC oxidation products from the gas to particle phase) so this could be mentioned in the Introduction too.

Response: Thanks. We have added some discussions on this aspect.

Changes in manuscript:

Introduction

(L68–74): “In addition, moderate increases in temperature can significantly enhance the emissions of biogenic volatile organic compounds (BVOCs) by stimulating the activity of the synthase enzyme. However, when temperatures exceed the physiological tolerance threshold of plants, decreased enzyme activity or metabolic disruption may suppress emissions(Lindwall et al., 2016; Kleist et al., 2012). Therefore, temperature changes can influence atmospheric PM_2.5 concentrations by modulating the emissions of BVOCs.”

(L89–94): “It is worth noting that elevated CO₂ concentrations may also directly inhibit BVOCs emissions by reducing the activity of BVOCs synthase enzymes(Heald et al., 2009; Pegoraro et al., 2004). Therefore, the impact of increased CO₂ on vegetation BVOCs emissions can be either positive or negative, depending primarily on the relative strength of the inhibitory effect from enzyme suppression versus the stimulatory effect from enhanced photosynthesis(Sun et al., 2012).”

References

Heald, C. L., Wilkinson, M. J., Monson, R. K., Alo, C. A., Wang, G. L., and Guenther, A.: Response of isoprene emission to ambient CO2 changes and implications for global budgets, Global Change Biology, 15, 1127-1140, 10.1111/j.1365-2486.2008.01802.x, 2009.

Kleist, E., Mentel, T. F., Andres, S., Bohne, A., Folkers, A., Kiendler-Scharr, A., Rudich, Y., Springer, M., Tillmann, R., and Wildt, J.: Irreversible impacts of heat on the emissions of monoterpenes, sesquiterpenes, phenolic BVOC and green leaf volatiles from several tree species, Biogeosciences, 9, 5111-5123, 10.5194/bg-9-5111-2012, 2012

Lindwall, F., Schollert, M., Michelsen, A., Blok, D., and Rinnan, R.: Fourfold higher tundra volatile emissions due to arctic summer warming, Journal of Geophysical Research-Biogeosciences, 121, 895-902, 10.1002/2015jg003295, 2016.

Pegoraro, E., Rey, A., Bobich, E. G., Barron-Gafford, G., Grieve, K. A., Malhi, Y., and Murthy, R.: Effect of elevated CO2 concentration and vapour pressure deficit on isoprene emission from leaves of Populus deltoides during drought, Functional Plant Biology, 31, 1137-1147, 10.1071/fp04142, 2004.

Sun, Z. H., Niinemets, Ü., Hüve, K., Noe, S. M., Rasulov, B., Copolovici, L., and Vislap, V.: Enhanced isoprene emission capacity and altered light responsiveness in aspen grown under elevated atmospheric CO2 concentration, Global Change Biology, 18, 3423-3440, 10.1111/j.1365-2486.2012.02789.x, 2012.

Lines 82-85: specify here that you are referring to China as the same may not be true for other regions.

Response: Thanks. We have now specified that the region in question is China.

Changes in manuscript:

Introduction

(L97–99): “Numerous studies have used statistical models and numerical simulations to investigate the impacts of meteorological conditions and anthropogenic pollution emissions on PM_2.5 concentration changes in China.”

Line 168: can you be more specific than “more favourable meteorological conditions”? What are the main differences in meteorology in this region that lead to lower PM_2.5 concentrations?

Response: Thanks. We have included an explanation of “more favourable meteorological conditions” in the revised manuscript.

Changes in manuscript:

3.1 PM_2.5 variation

(L226–230): “In contrast, regions in western China (Yunnan, Gansu, Xinjiang) exhibit lower PM_2.5 levels due to limited industrial activity, lower population density, and more favorable meteorological conditions (Low water vapor content, lower temperatures, and weak solar radiation are unfavorable for the formation of secondary aerosols such as sulfates, nitrates, and organic aerosols) (Wei et al., 2021; Xue et al., 2020).

References

Wei, J., Li, Z. Q., Lyapustin, A., Sun, L., Peng, Y. R., Xue, W. H., Su, T. N., and Cribb, M.: Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications, Remote Sens Environ, 252, ARTN 11213610.1016/j.rse.2020.112136, 2021.

Xue, W. H., Zhang, J., Zhong, C., Ji, D. Y., and Huang, W.: Satellite-derived spatiotemporal PM2.5 concentrations and variations from 2006 to 2017 in China, Sci Total Environ, 712, 10.1016/j.scitotenv.2019.134577, 2020

Line 105: Model Description - it would be useful to include some details around how the model calculates BVOC emissions (since this process is important to your results) - specifically, how are these emissions affected by CO₂ concentration; I don’t think this is covered in your previous paper (Ma et al 2023a).

Response: Thanks. We have added some discussions on this aspect.

Changes in manuscript:

2.1 Model description

(L129–135): “The YIBs model employs a leaf-level BVOC emission scheme based on vegetation photosynthesis. Unlike the traditional MEGAN (Model of Emissions of Gases and Aerosols from Nature) model, this approach incorporates the influence of plant photosynthesis on BVOC emissions, making it more representative of actual plant physiological processes. In this scheme, leaf-level BVOC emission rates depend on the photosynthetic rate, leaf surface temperature, and intracellular CO₂ concentration (Yue and Unger, 2015; Lei et al., 2020; Yue et al., 2015).

References

Lei, Y. D., Yue, X., Liao, H., Gong, C., and Zhang, L.: Implementation of Yale Interactive terrestrial Biosphere model v1.0 into GEOS-Chem v12.0.0: a tool for biosphere-chemistry interactions, Geoscientific Model Development, 13, 1137-1153, 10.5194/gmd-13-1137-2020, 2020.

Yue, X. and Unger, N.: The Yale Interactive terrestrial Biosphere model version 1.0: description, evaluation and implementation into NASA GISS ModelE2, Geoscientific Model Development, 8, 2399-2417, 10.5194/gmd-8-2399-2015, 2015.

Yue, X., Unger, N., and Zheng, Y.: Distinguishing the drivers of trends in land carbon fluxes and plant volatile emissions over the past 3 decades, Atmospheric Chemistry and Physics, 15, 11931-11948, 10.5194/acp-15-11931-2015, 2015.

Figure 1: it would be better if the words in each box weren’t split across lines, as some of them currently are. This could be solved by making some of the boxes slightly larger.

Response: Thank you for your valuable suggestion. We have enlarged the boxes to prevent word wrapping.

Changes in manuscript:

Figure 1. Framework of the RegCM-Chem-YIBs Model.

Figure 3: Correct the units on the legend within the figure.

Response: Thanks. We have corrected the units on the legend within the figure.

Changes in manuscript:

Figure 3. Changes in near-surface PM_2.5 concentrations (μg/m³) during the EASM period from 2009 (a) to 2018 (j) relative to 2008 in East Asia (SIM_Base - SIM₂₀₀₈).

Line 207: In Section 3.2 it’s not completely clear which time periods the values you report are referring to, i.e., for the PreG period, are these the changes between 2008 and 2013? Or is it the average of 2008-2013 minus 2008. I think this is confused by Figure 4 where it’s not clear which time period the central and right-hand columns refer to. Some Figure captions specify May to August but add this to the others that don’t.

Response: Thanks. Sorry for the mistake. “PreG-2008” represents the average of 2008-2013 minus 2008. We have revised the corresponding figure titles accordingly and replaced “May–August” with “EASM” throughout.

Changes in manuscript:

Figure 5. The PM_2.5 (a–c, μg/m³), CO₂ (d–f, ppm), precipitation (g–i, mm/day), and isoprene (j–l, μg/m³) during the EASM period in 2008 (left), and their mean changes due to CO₂ emission variations in PreG (2009–2013, center) and PostG (2014–2018, right) phase relative to 2008 (SIM_Base - SIM_CO2=2008).

Figure 6. The total changes in PM_2.5 concentrations (All, SIM_Base - SIM₂₀₀₈), and the changes in PM_2.5 attributed to variations of anthropogenic pollutant emissions (Emis, All-Met-CO₂), meteorological conditions (Met, SIM_Base – SIM_MET=2008), and CO₂ emissions (CO₂, SIM_Base - SIM_CO2=2008) during the EASM period in PreG (2009–2013, left) and PostG (2014–2018, right) phase relative to 2008.

Figure 7. The total changes in PM_2.5 concentrations (All, SIM_Base - SIM₂₀₀₈) for the North China Plain (NCP), Fenwei Plain (FWP), Yangtze River Delta (YRD), Pearl River Delta (PRD), and Sichuan Basin (SCB) during the EASM period in PreG (2009–2013) and PostG (2014–2018) phase relative to 2008, along with the variations in PM_2.5 due to anthropogenic pollutant emissions (Emis, All-Met-CO₂), meteorological conditions (Met, SIM_Base – SIM_MET=2008), and CO₂ emission (CO₂, SIM_Base - SIM_CO2=2008) changes.

Figure 4: Correct this caption (currently refers to O₃).

Response: Thanks. Sorry for the mistake. We replaced O₃ with PM_2.5.

Line 237: In Section 3.3 (and same for Section 3.2), it would be useful to reiterate which simulations have been used to generate these results, and include this in the captions for Figure.

Response: Thanks for your valuable suggestion. We have reiterated in the text which simulations were used to generate these results and included this information in the figure captions. Revisions to the figure captions are detailed in response to question 13.

Changes in manuscript:

3.1 PM_2.5 variation

(L220–222): “Changes in PM_2.5 concentrations from 2009 to 2018 relative to 2008 were quantified by comparing simulation results from each year in the SIM_Base experiment with SIM₂₀₀₈ (SIM_Base - SIM₂₀₀₈).”

3.2 Contribution of meteorological conditions

(L287–288): “The impact of meteorological conditions variations on PM_2.5 concentrations were assed by compared SIM_Base results with those from SIM_MET=2008 for the same year (SIM_Base - SIM_MET=2008).”

3.3 Contribution of CO₂

(L326–328): “The contribution of CO₂ emission changes to PM_2.5 variability was quantified by comparing the SIM_Base experiment with the SIM_CO2=2008 experiment (SIM_Base - SIM_CO2=2008) within the same year.”

3.4 Contribution of anthropogenic pollutant emissions

(L363–363): “The contribution of changed anthropogenic pollutant emissions to PM_2.5 variation was determined by removing the effects of meteorological and CO₂ emission changes from the total variation.”

Line 267: Do you mean the average over the entire region?

Response: Thanks. This refers to the mean values, and we have revised the ambiguous wording accordingly.

Changes in manuscript:

(L364–365): “During the PreG period, PM_2.5 levels decreased by an average of 5 to 10 μg/m³ over East Asia.”

Line 283: Correct the units on the legend in this Figure (should be ug/m³).

Response: Thanks. Sorry for the mistake. We have correctted the units on the legend in this Figure

Changes in manuscript:

Figure 6. The total changes in PM_2.5 concentrations (All, SIM_Base - SIM₂₀₀₈), and the changes in PM_2.5 attributed to variations of anthropogenic pollutant emissions (Emis, All-Met-CO₂), meteorological conditions (Met, SIM_Base – SIM_MET=2008), and CO₂ emissions (CO₂, SIM_Base - SIM_CO2=2008) during the EASM period in PreG (2009–2013, left) and PostG (2014–2018, right) phase relative to 2008.

In the simulations where meteorology varies with the year and you see an increase in temperature, would the model also have simulated an increase in BVOC emissions? If so that needs to be discussed.

Response: Thanks. As shown in Table 1, the only difference between the SIM_Base and SIM_MET=2008 experiments lies in the meteorological conditions. Therefore, the difference SIM_Base − SIM_MET=2008 represents the impact of meteorological changes on PM_2.5. Although elevated temperatures may lead to changes in BVOC emissions, Figure R1 indicates that BVOC changes are not significant and show little spatial correlation with PM₂.₅ trends (Figure 4 a-c). As a result, this aspect is not discussed further in the manuscript.

Table 1. The Numerical experimental in this study

Experiment	Time	Meteorological fields	CO₂ emissions	Anthropogenic pollutant emissions
SIM₂₀₀₈	2008	2008	2008	2008
SIM_Base	2009-2018	2009-2018	2009-2018	2009-2018

SIM_MET=2008	2009-2018 2009-2018	2008	2009-2018	2009-2018
SIM_CO2=2008	2009-2018	2008	2009-2018

Figure R1. The isoprene (μg/m³) during the EASM period in 2008 (left), and the changes due to meteorological variations in PreG (2009–2013, center) and PostG (2014–2018, right) phase relative to 2008 (SIM_Base - SIM_MET=2008).

Citation: https://doi.org/10.5194/egusphere-2025-10-AC2

Danyang Ma, Min Xie, Huan He, Tijian Wang, Mengzhu Xi, Lingyun Feng, Shuxian Zhang, and Shitong Chen

Supplement

https://doi.org/10.5194/egusphere-2025-10-supplement

Danyang Ma, Min Xie, Huan He, Tijian Wang, Mengzhu Xi, Lingyun Feng, Shuxian Zhang, and Shitong Chen

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Short summary

The PM_2.5 concentration in China underwent significant changes in 2013. We examined the underlying causes from three perspectives: anthropogenic pollutant emissions, meteorological conditions, and CO₂ concentration variations. Our study highlighted the importance of considering the role of CO₂ on vegetation when predicting PM_2.5 concentrations and developing corresponding control strategies.


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