In this study, we employed the RegESM, an improved extension of the RegCM-Chem-YIBs regional climate–chemistry–ecosystem modeling framework (Xie et al., 2024, 2019; Zhang et al., 2025). The original RegCM-Chem-YIBs couples the RegCM4 regional climate model (Giorgi et al., 2012), the radiative interactive gas-phase chemistry module Chem (Shalaby et al., 2012), and the YIBs terrestrial ecosystem model (Yue and Unger, 2015) to represent interactive processes among atmospheric dynamics, chemistry, and terrestrial carbon cycles (Xie et al., 2024). Building upon this foundation, RegESM strengthens two-way feedback among the atmosphere, atmospheric chemistry, and land surface processes, enabling a more realistic simulation of biogeochemical cycles (Zhang et al., 2025). The enhanced coupling allows land surface changes, such as vegetation dynamics and soil moisture variations, to more directly influence atmospheric composition, radiation, and meteorology, while atmospheric and chemical variations simultaneously affect ecosystem processes (Xie et al., 2024; Zhang et al., 2025). This bidirectional integration improves the model's capability to capture transient and spatially heterogeneous climate–ecosystem–chemistry interactions, which are crucial for regional climate change and carbon budget assessments (Zhang et al., 2025).

The RegESM framework used in this study integrates RegCM4 as the dynamical core for simulating regional climate processes at a high resolution, the Chem module for interactive gas-phase and aerosol chemistry coupled with radiation and meteorology, and the YIBs land surface model for calculating biophysical processes such as photosynthesis, transpiration, and energy balance, along with biogeochemical cycles of carbon and nitrogen (Giorgi et al., 2012; Shalaby et al., 2012; Xie et al., 2024; Yue and Unger, 2015). In RegESM, the influence of atmospheric nitrogen deposition on terrestrial carbon fluxes is represented through the online coupling between the chemistry and land components. Atmospheric nitrogen deposition is calculated online by the chemistry component as dry and wet deposition fluxes of reduced and oxidized nitrogen (NH_x and NO_y), which are then passed to the land component as external nitrogen inputs. These inputs affect soil inorganic nitrogen availability and subsequently influence plant productivity, ecosystem respiration, and net ecosystem productivity. Therefore, the effect of nitrogen deposition on carbon fluxes is represented as the integrated result of nitrogen input and land biogeochemical processes, rather than as a simple linear fertilization effect. These components are linked through an improved coupling mechanism that ensures the consistent exchange of meteorological, chemical, and biogeophysical variables at each model timestep, enabling fully interactive simulations in which land, atmosphere, and chemistry evolve in a physically coherent manner (Xie et al., 2024; Zhang et al., 2025). This model has been widely applied in East Asia (Xie et al., 2025, 2019; Zhang et al., 2025, 2024).

We used net ecosystem productivity (NEP) as an indicator for characterizing carbon sources and sinks (NEP > 0 suggests a carbon sink). NEP was calculated as the difference between gross primary production (GPP) and the sum of autotrophic respiration (Ra) and heterotrophic respiration (Rh) (Xie et al., 2025; Yue et al., 2021). It is noteworthy that the NEP estimated in this study does not account for lateral carbon transfers.

Once surface O₃ enters plants through the stomata, it directly damages plant cellular structures and suppresses the photosynthetic rate, thereby reducing vegetation productivity. In the YIBs vegetation module of the RegESM model, a semi-mechanistic parameterization scheme is employed to represent the impacts of O₃ on plants (Sitch et al., 2007; Yue and Unger, 2015): 1B=Btot⋅K where B denotes the photosynthetic rate under O₃ exposure, Btot represents the total leaf photosynthetic rate, and K is the remaining proportion of photosynthetic capacity after O₃ stress. This proportion is determined by the stomatal O₃ flux that exceeds a specified threshold: 2K=1-b⋅max⁡Kozn-Kozncrit,0 where b denotes the vegetation sensitivity parameter to O₃ derived from observational data. Kozncrit represents the threshold of O₃-induced damage to vegetation, and Kozn denotes the O₃ flux entering the leaf through stomata: 3Kozn=[O3]rb+κO3rs, where [O₃] denotes the O₃ concentration at the canopy top, rb is the boundary layer resistance, κO3 is the ratio of O₃ leaf resistance to water vapor blade resistance, and rs is the stomatal resistance accounting for the effects of O₃: 4rs=gs⋅K gs denotes the leaf conductance unaffected by O₃ exposure. By simultaneously solving Eqs. (2), (3), and (4), a quadratic term with respect to K is obtained, which can be solved analytically.

The simulation domain covers most of East Asia (Fig. S1 in the Supplement), centered at 36° N and 107° E. The horizontal resolution is 30 km, with 18 vertical layers. To quantify the independent contributions of aerosol, O₃ damage, and atmospheric nitrogen deposition to China's terrestrial carbon sink during 2010–2020, four sensitivity experiments were conducted (Table 1): a baseline simulation without these effects (Base), and three single-factor cases that enabled only aerosol (Ctrl_AOD), O₃-induced vegetation damage (Ctrl_O₃), and nitrogen deposition impacts (Ctrl_Ndep). In the Ctrl_AOD experiment, aerosols were fully coupled to meteorology, so that the direct aerosol radiative effect and the associated meteorological responses were represented in the simulations, while aerosol indirect effects through cloud processes were not included. The difference between each sensitivity case and the Base run represents the corresponding individual effect. All simulations were preceded by a one-year spin-up to reduce the influence of initial conditions. To further assess regional responses, China was divided into six representative subregions (Fig. S2), and statistical analyses were performed for each.

The initial and boundary meteorological fields were taken from the ECMWF (European Centre for Medium-Range Weather Forecasts) ERA-Interim reanalysis with a temporal resolution of 6 h and a horizontal resolution of 1.5° × 1.5° (Dee et al., 2011a; Hersbach et al., 2020). Aerosol initial and boundary conditions were provided by the global chemical transport model (MOZART) (Emmons et al., 2010; Horowitz et al., 2003). Background CO₂ fields were constrained by three-dimensional concentrations from NOAA CarbonTracker (CT) reanalysis (Peters et al., 2007). The initial parameters for the YIBs model were derived from soil carbon stocks based on equilibrium tree height and a 30-year harvest cycle (Yue and Unger, 2015). Vegetation cover was prescribed from MODIS and AVHRR (Advanced Very High Resolution Radiometer) datasets (Lawrence and Chase, 2007). Anthropogenic emissions in China were taken from the Multi-resolution Emission Inventory for China (MEIC) (Geng et al., 2024; Li et al., 2017; Zheng et al., 2018).

We employed monthly mean aerosol optical depth (AOD) data from the MODIS sensor onboard NASA's Terra satellite (MOD08_M3.061). The data have a spatial resolution of 1° × 1° and are retrieved using three algorithms: the Dark Target, Deep Blue, and combined approaches (Levy et al., 2013). Ground-level O₃ observations were obtained from 366 monitoring stations operated by the China National Environmental Monitoring Center (CNEMC). To evaluate the model's capability in simulating atmospheric nitrogen deposition, we employed publicly available datasets (Liu et al., 2024; Zhu et al., 2025). These datasets integrate observations with model outputs to provide nitrogen deposition estimates at both global and regional scales over China. To assess the reliability of simulated CO₂, we used observations from the World Data Centre for Greenhouse Gases (WDCGG). This dataset provides measured surface atmospheric CO₂ concentrations and was used to evaluate the model's ability to reproduce observed CO₂ levels. For the spatial distribution of CO₂, we additionally used CO₂ concentration fields from CT (Peters et al., 2007). For GPP and net primary production (NPP) validation, we used the global MODIS products MOD17A2H and MOD17A3H (Collection 6). The GPP data, at 8 d resolution, were derived using the radiation use efficiency algorithm, while NPP (NPP = GPP - Ra) data were produced by annually accumulating GPP values, with a spatial resolution of 500 m (He et al., 2018; Madani et al., 2014).

Aerosol-induced meteorological changes are highly interdependent, making it challenging to isolate their individual effects on terrestrial carbon cycling. To quantify the relative contributions of these meteorological responses to vegetation carbon fluxes, we applied a multiple linear regression framework. Standardized regression coefficients were used to assess the relative influence of each climate variable. This approach has been widely demonstrated as effective for disentangling the impacts of multiple environmental drivers on ecosystem processes (Jung et al., 2017; Xie et al., 2025; Zhang et al., 2024).

The regression model is expressed as follows: 5ΔY=A1×ΔX1RadD+A2×ΔX2RadF+A3×ΔX3Temp+A4×ΔX4Precip+A5×ΔX5VPD+ε where ΔY denotes the difference in terrestrial carbon flux between the simulations Base and Ctrl_AOD, respectively. ΔX1RadD,ΔX2RadF, ΔX3Temp, ΔX4Precip and ΔX5VPD denote the differences in direct radiation, diffuse radiation, temperature, precipitation, and vapor pressure deficit (VPD) between the simulations Base and Ctrl_AOD, respectively. Ai represents the partial regression coefficient for different meteorological factors, indicating the sensitivity of carbon flux to variations in these factors. ε is the residual term of the regression model. We use the following equation to calculate the standardized regression coefficient Bi for comparing the relative impacts of different meteorological factors: 6Bi=Ai×SDΔXi/SDΔY where SDΔXi and SDΔY represent the standard deviations of the changes in each meteorological factor and carbon flux, respectively. Bi quantifies the relative contribution of different meteorological factors to variations in carbon flux. This approach enables a quantitative assessment of the individual impacts of changes in each meteorological factor induced by aerosol radiative effects on terrestrial carbon flux.

During 2010–2020, surface O₃ in China increased and imposed a persistent suppression on terrestrial carbon sinks. Simulations show a strong reduction of GPP by 0.4–0.6 gC m⁻² d⁻¹ in most regions, with more than 0.8 gC m⁻² d⁻¹ in Southeast and Southwest China (Fig. 6a). NEP shows a similar spatial pattern (Fig. 6b). The largest decline occurs in the southeast (Yangtze River basin and South China coast), with NEP reduced by 0.06–0.08 gC m⁻² d⁻¹ and locally above 0.1 gC m⁻² d⁻¹, consistent with high O₃ and evergreen broadleaf forests (Yue et al., 2017). In the southwest (Sichuan Basin and Yunnan–Guizhou Plateau), NEP decreases by 0.03–0.06 gC m⁻² d⁻¹, related to complex terrain and dense forests. Impacts are weaker in Northeast and Northwest China, mostly below 0.02 gC m⁻² d⁻¹. In Shandong, Henan, and northern Jiangsu, the simulated losses are small, reflecting cropland-dominated land cover. However, earlier studies reported strong O₃ effects on crops (Ren et al., 2012), suggesting possible underestimation. This bias may stem from the simplified crop representation in the model (Fig. S2). Nationwide, O₃ reduces GPP and NEP by 749.44 and 51.33 TgC yr⁻¹, accounting for 10.17 % and 12.90 % of the totals. O₃ also decreased Rh by 288.17 TgC yr⁻¹, with the strongest reductions occurring in eastern and southern China (Fig. S10b). This indicates that the O₃-induced suppression of ecosystem carbon uptake was partly offset by a concurrent decline in heterotrophic respiration. This pattern suggests that reduced photosynthesis and carbon allocation under O₃ stress decreased litter input and belowground carbon supply, thereby limiting microbial substrate availability and weakening soil carbon decomposition. The suppression is attributed to reduced photosynthesis, altered stomatal conductance, and shifts in carbon allocation, which together weaken ecosystem sinks.

The annual effect intensifies until 2018 and then weakens (Fig. 6c). In 2010, O₃ reduces NEP by 42.93 TgC yr⁻¹, reaching 55.71 TgC yr⁻¹ in 2018. It then decreases to 51.98 TgC yr⁻¹ in 2019 and 51.77 TgC yr⁻¹ in 2020. These variations reflect air pollution control policies. Between 2013 and 2017, the first Clean Air Action reduced PM_2.5 and NO_x but left volatile organic compounds (VOCs) largely uncontrolled, thereby enhancing O₃ formation, especially in VOCs-limited regions (Lu et al., 2020). Both model and observations show higher O₃ during this stage (Fig. S4). After 2018, the second Clean Air Action introduced coordinated control of NO_x and VOCs in the Yangtze River Delta and Pearl River Delta, reducing O₃ during summer and easing sink suppression in 2019–2020. In contrast, O₃ continued to rise in North China, indicating uneven policy outcomes across regions.

Seasonal effects are distinct (Figs. 6d and S9). Summer shows the strongest suppression, with NEP reduced by 29.10 TgC (56.69 % of the annual effect). This results from the overlap of peak O₃ and peak photosynthesis, when high temperature and humidity keep stomata open and allow O₃ uptake. Spring is second, with NEP reduced by 11.67 TgC (22.74 %). The effect is linked to leaf expansion, rapid growth, and frequent transport events. Autumn and winter show weaker impacts due to lower photosynthesis, unfavorable O₃ chemistry, and reduced stomatal conductance. Regional differences are evident: in the south, suppression extends from spring to late autumn, while in the north it is confined to summer. This highlights the role of climate and phenology in modulating the impact of O₃ on carbon sinks.

The response of China's terrestrial ecosystems to atmospheric nitrogen deposition during 2010–2020 shows pronounced spatial heterogeneity (Fig. 7a, b). At the national scale, nitrogen deposition increased GPP and NEP by 668.88 and 37.98 TgC yr⁻¹, respectively. These increases account for 9.08 % of total GPP and 9.52 % of total NEP. Atmospheric nitrogen deposition also increased Rh by 297.26 TgC yr⁻¹ over China, indicating that the nitrogen-induced enhancement of carbon uptake was accompanied by stronger soil carbon decomposition. The net gains were mainly concentrated in the southeastern, southwestern, and central regions. In these areas, NEP increased by 0.04–0.08 gC m⁻² d⁻¹, forming the dominant contribution to the nitrogen-induced carbon sink. Although atmospheric nitrogen deposition is highest in eastern China (Fig. 2e, f), the regional variations in GPP and NEP induced by nitrogen deposition are more pronounced in southern China than in the east. The strong spatial gradient highlights that the ecological effects of nitrogen deposition are not uniform, but tightly linked to anthropogenic nitrogen emissions and ecosystem sensitivity (Shang et al., 2024). High responses were observed in regions with intensive agriculture and industry, where deposition exceeded 15 kg N ha⁻¹ yr⁻¹. Vegetation dominated by subtropical evergreen broadleaf forests, mixed forests, and croplands is generally nitrogen-limited. Additional nitrogen input alleviated nutrient constraints, enhanced photosynthesis and biomass accumulation, and shifted soil microbial processes. The spatial pattern of Rh (Fig. S10c) also shows pronounced positive responses in southern China, consistent with the regions of strong nitrogen-induced carbon uptake. This suggests that enhanced plant production and carbon input to soils stimulated microbial decomposition, so that the final NEP gain reflects the balance between increased NPP and increased Rh rather than a simple linear fertilization effect. When stimulation of GPP and NPP outweighed the increase in ER, NEP rose. Warm and humid climates, together with long growing seasons, further amplified these effects.

The impacts of nitrogen deposition on GPP and NEP varied strongly over time (Fig. 7c, d). In 2010, deposition enhanced NEP by 36.45 TgC yr⁻¹. The effect increased to a peak of 42.50 TgC yr⁻¹ in 2012, but then declined, reaching 34.65 TgC yr⁻¹ by 2020. This trajectory reflects the influence of China's air pollution control policies on ecosystem carbon dynamics. The temporal trend corresponds to changes in nitrogen deposition fluxes. Between 2010 and 2012, rapid industrialization and agriculture raised deposition from 15.85 to 17.91 kg N ha⁻¹ yr⁻¹ (+13 %). After 2013, emission reduction policies reduced nitrogen deposition, which fell to 13.25 kg N ha⁻¹ yr⁻¹ in 2020 (-26.02 %). Notably, the effect of nitrogen deposition on NEP leveled off after 2015, which can be attributed to the slower decline rate of atmospheric nitrogen deposition since 2015 (Fig. S5). The reduction in NEP (-18.47 %) was smaller than that in nitrogen input. This lagged response suggests that soil nitrogen pools accumulated from long-term deposition continued to supply nitrogen to vegetation, buffering the decline.

The influence of nitrogen deposition on NEP displayed clear seasonality (Fig. 8). Strong positive effects occurred in summer and spring, whereas autumn and winter showed overall suppression at the national scale. Summer accounted for the largest gain, with an NEP increase of 27.16 TgC. Spring followed with 14.12 TgC. In contrast, autumn and winter reduced NEP by 0.20 and 3.10 TgC, respectively. These seasonal differences result from the combined influence of multiple factors. During summer, optimal temperature, light, and water supported vigorous canopy photosynthesis. Plants assimilated nitrogen efficiently, leading to higher GPP and biomass accumulation. Spring growth stages were also nitrogen-sensitive, producing strong positive responses. In autumn and winter, however, plant activity slowed. Nitrogen inputs mainly stimulated heterotrophic respiration, while GPP and NPP remained low. As a result, NEP decreased, and carbon sink strength weakened outside the growing season. However, the negative response in autumn and winter was not spatially uniform. Positive NEP anomalies were still evident in parts of the eastern Qinghai–Tibet Plateau and western Sichuan, especially in autumn (Fig. 8c), whereas in winter this signal became much weaker and more localized (Fig. 8d). This spatial heterogeneity likely reflects the combined effects of persistent nitrogen limitation and weak heterotrophic respiration responses under cold, high-elevation conditions. In these regions, low temperatures constrain soil decomposition more strongly, so the stimulation of heterotrophic respiration by additional nitrogen is limited. Meanwhile, alpine grasslands, shrublands, and montane forests may still maintain some residual photosynthetic activity during the late growing season, allowing deposited nitrogen to support carbon uptake. As a result, nitrogen deposition can still enhance NEP locally in autumn, even though most other regions show seasonal carbon sink weakening.

To assess the combined influence of aerosols, O₃, and atmospheric nitrogen deposition on China's terrestrial carbon sink, the three independent effects were algebraically summed. During 2010–2020, the co-evolution of these atmospheric factors jointly drove substantial interannual variability and stage-dependent changes in carbon uptake, closely linked to the implementation of the CAA plan. The interannual trend (Fig. 9) shows that although aerosols and nitrogen deposition generally enhanced carbon sequestration, the strong carbon loss caused by O₃ largely offset these positive effects. The mean net effect was 4.58 TgC yr⁻¹, exhibiting pronounced fluctuations and a declining trend. Net enhancement was strong in the early years of the decade but weakened steadily and approached neutral levels by 2018–2020, when a slight negative value (-0.14 TgC yr⁻¹) first appeared. These changes indicate a gradual transition from an enhancement-dominated to an inhibition-dominated regime.

To further interpret this transition, the study period was divided into three phases according to key CAA milestones, and the dominant factors were identified (Fig. 9a). In pre-CAA (2010–2013), the mean annual net effect reached 8.22 TgC yr⁻¹, characterized by nitrogen-deposition-dominated enhancement. Nitrogen deposition provided the largest positive contribution (+39.47 TgC yr⁻¹), while the diffuse-radiation fertilization effect of aerosols offered a secondary gain (+16.08 TgC yr⁻¹). The negative impact of O₃ (-47.33 TgC yr⁻¹) was largely compensated by the two positive drivers, resulting in a pronounced increase in carbon sink strength. This stage corresponded to relatively high emissions of aerosol and nitrogen precursors, which maintained elevated aerosol loading and nitrogen deposition, while O₃ pollution had not yet reached the stronger suppressive level observed in later years. During CAA Phase I (2014–2017), the mean net effect decreased sharply to 3.50 TgC yr⁻¹, marking a transitional stage with competing influences. The positive effect of aerosols peaked (+19.22 TgC yr⁻¹), likely because aerosol changes during this period were more favorable for diffuse-radiation fertilization, despite the concurrent declines in both scattering and absorbing aerosols. However, this gain was largely offset by intensified O₃-induced inhibition (-53.80 TgC yr⁻¹). This phase coincided with strong reductions in emissions of SO₂, PM_2.5 and NO_x under the first Clean Air Action (Fig. S11), which substantially altered atmospheric composition. Although declining aerosol loading weakened the total radiative perturbation, the remaining aerosol conditions still supported a strong diffuse-radiation effect. Meanwhile, insufficient VOCs control favored O₃ formation in many regions, thereby amplifying O₃-induced suppression of the carbon sink. In CAA Phase II (2018–2020), the mean net effect further declined to 1.19 TgC yr⁻¹, forming an O₃-dominated pattern. This stage was associated with further declines in aerosol concentrations and nitrogen deposition under continued emission reductions, which weakened their positive contributions to NEP. At the same time, the coordinated control of NO_x and VOCs in key regions partly alleviated O₃ pollution, but this improvement was not sufficient to reverse the dominant suppressive role of O₃ at the national scale. Overall, the stage-dependent changes in the net carbon sink effect were broadly consistent with the temporal evolution of anthropogenic emissions during 2010–2020 (Fig. S11), highlighting the strong imprint of CAA-related emission controls on the balance among aerosols, O₃, and nitrogen deposition. With continued emission control, the aerosol-induced enhancement decreased from its peak (+18.66 TgC yr⁻¹), and the nitrogen-deposition gain weakened (+35.68 TgC yr⁻¹). Although O₃ suppression slightly eased (-53.15 TgC yr⁻¹), it still nearly balanced the combined positive contributions, indicating a fundamental shift in atmospheric drivers controlling China's terrestrial carbon sink.

The spatial overlay further supports these findings (Fig. 9b, c, d). In forested and industrialized regions of eastern and southern China, the cancellation between positive and negative effects was most pronounced. These areas, benefiting from nitrogen and aerosol fertilization but suffering from intense O₃ pollution, became hotspots of weakened or even reversed net effects. Overall, the CAA plan not only improved air quality but also altered atmospheric composition in ways that substantially affected China's terrestrial carbon sinks. Policy-driven emission changes transformed the system from a nitrogen–aerosol-enhanced regime to an O₃-dominated offset pattern. These results suggest that achieving synergistic benefits between air-quality improvement and carbon neutrality requires elevating O₃ mitigation to a higher strategic priority.

Although the RegESM framework captures the overall spatiotemporal variations of China's terrestrial carbon sink in response to atmospheric composition changes, several uncertainties remain that may influence the quantitative assessment of the individual and combined effects of aerosols, O₃, and nitrogen deposition.

First, this study only considered the direct radiative effects of aerosols, while aerosol–cloud interactions were excluded. The first and second indirect effects of aerosols on cloud formation and albedo involve large uncertainties (Haywood and Boucher, 2000) and were therefore not represented in our simulations. However, observations have shown that terrestrial carbon fluxes are highly sensitive to sky conditions and diffuse radiation changes (Oliphant et al., 2011; Yue and Unger, 2017). The omission of aerosol–cloud interactions may affect the magnitude and spatial pattern of aerosol impacts on radiation and photosynthesis, as cloud-mediated diffuse radiation responses remain uncertain. Future work should explicitly include aerosol–cloud–radiation feedbacks to better quantify their effects on ecosystem carbon exchange.

Second, uncertainties remain in evaluating vegetation responses to O₃ exposure. Field-based O₃ fumigation experiments across China are still limited, making it difficult to comprehensively assess ecosystem-level damage. In this study, the YIBs model applied different O₃ damage coefficients for plant functional types, and the parameterization has shown reasonable regional performance in simulating GPP–O₃ responses (Yue et al., 2017). Nevertheless, a wider range of site-level observations is required to constrain the O₃ damage functions across various vegetation types and climate zones at the national scale.

Third, nonlinear coupling among aerosols, O₃, and nitrogen deposition introduces systemic uncertainty in estimating their combined effects. Aerosol reduction alters photolysis rates and thereby affects O₃ formation (Tang et al., 2017; Yan et al., 2023; Yang et al., 2022), while O₃ and nitrogen jointly regulate stomatal conductance, photosynthetic efficiency, and water-use dynamics (Sitch et al., 2007; Zhang et al., 2018). In addition, the coexisting effects of these three drivers may not be strictly additive. Aerosol effects and O₃ damage may be partly antagonistic, as aerosol-induced changes in diffuse radiation, temperature, and vapor pressure deficit can modulate stomatal uptake and the physiological stress caused by O₃. Nitrogen deposition may partly offset O₃-induced reductions in plant productivity by alleviating nutrient limitation, whereas the interaction between aerosols and nitrogen deposition may vary regionally because nitrogen availability can influence the extent to which vegetation benefits from aerosol-induced radiation changes. These interactions may amplify or offset each other under changing climatic conditions, and the real-world coexisting effects of these atmospheric drivers may therefore differ from the linear sum of their independently quantified contributions. In the present study, the three factors were quantified separately to isolate their first-order effects, while a full assessment of their two-way and three-way interactions would require a dedicated factorial experimental framework. Therefore, although nonlinear interactions may modify the quantitative magnitudes of the simulated responses, the overall conclusion that the positive contributions of aerosols and nitrogen deposition weakened while the suppressive influence of O₃ became increasingly important during 2010–2020 is expected to remain qualitatively robust. These limitations further emphasize the need for high-resolution, fully coupled chemistry–ecosystem modeling frameworks to capture the co-evolution of multiple atmospheric processes.

In summary, despite these uncertainties, this study provides robust quantitative evidence that aerosols, O₃, and nitrogen deposition jointly modified the magnitude and spatial distribution of China's terrestrial carbon sink during 2010–2020. Future efforts should focus on incorporating aerosol–cloud interactions, expanding field-based O₃ response networks, and improving representation of multi-process coupling and nonlinear interactions to further constrain atmospheric–biosphere feedbacks under China's evolving air quality and carbon neutrality goals.