The ground-level air pollutant data for the YRD and BTH regions were acquired from the China National Environmental Monitoring Center (CNEMC) network (https://www.cnemc.cn/, last access: 31 December 2022), comprising hourly measurements of PM_2.5, PM₁₀, SO2, NO2, CO, and O3 concentrations from 2015 to 2022. Observations from multiple monitoring stations within the same city were averaged to derive city-level pollutant concentrations (site-specific details are provided in Table S1 and Fig. S1 in the Supplement). The monitoring network includes 80 stations in the BTH region, covering major cities and areas in Beijing, Tianjin, and Hebei Province, and 197 stations in the YRD region, spanning Shanghai, Jiangsu, Zhejiang, and adjacent provinces. To avoid ambiguity between two cities sharing the same English spelling “Taizhou”, Taizhou City in Jiangsu Province is denoted as TaizhouJS, while Taizhou City in Zhejiang Province is denoted as TaizhouZJ throughout this study.

All national monitoring stations strictly follow the Technical Specifications for Automatic Ambient Air Quality Monitoring (Ministry of Environmental Protection of China, 2013a), ensuring standardized field operation and quality control procedures. City-level PM_2.5 and PM₁₀ concentrations are released in accordance with the national reference gravimetric method (Ministry of Environmental Protection of China, 2011), which provides the calibration and traceability framework for automated particulate measurements across the monitoring network. Gaseous pollutants (SO2, NO2, CO, and O3) are measured using ultraviolet fluorescence, chemiluminescence, non-dispersive infrared absorption, and ultraviolet photometric analysis, respectively, following the national specifications for continuous gaseous monitoring (Ministry of Environmental Protection of China, 2013b). These unified procedures ensure accuracy, comparability, and long-term stability of pollutant observations.

Meteorological fields for 2015–2022 were obtained from the GEOS Forward Processing (GEOS-FP) product (http://geoschemdata.wustl.edu/ExtData/, last access: 31 December 2020) at a spatial resolution of 0.25°×0.3125°. GEOS-FP provides hourly assimilated meteorological variables with relatively high spatial and temporal resolution, enabling refined characterization of mesoscale atmospheric processes over the BTH and YRD regions. Its near–real-time data assimilation framework has been widely applied in regional atmospheric pollution and transport studies, supporting accurate representation of dynamic meteorological conditions (Yin et al., 2021b, 2022a, b). Its near–real-time data assimilation system further improves the accuracy of reanalysis-based meteorological fields and enhances representation of dynamic atmospheric processes (Sun et al., 2021a, b; Yin et al., 2019, 2020, 2021a). The meteorological parameters used in this study include total cloud fraction (CLDTOT), precipitation flux (PRECTOT), 2 m specific humidity (QV2M), 2 m air temperature (T2M), sea-level pressure (SLP), surface downward shortwave radiation (SWGDN), and 10 m zonal (U10M) and meridional (V10M) wind components.

Anthropogenic emission data for 2015–2022 were obtained from the Community Emissions Data System (CEDS), which provides monthly mean fluxes at a spatial resolution of 0.5°×0.5°. In this study, we use emissions of CH2O, CO, NH3, NOx, SO2, BC, OC, and paraffinic reactive primary emissions (PRPE). Emissions were further categorized into eight sectors: non-combustion agriculture, energy transformation and extraction, industrial combustion and processes, surface transportation, residential and commercial fuel use, solvents, waste treatment and disposal, and international shipping. Anthropogenic emission data for 2015–2022 were derived from the CEDS, a global inventory providing temporally resolved sector-specific emissions. The CEDS framework supports climate change projections and quantifies human-driven interactions between air pollutants and climate systems, critical for assessing health and ecosystem impacts.

City-level emission and meteorological variables were derived from gridded emission inventories and meteorological reanalysis datasets.For each city, polygon boundaries were obtained from the GADM Level-2 shapefile, and all grid cells whose centers fell within the city polygon were identified.

For emission data, which are expressed as surface fluxes (kgm-2s1), the city-scale total emission Ecity(t) was obtained by physical integration over all intersecting grid cells: 1Ecity(t)=∑i,jFij(t)Aijrij where Fij(t) is the emission flux of grid cell (i,j) at time t; Aij is the nominal grid-cell area; and rij is the fractional overlap between the grid cell and the city polygon. This area-weighted integration converts surface fluxes into physically consistent city-total emissions.

For meteorological variables, which represent intensive state quantities such as near-surface temperature (T2M), specific humidity (QV2M), and wind components (U10M/V10M), city-level averages were computed as the arithmetic mean of all grid-cell centers located inside the city polygon: 2Xcity(t)=1N∑i,jXij(t) where Xij(t) is the variable value in cell (i,j), and N is the number of valid grid cells within the city.

This center-based averaging is computationally efficient and provides a representative estimate of the mean meteorological condition. Because the study region lies mainly in the mid-to-low latitudes of China, where grid-cell area variation with latitude is minor, this approximation introduces negligible bias compared with full area-weighted averaging.

Both extraction procedures ensure spatial consistency between emission and meteorological datasets and yield temporally continuous, city-level time series for subsequent modeling.

Light Gradient Boosting Machine (LightGBM) is an efficient and scalable implementation of gradient boosting, extensively applied to regression, classification, and ranking tasks (Yin et al., 2021c). By using a histogram-based decision-tree algorithm, the LightGBM model drastically reduces both computation time and memory usage compared to traditional gradient-boosting methods such as XGBoost (Bian et al., 2023; Zhang et al., 2017). It supports the direct handling of categorical variables without one-hot encoding, improving efficiency when processing high-dimensional datasets. During training, LightGBM grows trees in a leaf-wise (best-first) manner, generating deeper splits along the branch that achieves the largest loss reduction.

In contrast, XGBoost and classical Gradient Boosting Decision Trees (GBDT) use a level-wise growth strategy, which provides stability but can become computationally slower for large-scale data. LightGBM also offers extensive hyperparameter controls, such as maximum tree depth, minimum data in leaf, and feature fraction, to balance model complexity and generalization (Ke et al., 2017). Due to its high predictive accuracy, efficient splitting mechanism, and robust computational capability, LightGBM has become one of the most widely used gradient-boosting frameworks in environmental modeling (Liu et al., 2023; Wang et al., 2022; Zhang et al., 2022b).

Prior to model training, all hourly emission and meteorological inputs were aggregated to monthly means, and the LightGBM models were trained on these monthly-resolved datasets. This temporal aggregation ensured that all variables shared the same temporal frequency, preventing inconsistencies between hourly and monthly features. It also matched the model input scale with the monthly trend analysis period (2015–2022), thereby avoiding time-scale mismatches between predictor variables and the evaluation framework.

The model performance was evaluated using three widely recognized regression metrics: the correlation coefficient (R), indicating the linear relationship between predicted and observed concentrations. the coefficient of determination (R2), measuring the proportion of variance in observations explained by the model; and the root-mean-square error (RMSE), representing the average magnitude of prediction errors. Higher R and R2 and lower RMSE indicate stronger predictive ability.

To quantify the interannual trends of PM_2.5 and PM₁₀ concentrations from 2015 to 2022, a linear regression model was employed in this study. For each city, the relationship between annual mean concentration y and year x was modeled as: 8y=β0+β1x+ϵ where β0 represents the intercept (baseline concentration), and ϵ denotes the error term. The slope β1, reflecting the annual rate of concentration change, was estimated via the ordinary least squares (OLS) method. Specifically, the parameters were optimized by minimizing the residual sum of squares (RSS): 9arg⁡min⁡β0,β1∑i=1n(yi-(β0+β1xi))2 where n is the sample size (e.g., n=6 for the period 2015–2022), xi denotes the year, and yi represents the corresponding annual mean concentration.

The slope β1 was derived as: 10β1=Cov(x,y)Var(x). The sign of β1 indicates the direction of concentration trends (negative for decreasing, positive for increasing), while its absolute value quantifies the magnitude of change.

To quantify meteorological and anthropogenic emission contributions, the trained models were applied in a parallel prediction experiment. For each year from 2016 to 2022, anthropogenic emission features were held fixed at their 2015 levels, while meteorological and temporal predictors were retained at their actual contemporaneous values. This produced a counterfactual concentration series driven solely by meteorological variability. This yielded pollutant concentrations driven solely by meteorological variations (denoted as ML2022met for 2022). The contribution metrics related to 2015 were calculated as follows:

Meteorological contribution (ML2022met): 11ML2022met=ML15-22-ML2015. ML15-22 is the non-emission condition unchanged, the emission condition is fixed as the model prediction result in 2015, and ML2015 is the model prediction result with unchanged meteorological and emission conditions.

Emission contribution (ML2022emis): 12ML2022emis=(Obs2022-Obs2015)-ML2022met. Obs2022 and Obs2015: Observed concentrations in 2022 and 2015, respectively.

Figure 1 illustrates the interannual evolution of ground-level PM_2.5 and PM₁₀ concentrations across the BTH and YRD regions from 2015 to 2022, with corresponding annual mean spatial distributions shown in Figs. S2 and S3. Both pollutants exhibit persistent downward trajectories across nearly all cities. The monthly time series and associated OLS trend lines (Fig. S4) further confirm that these declines remain stable throughout the study period, with the most pronounced reductions occurring before 2020. Statistical diagnostics from linear regression (Table S4) indicate that most cities display significant negative trends (p<0.05), underscoring the robustness and consistency of these decreases.

For PM_2.5, all cities show negative trends, with annual rates ranging from approximately -1.53 to -9.46 µgm-3yr-1. Within BTH, the most rapid declines occur in Baoding (-9.46±0.74 µgm-3yr-1), Hengshui (-8.36±0.67 µgm-3yr-1), and Xingtai (-8.02±0.56 µgm-3yr-1), while Chengde (-1.98±0.35 µgm-3yr-1) and Zhangjiakou (-2.00±0.42 µgm-3yr-1) exhibit more modest declines. In the YRD region, substantial decreases are observed in Huzhou (-5.69±1.22 µgm-3yr-1), Hefei (-4.83±0.33 µgm-3yr-1), and Chuzhou (-4.28±1.12 µgm-3yr-1), whereas Zhoushan (-2.11±0.14 µgm-3yr-1) and Taizhou-ZJ (-2.72±0.26 µgm-3yr-1) experience comparatively smaller reductions. These regional contrasts closely align with the initial concentration levels shown in Fig. S2, where inland BTH cities began with substantially higher baselines (e.g., Baoding and Hengshui exceeded 105 and 98 µgm-3 in 2015), enabling larger absolute declines.

For PM₁₀, all cities also exhibit significant decreases, with annual trends ranging from roughly -1.84 to -13.79 µgm-3yr-1. The largest reductions occur in BTH, particularly Hengshui (-13.79±1.82 µgm-3yr-1), Baoding (-13.07±1.46 µgm-3yr-1), and Xingtai (-12.76±1.29 µgm-3yr-1). In contrast, Chizhou (-1.84±2.03 µgm-3yr-1) and Zhoushan (-2.77±0.30 µgm-3yr-1) show the smallest declines, and the PM₁₀ trend in Chizhou is not statistically significant (p=0.40), consistent with its minimal decline rate. As with PM_2.5, cities starting with the highest PM₁₀ levels – such as Baoding (174.6 µgm-3 in 2015) and Hengshui (175.9 µgm-3) – exhibit the steepest reductions, whereas cities with lower initial levels (e.g., Zhoushan at ∼46.8 µgm-3) show correspondingly smaller absolute decreases.

Overall, both PM_2.5 and PM₁₀ decrease more rapidly in BTH than in YRD, reflecting regional differences in initial emissions, industrial structure, and the intensity of mitigation policies. The widespread statistical significance of the trends (Table S4) supports the conclusion that both regions experienced sustained and robust improvements in air quality during 2015–2022.

Figure 2 summarizes the predictive performance of the LightGBM models for PM_2.5 and PM₁₀ across all cities. The density scatterplots (Fig. 2a and c) show good agreement between predictions and observations, with overall R/R2 values of 0.82/0.67 for PM_2.5 and 0.81/0.65 for PM₁₀, respectively. City-level performance (Fig. 2b and d) further indicates that the majority of cities achieve correlation coefficients exceeding 0.70, as detailed in Table S5. While the predictive skill varies among cities, particularly for PM₁₀, such variability is expected given the stronger influence of coarse particle processes and regional heterogeneity. Importantly, subsequent robustness analyses (Figs. S5 and S6) confirm that this variability does not arise from model overfitting or instability, but rather reflects intrinsic differences in local PM dynamics across urban environments.

Figure 3 summarizes the SHAP-based overall feature importance. For PM_2.5, although T2M ranks as the single most influential variable in Fig. 3a, the broader pattern shows that emission-related predictors dominate the upper portion of the importance distribution, with SO2_total, BC_total, and NOx_total collectively contributing a larger share than any single meteorological factor. This implies that PM_2.5 variability during 2015–2022 remains strongly controlled by precursor emissions and their long-term reductions. In contrast, for PM₁₀ (Fig. 3b), meteorological variables, particularly QV2M and T2M, occupy the highest ranks. It should be noted that PM₁₀ inherently includes a substantial contribution from fine particles (PM_2.5). As a result, the similarity between the PM_2.5 and PM₁₀ feature importance rankings partly reflects shared driving processes affecting fine-mode particles, rather than a limitation of the modeling framework. Within this integrated metric, the elevated importance of humidity and temperature reflects their role in modulating particle mass through hygroscopic growth, boundary-layer conditions, and seasonally varying resuspension processes. Precursor emissions such as NOx_total and SO2_total remain influential, but their relative importance is reduced compared to PM_2.5.

To further assess whether the model can distinguish drivers associated with coarse particles, we additionally analyzed the coarse-mode mass defined as PM_2.5−10, calculated as PM₁₀ minus PM_2.5, using the same LOGO-based LightGBM modeling and SHAP attribution framework. The resulting feature importance rankings show systematic differences from those of PM₁₀. In particular, the dominance of humidity- and temperature-related variables and combustion-related emission proxies is reduced, whereas the relative influence of transport- and removal-related meteorological factors becomes more pronounced. Detailed results are provided in Fig. S7.

Region-specific SHAP rankings (Fig. S8) further show that PM_2.5 in the BTH region is more strongly influenced by SO2_total and BC_total, whereas the YRD exhibits greater sensitivity to meteorological conditions such as temperature and humidity. For PM₁₀, QV2M remains the dominant driver in both regions, while precursor importance (e.g., NOx_total) is consistently higher in BTH. These spatial contrasts reflect differences in emission structures and seasonal behavior between regions and underscore the need for differentiated control strategies targeting PM_2.5 and PM₁₀.

The spatial patterns of average emission and meteorological contributions (Fig. 4) reveal clear regional contrasts between northern cities in BTH and southern cities in YRD. For PM_2.5, emission contributions are strongly negative across most BTH cities such as Baoding, Hengshui, and Xingtai, often exceeding -30 µgm-3, reflecting substantial reductions from historically elevated emission levels. In contrast, YRD cities such as Suzhou, Hangzhou, and Ningbo display more moderate negative contributions, typically around -10 to -20 µgm-3, consistent with their lower baseline emissions. Meteorological contributions also exhibit north–south gradients: several coastal or humid subtropical YRD cities (e.g., Ningbo, Wenzhou) show slightly positive meteorological influences, whereas continental BTH cities (e.g., Beijing, Tianjin) show predominantly negative effects associated with winter stagnation, shallow boundary layers, and lower humidity. Similar spatial patterns are observed for PM₁₀ (Fig. 4b and d), where emission-driven reductions remain larger in BTH than in YRD, mirroring regional differences in emission intensity.

The interannual evolution shown in Fig. 5 further clarifies these relationships. Emission contributions remain consistently negative from 2016 to 2022 for both pollutants and intensify over time. For PM_2.5, the reductions increase from -9.13 µgm-3 in 2016 to -31.39 µgm-3 in 2022, while PM₁₀ reductions strengthen from -9.77 to -42.91 µgm-3 over the same period. These progressively stronger negative contributions are consistent with sustained decreases in major emission species, including NOx_total, SO2_total, and BC_total. In contrast, meteorological contributions remain positive throughout all years, on the order of approximately 2–4 µgm-3 for PM_2.5 and 0.5–3 µgm-3 for PM₁₀, indicating that interannual meteorological conditions, on average, tended to offset part of the emission-driven reductions rather than reinforce them. Although PM₁₀ is often considered more responsive to short-term meteorological variability, the smaller meteorology-driven contributions for PM₁₀ in Fig. 5 should be interpreted in the context of interannual net effects. Meteorological influences on PM₁₀ often involve multiple processes that can act in opposite directions, such as enhanced resuspension versus enhanced dispersion and removal, leading to partial cancellation when aggregated at annual timescales. By contrast, meteorological effects on PM_2.5, particularly those related to temperature and humidity, tend to influence secondary formation and particle growth in a more directionally consistent manner at the interannual scale. This consistency allows their impacts to accumulate into a clearer net contribution. As a result, the relative magnitude of meteorology-driven changes appears larger for PM_2.5 than for PM₁₀ in Fig. 5, without implying a weaker overall meteorological sensitivity of PM₁₀.

Temporal relationships between individual emission indicators and their contributions are shown in Fig. 6. For PM_2.5, month-to-month changes in NOx_total, SO2_total, and BC_total are strongly correlated with their corresponding emission-driven contributions (R=0.89–0.95). A similar correspondence is observed for PM₁₀ (R=0.89–0.93). These strong associations indicate that the estimated emission contributions respond consistently to changes in emission levels, with higher emissions generally linked to larger positive contributions and lower emissions associated with weaker contributions. This relationship persists despite the nonlinear structure of the model, which adjusts contribution magnitudes according to the prevailing meteorological conditions.

In contrast, meteorological drivers show a different temporal behavior (Fig. 7). Temperature and humidity exhibit strong negative correlations with their meteorology-driven contributions for both pollutants (R≈-0.96 to -0.98). Importantly, these correlations describe relative contributions at the interannual scale after emission-driven trends have been removed, rather than direct or instantaneous responses of particulate matter to meteorological forcing. At this aggregated temporal scale, years with higher temperature or moisture do not necessarily correspond to larger net meteorological contributions, because multiple meteorological influences with opposing effects can partially offset each other. These results therefore point to a scale-dependent influence of meteorology, rather than contradicting process-based studies that emphasize the role of temperature and humidity in promoting particle formation at shorter timescales.

Taken together, the increasingly negative emission-driven contributions shown in Fig. 5, combined with relatively modest and predominantly positive net meteorology-driven anomalies at the interannual scale, indicate that the observed improvements in PM_2.5 and PM₁₀ during 2016–2022 were mainly driven by sustained emission reductions. Meteorological conditions influenced year-to-year fluctuations but did not reverse the overall downward trends, highlighting the dominant role of emission control measures in shaping the long-term evolution of both pollutants.