Jianlei Lang

The impact of trade activities on the balance between interregional environmental and socio-economic development is critical for sustainable regional cooperation. This study integrates a nested multiregional input-output (MRIO) model with an atmospheric chemical transport model (WRF-CAMx) to quantify the complex impacts of embodied emission transfers on PM_2.5 concentrations under significant interregional differences in energy consumption and emission intensity, enabling a regionally development status-aligned assessment of environmental benefits; furthermore, by coupling environmental (PM_2.5 concentration), social (employment opportunity), and economic (value-added) benefits, a regional multidimensional balance index (RESI) was constructed. Results indicate that in 2017, trade between China and its 33 neighboring countries was generally balanced (RESI values close to zero), suggesting relatively equitable exchanges of environmental and socio-economic impacts. The path of China's sustainable development has played a positive role in promoting mutually beneficial and win-win regional cooperation. At the provincial level, developed coastal regions gained economic benefits while outsourcing pollution, whereas less-developed inland regions bore environmental burdens without receiving corresponding socio-economic returns. Incorporating PM_2.5 concentration and employment factors alters inequality assessments for some regions, providing a more realistic evaluation. This study provides a scientific basis for improving cross-regional environmental compensation and promoting more balanced and sustainable development.

Quantifying the relative contributions of local emissions and regional transport is critical for urban air quality management. Chemical transport models (CTMs) are widely applied for source apportionment, but they require detailed emission inventories, extensive input data, and substantial computational resources, which limit their operational use. In contrast, urban monitoring networks provide continuous and readily available observations. This study develops an observation-based framework that estimates regional contribution ratios (RCs) from inter-station concentration variability, quantified by the coefficient of variation (CV), using WRF–CAMx results as a reference. Using Linyi as the primary case, with Xi’an and Beijing for comparison, concentration-stratified regression was applied to establish CV–RC relationships. Results show a consistent nonlinear relationship between CV and RC, with coefficients of determination (R²) up to 0.86 for PM₁₀ (daily), 0.81 for NO₂ (hourly), and 0.78–0.79 for O₃. CV decreases markedly with increasing concentration; for PM_2.5, values decline from ~0.17–0.18 to 0.05–0.06 (≈65–70%), indicating enhanced spatial homogeneity under regional influence. The relationship is most stable within a 10–15 km spatial scale. Application-based evaluation for January 2022 shows moderate agreement between estimated and modeled RC (R = 0.55–0.65), reflecting pollutant-dependent uncertainties, partly associated with biases in the model-derived reference RC. These results demonstrate that inter-station concentration variability provides a first-order, computationally efficient indicator of the balance between local emissions and regional transport.

Predicting the anthropogenic economic pollutant emissions index helps balance economic growth and environmental protection. In this study, a set of constructed features was derived using provincial-level basic data from 1995 to 2023. By constructing a weighted ensemble strategy incorporating Extreme Gradient Boosting, light gradient boosting machine, Random Forest, and Multi-Layer Perceptron, and integrating it with the Autoregressive Integrated Moving Average model and Shapley Additive Explanations, an anthropogenic economic pollutant emission index (AS_GPI) forecasting model was finally established, with basic and constructed features employed as its inputs (AFEA-AG). The AS_GPI forecasting model (EA-AG) was developed without constructed features, with all other settings consistent with the AFEA-AG model for comparison. Results show that the proposed model achieves high forecasting accuracy for AS_GPI across four typical pollutants, with R² values exceeding 0.95 for the AS_GPI_NOx and AS_GPI_PM_2.5. The mean absolute percentage error was as low as 0.0744. The forecasting model with constructed features yielded lower errors and higher stability in its prediction results compared with the one without such features. Feature contribution analysis revealed differing key contributors, with AS_GPI lagged values and economic-related characteristics among underlying variables exhibiting strong predictive importance. The 2024 projection results indicated certain disparities in pollution control effectiveness between key and non-key regions. Further analysis of historical and predicted data revealed a nationwide decline in AS_GPI between 1995 and 2024. Beijing and Shanghai achieved notable environmental quality improvements through anthropogenic emission reductions. The coefficient of variation values of different AS_GPIs reveal spatial heterogeneity and differences. Nationwide efforts should prioritize the control of anthropogenic NMVOC and NOx emissions. This framework provides a prediction method that offers certain reference for the development of the economy and the environment.

Brown carbon (BrC) affects climate and the environment. Biomass burning (BB) is a major BrC emission source, yet insufficient understanding of long-term emission trends from different sources and their underlying drivers hinders the accurate assessment of regional climate and environmental effects, as well as the development of targeted mitigation strategies. We integrated provincial socioeconomic data, remote sensing data, and source-specific emission factors to quantify the magnitude, spatial variations, and trends (1980-2023) of BrC emissions from BB in China. Furthermore, we analyzed the period-specific drivers and projected future emissions under different policy scenarios. The BrC emissions rose and then fell (1980-2023), peaking in 2014 at 433 (95% CI: 323-635) Gg, with an increasing contribution from open emissions. The major emission regions migrated from areas with intensive indoor biomass use (e.g., South-Central China) to those with main crop-producing regions (e.g., Northeast China). Agricultural expansion, energy structure changes, and policy controls are the primary factors driving emission variations, while the main drivers of indoor and open emissions varied across different periods. Projections of the baseline scenario indicate that open emissions should be paid more attention, given their continued contribution (94%, 2035). Maintaining existing control efforts may lead to a slight increase in emissions (11%) due to growing straw output and control uncertainties. More rigorous control of straw-burning policies and differentiated management strategies could decrease emissions.

As climate change intensifies, enhancing numerical weather prediction (NWP) accuracy has been increasingly critical. While data assimilation optimizes NWP initial conditions, its effectiveness over complex terrain requires further systematic evaluation. This study implemented a high-resolution WRF/4D-Var data assimilation framework, overcoming its inherent limitation of not supporting two-layer nested assimilation across domains by designing a two-layer nested “assimilation-forecast” workflow. Representative winter and summer cases from February and June 2019 were selected to evaluate improvements in near-surface and upper-air meteorological parameters. The results indicated that the 4D-Var data assimilation significantly improved the correlation coefficients of near-surface variables during winter by 2.9% (temperature), 14.5% (relative humidity), 6.6% (wind speed), and 10.4% (wind direction), with even greater improvements observed in summer reaching 13.3%, 5.8%, 35.3%, and 42.3%, respectively. Meanwhile, 4D-Var considerably enhanced the atmospheric vertical profiling, with the middle troposphere (300–700 hPa) exhibiting the most pronounced improvement. Among different surface types, water bodies exhibited the strongest assimilation response. Results also revealed systematic corrections to the background fields, with February exhibiting more uniform adjustments in contrast to June’s complex spatiotemporal patterns. Positive effects persisted throughout the 24-h forecasts, with the maximum benefit occurring within the first 12 h. These results demonstrate the effectiveness of 4D-Var in regional meteorological forecasting, highlighting its value for constructing high-precision multidimensional meteorological fields to support both weather and air quality simulations.

Source inversion optimization using sensor observations is a key method for rapidly and accurately identifying unknown source parameters (source strength and location) in abrupt hazardous gas leaks. Sensor number and location distribution both play important roles in source inversion; however, their combined impacts on source inversion optimization remain poorly understood. In our study, the optimization inversion method is established based on the Gaussian plume model and the generation algorithm. A research strategy combining random sampling and coefficient of variation methods was proposed to simultaneously quantify their combined impacts in the case of a single emission source. The sensor layout impact difference was analyzed under varying atmospheric conditions (unstable, neutral, and stable) and source location information (known or unknown) using the Prairie Grass experiments. The results indicated that adding sensors improved the source strength estimation accuracy more when the source location was known than when it was unknown. The impacts of sensor location distribution were strongly negatively correlated (r ≤ −0.985) with the number of sensors across scenarios. For source strength estimation, the impacts of the sensor location distribution difference decreased non-linearly with more sensors for known locations but linearly for unknown ones. The impacts of sensor number and location distribution on source strength estimation were amplified under stable atmospheric conditions compared to unstable and neutral conditions. The minimum number of randomly scattered sensors required for stable source strength inversion accuracy was 11, 12, and 17 for known locations under unstable, neutral, and stable atmospheric conditions, respectively, and 24, 9, and 21 for unknown locations. The multi-layer arc distribution outperformed rectangular, single-layer arc, and downwind-axis distributions in source strength estimation. This study enhances the understanding of factors influencing source inversion optimization and provides valuable insights for optimizing sensor layouts.

Inter-provincial trade leads to changes in CO₂ and air pollutant emissions. However, there is a research gap regarding the coordinated effects (co-effects) between embodied CO₂ and air pollutant emissions in trade. Understanding co-effects in inter-provincial trade is a prerequisite for driving the green transformation of trade and achieving coordination between pollution and carbon reduction. Here, we calculated provincial-level CO₂ and air pollutant emission leakage in 2012 and 2017 based on a modified input–output model and, for the first time, investigated the co-effects between CO₂ and air pollutant emission leakage caused by emissions transfers in China. Three types of co-effects, categorized as co-benefits, trade-offs, and co-damage, were discovered and defined to reveal the provincial differences. Furthermore, combined with structural decomposition analysis (SDA), we calculated the interannual variation in trade-induced emissions and identified the key driving factors of provincial-level co-effects from 2012 to 2017. Optimizing the energy structure has led to the greatest co-benefits, while changes in the industrial structure and emission coefficients have led to limited co-benefits in specific provinces. Variations in trade volume have led to co-damages across all provinces, and changes in emission coefficients have led to trade-offs in the majority of provinces. The case analysis confirmed that identifying and adjusting the key driving factors of co-effects can promote the transformation from co-damage and trade-offs to co-benefits. The findings implied a new approach for the reduction in pollution and carbon through inter-provincial trade.