PM_2.5 pollution poses significant risks to human health and the environment, underscoring the importance of accurate PM_2.5 simulation. This study simulated a representative PM_2.5 pollution event using the Weather Research and Forecasting model coupled with chemistry (WRF-Chem), incorporating the assimilation of infrared atmospheric motion vector (AMV) data from the Fengyun-4A (FY-4A) satellite. A comprehensive analysis was conducted to examine the meteorological characteristics of the event and their influence on PM_2.5 concentration simulations. The results demonstrate that the assimilation of FY-4A infrared AMV data significantly enhanced the simulation performance of meteorological variables, particularly improving the wind field and capturing local and small-scale wind variations. Moreover, PM_2.5 concentrations simulated with AMV assimilation showed improved spatial and temporal agreement with ground-based observations, reducing the root mean square error (RMSE) by 8.2% and the mean bias (MB) by 15.2 µg/m³ relative to the control (CTL) experiment. In addition to regional improvements, the assimilation notably enhanced PM_2.5 simulation accuracy in severely polluted cities, such as Tangshan and Tianjin. Mechanistic analysis revealed that low wind speeds and weak atmospheric divergence restricted pollutant dispersion, resulting in higher near-surface concentrations. This was exacerbated by cooler nighttime temperatures and a lower planetary boundary layer height (PBLH). These findings underscore the utility of assimilating satellite-derived wind products to enhance regional air quality modeling and forecasting accuracy. This study highlights the potential of FY-4A infrared AMV data in improving regional pollution simulations, offering scientific support for the application of next-generation Chinese geostationary satellite data in numerical air quality forecasting.

Understanding how changes in anthropogenic carbon dioxide (CO2) emissions affect surface CO2 mole fraction (surface CO2), column-averaged dry-air CO2 mole fraction (XCO2), and the relative contributions of anthropogenic emissions and biospheric fluxes is essential for evaluating the atmospheric effects of emission mitigation. In this study, the Weather Research and Forecasting Model coupled with the Vegetation Photosynthesis and Respiration Model (WRF-VPRM) was used to simulate three emission scenarios: a 2016 baseline, a 2030 carbon peaking scenario, and a 2060 carbon neutrality scenario, under identical meteorological fields constrained by observations. Contribution decomposition, sensitivity experiments, backward trajectory analysis, and potential source contribution function (PSCF) analysis were combined to diagnose the response mechanisms of atmospheric CO2. Anthropogenic emissions increased by 18.1 % in 2030 relative to 2016, whereas surface CO2 and XCO2 increased by only 0.363 and 0.065 ppm, respectively. In 2060, emissions decreased by 90.3 %, reducing surface CO2 and XCO2 by 1.914 and 0.359 ppm, respectively. The XCO2 response was therefore much weaker than the surface CO2 response. Anthropogenic contributions dominated the differences among scenarios, while biospheric fluxes shaped seasonal variations and became relatively more important under deep emission reductions. The selected high-CO2 episodes in the Beijing-Tianjin-Hebei (BTH) region were strongly modulated by meteorological conditions. Local accumulation dominated under stagnant conditions, whereas upstream transport dominated under favorable transport conditions. These results indicate that atmospheric CO2 responses to carbon peaking and carbon neutrality pathways are jointly shaped by anthropogenic mitigation, biospheric fluxes, and regional transport.

Understanding how changes in anthropogenic carbon dioxide (CO₂) emissions affect surface CO₂ mole fraction (surface CO₂), column-averaged dry-air CO₂ mole fraction (XCO₂), and the relative contributions of anthropogenic emissions and biospheric fluxes is essential for evaluating the atmospheric effects of emission mitigation. In this study, the Weather Research and Forecasting Model coupled with the Vegetation Photosynthesis and Respiration Model (WRF-VPRM) was used to simulate three emission scenarios: a 2016 baseline, a 2030 carbon peaking scenario, and a 2060 carbon neutrality scenario, under identical meteorological fields constrained by observations. Contribution decomposition, sensitivity experiments, backward trajectory analysis, and potential source contribution function (PSCF) analysis were combined to diagnose the response mechanisms of atmospheric CO₂. Anthropogenic emissions increased by 18.1 % in 2030 relative to 2016, whereas surface CO₂ and XCO₂ increased by only 0.363 and 0.065 ppm, respectively. In 2060, emissions decreased by 90.3 %, reducing surface CO₂ and XCO₂ by 1.914 and 0.359 ppm, respectively. The XCO₂ response was therefore much weaker than the surface CO₂ response. Anthropogenic contributions dominated the differences among scenarios, while biospheric fluxes shaped seasonal variations and became relatively more important under deep emission reductions. The selected high-CO₂ episodes in the Beijing-Tianjin-Hebei (BTH) region were strongly modulated by meteorological conditions. Local accumulation dominated under stagnant conditions, whereas upstream transport dominated under favorable transport conditions. These results indicate that atmospheric CO₂ responses to carbon peaking and carbon neutrality pathways are jointly shaped by anthropogenic mitigation, biospheric fluxes, and regional transport.