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Beijing-Tianjin-Hebei metropolitan area is a significant carbon emission center. The region's early achievement of peak carbon targets is critical to the nation's achievement of peak carbon targets. In this paper, it is proposed to use different orders of grey models to classify into three scenarios. Based on three scenarios, the grey multivariate convolutional model with new information priority accumulation is adopted to predict carbon emissions in the Beijing-Tianjin-Hebei region and select the scenario suitable for local development. The results show that: (1) The Beijing region has already achieved peak carbon, the Tianjin region may not reach its peak carbon target by 2030, and the Hebei region is expected to reach its peak carbon target by 2030. (2) The high rate of carbon emission reduction scenario will greatly improve the air quality of Beijing. The low-speed growth carbon emission scenario is more in line with the future development of Tianjin city. The low-rate carbon reduction scenario is more in line with the synergistic governance of pollution reduction and carbon reduction in Hebei Province. (3) Beijing's population policy in the most recent years has been conducive to improving the local environment. Tianjin's medium-term population policy is more in line with the local area. Hebei's medium-term industrial structure reform is favorable to local development. • Grey correlation analysis is used to analyze the factors affected carbon emissions in a region. • The factors with high correlation are predicted by using grey prediction models of different orders. • The high rate of carbon emission reduction scenario will greatly improve the air quality of Beijing. [ABSTRACT FROM AUTHOR]

Atmospheric nanoparticle growth is a critical bridge between aerosol to clouds and climate. However, current mechanisms for nanoparticle growth are not yet fully understood due to unknown size-resolved chemical composition during the growth process. Here, we present comprehensive field measurements of the size-resolved chemical composition of the atmospheric nanoparticle growth in the size range from 10 to 100 nm using a custom-made Nanoparticle Chemical Ionization Mass Spectrometer (NPCIMS). The typical chemical compositions of nanoparticles are quantified in both positive and negative ion modes during growth processes after sensitivity calibration. The quantification results for chemical compositions show that CHO organics account for ∼80% of the newly formed particle in the growth process with diameters from 10 to 25 nm. However, the nitrate component is rarely observed during the nanoparticle growth under size-resolved mode (10–25 nm), indicating the little contribution of the nitrate component to growth at diameters below 25 nm. Different from the contribution of aminium salts to nanoparticle growth according to the previous urban observations in Mexico, the newly formed particle growth in urban Beijing is dominated by ammonium, sulfate, and organic components in the size-resolved modes (10–25 nm) in springtime. [Display omitted] • The newly formed particle growth in urban Beijing is dominated by ammonium, sulfate, and organic components in springtime. • The contribution of nitrate, protonated methylammonium and protonated C 2 amines to nanoparticles growth is little. • CHO organics account up to ～80% in the growth of the newly formed organic particle with diameters from 10 to 25 nm. [ABSTRACT FROM AUTHOR]