Based on the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, the impact factors of industrial carbon emission in Nanjing were considered as total population, industrial output value, labor productivity, industrialization rate, energy intensity, research and development (R&D) intensity, and energy structure. Among them, the total population, industrial output value, labor productivity, and industrial energy structure played a role in promoting the increase of industrial carbon emissions in Nanjing, and the degree of influence weakened in turn. For every 1% change in these four factors, carbon emissions increased by 0.52%, 0.49%, 0.17% and 0.12%, respectively. The industrialization rate, R&D intensity, and energy intensity inhibited the increase of industrial carbon emissions, and the inhibiting effect weakened in turn. Every 1% change in these three factors inhibited the increase of industrial carbon emissions in Nanjing by 0.03%, 0.07%, and 0.02%, respectively. Then, taking the relevant data of industrial carbon emissions in Nanjing from 2006 to 2020 as a sample, the gray rolling prediction model with one variable and one first-order equation (GRPM (1,1)) forecast and scenario analysis is used to predict the industrial carbon emission in Nanjing under the influence of the pandemic from 2021 to 2030, and the three development scenarios were established as three levels of high-carbon, benchmark and low-carbon, It was concluded that Nanjing’s industrial carbon emissions in 2030 would be 229.95 million tons under the high-carbon development scenario, 226.92 million tons under the benchmark development scenario, and 220.91 million tons under the low-carbon development scenario. It can not only provide data reference for controlling industrial carbon emissions in the future but also provide policy suggestions and development routes for urban planning decision-makers. Finally, it is hoped that this provides a reference for other cities with similar development as Nanjing.

Nitrogen self-doped hierarchical porous carbon for electrical double-layer capacitors was synthesized by direct carbonization of bean dregs-based tar with potassium acetate as the template agent. The pore structure parameters and chemical element composition were adjusted by varying the heating rate during the carbonization process. The electrochemical properties of the electrode materials were evaluated in a three electrode system with 6 M KOH as the electrolyte. The resultant bean dregs-based porous carbons (BDPCs) exhibited high specific surface area, unique hierarchical architecture (consisting of supermicro- and mesopores), and medium nitrogen content (0.66 to 0.78%). The BDPC-10 sample had the highest specific surface area of 1610 m2/g and reasonable pore size distribution, and consequently exhibited an excellent specific capacitance of 363.7 F/g at the current density of 1 A/g. Nevertheless, the capacitance was reduced to 280.5 F/g at 3 A/g, giving a capacitance retention ratio of 77.1%. This study suggests a facile and environmentally friendly template synthesis process for supercapacitor electrode materials preparation, but it also faces challenges to increase the rate capability.

Basic analysis of the flow field and aerosol deposition under different conditions when a spreader contains an upper airway tract is important to accurately predict the transmission of virus-laden aerosols. An upper airway was included to simulate aerosol transport and deposition. A flow field was simulated by the Transition SST model for validation. The simulation results show that, in the absence of the upper airway structure, an over-predicted aerosol deposition rate will occur. Higher upper-stream air velocity enhanced the intensity but added complexity to the recirculating flow between two manikins and increased the deposition rate of aerosol in the disseminator. A low-temperature environment can reduce the deposition rate of aerosol particles on the body of the disseminator due to a strong thermal plume. Therefore, the structure of the upper airway should be considered when predicting respiratory aerosol in order to increase the accuracy of aerosol propagation prediction.