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In recent years, China has implemented several measures to improve air quality. The Beijing-Tianjin-Hebei (BTH) region is one area that has suffered from the most serious air pollution in China and has undergone huge changes in air quality in the past few years. How to scientifically assess these change processes remain the key issue in further improving the air quality over this region in the future. To evaluate the changes in major air pollutant emissions over this region, this paper employs ensemble Kalman filtering (EnKF) for integrating the national ground monitoring pollutant observation data and the Nested Air Quality Prediction Modeling System (NAQPMS) simulation data to inversely estimate the emission rates of SO2, NOX, CO, and primary PM2.5 over BTH region in February from 2014 to 2019. The results show that SO2, NOX, CO, and primary PM2.5 emissions in the BTH region decreased in February from 2014 to 2019 by 83%, 37%, 41%, and 42%, while decreases in Beijing during this period were 86%, 67%, 59%, and 65%, respectively. Compared with the prior emission inventory, the inversion emission inventory reduces the uncertainty of multi-pollutant simulation in the BTH region, with simulated root mean square errors of the monthly average concentrations of SO2, NOX, PM2.5, and CO reduced by 41%, 30%, 31%, and 22%, respectively. The average uncertainties of SO2, NOX, PM2.5, and CO inversion emissions in 2014–19 are ±14.03% yr−1, ±28.91% yr−1, ±126.15% yr−1, and ±43.58% yr−1. Compared with the uncertainty of MEIC emission, the uncertainties of all species changed by +2% yr−1, −2% yr−1, −26% yr−1, and −4% yr−1, respectively. The spatial distribution results illustrate that air pollutant emissions are mainly distributed over the eastern and southern BTH regions. The spatial gap between the inversion emissions and MEIC emissions was further closed in 2019 compared to 2014. The results of this paper can provide a new reference for assessing changes in air pollution emissions over the BTH region in recent years and validating a bottom-up emission inventory. [ABSTRACT FROM AUTHOR]

The 2022 Winter Olympics were held in the three competition zones of Beijing, Yanqing and Zhangjiakou, China. The venues of this Winter Olympics were scattered and the terrain was complex. Moreover, the medical resources of Hebei and Beijing were relatively unbalanced. In the medical security of major events, the connection between first aid and in-hospital processes is of the utmost importance to rescue quality. 5th generation mobile network (5G) applications in medical scenarios are on the rise. It would be of great relevance to fully use 5G's low-latency and high-speed features to share the process information of patients, ambulance personnel, and the destination hospital's rescue team at emergency scenes and in transportation, improving rescue efficiency. This paper proposes a system scheme of cross-institutional emergency health information sharing based on 5G and augmented reality wearable devices. It also integrates the construction method of monitoring and other sign data sharing, in addition to testing the proposed scheme's service quality in 5G environments. In the deployment area of the 5G emergency medical rescue information sharing scheme for the Beijing Winter Olympic Games, we selected two designated medical support institutions for testing. The test adopted a combination of fixed-point and driving tests to experiment on the service data, voice service, and streaming media indicators. The 5G signal's coverage rate was close to 100%, the standalone connection's success rate was 100%, and the drop rate was 0. The average downlink rate of multiple scenarios was 620mbps, and the average uplink rate of 5G was over 71.8mbps, which is higher than the average 5G level in China. The downlink rate was more than 20 times larger than the 4th generation mobile network (4G) rate. This study's proposed scheme demonstrates the importance of 5G applications in emergency response and support, in addition to providing a suitable scheme for the integration of 5G networks in the medical scene. [ABSTRACT FROM AUTHOR]