Air pollutant emissions from vehicles, railways, and aircraft for freight and passenger transportation are major sources of air pollution, and strongly impact the air quality of Beijing, China. To better understand the variation characteristics of these emissions, we used the emission factor method to quantitatively determine the air pollutant emissions from the transportation sector. The emission intensity of different modes of transportation was estimated, and measures are proposed to prevent and control air pollutants emitted from the transportation sector. The results showed that air pollutant emissions from the transportation sector have been decreasing year by year as a result of the reduction in emissions from motor vehicles, benefiting from the structural adjustment of motor vehicles. A comparison of the emission intensity of primary air pollutants from different modes of transportation showed that the emission level of railway transportation was much lower than that of road transportation. However, Beijing relies heavily on road transportation, with road freight transportation accounting for 96% of freight transportation, whereas the proportion of railway transportation was low. Primary air pollutants from the transportation sector contributed significantly to the total emissions in Beijing. The proportion of NOX emissions increased from 54% in 2013 to 58% in 2018. To reduce air pollutant emissions from the transportation sector, further adjustments and optimization of the structure of transportation in Beijing are needed. As for the control of motor vehicle pollutant emissions, vehicle composition must be adjusted and the development of clean energy must be promoted, as well as the replacement of diesel vehicles with electric vehicles for passenger and freight transportation.

The Beijing government initiated the Clean Air Action Plan (CAAP) in 2013. Through a series of actions to control air pollution, the emissions of major atmospheric pollutants are reduced to improve urban air quality. In order to evaluate the effectiveness of control measures taken to mitigate atmospheric pollution, we investigated and analyzed the implementation of the CAAP in Beijing from 2013 to 2017, estimating the corresponding reduction in emissions of major air pollutants. The contribution of different control measures to the improvement of air quality was quantified and the experiences of managing air pollution were summarized, which provided references for the continuous improvement of air quality in Beijing and the surrounding areas. The results showed that the emission of SO 2 , NO X , PM 10 , PM 2.5 , and VOCs from air pollution source have been decreased by 119,924, 116,091, 116,810, 46,652, and 97,267 tons after the implementation of the CAAP. The sum of these five air pollutants emissions have been reduced by 39% in 2017 compared with 2013, the largest decrease in SO 2 emissions was 87%, which was related to the vigorous control on coal-fired combustion. The control measure with the greatest contribution to decreasing the ambient PM 2.5 concentration was the clean energy transformation of coal-fired power plants, which contributed 27% of the total reduced concentration and 6.1 µg/m 3 of the average PM 2.5 concentration reduction in Beijing. Clean Residential coal use also significantly decreased the PM 2.5 concentration by 5.4 µg/m 3 , which was 23% of the total reduction. In addition, the industrial restructuring and the management of automotive vehicle use and dust could also contribute to efficiently reducing the PM 2.5 concentration by 4.0, 3.2, and 2.3 µg/m 3 , or 17%, 14%, and 10% of the total reduction, respectively. Due to the implementation of control measures of Clean Air Action Plan, the energy and industrial structure of Beijing have been adjusted and optimized, leading to the reduction of pollutant emissions, which is the secret of urban long-term air quality improvement.

According to the traffic flow variation from January 2019 to August 2020, emissions of primary air pollutants from highway vehicles were calculated based on the emission factor method, which integrated the actual structure of on-road vehicles. The characteristics of on-highway traffic flow and pollution emissions were compared during various progression stages of coronavirus disease (COVID-19). The results showed that the average daily traffic volume decreased by 38.2% in 2020, with a decrease of 62% during the strict lockdown due to the impact of COVID-19. The daily emissions of primary atmospheric pollutants decreased by 29.2% in 2020 compared to the same period in 2019. As for the structure of on-highway vehicle types, the small and medium-sized passenger vehicles predominated, which accounted for 76.3% of traffic, while trucks and large passenger vehicles accounted for 19.7% and 4.0%, but contributed 58.4% and 33.9% of nitrogen oxide (NOx) emissions, respectively. According to the simulation results of the ADMS model, the average concentrations of NOx were reduced by 12.0 µg/m3 compared with the same period in 2019. As for the implication for future pollution control, it is necessary to further optimize the structure of on-highway and the road traffic vehicle types and increase the proportions of new-energy vehicles and vehicles with high emission standards.

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Digital rural construction is an important strategy for rural revitalization. In order to improve the precision of digital rural measurement, a set of evaluation indicators for digital rural construction was devised in this study. The study uses the entropy method, kernel density estimation and system clustering to quantify the level of China’s digital rural construction, as well as the Tobit test and other techniques. From the perspective of time series evolution, the construction of digital villages grew continuously, with a peak in 2020. After that, the speed of digital village construction slowed down slightly because of economic changes in both domestic and international environments. In terms of dynamic evolution, the core density curve of China’s digital rural construction shifted to the right between 2011 and 2021, accompanied by gradient influence and a multipolar development trend; local general budget revenue, the per capita disposable income of rural residents, rural infrastructure investment, computer ownership per 100 rural residents, added value of primary industry, education level, and rural power generation are some of the factors that affect the development level of China’s digital countryside. This study is helpful in understanding the influencing aspects of China’s digital rural construction, thereby facilitating the formation of suitable digital rural development strategies in various regions depending on the real scenario.