Vehicle emissions of primary air pollutants from 2009 to 2019 and projection for the 14th Five-Year Plan period in Beijing, China.

Over the past decade, the emission standards and fuel standards in Beijing have been upgraded twice, and the vehicle structure has been improved by accelerating the elimination of 2.95 million old vehicles. Through the formulation and implementation of these policies, the emissions of carbon monoxide (CO), volatile organic compounds (VOCs), nitrogen oxides (NOx), and fine particulate matter (PM2.5) in 2019 were 147.9, 25.3, 43.4, and 0.91 kton in Beijing, respectively. The emission factor method was adopted to better understand the emissions characteristics of primary air pollutants from combustion engine vehicles and to improve pollution control. In combination with the air quality improvement goals and the status of social and economic development during the 14th Five-Year Plan period in Beijing, different vehicle pollution control scenarios were established, and emissions reductions were projected. The results show that the emissions of four air pollutants (CO, VOCs, NOx, and PM2.5) from vehicles in Beijing decreased by an average of 68% in 2019, compared to their levels in 2009. The contribution of NOx emissions from diesel vehicles increased from 35% in 2009 to 56% in 2019, which indicated that clean and energy-saving diesel vehicle fleets should be further improved. Electric vehicle adoption could be an important measure to reduce pollutant emissions. With the further upgrading of vehicle structure and the adoption of electric vehicles, it is expected that the total emissions of the four vehicle pollutants can be reduced by 20%-41% by the end of the 14th Five-Year Plan period.

Characterization of air pollutant emissions from construction machinery in Beijing and evaluation of the effectiveness of control measures based on information code registration data.

Non-road mobile machinery (NRMM), mainly construction machinery, has a high emission intensity of air pollutants, significantly impacting urban air quality. Most previous estimates of NRMM emissions have employed a top-down approach mainly based on estimates of energy consumption, leading to large uncertainties. This study uses the information code registration data specified in the latest regulations to establish a bottom-up method for emission accounting to more precisely identify the characteristics of air pollutant emissions from construction machinery in Beijing in 2020. Moreover, the study evaluates the effectiveness of the implementation of the corresponding control measures in conjunction with the current situation of pollution control of NRMM in Beijing. The results show the following: (1) Based on the information code registration data, there are 37,000-based fuel construction machines, with excavators accounting for the largest proportion (56%), loaders and forklifts also accounting for large proportions (19% and 15%, respectively), representing the main types of construction machinery. (2) Information code registration data better reflect the actual situation of construction machinery emissions than the top-down method; the emissions of the main air pollutants NOx, PM2.5, and VOCs amount to 12,000 tons, 600 tons, and 1000 tons, respectively, which are overestimated to some extent by the top-down method. (3) Loaders and excavators have a large contribution to emissions, accounting for 80-91% of these three pollutants emissions; there is a large quantity of machinery classified into the China III standard, accounting for 64-68% of these pollutants emissions; the designation of low emission zones banning the use of high-emission machinery plays a positive role in pollution reduction, but high-emission machinery is still used in these regions, which requires further attention. (4) In the future, the scope of these regions banning high-emission machinery and the types of controlled machinery should be further expanded, and the supervision and enforcement should be strengthened, Furthermore, the structural adjustment and energy conservation of construction machinery should be promoted, and measures such as electrification should be implemented for part of the light construction machinery to continue to reduce pollutant emissions.

Emission Variations of Primary Air Pollutants from Highway Vehicles and Implications during the COVID-19 Pandemic in Beijing, China.

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.

Energy consumption and pollutant emission of diesel-fired combustion from 2009 to 2018 in Beijing, China.

Diesel-fired combustion is one of the main sources of air pollution in the world. In this study, to better understand the energy consumption and main air pollutant emissions of diesel-fired combustion, a practical investigation and historical data analyses were conducted to determine the variations and driving forces of diesel consumption, the distribution of diesel consumption, and the contribution of emissions among various industries. Based on the results of this study, future control measures can be proposed for diesel-fired combustion. The results show that economic development led to an increase in the total volume of passengers and freight transportation, and the number of diesel vehicles increased from 0.16 million in 2009 to 0.25 million in 2018. However, diesel consumption in Beijing decreased from 2.4 Mt in 2009 to 1.8 Mt in 2018 due to the dominant driving forces, such as structural optimization of the diesel vehicle fleet and stricter limit standards for single-vehicle fuel consumption. The use of diesel vehicles in the logistics and transportation industries and the use of diesel-fired machinery in the construction industry were the two main sources of diesel consumption, accounting for 55% and 23% of the total, respectively. The main air pollutant emissions from diesel-fired combustion from 2009 to 2018 first increased and then decreased, while the NOX emissions peaked at 74,800 tons in 2014, which was affected by the structural optimization of the vehicle fleet and the elimination of old diesel trucks. The emissions finally decreased to 54,000 tons in 2018, which was approximately 89% of the amount in 2009. However, the continuously increasing contribution of diesel combustion to the total emissions requires more attention. The electrification of diesel vehicles and the structural upgrading of diesel vehicles have played important roles in mitigating the emissions of diesel combustion. Our study suggests that consumption control targets should be set, reduction plans for key industries such as the logistics and transportation, construction, and tourism industries should be developed, and low-emission zones should be created to promote the elimination and updating of low-emission diesel vehicles and machinery.