Structural contribution and scenario simulation of highway passenger transit carbon emissions in the Beijing-Tianjin-Hebei metropolitan region, China

Ma, Haitao; Sun, Wei; Wang, Shaojian; Kang, Lei

doi:10.1016/j.resconrec.2018.09.028

Cited by 34 publications

(15 citation statements)

References 40 publications

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“… Li et al (2018) used a support vector machine–extreme learning machine model to forecast energy-related carbon emissions before 2030 in the BTH region. Ma et al (2019) also performed a similar research. However, energy-related carbon emissions do not directly pollute the local air even though climate change is aggravating.…”

Section: Literature Reviewmentioning

confidence: 90%

Has air pollution emission level in the Beijing–Tianjin–Hebei region peaked? A panel data analysis

Meng

Zhou

2020

Ecological Indicators

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Highlights Air pollution emission level of the Beijing–Tianjin–Hebei region is fitted. Fitting curve to air pollution emission level reached the peak in 2015. Air pollution emissions in 2025 will decrease to the level of 2007. Industrial waste gas emissions play a decisive role for the pollution level change. Air quality is sensitive to the operation of building material industries.

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Section: Literature Reviewmentioning

confidence: 90%

Has air pollution emission level in the Beijing–Tianjin–Hebei region peaked? A panel data analysis

Meng

Zhou

2020

Ecological Indicators

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“…Even under many control measures in the transportation sector, the number of vehicles in Beijing still reached 6.1 million in 2018, and the annual passenger and freight turnover on highways reached 5.04 billion person-km and 16.74 billion ton-km, respectively [ 17 ]. Highways are both an important medium of transportation and a component of road networks [ 18 ]. Driven by the economic development in Beijing, the construction and development of highways are rapid.…”

Section: Methodsmentioning

confidence: 99%

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

Cao

Tian

Shen

et al. 2021

IJERPH

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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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“…High concentrations of pollutants generated by human activity occur especially in industrial areas and cities, as well as in other spaces such as agricultural or recreational areas. These pollutants directly and indirectly affect the air quality not only around the source of the pollution, but also in the surrounding area [2][3][4][5][6][7]. The main pollutants according to criteria established for the protection of human health and plant protection are particulate matter (PM10, PM2.5, and increasingly PM1.0) and gaseous pollutants such as benzene, nitrogen dioxide, sulfur dioxide, carbon monoxide, ozone, lead, arsenic, cadmium, nickel, and benzo (a) pyrene in PM10 dust, as well as hydrogen sulfide (H 2 S) [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Spatial Analysis (Measurements at Heights of 10 m and 20 m above Ground Level) of the Concentrations of Particulate Matter (PM10, PM2.5, and PM1.0) and Gaseous Pollutants (H2S) on the University Campus: A Case Study

Cichowicz

Dobrzański

2021

Atmosphere

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Spatial analysis of the distribution of particulate matter PM10, PM2.5, PM1.0, and hydrogen sulfide (H2S) gas pollution was performed in the area around a university library building. The reasons for the subject matter were reports related to the perceptible odor characteristic of hydrogen sulfide and a general poor assessment of air quality by employees and students. Due to the area of analysis, it was decided to perform measurements at two heights, 10 m and 20 m above ground level, using measuring equipment attached to a DJI Matrice 600 unmanned aerial vehicle (UAV). The aim of the measurements was air quality assessment and investigate the convergence of the theory of air flow around the building with the spatial distribution of air pollutants. Considerable differences of up to 63% were observed in the concentrations of pollutants measured around the building, especially between opposite sides, depending on the direction of the wind. To explain these differences, the theory of aerodynamics was applied to visualize the probable airflow in the direction of the wind. A strong convergence was observed between the aerodynamic model and the spatial distribution of pollutants. This was evidenced by the high concentrations of dust in the areas of strong turbulence at the edges of the building and on the leeward side. The accumulation of pollutants was also clearly noticeable in these locations. A high concentration of H2S was recorded around the library building on the side of the car park. On the other hand, the air turbulence around the building dispersed the gas pollution, causing the concentration of H2S to drop on the leeward side. It was confirmed that in some analyzed areas the permissible concentration of H2S was exceeded.

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Structural contribution and scenario simulation of highway passenger transit carbon emissions in the Beijing-Tianjin-Hebei metropolitan region, China

Cited by 34 publications

References 40 publications

Has air pollution emission level in the Beijing–Tianjin–Hebei region peaked? A panel data analysis

Has air pollution emission level in the Beijing–Tianjin–Hebei region peaked? A panel data analysis

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

Spatial Analysis (Measurements at Heights of 10 m and 20 m above Ground Level) of the Concentrations of Particulate Matter (PM10, PM2.5, and PM1.0) and Gaseous Pollutants (H2S) on the University Campus: A Case Study

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