Emission inventory and control policy for non-road construction machinery in Tianjin

Zhang, Qijun; Wei, Ning; Yang, Liu; Feng, Xi; Zhang, Yanjie; Wu, Lin; Mao, Hongjun

doi:10.1007/s11356-021-16218-x

Cited by 11 publications

(3 citation statements)

References 26 publications

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“…In recent years, with the rapid development of social economy and urbanization, as well as the continuous improvement of people's living standard and travel demand, the number of vehicles in China has shown an explosive growth [1,2]. The huge number of vehicles and their high activity level have led to the increasing contribution rate of vehicle pollutant emissions to haze pollution represented by a high fine particle (PM 2.5 ) concentration, and photochemical pollution represented by high ozone (O 3 ) concentration, especially in urban areas [3,4].…”

Section: Introductionmentioning

confidence: 99%

Development of Vehicle Emission Model Based on Real-Road Test and Driving Conditions in Tianjin, China

et al. 2022

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Based on the demand of vehicle emission research and control, this paper presents the development of a portable vehicle measurement system (PEMS) based on SEMTECH-DS and ELPI+, the vehicle emission tests carried out on actual roads, and the data obtained for the establishment and validation of a vehicle emission model. Based on the results of the vehicle emission test, it was found that vehicle driving conditions (speed, acceleration, vehicle specific power (VSP), etc.) had a significant impact on the pollutant emission rate. In addition, local driving cycles were generated and the frequency distribution of VSP-bin under different cycles was analyzed. Then, through the establishment of an emission rate database, calculation of emission factors and validation of the emission model, a vehicle emission model based on actual road driving conditions was developed by taking VSP as the “surrogate variables”. It showed that the emission factor model established in this study could better reflect the vehicle transient emissions on the actual road with high accuracy and local adaptability. Through this study, it could be found that due to the great differences in traffic development modes and vehicle driving conditions in different cities in China, the emission model based on driving conditions was a better choice to carry out the research on vehicle emission in Chinese cities. Compared with directly applying international models or quoting the recommended values of relevant macroscopic guidelines, the emission factor model established in this study, using actual driving conditions, could better reflect the vehicle transient emissions on the actual road with high accuracy and local adaptability. In addition, due to the rapid development of China’s urban traffic and the rapid change of driving conditions, it was of great significance to regularly update China’s urban conditions to improve the accuracy of the model, no matter which model was chosen.

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Section: Introductionmentioning

confidence: 99%

Development of Vehicle Emission Model Based on Real-Road Test and Driving Conditions in Tianjin, China

et al. 2022

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“…According to our previous research [ 32 ], the Wujinglu Tunnel traffic is mainly composed of gasoline vehicles (with diesel vehicles accounting for only about 2%). As an important gathering and distribution channel for Tianjin Port, which has the eighth largest cargo volume in the world, Teda Street traffic is mainly composed of diesel vehicles transporting goods [ 33 ]. Therefore, the Wujinglu Tunnel and Teda Street can represent the actual traffic pollution exposure levels of gasoline and diesel vehicles as the main vehicle types, respectively.…”

Section: Methodsmentioning

confidence: 99%

Lipid Dysregulation Induced by Gasoline and Diesel Exhaust Exposure and the Interaction with Age

Gao,

Zhang,

et al. 2024

Toxics

Self Cite

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Limited knowledge exists regarding gasoline and diesel exhaust effects on lipid metabolism. This study collected gasoline and diesel exhaust under actual driving conditions and conducted inhalation exposure on male young and middle-aged C57BL/6J mice for 4 h/day for 5 days to simulate commuting exposure intensity. Additionally, PM2.5 from actual roadways, representing gasoline and diesel vehicles, was generated for exposure to human umbilical vein endothelial cells (HUVECs) and normal liver cells (LO2) for 24, 48, and 72 h to further investigate exhaust particle toxicity. Results showed that diesel exhaust reduced total cholesterol and low-density lipoprotein cholesterol levels in young mice, indicating disrupted lipid metabolism. Aspartate aminotransferase and alanine aminotransferase levels increased by 53.7% and 21.7%, respectively, suggesting potential liver injury. Diesel exhaust exposure decreased superoxide dismutase and increased glutathione peroxidase levels. Cell viability decreased, and reactive oxygen species levels increased in HUVECs and LO2 following exposure to exhaust particles, with dose- and time-dependent effects. Diesel exhaust particles exhibited more severe inhibition of cell proliferation and oxidative damage compared to gasoline exhaust particles. These findings provide novel evidence of the risk of disrupted lipid metabolism due to gasoline and diesel exhaust, emphasizing the toxicity of diesel exhaust.

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“…These models define various vehicle types for which emissions per unit distance can differ significantly by several or even hundreds of times with parameters such as fuel type, emission standards, weight, and displacement [9,10]. Moreover, the spatiotemporal dynamic characteristics of vehicle travel mean that each type of vehicle exhibits varying travel distance proportions across different hour periods and road segments [11]. Thus, the differences in emissions per unit distance and dynamics of vehicle travel may bring significant uncertainty to the quantification of road traffic emissions [12].…”

Section: Introductionmentioning

confidence: 99%

Managing Uncertainty in Urban Road Traffic Emissions Associated with Vehicle Fleet Composition: From the Perspective of Spatiotemporal Sampling Coverage

Cai,

Zeng,

et al. 2024

Sustainability

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With pronounced differences in emission factors among vehicle types and marked spatiotemporal heterogeneity of vehicle fleet composition, extrapolating fleet composition from insufficient sample hour periods and road segments will introduce significant uncertainty in calculating regional daily road traffic emissions. We proposed a framework to manage uncertainty in urban road traffic emissions associated with vehicle fleet composition from the perspective of spatiotemporal sampling coverage. Initially, the respective relationships of the temporal and spatial sampling coverages of fleet composition with the resulting regional daily road traffic emission uncertainties were determined, using the core area of a typical small and medium-sized city in China with the widely-used International Vehicle Emissions (IVE) model as example. Subsequently, function models were developed to explore the determination of the spatiotemporal sampling coverage of fleet composition. These results of emission uncertainties and function models implied that gases with larger emission factor discrepancies between vehicle types, such as NOx, required greater spatiotemporal sampling coverage than gases with smaller discrepancies, such as CO2, under the same uncertainties target. Therefore, sampling efforts should be prioritized for gases with larger emission factor discrepancies. Additionally, increasing sampling coverage in one dimension (either spatial or temporal) can reduce the minimum required coverage in the other dimension. To further reduce uncertainty, enhancing both spatial and temporal sampling coverage of the fleet composition is more effective than enhancing one type of coverage alone. The framework and results proposed in this work can reduce the uncertainty of emissions calculations caused by insufficient sampling coverage and contribute to more accurate transport emission reduction policy formulation.

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Emission inventory and control policy for non-road construction machinery in Tianjin

Cited by 11 publications

References 26 publications

Development of Vehicle Emission Model Based on Real-Road Test and Driving Conditions in Tianjin, China

Development of Vehicle Emission Model Based on Real-Road Test and Driving Conditions in Tianjin, China

Lipid Dysregulation Induced by Gasoline and Diesel Exhaust Exposure and the Interaction with Age

Managing Uncertainty in Urban Road Traffic Emissions Associated with Vehicle Fleet Composition: From the Perspective of Spatiotemporal Sampling Coverage

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