Real-world operation conditions and on-road emissions of Beijing diesel buses measured by using portable emission measurement system and electric low-pressure impactor

Liu, Zhihua; Ge, Yunshan; Johnson, Kent C.; Shah, Asad Naeem; Tan, Jianwei; Wang, Chu; Yu, Linxiao

doi:10.1016/j.scitotenv.2010.12.042

Cited by 83 publications

(36 citation statements)

References 20 publications

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“…However, it should be noted that those test conditions do not represent on-road driving conditions; when considering real-world driving speed, engine conditions, and load mass, there might be significantly different results with those fixed experimental conditions. Furthermore, on-road vehicular PM emissions could be tested by using on-board portable emission measurement systems (Liu et al 2011;Wu et al 2012b), on-road vehicle chasing (Tang and Wang 2006;Wang et al 2011Wang et al , 2012, remote sensing (Kuhns et al 2004), and tunnel measurement (Grieshop et al 2006). However, compared to the on-board PEMS, the other on-road measurement technologies mentioned above could only provide emission profiles for a rather short period without detailed record of instantaneous driving conditions for tested vehicles.…”

Section: Introductionmentioning

confidence: 99%

Impacts of load mass on real-world PM1 mass and number emissions from a heavy-duty diesel bus

Wang

Jiang

et al. 2014

Int. J. Environ. Sci. Technol.

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On-road emission profiles of ultra-fine particle matter with aerodynamic diameter \1 lm (PM 1 ) were obtained from a Euro IV diesel bus in Beijing under two typical load mass conditions. PM 1 mass and number emissions were recorded second by second using an electrical low-pressure impactor (ELPI). An on-road driving mode binning method was applied to relate instantaneous emission rate with real-time on-road driving conditions, and an engine-operating mode binning method was used to explore the impacts of engine load on PM 1 emission characteristics. Our results show that higher load mass will increase both particle mass and number emissions. Measured PM 1 mass emission factors were 0.147 and 0.185 g/ km under low and high load mass conditions, respectively. PM 1 number emission factors were 3.71 9 10 13 and 4.58 9 10 13 #/km. In addition, load mass also influenced the size-resolved concentration of PM mass and number from that sampled bus. For example, under low load mass conditions, an obvious peak of PM number concentration was observed situated in the second channel of the ELPI corresponding to the median diameter 63 nm. By contrast, peak concentration of the PM number was increased under high load mass conditions and situated in the third channel of the ELPI corresponding to the median diameter 109 nm.

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Section: Introductionmentioning

confidence: 99%

Impacts of load mass on real-world PM1 mass and number emissions from a heavy-duty diesel bus

Wang

Jiang

et al. 2014

Int. J. Environ. Sci. Technol.

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“…Based on previous research on an urban carbon and oxygen balance model (UCOB) [89], calculations were introduced, such as a method for calculating transportation fuel consumption [71,72,93] and municipal solid waste [94], to improve the accuracy of the results. The carbon sources discussed in this study are human respiration, industrial fossil fuel consumption, transportation fuel consumption, domestic energy use, and municipal solid waste.…”

Section: Urban Carbon Emission Modelmentioning

confidence: 99%

“…inventories method recommended by the IPCC [9,23,52,67]; statistical methods that use information from yearbooks, statistical data or other experimental site data [53,54,[68][69][70][71][72][73][74][75]; the GAINS-City integrated assessment model [76]; life-cycle assessment models [77]; economic input-output models [9,50,55,78,79], which can provide detailed inventories of main sources (e.g., energy production, agricultural activities, and waste treatment) [80]; carbon sink estimations with LULC data and empirical coefficients [81,82]; and other approaches. However, the eddy covariance method does not provide valuable partitioning information for CO2 emissions and cannot be used to identify specific carbon sources and sinks [65,68,83].…”

mentioning

confidence: 99%

Evaluation of Carbon and Oxygen Balances in Urban Ecosystems Using Land Use/Land Cover and Statistical Data

Yin

Tian

et al. 2014

Sustainability

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Urban areas play an important role in the global carbon cycle, and human-induced carbon emissions from urban areas urgently need to be reduced. Therefore, understanding the relationship between carbon sources and sinks is the first step toward mitigating the effect of urban areas on climate change. Combined with the land use and land cover (LULC) empirical coefficients and statistical methods, urban carbon and oxygen balances in Beijing were evaluated. In this study, the carbon sequestration and oxygen emission capabilities of various LULC types were calculated, and the partitioning of carbon emissions and oxygen consumption in Beijing were estimated. The evaluation results indicated that the ecosystem services from the LULC in an area were not adequate to offset the urban carbon emissions and oxygen consumption. The areas with a high capacity for carbon sequestration and oxygen emissions were primarily distributed in the exurban districts of Beijing, and the carbon and oxygen balances in the exurban districts were superior to those of core urban areas. Industrial fossil fuel consumption dominated all of the human-induced carbon sources. The methods developed in this research were shown to be viable for the quantitative evaluation of urban ecosystem carbon and oxygen balances. OPEN ACCESSSustainability 2015, 7 196

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“…Previous studies have conducted numerous tests to measure the emissions from different type vehicles using PEMS, which revealed that vehicle emissions are strongly related with driving cycle, fuel quality, and after-treatment equipment [8,9]. PEMS testings have also been carried out in Chinese cities [10][11][12]. These studies have produced many results for in-use vehicle emission characteristics and have laid a foundation for policy-making in vehicle pollution control in China.…”

Section: Introductionmentioning

confidence: 99%

On-road Emission Characteristics of Logistics Transportation Vehicles in Chengdu

Zhang¹,

Wu²,

Liu³

et al. 2014

Advances in Social Science, Education and Humanities Research

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Abstract. On-road vehicle tests of three diesel trucks were conducted by a portable emission measurement system (PEMS) in Chengdu, China. SEMTECH-ECOSTAR provided by Sensors, Inc and MI2, an emissions measuring instrument powered by the Pegasor Particulate Sensor (PPS) were employed to detect gaseous and particle emissions during the tests. The impacts of speed and acceleration on emissions were analyzed. The average NOx emission factors of the heavy duty diesel truck (HDDT), medium-duty diesel truck (MDDT), light duty diesel truck (LDDT) were 7.29, 5.29 and 5.53 g/km, all of which were in excess of their emission limits. The particle emission factors were 0.60, 0.30 and 0.14 g/km, generally higher than the results for similar reported in the previous studies. Both gaseous and particle emission exhibit significant correlations with the change in vehicle speed, acceleration and power demand. The highest emission for each vehicle were generally found in high-VSP. High engine power caused by aggressive driving or heavy load is the main factor of high emissions for the vehicles in real-world situation.

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Real-world operation conditions and on-road emissions of Beijing diesel buses measured by using portable emission measurement system and electric low-pressure impactor

Cited by 83 publications

References 20 publications

Impacts of load mass on real-world PM1 mass and number emissions from a heavy-duty diesel bus

Impacts of load mass on real-world PM1 mass and number emissions from a heavy-duty diesel bus

Evaluation of Carbon and Oxygen Balances in Urban Ecosystems Using Land Use/Land Cover and Statistical Data

On-road Emission Characteristics of Logistics Transportation Vehicles in Chengdu

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