On-road remote sensing of petrol vehicle emissions measurement and emission factors estimation in Hong Kong

Chan, Tat Leung; Ning, Zhi; Leung, Chun Wah; Cheung, Chun Shun; Hung, W.T.; Dong, Guodan

doi:10.1016/j.atmosenv.2004.01.031

Cited by 72 publications

(33 citation statements)

References 15 publications

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“…7 Other means of estimating the real-world emission factors include direct measurements of the tail pipe emissions either by using remote sensing techniques, such as open path Fourier transform infrared spectroscopy, 8,9 or using on-board instruments to measure emissions. 10 An adaptation of the mass conservation box model of open road measurements method was used in the Borman Expressway project, and wind tunnel measurements were used to address some problems in estimating the turbulent mixing volume caused by the moving vehicles, as will be shown later.…”

Section: Introductionmentioning

confidence: 99%

Development of an Empirical Model to Estimate Real-World Fine Particulate Matter Emission Factors: The Traffic Air Quality Model

Soliman

Jacko

Palmer

2006

Journal of the Air & Waste Management Association

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The purpose of the study was to quantify the impact of traffic conditions, such as free flow and congestion, on local air quality. The Borman Expressway (I-80/94) in Northwest Indiana is considered a test bed for this research because of the high volume of class 9 truck traffic traveling on it, as well as the existing and continuing installation of the Intelligent Transportation System (ITS) to improve traffic management along the highway stretch. An empirical traffic air quality (TAQ) model was developed to estimate the fine particulate matter (PM 2.5 ) emission factors (grams per kilometer) based solely on the measured traffic parameters, namely, average speed, average acceleration, and class 9 truck density. The TAQ model has shown better predictions that matched the measured emission factor values more than the U.S. Environmental Protection Agency (EPA)-PART5 model. During congestion (defined as flow-speeds Ͻ50 km/hr [30 mi/hr]), the TAQ model, on average, overpredicted the measured values only by a factor of 1.2, in comparison to a fourfold underprediction using the EPA-PART5 model. On the other hand, during free flow (defined as flowspeeds Ͼ80 km/hr [50 mi/hr]), the TAQ model was conservative in that it overpredicted the measured values by 1.5-fold.

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Section: Introductionmentioning

confidence: 99%

Development of an Empirical Model to Estimate Real-World Fine Particulate Matter Emission Factors: The Traffic Air Quality Model

Soliman

Jacko

Palmer

2006

Journal of the Air & Waste Management Association

View full text Add to dashboard Cite

show abstract

“…For complex networks, the link-bylink approach is recommended to evaluate emission patterns, but for simpler networks, the roadway functional class basis or even the overall -average approach may be adequate. Chan et al (2004) studied on road petrol vehicle emission of carbon monoxide, hydrocarbon and nitric oxide at nine sites in Hong Kong. Their results show that petrol vehicle model year, engine size and driving patterns have a strong correlation on their emission factors.…”

Section: Literature Reviewmentioning

confidence: 99%

Developing Speed Dependent Emission Factors Using on-Board Emission Measuring Equipment in India

Sharma¹,

Mathew²

2016

IJTTE

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Vehicular emission models are important tools in several environmental impact studies. Although several studies have been conducted for emission control purposes, few attempts have been made on the planning side. For instance, long-term transportation network capacity improvement models do not explicitly consider emission in the objective function. Incorporating vehicular emission into the objective function is effective only if speed dependent emission factor is used in the estimation of emission. Although this issue is well addressed in the developed countries, owing to the heterogeneity of vehicles and absence of speed dependent emission factors the benefit from network investment is often underestimated in developing countries like India. Therefore, an attempt is made to explore the possibility of developing speed dependent emission factor for Indian conditions and vehicles. For accurate measurement an onboard test is conducted on typical vehicles; namely, a passenger car, a sports utility vehicle, and a truck. On board test equipment collected the data while the vehicle traversed with different speed ranges. The data obtained is processed and used for developing emission factor in the form of second degree polynomial with speed as the dependent variable. The emission factors for the three types of vehicles and for CO, CO 2 , NO X , and HC are developed. The results have been compared with Indian driving cycle based emission factors as well as UK based speed dependent emission factors for car in particular. The study gave a preliminary insight into the behaviors of pollutants with respect to speed. However, there is a need to develop such factor using large spectrum of vehicles and diverse driving conditions.

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“…15 The CO emission of petrol passenger cars in Macao agrees well with that used by the Hong Kong government, 16 whereas the NO x emission in Macao is significantly lower. However, another remote sensing study in Hong Kong 17 showed an NO x emission of 0.18 g/km, which is close to the 0.28 g/km in Macao. For diesel buses in Macao, the CO, NO x , and HC emissions are much lower than those in Hong Kong 16 because Macao uses single-decker buses, whereas the reference data in Hong Kong are for doubledecker buses.…”

Section: Emission Factors Of Vehicle Categoriesmentioning

confidence: 99%

Determining Gaseous Emission Factors and Driver’s Particle Exposures during Traffic Congestion by Vehicle-Following Measurement Techniques

Tang

Wang

2006

Journal of the Air & Waste Management Association

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Vehicle gaseous emissions (NO, CO, CO 2 , and hydrocarbon [HC]) and driver's particle exposures (particulate matter Ͻ1 m [PM 1 ], Ͻ2.5 m [PM 2.5 ], and Ͻ10 m [PM 10 ]) were measured using a mobile laboratory to follow a wide variety of vehicles during very heavy traffic congestion in Macao, Special Administrative Region, People's Republic of China, an urban area having one of the highest population densities in the world. The measurements were taken with high time resolution so that fluctuations in the emissions can be seen readily during vehicle acceleration, cruising, deceleration, and idling. The tests were conducted in close proximity to the vehicles, with the inlet of a five-gas analyzer mounted on the front bumper of the mobile laboratory, and the distance between the vehicles was usually within several meters. To measure the driver's particle exposures, the inlets of the particle analyzers were mounted at the height of the driver's breathing position in the mobile laboratory, with the driver's window open. A total of 178 and 113 vehicles were followed individually to determine the gaseous emission factor and the driver's particle exposures, respectively, for motorcycle, passenger car, taxi, truck, and bus. The gaseous emission factors were used to model the roadside air quality, and good correlations between the modeled and monitored CO, NO 2 , and nitrogen oxide (NO x ) verified the reliability of the experiments. Compared with petrol passenger cars and petrol trucks, diesel taxies and diesel trucks emitted less CO but more NO x . The impact of urban canyons is shown to cause a significant increase in the PM 1 peak. The background concentrations contributed a significant amount of the driver's particle exposures.

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On-road remote sensing of petrol vehicle emissions measurement and emission factors estimation in Hong Kong

Cited by 72 publications

References 15 publications

Development of an Empirical Model to Estimate Real-World Fine Particulate Matter Emission Factors: The Traffic Air Quality Model

Development of an Empirical Model to Estimate Real-World Fine Particulate Matter Emission Factors: The Traffic Air Quality Model

Developing Speed Dependent Emission Factors Using on-Board Emission Measuring Equipment in India

Determining Gaseous Emission Factors and Driver’s Particle Exposures during Traffic Congestion by Vehicle-Following Measurement Techniques

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