Statistical evaluation of on-road vehicle emissions measurement using a dual remote sensing technique

Huang, Yuhan; Yu, Yang; Yam, Yat-shing; Zhou, John L.; Lei, Chengwang; Organ, Bruce; Zhuang, Yuan; Mok, Wai-Chuen; Chan, Eric

doi:10.1016/j.envpol.2020.115456

Cited by 11 publications

(4 citation statements)

References 40 publications

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“…The response time of oxygen sensor switching from high to low output voltage has to be very fast, at a frequency higher than 1 Hz. Our research showed that the two emission factors measured by dual RS technique demonstrated little correlation ( 31 ), which proved the ability of the 1-s separation distance to capture exhaust emissions under different engine combustion conditions. Therefore, if both RS readings exceed the cut points, there is a high probability of failure from oxygen sensor and/or TWC, leading to a persistent high emitter.…”

Section: Resultsmentioning

confidence: 89%

“…1A) results, it is noted that the false negative is comparable to the double-blind test for taxis but higher for petrol cars. This is due to the fact that petrol car utilization is much lower than taxis so that the RS scans per petrol car (1.6 scans/year) are much lower than for taxis (5.9 scans/year) ( 31 ). To reduce the false negatives, the intensity of the RS scanning per vehicle type needs to be increased in the design of the enforcement program.…”

Section: Resultsmentioning

confidence: 99%

“…4 (B to D). This is because the emissions of both the upstream and downstream RS systems converge to the same gamma distribution when sufficient measurements are used (i.e., >200) (31,33) so that both datasets have the same average emissions and trends for a large dataset (e.g., more than 1 million valid measurements in this study). As shown in Fig.…”

Section: Evaluation Of Rs Enforcement Programmentioning

confidence: 98%

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Rapid detection of high-emitting vehicles by on-road remote sensing technology improves urban air quality

Huang

Lee²,

Yam³

et al. 2022

Sci. Adv.

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Vehicle emissions are the most important source of air pollution in the urban environment worldwide, and their detection and control are critical for protecting public health. Here, we report the use of on-road remote sensing (RS) technology for fast, accurate, and cost-effective identification of high-emitting vehicles as an enforcement program for improving urban air quality. Using large emission datasets from chassis dynamometer testing, RS, and air quality monitoring, we found that significant percentages of in-use petrol and LPG vehicles failed the emission standards, particularly the high-mileage fleets. The RS enforcement program greatly cleaned these fleets, in terms of high-emitter percentages, fleet average emissions, roadside and ambient pollutant concentrations, and emission inventory. The challenges of the current enforcement program are conservative setting of cut points, single-lane measurement sites, and lack of application experience in diesel vehicles. Developing more accurate and vertical RS systems will improve and extend their applications.

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Section: Resultsmentioning

confidence: 89%

Section: Resultsmentioning

confidence: 99%

Section: Evaluation Of Rs Enforcement Programmentioning

confidence: 98%

See 1 more Smart Citation

Rapid detection of high-emitting vehicles by on-road remote sensing technology improves urban air quality

Huang

Lee²,

Yam³

et al. 2022

Sci. Adv.

Self Cite

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“…Urgent measures need to be taken to reduce vehicle emissions. The vehicle emission inventories of different cities and road grades have been researched [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Unregulated Emissions from Natural Gas Taxi Based on IVE Model

Zhao

et al. 2021

Atmosphere

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Emissions from motor vehicles have gained the attention of government agencies. To alleviate air pollution and reduce the petroleum demand from vehicles in China, the policy of “oil to gas” was vigorously carried out. Qingdao began to promote the use of natural gas vehicles (NGVs) in 2003. By the end of 2016, there were 9460 natural gas (NG) taxis in Qingdao, which accounted for 80% of the total taxis. An understanding of policy implementation for emission reductions is required. Experiments to obtain the taxi driving conditions and local parameters were investigated and an international vehicle emissions (IVE) localization model was established. Combined with vehicle mass analysis system (VMAS) experiments, the IVE localization model was amended and included the taxi pollutant emission factors. The result indicates that annual total carbon monoxide (CO) emissions from actual taxis are 6411.87 t, carbureted hydrogen (HC) emissions are 124.85 t, nitrogen oxide (NOx) emissions are 1397.44 t and particulate matter (PM) emissions are 8.9 t. When the taxis are running on pure natural gas, the annual emissions of CO, HC, NOx and PM are 4942.3 t, 48.15 t, 1496.01 t and 5.13 t, respectively. Unregulated emissions of annual total formaldehydes, benzene, acetaldehyde, 1,3-butadience emissions from an actual taxi are 65.99 t, 4.68 t, 1.04 t and 8.83 t. When the taxi is running on pure natural gas, the above unregulated emissions are 12.11 t, 1.27 t, 1.5 t and 0.02 t, respectively.

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Emission profiles, source identifications, and health risk of polycyclic aromatic hydrocarbons (PAHs) in a road tunnel located in Xi’an, China

Liu

Deng

Tong

et al. 2023

Environ Sci Pollut Res

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Statistical evaluation of on-road vehicle emissions measurement using a dual remote sensing technique

Abstract: Statistical evaluation of on-road vehicle emissions measurement using a dual remote sensing technique.

Cited by 11 publications

References 40 publications

Rapid detection of high-emitting vehicles by on-road remote sensing technology improves urban air quality

Rapid detection of high-emitting vehicles by on-road remote sensing technology improves urban air quality

Unregulated Emissions from Natural Gas Taxi Based on IVE Model

Emission profiles, source identifications, and health risk of polycyclic aromatic hydrocarbons (PAHs) in a road tunnel located in Xi’an, China

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