A remote sensing emissions monitoring programme reduces emissions of gasoline and LPG vehicles

A review of strategies for mitigating roadside air pollution in urban street canyons

Urban street canyons formed by high-rise buildings restrict the dispersion of vehicle emissions, which pose severe health risks to the public by aggravating roadside air quality. However, this issue is often overlooked in city planning. This paper reviews the mechanisms controlling vehicle emission dispersion in urban street canyons and the strategies for managing roadside air pollution. Studies have shown that air pollution hotspots are not all attributed to heavy traffic and proper urban design can mitigate air pollution. The key factors include traffic conditions, canyon geometry, weather conditions and chemical reactions. Two categories of mitigation strategies are identified, namely traffic interventions and city planning. Popular traffic interventions for street canyons include low emission zones and congestion charges which can moderately improve roadside air quality. In comparison, city planning in terms of building geometry can significantly promote pollutant dispersion in street canyons. General design guidelines, such as lower canyon aspect ratio, alignment between streets and prevailing winds, non-uniform building heights and ground-level building porosity, may be encompassed in new development. Concurrently, in-street barriers are widely applicable to rectify the poor roadside air quality in existing street canyons. They are broadly classified into porous (e.g. trees and hedges) and solid (e.g. kerbside parked cars, noise fences and viaducts) barriers that utilize their aerodynamic advantages to ease roadside air pollution. Post-evaluations are needed to review these strategies by real-world field experiments and more detailed modelling in the practical perspective.

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

On-road remote sensing (RS) is a rapid, non-intrusive and economical tool to monitor and control the emissions of in-use vehicles, and currently is gaining popularity globally. However, a majority of studies used a single RS technique, which may bias the measurements since RS only captures a snapshot of vehicle emissions. This study aimed to use a unique dual RS technique to assess the characteristics of on-road vehicle emissions. The results show that instantaneous vehicle emissions are highly dynamic under real-world driving conditions. The two emission factors measured by the dual RS technique show little correlation, even under the same driving condition. This indicates that using the single RS technique may be insufficient to accurately represent the emission level of a vehicle based on one measurement. To increase the accuracy of identifying high-emitting vehicles, using the dual RS technique is essential. Despite little correlation, the dual RS technique measures the same average emission factors as the single RS technique does when a large number of measurements are available. Statistical analysis shows that both RS systems demonstrate the same Gamma distribution with ≥200 measurements, leading to converged mean emission factors for a given vehicle group. These findings point to the need for a minimum sample size of 200 RS measurements in order to generate reliable emission factors for on-road vehicles. In summary, this study suggests that using the single or dual RS technique will depend on the purpose of applications. Both techniques have the same accuracy in calculating average emission factors when sufficient measurements are available, while the dual RS technique is more accurate in identifying high-emitters based on one measurement only.

Evaluating in-use vehicle emissions using air quality monitoring stations and on-road remote sensing systems

This study investigated real world in-use vehicle emissions using two regulatory techniques simultaneously, namely on-road remote sensing (RS) systems and air quality (AQ) monitoring stations, aiming to provide a full pollution profile from tailpipe to roadside and atmosphere. Two large AQ and RS datasets collected during 2012-2018 were analyzed. The effects of various emission control programmes on the trends of tailpipe emissions and air quality were evaluated. Correlations between tailpipe emissions and roadside and ambient air quality were also explored. The results showed a decreasing trend of NO₂ at both roadside and ambient AQ stations from 2013 to 2016, which was attributed to the intensive implementation of a series of vehicle emissions control programmes. Although NO₂ was decreasing, O₃ was generally increasing for all AQ stations. AQ data showed that O₃ had little correlation with either NO₂ or NO_x, but was mainly determined by NO₂/NO_x ratio. Roadside NO₂/NO_x ratio increased first and then decreased or stabilized after 2014, while ambient NO₂/NO_x ratio increased steadily. RS data showed that the overall NO decreased quickly during 2012-2015 and then decreased moderately after 2015. The decrease was mainly attributed to the effective NO reduction from LPG vehicles. However, diesel NO remained high and reduced relatively slowly during the study period. Gasoline vehicles were relatively clean compared with LPG and diesel vehicles. Finally, good correlations were demonstrated between NO measured by RS sites and NO_x measured by roadside AQ stations, indicating that vehicle emissions were the major contributor to roadside NO_x pollution. Ambient NO_x emissions could be affected by various sources, leading to different correlation levels between RS and ambient AQ results.

Simulation of engine faults and their impact on emissions and vehicle performance for a liquefied petroleum gas taxi

The deterioration of emissions control systems in a spark ignition engine is predominantly a gradual process of wear and tear occurring as vehicles accumulate mileage. As new innovations in engine and emissions technology have been progressively introduced to meet lower emissions targets, the impact of gradual deterioration of hardware has become more challenging to identify and quantify in the repair industry. When a pioneering emissions control programme utilising remote sensing to detect high emitting gasoline and liquefied petroleum gas (LPG) vehicles was to be introduced in Hong Kong, it became apparent the repair industry needed specialised training to assist with identifying the types of failures which would lead to high vehicle emissions. To identify the impact of hardware deterioration and failures, a Toyota Crown Comfort LPG taxi was used to demonstrate simulated failures of engine hardware systems to measure their impact on emissions, fuel consumption and drivability using a chassis dynamometer. This novel study simulated a broad range of deterioration and failures covering the intake, fuel supply, ignition, and exhaust systems. The results of the study showed significant THC and CO increases of up to 317% (0.604 g/km) and 782% (5.351 g/km) respectively for a simulated oxygen sensor high voltage fault and a sticky mixture control valve. The largest increase in NO_x emissions was for restricted main fuel supply in the LPG vapouriser, producing an increase of 282% (1.41 g/km). Fuel consumption varied with increases of up to 15.5%. Drivability was impacted with poor idle from a number of faults and especially by a worn throttlebody which produced rough acceleration characteristics as well. This study clearly highlights the importance of having properly maintained emissions and engine hardware systems to achieve optimal fuel economy and compliant emissions levels, which could be reproduced in other regions for prescribed emissions regulation.

Large eddy simulation of vehicle emissions dispersion: Implications for on-road remote sensing measurements

On-road remote sensing technology measures the concentration ratios of pollutants over CO₂ in the exhaust plume in half a second when a vehicle passes by a measurement site, providing a rapid, non-intrusive and economic tool for vehicle emissions monitoring and control. A key assumption in such measurement is that the emission ratios are constant for a given plume. However, there is a lack of study on this assumption, whose validity could be affected by a number of factors, especially the engine operating conditions and turbulence. To guide the development of the next-generation remote sensing system, this study is conducted to investigate the effects of various factors on the emissions dispersion process in the vehicle near-wake region and their effects on remote sensing measurement. The emissions dispersion process is modelled using Large Eddy Simulation (LES). The studied factors include the height of the remote sensing beam, vehicle speed, acceleration and side wind. The results show that the measurable CO₂ and NO exhaust plumes are relatively short at 30 km/h cruising speed, indicating that a large percentage of remote sensing readings within the measurement duration (0.5 s) are below the sensor detection limit which would distort the derived emission ratio. In addition, the valid measurement region of NO/CO₂ emission ratio is even shorter than the measurable plume and is at the tailpipe height. The effect of vehicle speed (30-90 km/h) on the measurable plume length is insignificant. Under deceleration condition, the length of the valid NO/CO₂ measurement region is shorter than under cruising and acceleration conditions. Side winds from the far-tailpipe direction have a significant effect on remote sensing measurements. The implications of these findings are discussed and possible solutions to improve the accuracy of remote sensing measurement are proposed.

A Method to Assess and Reduce Pollutant Emissions of Logistic Transportation under Adverse Weather

With the increase of vehicle activities and adverse weather under the background of modernization and global warming, more attention should be paid to vehicle emissions reduction in such circumstances for environmental protection and sustainable transportation. Different from some typical measures, e.g., relevant government policies, improvement of vehicle hardware technologies, and optimization of traffic management, this paper develop a new method based on emergency path optimization to evaluate and reduce pollutant emissions of logistic transportation under adverse weather. Firstly, we establish a calculation model of pollutant emissions (LT-PE model) to calculate the amount of vehicle pollutant emissions produced under adverse weather. Then, a co-evolving path optimization (CEPO) method-based ripple-spreading algorithm is proposed in order to reduce pollutant emissions. To validate the effectiveness of the proposed method, this paper selects fruit logistics transportation affected by typhoon in China’s Hainan Island as a case study. The results show that total vehicle pollutant emissions from the fruit transportation of 35 farms under the typhoon are 28.1% more than when there is no typhoon. The proposed method can reduce pollutant emissions by 21.2% compared with the traditional methods under typhoon.