Hyperfine-resolution mapping of on-road vehicle emissions with comprehensive traffic monitoring and an intelligent transportation system

Abstract. Urban on-road vehicle emissions affect air quality and human health locally and globally. Given uneven sources, they typically exhibit distinct spatial heterogeneity, varying sharply over short distances (10 m–1 km). However, all-around observational constraints on the emission sources are limited in much of the world. Consequently, traditional emission inventories lack the spatial resolution that can characterize the on-road vehicle emission hotspots. Here we establish a bottom-up approach to reveal a unique pattern of urban on-road vehicle emissions at a spatial resolution 1–3 orders of magnitude higher than current emission inventories. We interconnect all-around traffic monitoring (including traffic fluxes, vehicle-specific categories, and speeds) via an intelligent transportation system (ITS) over Xiaoshan District in the Yangtze River Delta (YRD) region. This enables us to calculate single-vehicle-specific emissions over each fine-scale (10 m–1 km) road segment. Thus, the most hyperfine emission dataset of its type is achieved, and on-road emission hotspots appear. The resulting map shows that the hourly average on-road vehicle emissions of CO, NOx, HC, and PM2.5 are 74.01, 40.35, 8.13, and 1.68 kg, respectively. More importantly, widespread and persistent emission hotspots emerged. They are of significantly sharp small-scale variability, up to 8–15 times within individual hotspots, attributable to distinct traffic fluxes, road conditions, and vehicle categories. On this basis, we investigate the effectiveness of routine traffic control strategies on on-road vehicle emission mitigation. Our results have important implications for how the strategies should be designed and optimized. Integrating our traffic-monitoring-based approach with urban air quality measurements, we could address major data gaps between urban air pollutant emissions and concentrations.

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Hyperfine-Resolution Mapping of On-Road Vehicle Emissions with Comprehensive Traffic Monitoring and Intelligent Transportation System

10.5194/acp-2021-533 ◽

2021 ◽

Author(s):

Linhui Jiang ◽

Yan Xia ◽

Lu Wang ◽

Xue Chen ◽

Jianjie Ye ◽

...

Keyword(s):

Air Quality ◽

Spatial Resolution ◽

Vehicle Emissions ◽

Intelligent Transportation System ◽

Intelligent Transportation ◽

Traffic Monitoring ◽

Transportation System ◽

Vehicle Emission ◽

Urban Air ◽

Road Vehicle

Abstract. Urban on-road vehicle emissions affect air quality and human health locally and globally. Such emissions typically exhibit distinct spatial heterogeneity, varying sharply over short distances (10 m ~ 1 km). However, all-around observational constraints on the emission sources are limited in much of the world. Consequently, traditional emission inventories lack the spatial resolution that can characterize on-road vehicle emission hotspots. Here we establish a bottom-up approach to reveal a unique pattern of urban on-road vehicle emissions at 1 ~ 3 orders of magnitude higher spatial resolution than current inventories. We interconnect all-around traffic monitoring (including traffic fluxes, vehicle-specific categories, and speeds) via an intelligent transportation system (ITS) over the Xiaoshan District in the Yangtze River Delta (YRD) region. This enables us to calculate single-vehicle-specific emissions over each fine-scale (10 m ~ 1 km) road segment. Thus, a hyperfine emission dataset is achieved, and on-road emission hotspots appear. The resulting map shows that the hourly average on-road vehicle emissions of CO, NOx, HC, and PM2.5 are 74.01 kg, 40.35 kg, 8.13 kg, and 1.68 kg, respectively. More importantly, widespread and persistent emission hotspots emerge, of significantly sharp small-scale variability, up to 8 ~ 15 times, attributable to distinct traffic fluxes, road conditions, and vehicle categories. On this basis, we investigate the effectiveness of routine traffic control strategies on the on-road vehicle emission mitigation. Our results have important implications for how the strategies should be designed and optimized. Integrating our traffic-monitoring-based approach with urban air quality measurements, we could address major data gaps between urban air pollutant emissions and concentrations.

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Supplementary material to "Hyperfine-Resolution Mapping of On-Road Vehicle Emissions with Comprehensive Traffic Monitoring and Intelligent Transportation System"

10.5194/acp-2021-533-supplement ◽

2021 ◽

Author(s):

Linhui Jiang ◽

Yan Xia ◽

Lu Wang ◽

Xue Chen ◽

Jianjie Ye ◽

...

Keyword(s):

Vehicle Emissions ◽

Intelligent Transportation System ◽

Intelligent Transportation ◽

Traffic Monitoring ◽

Transportation System ◽

Road Vehicle ◽

Resolution Mapping ◽

Supplementary Material

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LPWAN-based hybrid backhaul communication for intelligent transportation systems: architecture and performance evaluation

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-021-01918-2 ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Taghi Shahgholi ◽

Amir Sheikhahmadi ◽

Keyhan Khamforoosh ◽

Sadoon Azizi

Keyword(s):

Low Power ◽

Intelligent Transportation Systems ◽

Intelligent Transportation System ◽

Road Traffic ◽

Intelligent Transportation ◽

Traffic Monitoring ◽

Transportation Systems ◽

Area Network ◽

Emergency Vehicle ◽

Wide Area Network

AbstractIncreased number of the vehicles on the streets around the world has led to several problems including traffic congestion, emissions, and huge fuel consumption in many regions. With advances in wireless and traffic technologies, the Intelligent Transportation System (ITS) has been introduced as a viable solution for solving these problems by implementing more efficient use of the current infrastructures. In this paper, the possibility of using cellular-based Low-Power Wide-Area Network (LPWAN) communications, LTE-M and NB-IoT, for ITS applications has been investigated. LTE-M and NB-IoT are designed to provide long range, low power and low cost communication infrastructures and can be a promising option which has the potential to be employed immediately in real systems. In this paper, we have proposed an architecture to employ the LPWAN as a backhaul infrastructure for ITS and to understand the feasibility of the proposed model, two applications with low and high delay requirements have been examined: road traffic monitoring and emergency vehicle management. Then, the performance of using LTE-M and NB-IoT for providing backhaul communication infrastructure has been evaluated in a realistic simulation environment and compared for these two scenarios in terms of end-to-end latency per user. Simulation of Urban MObility has been used for realistic traffic generation and a Python-based program has been developed for evaluation of the communication system. The simulation results demonstrate the feasibility of using LPWAN for ITS backhaul infrastructure mostly in favor of the LTE-M over NB-IoT.

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Evaluating Vehicle Emission Control Policies using on-Road Mobile Measurements and Continuous Wavelet Transform: a Case Study during the Asia-Pacific Economic Cooperation Forum, China 2014

10.5194/acp-2016-460 ◽

2016 ◽

Cited By ~ 1

Author(s):

Ziqiang Tan ◽

Yanwen Wang ◽

Chunxiang Ye ◽

Yi Zhu ◽

Yingruo Li ◽

...

Keyword(s):

Wavelet Transform ◽

Air Quality ◽

Continuous Wavelet Transform ◽

Vehicle Emissions ◽

Emission Control ◽

Control Policy ◽

Asia Pacific ◽

Vehicle Emission ◽

Continuous Wavelet ◽

Asia Pacific Economic Cooperation

Abstract. Vehicle emissions are major sources of atmospheric pollutants in urban areas, especially in megacities around the world. Various vehicle emission control policies have been implemented to improve air quality. However, the effectiveness of these policies is unclear, due to a lack of systematic evaluation and sound methodologies. During the Asia-Pacific Economic Cooperation (APEC) Forum, China 2014, the Chinese government implemented the strictest vehicle emission control policy in the country's history, which provided an opportunity to evaluate its effectiveness, based on our recently developed method. To evaluate the vehicle emission reduction, we used a mobile research platform to measure the main air pollutants (PM2.5, black carbon (BC), SO2, CO, NOx and O3) on the 4th ring road of the city of Beijing, combined with a continuous wavelet transform method (CWT) to separate out "instantaneous emissions" by passing vehicles. The results suggested that our measurements captured the spatial distribution and variation of atmospheric pollutant concentrations on the 4th ring road. The "instantaneous concentration" decomposed by the CWT method represents on-road emissions better than other methods reported in the literature. With this method, we found that the daytime vehicle emission of CO and NOx decreased by 28.1 and 16.3 %, respectively, during the APEC period relative to the period before APEC, and by 39.3 and 38.5 %, respectively, relative to the period after APEC. The nighttime vehicle emissions of CO and NOx decreased by 56.0 and 60.7 %, respectively, during the APEC period relative to the period after APEC. Because vehicle emissions of NOx and CO contribute considerably to the total emissions of these pollutants in Beijing, the vehicle emission control policy implementation was extremely successful in controlling air quality during APEC 2014, China.

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Near-road vehicle emissions air quality monitoring for exposure modeling

Atmospheric Environment ◽

10.1016/j.atmosenv.2020.117318 ◽

2020 ◽

Vol 224 ◽

pp. 117318 ◽

Cited By ~ 6

Author(s):

Jennifer L. Moutinho ◽

Donghai Liang ◽

Rachel Golan ◽

Stefanie E. Sarnat ◽

Rodney Weber ◽

...

Keyword(s):

Air Quality ◽

Vehicle Emissions ◽

Quality Monitoring ◽

Air Quality Monitoring ◽

Road Vehicle ◽

Exposure Modeling

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Intelligent Transportation System Data Archiving: Statistical Techniques for Determining Optimal Aggregation Widths for Inductive Loop Detector Speed Data

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/1719-11 ◽

2000 ◽

Vol 1719 (1) ◽

pp. 85-93 ◽

Cited By ~ 14

Author(s):

Byron J. Gajewski ◽

Shawn M. Turner ◽

William L. Eisele ◽

Clifford H. Spiegelman

Keyword(s):

Traffic Management ◽

Intelligent Transportation System ◽

Intelligent Transportation ◽

Traffic Monitoring ◽

Transportation System ◽

Monitoring Data ◽

Statistical Techniques ◽

Parameter Distribution ◽

Data Archiving ◽

Wide Range

Although most traffic management centers collect intelligent transportation system (ITS) traffic monitoring data from local controllers in 20-s to 30-s intervals, the time intervals for archiving data vary considerably from 1 to 5, 15, or even 60 min. Presented are two statistical techniques that can be used to determine optimal aggregation levels for archiving ITS traffic monitoring data: the cross-validated mean square error and the F-statistic algorithm. Both techniques seek to determine the minimal sufficient statistics necessary to capture the full information contained within a traffic parameter distribution. The statistical techniques were applied to 20-s speed data archived by the TransGuide center in San Antonio, Texas. The optimal aggregation levels obtained by using the two algorithms produced reasonable and intuitive results—both techniques calculated optimal aggregation levels of 60 min or more during periods of low traffic variability. Similarly, both techniques calculated optimal aggregation levels of 1 min or less during periods of high traffic variability (e.g., congestion). A distinction is made between conclusions about the statistical techniques and how the techniques can or should be applied to ITS data archiving. Although the statistical techniques described may not be disputed, there is a wide range of possible aggregation solutions based on these statistical techniques. Ultimately, the aggregation solutions may be driven by nonstatistical parameters such as cost (e.g., “How much do we/the market value the data?”), ease of implementation, system requirements, and other constraints.

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