Evaluation of the accuracy of remote emission sensing measurements via real-world vehicle dynamic tests
To evaluate the measurement accuracy of horizontal and vertical remote emission sensing (RES) equipment, a real-world dynamic test was carried out in Chengdu by using electric vehicles equipped with various concentrations of standard gases. In addition, a new Image-based Spectral Processing Algorith...
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| Published in | Environmental pollution (1987) Vol. 360; p. 124780 |
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| Main Authors | , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
Elsevier Ltd
01.11.2024
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0269-7491 1873-6424 1873-6424 |
| DOI | 10.1016/j.envpol.2024.124780 |
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| Abstract | To evaluate the measurement accuracy of horizontal and vertical remote emission sensing (RES) equipment, a real-world dynamic test was carried out in Chengdu by using electric vehicles equipped with various concentrations of standard gases. In addition, a new Image-based Spectral Processing Algorithm (ISPA) for vertical remote sensing spectral data was developed to improve the measurement capability. The results showed that the ISPA provided a greater percentage of valid data and lower relative errors; thus, our new algorithm could more effectively analyze the spectral data to measure vehicle emission levels. The percentages of valid horizontal and vertical RES data were 71% and 84%, respectively. The mean relative errors of CO2, CO, HC and NO measured by the vertical RES were about 5%, 20%, 20% and 40%, respectively, and those of CO2, CO and NO measured by the horizontal RES were 3%, 13% and 15%, respectively. For the common vehicle emission concentration, the percentage of valid data for the two RES types increased with increasing gas concentration. As the vehicle speed increased, the relative errors of the horizontal RES equipment showed an increasing trend for the same concentration of gas. Furthermore, for the same speed segment, the relative errors of the horizontal RES equipment increased as the simulated emission concentration decreased. The vertical RES equipment did not exhibit a consistent trend in terms of changes. This study provides a data quality reference for further RES applications.
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•Accuracy of horizontal and vertical remote emission sensing were evaluated.•A new vertical RES spectral data algorithm was developed to reduce NO error.•Mean relative errors of NO from V-RES and H-RES were 15% and 40%, respectively.•Both RES equipment can better evaluate fleet emissions and screen higher emitters. |
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| AbstractList | To evaluate the measurement accuracy of horizontal and vertical remote emission sensing (RES) equipment, a real-world dynamic test was carried out in Chengdu by using electric vehicles equipped with various concentrations of standard gases. In addition, a new Image-based Spectral Processing Algorithm (ISPA) for vertical remote sensing spectral data was developed to improve the measurement capability. The results showed that the ISPA provided a greater percentage of valid data and lower relative errors; thus, our new algorithm could more effectively analyze the spectral data to measure vehicle emission levels. The percentages of valid horizontal and vertical RES data were 71% and 84%, respectively. The mean relative errors of CO2, CO, HC and NO measured by the vertical RES were about 5%, 20%, 20% and 40%, respectively, and those of CO2, CO and NO measured by the horizontal RES were 3%, 13% and 15%, respectively. For the common vehicle emission concentration, the percentage of valid data for the two RES types increased with increasing gas concentration. As the vehicle speed increased, the relative errors of the horizontal RES equipment showed an increasing trend for the same concentration of gas. Furthermore, for the same speed segment, the relative errors of the horizontal RES equipment increased as the simulated emission concentration decreased. The vertical RES equipment did not exhibit a consistent trend in terms of changes. This study provides a data quality reference for further RES applications.
[Display omitted]
•Accuracy of horizontal and vertical remote emission sensing were evaluated.•A new vertical RES spectral data algorithm was developed to reduce NO error.•Mean relative errors of NO from V-RES and H-RES were 15% and 40%, respectively.•Both RES equipment can better evaluate fleet emissions and screen higher emitters. To evaluate the measurement accuracy of horizontal and vertical remote emission sensing (RES) equipment, a real-world dynamic test was carried out in Chengdu by using electric vehicles equipped with various concentrations of standard gases. In addition, a new vertical remote sensing spectral data algorithm based on image processing (ISPA) was developed to improve the measurement capability. The results showed that the ISPA provided a greater percentage of valid data and lower relative errors; thus, our new algorithm could more effectively analyze the spectral data to measure vehicle emission levels. The percentages of valid horizontal and vertical RES data were 71% and 84%, respectively. The mean relative errors of CO , CO, HC and NO measured by the vertical RES were about 5%, 20%, 20% and 40%, respectively, and those of CO , CO and NO measured by the horizontal RES were 3%, 13% and 15%, respectively. For the common vehicle emission concentration, the percentage of valid data for the two RES types increased with increasing gas concentration. As the vehicle speed increased, the relative errors of the horizontal RES equipment showed an increasing trend for the same concentration of gas. Furthermore, for the same speed segment, the relative errors of the horizontal RES equipment increased as the simulated emission concentration decreased. The vertical RES equipment did not exhibit a consistent trend in terms of changes. This study provides a data quality reference for further RES applications. To evaluate the measurement accuracy of horizontal and vertical remote emission sensing (RES) equipment, a real-world dynamic test was carried out in Chengdu by using electric vehicles equipped with various concentrations of standard gases. In addition, a new Image-based Spectral Processing Algorithm (ISPA) for vertical remote sensing spectral data was developed to improve the measurement capability. The results showed that the ISPA provided a greater percentage of valid data and lower relative errors; thus, our new algorithm could more effectively analyze the spectral data to measure vehicle emission levels. The percentages of valid horizontal and vertical RES data were 71% and 84%, respectively. The mean relative errors of CO2, CO, HC and NO measured by the vertical RES were about 5%, 20%, 20% and 40%, respectively, and those of CO2, CO and NO measured by the horizontal RES were 3%, 13% and 15%, respectively. For the common vehicle emission concentration, the percentage of valid data for the two RES types increased with increasing gas concentration. As the vehicle speed increased, the relative errors of the horizontal RES equipment showed an increasing trend for the same concentration of gas. Furthermore, for the same speed segment, the relative errors of the horizontal RES equipment increased as the simulated emission concentration decreased. The vertical RES equipment did not exhibit a consistent trend in terms of changes. This study provides a data quality reference for further RES applications.To evaluate the measurement accuracy of horizontal and vertical remote emission sensing (RES) equipment, a real-world dynamic test was carried out in Chengdu by using electric vehicles equipped with various concentrations of standard gases. In addition, a new Image-based Spectral Processing Algorithm (ISPA) for vertical remote sensing spectral data was developed to improve the measurement capability. The results showed that the ISPA provided a greater percentage of valid data and lower relative errors; thus, our new algorithm could more effectively analyze the spectral data to measure vehicle emission levels. The percentages of valid horizontal and vertical RES data were 71% and 84%, respectively. The mean relative errors of CO2, CO, HC and NO measured by the vertical RES were about 5%, 20%, 20% and 40%, respectively, and those of CO2, CO and NO measured by the horizontal RES were 3%, 13% and 15%, respectively. For the common vehicle emission concentration, the percentage of valid data for the two RES types increased with increasing gas concentration. As the vehicle speed increased, the relative errors of the horizontal RES equipment showed an increasing trend for the same concentration of gas. Furthermore, for the same speed segment, the relative errors of the horizontal RES equipment increased as the simulated emission concentration decreased. The vertical RES equipment did not exhibit a consistent trend in terms of changes. This study provides a data quality reference for further RES applications. |
| ArticleNumber | 124780 |
| Author | Yang, Xinping Fu, Mingliang Zhang, Yingzhi Tian, Qili Zhang, Hefeng Cao, Yang Kang, Yu Liu, Jin Wang, Xiaohu Jiang, Han |
| Author_xml | – sequence: 1 givenname: Qili surname: Tian fullname: Tian, Qili organization: State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China – sequence: 2 givenname: Xinping surname: Yang fullname: Yang, Xinping organization: State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China – sequence: 3 givenname: Han surname: Jiang fullname: Jiang, Han organization: State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China – sequence: 4 givenname: Xiaohu surname: Wang fullname: Wang, Xiaohu organization: Anhui Baolong Environmental Protection Technology Co., Ltd, Hefei, 230000, China – sequence: 5 givenname: Jin surname: Liu fullname: Liu, Jin organization: Anhui Baolong Environmental Protection Technology Co., Ltd, Hefei, 230000, China – sequence: 6 givenname: Yingzhi surname: Zhang fullname: Zhang, Yingzhi organization: Anhui Baolong Environmental Protection Technology Co., Ltd, Hefei, 230000, China – sequence: 7 givenname: Yang surname: Cao fullname: Cao, Yang organization: Institute of Advanced Technology, University of Science and Technology of China, Hefei, 230088, China – sequence: 8 givenname: Yu surname: Kang fullname: Kang, Yu organization: Institute of Advanced Technology, University of Science and Technology of China, Hefei, 230088, China – sequence: 9 givenname: Mingliang surname: Fu fullname: Fu, Mingliang email: fumingliang160@163.com organization: State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China – sequence: 10 givenname: Hefeng surname: Zhang fullname: Zhang, Hefeng email: zhanghf@vecc.org.cn organization: State Environmental Protection Key Laboratory of Vehicle Emission Control and Simulation, Chinese Research Academy of Environmental Sciences, Beijing, 100012, China |
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| Cites_doi | 10.1016/j.atmosenv.2016.09.021 10.1016/j.jclepro.2019.04.095 10.1016/j.envpol.2018.02.043 10.1016/j.scitotenv.2020.142088 10.1080/10962247.2012.699015 10.1016/j.atmosenv.2020.117877 10.1016/j.scitotenv.2021.146750 10.1016/j.envpol.2019.04.130 10.1016/j.atmosenv.2013.09.026 10.3390/atmos10090516 10.1016/j.envpol.2020.116384 10.1016/j.atmosenv.2015.09.048 10.1016/j.pecs.2016.12.004 10.1088/1748-9326/aa8850 10.1016/j.atmosenv.2021.118317 10.1016/j.atmosenv.2018.03.035 10.1016/j.scitotenv.2019.02.144 10.1016/j.scitotenv.2018.12.349 10.1016/j.atmosenv.2017.09.020 10.1016/j.scitotenv.2020.139868 10.1016/j.atmosenv.2013.01.006 10.1016/j.envpol.2020.113974 10.1021/ac00185a746 10.1016/j.envint.2021.106977 10.1016/j.enpol.2012.05.081 10.1088/1748-9326/ac5c9e 10.1016/j.jclepro.2020.122000 10.1016/j.scitotenv.2022.153699 10.1016/j.trpro.2023.11.295 |
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| Keywords | Remote emission sensing Vehicle emissions Accuracy evaluation Dynamic test |
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| References | Kang, Li, Lv, Xu, Zheng, Chang (bib17) 2021; 244 Lau, Rakowska, Townsend, Brimblecombe, Chan, Yam, Mocnik, Ning (bib18) 2015; 122 Mahesh, McNabola, Smith, Timoney, Ekhtiari, Fowler, Willis, Rose, Wareham, Walker, Ghosh (bib21) 2023; 117 O'Driscoll, ApSimon, Oxley, Molden, Stettler, Thiyagarajah (bib22) 2016; 145 Huang, Organ, Zhou, Surawski, Hong, Chan, Yam (bib12) 2018; 182 Buhigas, Alonso de Lomas, Lumoz (bib36) 2023; 72 Qiu, Borken-Kleefeld (bib23) 2022; 17 Bishop, Starkey, Ihlenfeldt, Williams, Stedman (bib2) 1989; 61 Huang, Organ, Zhou, Surawski, Hong, Chan, Yam (bib11) 2018; 237 Carslaw, Rhys-Tyler (bib3) 2013; 81 Stedman, Bishop (bib28) 1990 Rushton, Tate, Shepherd (bib24) 2021; 750 Yang, Tate, Rushton, Morganti, Shepherd (bib33) 2022; 823 Huang, Tao, Lou, Hu, Wang, Wang, Li, Wang, Liu, Quan, Zhou (bib9) 2017; 169 Li, Kang, Lv, Zhao (bib20) 2016 Smit, Bainbridge, Kennedy, Kingston (bib26) 2021; 252 Demuynck, Bosteels, De Paepe, Favre, May, Verhelst (bib4) 2012; 49 Huang, Organ, Zhou, Surawski, Yam, Chan (bib13) 2019; 252 Lee, Lee, Lee, Choi, Min (bib19) 2021; 782 Bishop, Schuchmann, Stedman, Lawson (bib1) 2012; 62 Franco, Kousoulidou, Muntean, Ntziachristos, Hausberger, Dilara (bib7) 2013; 70 Xu, Kang, Lv (bib32) 2017 Wei, Zhang, Zhang, Jin, Chang, Yang, Ma, Jia, Ren, Wu, Peng, Mao (bib31) 2022; 158 Environment (bib5) 2017 Smit, Kingston (bib27) 2019; 10 Huang, Surawski, Organ, Zhou, Tang, Chan (bib15) 2019; 659 Zhang, Wei, Zou, Mao (bib34) 2021; 271 Huang, Mok, Yam, Zhou, Surawski, Organ, Chan, Mofijur, Mahlia, Ong (bib14) 2020; 740 Triantafyllopoulos, Dimaratos, Ntziachristos, Bernard, Dornoff, Samaras (bib29) 2019; 666 Gruening, Bonnel, Clairotte, Giechaskiel, Valverde, Zardini, Carriero (bib37) 2019 Guo, Meng (bib8) 2019; 226 Huang, Ng, Surawski, Yam, Mok, Liu, Zhou, Organ, Chan (bib10) 2020; 259 (bib35) 2023 Schildknecht (bib25) 2020 Fontaras, Zacharof, Ciuffo (bib6) 2017; 60 Jonson, Borken-Kleefeld, Simpson, Nyíri, Posch, Heyes (bib16) 2017; 12 Wang, Wood, Wang, Geng, Long (bib30) 2020; 266 Demuynck (10.1016/j.envpol.2024.124780_bib4) 2012; 49 Lau (10.1016/j.envpol.2024.124780_bib18) 2015; 122 Guo (10.1016/j.envpol.2024.124780_bib8) 2019; 226 Huang (10.1016/j.envpol.2024.124780_bib10) 2020; 259 Wang (10.1016/j.envpol.2024.124780_bib30) 2020; 266 Huang (10.1016/j.envpol.2024.124780_bib9) 2017; 169 Fontaras (10.1016/j.envpol.2024.124780_bib6) 2017; 60 Lee (10.1016/j.envpol.2024.124780_bib19) 2021; 782 Yang (10.1016/j.envpol.2024.124780_bib33) 2022; 823 Franco (10.1016/j.envpol.2024.124780_bib7) 2013; 70 Wei (10.1016/j.envpol.2024.124780_bib31) 2022; 158 Bishop (10.1016/j.envpol.2024.124780_bib1) 2012; 62 Li (10.1016/j.envpol.2024.124780_bib20) 2016 Carslaw (10.1016/j.envpol.2024.124780_bib3) 2013; 81 Smit (10.1016/j.envpol.2024.124780_bib26) 2021; 252 Bishop (10.1016/j.envpol.2024.124780_bib2) 1989; 61 Jonson (10.1016/j.envpol.2024.124780_bib16) 2017; 12 (10.1016/j.envpol.2024.124780_bib35) 2023 Qiu (10.1016/j.envpol.2024.124780_bib23) 2022; 17 Huang (10.1016/j.envpol.2024.124780_bib13) 2019; 252 Kang (10.1016/j.envpol.2024.124780_bib17) 2021; 244 Huang (10.1016/j.envpol.2024.124780_bib12) 2018; 182 O'Driscoll (10.1016/j.envpol.2024.124780_bib22) 2016; 145 Triantafyllopoulos (10.1016/j.envpol.2024.124780_bib29) 2019; 666 Huang (10.1016/j.envpol.2024.124780_bib11) 2018; 237 Xu (10.1016/j.envpol.2024.124780_bib32) 2017 Buhigas (10.1016/j.envpol.2024.124780_bib36) 2023; 72 Rushton (10.1016/j.envpol.2024.124780_bib24) 2021; 750 Huang (10.1016/j.envpol.2024.124780_bib14) 2020; 740 Mahesh (10.1016/j.envpol.2024.124780_bib21) 2023; 117 Gruening (10.1016/j.envpol.2024.124780_bib37) 2019 Huang (10.1016/j.envpol.2024.124780_bib15) 2019; 659 Smit (10.1016/j.envpol.2024.124780_bib27) 2019; 10 Zhang (10.1016/j.envpol.2024.124780_bib34) 2021; 271 Schildknecht (10.1016/j.envpol.2024.124780_bib25) 2020 Environment (10.1016/j.envpol.2024.124780_bib5) 2017 Stedman (10.1016/j.envpol.2024.124780_bib28) 1990 |
| References_xml | – volume: 72 start-page: 4484 year: 2023 end-page: 4491 ident: bib36 article-title: Performance of a remote sensing device based on a spectroscopic approach for the remote measurement of vehicle emissions publication-title: Transport. Res. Procedia – volume: 145 start-page: 81 year: 2016 end-page: 91 ident: bib22 article-title: A Portable Emissions Measurement System (PEMS) study of NOx and primary NO2 emissions from Euro 6 diesel passenger cars and comparison with COPERT emission factors publication-title: Atmos. Environ. – volume: 12 year: 2017 ident: bib16 article-title: Impact of excess NO publication-title: Environ. Res. Lett. – year: 2023 ident: bib35 article-title: China Mobile Source Environmental Management Annual Report (2023) – volume: 266 year: 2020 ident: bib30 article-title: CO publication-title: J. Clean. Prod. – volume: 60 start-page: 97 year: 2017 end-page: 131 ident: bib6 article-title: Fuel consumption and CO publication-title: Prog. Energy Combust. Sci. – volume: 122 start-page: 171 year: 2015 end-page: 182 ident: bib18 article-title: Evaluation of diesel fleet emissions and control policies from plume chasing measurements of on-road vehicles publication-title: Atmos. Environ. – volume: 10 start-page: 516 year: 2019 ident: bib27 article-title: Measuring on-road vehicle emissions with multiple instruments including remote sensing publication-title: Atmosphere – volume: 117 year: 2023 ident: bib21 article-title: On-road remote sensing of vehicles in Dublin: Measurement and emission factor estimation publication-title: Transport. Res. Transport Environ. – year: 2017 ident: bib5 article-title: Measurement Method and Technical Specification for PEMS Test of Exhaust Pollutants from Heavy-Duty Diesel and Gas Fuelled Vehicles – volume: 17 year: 2022 ident: bib23 article-title: Using snapshot measurements to identify high-emitting vehicles publication-title: Environ. Res. Lett. – volume: 782 year: 2021 ident: bib19 article-title: Characteristics of NOx emission of light-duty diesel vehicle with LNT and SCR system by season and RDE phase publication-title: Sci. Total Environ. – volume: 237 start-page: 133 year: 2018 end-page: 142 ident: bib11 article-title: Emission measurement of diesel vehicles in Hong Kong through on-road remote sensing: performance review and identification of high-emitters publication-title: Environ. Pollut. – volume: 750 year: 2021 ident: bib24 article-title: A novel method for comparing passenger car fleets and identifying high-chance gross emitting vehicles using kerbside remote sensing data publication-title: Sci. Total Environ. – volume: 226 start-page: 692 year: 2019 end-page: 705 ident: bib8 article-title: Exploring the driving factors of carbon dioxide emission from transport sector in Beijing-Tianjin-Hebei region publication-title: J. Clean. Prod. – year: 2020 ident: bib25 article-title: The Saturation of the Infrared Absorption by Carbon Dioxide in the Atmosphere – volume: 169 start-page: 193 year: 2017 end-page: 203 ident: bib9 article-title: Evaluation of emission factors for light-duty gasoline vehicles based on chassis dynamometer and tunnel studies in Shanghai, China publication-title: Atmos. Environ. – volume: 244 year: 2021 ident: bib17 article-title: High-emitting vehicle identification by on-road emission remote sensing with scarce positive labels publication-title: Atmos. Environ. – volume: 252 year: 2021 ident: bib26 article-title: A decade of measuring on-road vehicle emissions with remote sensing in Australia publication-title: Atmos. Environ. – year: 2019 ident: bib37 article-title: Potential of Remote Sensing Devices (RSDs) to Screen Vehicle Emissions: Assessment of RSD Measurement Performance – volume: 70 start-page: 84 year: 2013 end-page: 97 ident: bib7 article-title: Road vehicle emission factors development: a review publication-title: Atmos. Environ. – volume: 259 year: 2020 ident: bib10 article-title: Large eddy simulation of vehicle emissions dispersion: implications for on-road remote sensing measurements publication-title: Environ. Pollut. – start-page: 4029 year: 2017 end-page: 4033 ident: bib32 article-title: Analysis and prediction of vehicle exhaust emission using ANN publication-title: 2017 36th Chinese Control Conference (CCC) – volume: 666 start-page: 337 year: 2019 end-page: 346 ident: bib29 article-title: A study on the CO publication-title: Sci. Total Environ. – volume: 659 start-page: 275 year: 2019 end-page: 282 ident: bib15 article-title: Fuel consumption and emissions performance under real driving: comparison between hybrid and conventional vehicles publication-title: Sci. Total Environ. – volume: 158 year: 2022 ident: bib31 article-title: Super-learner model realizes the transient prediction of CO2 and NOx of diesel trucks: model development, evaluation and interpretation publication-title: Environ. Int. – volume: 61 start-page: 671A year: 1989 end-page: 677A ident: bib2 article-title: IR long-path photometry: a remote sensing tool for automobile emissions publication-title: Anal. Chem. – volume: 81 start-page: 339 year: 2013 end-page: 347 ident: bib3 article-title: New insights from comprehensive on-road measurements of NOx, NO2 and NH3 from vehicle emission remote sensing in London, UK publication-title: Atmos. Environ. – volume: 62 start-page: 1127 year: 2012 end-page: 1133 ident: bib1 article-title: Multispecies remote sensing measurements of vehicle emissions on Sherman Way in Van Nuys, California publication-title: J. Air Waste Manag. Assoc. – volume: 271 year: 2021 ident: bib34 article-title: Evaluating the ammonia emission from in-use vehicles using on-road remote sensing test publication-title: Environ. Pollut. – volume: 49 start-page: 234 year: 2012 end-page: 242 ident: bib4 article-title: Recommendations for the new WLTP cycle based on an analysis of vehicle emission measurements on NEDC and CADC publication-title: Energy Pol. – volume: 252 start-page: 31 year: 2019 end-page: 38 ident: bib13 article-title: Characterisation of diesel vehicle emissions and determination of remote sensing cutpoints for diesel high-emitters publication-title: Environ. Pollut. – year: 1990 ident: bib28 article-title: Analysis of On-Road Remote Sensing as a Tool for Automobile Emissions Control – volume: 182 start-page: 58 year: 2018 end-page: 74 ident: bib12 article-title: Remote sensing of on-road vehicle emissions: mechanism, applications and a case study from Hong Kong publication-title: Atmos. Environ. – volume: 823 year: 2022 ident: bib33 article-title: Detecting candidate high NOx emitting light commercial vehicles using vehicle emission remote sensing publication-title: Sci. Total Environ. – volume: 740 year: 2020 ident: bib14 article-title: Evaluating in-use vehicle emissions using air quality monitoring stations and on-road remote sensing systems publication-title: Sci. Total Environ. – start-page: 561 year: 2016 end-page: 566 ident: bib20 article-title: Remote sensing and artificial neural network estimation of on-road vehicle emissions publication-title: 2016 International Conference on Advanced Robotics and Mechatronics (ICARM) – year: 2017 ident: 10.1016/j.envpol.2024.124780_bib5 – volume: 145 start-page: 81 year: 2016 ident: 10.1016/j.envpol.2024.124780_bib22 article-title: A Portable Emissions Measurement System (PEMS) study of NOx and primary NO2 emissions from Euro 6 diesel passenger cars and comparison with COPERT emission factors publication-title: Atmos. Environ. doi: 10.1016/j.atmosenv.2016.09.021 – volume: 226 start-page: 692 year: 2019 ident: 10.1016/j.envpol.2024.124780_bib8 article-title: Exploring the driving factors of carbon dioxide emission from transport sector in Beijing-Tianjin-Hebei region publication-title: J. Clean. Prod. doi: 10.1016/j.jclepro.2019.04.095 – volume: 117 year: 2023 ident: 10.1016/j.envpol.2024.124780_bib21 article-title: On-road remote sensing of vehicles in Dublin: Measurement and emission factor estimation publication-title: Transport. Res. Transport Environ. – volume: 237 start-page: 133 year: 2018 ident: 10.1016/j.envpol.2024.124780_bib11 article-title: Emission measurement of diesel vehicles in Hong Kong through on-road remote sensing: performance review and identification of high-emitters publication-title: Environ. Pollut. doi: 10.1016/j.envpol.2018.02.043 – volume: 750 year: 2021 ident: 10.1016/j.envpol.2024.124780_bib24 article-title: A novel method for comparing passenger car fleets and identifying high-chance gross emitting vehicles using kerbside remote sensing data publication-title: Sci. Total Environ. doi: 10.1016/j.scitotenv.2020.142088 – volume: 62 start-page: 1127 year: 2012 ident: 10.1016/j.envpol.2024.124780_bib1 article-title: Multispecies remote sensing measurements of vehicle emissions on Sherman Way in Van Nuys, California publication-title: J. Air Waste Manag. Assoc. doi: 10.1080/10962247.2012.699015 – year: 2019 ident: 10.1016/j.envpol.2024.124780_bib37 article-title: Potential of Remote Sensing Devices (RSDs) to Screen Vehicle Emissions: Assessment of RSD Measurement Performance – volume: 244 year: 2021 ident: 10.1016/j.envpol.2024.124780_bib17 article-title: High-emitting vehicle identification by on-road emission remote sensing with scarce positive labels publication-title: Atmos. Environ. doi: 10.1016/j.atmosenv.2020.117877 – volume: 782 year: 2021 ident: 10.1016/j.envpol.2024.124780_bib19 article-title: Characteristics of NOx emission of light-duty diesel vehicle with LNT and SCR system by season and RDE phase publication-title: Sci. Total Environ. doi: 10.1016/j.scitotenv.2021.146750 – volume: 252 start-page: 31 year: 2019 ident: 10.1016/j.envpol.2024.124780_bib13 article-title: Characterisation of diesel vehicle emissions and determination of remote sensing cutpoints for diesel high-emitters publication-title: Environ. Pollut. doi: 10.1016/j.envpol.2019.04.130 – volume: 81 start-page: 339 year: 2013 ident: 10.1016/j.envpol.2024.124780_bib3 article-title: New insights from comprehensive on-road measurements of NOx, NO2 and NH3 from vehicle emission remote sensing in London, UK publication-title: Atmos. Environ. doi: 10.1016/j.atmosenv.2013.09.026 – volume: 10 start-page: 516 year: 2019 ident: 10.1016/j.envpol.2024.124780_bib27 article-title: Measuring on-road vehicle emissions with multiple instruments including remote sensing publication-title: Atmosphere doi: 10.3390/atmos10090516 – start-page: 561 year: 2016 ident: 10.1016/j.envpol.2024.124780_bib20 article-title: Remote sensing and artificial neural network estimation of on-road vehicle emissions – volume: 271 year: 2021 ident: 10.1016/j.envpol.2024.124780_bib34 article-title: Evaluating the ammonia emission from in-use vehicles using on-road remote sensing test publication-title: Environ. Pollut. doi: 10.1016/j.envpol.2020.116384 – volume: 122 start-page: 171 year: 2015 ident: 10.1016/j.envpol.2024.124780_bib18 article-title: Evaluation of diesel fleet emissions and control policies from plume chasing measurements of on-road vehicles publication-title: Atmos. Environ. doi: 10.1016/j.atmosenv.2015.09.048 – volume: 60 start-page: 97 year: 2017 ident: 10.1016/j.envpol.2024.124780_bib6 article-title: Fuel consumption and CO2 emissions from passenger cars in Europe Laboratory versus real-world emissions publication-title: Prog. Energy Combust. Sci. doi: 10.1016/j.pecs.2016.12.004 – volume: 12 year: 2017 ident: 10.1016/j.envpol.2024.124780_bib16 article-title: Impact of excess NOx emissions from diesel cars on air quality, public health and eutrophication in Europe publication-title: Environ. Res. Lett. doi: 10.1088/1748-9326/aa8850 – year: 2023 ident: 10.1016/j.envpol.2024.124780_bib35 – volume: 252 year: 2021 ident: 10.1016/j.envpol.2024.124780_bib26 article-title: A decade of measuring on-road vehicle emissions with remote sensing in Australia publication-title: Atmos. Environ. doi: 10.1016/j.atmosenv.2021.118317 – volume: 182 start-page: 58 year: 2018 ident: 10.1016/j.envpol.2024.124780_bib12 article-title: Remote sensing of on-road vehicle emissions: mechanism, applications and a case study from Hong Kong publication-title: Atmos. Environ. doi: 10.1016/j.atmosenv.2018.03.035 – year: 2020 ident: 10.1016/j.envpol.2024.124780_bib25 – volume: 666 start-page: 337 year: 2019 ident: 10.1016/j.envpol.2024.124780_bib29 article-title: A study on the CO2 and NOx emissions performance of Euro 6 diesel vehicles under various chassis dynamometer and on-road conditions including latest regulatory provisions publication-title: Sci. Total Environ. doi: 10.1016/j.scitotenv.2019.02.144 – start-page: 4029 year: 2017 ident: 10.1016/j.envpol.2024.124780_bib32 article-title: Analysis and prediction of vehicle exhaust emission using ANN – volume: 659 start-page: 275 year: 2019 ident: 10.1016/j.envpol.2024.124780_bib15 article-title: Fuel consumption and emissions performance under real driving: comparison between hybrid and conventional vehicles publication-title: Sci. Total Environ. doi: 10.1016/j.scitotenv.2018.12.349 – volume: 169 start-page: 193 year: 2017 ident: 10.1016/j.envpol.2024.124780_bib9 article-title: Evaluation of emission factors for light-duty gasoline vehicles based on chassis dynamometer and tunnel studies in Shanghai, China publication-title: Atmos. Environ. doi: 10.1016/j.atmosenv.2017.09.020 – volume: 740 year: 2020 ident: 10.1016/j.envpol.2024.124780_bib14 article-title: Evaluating in-use vehicle emissions using air quality monitoring stations and on-road remote sensing systems publication-title: Sci. Total Environ. doi: 10.1016/j.scitotenv.2020.139868 – year: 1990 ident: 10.1016/j.envpol.2024.124780_bib28 – volume: 70 start-page: 84 year: 2013 ident: 10.1016/j.envpol.2024.124780_bib7 article-title: Road vehicle emission factors development: a review publication-title: Atmos. Environ. doi: 10.1016/j.atmosenv.2013.01.006 – volume: 259 year: 2020 ident: 10.1016/j.envpol.2024.124780_bib10 article-title: Large eddy simulation of vehicle emissions dispersion: implications for on-road remote sensing measurements publication-title: Environ. Pollut. doi: 10.1016/j.envpol.2020.113974 – volume: 61 start-page: 671A year: 1989 ident: 10.1016/j.envpol.2024.124780_bib2 article-title: IR long-path photometry: a remote sensing tool for automobile emissions publication-title: Anal. Chem. doi: 10.1021/ac00185a746 – volume: 158 year: 2022 ident: 10.1016/j.envpol.2024.124780_bib31 article-title: Super-learner model realizes the transient prediction of CO2 and NOx of diesel trucks: model development, evaluation and interpretation publication-title: Environ. Int. doi: 10.1016/j.envint.2021.106977 – volume: 49 start-page: 234 year: 2012 ident: 10.1016/j.envpol.2024.124780_bib4 article-title: Recommendations for the new WLTP cycle based on an analysis of vehicle emission measurements on NEDC and CADC publication-title: Energy Pol. doi: 10.1016/j.enpol.2012.05.081 – volume: 17 year: 2022 ident: 10.1016/j.envpol.2024.124780_bib23 article-title: Using snapshot measurements to identify high-emitting vehicles publication-title: Environ. Res. Lett. doi: 10.1088/1748-9326/ac5c9e – volume: 266 year: 2020 ident: 10.1016/j.envpol.2024.124780_bib30 article-title: CO2 emission in transportation sector across 51 countries along the Belt and Road from 2000 to 2014 publication-title: J. Clean. Prod. doi: 10.1016/j.jclepro.2020.122000 – volume: 823 year: 2022 ident: 10.1016/j.envpol.2024.124780_bib33 article-title: Detecting candidate high NOx emitting light commercial vehicles using vehicle emission remote sensing publication-title: Sci. Total Environ. doi: 10.1016/j.scitotenv.2022.153699 – volume: 72 start-page: 4484 year: 2023 ident: 10.1016/j.envpol.2024.124780_bib36 article-title: Performance of a remote sensing device based on a spectroscopic approach for the remote measurement of vehicle emissions publication-title: Transport. Res. Procedia doi: 10.1016/j.trpro.2023.11.295 |
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