High-resolution mapping of on-road vehicle emissions with real-time traffic datasets based on big data

Wang, Yujia; Wang, Hongbin; Zhang, Bo; Liu, Peng; Wang, Xinfeng; Si, Shuchun; Xue, Likun; Zhang, Qingzhu; Wang, Qiao

doi:https://doi.org/10.5194/egusphere-2024-2791

Preprints

https://doi.org/10.5194/egusphere-2024-2791

Preprints

11 Oct 2024

| 11 Oct 2024

High-resolution mapping of on-road vehicle emissions with real-time traffic datasets based on big data

Yujia Wang, Hongbin Wang, Bo Zhang, Peng Liu, Xinfeng Wang, Shuchun Si, Likun Xue, Qingzhu Zhang, and Qiao Wang

Abstract. On-road vehicle emissions play a crucial role in affecting fine-scale air quality and exposure equity in traffic-dense urban areas. They vary largely in both spatial and temporal scales due to the complex distribution patterns of vehicle types and traffic conditions. With the deployment of traffic cameras and big data approaches, we established a bottom-up model that employed interpolation to obtain a spatially continuous on-road vehicle emission mapping for the main urban area of Jinan, revealing fine-scale gradients and emission hotspots intuitively. The results show that the hourly average emissions of nitrogen oxides, carbon monoxide, hydrocarbons, and fine particulate matters from on-road vehicles in urban Jinan were 345.2, 789.7, 69.5, and 5.4 kg, respectively. The emission intensity varied largely with a factor of up to 3 within 1 km on the same road segment. The unique patterns of road vehicle emissions within urban area were further examined through time series clustering and hotspot analysis. When spatial hotspots coincided with peak hours, emissions were significantly enhanced, making them key targets for traffic pollution control. Based on the established emission model, we predicted that the benefits of vehicle electrification in reducing vehicle emissions could reach 40 %–80 %. Overall, this work provides new methods for developing a high-resolution vehicle emission inventory in urban areas, and offers detailed and accurate emission data and fine spatiotemporal variation patterns in urban Jinan, which are of great implications for air pollution control, traffic management, policy making, and public awareness enhancement.

Received: 05 Sep 2024 – Discussion started: 11 Oct 2024

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Journal article(s) based on this preprint

04 Jun 2025

High-resolution mapping of on-road vehicle emissions with real-time traffic datasets based on big data

Yujia Wang, Hongbin Wang, Bo Zhang, Peng Liu, Xinfeng Wang, Shuchun Si, Likun Xue, Qingzhu Zhang, and Qiao Wang

Atmos. Chem. Phys., 25, 5537–5555, https://doi.org/10.5194/acp-25-5537-2025,https://doi.org/10.5194/acp-25-5537-2025, 2025

Short summary

Yujia Wang, Hongbin Wang, Bo Zhang, Peng Liu, Xinfeng Wang, Shuchun Si, Likun Xue, Qingzhu Zhang, and Qiao Wang

Interactive discussion

Status: closed

CC1:
'Comment on egusphere-2024-2791', Manmeet Singh, 11 Nov 2024

The article "High-resolution mapping of on-road vehicle emissions with real-time traffic datasets based on big data" represents a significant advancement in urban emission inventorying by leveraging real-time data from traffic monitoring networks. This study, conducted in Jinan, China, presents a methodologically robust framework for capturing the spatiotemporal complexity of on-road emissions with unprecedented resolution. By integrating traffic camera networks with advanced big data methods, the authors developed a 50 m by 50 m, high-resolution map of emissions across pollutants, including NOx, CO, HC, and PM2.5.
The study is particularly innovative in its bottom-up approach, employing spatial interpolation methods to address the challenges posed by gaps between monitoring points. The use of Gaussian smoothing and nearest-neighbor interpolation effectively compensates for the spatial discontinuities typical in urban traffic networks. The authors also apply clustering techniques to analyze and interpret diurnal emission patterns, uncovering "hotspot" areas with temporal overlap during peak traffic periods. This attention to dynamic, real-world traffic flows yields an emissions inventory that is not only spatially continuous but also highly reflective of the real-world variability in urban emissions.
One of the key strengths of this paper lies in its interdisciplinary approach, combining atmospheric science, AI, and traffic monitoring technologies to generate actionable insights for urban policymakers. The authors demonstrate how time series clustering and hotspot analysis can aid in targeted interventions, such as the prioritization of NEV deployment in high-emission zones and timing traffic management measures to mitigate peak pollution periods. The scenarios for NEV penetration are also forward-thinking, modeling emissions reductions with increased adoption of electric vehicles (EVs) and revealing reductions of up to 80% in certain pollutants in high-penetration scenarios. Such modeling highlights the potential benefits of decarbonizing transportation sectors within heavily trafficked urban centers.
This work is a valuable addition to the field of atmospheric chemistry and urban environmental management, providing a replicable model for other cities aiming to control air pollution at a fine scale. Future research could further enhance this study by incorporating low-cost sensor data or machine-learning-based gap-filling to expand coverage and reduce dependency on fixed monitoring networks. The paper underscores the transformative potential of AI and big data in developing comprehensive, dynamically updated emissions inventories that support air quality improvements and health outcomes.
Following are areas of improvement:
1. The current reliance on fixed traffic cameras could be complemented by integrating data from mobile low-cost sensors on public vehicles (e.g., buses or taxis) and citizen monitoring devices. This would improve coverage, especially in areas with fewer monitoring points, and provide higher temporal resolution. High-resolution satellite imagery or drone footage could supplement ground-level data, helping to capture emissions near intersections, construction zones, or other areas prone to congestion where cameras may have limited views. Authors of https://arxiv.org/abs/2410.19773 have shown some success in this direction
2. Traditional emission factors could be replaced or augmented by machine learning models that dynamically predict emissions based on vehicle type, traffic density, and weather conditions. This could improve accuracy, especially for rapidly changing urban traffic conditions. Adding localized, real-time meteorological data (e.g., wind speed, temperature) from additional sources such as local weather stations or remote sensors would improve the emission model by accounting for variations in atmospheric dispersion conditions.
3. Expanding vehicle classifications (e.g., electric, plug-in hybrid, fuel cell, diesel) would allow for finer distinctions in emissions and support more precise electrification scenarios. Integrating land use data (e.g., residential, industrial, commercial) could reveal patterns in emissions by area type, helping tailor policies for specific zones, such as low-emission zones near schools or hospitals.

Citation: https://doi.org/10.5194/egusphere-2024-2791-CC1
- AC1: 'Reply on CC1', Xinfeng Wang, 07 Feb 2025
  
  We greatly appreciate Dr. Manmeet Singh for the constructive and helpful comments, the incorporation of which has led to an improved manuscript. We have addressed all the comments and thoroughly revised the manuscript accordingly. Please refer to the attachment for our detailed responses.
  
  Citation: https://doi.org/10.5194/egusphere-2024-2791-AC1
RC1:
'Comment on egusphere-2024-2791', Leonardo Hoinaski, 13 Dec 2024

This article makes a valuable contribution to the field of vehicular emission inventories. It provides detailed, street-level information on traffic flow and emissions for both light- and heavy-duty vehicles. Additionally, it offers new insights into the disaggregation of vehicular emission inventories in situations where such refined data is unavailable. The authors effectively utilized the dataset to analyze weekly variability, peak traffic hours, and the spatial distribution of emission hotspots. Furthermore, the study examines the potential impact of fleet electrification, adding another important dimension to its findings.

Citation: https://doi.org/10.5194/egusphere-2024-2791-RC1
- AC2: 'Reply on RC1', Xinfeng Wang, 07 Feb 2025
  
  Thank you very much for the positive feedback on our work. We are delighted to hear that you consider our paper a valuable contribution to the field of vehicular emission inventories. Your kind recognition of the detailed, street-level information on traffic flow and emissions for both light- and heavy-duty vehicles, as well as the new insight regarding the disaggregation of vehicular emission inventories in situations where such refined data is unavailable, is greatly appreciated. Your acceptance of this contribution motivates us to continue refining and developing such methods.
  We are also pleased that you found our analysis of weekly variability, peak traffic hours, and the spatial distribution of emission hotspots useful. These aspects are essential for understanding emission patterns at a local level and for informing future emission reduction strategies. Moreover, we are grateful for your recognition of our exploration of fleet electrification and its potential impact. This is an area we aim to continue exploring, and your feedback encourages us to further enhance the relevant research work. We are committed to improving and extending our findings in future work.
  Once again, thank you for your valuable comments and encouragement. We are dedicated to further refining our methods and expanding the scope of our research to provide more meaningful insights to the field.
  
  Citation: https://doi.org/10.5194/egusphere-2024-2791-AC2
RC2:
'Comment on egusphere-2024-2791', Anonymous Referee #2, 29 Dec 2024

General comment

The paper reports a methodology and an analysis of high spatial resolution emissions of road vehicles in Jihan (China) based on real-time traffic data. Some future scenarios are discussed related to penetration of electric vehicles. The topic is interesting and useful for planning future mitigation strategies in urban areas. However, some aspects are not clear and a revision will be beneficial.

Specific comments

Lines 35-50. In this discussion, I suggest to mention the methodologies used to determine emissions of vehicles in real operating conditions such as eddy-covariance approach, see foe example, Conte et al. (Environmental Pollution 251, 830-838, 2019).

Section 2.2. It is not clear how the meteo data of ERA are used in this approach even considering that the spatial resolution is much lower than that of calculated emissions. In addition, what are the emission factors used?

Another aspect, section 2.3, not clear if the electrical vehicles. It is assumed that these have zero emissions but this is not true in general. A large fraction (more than 50%) of emissions in non-electric vehicles is due to non-exhaust and this will be present, and likely increased, in electric vehicles. Please justify this choice and if it was not considered the non-exhaust certainly the reduction to new vehicles penetration is largely overestimated.

Lines 159-161. What kind of correction and with what spatial resolution? The same in line 339.

Line 529. Better to say future scenarios, otherwise the sentence is strange.

Citation: https://doi.org/10.5194/egusphere-2024-2791-RC2
- AC3: 'Reply on RC2', Xinfeng Wang, 07 Feb 2025
  
  We greatly appreciate the referee for the valuable and constructive comments, which are helpful to improve the quality of our research work. We have revised the manuscript appropriately and addressed all the comments in our point-by-point responses. Please refer to the attachment for our detailed responses.
  
  Citation: https://doi.org/10.5194/egusphere-2024-2791-AC3

Interactive discussion

Status: closed

CC1:
'Comment on egusphere-2024-2791', Manmeet Singh, 11 Nov 2024

The article "High-resolution mapping of on-road vehicle emissions with real-time traffic datasets based on big data" represents a significant advancement in urban emission inventorying by leveraging real-time data from traffic monitoring networks. This study, conducted in Jinan, China, presents a methodologically robust framework for capturing the spatiotemporal complexity of on-road emissions with unprecedented resolution. By integrating traffic camera networks with advanced big data methods, the authors developed a 50 m by 50 m, high-resolution map of emissions across pollutants, including NOx, CO, HC, and PM2.5.
The study is particularly innovative in its bottom-up approach, employing spatial interpolation methods to address the challenges posed by gaps between monitoring points. The use of Gaussian smoothing and nearest-neighbor interpolation effectively compensates for the spatial discontinuities typical in urban traffic networks. The authors also apply clustering techniques to analyze and interpret diurnal emission patterns, uncovering "hotspot" areas with temporal overlap during peak traffic periods. This attention to dynamic, real-world traffic flows yields an emissions inventory that is not only spatially continuous but also highly reflective of the real-world variability in urban emissions.
One of the key strengths of this paper lies in its interdisciplinary approach, combining atmospheric science, AI, and traffic monitoring technologies to generate actionable insights for urban policymakers. The authors demonstrate how time series clustering and hotspot analysis can aid in targeted interventions, such as the prioritization of NEV deployment in high-emission zones and timing traffic management measures to mitigate peak pollution periods. The scenarios for NEV penetration are also forward-thinking, modeling emissions reductions with increased adoption of electric vehicles (EVs) and revealing reductions of up to 80% in certain pollutants in high-penetration scenarios. Such modeling highlights the potential benefits of decarbonizing transportation sectors within heavily trafficked urban centers.
This work is a valuable addition to the field of atmospheric chemistry and urban environmental management, providing a replicable model for other cities aiming to control air pollution at a fine scale. Future research could further enhance this study by incorporating low-cost sensor data or machine-learning-based gap-filling to expand coverage and reduce dependency on fixed monitoring networks. The paper underscores the transformative potential of AI and big data in developing comprehensive, dynamically updated emissions inventories that support air quality improvements and health outcomes.
Following are areas of improvement:
1. The current reliance on fixed traffic cameras could be complemented by integrating data from mobile low-cost sensors on public vehicles (e.g., buses or taxis) and citizen monitoring devices. This would improve coverage, especially in areas with fewer monitoring points, and provide higher temporal resolution. High-resolution satellite imagery or drone footage could supplement ground-level data, helping to capture emissions near intersections, construction zones, or other areas prone to congestion where cameras may have limited views. Authors of https://arxiv.org/abs/2410.19773 have shown some success in this direction
2. Traditional emission factors could be replaced or augmented by machine learning models that dynamically predict emissions based on vehicle type, traffic density, and weather conditions. This could improve accuracy, especially for rapidly changing urban traffic conditions. Adding localized, real-time meteorological data (e.g., wind speed, temperature) from additional sources such as local weather stations or remote sensors would improve the emission model by accounting for variations in atmospheric dispersion conditions.
3. Expanding vehicle classifications (e.g., electric, plug-in hybrid, fuel cell, diesel) would allow for finer distinctions in emissions and support more precise electrification scenarios. Integrating land use data (e.g., residential, industrial, commercial) could reveal patterns in emissions by area type, helping tailor policies for specific zones, such as low-emission zones near schools or hospitals.

Citation: https://doi.org/10.5194/egusphere-2024-2791-CC1
- AC1: 'Reply on CC1', Xinfeng Wang, 07 Feb 2025
  
  We greatly appreciate Dr. Manmeet Singh for the constructive and helpful comments, the incorporation of which has led to an improved manuscript. We have addressed all the comments and thoroughly revised the manuscript accordingly. Please refer to the attachment for our detailed responses.
  
  Citation: https://doi.org/10.5194/egusphere-2024-2791-AC1
RC1:
'Comment on egusphere-2024-2791', Leonardo Hoinaski, 13 Dec 2024

This article makes a valuable contribution to the field of vehicular emission inventories. It provides detailed, street-level information on traffic flow and emissions for both light- and heavy-duty vehicles. Additionally, it offers new insights into the disaggregation of vehicular emission inventories in situations where such refined data is unavailable. The authors effectively utilized the dataset to analyze weekly variability, peak traffic hours, and the spatial distribution of emission hotspots. Furthermore, the study examines the potential impact of fleet electrification, adding another important dimension to its findings.

Citation: https://doi.org/10.5194/egusphere-2024-2791-RC1
- AC2: 'Reply on RC1', Xinfeng Wang, 07 Feb 2025
  
  Thank you very much for the positive feedback on our work. We are delighted to hear that you consider our paper a valuable contribution to the field of vehicular emission inventories. Your kind recognition of the detailed, street-level information on traffic flow and emissions for both light- and heavy-duty vehicles, as well as the new insight regarding the disaggregation of vehicular emission inventories in situations where such refined data is unavailable, is greatly appreciated. Your acceptance of this contribution motivates us to continue refining and developing such methods.
  We are also pleased that you found our analysis of weekly variability, peak traffic hours, and the spatial distribution of emission hotspots useful. These aspects are essential for understanding emission patterns at a local level and for informing future emission reduction strategies. Moreover, we are grateful for your recognition of our exploration of fleet electrification and its potential impact. This is an area we aim to continue exploring, and your feedback encourages us to further enhance the relevant research work. We are committed to improving and extending our findings in future work.
  Once again, thank you for your valuable comments and encouragement. We are dedicated to further refining our methods and expanding the scope of our research to provide more meaningful insights to the field.
  
  Citation: https://doi.org/10.5194/egusphere-2024-2791-AC2
RC2:
'Comment on egusphere-2024-2791', Anonymous Referee #2, 29 Dec 2024

General comment

The paper reports a methodology and an analysis of high spatial resolution emissions of road vehicles in Jihan (China) based on real-time traffic data. Some future scenarios are discussed related to penetration of electric vehicles. The topic is interesting and useful for planning future mitigation strategies in urban areas. However, some aspects are not clear and a revision will be beneficial.

Specific comments

Lines 35-50. In this discussion, I suggest to mention the methodologies used to determine emissions of vehicles in real operating conditions such as eddy-covariance approach, see foe example, Conte et al. (Environmental Pollution 251, 830-838, 2019).

Section 2.2. It is not clear how the meteo data of ERA are used in this approach even considering that the spatial resolution is much lower than that of calculated emissions. In addition, what are the emission factors used?

Another aspect, section 2.3, not clear if the electrical vehicles. It is assumed that these have zero emissions but this is not true in general. A large fraction (more than 50%) of emissions in non-electric vehicles is due to non-exhaust and this will be present, and likely increased, in electric vehicles. Please justify this choice and if it was not considered the non-exhaust certainly the reduction to new vehicles penetration is largely overestimated.

Lines 159-161. What kind of correction and with what spatial resolution? The same in line 339.

Line 529. Better to say future scenarios, otherwise the sentence is strange.

Citation: https://doi.org/10.5194/egusphere-2024-2791-RC2
- AC3: 'Reply on RC2', Xinfeng Wang, 07 Feb 2025
  
  We greatly appreciate the referee for the valuable and constructive comments, which are helpful to improve the quality of our research work. We have revised the manuscript appropriately and addressed all the comments in our point-by-point responses. Please refer to the attachment for our detailed responses.
  
  Citation: https://doi.org/10.5194/egusphere-2024-2791-AC3

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

AR by Xinfeng Wang on behalf of the Authors (07 Feb 2025) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (08 Mar 2025) by Qiang Zhang

RR by Anonymous Referee #2 (09 Mar 2025)

ED: Publish as is (13 Mar 2025) by Qiang Zhang

AR by Xinfeng Wang on behalf of the Authors (13 Mar 2025) Manuscript

Journal article(s) based on this preprint

04 Jun 2025

High-resolution mapping of on-road vehicle emissions with real-time traffic datasets based on big data

Yujia Wang, Hongbin Wang, Bo Zhang, Peng Liu, Xinfeng Wang, Shuchun Si, Likun Xue, Qingzhu Zhang, and Qiao Wang

Atmos. Chem. Phys., 25, 5537–5555, https://doi.org/10.5194/acp-25-5537-2025,https://doi.org/10.5194/acp-25-5537-2025, 2025

Short summary

Yujia Wang, Hongbin Wang, Bo Zhang, Peng Liu, Xinfeng Wang, Shuchun Si, Likun Xue, Qingzhu Zhang, and Qiao Wang

Supplement

https://doi.org/10.5194/egusphere-2024-2791-supplement

Data sets

Data of High-resolution mapping of on-road vehicle emissions with real-time traffic datasets based on big data Xinfeng Wang and Yujia Wang https://doi.org/10.17632/24t54p6rj2.1

ERA5 hourly data on single levels from 1940 to present H. Hersbach et al. https://doi.org/10.24381/cds.adbb2d47

Yujia Wang, Hongbin Wang, Bo Zhang, Peng Liu, Xinfeng Wang, Shuchun Si, Likun Xue, Qingzhu Zhang, and Qiao Wang

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Short summary

This study established a bottom-up approach that employs real-time traffic flows and interpolation to obtain a spatially continuous on-road vehicle emission mapping for the main urban area of Jinan. The diurnal variation, spatial distribution, and emission hotspots were analyzed with clustering and hotspot analysis, showing unique fine-scale variation characteristics of on-road vehicle emissions. Future scenario analysis demonstrates remarkable benefits of electrification on emission reduction.


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