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Luo et al. Carbon Footprints 2025, 4, 14 Carbon Footprints
DOI: 10.20517/cf.2024.53

Original Article Open Access

Concentration distribution and group disparity of

traffic-derived NO exposure in Baoshan District
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Xiao Luo , Siqi Wang , Chao Liu , Qingyan Fu , Huizi Wang , Min Yi , Xi Guo , Qian Wang , Yangjing Fu 1
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College of Transportation Engineering, Tongji University, Shanghai 201804, China.
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College of Architecture and Urban Planning, Tongji University, Shanghai 200092, China.
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State Environmental Protection Key Laboratory of Formation and Prevention of Urban Air Pollution Complex, Shanghai Academy
of Environmental Sciences, Shanghai 200233, China.
4School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.
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Shenzhen Urban Transport Planning Center CO., LTD, Shenzhen 518057, Guangdong, China.
Correspondence to: Qian Wang, Shenzhen Urban Transport Planning Center CO., LTD, Intersection of Hongmu 1st Street and
Hongmu 3rd Street, Longhua District, Shenzhen 518057, Guangdong, China. E-mail: wangqian@sutpc.com; Yangjing Fu, College
of Transportation Engineering, Tongji University, No. 4800, Cao'an Road, Jiading District, Shanghai 201804, China. E-mail:
2331723@tongji.edu.cn
How to cite this article: Luo, X.; Wang, S.; Liu, C.; Fu, Q.; Wang, H.; Yi, M.; Guo, X.; Wang, Q.; Fu, Y. Concentration distribution
and group disparity of traffic-derived NO exposure in Baoshan District. Carbon Footprints 2025, 4, 14. https://dx.doi.org/10.
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20517/cf.2024.53
Received: 3 Dec 2024 First Decision: 18 Mar 2025 Revised: 16 Apr 2025 Accepted: 13 May 2025 Published: 21 May 2025
Academic Editor: Yuli Shan Copy Editor: Fangling Lan Production Editor: Fangling Lan
Abstract
Motor vehicles are a major source of NO emissions, making traffic-related pollution a key target for urban air
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pollution control management. However, research on traffic-related NO exposure risks in China remains nascent,
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particularly regarding spatio-temporal variations and exposure inequities. To support evidence-based public health
policies, it is essential to investigate group disparities in exposure across both spatial and temporal dimensions.
This study utilizes the CALPUFF model and mobile phone signal data to examine the spatio-temporal patterns and
population group disparities in NO exposure within Baoshan District, Shanghai, China. The findings reveal a
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bimodal diurnal pattern, with higher NO exposure levels on weekdays and lower levels on weekends. Areas with
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heavy traffic and high population density, such as port zones and the outer ring expressway, are identified as the
most vulnerable. Furthermore, males and younger age groups experience greater exposure to traffic-related NO ,
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whereas elderly individuals are comparatively less exposed.

Keywords: CALPUFF model, pollution modeling, spatio-temporal variation, NO exposure risk, environmental
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justice

© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0
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