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Page 2 of 24 Luo et al. Carbon Footprints 2025, 4, 14 https://dx.doi.org/10.20517/cf.2024.53

INTRODUCTION
Nitrogen dioxide (NO ), a primary contributor to atmospheric acid deposition, photochemical smog, and
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other pollution issues, has increasingly become a key focus of air pollution control efforts. While emissions
from industrial and domestic sources have steadily declined over time, those from transportation and other
mobile sources have increased, reaching 6.336 million tons and accounting for over 51.3% of total NO
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[1]
emissions . In urban areas, motor vehicles are a major source of NO pollution. Several studies have
2
confirmed that short-term exposure to NO can result in airway hyperresponsiveness and impaired lung
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function, while long-term exposure may compromise immunity and heighten the risk of respiratory
infections . With rapid urbanization, NO pollution from road traffic is expected to rise, posing greater and
[2]
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more widespread risks to public health.
Exposure levels are influenced by both pollutant concentrations in the areas individuals occupy and the
[3,4]
duration of time spent in those locations . Thus, exposure is determined by the spatial and temporal
distribution of both air pollutants and population presence. However, research on traffic-related exposure
risk in China remains in its early stages, particularly regarding environmental justice. It remains unclear
whether vulnerable groups are disproportionately affected or how such disparities evolve over time and
space.

To address this gap, we propose an evaluation framework to assess the exposure risk from traffic-derived
NO pollution, using Shanghai’s Baoshan District as a case study. Beyond pollution simulation, this study
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makes two additional contributions to the pollution modeling literature. First, it introduces a novel
approach that integrates the CALPUFF atmospheric dispersion model with high-resolution mobile phone
signaling data, enabling a dynamic exposure risk assessment that accounts for real-time human mobility
patterns. The fine spatio-temporal resolution of these data allows for more accurate identification of
vulnerable groups, especially during peak traffic hours and in high-emission zones, thereby significantly
improving the accuracy and realism of exposure assessments. Second, from an environmental justice
perspective, we investigate disparities in exposure across gender and age groups using time-resolved,
activity-based population data rather than static census data. This approach uncovers hidden inequalities in
traffic-related NO exposure that are often overlooked in traditional studies. Notably, this research pioneers
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the application of dynamic exposure modeling to assess intra-urban exposure inequities within a Chinese
context, a topic that remains underexplored in current literature. Our findings provide both empirical
evidence and methodological advancement that support the development of equity-oriented, fine-grained
traffic pollution mitigation policies in rapidly urbanizing regions.

BACKGROUND
Application of the CALPUFF model
In current environmental impact assessments and air pollutant dispersion simulation studies, small- to
medium-scale air quality models are widely applied in scenarios such as industrial emissions and urban
pollution control. Commonly used models include AERMOD, ADMS (Atmospheric Dispersion Modelling
System), and CALPUFF. Among these, AERMOD, a steady-state Gaussian model recommended by the U.S.
Environmental Protection Agency (EPA), is extensively employed to simulate pollutant dispersion over
[5,6]
short ranges and relatively flat terrains . Its advantages include a well-established framework,
computational efficiency, and ease of integration with meteorological data. However, AERMOD performs
poorly under conditions of low wind speeds, complex topography, or non-steady-state atmospheric
dynamics. ADMS, developed by the UK’s Cambridge Environmental Research Consultants (CERC), is also
a steady-state model but features pollutant chemistry compared to AERMOD, demonstrating superior
performance in urban micro-meteorological environments . Nevertheless, ADMS is less suitable for long-
[7]