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Page 10 of 24 Luo et al. Carbon Footprints 2025, 4, 14 https://dx.doi.org/10.20517/cf.2024.53

strong correlations. The paired sample t-test results are 0.997, 0.627, and 0.499, all exceeding the 0.05
significance threshold. These results suggest that there are no significant differences in the age structure of
the three groups, confirming that mobile signaling data can reliably reflect the age structure of different
regions in Shanghai.

Traffic-derived NO pollution exposure assessment
2
This study adopted the population-weighted exposure level (PWEL) as the indicator for assessing exposure
to traffic-derived NO pollution, as proposed by Fu and Kan . PWEL was calculated by combining
[25]
2
predicted NO concentrations with dynamic population distribution data. The CALPUFF model was used
2
to estimate NO concentrations for each standardized 1,000 × 1,000 m grid across different time periods.
2
Hourly gridded population data were obtained from mobile phone records. PWEL was then used to
evaluate the risk of exposure to traffic-derived NO pollution in each grid over various time intervals. The
2
formula is as follows:

E = (P × C)/ P (2)
i
i
i
i
where i denotes the grid index, n is the total number of grids, E represents the potential population
i
exposure in grid i, P is the population of grid i during a certain period, and C is the NO concentration in
i
i
2
grid i. The overall population-weighted exposure level of traffic-derived NO pollution in Shanghai was
2
assessed using Formula (3), where E represents the total potential exposure across the city:
E = E (3)
i

RESULTS
Temporal variation in the NO /NO conversion rate in Shanghai
x
2
The NO /NO conversion rate in Shanghai predominantly falls within the range of 0.3-0.7 [Figure 3], which
x
2
is comparable to the range reported in Seoul (0.4-0.8) . However, it significantly deviates from the value
[38]
suggested by China's Ministry of Ecology and Environment, as previously mentioned. Therefore, estimating
NO concentrations using hourly data from air quality monitoring stations and a ratio-based hourly
2
simulation method is crucial for accurately modeling the temporal and spatial distribution of NO exposure
2
risk.
Spatiotemporal distribution of traffic-related NO
2
There is significant spatiotemporal variability in NO concentrations related to road traffic. Temporally,
2
NO concentrations exhibit a “bimodal” distribution that aligns with the typical daily traffic pattern: the
2
3
highest concentrations occur during morning and evening rush hours, reaching 15.51 and 18.36 μg/m ,
respectively. Concentrations are slightly lower during the daytime, averaging around 12.62 μg/m , and drop
3
3
to their lowest levels at night, averaging just 7.12 μg/m .
Spatially, NO concentrations tend to decrease from the center of the road outward to both sides. Overall,
2
higher concentrations are observed along major roadways, including the east-west Shanghai Ring
Expressway (G1503) and Outer Ring Expressway (S20), as well as the north-south Hutai Road and
Yunchuan Road. Elevated concentrations are also found near warehousing and logistics hubs, such as
Baoyang Road at the confluence of two rivers. Furthermore, areas prone to frequent traffic congestion, such
as expressway toll stations, entrance and exit ramps, and overpasses, exhibit higher pollutant levels
compared to other road segments [Figure 4].