Page 78 - Read Online

Luo et al. Carbon Footprints 2025, 4, 14 https://dx.doi.org/10.20517/cf.2024.53 Page 19 of 24

Compared to other age groups, older adults primarily engage in non-commuting travel, which is
characterized by lower frequency, shorter distances, and briefer durations. On average, the elderly make
2.03 trips per day, slightly fewer than the 2.16 daily trips reported in Shanghai's Fifth Comprehensive Traffic
[58]
Survey . Regarding travel distance and duration, juveniles, young adults, middle-aged adults, and older
adults average 5.4, 7.7, 7.1, and 2.8 km per day, respectively. Corresponding average travel times are 24.4,
33.6, 31.7, and 16.2 min, respectively. Clearly, the elderly travel shorter distances and for less time than all
other age groups.

Additionally, Huang et al. found that the elderly’s activities are largely confined to their neighborhoods,
with walking being the predominant mode of transport, supplemented by public transit and bicycles. When
[59]
feasible, approximately 80% of seniors prioritize walking . This pattern results in a strong preference for
low-grade roads, such as neighborhood and branch roads, and minimal use of expressways and arterial
roads. As a result, the elderly group is exposed to significantly lower levels of traffic-related pollution
compared to other groups.

It is noteworthy that the greatest disparity in exposure risk among age groups occurs during evening peak
hours, with a maximum difference of 2.88 μg/m , followed by daytime (up to 2.27 μg/m ). Differences
3
3
during the morning peak and nighttime are relatively smaller, at 1.37 and 1.25 μg/m respectively. These
3
variations align with age-specific travel behavior. For instance, older adults demonstrate a reverse parabolic
pattern in travel intensity - peaking during the morning and evening and tapering off in the afternoon .
[60]
Uncertainty analysis
This study integrates the CALPUFF dispersion model with dynamic mobile phone signaling data to
enhance the spatiotemporal resolution of exposure assessments. However, several sources of uncertainty
remain and warrant further refinement:

(1) Data Representativeness and Sampling Bias

Although the spatial distribution, gender, and age structure of the mobile phone signaling data are broadly
consistent with the Seventh National Population Census, the sample covers only 23.5% of the city’s
population. This may lead to underrepresentation of low-mobility groups (e.g., older adults or children).
Furthermore, the inference-based classification of gender and age may introduce additional errors.

(2) Uncertainty in NO Concentration Estimation
2

NO concentrations are estimated from NO emissions using hourly NO /NO conversion rates (range:
2
x
x
2
0.3-0.7), which improves the temporal accuracy compared to fixed conversion factors. While this approach
better captures diurnal pollution variations, it remains influenced by meteorological conditions and
photochemical processes, potentially overlooking short-term spikes or atypical weather patterns.
(3) Regional Limitations

This study focuses on Baoshan District, an industrial area on the outskirts of Shanghai characterized by
freight-intensive transportation, which may limit the generalizability of the findings to central urban areas
or non-port cities. Nevertheless, the proposed methodology is highly transferable and offers a replicable
framework for application in other regions.