Page 62 - Read Online

Luo et al. Carbon Footprints 2025, 4, 14 https://dx.doi.org/10.20517/cf.2024.53 Page 3 of 24

range transport modeling and often incurs higher costs due to commercial licensing requirements.

In contrast, CALPUFF employs a non-steady-state Lagrangian puff modeling approach, enabling dynamic
simulation of pollutant dispersion under changing wind directions and speeds. This makes it particularly
suited for simulating long-distance transport, complex terrains, and coastal or land-sea interface pollution
scenarios . The model supports time-varying emission sources, dynamic meteorological inputs, and
[8]
[9]
integration with diverse meteorological models , thereby enabling high-resolution spatiotemporal
simulations. With its non-steady-state modeling capability and flexible input mechanisms, CALPUFF
exhibits strong adaptability and accuracy, positioning it as one of the most promising tools for simulating
complex pollution dispersion scenarios.

In the transportation sector, CALPUFF has been widely applied in four key areas: (1) spatiotemporal
simulation of air pollution; (2) assessment of exposure risks to traffic-related pollutants; (3) investigation
into emission inventories; and (4) analysis of traffic impact scenarios.

Regarding spatiotemporal simulation, research primarily targets pollutants closely associated with vehicular
emissions, including nitrogen oxides (NO x [10,11] , carbon monoxide (CO) , carbon dioxide (CO ) , sulfur
[12]
[13]
2
dioxide (SO ) , and particulate matter (PM) [15,16] ). Most studies examine the spatial and temporal
[14]
2
distribution characteristics of two or three types of pollutants simultaneously.
For assessing exposure risks, a study in the Montreal region of Canada employed a four-stage traffic model,
an emissions model, and CALPUFF to quantify the total emissions associated with residents' daily travel.
The findings revealed that exposure risk increases significantly during outdoor activities, with average
outdoor exposure levels 23%-44% higher than those indoors, regardless of indoor air quality conditions .
[17]
These results underscore the importance of incorporating travel trajectory data when evaluating individual
exposure risks.

In the context of emission inventory development and traffic scenario analysis, it is crucial to allocate
responsibility for emissions and formulate targeted mitigation strategies. The Montreal case study revealed
that improving vehicle performance yields more substantial reductions in traffic-related pollution than
[17]
investments in public transportation or other policy interventions .

Air pollution exposure assessment
Accurate assessment of air pollution exposure requires not only high-resolution measurements of pollutant
concentrations but also a comprehensive understanding of human activity patterns . Previous studies have
[18]
largely focused on static population data, such as census statistics [19,20] and nighttime light imagery [21,22] . At
the macro-static level, exposure risk is typically evaluated using indicators such as air quality
concentration , population exposure intensity , and population-weighted exposure level (PWEL) .
[24]
[25]
[23]
However, air quality concentration alone does not account for the spatial heterogeneity of population
distribution and has been criticized for its theoretical limitations . Population exposure intensity is highly
[26]
sensitive to population density, and substantial regional differences can lead to polarized exposure risk
assessments. By comparison, PWEL is capable of capturing fine-scale spatial variations in exposure risk and
is widely used in urban-level studies in China .
[27]
To overcome the limitations of macro-static assessments that overlook individual mobility, recent studies
have incorporated dynamic population distribution data, such as travel surveys [28,29] and mobile phone signal
data [30,31] , to analyze exposure during various daily activities. For example, Guo et al. used mobile phone