Comprehensive review of intake fraction methods for assessing traffic-related air pollution exposure: insights,
Shuyan Meng1, Ling Qi1, Pengpeng Wu1
1School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing, China.
Background:
Traffic-related air pollution (TRAP) poses significant risks to human health, particularly in urban areas with high traffic volumes. Intake fraction (iF) quantifies the relationship between emissions and exposure, defined as the ratio of the total inhalation increment of all exposed individuals in a target population to the emissions from specific pollution sources over a certain period.
Objective:
The overarching objective of this study is to unravel the underlying value and significance of the iF method in evaluating TRAP exposure risks, while also exploring its future development trajectories and potential avenues for application.
Methods:
We conducted a comprehensive review of iF to assess TRAP exposure. We employed a search strategy to identify and analyze literature on iF methods related to TRAP exposure across academic databases covering the period from 2002 to 2024. After deduplication, title and abstract screening, and full-text review, we ultimately included 25 studies on iF related to TRAP.
Results:
We classified the measurement methods of iF into four types: simple estimation method, dispersion simulation method, numerical simulation method, and exposure monitoring method. We found orders of magnitude of differences in iF among studies. Population density, pollutant concentration, and breathing rate explain a significant portion of the variations. iF values of nitrogen oxides (NOx), carbon monoxide (CO), and fine particulate matter (PM2.5) are higher than those of diesel particulate matter (DPM), ultrafine particles (UFP), and benzene. Compared to power plants, TRAP has higher iF values, emphasizing the control priority of TRAP. Future research should expand to under-researched regions, strengthen investigations on UFP and secondary pollutants, and refine iF calculation methods using high-resolution and mobility data.
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