Understanding Racial Disparities in Exposure to Traffic-Related Air Pollution: Considering the Spatiotemporal Dynamics of Population Distribution.
Yoo Min ParkMei-Po KwanPublished in: International journal of environmental research and public health (2020)
This study investigates the effect of spatiotemporal distributions of racial groups on disparities in exposure to traffic-related air pollution by considering people's daily movement patterns. Due to human mobility, a residential neighborhood does not fully represent the true geographic context in which people experience racial segregation and unequal exposure to air pollution. Using travel-activity survey data containing individuals' activity locations and time spent at each location, this study measures segregation levels that an individual might experience during the daytime and nighttime, estimates personal exposure by integrating hourly pollution maps and the survey data, and examines the association between daytime/nighttime segregation and exposure levels. The proximity of each activity location to major roads is also evaluated to further examine the unequal exposure. The results reveal that people are more integrated for work in high-traffic areas, which contributes to similarly high levels of exposure for all racial groups during the daytime. However, white people benefit from living in suburbs/exurbs away from busy roads. The finding suggests that policies for building an extensive and equitable public transit system should be implemented together with the policies for residential mixes among racial groups to reduce everyone's exposure to traffic-related air pollution and achieve environmental justice.
Keyphrases
- air pollution
- particulate matter
- lung function
- african american
- obstructive sleep apnea
- public health
- sleep quality
- physical activity
- mental health
- electronic health record
- heavy metals
- risk assessment
- big data
- cross sectional
- dna methylation
- emergency department
- depressive symptoms
- single cell
- cystic fibrosis
- human health
- machine learning
- artificial intelligence
- affordable care act
- water quality