Air Pollution Health Literacy among Active Commuters in Hamilton, Ontario.
Reed CiarloniK Bruce NewboldPublished in: International journal of environmental research and public health (2023)
The promotion of active transportation (AT), which has been broadly defined as a physical effort performed by the traveller to produce motion, has been a popular strategy to reduce vehicular emissions, improve air quality, and promote physical activity. However, individuals who engage in AT may incur increased exposure to air pollutants and thus potential health impacts. This research sought to determine how active commuters understand the health risks associated with air pollution during their commutes, and whether they engage in any behaviours to limit exposure. An online survey was adapted from the Environmental Health Literacy framework to assess air pollution health literacy among active commuters in Hamilton, ON, and generated a sample size of 192 AT users. Analyses involved the use of frequency tables and cross-tabulations for the quantitative data, and the Health Belief Model and thematic analysis to interpret the qualitative data. Results revealed that most AT users do not adopt behaviours that would limit air pollution exposure on commutes and exhibited low self-rated knowledge of the health impacts of air pollution exposure. Issues of perceived susceptibility and severity, barriers, cues to action, and self-efficacy all further impacted the likelihood of adopting health protective behaviours. Conclusively, air pollution is an often-neglected consideration among active commuters, with air pollution knowledge predicting the likelihood of behavioural modification.
Keyphrases
- air pollution
- healthcare
- particulate matter
- health information
- physical activity
- lung function
- mental health
- public health
- human health
- electronic health record
- body mass index
- social media
- high resolution
- risk assessment
- depressive symptoms
- heavy metals
- machine learning
- social support
- mass spectrometry
- chronic obstructive pulmonary disease
- single cell
- deep learning