Project at a Glance

Title: Sources of On-Road Vehicle Emissions and their Impacts on Respiratory Disease Symptoms in California

Principal Investigator / Author(s): Su, Guanquan (Jason)

Contractor: UC Berkeley

Contract Number: 19RD004

Research Program Area: Health & Exposure

Topic Areas: Health Effects of Air Pollution, Vulnerable Populations

Research Summary:

Regulations and technological upgrades have resulted in a steady decline in vehicle tailpipe emissions in California. However, despite the recent reductions in tailpipe emissions, some communities continue to be disproportionately exposed due to their proximity to heavily trafficked freeways and vehicular congestion, as well as their proximity to area-based traffic related exposure such as shopping centers, parking lots and distribution centers. Several studies have been published showing that communities exposed to these on-road emissions are at greater risk for respiratory disease exacerbations. Another important on-road source is non-exhaust emissions from tire and brake wear, which will become increasingly important as the benefits of implementation of tailpipe emission regulations become more widespread. The objective of this research is to quantify the relationship between on-road vehicle emissions including on-road non-exhaust pollutants and sub-acute respiratory disease symptoms. The health endpoint studied, sub-acute respiratory disease symptoms, is represented in this project by the use of short-acting beta agonist (SABA) for the acute relief of respiratory disease symptoms, such as asthma and chronic obstructive pulmonary disease (COPD). Health data was collected from June 2012 to May 2019 for 2,870 patients, in the major metropolitan areas of CA. The number of SABA uses per person per day will be used as the analysis outcome. Daily land use regression (LUR) models for pollutants and trace metals will be developed and analyzed with the health data using traditional environmental epidemiology models (e.g., linear mixed models) compared with advanced machine learning models (e.g., random forest models). The results can help CARB identify communities with disproportionate exposures and identify sources of these exposures for possible mitigations strategies. In addition, this information will be used to add respiratory disease symptoms as a health endpoint in CARB's quantitative health impacts analysis.

For questions regarding this research project, including available data and progress status, contact: Research Division staff at (916) 445-0753

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