Designing More Effective Air Quality Advisories

Background: There is increasing concern about the impact of air quality on human health in urban environments and how to best reduce impacts through public policies. An NSF Biocomplexity Project "Feedbacks between Urban Systems and the Environment" (FUSE), led by Portland State University, studies the human feedbacks and responses to air quality and heat advisories through data collected in city-wide phone surveys in Portland, OR and Houston, TX. On days when ozone levels are predicted to exceed air quality standards, regulatory agencies issue air quality advisories which ask residents to reduce certain air polluting behaviors such as driving and lawn mowing. However, there have been very few studies of the effectiveness of these systems. Previous work documented that in both Portland and Houston, around ~10% of the population respond to voluntary advisories (Semenza et. al, 2008). In the testbed cities, the survey also asked respondents questions about their personal emissions-related behavior such as driving, mowing, use of household products, etc., as well as how that behavior was effected by an air quality advisory. Objectives: In this project, the survey data is used to accomplish three major tasks. Firstly, to estimate emissions based on the survey responses. For example, estimating VOC emissions based on reported mowing behavior (size of lawn, frequency of mowing, and type of mower). Secondly, to compare reported behavior during normal days to behavior during air advisory days. And finally, to compare the emissions estimates to demographic data (such as population density) to find any trends. For example, can variations in emission patterns be predicted using a demographic characteristic such as housing density? Currently the EPA and the Oregon DEQ estimate many types of emissions on a per capita basis. These per capita emissions numbers are generally not adjusted for any local demographic information. One 3 additional use of this research could be to improve current emissions estimation methods. Methods: Survey responses to questions about emissions related behavior were used as activity factors in emissions calculations. The emission estimations were then tested against demographics such as housing density. For the purpose of graphical representation, housing density was broken into four quartiles to represent four different levels of density. For statistical analysis housing density was not categorized. Results: Emissions behavior, in several cases, appears to be linked to demographic features such as housing density. Per capita lawn care hydrocarbon emissions, vehicle emissions, and several consumer product category emissions trend higher towards lower housing density in both Portland and Houston. Conclusions: The results of this research project could be used to tailor air quality advisories to better target their audience with appropriate messages, thereby more effectively improving air quality during potentially hazardous air quality conditions. Understanding demographic elements to emissions estimation could be used by environmental agencies to produce better emission estimations.