Citizen Mapping: A Tool for Engagement and Improving Neighborhood Air Quality Estimates

Citizen science and activism is often critical to successfully turning the results of local air quality studies into lasting change. One means of increasing citizen involvement in air quality projects is for the community to be involved in the collection of quantitative monitoring data and qualitative environmental data. The value of qualitative data is sometimes overlooked in studies that focus on assessing pollutant concentrations, however, this local knowledge can provide unique insight on community sources of air pollution. In this study, we use multiple land use regression (LUR) models to quantify the impact of including local knowledge, in addition to more traditional geospatial datasets. To build the monitoring dataset, we deploy 13 low-cost sensors in a Vancouver, BC neighborhood with a history of community organizing and an interest in their exposure to air pollution. With this finer scale monitoring network, we collect data on PM2.5, CO, NO, NO2, and O3 for a 6-month period and use the data to create a traditional LUR. Through engagement with community members, including a community workshop to identify and map local air pollution sources such as construction sites and where trucks idle, we will build a second LUR that incorporates community knowledge as a feature. We will compare the results of both models and attempt to quantify the value added by including local knowledge in air quality modeling. We expect that the inclusion of local knowledge will improve our concentration estimates from LUR modeling and increase community engagement around air quality.