Incorporating Community Knowledge Into Analysis of Air Quality Monitoring Network Data

We conducted a pilot study to explore methods of incorporating qualitative community knowledge into quantitative assessment of temporal and spatial air quality patterns in a neighborhood in Vancouver, British Columbia. We deployed a low-cost sensor network measuring NO, NO₂, and PM_2.5. We used a variety of sources of community knowledge to complement a timeseries analysis and spatial model: a survey by the residents' association; odor reports from a citizen science project; and data from a community mapping event. Community knowledge highlighted, among other sources, areas where cars and heavy-duty vehicles idle, locations of construction, and locations of wood stoves. When creating a "traditional" land use regression (LUR) using easily accessible, and publicly available data sources, and a "community" LUR that uses community-reported air pollution sources, model fit was improved in the community LURs for NO₂ and NO_x. This suggests that community knowledge can provide insight into sources that are not well captured in commonly used, publicly available data sets due to their transient and informal nature. Not all community-reported short-term events corresponded to peaks in monitor data, which could reflect that reports were more correlated with unmeasured pollutants. We suggest that future studies collecting community knowledge on short-term pollution events through community mapping lower barriers to participation (i.e., through hosting a series of drop-in events, providing childcare, or timing any event to coincide with neighborhood-wide events). With these examples, we showcased ways to include community knowledge in quantitative air pollution studies and highlight opportunities to expand on these methods.