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Epidemiological research has demonstrated an adverse relationship between fine particulate matter (PM2.5) and human health. While PM2.5 continues to pose a signficant global health risk, there is still the need to further characterize exposures at the intra-urban scale. Land use regression is a statistical modeling technique which is used to predict air pollution concentrations at high resolution from a limited number of monitoring sites. However, the existing regulatory monitoring networks are typically not dense enough to apply these techniques. We explored the potential of using low-cost PM2.5 sensor networks to overcome the limitations of the existing regulatory monitoring infrastructure, and identified the need to determine sensor-specific correction factors based on the local PM2.5 source profile. Once calibrated, a land use regression model (R2 = 0.89) was developed using the low-cost sensor network (n ≈ 20), alongside several land use and meteorological variables, to predict daily particulate matter concentrations at a 50 m spatial resolution during a two year period within Portland, Oregon. From this model, we assessed the relative strengths of expected sources and sinks of fine particulate matter, focusing specifically on the role that the urban canopy may play in mitigating PM2.5 exposure. This model showed a modest but observable spatial pattern in PM2.5, but attributed the majority of PM2.5 variation to temporal predictors (e.g. ambient background PM2.5, wind speed, temperature). Neither proxies for traffic-related sources, or vegetationrelated sinks were identified as significant predictors of PM2.5. Our research also demonstrated the importance of sensor placement, as a considerably different set of predictors was selected after the inclusion of four additional monitoring

Modeling Spatiotemporal Patterns of PM 2.5 at the Sub-Neighborhood Scale Using Low-Cost Sensor Networks