Using Mobile Monitoring to Develop Hourly Empirical Models of Particulate Air Pollution in a Rural Appalachian Community

Abstract

Most empirical air quality models (e.g., land use regression) focus on urban areas. Mobile monitoring for model development offers the opportunity to explore smaller, rural communities — an understudied population. We use mobile monitoring to systematically sample all daylight hours (7 am to 7 pm) to develop empirical models capable of estimating hourly concentrations in Blacksburg, VA, a small town in rural Appalachia (population: 182 635). We collected ∼120 h of mobile monitoring data for particle number (PN) and black carbon (BC). We developed (1) daytime (12-h average) models that approximate long-term concentrations and (2) spatiotemporal models for estimating hourly concentrations. Model performance for the daytime models is consistent with previous fixed-site and short-term sampling studies; adjusted R2 (10-fold CV R2) was 0.80 (0.69) for the PN model and 0.67 (0.58) for the BC model. The spatiotemporal models had comparable performance (10-fold CV R2 for the PN [BC] models: 0.42 [0.25]) to previous mobile monitoring studies that isolate specific time periods. Temporal and spatial model coefficients had similar magnitudes in the spatiotemporal models suggesting both factors are important for exposure. We observed similar spatial patterns in Blacksburg (e.g., roadway gradients) as in other studies in urban areas suggesting similar exposure disparities exist in small, rural communities.