Application of plume analysis to build land use regression models from mobile sampling to improve model transferability

- We observed spatial gradients of polycyclic aromatic hydrocarbons (PB-PAH) and BC.
- BC and PB-PAH variability is driven by plumes from high emitting vehicles.
- Two-layer models (plume + background) were developed to describe spatial patterns.
- The model plume layer is transferable to an independent holdout dataset.

Mobile monitoring of traffic-related air pollutants was conducted in Pittsburgh, PA. The data show substantial spatial variability of particle-bound polycyclic aromatic hydrocarbons (PB-PAH) and black carbon (BC). This variability is driven in large part by pollutant plumes from high emitting vehicles (HEVs). These plumes contribute a disproportionately large fraction of the near-road exposures of PB-PAH and BC. We developed novel statistical models to describe the spatial patterns of PB-PAH and BC exposures. The models consist of two layers: a plume layer to describe the contributions of high emitting vehicles using a near-roadway kernel, and an urban-background layer that predicts the spatial pattern of other sources using land use regression. This approach leverages unique information content of highly time resolved mobile monitoring data and provides insight into source contributions. The two-layer model describes 76% of observed PB-PAH variation and 61% of BC variation. On average, HEVs contribute at least 32% of outdoor PB-PAH and 14% of BC. The transferability of the models was examined using measurements from 36 hold-out validation sites. The plume layer performed well at validation sites, but the background layer showed little transferability due to the large difference in land use between the city and outer suburbs.

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