Using advanced dispersion models and mobile monitoring to characterize spatial patterns of ultrafine particles in an urban area

Abstract In urban settings with elevated bridges, buildings, and other complex terrain, the relationship between traffic and air pollution can be highly variable and difficult to accurately characterize. Atmospheric dispersion models are often used in this context, but incorporating background concentrations and characterizing emissions at high spatiotemporal resolution is challenging, especially for ultrafine particles (UFPs). Ambient pollutant monitoring can characterize this relationship, especially when using continuous real-time monitoring. However, it is challenging to quantify local source contributions over background or to characterize spatial patterns across a neighborhood. The goal of this study is to evaluate contributions of traffic to neighborhood-scale air pollution using a combination of regression models derived from mobile UFP monitoring observations collected in Brooklyn, NY and outputs from the Quick Urban & Industrial Complex (QUIC) model. QUIC is a dispersion model that can explicitly take into account the three-dimensional shapes of buildings. The monitoring-based regression model characterized concentration gradients from a major elevated roadway, controlling for real-time traffic volume, meteorological variables, and other local sources. QUIC was applied to simulate dispersion from this same major roadway. The relative concentration decreases with distance from the roadway estimated by the monitoring-based regression model after removal of background and by QUIC were similar. Horizontal contour plots with both models demonstrated non-uniform patterns related to building configuration and source heights. We used the best-fit relationship between the monitoring-based regression model after removal of background and the QUIC outputs ( R 2  = 0.80) to estimate a UFP emissions factor of 5.7 × 10 14 particles/vehicle-km, which was relatively consistent across key model assumptions. Our joint applications of novel techniques for analyzing mobile monitoring data and the advanced dispersion model QUIC provide insight about source contributions above background levels and spatiotemporal air pollution patterns in urban areas.

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