Operational evaluation of the RLINE dispersion model for studies of traffic-related air pollutants. (June 2018) - Milando, Chad W.; Batterman, Stuart A.
Abstract: Exposure to traffic-related air pollutants (TRAP) remains a key public health issue, and improved exposure measures are needed to support health impact and epidemiologic studies and inform regulatory responses. The recently developed Research LINE source model (RLINE), a Gaussian line source dispersion model, has been used in several epidemiologic studies of TRAP exposure, but evaluations of RLINE's performance in such applications have been limited. This study provides an operational evaluation of RLINE in which predictions of NOx, CO and PM2.5 are compared to observations at air quality monitoring stations located near high traffic roads in Detroit, MI. For CO and NOx, model performance was best at sites close to major roads, during downwind conditions, during weekdays, and during certain seasons. For PM2.5, the ability to discern local and particularly the traffic-related portion was limited, a result of high background levels, the sparseness of the monitoring network, and large uncertainties for certain processes (e.g., formation of secondary aerosols) and non-mobile sources (e.g., area, fugitive). Overall, RLINE's performance in near-road environments suggests its usefulness for estimating spatially- and temporally-resolved exposures. The study highlights considerations relevant to health impact and epidemiologic applications, including the importance of selecting appropriate pollutants, using appropriate monitoring approaches, considering prevailing wind directions during study design, and accounting for uncertainty. Highlights: Exposure to traffic-related air pollutants (TRAP) remains a key public health issue. The Research Line-source model (RLINE) is used to predict TRAP in Detroit, MI. Model performance was assessed in an operational evaluation using common metrics. Performance was best near major roads, during downwind conditions, and on weekdays. This variability could cause exposure measurement errors in epidemiological studies.