A Novel Model for Predicting Ambient Ultrafine Particle Number Concentrations in Urban Neighborhoods - Simon, Matthew. (2018)
Abstract: Traffic-related ultrafine particles (UFP; <100 nanometers diameter) are ubiquitous in urban air. Epidemiological evidence of health effects, which is needed to inform risk assessment at the population scale, is limited due to challenges of accurately estimating UFP exposures. Model improvements are needed to better predict UFP concentrations for use in epidemiological studies. This study used a novel methodology to combine mobile and stationary monitoring to inform models of UFP in Chelsea and Boston (MA, USA). The objectives were to build hourly particle number concentration (PNC; a proxy for UFP) models, compare modeled to measured concentrations at residential sites in both study areas, and assess the ability of the models to predict PNC outside the data-collection period. Our results show differences between monitoring strategies: mean one-minute PNC on roads were higher (64,000 and 32,000 particles/cm3 in Boston and Chelsea, respectively) compared to central-site measurements (23,000 and 19,000 particles/cm3) and both were higher than at residences (14,000 and 15,000 particles/cm3). In both study areas, PNC was highest during winter and lowest during summer. The combined mobile-and-stationary modeling approach was an improvement over a mobile-monitoring-only model when compared to ambient PNC at residences: Pearson correlations of modeled and measured natural log-transformed PNC [ln(PNC)] were 10-50% higher with the combined models; and combined models increased model precision by as much as 32%. We also showed that adding a proxy variable to the models for secondary particle formation explained an additional 2-3% of PNC variability. Models overpredicted hourly PNC at residences, but adding an intercept into the models corrected for this, resulting in models with an under/overprediction interquartile range of -3,700-2,900 particles/cm3 (-37-27%) in Boston and -4,100-3,600 particles/cm3 (-29-49%) in Chelsea. Additionally, these models demonstrated they can be applied to time periods outside of the original monitoring window (as much as 9 years) and maintain high correlations with ambient measurements. Sensitivity analyses were conducted to explore model limitations. These results suggest that PNC models informed by both mobile and stationary monitoring reduce exposure error and can be applied to longitudinal epidemiological studies.; Thesis (Ph.D.)--Tufts University, 2017.; Submitted to the Dept. of Civil Engineering.; Advisor: John Durant.; Committee: Doug Brugge, Jonathan Levy, and Elena Naumova.; Keyword: Environmental engineering.