Agreement of Land Use Regression Models with Personal Exposure Measurements of Particulate Matter and Nitrogen Oxides Air Pollution

Abstract

Land use regression (LUR) models are often used to predict long-term average concentrations of air pollutants. Little is known how well LUR models predict personal exposure. In this study, the agreement of LUR models with measured personal exposure was assessed. The measured components were particulate matter with a diameter smaller than 2.5 μm (PM_2.5), soot (reflectance of PM_2.5), nitrogen oxides (NO_<i>x</i>), and nitrogen dioxide (NO₂). In Helsinki, Utrecht, and Barcelona, 15 volunteers (from semiurban, urban background, and traffic sites) followed prescribed time activity patterns. Per participant, six 96 h outdoor, indoor, and personal measurements spread over three seasons were conducted. Soot LUR models were significantly correlated with measured average outdoor and personal soot concentrations. Soot LUR models explained 39%, 44%, and 20% of personal exposure variability (<i>R</i>²) in Helsinki, Utrecht, and Barcelona. NO₂ LUR models significantly predicted outdoor concentrations and personal exposure in Utrecht and Helsinki, whereas NO_<i>x</i> and PM_2.5 LUR models did not predict personal exposure. PM_2.5, NO₂, and NO_<i>x</i> models were correlated with personal soot, the component least affected by indoor sources. LUR modeled and measured outdoor, indoor, and personal concentrations were highly correlated for all pollutants when data from the three cities were combined. This study supports the use of intraurban LUR models for especially soot in air pollution epidemiology