Relationships among particle number, surface area, and respirable mass concentrations in automotive engine...

This study investigated the relationships between particle number, surface area, and respirable mass concentration measured simultaneously in a foundry and an automotive engine machining and assembly center. Aerosol concentrations were measured throughout each plant with a condensation particle counter for number concentration, a diffusion charger for active surface area concentration, and an optical particle counter for respirable mass concentration. At selected locations, particle size distributions were characterized with the optical particle counter and an electrical low pressure impactor. Statistical analyses showed that active surface area concentration was correlated with ultrafine particle number concentration and weakly correlated with respirable mass concentration. Correlation between number and active surface area concentration was stronger during winter (R2 = 0.6 for both plants) than in the summer (R2 = 0.38 and 0.36 for the foundry and engine plant respectively). The stronger correlation in winter was attributed to use of direct-fire gas fired heaters that produced substantial numbers of ultrafine particles with a modal diameter between 0.007 and 0.023 mu m. These correlations support findings obtained through theoretical analysis. Such analysis predicts that active surface area increasingly underestimates geometric surface area with increasing particle size, particularly for particles larger than 100 nm. Thus, a stronger correlation between particle number concentration and active surface area concentration is expected in the presence of high concentrations of ultrafine particles. In general, active surface area concentration may be a concentration metric that is distinct from particle number concentration and respirable mass concentration. For future health effects or toxicological studies involving nano-materials or ultrafine aerosols, this finding needs to be considered, as exposure metrics may influence data interpretation.