posted on 2018-05-15, 00:00authored byMatthew C. Simon, Allison P. Patton, Elena N. Naumova, Jonathan I. Levy, Prashant Kumar, Doug Brugge, John L. Durant
Significant
spatial and temporal variation in ultrafine particle
(UFP; <100 nm in diameter) concentrations creates challenges in
developing predictive models for epidemiological investigations. We
compared the performance of land-use regression models built by combining
mobile and stationary measurements (hybrid model) with a regression
model built using mobile measurements only (mobile model) in Chelsea
and Boston, MA (USA). In each study area, particle number concentration
(PNC; a proxy for UFP) was measured at a stationary reference site
and with a mobile laboratory driven along a fixed route during an
ā¼1-year monitoring period. In comparing PNC measured at 20
residences and PNC estimates from hybrid and mobile models, the hybrid
model showed higher Pearson correlations of natural log-transformed
PNC (r = 0.73 vs 0.51 in Chelsea; r = 0.74 vs 0.47 in Boston) and lower root-mean-square error in Chelsea
(0.61 vs 0.72) but no benefit in Boston (0.72 vs 0.71). All models
overpredicted log-transformed PNC by 3ā6% at residences, yet
the hybrid model reduced the standard deviation of the residuals by
15% in Chelsea and 31% in Boston with better tracking of overnight
decreases in PNC. Overall, the hybrid model considerably outperformed
the mobile model and could offer reduced exposure error for UFP epidemiology.