Transferabilityand Generalizability of RegressionModels of Ultrafine Particles in Urban Neighborhoods in the BostonArea

摘要

Land use regression (LUR) models have been used to assess air pollutant exposure, but limited evidence exists on whether location-specific LUR models are applicable to other locations (transferability) or general models are applicable to smaller areas (generalizability). We tested transferability and generalizability of spatial-temporal LUR models of hourly particle number concentration (PNC) for Boston-area (MA, U.S.A.) urban neighborhoods near Interstate 93. Four neighborhood-specific regression models and one Boston-area model were developed from mobile monitoring measurements (34–46 dayseighborhood over one year each). Transferability was tested by applying each neighborhood-specific model to the other neighborhoods; generalizability was tested by applying the Boston-area model to each neighborhood. Both the transferability and generalizability of models were tested with and without neighborhood-specific calibration. Important PNC predictors (adjusted-R² = 0.24–0.43) included wind speed and direction, temperature, highway traffic volume, and distance from the highway edge. Direct model transferability waspoor (R² < 0.17). Locally-calibrated transferred models(R² = 0.19–0.40) and the Boston-area model (adjusted-R² = 0.26, range: 0.13–0.30) performed similarly to neighborhood-specificmodels; however, some coefficients of locally calibrated transferredmodels were uninterpretable. Our results show that transferabilityof neighborhood-specific LUR models of hourly PNC was limited, butthat a general model performed acceptably in multiple areas when calibratedwith local data.