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Land use regression (LUR) models have become popular to explain the spatial variation of air pollution concentrations. Independent evaluation is important. We developed LUR models for nitrogen dioxide (NO2) using measurements conducted at 144 sampling sites in The Netherlands. Sites were randomly divided into training data sets with a size of 24, 36, 48, 72, 96, 108, and 120 sites. LUR models were evaluated using (1) internal “leave-one-out-cross-validation (LOOCV)” within the training data sets and (2) external “hold-out” validation (HV) against independent test data sets. In addition, we calculated Mean Square Error based validation R2s. The mean adjusted model and LOOCV R2 slightly decreased from 0.87 to 0.82 and 0.83 to 0.79, respectively, with an increasing number of training sites. In contrast, the mean HV R2 was lowest (0.60) with the smallest training sets and increased to 0.74 with the largest training sets. Predicted concentrations were more accurate in sites with out of range values for predict...

There are currently no epidemiological studies on health effects of long-term exposure to ultrafine particles (UFP), largely because data on spatial exposure contrasts for UFP is lacking. The objective of this study was to develop a land use regression (LUR) model for UFP in the city of Amsterdam. Total particle number concentrations (PNC), PM10, PM2.5, and its soot content were measured directly outside 50 homes spread over the city of Amsterdam. Each home was measured during one week. Continuous measurements at a central urban background site were used to adjust the average concentration for temporal variation. Predictor variables (traffic, address density, land use) were obtained using geographic information systems. A model including the product of traffic intensity and the inverse distance to the nearest road squared, address density, and location near the port explained 67% of the variability in measured PNC. LUR models for PM2.5, soot, and coarse particles (PM10, PM2.5) explained 57%, 76%, and 37% of the variability in measured concentrations. Predictions from the PNC model correlated highly with predictions from LUR models for PM2.5, soot, and coarse particles. A LUR model for PNC has been developed, with similar validity as previous models for more commonly measured pollutants.

Through analytical characterization, and toxicity end epidemiologic studies, the health implications of airborne particulate matter are slowly being elucidated.

The validity of traffic intensity near the home as an estimate for the personal long-term exposure to traffic-related air pollution in an adult population was tested. Personal and near-home outdoor exposure to PM2.5, soot, NO, NO2, and NOx was monitored four to five times during 48 h periods in older adults. A group of 23 participants lived in high traffic intensity streets (>10000 vehicles/(24, h)), and 22 lived in low traffic intensity streets. The relation between average personal exposure and traffic intensity at the residential address was explored by taking indoor sources into account. Large differences in the measured outdoor concentrations between locations in high traffic and low traffic intensity streets were found for soot (68%), NO (127%), and NOx (35%). Differences were smaller for PM2.5 (14%) and NO2 (22%). Slightly elevated ratios were found for personal exposure to soot (1.15; 95% confidence interval (CI), 1.01-1.30) when comparing adults living in high traffic intensity streets with adults living in low traffic intensity streets. For NO, increased personal exposure (1.16) was seen for the same comparison, but this difference failed to reach statistical significance (CI, 0.80-1.66). Traffic intensity on the street of residence predicted personal exposure to soot but not to PM2.5 or nitrogen oxides. Traffic intensity may not correlate well to personal exposure and accordingly substantial misclassification of exposure may occur when traffic intensity is used as an exposure indicator in epidemiological studies. Time spent in traffic and spending time outdoors were associated with increased personal exposure of soot and PM2.5, but not NO,.

Land use regression (LUR) has been successfully used to assess the intraurban variability of air pollution. In the INMA (Environment and Childhood) Study, ambient nitrogen oxides (NO(x) and NO(2)) and aromatic hydrocarbons (BTEX) were measured at 57 sampling sites in Sabadell (northeast Spain). Multiple regression models were developed to predict residential outdoor concentrations in a cohortof pregnantwomen (n = 657), using geographic data as predictor variables. The models accounted for 68 and 69% of the variance in NO(x) and NO(2) levels, respectively, with four predictor variables (altitude, land coverage, and two road length indicators). These percentages of explained variability could be further improved by replacing the two road length indicators with an ordinal indicator (road type). To our knowledge, this is the first study using LUR to assess the intraurban variability of BTEX in Europe, with a model including altitude and source-proximity variables that explained 74% of the variance in BTEX levels. These models will be used to study the association between prenatal exposure to air pollution and adverse pregnancy outcomes and early childhhod effects in the cohort.