Land Use Regression (LUR) models have been used increasingly for modeling small-scale spatial variation in air pollution concentrations and estimating individual exposure for participants of cohort studies. Within the ESCAPE project, concentrations of PM(2.5), PM(2.5) absorbance, PM(10), and PM(coarse) were measured in 20 European study areas at 20 sites per area. GIS-derived predictor variables (e.g., traffic intensity, population, and land-use) were evaluated to model spatial variation of annual average concentrations for each study area. The median model explained variance (R(2)) was 71% for PM(2.5) (range across study areas 35-94%). Model R(2) was higher for PM(2.5) absorbance (median 89%, range 56-97%) and lower for PM(coarse) (median 68%, range 32- 81%). Models included between two and five predictor variables, with various traffic indicators as the most common predictors. Lower R(2) was related to small concentration variability or limited availability of predictor variables, especially traffic intensity. Cross validation R(2) results were on average 8-11% lower than model R(2). Careful selection of monitoring sites, examination of influential observations and skewed variable distributions were essential for developing stable LUR models. The final LUR models are used to estimate air pollution concentrations at the home addresses of participants in the health studies involved in ESCAPE.

h i g h l i g h t s < LUR models were developed in 36 study areas in Europe using a standardized approach. < NO 2 models explained a large fraction of concentration variability (median R 2 82%). < Local traffic intensity data were important predictors for LUR model development.

Despite the important contribution of traffic sources to urban air quality, relatively few studies have evaluated the effects of traffic-related air pollution on health, such as its influence on the development of asthma and other childhood respiratory diseases. We examined the relationship between traffic-related air pollution and the development of asthmatic/allergic symptoms and respiratory infections in a birth cohort (n approximately 4,000) study in The Netherlands. A validated model was used to assign outdoor concentrations of traffic-related air pollutants (nitrogen dioxide, particulate matter less than 2.5 micro m in aerodynamic diameter, and "soot") at the home of each subject of the cohort. Questionnaire-derived data on wheezing, dry nighttime cough, ear, nose, and throat infections, skin rash, and physician-diagnosed asthma, bronchitis, influenza, and eczema at 2 years of age were analyzed in relation to air pollutants. Adjusted odds ratios for wheezing, physician-diagnosed asthma, ear/nose/throat infections, and flu/serious colds indicated positive associations with air pollutants, some of which reached borderline statistical significance. No associations were observed for the other health outcomes analyzed. Sensitivity analyses generally supported these results and suggested somewhat stronger associations with traffic, for asthma that was diagnosed before 1 year of age. These findings are subject to confirmation at older ages, when asthma can be more readily diagnosed.

PM 2.5 , mass concentration of particles less than 2.5 mm in size; PM 2.5 absorbance, measurement of the blackness of PM 2.5 filters, this is a proxy for elemental carbon, which is the dominant light absorbing substance; PM 10 , mass concentration of particles less than 10 mm in size; PM coarse , mass concentration of the coarse fraction of particles between 2.5 mm and 10 mm in size; RB, regional background; RH, relative humidity; ST, Street; TRAPCA, Traffic-Related Air Pollution and Childhood Asthma; UB, urban background; US EPA, United States Environmental Protection Agency.

In this study, the authors evaluated whether the association between low educational level and increased risk of Alzheimer's disease (AD) and dementia may be explained by occupation-based socioeconomic status (SES). A cohort of 931 nondemented subjects aged > or = 75 years from the Kungsholmen Project, Stockholm, Sweden, was followed for 3 years between 1987 and 1993. A total of 101 incident cases of dementia, 76 involving AD, were detected. Less-educated subjects had an adjusted relative risk of developing AD of 3.4 (95% confidence interval: 2.0, 6.0), and subjects with lower SES had an adjusted relative risk of 1.6 (95% confidence interval: 1.0, 2.5). When both education and SES were introduced into the same model, only education remained significantly associated with AD. Combinations of low education with low or high SES were associated with similar increased risks of AD, but well-educated subjects with low SES were not at high risk. Low SES at 20 years of age, even when SES was high at age 40 or 60 years, was associated with increased risk; however, this increase disappeared when education was entered into the model. In conclusion, the association between low education and increased AD risk was not mediated by adult SES or socioeconomic mobility. This suggests that early life factors may be relevant.