Development of land use regression models for nitrogen dioxide, ultrafine particles, lung deposited surface area, and four other markers of particulate matter pollution in the Swiss SAPALDIA regions

Environmental Health

Research

Dirk Keidel¹  Regina Ducret-Stich¹  Nicole Probst-Hensch¹  Christian Schindler¹  Reto Meier¹  Alex Ineichen¹  Marloes Eeftens¹  Nino Künzli¹  Inmaculada Aguilera¹  Harish Phuleria²  Mark Davey³  Ming-Yi Tsai³ 

[1] Department of Epidemiology and Public Health, Swiss Tropical & Public Health Institute, P.O. Box 4002, Socinstrasse 57, Basel, Switzerland;University of Basel, Basel, Switzerland;Department of Epidemiology and Public Health, Swiss Tropical & Public Health Institute, P.O. Box 4002, Socinstrasse 57, Basel, Switzerland;University of Basel, Basel, Switzerland;CESE, Indian Institute of Technology Bombay, Mumbai, India;Department of Epidemiology and Public Health, Swiss Tropical & Public Health Institute, P.O. Box 4002, Socinstrasse 57, Basel, Switzerland;University of Basel, Basel, Switzerland;Department of Environmental & Occupational Health Sciences, University of Washington, Seattle, USA;

关键词: SAPALDIA;    Air pollution;    Long term;    Traffic;    Particulate matter;    Nanoparticles;    Land use regression;    LUR;    NO;    PM;    Absorbance;    PM;    Coarse fraction;    PNC;    LDSA;   

DOI  :  10.1186/s12940-016-0137-9

 received in 2015-09-29, accepted in 2016-04-11,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundLand Use Regression (LUR) is a popular method to explain and predict spatial contrasts in air pollution concentrations, but LUR models for ultrafine particles, such as particle number concentration (PNC) are especially scarce. Moreover, no models have been previously presented for the lung deposited surface area (LDSA) of ultrafine particles. The additional value of ultrafine particle metrics has not been well investigated due to lack of exposure measurements and models.MethodsAir pollution measurements were performed in 2011 and 2012 in the eight areas of the Swiss SAPALDIA study at up to 40 sites per area for NO2 and at 20 sites in four areas for markers of particulate air pollution. We developed multi-area LUR models for biannual average concentrations of PM2.5, PM2.5 absorbance, PM10, PMcoarse, PNC and LDSA, as well as alpine, non-alpine and study area specific models for NO2, using predictor variables which were available at a national level. Models were validated using leave-one-out cross-validation, as well as independent external validation with routine monitoring data.ResultsModel explained variance (R2) was moderate for the various PM mass fractions PM2.5 (0.57), PM10 (0.63) and PMcoarse (0.45), and was high for PM2.5 absorbance (0.81), PNC (0.87) and LDSA (0.91). Study-area specific LUR models for NO2 (R2 range 0.52–0.89) outperformed combined-area alpine (R2 = 0.53) and non-alpine (R2 = 0.65) models in terms of both cross-validation and independent external validation, and were better able to account for between-area variability. Predictor variables related to traffic and national dispersion model estimates were important predictors.ConclusionsLUR models for all pollutants captured spatial variability of long-term average concentrations, performed adequately in validation, and could be successfully applied to the SAPALDIA cohort. Dispersion model predictions or area indicators served well to capture the between area variance. For NO2, applying study-area specific models was preferable over applying combined-area alpine/non-alpine models. Correlations between pollutants were higher in the model predictions than in the measurements, so it will remain challenging to disentangle their health effects.

【 授权许可】

CC BY   
© Eeftens et al. 2016