Performance of multi-city land use regression models for nitrogen dioxide and fine particles

Abstract

BACKGROUND: Land use regression (LUR) models have mostly been developed to explain intra-urban variations in air pollution based on often small local monitoring campaigns. Transferability of LUR models from city to city has been investigated, but little is known about the performance of models based on large numbers of monitoring sites covering a large area. OBJECTIVES: To develop European and regional LUR models and to examine their transferability to areas not used for model development. METHODS: We evaluated LUR models for nitrogen dioxide (NO2) and Particulate Matter (PM2.5, PM2.5 absorbance) by combining standardized measurement data from 17 (PM) and 23 (NO2) ESCAPE study areas across 14 European countries for PM and NO2. Models were evaluated with cross validation (CV) and hold-out validation (HV). We investigated the transferability of the models by successively excluding each study area from model building. RESULTS: The European model explained 56% of the concentration variability across all sites for NO2, 86% for PM2.5 and 70% for PM2.5 absorbance. The HV R(2)s were only slightly lower than the model R(2) (NO2: 54%, PM2.5: 80%, absorbance: 70%). The European NO2, PM2.5 and PM2.5 absorbance models explained a median of 59%, 48% and 70% of within-area variability in individual areas. The transferred models predicted a modest to large fraction of variability in areas which were excluded from model building (median R(2): 59% NO2; 42% PM2.5; 67% PM2.5 absorbance). CONCLUSIONS: Using a large dataset from 23 European study areas, we were able to develop LUR models for NO2 and PM metrics that predicted measurements made at independent sites and areas reasonably well. This finding is useful for assessing exposure in health studies conducted in areas where no measurements were conducted.