Dataset Information

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

ABSTRACT:

Background

Land use regression (LUR) models have been developed mostly to explain intraurban 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

We aimed 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 (PM; PM2.5, PM2.5 absorbance) by combining standardized measurement data from 17 (PM) and 23 (NO2) ESCAPE (European Study of Cohorts for Air Pollution Effects) 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 R2s were only slightly lower than the model R2 (NO2, 54%; PM2.5, 80%; PM2.5 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 that were excluded from model building (median R2: NO2, 59%; PM2.5, 42%; PM2.5 absorbance, 67%).

Conclusions

Using a large data set 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.

SUBMITTER: Wang M

PROVIDER: S-EPMC4123024 | biostudies-literature | 2014 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Wang Meng M Beelen Rob R Bellander Tom T Birk Matthias M Cesaroni Giulia G Cirach Marta M Cyrys Josef J de Hoogh Kees K Declercq Christophe C Dimakopoulou Konstantina K Eeftens Marloes M Eriksen Kirsten T KT Forastiere Francesco F Galassi Claudia C Grivas Georgios G Heinrich Joachim J Hoffmann Barbara B Ineichen Alex A Korek Michal M Lanki Timo T Lindley Sarah S Modig Lars L Mölter Anna A Nafstad Per P Nieuwenhuijsen Mark J MJ Nystad Wenche W Olsson David D Raaschou-Nielsen Ole O Ragettli Martina M Ranzi Andrea A Stempfelet Morgane M Sugiri Dorothea D Tsai Ming-Yi MY Udvardy Orsolya O Varró Mihaly J MJ Vienneau Danielle D Weinmayr Gudrun G Wolf Kathrin K Yli-Tuomi Tarja T Hoek Gerard G Brunekreef Bert B

Environmental health perspectives 20140502 8

<h4>Background</h4>Land use regression (LUR) models have been developed mostly to explain intraurban 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.<h4>Objectives</h4>We aimed to develop European and regional LUR models and to examine their transferability to areas not used for mod ...[more]

PMID: 24787034

Similar Datasets

Project description:BackgroundOxidative potential (OP) has been suggested to be a more health-relevant metric than particulate matter (PM) mass. Land use regression (LUR) models can estimate long-term exposure to air pollution in epidemiological studies, but few have been developed for OP.ObjectivesWe aimed to characterize the spatial contrasts of two OP methods and to develop and evaluate LUR models to assess long-term exposure to the OP of PM2.5.MethodsThree 2-week PM2.5 samples were collected at 10 regional background, 12 urban background, and 18 street sites spread over the Netherlands/Belgium in 1 year and analyzed for OP using electron spin resonance (OP(ESR)) and dithiothreitol (OP(DTT)). LUR models were developed using temporally adjusted annual averages and a range of land-use and traffic-related GIS variables.ResultsStreet/urban background site ratio was 1.2 for OP(DTT) and 1.4 for OP(ESR), whereas regional/urban background ratio was 0.8 for both. OP(ESR) correlated moderately with OP(DTT) (R2 = 0.35). The LUR models included estimated regional background OP, local traffic, and large-scale urbanity with explained variance (R2) of 0.60 for OP(DTT) and 0.67 for OP(ESR). OP(DTT) and OP(ESR) model predictions were moderately correlated (R2 = 0.44). OP model predictions were moderately to highly correlated with predictions from a previously published PM2.5 model (R2 = 0.37-0.52), and highly correlated with predictions from previously published models of traffic components (R2 > 0.50).ConclusionLUR models explained a large fraction of the spatial variation of the two OP metrics. The moderate correlations among the predictions of OP(DTT), OP(ESR), and PM2.5 models offer the potential to investigate which metric is the strongest predictor of health effects.CitationYang A, Wang M, Eeftens M, Beelen R, Dons E, Leseman DL, Brunekreef B, Cassee FR, Janssen NA, Hoek G. 2015. Spatial variation and land use regression modeling of the oxidative potential of fine particles. Environ Health Perspect 123:1187-1192; http://dx.doi.org/10.1289/ehp.1408916.

Dataset Information

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

Background

Objectives

Methods

Results

Conclusions

Publications

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets