Elsevier

Atmospheric Environment

Volume 72, June 2013, Pages 10-23
Atmospheric Environment

Development of NO2 and NOx land use regression models for estimating air pollution exposure in 36 study areas in Europe – The ESCAPE project

https://doi.org/10.1016/j.atmosenv.2013.02.037Get rights and content

Abstract

Estimating within-city variability in air pollution concentrations is important. Land use regression (LUR) models are able to explain such small-scale within-city variations. Transparency in LUR model development methods is important to facilitate comparison of methods between different studies. We therefore developed LUR models in a standardized way in 36 study areas in Europe for the ESCAPE (European Study of Cohorts for Air Pollution Effects) project.

Nitrogen dioxide (NO2) and nitrogen oxides (NOx) were measured with Ogawa passive samplers at 40 or 80 sites in each of the 36 study areas. The spatial variation in each area was explained by LUR modelling. Centrally and locally available Geographic Information System (GIS) variables were used as potential predictors. A leave-one out cross-validation procedure was used to evaluate the model performance.

There was substantial contrast in annual average NO2 and NOx concentrations within the study areas. The model explained variances (R2) of the LUR models ranged from 55% to 92% (median 82%) for NO2 and from 49% to 91% (median 78%) for NOx. For most areas the cross-validation R2 was less than 10% lower than the model R2. Small-scale traffic and population/household density were the most common predictors. The magnitude of the explained variance depended on the contrast in measured concentrations as well as availability of GIS predictors, especially traffic intensity data were important. In an additional evaluation, models in which local traffic intensity was not offered had 10% lower R2 compared to models in the same areas in which these variables were offered.

Within the ESCAPE project it was possible to develop LUR models that explained a large fraction of the spatial variance in measured annual average NO2 and NOx concentrations. These LUR models are being used to estimate outdoor concentrations at the home addresses of participants in over 30 cohort studies.

Highlights

► LUR models were developed in 36 study areas in Europe using a standardized approach. ► NO2 models explained a large fraction of concentration variability (median R2 82%). ► Local traffic intensity data were important predictors for LUR model development.

Introduction

Many epidemiological studies have suggested that traffic-related air pollution contributes to health effects associated with long-term exposure to air pollution. Current estimates of the European health impact of air pollution are large (Brunekreef and Holgate, 2002). These estimates are, however, primarily based on exposure response relationships established in studies in North America (especially Pope et al., 2002). There is therefore an urgent need to perform long-term air pollution exposure and health effect studies in Europe. The European Study of Cohorts for Air Pollution Effects (ESCAPE) project was designed to help to fill this gap.

Recent epidemiological research demonstrated the importance of accounting for within-city variability in estimating air pollution concentrations (Jerrett et al., 2005; Hoek et al., 2008; Beelen et al., 2008; Brauer et al., 2003). Several methods can explain such small-scale within-city variations such as geostatistical interpolation, dispersion models, and Land Use Regression (LUR) models. Geostatistical interpolation of monitored concentrations is problematic whenever networks are not dense enough, and therefore fail to capture variability of concentrations over short distances. Dispersion models depend on detailed and spatially resolved input data if they are to capture small-scale spatial variations in air pollutants adequately. LUR modelling uses multiple linear regression to analyse associations between measured pollutant concentrations at a number of monitoring sites and predictor variables such as traffic, land use and topography. LUR models have been shown to be a cost-effective method to explain the spatial variation in air pollution in a number of studies (Hoek et al., 2008; Marshall et al., 2008).

Within the ESCAPE project LUR models for 36 study areas have been developed to estimate outdoor pollutant concentrations at the home addresses of participants in a large number of cohort studies conducted all over Europe. In this paper, we describe the standardized approach we used to develop these models for nitrogen dioxide (NO2) and nitrogen oxides (NOx), and we discuss the performance of the models in terms of explained variance and cross-validation. We also discuss some of the methodological issues occurring in LUR model development. LUR models for particulate matter have been published elsewhere (Eeftens et al., 2012a).

Section snippets

Methods

Annual average NO2 and NOx concentrations from an intensive monitoring campaign and predictor variables were used to develop LUR models in each ESCAPE study area. A standardized approach described in the ESCAPE exposure manual (available on www.escapeproject.eu) was used to develop LUR models in all areas. Predictor variables were derived from Europe-wide and local Geographic Information System (GIS) databases. European-wide GIS data were centrally obtained to facilitate consistency between

Model input data

Descriptive statistics of the air pollution measurements have been reported previously (Cyrys et al., 2012). Tables 2 and 3 describe the mean and range in concentrations for NO2 and NOx. Substantial spatial variations were found which were larger for NOx than for NO2. Within-area contrasts were largest for Catalunya, Barcelona and London-Oxford and smallest for Kaunas, Gyor and Bradford. The number of selected traffic sites differed per area, with a range of 5 traffic sites in Umeå Region and

Discussion

The models developed for all sites explained a large fraction of the measured NO2 and NOx concentrations. This is consistent with the range in model R2 in a recent LUR review paper (NO2: 0.51–0.97 and NOx: 0.73–0.96) (Hoek et al., 2008).

Conclusion

Within the ESCAPE project it was possible to develop LUR models using a standardized approach that explained a large fraction of the spatial variance in measured annual average NO2 and NOx concentrations. Results showed that it is especially important to have accurate local traffic intensity data as predictor variables available and evaluate influential observations in LUR model development. These LUR models are being used to estimate outdoor concentrations at the home addresses of participants

Acknowledgements

The research leading to these results has received funding from the European Community's Seventh Framework Program (FP7/2007–2011) under grant agreement number: 211250. The funding source had no role in the study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the manuscript for publication.

We thank all those who are responsible for data management, model development and supervision in all study areas and especially:

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