Data Download

This platform offers free access to models and data produced by the Environmental Exposures Group


Models and code       Environmental data       Socioeconomic data       Population data


Models and code:

Noise model (TRANEX)

The TRAffic Noise EXposure (TRANEX) model has been designed for spatial assessment of noise level exposures in an epidemiological context (i.e. it is not intended for detailed noise assessments). The model is an adaption of the Calculation of Road Traffic Noise method (CoRTN) (Department of Transport, 1988, HMSO, London), which has been used for strategic noise mapping in the UK. TRANEX was programmed in an open-source geographic information system (PostGIS, GRASS and R) and is freely available via the link on this page. The link provides background information about model development, its use and the script. TRANEX may be used to estimate hourly noise levels (one-hour A-weighted L10 (dB)) and the standard noise metrics LAeq,16hr and Lnight (i.e. European Noise Directive – END) at point locations (e.g. addresses or postcodes).TRANEX has been applied for noise level estimation in the ‘Traffic and Health in London – TRAFFIC’ study.


Modified CNOSSOS-EU model

The BioSHaRE-CNOSSOS-EU is a modified version of the CNOSSOS-EU noise model which simplifies the input data and some algorithms to makes the model suitable for international scale noise exposure modelling for epidemiological studies. The model is programmed in PostGIS, and is frrely available. 

Link to publication:



Environmental data:

NO2 and PM2.5 air pollution grids for Europe, 100m resolution (annual means, ug/m3), 2010

Projection: ETRS 1989 LAEA 52, 10

Reference: de Hoogh K, Gulliver J, et al. 2016. Development of West-European PM2.5 and NO2 land use regression models incorporating satellite-derived and chemical transport modelling data, Environmental Research, Volume 151, November 2016, Pages 1-10, ISSN 0013-9351

Summary: The NO2 and PM2.5 air pollution grids are based on European wide models for NO2 and PM2.5, developed for 2010 which are based on routine air pollution monitoring data (AIRBASE database) incorporating satellite-derived and chemical transport model estimates plus road and land use data. Both NO2 and PM2.5 models explained ~60% of spatial variation in measured NO2 and PM2.5 concentrations.

Link to publication:


NO2 and PM10 air pollution grids for Europe, 100m resolution (annual means, ug/m3), 2005-2007

Projection: ETRS 1989 LAEA 52, 10

Reference: Vienneau D, de Hoogh K, et al. 2013. Western European land use regression incorporating satellite- and ground-based measurements of NO2 and PM10. Environ. Sci. Technol. 47 (23), pp 13555-13564.

Abstract: Land use regression (LUR) models typically investigate within-urban variability in air pollution. Recent improvements in data quality and availability, including satellite-derived pollutant measurements, support fine-scale LUR modeling for larger areas. Here, we describe NO2 and PM10 LUR models for Western Europe (years: 2005–2007) based on >1500 EuroAirnet monitoring sites covering background, industrial, and traffic environments. Predictor variables include land use characteristics, population density, and length of major and minor roads in zones from 0.1 km to 10 km, altitude, and distance to sea. We explore models with and without satellite-based NO2 and PM2.5 as predictor variables, and we compare two available land cover data sets (global; European). Model performance (adjusted R2) is 0.48–0.58 for NO2 and 0.22–0.50 for PM10. Inclusion of satellite data improved model performance (adjusted R2) by, on average, 0.05 for NO2 and 0.11 for PM10. Models were applied on a 100 m grid across Western Europe; to support future research, these data sets are publicly available.

Link to publication:



Socioeconomic data:


Adapted national deprivation incides (Carstairs 2011 and English Index of Deprivation 2010) for rural settings


Abstract: Deprivation indices have been widely used in healthcare research and planning in the United Kingdom. Existing indices, however, are dominated by characteristics of urban populations that may be less relevant in capturing the nature of rural deprivation. We explore if deprivation indices can be modified to make them more sensitive to displaying rural disadvantage in England. The analysis focussed on the 2011 Carstairs Index (Carstairs2011) and the 2010 English Index of Multiple Deprivation (IMD2010). We removed all urban areas as identified by the Office for National Statistics Urban-Rural Area Classifications and mapped the Carstairs2011 and IMD2010 across the remaining rural areas using rural-specific quintiles. Our method was effective in displaying much greater heterogeneity in rural areas than was apparent in the original indices. We received positive feedback from Directors of Public Health who confirmed that the observed patterns mirror their experiences and first-hand knowledge on the ground. Our maps of rural Carstairs2011 and IMD2010 might strengthen the evidence base for rural planning and service provision. The modified deprivation indices, however, were not specifically formulated for rural populations and further work is needed to explore alternative input variables to produce a more rural-specific measure of deprivation.

Link to publication