Kees de Hoogh, Michal Korek, Danielle Vienneau, Menno Keuken, Jaakko Kukkonen, Mark J. Nieuwenhuijsen, Chiara Badaloni, Rob Beelen, Andrea Bolignano, Giulia Cesaroni, Marta Cirach Pradas, Josef Cyrys, John Douros, Marloes Eeftens, Francesco Forastiere, Bertil Forsberg, Kateryna Fuks, Ulrike Gehring, Alexandros Gryparis, John Gulliver, Anna L Hansell, Barbara Hoffmann, Christer Johansson, Sander Jonkers, Leena Kangas, Klea Katsouyanni, Nino Künzli, Timo Lanki, Michael Memmesheimer, Nicolas Moussiopoulos, Lars Modig, Göran Pershagen, Nicole Probst-Hensch, Christian Schindler, Tamara Schikowski, Dorothee Sugiri, Oriol Teixidó, Ming-Yi Tsai, Tarja Yli-Tuomi, Bert Brunekreef, Gerard Hoek, Tom Bellander
- Background
Land-use regression (LUR) and dispersion models (DM) are commonly used for estimating individual air pollution exposure in population studies. Few comparisons have however been made of the performance of these methods.
Objectives
Within the European Study of Cohorts for Air Pollution Effects (ESCAPE) we explored the differences between LUR and DM estimates for NO2, PM10 and PM2.5.
Methods
The ESCAPE study developed LUR models for outdoor air pollution levels based on a harmonised monitoring campaign. In thirteen ESCAPE study areas we further applied dispersion models. We compared LUR and DM estimates at the residential addresses of participants in 13 cohorts for NO2; 7 for PM10 and 4 for PM2.5. Additionally, we compared the DM estimates with measured concentrations at the 20–40 ESCAPE monitoring sites in each area.
Results
The median Pearson R (range) correlation coefficients between LUR and DM estimates for the annual average concentrations of NO2, PM10 and PM2.5Background
Land-use regression (LUR) and dispersion models (DM) are commonly used for estimating individual air pollution exposure in population studies. Few comparisons have however been made of the performance of these methods.
Objectives
Within the European Study of Cohorts for Air Pollution Effects (ESCAPE) we explored the differences between LUR and DM estimates for NO2, PM10 and PM2.5.
Methods
The ESCAPE study developed LUR models for outdoor air pollution levels based on a harmonised monitoring campaign. In thirteen ESCAPE study areas we further applied dispersion models. We compared LUR and DM estimates at the residential addresses of participants in 13 cohorts for NO2; 7 for PM10 and 4 for PM2.5. Additionally, we compared the DM estimates with measured concentrations at the 20–40 ESCAPE monitoring sites in each area.
Results
The median Pearson R (range) correlation coefficients between LUR and DM estimates for the annual average concentrations of NO2, PM10 and PM2.5 were 0.75 (0.19–0.89), 0.39 (0.23–0.66) and 0.29 (0.22–0.81) for 112,971 (13 study areas), 69,591 (7) and 28,519 (4) addresses respectively. The median Pearson R correlation coefficients (range) between DM estimates and ESCAPE measurements were of 0.74 (0.09–0.86) for NO2; 0.58 (0.36–0.88) for PM10 and 0.58 (0.39–0.66) for PM2.5.
Conclusions
LUR and dispersion model estimates correlated on average well for NO2 but only moderately for PM10 and PM2.5, with large variability across areas. DM predicted a moderate to large proportion of the measured variation for NO2 but less for PM10 and PM2.5.…
