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1. Development of West-European PM2.5 and NO2 land use regression models incorporating satellite-derived and chemical transport modelling data

Author

de Hoogh, Kees, Gulliver, John, Donkelaar, Aaron van, Martin, Randall V, Marshall, Julian D, Bechle, Matthew J, Cesaroni, Giulia, Pradas, Marta Cirach, Dedele, Audrius, Eeftens, Marloes, Forsberg, Bertil, Galassi, Claudia, Heinrich, Joachim, Hoffmann, Barbara, Jacquemin, Bénédicte, Katsouyanni, Klea, Korek, Michal, Künzli, Nino, Lindley, Sarah J, Lepeule, Johanna, Meleux, Frederik, de Nazelle, Audrey, Nieuwenhuijsen, Mark, Nystad, Wenche, Raaschou-Nielsen, Ole, Peters, Annette, Peuch, Vincent-Henri, Rouil, Laurence, Udvardy, Orsolya, Slama, Rémy, Stempfelet, Morgane, Stephanou, Euripides G, Tsai, Ming Y, Yli-Tuomi, Tarja, Weinmayr, Gudrun, Brunekreef, Bert, Vienneau, Danielle, Hoek, Gerard, dIRAS RA-2, dIRAS RA-I&I RA, Risk Assessment, Imperial College London, Center for Research in Environmental Epidemiology (CREAL), Universitat Pompeu Fabra [Barcelona] (UPF)-Catalunya ministerio de salud, CIBER de Epidemiología y Salud Pública (CIBERESP), Universitat Pompeu Fabra [Barcelona] (UPF), Institute for Advanced Biosciences / Institut pour l'Avancée des Biosciences (Grenoble) (IAB), Centre Hospitalier Universitaire [Grenoble] (CHU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Etablissement français du sang - Auvergne-Rhône-Alpes (EFS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Institut National de l'Environnement Industriel et des Risques (INERIS), Department of Environmental Science [Roskilde] (ENVS), Aarhus University [Aarhus], Institut de Veille Sanitaire (INVS), dIRAS RA-2, dIRAS RA-I&I RA, and Risk Assessment

Subjects
Fine particulate matter, 010504 meteorology & atmospheric sciences, Chemical transport model, Meteorology, Air pollution, nitrogendioxide, 010501 environmental sciences, Land use regression, 01 natural sciences, Biochemistry, Exposure, PM10, AREAS, ABSORBENCY, High spatial resolution, EXPOSURE, Nitrogen dioxide, 0105 earth and related environmental sciences, General Environmental Science, fine particulatematter, NITROGEN DIOXIDE, Spatial modelling, ESCAPE PROJECT, Regression analysis, AIR-POLLUTION, 15. Life on land, EXPOSURE ASSESSMENT, PMCOARSE, Ground level, AIR POLLUTION, SPATIAL MODELLING, 13. Climate action, Climatology, [SDE]Environmental Sciences, Environmental science, Satellite, Spatial variability, FINE PARTICULATE MATTER, AEROSOL OPTICAL DEPTH, Scale (map), MACC REANALYSIS
Abstract

Satellite-derived (SAT) and chemical transport model (CTM) estimates of PM2.5 and NO2 are increasingly used in combination with Land Use Regression (LUR) models. We aimed to compare the contribution of SAT and CTM data to the performance of LUR PM2.5 and NO2 models for Europe.Four sets of models, all including local traffic and land use variables, were compared (LUR without SAT or CTM, with SAT only, with CTM only, and with both SAT and CTM). LUR models were developed using two monitoring data sets: PM2.5 and NO2 ground level measurements from the European Study of Cohorts for Air Pollution Effects (ESCAPE) and from the European AIRBASE network.LUR PM2.5 models including SAT and SAT+CTM explained similar to 60% of spatial variation in measured PM2.5 concentrations, substantially more than the LUR model without SAT and CTM (adjR(2): 0.33-0.38). For NO2 CTM improved prediction modestly (adjR2: 0.58) compared to models without SAT and CTM (adjR2: 0.47-0.51). Both monitoring networks are capable of producing models explaining the spatial variance over a large study area.SAT and CTM estimates of PM2.5 and NO2 significantly improved the performance of high spatial resolution LUR models at the European scale for use in large epidemiological studies. (C) 2016 Elsevier Inc. All rights reserved.

Published
2016

2. Comparing land use regression and dispersion modelling to assess residential exposure to ambient air pollution for epidemiological studies

Author

de Hoogh, Kees, Korek, Michal, Vienneau, Danielle, Keuken, Menno, Kukkonen, Jaakko, Nieuwenhuijsen, Mark J, Badaloni, Chiara, Beelen, Rob, Bolignano, Andrea, Cesaroni, Giulia, Pradas, Marta Cirach, Cyrys, Josef, Douros, John, Eeftens, Marloes, Forastiere, Francesco, Forsberg, Bertil, Fuks, Kateryna, Gehring, Ulrike, Gryparis, Alexandros, Gulliver, John, Hansell, Anna L, Hoffmann, Barbara, Johansson, Christer, Jonkers, Sander, Kangas, Leena, Katsouyanni, Klea, Künzli, Nino, Lanki, Timo, Memmesheimer, Michael, Moussiopoulos, Nicolas, Modig, Lars, Pershagen, Göran, Probst-Hensch, Nicole, Schindler, Christian, Schikowski, Tamara, Sugiri, Dorothee, Teixidó, Oriol, Tsai, Ming-Yi, Yli-Tuomi, Tarja, Brunekreef, Bert, Hoek, Gerard, Bellander, Tom, LS IRAS EEPI ME (Milieu epidemiologie), Dep IRAS, Risk Assessment of Toxic and Immunomodulatory Agents, IRAS RATIA2, IRAS RATIA-SIB, LS IRAS EEPI ME (Milieu epidemiologie), Dep IRAS, Risk Assessment of Toxic and Immunomodulatory Agents, IRAS RATIA2, and IRAS RATIA-SIB

Subjects
Air pollution exposure, Population, Air pollution, medicine.disease_cause, Land use regression, complex mixtures, Dispersion modelling, Exposure, Arbetsmedicin och miljömedicin, Environmental health, medicine, Humans, Statistical dispersion, Least-Squares Analysis, education, lcsh:Environmental sciences, General Environmental Science, lcsh:GE1-350, Air Pollutants, education.field_of_study, Ambient air pollution, Cohort, Occupational Health and Environmental Health, Environmental Exposure, Models, Theoretical, Atmospheric dispersion modeling, Regression, Epidemiologic Studies, 13. Climate action, Environmental science, Female
Abstract

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 PM2.5 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. Keywords: Land use regression, Dispersion modelling, Air pollution, Exposure, Cohort

Published
2014

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