Yann Sellier1, Julien Galineau2, Agnes Hulin3, Fabrice Caini3, Nathalie Marquis2, Vladislav Navel3, Sebastien Bottagisi1, Lise Giorgis-Allemand1, Claire Jacquier2, Remy Slama1, Johanna Lepeule4. 1. Inserm, U823, Institut Albert Bonniot, Team of Environmental Epidemiology Applied to Reproduction and Respiratory Health, Grenoble, France; Université Joseph Fourier, Grenoble, France. 2. Air Lorraine, Nancy, France. 3. ATMO Poitou-Charentes, La Rochelle, France. 4. Inserm, U823, Institut Albert Bonniot, Team of Environmental Epidemiology Applied to Reproduction and Respiratory Health, Grenoble, France; Université Joseph Fourier, Grenoble, France; Exposure, Epidemiology, and Risk Program, Department of Environmental Health, Harvard School of Public Health, Boston, MA 02115, USA. Electronic address: jlepeule@hsph.harvard.edu.
Abstract
BACKGROUND: Spatially resolved exposure models are increasingly used in epidemiology. We previously reported that, although exhibiting a moderate correlation, pregnancy nitrogen dioxide (NO2) levels estimated by the nearest air quality monitoring station (AQMS) model and a geostatistical model, showed similar associations with infant birth weight. OBJECTIVES: We extended this study by comparing a total of four exposure models, including two highly spatially resolved models: a land-use regression (LUR) model and a dispersion model. Comparisons were made in terms of predicted NO2 and particle (aerodynamic diameter<10 μm, PM10) exposure and adjusted association with birth weight. METHODS: The four exposure models were implemented in two French metropolitan areas where 1026 pregnant women were followed as part of the EDEN mother-child cohort. RESULTS: Correlations between model predictions were high (≥ 0.70), except for NO2 between the AQMS and both the LUR (r = 0.54) and dispersion models (r = 0.63). Spatial variations as estimated by the AQMS model were greater for NO2 (95%) than for PM10 (22%). The direction of effect estimates of NO2 on birth weight varied according to the exposure model, while PM10 effect estimates were more consistent across exposure models. CONCLUSIONS: For PM10, highly spatially resolved exposure model agreed with the poor spatial resolution AQMS model in terms of estimated pollutant levels and health effects. For more spatially heterogeneous pollutants like NO2, although predicted levels from spatially resolved models (all but AQMS) agreed with each other, our results suggest that some may disagree with each other as well as with the AQMS regarding the direction of the estimated health effects.
BACKGROUND: Spatially resolved exposure models are increasingly used in epidemiology. We previously reported that, although exhibiting a moderate correlation, pregnancy nitrogen dioxide (NO2) levels estimated by the nearest air quality monitoring station (AQMS) model and a geostatistical model, showed similar associations with infant birth weight. OBJECTIVES: We extended this study by comparing a total of four exposure models, including two highly spatially resolved models: a land-use regression (LUR) model and a dispersion model. Comparisons were made in terms of predicted NO2 and particle (aerodynamic diameter<10 μm, PM10) exposure and adjusted association with birth weight. METHODS: The four exposure models were implemented in two French metropolitan areas where 1026 pregnant women were followed as part of the EDEN mother-child cohort. RESULTS: Correlations between model predictions were high (≥ 0.70), except for NO2 between the AQMS and both the LUR (r = 0.54) and dispersion models (r = 0.63). Spatial variations as estimated by the AQMS model were greater for NO2 (95%) than for PM10 (22%). The direction of effect estimates of NO2 on birth weight varied according to the exposure model, while PM10 effect estimates were more consistent across exposure models. CONCLUSIONS: For PM10, highly spatially resolved exposure model agreed with the poor spatial resolution AQMS model in terms of estimated pollutant levels and health effects. For more spatially heterogeneous pollutants like NO2, although predicted levels from spatially resolved models (all but AQMS) agreed with each other, our results suggest that some may disagree with each other as well as with the AQMS regarding the direction of the estimated health effects.
Authors: Laura A McGuinn; Cavin Ward-Caviness; Lucas M Neas; Alexandra Schneider; Qian Di; Alexandra Chudnovsky; Joel Schwartz; Petros Koutrakis; Armistead G Russell; Val Garcia; William E Kraus; Elizabeth R Hauser; Wayne Cascio; David Diaz-Sanchez; Robert B Devlin Journal: Environ Res Date: 2017-07-29 Impact factor: 6.498
Authors: Inyang Uwak; Natalie Olson; Angelica Fuentes; Megan Moriarty; Jairus Pulczinski; Juleen Lam; Xiaohui Xu; Brandie D Taylor; Samuel Taiwo; Kirsten Koehler; Margaret Foster; Weihsueh A Chiu; Natalie M Johnson Journal: Environ Int Date: 2021-01-25 Impact factor: 9.621
Authors: Marion Ouidir; Lise Giorgis-Allemand; Sarah Lyon-Caen; Xavier Morelli; Claire Cracowski; Sabrina Pontet; Isabelle Pin; Johanna Lepeule; Valérie Siroux; Rémy Slama Journal: Environ Int Date: 2015-08-24 Impact factor: 9.621