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
Swiss Tropical and Public Health Institute, Socinstrasse 57, 4051 Basel, Switzerland; University of Basel, Petersplatz 1, 4001 Basel, Switzerland.
MRC-PHE Centre for Environment and Health, Department of Epidemiology and Biostatistics, Imperial College London, St Mary's Campus, Norfolk Place, W2 1PG London, United Kingdom.
Environ Res. 2016 Nov;151:1-10. doi: 10.1016/j.envres.2016.07.005. Epub 2016 Jul 20.
Satellite-derived (SAT) and chemical transport model (CTM) estimates of PM and NO 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 PM and NO 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: PM and NO ground level measurements from the European Study of Cohorts for Air Pollution Effects (ESCAPE) and from the European AIRBASE network. LUR PM models including SAT and SAT+CTM explained ~60% of spatial variation in measured PM concentrations, substantially more than the LUR model without SAT and CTM (adjR: 0.33-0.38). For NO CTM improved prediction modestly (adjR: 0.58) compared to models without SAT and CTM (adjR: 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 PM and NO significantly improved the performance of high spatial resolution LUR models at the European scale for use in large epidemiological studies.
基于卫星数据（SAT）和化学传输模型（CTM）对颗粒物（PM）和氮氧化物（NO）的估算越来越多地与土地利用回归（LUR）模型结合使用。我们旨在比较SAT和CTM数据对欧洲LUR PM和NO模型性能的贡献。比较了四组模型，所有模型均包括本地交通和土地利用变量（无SAT或CTM的LUR模型、仅含SAT的模型、仅含CTM的模型以及同时包含SAT和CTM的模型）。LUR模型是利用两个监测数据集开发的：来自欧洲空气污染影响队列研究（ESCAPE）和欧洲航空基地网络的PM和NO地面测量数据。包含SAT和SAT+CTM的LUR PM模型解释了实测PM浓度约60%的空间变化，远高于不含SAT和CTM的LUR模型（调整决定系数：0.33 - 0.38）。对于NO，与不含SAT和CTM的模型（调整决定系数：0.47 - 0.51）相比，CTM适度改善了预测效果（调整决定系数：0.58）。两个监测网络都能够生成解释大研究区域空间方差的模型。SAT和CTM对PM和NO的估算显著提高了欧洲尺度上高空间分辨率LUR模型在大型流行病学研究中的性能。
