Development of West-European PM2.5 and NO2 land use regression models incorporating satellite-derived and chemical transport modelling data
Development of West-European PM2.5 and NO2 land use regression models incorporating satellite-derived and chemical transport modelling data
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DOI：
10.1016/j.envres.2016.07.005
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发表时间：
2016-11-01
影响因子：
8.3
通讯作者：
Hoek, Gerard
中科院分区：
文献类型：
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作者：
de Hoogh, Kees;Gulliver, John;Hoek, Gerard
PM2.5和NO2的卫星衍生（SAT）和化学传输模型（CTM）估计越来越多地与土地利用回归（LUR）模型结合使用。我们的目的是比较SAT和CTM数据的贡献LUR PM2.5和NO2模型的性能为Europe.4套模型，所有包括当地的交通和土地利用变量，进行了比较（LUR没有SAT或CTM，与SAT只，与CTM只，与SAT和CTM）。LUR模型是使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AIRBASE网络的PM2.5和NO2地面测量值。包括SAT和SAT+CTM的LUR PM2.5模型解释了测量的PM2.5浓度的60%的空间变化，远高于没有SAT和CTM的LUR模型（adjR（2）：0.33-0.38）。对于NO2，与没有SAT和CTM的模型（adjR 2：0.47-0.51）相比，CTM适度改善了预测（adjR 2：0.58）。这两个监测网络都能够产生模型解释的空间方差在一个大的研究area.SAT和CTM估计的PM2.5和NO2显着提高了性能的高空间分辨率LUR模型在欧洲规模的大型流行病学研究中使用。(C)2016 Elsevier Inc. All rights reserved.
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.