van Nunen Erik, Vermeulen Roel, Tsai Ming-Yi, Probst-Hensch Nicole, Ineichen Alex, Davey Mark, Imboden Medea, Ducret-Stich Regina, Naccarati Alessio, Raffaele Daniela, Ranzi Andrea, Ivaldi Cristiana, Galassi Claudia, Nieuwenhuijsen Mark, Curto Ariadna, Donaire-Gonzalez David, Cirach Marta, Chatzi Leda, Kampouri Mariza, Vlaanderen Jelle, Meliefste Kees, Buijtenhuijs Daan, Brunekreef Bert, Morley David, Vineis Paolo, Gulliver John, Hoek Gerard
Institute for Risk Assessment Sciences (IRAS), division of Environmental Epidemiology (EEPI), Utrecht University , Utrecht, The Netherlands.
Swiss Tropical and Public Health (TPH) Institute, University of Basel , Basel, Switzerland.
Environ Sci Technol. 2017 Mar 21;51(6):3336-3345. doi: 10.1021/acs.est.6b05920. Epub 2017 Mar 13.
Long-term ultrafine particle (UFP) exposure estimates at a fine spatial scale are needed for epidemiological studies. Land use regression (LUR) models were developed and evaluated for six European areas based on repeated 30 min monitoring following standardized protocols. In each area; Basel (Switzerland), Heraklion (Greece), Amsterdam, Maastricht, and Utrecht ("The Netherlands"), Norwich (United Kingdom), Sabadell (Spain), and Turin (Italy), 160-240 sites were monitored to develop LUR models by supervised stepwise selection of GIS predictors. For each area and all areas combined, 10 models were developed in stratified random selections of 90% of sites. UFP prediction robustness was evaluated with the intraclass correlation coefficient (ICC) at 31-50 external sites per area. Models from Basel and The Netherlands were validated against repeated 24 h outdoor measurements. Structure and model R of local models were similar within, but varied between areas (e.g., 38-43% Turin; 25-31% Sabadell). Robustness of predictions within areas was high (ICC 0.73-0.98). External validation R was 53% in Basel and 50% in The Netherlands. Combined area models were robust (ICC 0.93-1.00) and explained UFP variation almost equally well as local models. In conclusion, robust UFP LUR models could be developed on short-term monitoring, explaining around 50% of spatial variance in longer-term measurements.
流行病学研究需要在精细空间尺度上进行长期超细颗粒物（UFP）暴露估计。基于标准化协议下重复30分钟的监测，针对六个欧洲地区开发并评估了土地利用回归（LUR）模型。在每个地区，即巴塞尔（瑞士）、伊拉克利翁（希腊）、阿姆斯特丹、马斯特里赫特和乌得勒支（“荷兰”）、诺维奇（英国）、萨巴德尔（西班牙）和都灵（意大利），监测了160 - 240个站点，通过监督逐步选择地理信息系统（GIS）预测因子来开发LUR模型。对于每个地区以及所有地区的组合，在90%站点的分层随机选择中开发了10个模型。通过每个地区31 - 50个外部站点的组内相关系数（ICC）评估UFP预测的稳健性。巴塞尔和荷兰的模型根据重复的24小时室外测量进行了验证。各地区内部局部模型的结构和模型R相似，但地区之间有所不同（例如，都灵为38 - 43%；萨巴德尔为25 - 31%）。各地区内预测的稳健性较高（ICC为0.73 - 0.98）。巴塞尔的外部验证R为53%，荷兰为50%。组合区域模型稳健（ICC为0.93 - 1.00），解释UFP变化的程度与局部模型几乎相同。总之，基于短期监测可以开发出稳健的UFP LUR模型，解释长期测量中约50%的空间方差。
