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LandUse Regression Models for Ultrafine Particlesin Six European Areas

机译:土地对超细颗粒使用回归模型在六个欧洲地区

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摘要

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 R2 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 R2 was 53% in Baseland 50% in The Netherlands. Combined area models were robust (ICC0.93–1.00) and explained UFP variation almost equally wellas local models. In conclusion, robust UFP LUR models could be developedon short-term monitoring, explaining around 50% of spatial variancein longer-term measurements.
机译:流行病学研究需要在精细的空间尺度上进行长期超细颗粒物(UFP)暴露估计。在遵循标准化协议的30分钟重复监测的基础上,针对六个欧洲地区开发并评估了土地利用回归(LUR)模型。在每个区域;对巴塞尔(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”),诺里奇(英国),萨瓦德尔(西班牙)和都灵(意大利)进行了监测,监测了160-240个站点​​以开发LUR模型通过监督逐步选择GIS预测器。对于每个区域和所有区域,在90%的地点的分层随机选择中开发了10个模型。通过在每个区域31-50个外部站点的类内相关系数(ICC)来评估UFP预测的稳健性。来自巴塞尔和荷兰的模型针对重复的24小时户外测量进行了验证。内部模型的结构和模型R 2 在内部相似,但在区域之间有所不同(例如,都灵38-43%;萨瓦德尔25-31%)。区域内预测的稳健性很高(ICC 0.73–0.98)。在巴塞尔,外部验证R 2 为53%在荷兰占50%。组合区域模型很健壮(ICC0.93–1.00),并且几乎可以很好地解释UFP的变化作为本地模型。总之,可以开发可靠的UFP LUR模型进行短期监测,解释约50%的空间差异在长期测量中。

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