The transferability of NO and NO2 land use regression models between cities and pollutants
The transferability of NO and NO2 land use regression models between cities and pollutants
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DOI：
10.1016/j.atmosenv.2010.10.002
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发表时间：
2011-01-01
影响因子：
5
通讯作者：
Brauer, Michael
中科院分区：
文献类型：
--
作者：
Allen, Ryan W.;Amram, Ofer;Brauer, Michael
在交通相关空气污染的流行病学研究中，土地利用回归(LUR)模型常用于暴露评估。不同城市的模型往往包含相似的预测因素，这表明模型可能可以在具有相似特征的城市之间转移。NO或NO2的LUR模型也可用于估计交通污染混合物中其他污染物的暴露情况。我们评估了NO和NO2 Lur模式在温尼伯、马尼托巴省和埃德蒙顿之间的可转移性。艾伯塔省，以及温尼伯NO和NO2 Lur模型预测苯和甲苯浓度的能力。在这两个城市，Lur模型是基于小川被动采样器在两次14天的采样活动中在50个地点进行测量而开发的，而温尼伯的苯和甲苯浓度是在46个独立地点使用3M#3500被动徽章在14天期间进行测量的。当地校准的Lur模型解释了NO2(R-2：0.81-0.84)比NO(R-2：0.55-0.56)更多的变异性。尽管转移到相反城市的模型在NO2(R-2：0.37-0.52)或NO(R-2：0.24-0.41)方面的表现不如当地校准的模型，但转移的模型解释了比简单的二进制或连续道路接近度量度(R-2)更多的可变性
Land use regression (LUR) models are commonly used for exposure assessment in epidemiologic studies of traffic-related air pollution. Models in different cities often contain similar predictors, suggesting that models may be transferable between cities with similar characteristics. LUR models of NO or NO2 may also be useful for estimating exposure to other pollutants in the traffic pollution mixture. We evaluated the transferability of NO and NO2 LUR models between Winnipeg, Manitoba and Edmonton. Alberta, and the ability of the Winnipeg NO and NO2 LUR models to predict concentrations of benzene and toluene. In both cities, LUR models were developed based on measurements with Ogawa passive samplers at 50 locations during two 14-day sampling campaigns, while benzene and toluene concentrations in Winnipeg were measured at 46 independent locations using 3M #3500 passive badges during a single 14-day period. Locally calibrated LUR models explained more variability in NO2 (R-2: 0.81-0.84) than NO (R-2: 0.55-0.56). Although models transferred to the opposite city did not perform as well as the locally calibrated models for NO2 (R-2: 0.37-0.52) or NO (R-2: 0.24-0.41), the transferred models explained more variability than simple binary or continuous road proximity metrics (R-2