Development and evaluation of land use regression models for black carbon based on bicycle and pedestrian measurements in the urban environment
Development and evaluation of land use regression models for black carbon based on bicycle and pedestrian measurements in the urban environment
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DOI：
10.1016/j.envsoft.2017.09.019
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发表时间：
2018-01-01
影响因子：
4.9
通讯作者：
Theunis, Jan
中科院分区：
文献类型：
--
作者：
Van den Bossche, Joris;De Baets, Bernard;Theunis, Jan
土地利用回归（LUR）模型越来越多地用于流行病学研究，以预测空气污染暴露。然而，在有限数量的地点使用固定测量来建立LUR模型可能会导致对其预测能力的高估。我们利用机会移动监测收集高空间分辨率的数据，建立LUR模型来预测黑碳（BC）的年平均浓度。这些模型解释了BC浓度差异的重要部分。然而，由于输入的不确定性和缺乏能够正确捕捉局部浓度复杂特征的预测变量，整体预测性能仍然很低。我们强调使用适当的交叉验证方案来估计模型的预测性能的重要性。通过使用独立数据进行验证，并在模型构建过程中的变量选择过程中排除这些数据，可以避免过于乐观的性能估计。(C) 2017 Elsevier Ltd.版权所有。
Land use regression (LUR) modelling is increasingly used in epidemiological studies to predict air pollution exposure. The use of stationary measurements at a limited number of locations to build a LUR model, however, can lead to an overestimation of its predictive abilities. We use opportunistic mobile monitoring to gather data at a high spatial resolution to build LUR models to predict annual average concentrations of black carbon (BC). The models explain a significant part of the variance in BC concentrations. However, the overall predictive performance remains low, due to input uncertainty and lack of predictive variables that can properly capture the complex characteristics of local concentrations. We stress the importance of using an appropriate cross-validation scheme to estimate the predictive performance of the model. By using independent data for the validation and excluding those data also during variable selection in the model building procedure, overly optimistic performance estimates are avoided. (C) 2017 Elsevier Ltd. All rights reserved.