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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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Abstract
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.
Keywords
PARTICULATE MATTER COMPONENTS, AMBIENT ULTRAFINE PARTICLES, AIR-POLLUTION EXPOSURE, SPATIAL VARIATION, ESCAPE PROJECT, NO2, INTRAURBAN, AREAS, VARIABILITY, ANTWERP, Land use regression, Spatial cross-validation, Mobile measurements, Opportunistic monitoring, Black carbon, Urban air quality

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Citation

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Chicago
Van den Bossche, Joris, Bernard De Baets, Jan Verwaeren, Dick Botteldooren, and Jan Theunis. 2018. “Development and Evaluation of Land Use Regression Models for Black Carbon Based on Bicycle and Pedestrian Measurements in the Urban Environment.” Environmental Modelling & Software 99: 58–69.
APA
Van den Bossche, Joris, De Baets, B., Verwaeren, J., Botteldooren, D., & Theunis, J. (2018). Development and evaluation of land use regression models for black carbon based on bicycle and pedestrian measurements in the urban environment. ENVIRONMENTAL MODELLING & SOFTWARE, 99, 58–69.
Vancouver
1.
Van den Bossche J, De Baets B, Verwaeren J, Botteldooren D, Theunis J. Development and evaluation of land use regression models for black carbon based on bicycle and pedestrian measurements in the urban environment. ENVIRONMENTAL MODELLING & SOFTWARE. Oxford: Elsevier Sci Ltd; 2018;99:58–69.
MLA
Van den Bossche, Joris, Bernard De Baets, Jan Verwaeren, et al. “Development and Evaluation of Land Use Regression Models for Black Carbon Based on Bicycle and Pedestrian Measurements in the Urban Environment.” ENVIRONMENTAL MODELLING & SOFTWARE 99 (2018): 58–69. Print.
@article{8546573,
  abstract     = {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.},
  author       = {Van den Bossche, Joris and De Baets, Bernard and Verwaeren, Jan and Botteldooren, Dick and Theunis, Jan},
  issn         = {1364-8152},
  journal      = {ENVIRONMENTAL MODELLING \& SOFTWARE},
  keyword      = {PARTICULATE MATTER COMPONENTS,AMBIENT ULTRAFINE PARTICLES,AIR-POLLUTION EXPOSURE,SPATIAL VARIATION,ESCAPE PROJECT,NO2,INTRAURBAN,AREAS,VARIABILITY,ANTWERP,Land use regression,Spatial cross-validation,Mobile measurements,Opportunistic monitoring,Black carbon,Urban air quality},
  language     = {eng},
  pages        = {58--69},
  publisher    = {Elsevier Sci Ltd},
  title        = {Development and evaluation of land use regression models for black carbon based on bicycle and pedestrian measurements in the urban environment},
  url          = {http://dx.doi.org/10.1016/j.envsoft.2017.09.019},
  volume       = {99},
  year         = {2018},
}

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