@article{8658791,
  abstract     = {{Black carbon is often used as an indicator for combustion-related air pollution. In urban environments, on-road black carbon concentrations have a large spatial variability, suggesting that the personal exposure of a cyclist to black carbon can heavily depend on the route that is chosen to reach a destination. In this paper, we describe the development of a cyclist routing procedure that minimizes personal exposure to black carbon. Firstly, a land use regression model for predicting black carbon concentrations in an urban environment is developed using mobile monitoring data, collected by cyclists. The optimal model is selected and validated using a spatially stratified cross-validation scheme. The resulting model is integrated in a dedicated routing procedure that minimizes personal exposure to black carbon during cycling. The best model obtains a coefficient of multiple correlation of R = 0.520. Simulations with the black carbon exposure minimizing routing procedure indicate that the inhaled amount of black carbon is reduced by 1.58% on average as compared to the shortest-path route, with extreme cases where a reduction of up to 13.35% is obtained. Moreover, we observed that the average exposure to black carbon and the exposure to local peak concentrations on a route are competing objectives, and propose a parametrized cost function for the routing problem that allows for a gradual transition from routes that minimize average exposure to routes that minimize peak exposure.}},
  articleno    = {{108619}},
  author       = {{Van den Hove, Annelies and Verwaeren, Jan and Van den Bossche, Joris and Theunis, Jan and De Baets, Bernard}},
  issn         = {{0013-9351}},
  journal      = {{ENVIRONMENTAL RESEARCH}},
  keywords     = {{Biochemistry,General Environmental Science,Black carbon,Air quality,Land use regression,Routing,STREET CANYONS,AIR-QUALITY,EXPOSURE,PARTICLES,ULTRAFINE,BICYCLE,AREAS,PM2.5}},
  language     = {{eng}},
  pages        = {{14}},
  title        = {{Development of a land use regression model for black carbon using mobile monitoring data and its application to pollution-avoiding routing}},
  url          = {{http://doi.org/10.1016/j.envres.2019.108619}},
  volume       = {{183}},
  year         = {{2020}},
}

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