A spatio-temporal land use regression model to assess street-level exposure to black carbon. (November 2020)

• Record Type: Journal Article
• Authors: Van den Bossche, Joris; De Baets, Bernard; Botteldooren, Dick; Theunis, Jan
• Is Part Of: Environmental modelling & software. Volume 133(2020)
• Publication Date: 2020-11
• Subjects: Land use regression -- Spatio-temporal cross-validation -- Mobile measurements -- Opportunistic monitoring -- Black carbon -- Urban air quality; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2020.104837
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Estimation of exposure to air pollution using land use regression (LUR) models often focuses on spatial variation in (annual) average concentration. However, temporal variability is known to be an important factor for exposure. To estimate the short-term street-level exposure to black carbon (BC), we build a spatio-temporal LUR model by including time-dependent variables as predictor variables. We developed and evaluated the model based on data from an opportunistic mobile monitoring campaign in which city employees measured black carbon (BC) during their surveillance tours. Exposure estimates based on the hourly LUR model are more accurate than those based on a fixed site monitoring station or on a spatial LUR model, and can be used to estimate exposure of cyclists or pedestrians to traffic-related pollution based on a GPS track. We demonstrate the potential of building a real-time dynamic pollution map based on unstructured opportunistic measurements to provide personalized exposure information. Graphical abstract: Image 1 Highlights: A spatio-temporal model is developed based on an opportunistic measurement campaign. Different spatial and temporal cross-validation schemes are used to evaluate the model. The model can predict trip exposure more accurately than a fixed site monitoring station or a spatial LUR model. This shows the potential of building a real-time dynamic pollution map from opportunistic measurements.