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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/15455

Title: Modeling temporal and spatial variability of traffic-related air pollution: Hourly land use regression models for black carbon
Authors: DONS, Evi
Van Poppel, Martine
WETS, Geert
Issue Date: 2013
Citation: ATMOSPHERIC ENVIRONMENT, 74, p. 237-246
Abstract: Land use regression (LUR) modeling is a statistical technique used to determine exposure to air pollutants in epidemiological studies. Time-activity diaries can be combined with LUR models, enabling detailed exposure estimation and limiting exposure misclassification, both in shorter and longer time lags. In this study, the traffic related air pollutant black carbon was measured with mu-aethalometers on a 5-mm time base at 63 locations in Flanders, Belgium. The measurements show that hourly concentrations vary between different locations, but also over the day. Furthermore the diurnal pattern is different for street and background locations. This suggests that annual LUR models are not sufficient to capture all the variation. Hourly LUR models for black carbon are developed using different strategies: by means of dummy variables, with dynamic dependent variables and/or with dynamic and static independent variables. The LUR model with 48 dummies (weekday hours and weekend hours) performs not as good as the annual model (explained variance of 0.44 compared to 0.77 in the annual model). The dataset with hourly concentrations of black carbon can be used to recalibrate the annual model, resulting in many of the original explaining variables losing their statistical significance, and certain variables having the wrong direction of effect. Building new independent hourly models, with static or dynamic covariates, is proposed as the best solution to solve these issues. R-2 values for hourly LUR models are mostly smaller than the R2 of the annual model, ranging from 0.07 to 0.8. Between 6 a.m. and 10 p.m. on weekdays the R2 approximates the annual model R-2. Even though models of consecutive hours are developed independently, similar variables turn out to be significant. Using dynamic covariates instead of static covariates, i.e. hourly traffic intensities and hourly population densities, did not significantly improve the models' performance. (C) 2013 Elsevier Ltd. All rights reserved.
Notes: [Dons, Evi; Van Poppel, Martine; Int Panis, Luc] Vlaamse Instelling Technol Onderzoek, Flemish Inst Technol Res, B-2400 Mol, Belgium. [Dons, Evi; Kochan, Bruno; Wets, Geert; Int Panis, Luc] Hasselt Univ, IMOB, Transportat Res Inst, B-3590 Diepenbeek, Belgium. evi.dons@vito.be
URI: http://hdl.handle.net/1942/15455
DOI: 10.1016/j.atmosenv.2013.03.050
ISI #: 000320969500027
ISSN: 1352-2310
Category: A1
Type: Journal Contribution
Validation: ecoom, 2014
Appears in Collections: Research publications

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