Please use this identifier to cite or link to this item:
Title: An instantaneous spatiotemporal model to predict a bicyclist's Black Carbon exposure based on mobile noise measurements.
Authors: Dekoninck, Luc
Botteldooren, Dick
Panis, Luc Int 
Issue Date: 2013
Source: ATMOSPHERIC ENVIRONMENT, 79, p. 623-631
Abstract: Several studies have shown that a significant amount of daily air pollution exposure, in particular black carbon (BC), is inhaled during trips. Assessing this contribution to exposure remains difficult because on the one hand local air pollution maps lack spatio-temporal resolution, at the other hand direct measurement of particulate matter concentration remains expensive. This paper proposes to use in-traffic noise measurements in combination with geographical and meteorological information for predicting BC exposure during commuting trips. Mobile noise measurements are cheaper and easier to perform than mobile air pollution measurements and can easily be used in participatory sensing campaigns. The uniqueness of the proposed model lies in the choice of noise indicators that goes beyond the traditional overall A-weighted noise level used in previous work. Noise and BC exposures are both related to the traffic intensity but also to traffic speed and traffic dynamics. Inspired by theoretical knowledge on the emission of noise and BC, the low frequency engine related noise and the difference between high frequency and low frequency noise that indicates the traffic speed, are introduced in the model. In addition, it is shown that splitting BC in a local and a background component significantly improves the model. The coefficients of the proposed model are extracted from 200 commuter bicycle trips. The predicted average exposure over a single trip correlates with measurements with a Pearson coefficient of 0.78 using only four parameters: the low frequency noise level, wind speed, the difference between high and low frequency noise and a street canyon index expressing local air pollution dispersion properties.
Document URI:
ISSN: 1352-2310
e-ISSN: 1873-2844
DOI: 10.1016/j.atmosenv.2013.06.054
ISI #: 000325834700070
Category: A1
Type: Journal Contribution
Validations: ecoom 2014
Appears in Collections:Research publications

Files in This Item:
File Description SizeFormat 
LDk BC and noise AE 2013.pdf
  Restricted Access
Main article Dekoninck et al AE 20132.08 MBAdobe PDFView/Open    Request a copy
Dekoninck_Spatiotemporal_Biking_Model_20130620_paper.pdfPreprint version263.54 kBAdobe PDFView/Open
Show full item record


checked on Sep 2, 2020


checked on May 13, 2022

Page view(s)

checked on May 15, 2022


checked on May 15, 2022

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.