Applicability of a noise-based model to estimate in-traffic exposure to black carbon and particle number concentrations in different cultures Luc Dekoninck a, ⁎, Dick Botteldooren a , Luc Int Panis b,c , Steve Hankey d , Grishma Jain d , Karthik S d , Julian Marshall d a Information Technology, Acoustics Group, Ghent University, Sint-Pietersnieuwsstraat 41, 9000 Ghent, Belgium b Flemish Institute for Technological Research (VITO), Boeretang 200, 2400 Mol, Belgium c Transportation Research Institute (IMOB), Hasselt University, Wetenschapspark 5 Bus 6, 3590 Diepenbeek, Belgium d Department of Civil Engineering, University of Minnesota, 500 Pillsbury Dr SE, Minneapolis, MN 55455, United States abstract article info Article history: Received 30 June 2014 Accepted 3 October 2014 Available online 30 October 2014 Keywords: Black carbon Particulate matter Particle number concentration Vehicle noise Personal exposure Air pollution Several studies show that a significant portion of daily air pollution exposure, in particular black carbon (BC), occurs during transport. In a previous work, a model for the in-traffic exposure of bicyclists to BC was proposed based on spectral evaluation of mobile noise measurements and validated with BC measurements in Ghent, Belgium. In this paper, applicability of this model in a different cultural context with a totally different traffic and mobility situation is presented. In addition, a similar modeling approach is tested for particle number (PN) concentration. Indirectly assessing BC and PN exposure through a model based on noise measurements is advantageous because of the availability of very affordable noise monitoring devices. Our previous work showed that a model including specific spectral components of the noise that relate to engine and rolling emission and basic meteorological data, could be quite accurate. Moreover, including a background concentration adjustment improved the model considerably. To explore whether this model could also be used in a different context, with or without tuning of the model parameters, a study was conducted in Bangalore, India. Noise measurement equipment, data storage, data processing, continent, country, measurement operators, vehicle fleet, driving behavior, biking facilities, background concentration, and meteorology are all very different from the first measurement campaign in Belgium. More than 24 h of combined in-traffic noise, BC, and PN measurements were collected. It was shown that the noise-based BC exposure model gives good predictions in Bangalore and that the same approach is also successful for PN. Cross validation of the model parameters was used to compare factors that impact exposure across study sites. A pooled model (combining the measurements of the two locations) results in a correlation of 0.84 when fitting the total trip exposure in Bangalore. Estimating particulate matter exposure with traffic noise measurements was thus shown to be a valid approach across countries and cultures. © 2014 Elsevier Ltd. All rights reserved. 1. Introduction Particulate matter (PM) is currently regulated in Europe, the US, India and other countries based on specific particle size fractions (e.g., PM 10 , PM 2.5 ). Black carbon (BC) and particle number (pN) concen- trations are associated with transportation emissions but are typically unregulated. The World Health Organization suggests including BC when evaluating traffic-related health effects (WHO Europe, 2012). Recent epidemiological results for BC suggest that health effects per mass may be up to 10 times higher than PM 10 (Janssen et al., 2011). Research into the health effects of traffic-related particulates is cons- trained by the stronger spatial variability for BC and PN concentrations relative to PM 10 and PM 2.5 . Detailed measurements for near-road settings have shown large spatial gradients for certain aspects of partic- ulate air pollution. For example, ultrafine particles and BC show decreases of over 50% within the first 150 m from the edge of the road (Karner et al., 2010) and significant street-to-street differences in PN and BC have been reported by several authors (Boogaard et al., 2011; Dons et al., 2012, 2013). Building a fixed-site monitoring network for PN and BC to provide robust estimates of exposure patterns, would therefore be a daunting task. In a previous work a novel way to predict a bicyclist's in-traffic BC exposure was presented based on mobile measurements of traffic- related noise and BC in Ghent (Belgium) (Dekoninck et al., 2013). The noise-based model yields spatially and temporally precise estimates of Environment International 74 (2015) 89–98 ⁎ Corresponding author. Tel.: +32 9 264 99 95. E-mail addresses: luc.dekoninck@intec.ugent.be (L. Dekoninck), dick.botteldooren@intec.ugent.be (D. Botteldooren), luc.intpanis@vito.be, luc.intpanis@uhasselt.be (L.I. Panis), shankey1028@gmail.com (S. Hankey), julian@umn.edu (J. Marshall). http://dx.doi.org/10.1016/j.envint.2014.10.002 0160-4120/© 2014 Elsevier Ltd. All rights reserved. Contents lists available at ScienceDirect Environment International journal homepage: www.elsevier.com/locate/envint