Literature DB >> 25454224

Applicability of a noise-based model to estimate in-traffic exposure to black carbon and particle number concentrations in different cultures.

Luc Dekoninck1, Dick Botteldooren2, Luc Int Panis3, Steve Hankey4, Grishma Jain5, Karthik S5, Julian Marshall6.   

Abstract

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 24h 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.
Copyright © 2014 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Air pollution; Black carbon; Particle number concentration; Particulate matter; Personal exposure; Vehicle noise

Mesh:

Substances:

Year:  2014        PMID: 25454224     DOI: 10.1016/j.envint.2014.10.002

Source DB:  PubMed          Journal:  Environ Int        ISSN: 0160-4120            Impact factor:   9.621


  6 in total

1.  An accurate filter loading correction is essential for assessing personal exposure to black carbon using an Aethalometer.

Authors:  Nicholas Good; Anna Mölter; Jennifer L Peel; John Volckens
Journal:  J Expo Sci Environ Epidemiol       Date:  2016-12-21       Impact factor: 5.563

2.  An international application of the city-wide mobile noise mapping methodology: Retro-active traffic attribution on a bicycle commuters health study in New York City.

Authors:  Luc Dekoninck; Qiang Yang; Haokai Zhao; James Ross; Darby Jack; Steven Chillrud
Journal:  Proc Int Congr Noise Control Eng       Date:  2019-09-30

3.  Design of a Mobile Low-Cost Sensor Network Using Urban Buses for Real-Time Ubiquitous Noise Monitoring.

Authors:  Rosa Ma Alsina-Pagès; Unai Hernandez-Jayo; Francesc Alías; Ignacio Angulo
Journal:  Sensors (Basel)       Date:  2016-12-29       Impact factor: 3.576

4.  Wearable Ultrafine Particle and Noise Monitoring Sensors Jointly Measure Personal Co-Exposures in a Pediatric Population.

Authors:  Douglas Leaffer; Christopher Wolfe; Steve Doroff; David Gute; Grace Wang; Patrick Ryan
Journal:  Int J Environ Res Public Health       Date:  2019-01-23       Impact factor: 3.390

5.  Noise and air pollution during Covid-19 lockdown easing around a school site.

Authors:  Prashant Kumar; Hamid Omidvarborna; Abhijith Kooloth Valappil; Abigail Bristow
Journal:  J Acoust Soc Am       Date:  2022-02       Impact factor: 1.840

6.  Extending Participatory Sensing to Personal Exposure Using Microscopic Land Use Regression Models.

Authors:  Luc Dekoninck; Dick Botteldooren; Luc Int Panis
Journal:  Int J Environ Res Public Health       Date:  2017-05-31       Impact factor: 3.390

  6 in total

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