A comparison of airborne wear particle emission models based on metro station measurements
(2019) 10th International Scientific Conference BALTTRIB 2019 p.150-157- Abstract
The main sources of non-exhaust particles around metro systems are the wear from wheel-rail contact, brake contact and the contact between mechanical parts in electric power systems. In order to predict the PM10 levels on underground metro platforms, the relation among time, train frequency and PM10 level should be investigated. Two types of particle emission models have previously been published to determine the PM10 level on underground train platform; these are the linear model and the conservation model. The aim of this study is to compare the results from the two models with a set of field measurements PM10. In 2016, a set of field measurements are performed on four underground metro platforms in Stockholm. The predicted PM10... (More)
The main sources of non-exhaust particles around metro systems are the wear from wheel-rail contact, brake contact and the contact between mechanical parts in electric power systems. In order to predict the PM10 levels on underground metro platforms, the relation among time, train frequency and PM10 level should be investigated. Two types of particle emission models have previously been published to determine the PM10 level on underground train platform; these are the linear model and the conservation model. The aim of this study is to compare the results from the two models with a set of field measurements PM10. In 2016, a set of field measurements are performed on four underground metro platforms in Stockholm. The predicted PM10 values from the two models are compared with the measurement data. The accuracy of the two models is analysed and the behaviours of the two models in high and low train frequency regions are separately discussed.
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- author
- Tu, Minghui ; Wahlström, Jens LU ; Lyu, Yezhe LU and Olofsson, Ulf
- publishing date
- 2019-01-01
- type
- Contribution to conference
- publication status
- published
- subject
- keywords
- Model comparison, Particle emission, PM10, Underground metro platform
- pages
- 8 pages
- conference name
- 10th International Scientific Conference BALTTRIB 2019
- conference location
- Kaunas, Lithuania
- conference dates
- 2019-11-14 - 2019-11-16
- external identifiers
-
- scopus:85084341767
- DOI
- 10.15544/balttrib.2019.25
- language
- English
- LU publication?
- no
- id
- 3c788f5a-7a4e-4b2c-bfc0-5010eaddd67b
- alternative location
- https://hdl.handle.net/20.500.12259/103207
- date added to LUP
- 2020-05-19 12:30:33
- date last changed
- 2022-04-18 22:17:24
@misc{3c788f5a-7a4e-4b2c-bfc0-5010eaddd67b, abstract = {{<p>The main sources of non-exhaust particles around metro systems are the wear from wheel-rail contact, brake contact and the contact between mechanical parts in electric power systems. In order to predict the PM10 levels on underground metro platforms, the relation among time, train frequency and PM10 level should be investigated. Two types of particle emission models have previously been published to determine the PM10 level on underground train platform; these are the linear model and the conservation model. The aim of this study is to compare the results from the two models with a set of field measurements PM10. In 2016, a set of field measurements are performed on four underground metro platforms in Stockholm. The predicted PM10 values from the two models are compared with the measurement data. The accuracy of the two models is analysed and the behaviours of the two models in high and low train frequency regions are separately discussed.</p>}}, author = {{Tu, Minghui and Wahlström, Jens and Lyu, Yezhe and Olofsson, Ulf}}, keywords = {{Model comparison; Particle emission; PM10; Underground metro platform}}, language = {{eng}}, month = {{01}}, pages = {{150--157}}, title = {{A comparison of airborne wear particle emission models based on metro station measurements}}, url = {{http://dx.doi.org/10.15544/balttrib.2019.25}}, doi = {{10.15544/balttrib.2019.25}}, year = {{2019}}, }