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A coupled road dust and surface moisture model to predict non-exhaust road traffic induced particle emissions (NORTRIP). Part 2: Surface moisture and salt impact modelling
The Norwegian Institute for Air Research (NILU).
The Norwegian Institute for Air Research (NILU).
Stockholms universitet.
Helsinki Metropolia University of Applied Sciences.
Show others and affiliations
2013 (English)In: Atmospheric Environment, ISSN 1352-2310, E-ISSN 1873-2844, Vol. 81, 485-503 p.Article in journal (Refereed) Published
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Text
Abstract [en]

Non-exhaust traffic induced emissions are a major source of airborne particulate matter in most European countries. This is particularly important in Nordic and Alpine countries where winter time road traction maintenance occurs, e.g. salting and sanding, and where studded tyres are used. Though the total mass generated by wear sources is a key factor in non-exhaust emissions, these emissions are also strongly controlled by surface moisture conditions. In this paper, Part 2, the road surface moisture submodel of a coupled road dust and surface moisture model (NORTRIP) is described.

We present a description of the road surface moisture part of the model and apply the coupled model to seven sites in Stockholm, Oslo, Helsinki and Copenhagen over 18 separate periods, ranging from 3.5 to 24 months. At two sites surface moisture measurements are available and the moisture sub-model is compared directly to these observations. The model predicts the frequency of wet roads well at both sites, with an average fractional bias of -2.6%. The model is found to correctly predict the hourly surface state, wet or dry, 85% of the time. From the 18 periods modelled using the coupled model an average absolute fractional bias of 15% for PM10 concentrations was found. Similarly the model predicts the 90'th daily mean percentiles of PMio with an average absolute bias of 19% and an average correlation (R-2) of 0.49. When surface moisture is not included in the modelling then this average correlation is reduced to 0.16, demonstrating the importance of the surface moisture conditions. Tests have been carried out to assess the sensitivity of the model to model parameters and input data. The model provides a useful tool for air quality management and for improving our understanding of non-exhaust traffic emissions.

Place, publisher, year, edition, pages
Elsevier, 2013. Vol. 81, 485-503 p.
Keyword [en]
Particulate matter, Mathematical model, Salt (deicing), Surfacing, Moisture, Prediction, Correlation
National Category
Meteorology and Atmospheric Sciences
Research subject
10 Road: Transport, society, policy and planning, 15 Road: Environment
Identifiers
URN: urn:nbn:se:vti:diva-9994DOI: 10.1016/j.atmosenv.2013.09.003ISI: 000329377600052OAI: oai:DiVA.org:vti-9994DiVA: diva2:1144881
Available from: 2017-09-27 Created: 2017-09-27 Last updated: 2017-09-28Bibliographically approved

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Ketzel, MatthiasGustafsson, MatsBlomqvist, Göran
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Citation style
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