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Reliable and self-consistent data on air quality are needed for an extensive period of time for conducting long-term, or even lifetime health impact assessments. We have modelled the urban-scale concentrations of fine particulate matter (PM₂.₅) in the Helsinki Metropolitan Area for a period of 35 years, from 1980 to 2014. The regional background concentrations were evaluated based on reanalyses of the atmospheric composition on global and European scales, using the SILAM model. The high-resolution urban computations included both the emissions originated from vehicular traffic (separately exhaust and suspension emissions) and those from small-scale combustion, and were conducted using the road network dispersion model CAR-FMI and the multiple-source Gaussian dispersion model UDM-FMI. The modelled concentrations of PM2.5 agreed fairly well with the measured data at a regional background station and at four urban measurement stations, during 1999–2014. The modelled concentration trends were also evaluated for earlier years, until 1988, using proxy analyses. There was no systematic deterioration of the agreement of predictions and data for earlier years (the 1980s and 1990s), compared with the results for more recent years (2000s and early 2010s). The local vehicular emissions were about 5 times higher in the 1980s, compared with the emissions during the latest considered years. The local small-scale combustion emissions increased slightly over time. The highest urban concentrations of PM₂.₅ occurred in the 1980s; these have since decreased to about to a half of the highest values. In general, regional background was the largest contribution in this area. Vehicular exhaust has been the most important local source, but the relative shares of both small-scale combustion and vehicular non-exhaust emissions have increased in time. The study has provided long-term, high-resolution concentration databases on regional and urban scales that can be used for the assessm

Nordic countries suffer from periodic worsening of the air quality during spring with high peak PM10 concentrations (airborne particulate matter with diameter less than 10 µm or 0.01 mm). Characteristic for the high springtime PM10 concentrations are high shares of coarse particles (with diameters between 2.5 and 10µm), a signature of non-exhaust traffic dust formed via abrasion and wear of pavement, traction control materials, vehicle brakes and tyres. This Policy Brief summarizes the current understanding of the road dust system and presents the mitigation measures and policies currently in place in the Nordic countries. It has been compiled as part of the NORTRIP project funded by the Climate and air pollution working group of the Nordic Council of Ministers by researchers from 11 Nordic institutes studying different aspects of traffic non-exhaust emissions and road dust.

Nordic countries suffer from periodic worsening of the air quality during spring with high peak PM10 concentrations (airborne particulate matter with diameter less than 10 µm or 0.01 mm). Characteristic for the high springtime PM10 concentrations are high shares of coarse particles (with diameters between 2.5 and 10µm), a signature of non-exhaust traffic dust formed via abrasion and wear of pavement, traction control materials, vehicle brakes and tyres. This Policy Brief summarizes the current understanding of the road dust system and presents the mitigation measures and policies currently in place in the Nordic countries. It has been compiled as part of the NORTRIP project funded by the Climate and air pollution working group of the Nordic Council of Ministers by researchers from 11 Nordic institutes studying different aspects of traffic non-exhaust emissions and road dust.

We refined an urban-scale dispersion modelling system by adding a road dust suspension model, FORE. The deterministic modelling includes both vehicular exhaust emissions (including cold start and cold driving) and suspended road dust. The urban scale modelling system was used in combination with the regional scale chemical transport model LOTOS- EUROS, for 2008, and the measured regional background concentrations, for 2010. The predictions were compared against measured concentrations of PM 2.5 and PM 10 . PM 2.5 concentrations were slightly and the PM 10 concentrations substantially under-predicted in 2008, mainly due to the under-predicted regional background concentration. Source contri- butions of suspended road dust varied from 2% to 8% and from 12% to 38% for PM 2.5 and PM 10 , respectively. Long-range transported contributions at the urban traffic stations were 72% to 92% for PM 2.5 and 50% to 83% for PM 10 .

The predictions of two road dust suspension emission models were compared with the on-site mobile measurements of suspension emission factors. Such a quantitative comparison has not previously been reported in the reviewed literature. The models used were the Nordic collaboration model NORTRIP (NOn-exhaust Road TRaffic Induced Particle emissions) and the Swedish-Finnish FORE model (Forecasting Of Road dust Emissions). These models describe particulate matter generated by the wear of road surface due to traction control methods and processes that control the suspension of road dust particles into the air. An experimental measurement campaign was conducted using a mobile laboratory called SNIFFER, along two selected road segments in central Helsinki in 2007 and 2008. The suspended PM10 concentration was measured behind the left rear tyre and the street background PM10 concentration in front of the van. Both models reproduced the measured seasonal variation of suspension emission factors fairly well during both years at both measurement sites. However, both models substantially under-predicted the measured emission values. The article illustrates the challenges in conducting road suspension measurements in densely trafficked urban conditions, and the numerous requirements for input data that are needed for accurately applying road suspension emission models.

The predictions of two road dust suspension emission models were compared with the on-site mobile measurements of suspension emission factors. Such a quantitative comparison has not previously been reported in the reviewed literature. The models used were the Nordic collaboration model NORTRIP (NOn-exhaust Road TRaffic Induced Particle emissions) and the Swedish-Finnish FORE model (Forecasting Of Road dust Emissions). These models describe particulate matter generated by the wear of road surface due to traction control methods and processes that control the suspension of road dust particles into the air. An experimental measurement campaign was conducted using a mobile laboratory called SNIFFER, along two selected road segments in central Helsinki in 2007 and 2008. The suspended PM10 concentration was measured behind the left rear tyre and the street background PM10 concentration in front of the van. Both models reproduced the measured seasonal variation of suspension emission factors fairly well during both years at both measurement sites. However, both models substantially under-predicted the measured emission values. The article illustrates the challenges in conducting road suspension measurements in densely trafficked urban conditions, and the numerous requirements for input data that are needed for accurately applying road suspension emission models.

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 sub-model 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 PM10 with an average absolute bias of 19% and an average correlation (R2) 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., AuthorCount: 11Funding agencies:Nordic Council of Ministers Project NORTRIP BLS-306-00064; Norwegian Climate and Pollution Agency KLIF 4011009; Danish Environmental Protection Agency