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28 results on '"Kauhaniemi, M."'

1. Supporting the improvement of air quality management practices: The “FAIRMODE pilot” activity

Author

Pisoni, E., Guerreiro, C., Lopez-Aparicio, S., Guevara, M., Tarrason, L., Janssen, S., Thunis, P., Pfäfflin, F., Piersanti, A., Briganti, G., Cappelletti, A., D'Elia, I., Mircea, M., Villani, M.G., Vitali, L., Matavž, L., Rus, M., Žabkar, R., Kauhaniemi, M., Karppinen, A., Kousa, A., Väkevä, O., Eneroth, K., Stortini, M., Delaney, K., Struzewska, J., Durka, P., Kaminski, J.W., Krmpotic, S., Vidic, S., Belavic, M., Brzoja, D., Milic, V., Assimakopoulos, V.D., Fameli, K.M., Polimerova, T., Stoyneva, E., Hristova, Y., Sokolovski, E., and Cuvelier, C.

Published
2019
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2. Assessment of Population Exposure to Particulate Matter for London and Helsinki

Author

Kukkonen, J., Singh, V., Sokhi, R. S., Soares, J., Kousa, A., Matilainen, L., Kangas, L., Kauhaniemi, M., Riikonen, K., Jalkanen, J.-P., Rasila, T., Hänninen, O., Koskentalo, T., Aarnio, M., Hendriks, C., Karppinen, A., Abarbanel, Henry, Series editor, Braha, Dan, Series editor, Érdi, Péter, Series editor, Friston, Karl, Series editor, Haken, Hermann, Series editor, Jirsa, Viktor, Series editor, Kacprzyk, Janusz, Series editor, Kaneko, Kunihiko, Series editor, Kelso, Scott, Series editor, Kirkilionis, Markus, Series editor, Kurths, Jürgen, Series editor, Nowak, Andrzej, Series editor, Qudrat-Ullah, Hassan, Series editor, Reichl, Linda, Series editor, Schuster, Peter, Series editor, Schweitzer, Frank, Series editor, Sornette, Didier, Series editor, Thurner, Stefan, Series editor, Steyn, Douw G., editor, and Chaumerliac, Nadine, editor

Published
2016
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4. Road dust and PM10 in the Nordic countries

Author

Kupiainen, K, primary, Denby, B.R., additional, Gustafsson, C, additional, Johansson, J, additional, Ketzel, M, additional, Kukkonen, J, additional, Norman, M, additional, Pirjola, L, additional, Sundvor, I, additional, Blomqvist, G, additional, Janhäll, S, additional, Karppinen, A, additional, Kauhaniemi, M, additional, Malinen, A, additional, and Stojiljkovic, A, additional

Published
2017
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10. Modelling of the urban concentrations of PM₂.₅ on a high resolution for a period of 35 years, for the assessment of lifetime exposure and health effects

Author

Kukkonen, J. (Jaakko), Kangas, L. (Leena), Kauhaniemi, M. (Mari), Sofiev, M. (Mikhail), Aarnio, M. (Mia), Jaakkola, J. J. (Jouni J. K.), Kousa, A. (Anu), and Karppinen, A. (Ari)

Abstract

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 assessment of health effects associated with air pollution.

Published
2018

12. Road dust and PM10 in the Nordic countries : Measures to Reduce Road Dust Emissions from Traffic

Author

Kupiainen, K, Denby, B.R, Gustafsson, M, Johansson, C, Ketzel, M, Kukkonen, J, Norman, M, Pirjola, L, Sundvor, I, Bennet, C, Blomqvist, G, Janhäll, S, Karppinen, A, Kauhaniemi, M, Malinen, A, Stojiljkovic, A, Kupiainen, K, Denby, B.R, Gustafsson, M, Johansson, C, Ketzel, M, Kukkonen, J, Norman, M, Pirjola, L, Sundvor, I, Bennet, C, Blomqvist, G, Janhäll, S, Karppinen, A, Kauhaniemi, M, Malinen, A, and Stojiljkovic, A

Abstract

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.

Published
2017
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13. Modelling of particulate matter concentrations and source contributions in the Helsinki Metropolitan Area in 2008 and 2010

Author

Aarnio, M.A., Kukkonen, J., Kangas, L., Kauhaniemi, M., Kousa, A., Hendriks, C., Yli-Tuomi, T., Lanki, T., Hoek, G., Brunekreef, B., Elolähde, T., Karppinen, A., LS IRAS EEPI ME (Milieu epidemiologie), Dep IRAS, and dIRAS RA-2

Subjects
Urban Mobility & Environment, Urbanisation, CAS - Climate, Air and Sustainability, ELSS - Earth, Life and Social Sciences, Environment, Environment & Sustainability
Abstract

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 .

Published
2016

14. Modelling of particulate matter concentrations and source contributions in the Helsinki Metropolitan Area in 2008 and 2010.

Author

LS IRAS EEPI ME (Milieu epidemiologie), Dep IRAS, dIRAS RA-2, Aarnio, M.A., Kukkonen, J., Kangas, L., Kauhaniemi, M., Kousa, A., Hendriks, C., Yli-Tuomi, T., Lanki, T., Hoek, G., Brunekreef, B., Elolähde, T., Karppinen, A., LS IRAS EEPI ME (Milieu epidemiologie), Dep IRAS, dIRAS RA-2, Aarnio, M.A., Kukkonen, J., Kangas, L., Kauhaniemi, M., Kousa, A., Hendriks, C., Yli-Tuomi, T., Lanki, T., Hoek, G., Brunekreef, B., Elolähde, T., and Karppinen, A.

Published
2016

15. Comparison of the predictions of two road dust emission models with the measurements of a mobile van

Author

Kauhaniemi, M., Stojiljkovic, A., Pirjola, L., Karppinen, A., Harkonen, J., Kupiainen, K., Kangas, L., Aarnio, M. A., Omstedt, Gunnar, Denby, B. R., Kukkonen, J., Kauhaniemi, M., Stojiljkovic, A., Pirjola, L., Karppinen, A., Harkonen, J., Kupiainen, K., Kangas, L., Aarnio, M. A., Omstedt, Gunnar, Denby, B. R., and Kukkonen, J.

Abstract

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.

Published
2014
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18. Refinement of a model for evaluating the population exposure in an urban area

Author

Soares, J., primary, Kousa, A., additional, Kukkonen, J., additional, Matilainen, L., additional, Kangas, L., additional, Kauhaniemi, M., additional, Riikonen, K., additional, Jalkanen, J.-P., additional, Rasila, T., additional, Hänninen, O., additional, Koskentalo, T., additional, Aarnio, M., additional, Hendriks, C., additional, and Karppinen, A., additional

Published
2014
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19. 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

Author

Denby, B. R., Sundvor, I., Johansson, Christer, Pirjola, L., Ketzel, M., Norman, M., Kupiainen, K., Gustafsson, M., Blomqvist, G., Kauhaniemi, M., Omstedt, G., Denby, B. R., Sundvor, I., Johansson, Christer, Pirjola, L., Ketzel, M., Norman, M., Kupiainen, K., Gustafsson, M., Blomqvist, G., Kauhaniemi, M., and Omstedt, G.

Abstract

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

Published
2013
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23. Evaluation of a road dust suspension model for predicting the concentrations of PM10 in a street canyon

Author

Kauhaniemi, M., Kukkonen, J., Härkönen, J., Nikmo, J., Kangas, L., Omstedt, G., Ketzel, M., Kousa, A., Haakana, M., and Karppinen, A.

Subjects
*DUST, *ROADS, *SUSPENSIONS (Chemistry), *PARTICULATE matter, *MECHANICAL wear, *MATHEMATICAL models, *METEOROLOGICAL precipitation
Abstract

Abstract: We have slightly refined, evaluated and tested a mathematical model for predicting the vehicular suspension emissions of PM10. The model describes particulate matter generated by the wear of road pavement, traction sand, and the processes that control the suspension of road dust particles into the air. However, the model does not address the emissions from the wear of vehicle components. The performance of this suspension emission model has been evaluated in combination with the street canyon dispersion model OSPM. We used data from a measurement campaign that was conducted in the street canyon Runeberg Street in Helsinki from 8 January to 2 May, 2004. The model reproduced fairly well the seasonal variation of the PM10 concentrations, also during the time periods, when studded tyres and anti-skid treatments were commonly in use. For instance, the index of agreement (IA) was 0.83 for the time series of the hourly predicted and observed concentrations of PM10. The predictions of the model were found to be sensitive to precipitation and street traction sanding. The main uncertainties in the predictions are probably caused by (i) the cleaning processes of the streets, which are currently not included in the model, (ii) the uncertainties in the estimation of the sanding days, and (iii) the uncertainties in the evaluation of precipitation. This study provides more confidence that this model could potentially be a valuable tool of assessment to evaluate and forecast the suspension PM10 emissions worldwide. However, a further evaluation of the model is needed against other datasets in various vehicle fleet, speed and climatic conditions. [Copyright &y& Elsevier]

Published
2011
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24. Evaluation of a modelling system for predicting the concentrations of PM2.5 in an urban area

Author

Kauhaniemi, M., Karppinen, A., Härkönen, J., Kousa, A., Alaviippola, B., Koskentalo, T., Aarnio, P., Elolähde, T., and Kukkonen, J.

Subjects
*URBAN pollution, *PARTICULATE matter, *AEROSOLS, *ATMOSPHERIC models, *EMISSIONS (Air pollution), *AUTOMOBILE emissions, *DISPERSION (Chemistry)
Abstract

We present a modelling system that contains a treatment of the emissions and atmospheric dispersion of fine particulate matter (PM2.5) on an urban scale, combined with a statistical model for estimating the contribution of long-range transported aerosols. The model of PM2.5 emissions includes exhaust emissions, cold starts and driving, as well as, non-exhaust emissions originated from urban vehicular traffic. The influence of primary vehicular emissions from the road and street network was evaluated using a roadside emission and dispersion model, CAR-FMI, in combination with a meteorological pre-processing model, MPP-FMI. We have computed hourly sequential time series of the PM2.5 concentrations in 2002 in a numerical grid in the Helsinki Metropolitan Area. The predicted results were compared against measured data at two locations in central Helsinki: urban roadside station of Vallila and urban background station of Kallio. The predicted daily average PM2.5 concentrations agreed well with the measured values; e.g., the index of agreement values were 0.83 and 0.86 at Vallila and Kallio, respectively, and the absolute values of fractional bias ⩽0.13. As expected, the scatter of data points is substantially wider for the hourly concentration values; e.g., the index of agreement values were 0.69 and 0.74. We also computed the spatial concentration distributions of PM2.5. The predicted contribution from long-range transport to the street level PM2.5 varied spatially from 40% in the most trafficked areas to nearly 100% in the outskirts of the area. The emissions originated from cold starts and driving were responsible for <4% of the annual average concentrations at the roadside and urban stations. The model can potentially be used as a practical tool of assessment of urban PM2.5 contributions in various European regions. [Copyright &y& Elsevier]

Published
2008
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26. Modelling of particulate matter concentrations and source contributions in the Helsinki Metropolitan Area in 2008 and 2010

Author

Aarnio, M. A., Kukkonen, J., Kangas, L., Kauhaniemi, M., Anu Kousa, Hendriks, C., Yli-Tuomi, T., Lanki, T., Hoek, G., Brunekreef, B., Elolähde, T., and Karppinen, A.

Subjects
Urban Mobility & Environment, Urbanisation, CAS - Climate, Air and Sustainability, ELSS - Earth, Life and Social Sciences, Environment, Environment & Sustainability
Abstract

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 LOTOSEUROS, for 2008, and the measured regional background concentrations, for 2010. The predictions were compared against measured concentrations of PM2.5 and PM10. PM2.5 concentrations were slightly and the PM10concentrations substantially under-predicted in 2008, mainly due to the under-predicted regional background concentration. Source contributions of suspended road dust varied from 2% to 8% and from 12% to 38% for PM2.5 and PM10, respectively. Long-range transported contributions at the urban traffic stations were 72% to 92% for PM2.5 and 50% to 83% for PM10. © 2016.

27. Supporting the improvement of air quality management practices: The 'FAIRMODE pilot' activity

Author

Leonor Tarrasón, Joanna Struzewska, Mari Kauhaniemi, E. Sokolovski, F. Pfäfflin, Vasiliki D. Assimakopoulos, Philippe Thunis, Lina Vitali, Ari Karppinen, M. Rus, Y. Hristova, E. Stoyneva, K. Eneroth, K.M. Fameli, S. Krmpotic, Enrico Pisoni, Mihaela Mircea, D. Brzoja, C. Cuvelier, Antonio Piersanti, Stijn Janssen, Marc Guevara, Ilaria D'Elia, T. Polimerova, Anu Kousa, K. Delaney, Maria Gabriella Villani, Andrea Cappelletti, M. Belavic, S. Vidic, Susana Lopez-Aparicio, Jacek W. Kaminski, C. Guerreiro, L. Matavž, V. Milic, P. Durka, M. Stortini, R. Žabkar, O. Väkevä, Gino Briganti, Pisoni, E., Guerreiro, C., Lopez-Aparicio, S., Guevara, M., Tarrason, L., Janssen, S., Thunis, P., Pfafflin, F., Piersanti, A., Briganti, G., Cappelletti, A., D'Elia, I., Mircea, M., Villani, M. G., Vitali, L., Matavz, L., Rus, M., Zabkar, R., Kauhaniemi, M., Karppinen, A., Kousa, A., Vakeva, O., Eneroth, K., Stortini, M., Delaney, K., Struzewska, J., Durka, P., Kaminski, J. W., Krmpotic, S., Vidic, S., Belavic, M., Brzoja, D., Milic, V., Assimakopoulos, V. D., Fameli, K. M., Polimerova, T., Stoyneva, E., Hristova, Y., Sokolovski, E., and Cuvelier, C.

Subjects
Environmental Engineering, Computer science, 0208 environmental biotechnology, Air pollution, 02 engineering and technology, Plan (drawing), 010501 environmental sciences, Management, Monitoring, Policy and Law, medicine.disease_cause, 01 natural sciences, Article, Air Pollution, Limit (music), Environmental monitoring, medicine, Frame (artificial intelligence), Waste Management and Disposal, Air quality index, 0105 earth and related environmental sciences, Air Pollutants, Suite, Environmental Monitoring, General Medicine, Directive, 020801 environmental engineering, Risk analysis (engineering), Air Pollutant
Abstract

This paper presents the first outcomes of the “FAIRMODE pilot” activity, aiming at improving the way in which air quality models are used in the frame of the European “Air Quality Directive”. Member States may use modelling, combined with measurements, to “assess” current levels of air quality and estimate future air quality under different scenarios. In case of current and potential exceedances of the Directive limit values, it is also requested that they “plan” and implement emission reductions measures to avoid future exceedances. In both “assessment” and “planning”, air quality models can and should be used; but to do so, the used modelling chain has to be fit-for-purpose and properly checked and verified. FAIRMODE has developed in the recent years a suite of methodologies and tools to check if emission inventories, model performance, source apportionment techniques and planning activities are fit-for-purpose. Within the “FAIRMODE pilot”, these tools are used and tested by regional/local authorities, with the two-fold objective of improving management practices at regional/local scale, and providing valuable feedback to the FAIRMODE community. Results and lessons learnt from this activity are presented in this paper, as a showcase that can potentially benefit other authorities in charge of air quality assessment and planning., Highlights • Air quality is still a challenge in various areas in Europe. • FAIRMODE provides methodologies and tools for supporting air quality improvement. • Specifically, a new FAIRMODE initiative for better management practices has been recently launched. • The initiative provides a “live tutorial” to interested authorities. • First results on improved emission inventories and concentrations are presented.

Published
2019

28. Long-term exposure to ambient fine particulate matter originating from traffic and residential wood combustion and the prevalence of depression.

Author

Allaouat S, Yli-Tuomi T, Tiittanen P, Turunen AW, Siponen T, Kukkonen J, Kangas L, Kauhaniemi M, Aarnio M, Ngandu T, and Lanki T

Subjects
Cross-Sectional Studies, Depression epidemiology, Environmental Exposure adverse effects, Environmental Exposure analysis, Humans, Particulate Matter adverse effects, Particulate Matter analysis, Prevalence, Wood chemistry, Air Pollutants adverse effects, Air Pollutants analysis, Air Pollution adverse effects, Air Pollution analysis
Abstract

Introduction: Air pollution has been suggested to be associated with depression. However, current evidence is conflicting, and no study has considered different sources of ambient particulate matter with an aerodynamic diameter below 2.5 µm (PM 2.5 ). We evaluated the associations of long-term exposure to PM 2.5 from road traffic and residential wood combustion with the prevalence of depression in the Helsinki region, Finland., Methods: We conducted a cross-sectional analysis based on the Helsinki Capital Region Environmental Health Survey 2015-2016 (N=5895). Modelled long-term outdoor concentrations of PM 2.5 were evaluated using high-resolution emission and dispersion modelling on an urban scale and linked to the home addresses of study participants. The outcome was self-reported doctor-diagnosed or treated depression. We applied logistic regression and calculated the OR for 1 μg/m 3 increase in PM 2.5 , with 95% CI. Models were adjusted for potential confounders, including traffic noise and urban green space., Results: Of the participants, 377 reported to have been diagnosed or treated for depression by a doctor. Long-term exposure to PM 2.5 from road traffic (OR=1.23, 95% CI 0.86 to 1.73; n=5895) or residential wood combustion (OR=0.78, 95% CI 0.43 to 1.41; n=5895) was not associated with the prevalence of depression. The estimates for PM 2.5 from road traffic were elevated, but statistically non-significant, for non-smokers (OR=1.38, 95% CI 0.94 to 2.01; n=4716)., Conclusions: We found no convincing evidence of an effect of long-term exposure to PM 2.5 from road traffic or residential wood combustion on depression., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Published
2021
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