A
Ari Karppinen
Researcher at Finnish Meteorological Institute
Publications - 143
Citations - 4370
Ari Karppinen is an academic researcher from Finnish Meteorological Institute. The author has contributed to research in topics: Air quality index & Atmospheric dispersion modeling. The author has an hindex of 32, co-authored 134 publications receiving 3881 citations. Previous affiliations of Ari Karppinen include RAND Corporation & Finnish Institute of Occupational Health.
Papers
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Extensive evaluation of neural network models for the prediction of NO2 and PM10 concentrations, compared with a deterministic modelling system and measurements in central Helsinki
Jaakko Kukkonen,Leena Partanen,Ari Karppinen,Juhani Ruuskanen,Heikki Junninen,Mikko Kolehmainen,Harri Niska,Stephen Dorling,Tim Chatterton,Rob Foxall,Gavin C. Cawley +10 more
TL;DR: In this article, five neural network (NN) models, a linear statistical model and a deterministic modelling system (DET) were evaluated for the prediction of urban NO2 and PM10 concentrations.
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Evolving the neural network model for forecasting air pollution time series
TL;DR: A parallel genetic algorithm is used for selecting the inputs and designing the high-level architecture of a multi-layer perceptron model for forecasting hourly concentrations of nitrogen dioxide at a busy urban traffic station in Helsinki and the results showed that the GA is a capable tool for tackling the practical problems of neural network design.
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Retrieval of mixing height and dust concentration with lidar ceilometer
TL;DR: The Vaisala ceilometers CT25K and CL31 are eye-safe single lens lidar systems reporting attenuated backscatter profiles; they often operate 24 h a day in fully automated, hands-off operation mode as mentioned in this paper.
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Intercomparison of air quality data using principal component analysis, and forecasting of PM10 and PM2.5 concentrations using artificial neural networks, in Thessaloniki and Helsinki
Dimitris Voukantsis,Kostas Karatzas,Jaakko Kukkonen,Teemu Räsänen,Ari Karppinen,Mikko Kolehmainen +5 more
TL;DR: A methodology consisting of specific computational intelligence methods, i.e. principal component analysis and artificial neural networks, is proposed in order to inter-compare air quality and meteorological data, and to forecast the concentration levels for environmental parameters of interest (air pollutants).
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Chemical composition of aerosols during a major biomass burning episode over northern Europe in spring 2006: Experimental and modelling assessments
Sanna Saarikoski,Markus Sillanpää,Mikhail Sofiev,Hilkka Timonen,Karri Saarnio,Kimmo Teinilä,Ari Karppinen,Jaakko Kukkonen,Risto Hillamo +8 more
TL;DR: The long-range transported smoke emitted by biomass burning had a strong impact on the PM 2.5 mass concentrations in Helsinki over the 12 days period in April and May 2006 as mentioned in this paper.