Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Forecasting size-fractionated particle number concentrations in the urban atmosphere
Abstract Airborne particulate matter affects human health, especially in urban areas where air pollutant concentrations are high. In order to reduce exposure to particulates and other pollutants it is essential to forecast concentrations of these. In this work we introduce a statistical model to forecast size-fractionated particle number concentrations. Our forecasting approach is based on a parametric regression model that utilises traffic intensity and meteorological parameters as covariates and has an autoregressive dependence structure for the error terms. We use a Bayesian framework with a Markov Chain Monte Carlo (MCMC) implementation to derive the forecasts numerically from a given set of learning data. The particle concentration forecast is provided as probability distributions for a few days ahead. For the development and test of the model we used weather and particle size distribution data from an urban background station in Helsinki, and traffic data. Comparison of forecast distributions and measurements shows that the adopted probabilistic characterisation of particle number concentrations is adequate. In particular, forecasts of the log number concentration of ultra-fine particles (diameter < 100 nm) for the upcoming day have R 2 equal to 0.67 (at 3 h time resolution). Our model is flexible and it may be implemented for other urban locations, provided that measurements of number concentrations, as well as measurements and forecasts of covariates are available.
Highlights ► Development of a statistical forecast model for particle number concentrations. ► Bayesian inference for the probability distributions. ► Model evaluation against long-term aerosol data in the urban background of Helsinki.
Forecasting size-fractionated particle number concentrations in the urban atmosphere
Abstract Airborne particulate matter affects human health, especially in urban areas where air pollutant concentrations are high. In order to reduce exposure to particulates and other pollutants it is essential to forecast concentrations of these. In this work we introduce a statistical model to forecast size-fractionated particle number concentrations. Our forecasting approach is based on a parametric regression model that utilises traffic intensity and meteorological parameters as covariates and has an autoregressive dependence structure for the error terms. We use a Bayesian framework with a Markov Chain Monte Carlo (MCMC) implementation to derive the forecasts numerically from a given set of learning data. The particle concentration forecast is provided as probability distributions for a few days ahead. For the development and test of the model we used weather and particle size distribution data from an urban background station in Helsinki, and traffic data. Comparison of forecast distributions and measurements shows that the adopted probabilistic characterisation of particle number concentrations is adequate. In particular, forecasts of the log number concentration of ultra-fine particles (diameter < 100 nm) for the upcoming day have R 2 equal to 0.67 (at 3 h time resolution). Our model is flexible and it may be implemented for other urban locations, provided that measurements of number concentrations, as well as measurements and forecasts of covariates are available.
Highlights ► Development of a statistical forecast model for particle number concentrations. ► Bayesian inference for the probability distributions. ► Model evaluation against long-term aerosol data in the urban background of Helsinki.
Forecasting size-fractionated particle number concentrations in the urban atmosphere
Mølgaard, Bjarke (Autor:in) / Hussein, Tareq (Autor:in) / Corander, Jukka (Autor:in) / Hämeri, Kaarle (Autor:in)
Atmospheric Environment ; 46 ; 155-163
03.10.2011
9 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch