scholarly journals Overview of the European project FUMAPEX

2006 ◽  
Vol 6 (7) ◽  
pp. 2005-2015 ◽  
Author(s):  
A. Baklanov
Keyword(s):  
Air Pollution ◽  
Prediction Models ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Urban Air Pollution ◽  
Population Exposure ◽  
Urban Air ◽  
Exposure Models ◽  
Numerical Weather Prediction Models

Abstract. The quality of the urban air pollution forecast critically depends on the mapping of emissions, the urban air pollution models, and the meteorological data. The quality of the meteorological data should be largely enhanced by using downscaled data from advanced numerical weather prediction models. These different topics, as well as the application of population exposure models, have traditionally been treated in distinct scientific communities whose expertise needs to be combined to enhance the possibilities of forecasting air pollution episodes in European cities. For this purpose the EU project "Integrated Systems for Forecasting Urban Meteorology, Air Pollution and Population Exposure'' (FUMAPEX) (http://fumapex.dmi.dk), involving 22 organizations from 10 European countries, was initiated. The main objectives of the project are the improvement of meteorological forecasts for urban areas, the connection of numerical weather prediction models to urban air pollution and population exposure models, the building of improved Urban Air Quality Information and Forecasting Systems, and their application in cities in various European climates. This paper overviews the project items and first two-years results, it is an introduction to the whole ACP issue.

scholarly journals Overview of the European project FUMAPEX

2005 ◽  
Vol 5 (6) ◽  
pp. 11679-11702 ◽  
Author(s):  
A. Baklanov
Keyword(s):  
Air Pollution ◽  
Prediction Models ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Urban Air Pollution ◽  
Population Exposure ◽  
Urban Air ◽  
Exposure Models ◽  
Numerical Weather Prediction Models

Abstract. The quality of the urban air pollution forecast critically depends on the mapping of emissions, the urban air pollution models, and the meteorological data. The quality of the meteorological data should be largely enhanced by using downscaled data from advanced numerical weather prediction models. These different topics, as well as the application of population exposure models, have traditionally been treated in distinct scientific communities whose expertise needs to be combined to enhance the possibilities of forecasting air pollution episodes in European cities. For this purpose the EU project ''Integrated Systems for Forecasting Urban Meteorology, Air Pollution and Population Exposure'' (FUMAPEX) (http://fumapex.dmi.dk), involving 22 organizations from 10 European countries, was initiated. The main objectives of the project are the improvement of meteorological forecasts for urban areas, the connection of numerical weather prediction models to urban air pollution and population exposure models, the building of improved Urban Air Quality Information and Forecasting Systems, and their application in cities in various European climates. This paper overviews the project items and first two-years results, it is an introduction to the whole ACP issue.

scholarly journals Integrated systems for forecasting urban meteorology, air pollution and population exposure

2007 ◽  
Vol 7 (3) ◽  
pp. 855-874 ◽  
Author(s):  
A. Baklanov ◽  
O. Hänninen ◽  
L. H. Slørdal ◽  
J. Kukkonen ◽  
N. Bjergene ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Health Effects ◽  
Weather Prediction ◽  
Urban Air Pollution ◽  
Urban Air Quality ◽  
Population Exposure ◽  
Quality Information ◽  
Urban Air ◽  
Initial Development

Abstract. Urban air pollution is associated with significant adverse health effects. Model-based abatement strategies are required and developed for the growing urban populations. In the initial development stage, these are focussed on exceedances of air quality standards caused by high short-term pollutant concentrations. Prediction of health effects and implementation of urban air quality information and abatement systems require accurate forecasting of air pollution episodes and population exposure, including modelling of emissions, meteorology, atmospheric dispersion and chemical reaction of pollutants, population mobility, and indoor-outdoor relationship of the pollutants. In the past, these different areas have been treated separately by different models and even institutions. Progress in computer resources and ensuing improvements in numerical weather prediction, air chemistry, and exposure modelling recently allow a unification and integration of the disjunctive models and approaches. The current work presents a novel approach that integrates the latest developments in meteorological, air quality, and population exposure modelling into Urban Air Quality Information and Forecasting Systems (UAQIFS) in the context of the European Union FUMAPEX project. The suggested integrated strategy is demonstrated for examples of the systems in three Nordic cities: Helsinki and Oslo for assessment and forecasting of urban air pollution and Copenhagen for urban emergency preparedness.

scholarly journals Integrated systems for forecasting urban meteorology, air pollution and population exposure

2006 ◽  
Vol 6 (2) ◽  
pp. 1867-1913 ◽  
Author(s):  
A. Baklanov ◽  
O. Hänninen ◽  
L. H. Slørdal ◽  
J. Kukkonen ◽  
N. Bjergene ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Health Effects ◽  
Weather Prediction ◽  
Urban Air Pollution ◽  
Urban Air Quality ◽  
Population Exposure ◽  
Quality Information ◽  
Urban Air ◽  
Initial Development

Abstract. Urban air pollution is associated with significant adverse health effects. Model-based abatement strategies are required and developed for the growing urban populations. In the initial development stage, these are focussed on exceedances of air quality standards caused by high short-term pollutant concentrations. Prediction of health effects and implementation of urban air quality information and abatement systems require accurate forecasting of air pollution episodes and population exposure, including modelling of emissions, meteorology, atmospheric dispersion and chemical reaction of pollutants, population mobility, and indoor-outdoor relationship of the pollutants. In the past, these different areas have been treated separately by different models and even institutions. Progress in computer resources and ensuing improvements in numerical weather prediction, air chemistry, and exposure modelling recently allow a unification and integration of the disjunctive models and approaches. The current work presents a novel approach that integrates the latest developments in meteorological, air quality, and population exposure modelling into Urban Air Quality Information and Forecasting Systems (UAQIFS) in the context of the European Union FUMAPEX project. The suggested integrated strategy is demonstrated for examples of the systems in three Nordic cities: Helsinki and Oslo for assessment and forecasting of urban air pollution and Copenhagen for urban emergency preparedness.

Spatially differentiated and source-specific population exposure to ambient urban air pollution

2009 ◽  
Vol 43 (26) ◽  
pp. 3981-3988 ◽  
Author(s):  
Bin Zou ◽  
J. Gaines Wilson ◽  
F. Benjamin Zhan ◽  
Yongnian Zeng
Keyword(s):  
Air Pollution ◽  
Urban Air Pollution ◽  
Population Exposure ◽  
Urban Air ◽  
Specific Population ◽  
Spatially Differentiated

scholarly journals Evaluation of very high-resolution simulations with the non-hydrostatic numerical weather prediction model Lokalmodell for urban air pollution episodes in Helsinki, Oslo and Valencia

2005 ◽  
Vol 5 (5) ◽  
pp. 8233-8284 ◽  
Author(s):  
B. Fay ◽  
L. Neunhäuserer
Keyword(s):  
Air Pollution ◽  
Meteorological Parameters ◽  
Weather Prediction ◽  
Urban Air Pollution ◽  
Numerical Weather Prediction Model ◽  
Urban Air ◽  
Urban Air Pollution Episodes ◽  
Air Pollution Episodes ◽  
Physiographic Parameters ◽  
Pollution Episodes

Abstract. The operational numerical weather prediction model Lokalmodell LM with 7 km horizontal resolution was evaluated for simulations of the meteorological conditions during observed urban air pollution episodes. The resolution was increased to experimental 2.8 km and 1.1 km resolution by one-way interactive nesting without introducing urbanisation of physiographic parameters or parameterisations. The episodes examined are two severe winter inversion-induced episodes in Helsinki in December 1995 and Oslo in January 2003, three suspended dust episodes in spring and autumn in Helsinki and Oslo, and a late-summer photochemical episode in the Valencia area. The evaluation was basically performed against observations and radiosoundings and focused on the LM skill at forecasting the key meteorological parameters characteristic for the specific episodes. These included temperature inversions, atmospheric stability and low wind speed for the Scandinavian episodes and the development of mesoscale recirculations in the Valencia area. LM forecasts often improved due to higher model resolution especially in mountainous areas like Oslo and Valencia where features depending on topography like temperature, wind fields and mesoscale valley circulations were better described. At coastal stations especially in Helsinki, forecast gains were due to the improved physiographic parameters (land fraction, soil type, or roughness length). The Helsinki and Oslo winter inversions with extreme nocturnal inversion strengths of 18°C were not sufficiently predicted with all LM resolutions. In Helsinki, overprediction of surface temperatures and low-level wind speeds basically led to underpredicted inversion strength. In the Oslo episode, the situation was more complex involving erroneous temperature advection and mountain-induced effects for the higher resolutions. Possible explanations include the influence of the LM treatment of snow cover, sea ice and stability-dependence of transfer and diffusion coefficients. The LM simulations distinctly improved for winter daytime and nocturnal spring and autumn inversions and showed good skill at forecasting further episode-relevant meteorological parameters. The evaluation of the photochemical Valencia episode concentrated on the dominating mesoscale circulation patterns and showed that the LM succeeds well in describing all the qualitative features observed in the region. LM performance in forecasting the examined episodes thus depends on the key episode characteristics and also the season of the year with a need to improve model performance in very stable inversion conditions not only for urban simulations.

scholarly journals A Traffic-Based Method to Predict and Map Urban Air Quality

2020 ◽  
Vol 10 (6) ◽  
pp. 2035 ◽  
Author(s):  
Rasa Zalakeviciute ◽  
Marco Bastidas ◽  
Adrian Buenaño ◽  
Yves Rybarczyk
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Traffic Flow ◽  
Low Income ◽  
Urban Air Pollution ◽  
Monitoring Network ◽  
Time Of Day ◽  
Low Income Countries ◽  
Population Exposure ◽  
Urban Air

As global urbanization, industrialization, and motorization keep worsening air quality, a continuous rise in health problems is projected. Limited spatial resolution of the information on air quality inhibits full comprehension of urban population exposure. Therefore, we propose a method to predict urban air pollution from traffic by extracting data from Web-based applications (Google Traffic). We apply a machine learning approach by training a decision tree algorithm (C4.8) to predict the concentration of PM2.5 during the morning pollution peak from: (i) an interpolation (inverse distance weighting) of the value registered at the monitoring stations, (ii) traffic flow, and (iii) traffic flow + time of the day. The results show that the prediction from traffic outperforms the one provided by the monitoring network (average of 65.5% for the former vs. 57% for the latter). Adding the time of day increases the accuracy by an average of 6.5%. Considering the good accuracy on different days, the proposed method seems to be robust enough to create general models able to predict air pollution from traffic conditions. This affordable method, although beneficial for any city, is particularly relevant for low-income countries, because it offers an economically sustainable technique to address air quality issues faced by the developing world.

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