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An integrated air quality forecast system for a metropolitan area
Claudio Carnevale1, Giovanna Finzi, Enrico Pisoni
1Department of Information Engineering, University of Brescia, Via Branze 38, I-25123, Brescia, Italy.
Journal of Environmental Monitoring : JEM
|November 1, 2011
Summary
This study developed an integrated system for air quality forecasting, predicting ozone and PM10 pollution up to two days ahead. The system uses artificial neural networks and cokriging for reliable environmental health assessments.
Area of Science:
- Environmental Science
- Atmospheric Chemistry
- Public Health
Background:
- Air quality forecasting is crucial due to the health impacts of air pollutants.
- Accurate predictions are needed to implement public health protection measures.
Purpose of the Study:
- To develop and validate an integrated modeling system for forecasting daily maximum 8-hour ozone and daily mean PM10 concentrations.
- To provide air quality forecasts up to two days in advance for urban areas.
Main Methods:
- An integrated modeling system combining artificial neural networks (ANNs) for point-wise forecasting and cokriging for spatial interpolation.
- ANNs were used for initial predictions at monitoring sites.
- Cokriging enhanced spatial interpolation, especially with sparse data.
Main Results:
- The system successfully forecasted daily maximum 8-hour ozone and daily mean PM10 concentrations.
- Validation was performed over Northern Italy for the year 2004.
- The study analyzed compliance with European Commission health protection limit values.
Conclusions:
- The integrated modeling system offers a fast and reliable method for air quality forecasting.
- It supports decision-makers and the public in implementing prevention and precautionary measures for environmental health.
- The approach is effective for urban air quality management and health protection.
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