Improving of local ozone forecasting by integrated models.

Area of Science:

• Environmental Science
• Atmospheric Chemistry
• Artificial Intelligence in Environmental Monitoring

Background:

• Accurate forecasting of maximum ozone concentrations in urban areas is essential for timely public health alerts.
• Existing air-quality and meteorological models often lack the necessary local resolution for effective urban forecasting.
• Empirical methods offer better local forecast accuracy but may not capture complex atmospheric dynamics.

Purpose of the Study:

• To propose and evaluate an integrated modeling methodology for improving 1-day-ahead ozone concentration forecasts in urban microlocations.
• To enhance the reliability of air pollution alert systems by improving forecast accuracy at the local level.

Main Methods:

• Development of an integrated model utilizing multilayer perceptron neural networks.
• Input data includes outputs from the QualeAria air-quality model, the WRF numerical weather prediction model, and onsite meteorological and air pollution measurements.
• The neural network integrates diverse data sources to leverage the strengths of both large-scale models and local empirical data.

Main Results:

• The integrated model demonstrated a noticeable improvement in 1-day-ahead ozone forecasts for representative locations in Slovenia.
• Validation results confirm the enhanced accuracy of the proposed integrated approach compared to standalone models.
• The improved forecasts contribute to more effective and reliable public alert systems for ozone pollution.

Conclusions:

• Integrated modeling, combining numerical predictions with ground-level measurements via neural networks, is a highly effective strategy for urban ozone forecasting.
• This methodology significantly enhances the accuracy of maximum daily ozone concentration predictions.
• The findings support the implementation of advanced integrated models for better air quality management and public health protection in urban environments.