Predicting photochemical pollution in an industrial area

Area of Science:

• Environmental Science
• Atmospheric Chemistry
• Data Science

Background:

• The Thriassion Plain, particularly Elefsis, faces significant pollution challenges.
• Accurate forecasting of air pollutants is crucial for environmental management and public health.

Purpose of the Study:

• To develop and evaluate a predictive model for daily maximum concentrations of nitrogen oxides (NOx) and ozone (O3).
• To provide reliable air quality forecasts for the Elefsis region.

Main Methods:

• Utilized hourly pollutant data (NOx, NO2, O3) and meteorological data from 1993-1999.
• Employed three statistical fitting methods: ordinary least squares, piecewise regression, and quantile regression.
• Validated model performance using 2000 measurement data.

Main Results:

• The developed models demonstrated satisfactory forecasting capabilities.
• Predicted pollutant concentrations generally followed observed trends.
• Quantile regression showed particular promise in capturing pollution variability.

Conclusions:

• The forecasting models offer a valuable tool for monitoring and managing air quality in Elefsis.
• The study highlights the effectiveness of statistical modeling in predicting air pollution events.
• Continued refinement of these models can enhance environmental protection strategies.