Exploring the applicability of future air quality predictions based on synoptic system forecasts.

Area of Science:

• Atmospheric chemistry and physics
• Climate science
• Environmental monitoring

Background:

• Atmospheric conditions significantly influence air pollutant levels and distribution.
• Variability in air quality is primarily driven by synoptic-scale atmospheric patterns, beyond daily and seasonal cycles.

Purpose of the Study:

• To introduce a straightforward methodology for predicting future air pollutant concentrations.
• To evaluate the effectiveness of this method for both short-term forecasting and long-term climate change impact assessments.

Main Methods:

• Utilizing atmospheric synoptic forecasts to predict air pollutant concentrations.
• Comparing the methodology's performance against persistence and seasonal forecast models for short-term air quality classification.
• Applying the method to assess air quality variability under climate change scenarios.

Main Results:

• The proposed methodology demonstrates comparable or superior performance to persistence and seasonal forecasts in classifying pollution levels over short time scales.
• The approach is validated for long-term air quality studies, particularly in the context of climate change where traditional methods are insufficient.
• Air quality variations resulting from changes in emissions are projected to be substantially greater than those attributable to climate change impacts.

Conclusions:

• A synoptic forecast-based methodology offers a viable approach for assessing future air pollutant concentrations.
• This method is applicable across different time scales, from short-term predictions to long-term climate change analyses.
• Emissions changes are a more dominant driver of future air quality variability than climate change effects.