Numerical: Air Quality

1. Regional Air Quality Deterministic Prediction System (RAQDPS)

Description:

The RAQDPS is designed to produce deterministic forecasts of air quality by accounting for various physical and chemical processes in the atmosphere. It focuses on predicting concentrations of chemical species that are significant to air quality.

eccc-msc.github.io

Available Variables:

• Ozone (O₃): Predicted concentrations of ozone near the surface.

• Sulfur Dioxide (SO₂): Forecasted levels of sulfur dioxide.

• Nitric Oxide (NO) and Nitrogen Dioxide (NO₂): Predicted concentrations of these nitrogen oxides.

• Fine Particulate Matter (PM₂.₅): Forecasted concentrations of particulate matter with a diameter of 2.5 micrometers or less.

• Coarse Particulate Matter (PM₁₀): Predicted levels of particulate matter with a diameter of 10 micrometers or less.

Potential AI/ML Applications:

• Developing predictive models for air quality indices.

• Assessing the impact of various pollutants on public health.

• Enhancing real-time air quality monitoring systems.

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2. Regional Deterministic Air Quality Analysis (RDAQA)

Description:

The RDAQA system provides analyses of air quality by integrating observational data and model outputs. It focuses on key pollutants such as ozone and fine particulate matter (PM₂.₅) at the surface level.

researchgate.net

Available Variables:

• Ozone (O₃): Analyzed concentrations at the surface level.

• Fine Particulate Matter (PM₂.₅): Analyzed surface-level concentrations.

Potential AI/ML Applications:

• Improving the accuracy of air quality forecasts through data assimilation techniques.

• Identifying spatial and temporal patterns in pollutant distribution.

• Evaluating the effectiveness of emission reduction strategies.