Rapid Estimation of Climate-Air Quality Interactions in Integrated Assessment Using a Response Surface Model

Area of Science:

• Environmental Science
• Atmospheric Chemistry
• Climate Modeling

Background:

• Air quality and climate change are interconnected sustainability issues.
• Current integrated assessment models (IAMs) often lack the fidelity to capture complex air quality responses to climate policy.
• A computational gap exists between high-fidelity simulations and policy-relevant models.

Purpose of the Study:

• To develop a computationally efficient approach for assessing combined climate and air quality interventions.
• To bridge the gap between high-fidelity atmospheric simulations and IAMs.
• To quantify spatial heterogeneity in air quality outcomes and equity metrics.

Main Methods:

• Fitting response surfaces to high-fidelity model simulation output for 1525 global locations.
• Developing a method to capture complex atmospheric chemistry and spatial variations.
• Implementing the approach for rapid estimation of air quality responses in IAMs.

Main Results:

• The sensitivity of air quality to climate change and emission reductions varies significantly by region.
• Ignoring simultaneous air quality interventions in climate policy assessments can lead to inaccurate co-benefit calculations.
• The air quality impact of climate policy is contingent on emission control stringency.

Conclusions:

• The developed approach enables rapid, spatially resolved estimation of air quality impacts from combined interventions.
• Regional differences in air quality response highlight the need for tailored policy design.
• The method can be extended to incorporate higher-resolution data and other sustainable development interventions.