A non-linear optimization programming model for air quality planning including co-benefits for GHG emissions

Area of Science:

• Environmental Science
• Operations Research
• Climate Change Analysis

Background:

• Air quality management requires integrated strategies considering multiple pollutants and scales.
• Greenhouse gas (GHG) emissions reduction efforts can yield co-benefits for local air quality.
• Cost-effective planning is crucial for implementing air quality improvement measures.

Purpose of the Study:

• Introduce the Multi-dimensional Air Quality (MAQ) model for cost-effective air quality planning.
• Assess co-benefits of air quality plans on GHG emissions.
• Support decision-making for integrated environmental policies.

Main Methods:

• Developed a non-linear, multi-objective, multi-pollutant decision problem framework.
• Decision variables include emission abatement measures: energy consumption reduction, end-of-pipe technologies, and fuel switching.
• Optimized for minimization of tropospheric secondary pollution exposure and internal costs.

Main Results:

• The MAQ model effectively integrates air quality and climate change mitigation objectives.
• Identified potential win-win policies offering reductions in both pollution and GHG emissions.
• Demonstrated the model's applicability in a real-world, highly polluted region (Lombardy, Italy).

Conclusions:

• The MAQ model provides a robust tool for developing integrated and cost-effective environmental policies.
• Supports decision-makers in selecting strategies that simultaneously improve air quality and reduce GHG emissions.
• Highlights the importance of considering co-benefits in environmental planning for maximum societal benefit.