Abstract: Air quality and climate change are substantial and linked sustainability challenges, and there is a need for improved tools to assess the implications of addressing these challenges together. Due to the high computational cost of accurately assessing these challenges, integrated assessment models (IAMs) used in policy development often use global- or regional-scale marginal response factors to calculate air quality impacts of climate scenarios.
We bridge the gap between IAMs and high-fidelity simulation by developing a computationally-efficient approach to quantify how combined climate and air quality interventions affect air quality outcomes, including capturing spatial heterogeneity and complex atmospheric chemistry. We fit individual response surfaces to high-fidelity model simulation output for 1,525 locations worldwide under a variety of perturbation scenarios. Our approach captures known differences in atmospheric chemical regimes and can be straightforwardly implemented in IAMs, enabling researchers to rapidly estimate how air quality in different locations, and related equity-based metrics, will respond to large-scale changes in emissions policy.
We find that the sensitivity of air quality to climate change and air pollutant emissions reductions differs in sign and magnitude by region, suggesting that calculations of “co-benefits” of climate policy that do not account for the existence of simultaneous air quality interventions can lead to inaccurate conclusions. Although reductions in global mean temperature are effective in improving air quality in many locations and sometimes yield compounding benefits, we show that the air quality impact of climate policy depends on air quality precursor emissions stringency.
Our approach can be extended to include results from higher-resolution modeling, but also to incorporate other interventions towards sustainable development which interact with climate action and have spatially-distributed equity dimensions.
