Eastham Sebastian D, Monier Erwan, Rothenberg Daniel, Paltsev Sergey, Selin Noelle E
Laboratory for Aviation and the Environment, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
Joint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.
ACS Environ Au. 2023 Feb 14;3(3):153-163. doi: 10.1021/acsenvironau.2c00054. eCollection 2023 May 17.
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 1525 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 emission policy. We find that the sensitivity of air quality to climate change and air pollutant emission 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 emission stringency. Our approach can be extended to include results from higher-resolution modeling and also to incorporate other interventions toward sustainable development that interact with climate action and have spatially distributed equity dimensions.
空气质量和气候变化是重大且相互关联的可持续发展挑战，因此需要改进工具来评估共同应对这些挑战的影响。由于准确评估这些挑战的计算成本很高，政策制定中使用的综合评估模型（IAMs）通常使用全球或区域尺度的边际响应因子来计算气候情景对空气质量的影响。我们通过开发一种计算效率高的方法来弥合综合评估模型与高保真模拟之间的差距，以量化气候和空气质量综合干预如何影响空气质量结果，包括捕捉空间异质性和复杂的大气化学过程。我们针对全球1525个地点在各种扰动情景下的高保真模型模拟输出拟合了个体响应面。我们的方法捕捉了大气化学状态中已知的差异，并且可以直接在综合评估模型中实施，使研究人员能够快速估计不同地点的空气质量以及基于公平性的相关指标将如何应对排放政策的大规模变化。我们发现，空气质量对气候变化和空气污染物减排的敏感性在不同地区的正负和幅度上存在差异，这表明在不考虑同时进行的空气质量干预措施的情况下计算气候政策的“协同效益”可能会得出不准确的结论。虽然全球平均温度的降低在许多地方对改善空气质量有效，有时还会产生复合效益，但我们表明气候政策对空气质量的影响取决于空气质量前体排放的严格程度。我们的方法可以扩展到纳入更高分辨率建模的结果，还可以纳入其他与气候行动相互作用且具有空间分布公平维度的可持续发展干预措施。
