SO2 Emission Reductions Shift Sulfate Oxidation Pathways in the Northeastern United States: Evidence from Δ17O(ASO4), CMAQ, and Machine Learning

The Sulfur Tracking Mechanism in CMAQ was used to model the oxygen isotope anomaly (Δ17O) of aerosol sulfate (ASO4) for 2006, 2010, and 2014. The CMAQ model simulations were compared with monthly integrated Δ17O(ASO4) observations from Providence and West Greenwich, Rhode Island. The key drivers of Δ17O variability was diagnosed using machine learning approaches.本研究采用CMAQ中的硫追踪机制，对2006年、2010年及2014年的气溶胶硫酸盐（ASO4，aerosol sulfate）的氧同位素异常（Δ17O）开展模拟。将CMAQ模型的模拟结果与美国罗德岛州普罗维登斯和西格林威治获取的逐月积分Δ17O(ASO4)观测数据进行对比。本研究借助机器学习方法，解析了Δ17O变化的关键驱动因子。