Li Huimin, Yang Yang, Wang Hailong, Wang Pinya, Yue Xu, Liao Hong
Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China.
Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States.
Environ Sci Technol. 2022 Apr 5;56(7):3884-3893. doi: 10.1021/acs.est.1c04380. Epub 2022 Mar 16.
Projection of future aerosols and understanding the driver of the aerosol changes are of great importance in improving the atmospheric environment and climate change mitigation. The latest Coupled Model Intercomparison Project Phase 6 (CMIP6) provides various climate projections but limited aerosol output. In this study, future near-surface aerosol concentrations from 2015 to 2100 are predicted based on a machine learning method. The machine learning model is trained with global atmospheric chemistry model results and projects aerosols with CMIP6 multi-model simulations, creatively estimating future aerosols with all important species considered. PM (particulate matter less than 2.5 μm in diameter) concentrations in 2095 (2091-2100 mean) are projected to decrease by 40% in East Asia, 20-35% in South Asia, and 15-25% in Europe and North America, compared to those in 2020 (2015-2024 mean), under low-emission scenarios (SSP1-2.6 and SSP2-4.5), which are mainly due to the presumed emission reductions. Driven by the climate change alone, PM concentrations would increase by 10-25% in northern China and western U.S. and decrease by 0-25% in southern China, South Asia, and Europe under the high forcing scenario (SSP5-8.5). A warmer climate exerts a stronger modulation on global aerosols. Climate-driven global future aerosol changes are found to be comparable to those contributed by changes in anthropogenic emissions over many regions of the world in high forcing scenarios, highlighting the importance of climate change in regulating future air quality.
预测未来气溶胶并了解气溶胶变化的驱动因素对于改善大气环境和缓解气候变化至关重要。最新的耦合模式比较计划第六阶段（CMIP6）提供了各种气候预测，但气溶胶输出有限。在本研究中，基于机器学习方法预测了2015年至2100年未来近地面气溶胶浓度。该机器学习模型使用全球大气化学模型结果进行训练，并通过CMIP6多模式模拟预测气溶胶，创造性地在考虑所有重要物种的情况下估计未来气溶胶。在低排放情景（SSP1-2.6和SSP2-4.5）下，预计2095年（2091-2100年平均值）东亚的细颗粒物（直径小于2.5μm的颗粒物）浓度将比2020年（2015-2024年平均值）下降40%，南亚下降20-35%，欧洲和北美下降15-25%，这主要是由于假定的排放减少。在高辐射情景（SSP5-8.5）下，仅受气候变化驱动，中国北方和美国西部的细颗粒物浓度将增加10-25%，而中国南方、南亚和欧洲的细颗粒物浓度将下降0-25%。气候变暖对全球气溶胶的调制作用更强。发现在高辐射情景下，气候驱动的全球未来气溶胶变化与世界许多地区人为排放变化所导致的变化相当，这突出了气候变化在调节未来空气质量方面的重要性。
