使用机器学习和SHAP分析,揭示区域臭氧模拟偏差的关键来源
Xin Yuan1, Xinlong Hong1, Zhijiong Huang1
1College of Environment and Climate, Institute for Environmental and Climate Research, Jinan University, Guangzhou, 511443, China.
Environmental pollution (Barking, Essex : 1987)
|March 8, 2025
概括
机器学习准确地确定了氧化 (NOx) 排放是广东省臭氧模拟偏差的主要驱动因素. 改进NOx排放数据显著减少臭氧预测错误,并改善空气质量建模.
科学领域:
- 大气化学和空气质量建模.
背景情况:
- 大气化学物质运输模型 (CTM) 对空气质量管理至关重要,但存在显著的模拟偏差.
- 识别偏差源的传统方法在计算上昂贵,需要大量的模型运行.
研究的目的:
- 开发和应用一种高效的机器学习方法,XGBoost与SHAP分析,用于诊断CTM中的模拟偏差.
- 确定导致广东省臭氧模拟偏差的关键因素.
主要方法:
- 使用XGBoost和SHAP分析来确定臭氧建模中的偏差来源.
- 采用模型输入偏差作为特征和排除的数据容易产生观测不确定性.
- 专注于广东省的臭氧建模作为一个案例研究.
主要成果:
- 在NO2,NO,PM2.5度,温度和生物源排放方面的偏差被确定为臭氧模拟偏差的重要贡献者.
- 氧化物 (NOx) 排放成为主要的偏差来源,特别是在秋季和冬季的挥发性有机化合物 (VOC) 限制条件下.
- 低估的NOx排放扭曲了NO2-O3关系和珍珠河三角洲 (PRD) 的VOC限制制度的空间表示.
结论:
- 提高NOx排放估计减少了34%的PRD中臭氧模拟偏差.
- 机器学习方法有效地识别了关键偏差驱动因素,并改善了大气化学的表示.
- 这种方法为改善CTM和空气质量管理提供了计算效率高的替代方案.
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