[基于随机森林的北京城市臭氧敏感性分析]
Hong Zhou1, Ming Wang1, Wen-Xuan Chai2
1Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing 210044, China.
Huan jing ke xue= Huanjing kexue
|April 17, 2024
概括
这项研究分析了北京的臭氧 (O3) 和其前体,发现温度和氧化 (NOx) 显著影响O3水平. 研究表明,北京是北京.
科学领域:
- 大气化学和空气污染
- 环境科学 环境科学
- 数据科学和机器学习应用程序数据科学和机器学习应用程序
背景情况:
- 臭氧 (O3) 形成是一个复杂的过程,受其前体,挥发性有机化合物 (VOC) 和氧化 (NOx) 的影响.
- 了解O3及其前体之间的非线性关系对于有效的污染控制策略至关重要.
- 之前的研究已经使用了各种模型,但补充方法对灵敏度分析有价值.
研究的目的:
- 分析北京城市的O3和前体污染特征.
- 使用先进的建模技术,确定影响O3度的关键因素.
- 在北京确定O3-VOCs-NOx灵敏度制度.
主要方法:
- 在2020年4月至9月期间,收集了O3,VOC,NOx和气象元素的在线观测数据.
- 采用随机森林 (RF) 模型与SHAP值相结合,分析O3影响因素.
- 进行了多场景分析,以探索O3-VOCs-NOx灵敏度和生成的O3异位曲线 (EKMA曲线).
主要成果:
- 每小时的O3度与温度 (T) 有正相关性,与TVOC和NOx有负相关性.
- 每日O3度与T,TVOC和NOx正相关.
- 射频模型模拟与观察到的O3值密切匹配; T和NOx被确定为对O3最有影响的因素.
结论:
- 在北京城市的O3-VOCs-NOx灵敏度运行在一个VOCs限制的制度下.
- 随机森林模型是O3-VOCs-NOx灵敏度分析的可行补充工具.
- 这些发现支持有针对性的排放控制策略,重点关注VOC和NOx,以减少O3.
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