[基于随机森林模型的四川盆地臭氧污染预测]
Xiao-Tong Yang1, Ping Kang2, An-Yi Wang2
1High Impact Weather Key Laboratory of China Meteorological Administration, College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410000, China.
Huan jing ke xue= Huanjing kexue
|April 17, 2024
概括
四川盆地臭氧 (O3) 污染在2017-2020年间波动,受温度和湿度的影响. 一个随机森林模型准确地预测了O3趋势,为污染控制策略提供了洞察力.
科学领域:
- 环境科学 环境科学
- 大气化学 大气化学
- 数据科学数据科学数据科学
背景情况:
- 臭氧 (O3) 污染是一个重大的环境问题,特别是在城市聚集地.
- 了解O3污染的长期变化和影响因素对于有效的缓解策略至关重要.
研究的目的:
- 分析2017年至2020年四川盆地O3度的时空分布和长期变化.
- 确定影响O3污染的关键气象因素,并构建一个预测模型.
- 评估开发的O3预测模型的准确性和稳定性.
主要方法:
- 利用了来自四川盆地的18个城市 (2017-2020) 的地面O3度数据和气象观测数据.
- 采用随机森林模型来选主要的气象因素,并构建O3度的预测模型.
- 在2020年期间,在四川盆地城市聚集地进行了O3污染的预测分析.
主要成果:
- 在2017-2020年期间,O3度呈现波动趋势,2019年下降,2020年上升.
- 相对湿度,每日最高温度和日照时间被确定为影响O3波动的主要因素.
- 随机森林模型表现出强大的预测性能 (大多数城市的R2>0.80),高稳定性和O3度预测的概括能力.
结论:
- 气象因素显著影响四川盆地O3污染动态.
- 随机森林模型为准确预测长期O3度变化提供了可靠的工具.
- 这些发现支持该地区制定有针对性的空气质量管理战略.
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