基于机器学习方法的沿海城市核化模式粒子形成的新见解
Chen Yang1,2,3, Hesong Dong3,4, Yuping Chen1,2,3
1Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, Fujian 361021, People's Republic of China.
Environmental science & technology
|December 20, 2023
概括
机器学习模型准确地模拟了大气核化模式粒子数度 (PNCN). 影响PNCN的关键因素包括UV,H2SO4和有机分子,对于理解粒子形成至关重要.
科学领域:
- 大气化学 大气化学
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 超细大气颗粒由于其高度和毒性,对气候和健康产生重大影响.
- 了解驱动超细颗粒行为的因素对于空气质量管理至关重要.
- 机器学习 (ML) 为分析复杂的环境数据提供了强大的工具.
研究的目的:
- 开发和评估用于模拟核化模式粒子数度 (PNCN) 的机器学习模型.
- 使用现场观测数据,识别影响PNCN的关键大气因素.
- 探索ML作为空气污染物模拟的新方法的潜力.
主要方法:
- 基于现场观察的三种机器学习模型的构建.
- 使用开发的ML模型模拟PNCN.
- 特性重要性分析和双变的部分依赖图 (PDPs) 来识别影响因素.
主要成果:
- 这三种ML模型都显示出强大的PNCN复制 (R2>0.80).
- 随机森林 (RF) 模型在测试数据上取得了最高的准确性 (R2 = 0.89).
- 紫外线 (UV) 辐射,硫酸 (H2SO4),低挥发性氧化有机分子 (LOOM),温度和臭氧 (O3) 被确定为PNCN的主要驱动因素.
结论:
- 机器学习为模拟PNCN等大气污染物提供了一种新且有效的方法.
- 在夜间和多云条件下,H2SO4和LOOM对PNCN尤为重要.
- 通过将详细的排放和气象数据集成到ML模型中,可以实现未来的改进.
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