探索气溶垂直分布及其影响因素:MAX-DOAS和机器学习的洞察力
Sanbao Zhang1, Shanshan Wang1,2, Juntao Huo3
1Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China.
Environmental science & technology
|June 4, 2025
概括
了解气溶垂直分布是控制污染的关键. 这项研究使用先进的建模来揭示季节性模式和驱动因素,强调减排和大气化学,以制定有效的缓解策略.
科学领域:
- 大气科学 大气科学
- 环境科学 环境科学
- 遥感 遥感 遥感 遥感
背景情况:
- 气溶垂直分布对于空气污染管理至关重要,但由于数据有限,人们对其了解甚少.
- 准确的气溶分析对于制定有效的污染减缓策略至关重要.
研究的目的:
- 为了检索高分辨率的气溶光学特性,并了解它们在上海的垂直分布.
- 调查影响不同高度和季节气溶变化的关键驱动因素.
- 根据对气溶动态的多维理解,为有针对性的污染控制策略提供信息.
主要方法:
- 使用多轴差光学吸收光谱仪 (MAX-DOAS) 进行气溶测量.
- 采用合辐射转移模型-机器学习 (RTM-ML) 框架进行物业检索.
- 应用多因素驱动ML模型和沙普利增量解释 (SHAP) 来识别影响因素.
主要成果:
- 气溶通常随着高度的增加而减少,夏季高峰在上层大气层,冬季高峰在下层大气层.
- 气溶的高度显示季节性变化,并随着海拔高度的增加而增加.
- 在0.5公里以下,排放,东西运输和氧化占主导地位;在0.5公里以上,湿度和氧化是关键驱动因素.
- 北南运输对在0.5至1.6公里之间的气溶产生重大影响.
结论:
- 减少排放对于降低低大气层气溶是有效的.
- 强化大气氧化促进了二次气溶的形成,特别是在大气上层.
- 有效的污染减缓需要采用多维方法,考虑影响垂直气溶分布的各种因素.
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