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Atomic emission spectroscopy (AES) is an analytical technique used to determine the elemental composition of a sample by analyzing the light emitted from excited atoms. In AES, atoms in a sample are excited to higher energy levels by thermal energy from high-temperature sources, such as plasma, arcs, or sparks. When these excited atoms return to lower energy states, they emit light at specific wavelengths characteristic of each element. The resulting atomic emission spectrum, which consists of...
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Composition and Distribution Analysis of Bioaerosols Under Different Environmental Conditions
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高时分辨率PM2.5来源分配,辅助的是基于频谱的特征分析.

Jie Liu1, Fangjingxin Ma2, Tse-Lun Chen3

  • 1School of Water Conservancy & Civil Engineering, Northeast Agricultural University, Harbin 150030, China; Institute of Environmental Engineering (IfU), ETH Zürich, 8093 Zürich, Switzerland.

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高时分辨率的高时间分辨率.污染 PM ((2.5) 的污染.来源分配的分配方式频谱分析是一种分析.频谱特征提取 频谱特征提取

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科学领域:

  • 环境科学 环境科学
  • 大气化学 大气化学
  • 数据分析 数据分析

背景情况:

  • 颗粒物 (PM2.5) 污染需要有效的来源分配,以制定有效的缓解策略.
  • 频谱分析为提取PM2.5污染事件的特征提供了潜力.

研究的目的:

  • 通过光谱分析和受体建模,开发和应用PM2.5来源分配的综合框架.
  • 提取PM2.5污染异常的频谱特征,以改进来源识别.

主要方法:

  • 组合快速里叶变换 (FFT) 和连续波纹变换 (CWT) 用于光谱分析.
  • 使用正矩阵因子化 (PMF) 受体模型进行源贡献评估.
  • 在冬季加热期间,将框架应用于北京每小时的PM2.5数据.

主要成果:

  • 成功捕获了PM2.5污染异常的频谱特征 (频率,位置,持续时间,强度).
  • 在北京的冬季加热期间,确定二次无机气溶是占主导地位的PM2.5来源 (50.59%).
  • 其他来源的量化贡献:生物质燃烧 (15.01%),车辆排放 (11.00%),煤炭燃烧 (10.70%),道路灰尘 (5.31%),工业过程 (3.88%) 和烟花 (3.51%).

结论:

  • 综合光谱分析和PMF框架为PM2.5污染动态提供了强大的洞察力.
  • 这种方法提高了对PM2.5.5的时间演变,来源识别和贡献量化的理解.