A New Methodology for High Spatiotemporal Resolution Measurements of Air Volatile Organic Compounds: From Sampling to Data Deconvolution

反褶积 采样(信号处理) 环境科学 遥感 分辨率(逻辑) 样品(材料) 图像分辨率 挥发性有机化合物 计算机科学 化学 算法 地质学 人工智能 色谱法 有机化学 滤波器(信号处理) 计算机视觉
作者
Yanrong Yang,Jietao Zhou,Conghui Xie,Tian Wang,Ming Xue,Tianran Han,Keyu Chen,Yuheng Zhang,Yayong Liu,Yufei Huang,Haijiong Sun,Chang Liu,Shao‐Meng Li
出处
期刊:Environmental Science & Technology [American Chemical Society]
卷期号:58 (28): 12488-12497 被引量:3
标识
DOI:10.1021/acs.est.4c05669
摘要

Monitoring of volatile organic compounds (VOCs) in air is crucial for understanding their atmospheric impacts and advancing their emission reduction plans. This study presents an innovative integrated methodology suitable for achieving semireal-time high spatiotemporal resolution three-dimensional measurements of VOCs from ground to hundreds of meters above ground. The methodology integrates an active AirCore sampler, custom-designed for deployment from unmanned aerial vehicles (UAV), a proton-transfer-reaction mass spectrometry (PTR-MS) for sample analysis, and a data deconvolution algorithm for improved time resolution for measurements of multiple VOCs in air. The application of the deconvolution technique significantly improves the signal strength of data from PTR-MS analysis of AirCore samples and enhances their temporal resolution by 4 to 8 times to 4–11 s. A case study demonstrates that the methodology can achieve sample collection and analysis of VOCs within 45 min, resulting in >120–360 spatially resolved data points for each VOC measured and achieving a horizontal resolution of 20–55 m at a UAV flight speed of 5 m/s and a vertical resolution of 5 m. This methodology presents new possibilities for acquiring 3-dimensional spatial distributions of VOC concentrations, effectively tackling the longstanding challenge of characterizing three-dimensional VOC distributions in the lowest portion of the atmospheric boundary layer.
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