Journal of Remote Sensing (Jan 2023)

A Novel Hyperspectral Remote Sensing Technique with Hour-Hectometer Level Horizontal Distribution of Trace Gases: To Accurately Identify Emission Sources

  • Chuan Lu,
  • Qihua Li,
  • Chengzhi Xing,
  • Qihou Hu,
  • Wei Tan,
  • Hua Lin,
  • Jinan Lin,
  • Zhiguo Zhang,
  • Bowen Chang,
  • Cheng Liu

DOI
https://doi.org/10.34133/remotesensing.0098
Journal volume & issue
Vol. 3

Abstract

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High spatial-temporal resolution distribution of atmospheric gaseous pollutant is an important basis for tracing its emission, transport, and transformation. Typical methods for acquiring regional atmospheric gaseous pollutant distributions are satellite remote sensing and in situ observations. However, these approaches have limitations, such as sparse overpass times for satellites and restricted coverage for in situ monitoring. In this study, we propose a method for the long-term detection of the horizontal distribution of trace gases. This method based on effective optical paths (EOPs) as the instrument's detection range. It acquires the average trace gas concentration along the EOPs by utilizing different detection distances within the ultraviolet (UV) and visible (VIS) spectral bands. Subsequently, we use the onion-peeling method to obtain trace gas concentrations at two distinct distances. The obtained trace gas horizontal distribution was consistent with the in situ and mobile measurements. Compared with satellite remote sensing, this method achieved horizontal distribution results with higher spatial and temporal resolutions, and located several small high-value areas in Hefei, China. The tropospheric NO2 vertical column density (VCD) results of the satellite at transit time (13:30) were consistent with the hyperspectral NO2 horizontal distribution results at 13:00 to 14:00 on the same day but were not consistent with the daily average NO2 results. The hourly NO2 concentration in each area was 10% to 40% lower than the daytime average obtained by the hyperspectral remote sensing result. We evaluated the errors associated with the calculation of NO2 emissions based on the satellite results and found a bias of approximately 69.45% to 83.34%. The spatial distribution of NO2 concentration obtained from MAX-DOAS measurements may help in future bottom-up emission calculations.