Hyperspectral remote sensing for air pollutants: Stereoscopic monitoring, source localization & warning, and a dynamic emission inventory concept

With the continuous improvement of air quality in China, the characteristics of emission sources of pollutants have changed significantly, from their distribution to emitted atmospheric species and the corresponding emission concentrations and source localization has become increasingly challenging. The localization uncertainties of in situ observations are further amplified when combined with model simulations, which seriously restricts the realization of China's strategic goal of "reducing pollution and carbon." In this study, we established a localization and emission warning scheme for emission sources based on various hyperspectral remote sensing techniques with different observation spatial resolutions. These include satellite remote sensing, horizontal remote sensing, Unmanned Aerial Vehicle (UAV) remote sensing, and imaging. Based on this study, we aimed to locate high-concentration emission sources of NO₂ (coal-fired power plants), HCHO (chemical and coking industries), and CH₂CCH₃CHO (metallurgical and material synthesis industries) and provide excess emission warnings for these species. Moreover, hyperspectral imaging remote sensing technology provides a possible method to obtain a dynamic emission inventory of pollutants, and the emission concentrations of NO₂, SO₂, HCHO, CHOCHO, and CH₂CCH₃CHO emitted from the coking industry at different timescales were obtained. The localization and emission warning scheme of pollutants established based on stereoscopic remote sensing, as well as the dynamic emission inventory established based on hyperspectral imaging remote sensing, provides technical and data support for air pollution control efforts.