Remote Sensing on Carbon Dioxide Emissions in Power Plants and Urban Areas Based on DOAS Technology

Objective Carbon dioxide (CO2), methane (CH4), water vapor (H2O), and other greenhouse gases have the ability to absorb longwave radiation emitted by the earth to cause the greenhouse effect. The escalating greenhouse effect has resulted in a series of climate and environmental degradation issues. Accurate measurement of CO2 gas concentration and estimation of CO2 emission intensity from emission sources are of significance for controlling greenhouse gas emissions. Remote sensing methods based on passive differential optical absorption spectroscopy (DOAS) technology can measure the concentration of multiple gas components in real-time with high precision and can capture a wide range of gas concentration variations. Currently, researchers both domestically and internationally have made corresponding progress in measuring greenhouse gas concentrations and emission fluxes from emission sources using DOAS technology, mainly focusing on satellite and airborne platforms. However, there is relatively less research on emission flux estimation from typical emission sources using DOAS remote sensing methods on ground-based platforms. Focusing on typical emission sources and urban areas as the research targets, we employ a ground-based near-infrared spectroscopy remote sensing system coupled with differential absorption spectroscopy technology to retrieve the two-dimensional distribution of CO2 column concentration. Based on these results, the emission fluxes from typical emission sources are estimated to provide a reliable technique and method for remote sensing of carbon emissions. Methods The near-infrared DOAS algorithm for retrieving CO2 concentration information is first studied, and the spectral range and interfering gases for CO2 inversion are selected by analyzing the distribution of gas absorption line intensity in the HITRAN database. Meanwhile, we calculate the effective absorption cross-sections of the gases in the measurement environment through line broadening and convolution. The absorption cross-sections of CH4 and H2O are included in the retrieval to account for their interference in the absorption spectra. The influence of reference spectrum selection on the retrieval results is analyzed, and ultimately the zenith direction spectrum is chosen as the reference spectrum. The CO2 column concentration information is obtained using least-squares fitting. By subtracting the gas concentration in the first column upwind from the obtained CO2 column concentration and considering the angular information of the remote sensing system, the two-dimensional concentration distribution of CO2 columns in the vicinity of the power plant and the boundary layer of Hefei is obtained. The bicubic interpolation algorithm is applied to achieve high spatial resolution for the two-dimensional distribution of CO2 column concentrations. Furthermore, the CO2 emission flux from the power plant is calculated, and the error sources are analyzed. Results and Discussions We choose the background spectrum as the reference spectrum, and employ the DOAS algorithm to retrieve CO2 column density. The retrieval error can reach 0. 79%. The remote sensing results of a chimney in a power plant in Hefei show that high concentrations of CO2 are concentrated above the chimney outlet, and the slant column density of CO2 at the emission hotspot is approximately 3. 36x1021 molecule/cm(2) higher than the background concentration (Fig. 9). The high concentrations of CO2 emitted from the power plant are mainly distributed within a height range of 35 m above the chimney outlet (Fig. 10). By utilizing the bicubic interpolation algorithm, the spatial resolution of the power plant's CO2 concentration distribution map is improved from 5 mx5 m to 1. 25 mx1. 25 m (Fig. 11). The emission flux from the power plant is about 1925 kg and the distance estimation error is the largest error source in the flux measurement. The remote sensing results in the boundary layer of Hefei indicate that both the power plant and the urban area have higher concentrations compared to the suburban area. After subtracting the complex background, the highest concentration in the urban area reaches up to 2. 58x1021 molecule/cm(2). The thickness of the high-concentration layer in the power plant is approximately 279. 2 m, while it is 418. 8 m in the urban area (Fig. 13). Conclusions We introduce a near-infrared spectroscopy remote sensing system and estimation method for measuring CO2 column density distribution and emission fluxes, and investigate the distribution characteristics of CO2 from typical point sources and urban emissions. A power plant is selected as a point source for emission research. Scanning measurements are conducted in the vertical direction of the power plant plume dispersion to obtain a two-dimensional concentration distribution map of CO2 emissions from the power plant. The CO2 emission flux is calculated as about 1925 kg, and observations and research on the CO2 distribution characteristics in the atmospheric boundary layer of Hefei are also conducted. Preliminary results show that the high concentration values in the boundary layer are primarily distributed near the ground level, and the concentrations in the power plant and urban areas are significantly higher than those in the suburbs. This suggests that the fuel combustion in the power plant and emissions from transportation and manufacturing activities in urban areas play a major role in atmospheric CO2 concentration and distribution. Finally, we provide effective techniques and methods for estimating carbon emissions. The next step will involve adopting three-dimensional modeling to investigate the three-dimensional spatial distribution characteristics of the plume and combining precise wind speed measurement instruments for more accurate emission flux estimation.