Industrial activities represent the primary source of anthropogenic carbon dioxide (CO2) emissions, and accurate monitoring of industrial CO2 emissions is critical to mitigating greenhouse gas emissions. Due to the lack of quantifiable and direct measurement technologies, industrial CO2 emissions are typically calculated based on fuel combustion consumption and emission factors. However, the calculation method is not applicable to the quantification of fugitive emissions of CO2. This work demonstrates the capability of remotely measuring industrial CO2 emissions by mobile Differential Absorption Lidar (DIAL) system. The two-dimensional concentration distributions of the CO2 plume were remotely measured using DIAL system, and the CO2 emission rate was obtained with wind field information. The DIAL measurements were cross-validated using in-stack CEMS data and emission-factor calculations. Results show that the relative deviations of CO2 emission rates between DIAL and CEMS range from −5.83% to +2.57% across four tests, all within ±6%, and the coefficient of variation (CV) of 27 valid datasets is 7.24%. In contrast, the emission factor method yields consistently higher estimates, with relative deviations of +4.61% compared to DIAL measurements. Furthermore, the mobile DIAL system was deployed in three industrial scenarios with different emission intensities: a natural gas-fired industrial park, a photovoltaic glass manufacturing plant (low-emission steady-state), and a coal-fired power plant (high-emission dynamic), demonstrating its preliminary adaptability under different operating conditions. This study indicates the feasibility and potential reliability of the mobile DIAL system for high spatio-temporal resolution remote measurement of industrial CO2 emissions.
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Jinliang Zang
Liang Wu
Wanglong Shi
Applied Sciences
National Institute of Metrology
Shandong Institute of Metrology
Fujian Metrology Institute
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Zang et al. (Wed,) studied this question.
www.synapsesocial.com/papers/69fed1f0b9154b0b82879108 — DOI: https://doi.org/10.3390/app16094576