Application of a new UAV measurement methodology to the quantification of CO₂ and CH₄ emissions from a major coking plant

Abstract. The development in unmanned aerial vehicle (UAV) technologies over the past decade has led to a plethora of platforms that can potentially enable greenhouse gas measurements over the 3-dimensional space. Here, we report the development of a new air sampler, consisting of a pumped stainless tube of 150 m in length with controlled time-stamping, and its deployment from an industrial UAV to quantify CO₂ and CH₄ emissions from the main coking plant stacks of a major steel maker in eastern China. During flights, the air sampler starts sampling as soon as the UAV takes off, and stops sampling after landing. The air sample is immediately analyzed upon retrieval with a CRDS gas analyzer for CO₂ and CH₄ mixing ratios. Laboratory tests show that the time series of CO₂ and CH₄ measured using the sampling system is smoothed when compared to online measurement by the CRDS analyzer. Further analyses show that the smoothing is akin to a convolution of the true time series signals with a heavy-tailed digital filter. For field test, the air sampler was mounted on the UAV and flown virtual boxes around two stacks in the coking plant at Shagang Steel Group. Mole fractions of CO₂ and CH₄ in air and meteorological parameters were measured from the UAV during the test flight. A mass-balance computational algorithm was used on the data to estimate the CO₂ and CH₄ emission rates from the stacks. Using this algorithm, the emission rates for the two stacks from the coking plant were calculated to be 0.12 ± 0.014 t h⁻¹ for CH₄ and 110 ± 18 t h⁻¹ for CO₂, the latter being in excellent agreement with material balance based estimates. A Gaussian plume inversion approach was also used to derive the emission rates and the results were compared with those derived using the mass-balance algorithm, showing a good agreement between the two methods.