Quantification of greenhouse gas emission rates for point sources and cities via airborne measurements

Urban greenhouse gas emissions and urbanization are both expected to continue to increase in coming years. Accordingly, many cities have passed legislation or set goals for specific greenhouse gas reductions. However, high precision monitoring techniques are necessary to act on this legislation and to quantify the impact of effective mitigation strategies. Here we use the airborne mass balance technique to address this need.

Chapter 3 focuses on 23 flights at 14 natural gas-fired power plants (NGPPs) using an aircraft-based mass balance technique and methane/carbon dioxide enhancement ratios (ΔCH4/ΔCO2) measured from stack plumes to quantify the unburned fuel. Current research efforts on the atmospheric impacts of natural gas (NG) have focused heavily on the production, storage/transmission, and processing sectors, with less attention paid to the distribution and end use sectors. By comparing the ΔCH4/ΔCO2 ratio measured in stack plumes to that measured downwind, we determined that, within uncertainty of the measurement, all observed CH4 emissions were stack-based, that is, uncombusted NG from the stack rather than fugitive sources. Measured CH4 emission rates (ER) ranged from 8 (± 5) to 135 (± 27) kg CH4/h (± 1σ), with the fractional CH4 throughput lost (loss rate) ranging from -0.039% (± 0.076%) to 0.204% (± 0.054%). We attribute negative values to partial combustion of ambient CH4 in the power plant. The average calculated emission factor (EF) of 5.4 (+10/-5.4) g CH4/million British thermal units (MMBTU) is within uncertainty of the Environmental Protection Agency (EPA) EFs. However, one facility measured during startup exhibited substantially larger stack emissions with an EF of 440 (+660/-440) g CH4/MMBTU and a loss rate of 2.5% (+3.8/-2.5%).

Chapter 4 uses a slightly larger set of power plant flights, including most of those in Chapter 3, to assess the airborne mass balance technique. GHG quantification techniques must be highly precise to effectively monitor changes in GHG emissions to inform effective mitigation strategies and act on already existing goals and legislation towards reductions. Power plants are required to measure their CO2 emissions using continuous emissions monitoring systems (CEMS), providing an effective “known” emission rate to compare against those measured downwind using the airborne mass balance approach. The mean absolute error between measured and CEMS emission rates was calculated as 20% ± 13 and the slope of measured emission rates against CEMS emission rates was 0.871 ± 0.033. Additionally, power plants generally have consistent production/emission profiles through the typical midday hours of the experiments. This allows us to consider back to back experiments at the same facility as replicate experiments to assess the precision of the mass balance technique too. Across the campaign, the average relative standard deviation (1σ/mean) was 25% ± 16.

Chapter 5 focuses on measurements of greenhouse gases around New York City with 7 non-growing season research aircraft flights in 2018-2020 and used dispersion modelling to estimate CO2 emissions from New York City with a simple scaling factor approach. Cities are leading efforts to reduce greenhouse gas emissions. New York City has passed a suite of legislation outlining aggressive reduction targets, which has been supplemented by similar legislation covering New York State. However, appropriate techniques to quantify emission reductions over time are necessary to monitor the progress of such legislation and effectively inform continuing mitigation efforts. The average calculated CO2 emission rate for New York City, representative of afternoons in the non-growing season, was 67 kmol/s. By using a variety of priors, calculation methods, and meteorology products we also investigate the variability of the estimation introduced by each of these sources. Variability across flight days proved to be larger than the combined variability of all other sources, as seen in previous works. This work uses a pre-COVID dataset to introduce a scaling factor approach that can account for and isolate multiple sources of variability and that can be used for long term emission trend analyses, including analyses of emissions during and after shut-down conditions.