Measurement of methane emissions from CNG fueling stations in East China

Wang, Yifan; Tang, Jianfeng; Li, Fēi; Xie, Donglai; Zuo, Feng-Yuan; Yu, Xiao; Xu, Yujie; Chen, Jie

doi:10.1007/s11356-022-20929-0

Cited by 7 publications

(2 citation statements)

References 11 publications

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“…OTM 33A itself has its own uncertainties. OTM 33A inherent uncertainty due to the assumptions of the computational model is affected by the length of various time periods, data rates, and wind filters 12 , 20 , 21 . In addition, there are uncertainties in the measurement process, where the major factors will be the downwind distance and height differences between the emission source, or possible multi-sources, and the measuring point.…”

Section: Resultsmentioning

confidence: 99%

Temporal variation and grade categorization of methane emission from LNG fueling stations

Wang

Tang

Xie³

et al. 2022

Sci Rep

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Natural gas is increasingly seen as the fossil fuel of choice for China as it transitions to renewable sources. The significant development of China’s LNG vehicle application and fueling stations and the urgency of climate changes make it particularly important to quantify methane emission from LNG stations, where the data are extremely rare. We carried out a pilot study on direct measurement and quantitative analysis of methane emission from five LNG fueling stations located in Shandong, China following the standard stationary EPA OTM 33A method. The measured methane emission of these five stations vary from 0.01 to 8.76 kg/h. The loss rates vary from 0.004 to 0.257%. We demonstrated that the emission from LNG stations consist of continuous and intermittent contents. The intermittent emission shows a strong temporal variation. If a station is only monitored for 20 min, it may either under-estimate or over-estimate the total emission. Both the distribution of emission events and total emission rates among different stations are highly skewed. We found that these LNG fueling station emission can be categorized into 3 grades, as low, medium and high, corresponding to emission rates below 0.1 kg/h; between 0.1 and 1 kg/h and above 1 kg/h, which can be characterized by the measured average methane concentration enhancement.

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Section: Resultsmentioning

confidence: 99%

Temporal variation and grade categorization of methane emission from LNG fueling stations

Wang

Tang

Xie³

et al. 2022

Sci Rep

Self Cite

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“…Mobile measurement platforms are typically parked downwind of sources for 20-30 minute periods to determine how concentrations vary with wind direction. This technique has been used to measure CH 4 emissions from oil and gas infrastructure in the United States (Brantley et al, 2014), Canada (Vogt et al, 2022;Hugenholtz et al, 2021), Europe (Yacovitch et al, 2018;Kumar et al, 2022), and compressed fossil gas facilities in Eastern China (Wang et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

A Modified Gaussian Plume Model for Mobile in situ Greenhouse Gas Measurements

Gillespie,

Ars,

Williams

et al. 2023

Preprint

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Abstract. Atmospheric methane measurements are important for evaluating high resolution methane inventories and monitoring emissions reductions. Despite recent international efforts to harmonize measurement methodologies and techniques, currently there are no standardized or internationally accepted techniques for estimating emissions from mobile in situ concentration measurements. We present measurements from two different mobile in situ methane laboratories, and compare emission rates calculated from four Gaussian plume Bayesian optimal estimation strategies and a statistical algorithm. For mobile transects from the slower flow-rate instrument, we find a significant asymmetric smoothing artifact. The effect of this asymmetry is most significant for short transects of small (0–50 kg CH4 day−1), nearby methane sources, where the plume crossing time is comparable to the mean residence time of the instrument. We develop a model of this effect, demonstrate how this model can be applied to Gaussian plume inversions, and describe its limitations. We use these results to compute emissions rate estimates for two methane sources from Toronto’s wastewater management system to demonstrate the use and limitations of Gaussian plume inversions to quantify methane emissions in an urban environment. Overall, we highlight the importance of using observed plume enhancement areas rather than the more commonly used enhancement heights for determining comparable emissions estimates between different mobile laboratories.

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