Investigating aerial data pre-analysis schemes and site-level methane emission aggregation methods at LNG facilities Article Swipe
Olga Khaliukova
,
Yuanrui Zhu
,
William Daniels
,
Arvind Ravikumar
,
Gregory B. Ross
,
Selina Roman-White
,
Fiji George
,
Dorit Hammerling
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.26434/chemrxiv-2024-rgppp
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.26434/chemrxiv-2024-rgppp
Methane measurements at liquefied natural gas (LNG) facilities play an important role in characterizing methane emissions from the natural gas supply chain. The large size and complexity of LNG facilities make quantifying emissions with ground-based monitoring systems challenging, making aerial platforms one of the preferred methods for methane measurements at these sites. However, aerial measurements typically provide a snapshot of emissions at a given instance, necessitating further analytical steps to infer both annualized emissions and the range of possible emissions at different instants in time. This study uses aerial measurements at two LNG facilities from a Quantification, Monitoring, Reporting, and Verification project to characterize the distribution of temporally averaged emissions (i.e., annualized inventories) and possible site-level emissions at any given point in time (``instantaneous emissions''). The former provides uncertainty on the aerial measurement component of measurement-informed inventories representing annual averages, while the latter will help contextualize future snapshot measurements (e.g., aerial surveys or high-resolution satellite platforms) at LNG facilities. We find that instantaneous emissions may fall well outside of the distribution describing uncertainty in the annual inventory. We also compare different pre-analysis schemes for aerial data, as existing literature does not provide a clear consensus on methods for doing so, especially at LNG facilities.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.26434/chemrxiv-2024-rgppp
- https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/66c4c254f3f4b05290767f71/original/investigating-aerial-data-pre-analysis-schemes-and-site-level-methane-emission-aggregation-methods-at-lng-facilities.pdf
- OA Status
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- Cited By
- 3
- References
- 34
- Related Works
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- OpenAlex ID
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Raw OpenAlex JSON
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https://openalex.org/W4401814517Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.26434/chemrxiv-2024-rgpppDigital Object Identifier
- Title
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Investigating aerial data pre-analysis schemes and site-level methane emission aggregation methods at LNG facilitiesWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-08-23Full publication date if available
- Authors
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Olga Khaliukova, Yuanrui Zhu, William Daniels, Arvind Ravikumar, Gregory B. Ross, Selina Roman-White, Fiji George, Dorit HammerlingList of authors in order
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https://doi.org/10.26434/chemrxiv-2024-rgpppPublisher landing page
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https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/66c4c254f3f4b05290767f71/original/investigating-aerial-data-pre-analysis-schemes-and-site-level-methane-emission-aggregation-methods-at-lng-facilities.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://chemrxiv.org/engage/api-gateway/chemrxiv/assets/orp/resource/item/66c4c254f3f4b05290767f71/original/investigating-aerial-data-pre-analysis-schemes-and-site-level-methane-emission-aggregation-methods-at-lng-facilities.pdfDirect OA link when available
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Methane, Environmental science, Methane emissions, Natural gas, Waste management, Engineering, Chemistry, Organic chemistryTop concepts (fields/topics) attached by OpenAlex
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3Total citation count in OpenAlex
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2025: 1, 2024: 2Per-year citation counts (last 5 years)
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34Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.also | 178 |
| abstract_inverted_index.both | 71 |
| abstract_inverted_index.does | 189 |
| abstract_inverted_index.fall | 165 |
| abstract_inverted_index.find | 160 |
| abstract_inverted_index.from | 16, 94 |
| abstract_inverted_index.help | 144 |
| abstract_inverted_index.make | 30 |
| abstract_inverted_index.play | 8 |
| abstract_inverted_index.role | 11 |
| abstract_inverted_index.size | 24 |
| abstract_inverted_index.that | 161 |
| abstract_inverted_index.time | 122 |
| abstract_inverted_index.uses | 87 |
| abstract_inverted_index.well | 166 |
| abstract_inverted_index.will | 143 |
| abstract_inverted_index.with | 33 |
| abstract_inverted_index.(LNG) | 6 |
| abstract_inverted_index.clear | 193 |
| abstract_inverted_index.data, | 185 |
| abstract_inverted_index.doing | 198 |
| abstract_inverted_index.given | 63, 119 |
| abstract_inverted_index.infer | 70 |
| abstract_inverted_index.large | 23 |
| abstract_inverted_index.point | 120 |
| abstract_inverted_index.range | 76 |
| abstract_inverted_index.steps | 68 |
| abstract_inverted_index.study | 86 |
| abstract_inverted_index.these | 50 |
| abstract_inverted_index.time. | 84 |
| abstract_inverted_index.while | 140 |
| abstract_inverted_index.(e.g., | 149 |
| abstract_inverted_index.(i.e., | 110 |
| abstract_inverted_index.aerial | 39, 53, 88, 131, 150, 184 |
| abstract_inverted_index.annual | 138, 175 |
| abstract_inverted_index.chain. | 21 |
| abstract_inverted_index.former | 126 |
| abstract_inverted_index.future | 146 |
| abstract_inverted_index.latter | 142 |
| abstract_inverted_index.making | 38 |
| abstract_inverted_index.sites. | 51 |
| abstract_inverted_index.supply | 20 |
| abstract_inverted_index.Methane | 0 |
| abstract_inverted_index.compare | 179 |
| abstract_inverted_index.further | 66 |
| abstract_inverted_index.methane | 14, 47 |
| abstract_inverted_index.methods | 45, 196 |
| abstract_inverted_index.natural | 4, 18 |
| abstract_inverted_index.outside | 167 |
| abstract_inverted_index.project | 101 |
| abstract_inverted_index.provide | 56, 191 |
| abstract_inverted_index.schemes | 182 |
| abstract_inverted_index.surveys | 151 |
| abstract_inverted_index.systems | 36 |
| abstract_inverted_index.However, | 52 |
| abstract_inverted_index.averaged | 108 |
| abstract_inverted_index.existing | 187 |
| abstract_inverted_index.instants | 82 |
| abstract_inverted_index.possible | 78, 114 |
| abstract_inverted_index.provides | 127 |
| abstract_inverted_index.snapshot | 58, 147 |
| abstract_inverted_index.averages, | 139 |
| abstract_inverted_index.component | 133 |
| abstract_inverted_index.consensus | 194 |
| abstract_inverted_index.different | 81, 180 |
| abstract_inverted_index.emissions | 15, 32, 60, 73, 79, 109, 116, 163 |
| abstract_inverted_index.important | 10 |
| abstract_inverted_index.instance, | 64 |
| abstract_inverted_index.liquefied | 3 |
| abstract_inverted_index.platforms | 40 |
| abstract_inverted_index.preferred | 44 |
| abstract_inverted_index.satellite | 154 |
| abstract_inverted_index.typically | 55 |
| abstract_inverted_index.Reporting, | 98 |
| abstract_inverted_index.analytical | 67 |
| abstract_inverted_index.annualized | 72, 111 |
| abstract_inverted_index.complexity | 26 |
| abstract_inverted_index.describing | 171 |
| abstract_inverted_index.especially | 200 |
| abstract_inverted_index.facilities | 7, 29, 93 |
| abstract_inverted_index.inventory. | 176 |
| abstract_inverted_index.literature | 188 |
| abstract_inverted_index.monitoring | 35 |
| abstract_inverted_index.platforms) | 155 |
| abstract_inverted_index.site-level | 115 |
| abstract_inverted_index.temporally | 107 |
| abstract_inverted_index.Monitoring, | 97 |
| abstract_inverted_index.facilities. | 158, 203 |
| abstract_inverted_index.inventories | 136 |
| abstract_inverted_index.measurement | 132 |
| abstract_inverted_index.quantifying | 31 |
| abstract_inverted_index.uncertainty | 128, 172 |
| abstract_inverted_index.Verification | 100 |
| abstract_inverted_index.challenging, | 37 |
| abstract_inverted_index.characterize | 103 |
| abstract_inverted_index.distribution | 105, 170 |
| abstract_inverted_index.ground-based | 34 |
| abstract_inverted_index.inventories) | 112 |
| abstract_inverted_index.measurements | 1, 48, 54, 89, 148 |
| abstract_inverted_index.pre-analysis | 181 |
| abstract_inverted_index.representing | 137 |
| abstract_inverted_index.contextualize | 145 |
| abstract_inverted_index.emissions''). | 124 |
| abstract_inverted_index.instantaneous | 162 |
| abstract_inverted_index.necessitating | 65 |
| abstract_inverted_index.characterizing | 13 |
| abstract_inverted_index.Quantification, | 96 |
| abstract_inverted_index.high-resolution | 153 |
| abstract_inverted_index.(``instantaneous | 123 |
| abstract_inverted_index.measurement-informed | 135 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 8 |
| citation_normalized_percentile.value | 0.74824793 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |