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Record W2965367791 · doi:10.5194/acp-19-9865-2019

Emissions of halocarbons from India inferred through atmospheric measurements

2019· article· en· W2965367791 on OpenAlexaboutno aff
Daniel Say, Anita L. Ganesan, Mark F. Lunt, Matthew Rigby, Simon O’Doherty, Christina M. Harth, Alistair J. Manning, Paul B. Krummel, Stéphane Bauguitte

Bibliographic record

VenueAtmospheric chemistry and physics · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsnot available
FundersMinistry of Earth SciencesNatural Environment Research CouncilSight Research UK
KeywordsHalocarbonEnvironmental scienceGreenhouse gasMontreal ProtocolAtmospheric sciencesOzone depletionOzoneEmission inventoryTroposphereClimatologyOzone layerMeteorologyAir quality indexGeographyChemistryOceanography

Abstract

fetched live from OpenAlex

Abstract. As the second most populous country and third fastest growing economy, India has emerged as a global economic power. As such, its emissions of greenhouse and ozone-depleting gases are of global significance. However, unlike neighbouring China, the Indian sub-continent is very poorly monitored by atmospheric measurement networks. India's halocarbon emissions, here defined as chlorofluorocarbons (CFCs), hydrochlorofluorocarbons (HCFCs), hydrofluorocarbons (HFCs) and chlorocarbons, are not well-known. Previous measurements from the region have been obtained at observatories many hundreds of kilometres from source regions, or at high altitudes, limiting their value for the estimation of regional emission rates. Given the projected rapid growth in demand for refrigerants and solvents in India, emission estimates of these halocarbons are urgently needed to provide a benchmark against which future changes can be evaluated. In this study, we report atmospheric-measurement-derived halocarbon emissions from India. With the exception of dichloromethane, these top-down estimates are the first for India's halocarbons. Air samples were collected at low altitude during an aircraft campaign in June and July 2016, and emissions were derived from measurements of these samples using an inverse modelling framework. These results were evaluated to assess India's progress in phasing out ozone-depleting substances under the Montreal Protocol. India's combined CFC emissions are estimated to be 54 (27–86) Tg CO2 eq. yr−1 (5th and 95th confidence intervals are shown in parentheses). HCFC-22 emissions of 7.8 (6.0–9.9) Gg yr−1 are of similar magnitude to emissions of HFC-134a (8.2 (6.1–10.7) Gg yr−1). We estimate India's HFC-23 emissions to be 1.2 (0.9–1.5) Gg yr−1, and our results are consistent with resumed venting of HFC-23 by HCFC-22 manufacturers following the discontinuation of funding for abatement under the Clean Development Mechanism. We report small emissions of HFC-32 and HFC-143a and provide evidence to suggest that HFC-32 emissions were primarily due to fugitive emissions during manufacturing processes. A lack of significant correlation among HFC species and the small emissions derived for HFC-32 and HFC-143a indicate that in 2016, India's use of refrigerant blends R-410A, R-404A and R-507A was limited, despite extensive consumption elsewhere in the world. We also estimate emissions of the regulated chlorocarbons carbon tetrachloride and methyl chloroform from northern and central India to be 2.3 (1.5–3.4) and 0.07 (0.04–0.10) Gg yr−1 respectively. While the Montreal Protocol has been successful in reducing emissions of many ozone-depleting substances, growth in the global emission rates of the unregulated very short-lived substances poses an ongoing threat to the recovery of the ozone layer. Emissions of dichloromethane are found to be 96.5 (77.8–115.6) Gg yr−1, and our estimate suggests a 5-fold increase in emissions since the last estimate derived from atmospheric data in 2008. We estimate perchloroethene emissions from India and chloroform emissions from northern–central India to be 2.9 (2.5–3.3) and 32.2 (28.3–37.1) Gg yr−1 respectively. Given the rapid growth of India's economy and the likely increase in demand for halocarbons such as HFCs, the implementation of long-term atmospheric monitoring in the region is urgently required. Our results provide a benchmark against which future changes to India's halocarbon emissions may be evaluated.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.095
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0120.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.214
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations64
Published2019
Admission routes1
Has abstractyes

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