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Record W2041087132 · doi:10.1029/2000jd900513

Measurement and deduction of emissions of trichloroethene, tetrachloroethene, and trichloromethane (chloroform) in the northeastern United States and southeastern Canada

2000· article· en· W2041087132 on OpenAlexaboutno aff
Gary Kleiman, Ronald G. Prinn

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2000
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsHYSPLITTroposphereEnvironmental scienceEmission inventoryChloroformOptimal estimationAtmospheric sciencesGasolineMeteorologyAerosolChemistryAir quality indexMathematicsStatisticsGeographyGeology

Abstract

fetched live from OpenAlex

We present optimal estimates of the emission patterns for trichloroethene (TCE, CHCl = CCl2), tetrachloroethene (perchloroethylene, CCl2 = CCl2), and trichloromethane (chloroform, CHCl3) utilizing hourly gas chromatographie measurements at Nahant, Massachusetts (approximately 10 km northeast of Boston). Our analysis combines the measurements with back trajectory information obtained from the Hybrid Single‐Particle Lagrangian Integrated Trajectory (HYSPLIT‐4) model (National Oceanic and Atmospheric Administration Air Resources Laboratory, Silver Spring, Maryland). Using a Kaiman filter inverse method and an analytical solution of the continuity equation to estimate the effects of eddy diffusion, we calculate the surface emissions on a 1°×1° grid for the selected species necessary to optimally match the observations. These emissions are compared with the estimates determined by the Reactive Chlorine Emissions Inventory (RCEI) working group of the International Global Atmospheric Chemistry Program Global Emissions Inventory Activity (GEIA). The new emissions scenarios computed here provide an observation‐based assessment for comparison with the RCEI emissions inventories for the northeastern United States and southeastern Canada. Results indicate that the RCEI estimates of the anthropogenic emissions of these chemicals in the geographical domain studied differ from our optimal estimates but generally lie within the estimated error of these optimal estimates. Results are sensitive to the assumed vertical distributions and hence to the assumed vertical mixing rates. The current accuracy achievable through this observation‐based technique (±45%) is limited in large part by the uncertainty in the vertical distribution of these compounds in the troposphere over highly emitting regions. Within this accuracy the optimal estimates of emissions presented here indicate that the emissions for many grid cells do not need to be corrected significantly from the 1990 estimates presented in the RCEI. However, for trichloroethene and tetrachloroethene we calculate large statistically significant decreases in emissions for some highly populated urban locations on the East Coast and correspondingly large increases for some less populous grid cells in Pennsylvania and New York State. Only anthropogenic sources of trichloromethane (representing roughly 11% of estimated global emissions) were gridded in the RCEI inventory and included in the initial inventory used here. We find that these anthropogenic emissions are, as expected, too low to explain the observations and that most grid cells require small increases and several cells require substantial increases (∼5–10 nmoles m−2 h−1) to produce emissions estimates that are consistent with observations. This is reasonable given current knowledge of natural land sources (e.g., soil emissions) and a large oceanic source for this compound inferred from previous oceanic observations.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score0.175

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.020
GPT teacher head0.256
Teacher spread0.236 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations29
Published2000
Admission routes1
Has abstractyes

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