Measurement and deduction of emissions of trichloroethene, tetrachloroethene, and trichloromethane (chloroform) in the northeastern United States and southeastern Canada
Bibliographic record
Abstract
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".