XCO <sub>2</sub> in an emission hot-spot region: the COCCON Paris campaign 2015
Bibliographic record
Abstract
Providing timely information on urban greenhouse gas (GHG) emissions and their trends to stakeholders relies on reliable measurements of atmospheric concentrations and the understanding of how local emissions and atmospheric transport influence these observations. Portable Fourier transform infrared (FTIR) spectrometers were deployed at five stations in the Paris metropolitan area to provide column-averaged concentrations of CO 2 (XCO 2 ) during a field campaign in spring of 2015, as part of the Collaborative Carbon Column Observing Network (COCCON). Here, we describe and analyze the variations of XCO 2 observed at different sites and how they changed over time. We find that observations upwind and downwind of the city centre differ significantly in their XCO 2 concentrations, while the overall variability of the daily cycle is similar, i.e. increasing during night-time with a strong decrease (typically 2–3 ppm) during the afternoon. An atmospheric transport model framework (CHIMERE-CAMS) was used to simulate XCO 2 and predict the same behaviour seen in the observations, which supports key findings, e.g. that even in a densely populated region like Paris (over 12 million people), biospheric uptake of CO 2 can be of major influence on daily XCO 2 variations. Despite a general offset between modelled and observed XCO 2 , the model correctly predicts the impact of the meteorological parameters (e.g. wind direction and speed) on the concentration gradients between different stations. When analyzing local gradients of XCO 2 for upwind and downwind station pairs, those local gradients are found to be less sensitive to changes in XCO 2 boundary conditions and biogenic fluxes within the domain and we find the model–data agreement further improves. Our modelling framework indicates that the local XCO 2 gradient between the stations is dominated by the fossil fuel CO 2 signal of the Paris metropolitan area. This further highlights the potential usefulness of XCO 2 observations to help optimize future urban GHG emission estimates.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".