Investigation of the atmospheric boundary layer depth variability and its impact on the <sup>222</sup>Rn concentration at a rural site in France
Bibliographic record
Abstract
Abstract Continuous monitoring of the atmospheric boundary layer (ABL) depth (zi) is important for investigations of trace gases with near‐surface sources. The aim of this study is to examine the temporal variability of zi on both diurnal and seasonal time scales over a full year (2011) and relate these changes to the atmospheric 222Rn concentrations (CRn) measured near the top of a 200 m tower at a rural site (Trainou) in France. Continuous zi estimates were made using a combination of lidar and hourly four‐height carbon dioxide (CO2) profile measurements. Over the diurnal cycle, the 180 m CRn reached a maximum in the late morning as the growing ABL passed through the inlet height (180 m) transporting upward high CRn air from the nocturnal boundary layer. During late afternoon, a minimum in the CRn occurred mainly due to ABL‐mixing. We argue that ABL dilution occurs in two stages: first, during the rapid morning growth into the residual layer, and second, during afternoon with the free atmosphere when zi has reached its quasi‐stationary height (around 750 m in winter or 1700 m in summer). An anticorrelation (R2 of −0.49) was found while performing a linear regression analysis between the daily zi growth rates and the corresponding changes in the CRn illustrating the ABL‐dilution effect. We also analyzed the numerical proportions of the time within a season when zi remained lower than the inlet height and found a clear seasonal variability for the nighttime measurements with higher number of cases with shallow zi (<200 m) in winter (67.3%) than in summer (33.9%) and spring (54.5%). Thus, this pilot study helps delineate the impact of zi on CRn at the site mainly for different regimes of ABL, in particular, during the times when the zi is above the measurement height. It is suggested that when the zi is well below the inlet height, measurements are most possibly indicative of the residual layer 222Rn, an important issue that should be considered in the mass budget approach.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".