Impact of Increasing CO2, and Air Pollutants (NOx, SO2, O3) on the Stable Isotope Ratios in Tree Rings
Bibliographic record
Abstract
Abstract Anthropogenic activities such as industrialization, land use change and intensification of agriculture strongly contribute to changes in the concentrations of atmospheric trace gases. Carbon dioxide (CO 2 ), oxidized N compounds(NOx), sulfur dioxide (SO 2 ) and ozone(O 3 ) have particularly significant impacts on plant physiology. CO 2 , the substrate for plant photosynthesis, is in the focus of interest as the ambiguous effect of its increasing concentration is controversially discussed. Is its increase beneficial for plants or are plants non-responsive? NOx, a product of combustion and lightning, can have either fertilizing or toxic effects depending on the concentration and form. This is also the case for reduced forms of nitrogen(NHy), which are mostly emitted from agricultural and industrial activities. In combination CO 2 and N compounds can have a fertilizing effect. SO 2 and ground-level O 3 are mostly phytotoxic, depending on their concentrations, daily and seasonal exposure dynamics, and tree health condition. Elevated concentrations of both substances arise from industrial combustion processes and car emissions. All of the above-mentioned gaseous compounds affect plant metabolism in their specific ways and to different degrees. This impacts the isotope fractionation leaving specific fingerprints in the C, O, (H) and N isotope ratios of organic matter. In this chapter we will show how the impact of increasing CO 2 and air pollutants are reflected in the isotopic ratios of tree rings. Increasing CO 2 shows a considerable variation in responses of δ 13 C and to a minor degree in δ 18 O. Ozone and SO 2 exposure cause an overall increase of the δ 13 C values in tree rings and a slight decrease in δ 18 O, mimicking an increase in net photosynthesis (A N ) and to a minor degree in stomatal conductance (g s ). However, directly measured A N and g s values show the opposite, which does not always correspond with the isotope derived gas exchange data. NO 2 concentration as it is found near highly frequented freeways or industrial plants causes an increase of δ 13 C while δ 18 O decreases. This indicates an increase in both A N and g s , which corresponds well with directly measured gas exchange data. Thus the air quality situation must be taken in consideration for the interpretation of isotope values in tree rings.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".