Chemical Elements Recorded by Quercus mongolica Fisch. ex Ledeb. Tree Rings Reveal Trends of Pollution History in Harbin, China
Bibliographic record
Abstract
Rapid industrialization has led to a dramatic increase in air pollution. In China, the factors driving the abundance and composition of smog, particularly fine particulate matter, remain poorly understood, and short-term air pollution data are available from few air quality monitoring networks. Using laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS), chemical elements (Mg, Al, Si, S, K, Ca, Ti, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Sr, Tl, Pb, Bi) were analyzed in Quercus mongolica Fisch. ex Ledeb. tree rings from Harbin, China, in latewood at 5-year resolution over the period 1965–2020. The temporal trend of some elements was influenced by physiological factors, by environmental factors such as pollution, or influenced by both. Mg, K, Zn, Cu, Ni, Pb, As, Sr and Tl showed changes in pollution levels over time. The signal of K, Zn, Ni, Cu and Pb in trees from Harbin statistically did not differ from those at the control site after the 2000s. Our analysis confirmed the success of the undertaken emission reduction measures, which lead to an improvement in China’s urban air quality after 2010. However, As increased from 2000 to 2020 in Harbin which is consistent with rising As concentrations in China. Our study proved that dendrochemistry is a reliable tool to monitor the long-term history of pollution and to contribute to extending instrumental records of pollution back in time.
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 | 0.000 | 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".