Lead contamination in Chinese surface soils: Source identification, spatial-temporal distribution and associated health risks
Bibliographic record
Abstract
Soil lead (Pb) pollution is wide spread in China. The Chinese government is taking ambitious actions to tackle the soil pollution issue, with the latest soil quality standards and the Soil Pollution Prevention and Remediation Law enacted in 2018. This study assesses the spatio-temporal distribution, pollution levels, major sources and health risks of Pb in surface soils in China in the past three decades (1990–2017). Traffic emissions (mainly leaded gasoline), mining, smelting, and e-waste recycling were main contributors to soil Pb pollution and pose a risk to food security and human health. The weighted arithmetic mean of Pb concentrations was 35.9 ± 0.21 mg/kg. Southern China suffered from severer soil Pb pollution with hotspots of the Pearl River Delta, Yangtze River Delta, Shaanxi and Hunan. The average soil Pb concentration increased marginally during 1990–2001 due to increased industrial and transportation activities; afterwards, it decreased by ∼30% during 2001–2013, reflecting the effectiveness of the ban on leaded gasoline in 2000. However, there was a slight increase in recent years. Therefore, it is critical to establish a comprehensive evaluation and monitoring system, strengthen pollution source control, properly manage the environmental and health risks at severely contaminated sites, and conduct green and sustainable remediation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".