Abstract
Although soil combustible organic pollutants (COPs) pose a serious threat to human well-being, their spatial distribution patterns, responses to environmental constraints, and areas of risk throughout China are still unclear. This knowledge gap hinders the control of soil COPs, causing us to overlook their impact on climate change and the environment. In this study, a total of 420 soil samples, distributed in typical regions of China, were tested for COPs content, including black carbon (BC) and polycyclic aromatic hydrocarbons (PAHs). Interest points (POI) such as parking lots, gas stations, and car services have become the main factors that influence soil COPs enrichment, and can be considered new indicators in other organic pollution studies. By comparing various machine learning simulations and predictions, this study accurately predicted the content of soil COPs in China and pointed out that, as the "third pole of the world", the Qinghai Tibet Plateau will face an unprecedented crisis. We established a method for assessing the comprehensive risk of soil COPs and identified at-risk areas, which accounted for 38.9 % of China's total soil area. Our research findings emphasize the main driving factors for soil COPs and identify areas in China that require prioritized soil COPs control.
| Original language | English |
|---|---|
| Article number | 137588 |
| Journal | Journal of Hazardous Materials |
| Volume | 489 |
| DOIs | |
| State | Published - 5 Jun 2025 |
| Externally published | Yes |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 13 Climate Action
Keywords
- Combustible organic pollutants
- Driving mechanism
- Machine learning
- Risk identification
- Soils
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