采砂
沉积物
环境科学
热带
淤积
水文学(农业)
支流
出院手续
地质学
生态学
流域
地理
地图学
生物
古生物学
岩土工程
作者
Evan N. Dethier,Miles R. Silman,J. Leiva,Sarra Alqahtani,Luis E. Fernandez,Paúl Pauca,Seda Çamalan,Peter Tomhave,Francis J. Magilligan,Carl E. Renshaw,D. A. Lutz
出处
期刊:Nature
[Springer Nature]
日期:2023-08-23
卷期号:620 (7975): 787-793
被引量:16
标识
DOI:10.1038/s41586-023-06309-9
摘要
Increasing gold and mineral mining activity in rivers across the global tropics has degraded ecosystems and threatened human health1,2. Such river mineral mining involves intensive excavation and sediment processing in river corridors, altering river form and releasing excess sediment downstream2. Increased suspended sediment loads can reduce water clarity and cause siltation to levels that may result in disease and mortality in fish3,4, poor water quality5 and damage to human infrastructure6. Although river mining has been investigated at local scales, no global synthesis of its physical footprint and impacts on hydrologic systems exists, leaving its full environmental consequences unknown. We assemble and analyse a 37-year satellite database showing pervasive, increasing river mineral mining worldwide. We identify 396 mining districts in 49 countries, concentrated in tropical waterways that are almost universally altered by mining-derived sediment. Of 173 mining-affected rivers, 80% have suspended sediment concentrations (SSCs) more than double pre-mining levels. In 30 countries in which mining affects large (>50 m wide) rivers, 23 ± 19% of large river length is altered by mining-derived sediment, a globe-spanning effect representing 35,000 river kilometres, 6% (±1% s.e.) of all large tropical river reaches. Our findings highlight the ubiquity and intensity of mining-associated degradation in tropical river systems. The assembly and analysis of a 37-year satellite database covering almost 400 mining districts in 49 countries shows that a rise in river mineral mining has substantially increased riverine sediment load in tropical rivers worldwide.
科研通智能强力驱动
Strongly Powered by AbleSci AI