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Updated: Jul 10, 2026

Detection and Recovery of Palladium, Gold and Cobalt Metals from the Urban Mine Using Novel Sensors/Adsorbents Designated with Nanoscale Wagon-wheel-shaped Pores
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Detection and Recovery of Palladium, Gold and Cobalt Metals from the Urban Mine Using Novel Sensors/Adsorbents Designated with Nanoscale Wagon-wheel-shaped Pores

Published on: December 6, 2015

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揭开城市土壤的秘密:在柏林的重金属污染研究中,机器学习和光谱学考虑了空间数据

Mahsa Nakhostin Rouhi1, Mohammadmehdi Saberioon2, Mohsen Makki3

  • 1Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, 1417853933, Tehran, Tehran, Iran.

Environmental monitoring and assessment
|July 19, 2025
PubMed
概括

机器学习模型准确地预测了柏林重金属 (HM) 土壤污染. 按土地使用分层数据提高了对和等特定金属的预测准确度.

关键词:
土地使用/土地覆盖 (LULC)机器学习 (ML) 是指机器学习.土壤 土壤土壤.空间数据空间数据是指空间数据.频谱测量是一种光谱测量.

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Dissolved Solute Sampling Across an Oxic-Anoxic Soil-Water Interface Using Microdialysis Profilers11:43

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Microbial Bioremediation of Uranium

Microorganisms play a critical role in the transformation and immobilization of uranium in contaminated environments through four main pathways: bioreduction, biosorption, bioaccumulation, and biomineralization. These mechanisms reduce uranium’s toxicity and prevent its migration through groundwater systems, offering sustainable approaches for in situ bioremediation.Bioreduction of UraniumBioreduction is driven by anaerobic bacteria such as certain strains of Geobacter and Shewanella, which use...