A combination of cultivation-based and molecular-based approaches was used to reveal the culturable and molecular diversity of the microbes inhabiting an open-dumped Pb/Zn mine tailings that was undergoing intensive acid generation (pH 1.9). Culturable bacteria found in the extremely acidic mine tailings were Acidithiobacillus ferrooxidans, Leptospirillum ferriphilum, Sulfobacillus thermotolerans and Acidiphilium cryptum, where the number of acidophilic heterotrophs was ten times higher than that of the iron- and sulfur-oxidizing bacteria. Cloning and phylogenetic analysis revealed that, in contrast to the adjacent AMD, the mine tailings possessed a low microbial diversity with archaeal sequence types dominating the 16S rRNA gene library. Of the 141 clones examined, 132 were represented by two sequence types phylogenetically affiliated with the iron-oxidizing archaea Ferroplasma acidiphilum and three belonged to two tentative groups within the Thermoplasma lineage so far represented by only a few environmental sequences. Six clones in the library were represented by the only bacterial sequence type and were closely related to the well-described iron-oxidizer L. ferriphilum. The significant differences in the prokaryotic community structures of the extremely acidic mine tailings and the AMD associated with it highlights the importance of studying the microbial communities that are more directly involved in the iron and sulfur cycles of mine tailings.
Data uncertainty is widespread in a variety of applications. This paper proposes a new Bayesian classification algorithm for classifying uncertain data. In the paper, we apply probability and statistics theory on uncertain data model, and provide solutions for model parameter estimation for both uncertain numerical data and uncertain categorical data. We also prove the correctness of the solutions. The experimental results demonstrate the proposed uncertain Bayesian classifier can be efficiently constructed, and it significantly outperforms the traditional Bayesian classifier in prediction accuracy when data is highly uncertain.
Coal mining has environmental impacts on surrounding areas, including heavy metal contamination of soil. This study explores the feasibility of using hyperspectral remote sensing to determine the heavy metal (Cr, Ni, Cu, Zn, Cd, Pb) content of soils in a coal‐mining area in the city of Zoucheng, Shandong Province, China. We used a plasma mass spectrometer to measure the heavy metal contents of soils and an ASD Field Spec4 spectrometer to measure soil hyperspectral data. Savitzky–Golay (SG) convolution smoothing and multiplicative scatter correction (MSC) were applied to the data, along with multiple mathematical transformations. Finally, a regression model for estimating heavy metal content of soils was developed using partial least squares regression (PLSR) analysis. Results show that the average heavy metal content of study soils was lower than the national standard value of soil environmental quality. The model's predictive accuracy is extremely high for Ni (R2 = 0.923 and RMSE = 0.831 by modeling; R2 = 0.879 and RMSE = 1.292 by testing); ideal for Cr, Cu, Zn, and Pb; and insufficient for Cd. Preprocessing the reflectance spectra with SG convolution smoothing in combination with MSC and reciprocal logarithm transformation yields the highest model accuracy. Hyperspectral PLSR modeling can effectively predict heavy metal content of soils in coal‐mining areas, and preprocessing spectral data is crucial for achieving high prediction accuracy.
Core Ideas
Quantitative inversion can provide technical support for monitoring of soil heavy metals.
Quantitative inversion can provide theoretical information for further environmental recovery in mining areas.
Hyperspectral remote sensing can quickly evaluate the status of heavy metal contamination in soil.
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