No abstract

More than twenty abandoned coal mines in the Yudong River basin of Guizhou Province have discharged acid mine drainage (AMD) for a long time. The revelation of microbial community composition, interaction patterns and metabolic functions can contributes to the ecological remediation of AMD pollution. In this study, reference and contaminated soils were collected along the AMD ow path for high-throughput sequencing. Results showed that the long-term AMD pollution promoted the evolution of γ-Proteobacteria, and the acidophilic iron-oxidizing bacteria Ferrovum (relative abundance of 15.50%) and iron-reducing bacteria Metallibacterium (9.87%) belonging to this class became the dominant genera. Co-occurrence analysis revealed that the proportion of positive correlations among bacteria increased from 51.02% (reference soil) to 75.16% (contaminated soil), suggesting that acidic pollution promotes the formation of mutualistic interaction networks of microorganisms. Metabolic function prediction (Tax4Fun) revealed that AMD contamination enhanced the microbial functions such as translation, repair, and biosynthesis of peptidoglycan and lipopolysaccharide etc., which may be an adaptive mechanism for microbial survival in extremely acidic environment. In addition, the acidic pollution promoted the high expression of nitrogen xing genes in soil, and the discovery of autotrophic nitrogen xing bacteria such as Ferrovum provided the possibility of bioremediation of AMD pollution.

Discharge of acid mine drainage (AMD) from abandoned coal mines of the YuDong catchment in Kaili City, Guizhou Province, China, has severely damaged local ecological environments. In this study, a laboratory-scale dispersed alkaline substrate (DAS) was studied for the treatment of simulated AMD. The experimental conditions and reaction mechanisms were preliminarily explored. The treatment effect and variation law of vertical effluent water quality of the experimental conditions were thoroughly analysed. The results indicated that small-sized limestone (diameter 5–7 mm) having a 20:1 mixture ratio with shavings and minimum HRT of 20 hours result in increasing effluent pH from 3.5 to 6.6, achieving 66.2% and 99.1% removal of Fe and Al, respectively. There were obvious differences in each reaction layer for the removal of various pollutants from AMD along the depth by DAS, the main reaction zone was first 20–30 cm of reaction column. The removal process of metal ions and sulfate was accompanied by bio-mineralization reaction. This test provided a valuable support for the local practical engineering applications, enriched the AMD processing technology experimental cases, and provided reference for the treatment technology of similar polluted areas. HIGHLIGHT A dispersed alkaline substrate was designed to treat the acid mine drainage from abandoned coal mines of YuDong valley, which provides a reference for the further design of site device and other similar contaminated areas. Combining the physical and chemical parameters of the effluent, mineralogical characterization of the filler along with the microbial diversity of the system, the mechanism of DAS treatment of AMD was analyzed.

Depth estimation is a fundamental problem in the perception system of autonomous driving scenes. Although autonomous driving is challenging, much prior knowledge can still be utilized, by which the sophistication of the problem can be effectively restricted. Some previous works introduce the road plane prior to the depth estimation problem according to the Planar Parallax Geometry. However, we find that their usages are not effective, leaving the network cannot learn the geometric information. To this end, we analyze this problem in detail and reveal that explicit warping of consecutive frames and flow pre-training can effectively bring the geometric prior into learning. Furthermore, we propose Planar Position Embedding to deal with the intrinsic weakness of plane parallax geometry. Comprehensive experimental results on autonomous driving datasets like KITTI and Waymo Open Dataset (WOD) demonstrate that our Planar Parallax Network(PPNet) dramatically outperforms existing learning-based methods.

More than twenty abandoned coal mines in the Yudong River basin of Guizhou Province have discharged acid mine drainage (AMD) for a long time. The revelation of microbial community composition, interaction patterns and metabolic functions can contributes to the ecological remediation of AMD pollution. In this study, reference and contaminated soils were collected along the AMD flow path for high-throughput sequencing. Results showed that the long-term AMD pollution promoted the evolution of γ-Proteobacteria, and the acidophilic iron-oxidizing bacteria Ferrovum (relative abundance of 15.50%) and iron-reducing bacteria Metallibacterium (9.87%) belonging to this class became the dominant genera. Co-occurrence analysis revealed that the proportion of positive correlations among bacteria increased from 51.02% (reference soil) to 75.16% (contaminated soil), suggesting that acidic pollution promotes the formation of mutualistic interaction networks of microorganisms. Metabolic function prediction (Tax4Fun) revealed that AMD contamination enhanced the microbial functions such as translation, repair, and biosynthesis of peptidoglycan and lipopolysaccharide etc., which may be an adaptive mechanism for microbial survival in extremely acidic environment. In addition, the acidic pollution promoted the high expression of nitrogen fixing genes in soil, and the discovery of autotrophic nitrogen fixing bacteria such as Ferrovum provided the possibility of bioremediation of AMD pollution.