查看更多>>摘要：? 2022 Elsevier B.V.With the development of economy, soil heavy metal pollution becomes more severe, which causes soil resource degradation and ecosystem deterioration. In this study, we present the locations with excessive contamination and analyze heavy metal polluted provinces, as well as the status and primary factors of heavy metal pollution in Guangxi, Fujian, Liaoning and Yunnan provinces, which contain the hotspots at the city level. Results indicate that the heavy metal concentration increased from north to south in China. Soil heavy metal pollution is typical in Northeast China; Also, it is widespread in southern China, with heavy metals exceeding the standard levels in Central, Southwest and South China. Heat map reveals that mineral exploitation and industrial production are the primary causes in the most severely polluted provinces, while sewage irrigation or irrational application of fertilizer is the cause in agricultural areas. Urban development in densely populated provinces has become the third main factor. In addition, transportation and household refuse are potential driving factors. Network analysis suggests combined remediation methods are widely applied to improve remediation efficiency. Among them, physical-chemical technology, chemical-biological technology and physical-biological technology occupy 27.1%, 18.9% and 10.7% in the above four provinces. In particular, combining phytoremediation and microbial remediation techniques is the main research direction in future because of its environmental friendly. In brief, our study provides accurate spatial information, primary sources and appropriate remediation alternatives for soil heavy metal pollution in China, benefiting soil resource protection and land utilization.
查看更多>>摘要：? 2022In order to solve the difficulties encountered in E-wastes disposal automation, this work investigates the sensor-based sorting of waste washing machine parts. To satisfy the post-processing, we divided the parts into 6 categories (Piece, Cover, Base, Shell, Spin tub and Drain hose) based on the shape differences and used object recognition algorithm to classify them. In the recycling terminal, parts were divided based on types of materials. Piece and Cover contained acrylonitrile butadiene styrene (ABS) parts and polystyrene (PS) parts, while Base, Shell, Spin tub and Drain hose were polypropylene (PP) parts only. Therefore near-infrared (NIR) spectroscopy was applied for sorting ABS, PS and PP. Algorithm for object recognition and NIR spectroscopy both reached high precision. In object recognition, algorithm of YOLOv5 and its improved models, including focal loss and channel attention mechanism, were tested. YOLOv5 with channel attention achieved the best 98.7% mean Average Precision (mAP) and the overall over 98% Average Precision (AP). In the task of NIR spectroscopy, principal component analysis (PCA) coupled with support vector machine (SVM) reached the overall 97.8% classification precision. Finally, a sorting platform based on our device and algorithm was designed.
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