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This study aimed to investigate the occurrence and removal of 19 biocides in ten different wastewater treatment plants (WWTPs), then estimate the usages and emissions per capita of 19 biocides based on mass balance analysis approach. The results showed that target biocides were universally detected in the WWTPs and their receiving rivers, and 19 for liquid samples and 18 for solid samples. The prominent compound for liquid was DEET (N,N-diethyl-3-methylbenzamide), with its maximum concentration of 393ng/L in influent; while that for solid was triclocarban with its maximum concentration of 2.11×10ng/g in anaerobic sludge. Most biocides were readily removed from the liquid phase of ten WWTPs, and the mean removal rate to ∑19 biocides was up to 75%. The removals of target biocides were attributed to biodegradation and adsorption onto activated sludge. The mean input per capita for ∑19 biocides based on influent was 907μg/d/person, while the emissions per capita were 187μg/d/person for effluent, and 121μg/d/person for excess sludge. As demonstrated, the biocides contamination of the receiving rivers could pose potential ecological risks for aquatic organisms. Therefore, advanced wastewater treatment technologies should be developed to reduce the emission of biocides into the receiving environment.

The nanosized UiO-66-NH2 metal–organic framework (MOF) material was synthesized and modified by palmitoyl chloride to enhance the dispersibility and restrain the aggregation of MOF particles in the organic phase.

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Scene classification is one of the bases for automatic remote sensing image interpretation. Recently, deep convolutional neural networks have presented promising performance in high-resolution remote sensing scene classification research. In general, most researchers directly use raw deep features extracted from the convolutional networks to classify scenes. However, this strategy only considers single scale features, which cannot describe both the local and global features of images. In fact, the dissimilarity of scene targets in the same category may result in convolutional features being unable to classify them into the same category. Besides, the similarity of the global features in different categories may also lead to failure of fully connected layer features to distinguish them. To address these issues, we propose a scene classification method based on multi-scale deep feature representation (MDFR), which mainly includes two contributions: (1) region-based features selection and representation; and (2) multi-scale features fusion. Initially, the proposed method filters the multi-scale deep features extracted from pre-trained convolutional networks. Subsequently, these features are fused via two efficient fusion methods. Our method utilizes the complementarity between local features and global features by effectively exploiting the features of different scales and discarding the redundant information in features. Experimental results on three benchmark high-resolution remote sensing image datasets indicate that the proposed method is comparable to some state-of-the-art algorithms.

Cotton fabrics pretreated with cationic starch have been dyed with reactive dyes by a continuous dyeing method and the pretreatment conditions influencing dyeability of the treated cotton investigated. Cationised cotton has been found to give level dyeing without the presence of salt and dye fixation is improved compared with untreated cotton. The dyeings show good wash and rub fastness.

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