The phytotoxicity of added copper (Cu) and nickel (Ni) is influenced by soil properties and field aging. However, the differences in the chemical behavior between Cu and Ni are still unclear. Therefore, this study was conducted to investigate the extractability of added Cu and Ni in 6-year field experiments, as well as the link with their phytotoxicity. The results showed that the extractability of added Cu decreased by 6.63% (5.10%–7.90%), 22.5% (20.6%–23.9%), and 6.87% (0%–17.9%) on average for acidic, neutral, and alkaline soil from 1 to 6 years, although the phytotoxicity of added Cu and Ni did not change significantly from 1 to 6 years in the long term field experiment. Because of dissolution of Cu, when the pH decreased below 7.0, the extractability of Cu in alkaline soil by EDTA at pH 4.0 could not reflect the effects of aging. For Ni, the extractability decreased by 18.1% (10.1%–33.0%), 63.0% (59.2%–68.8%), and 22.0% (12.4%–31.8%) from 1 to 6 years in acidic, neutral, and alkaline soils, respectively, indicating the effects of aging on Ni were greater than on Cu. The sum of ten sequential extractions of Cu and Ni showed that added Cu was more extractable than Ni in neutral and alkaline soil, but similar in acidic soil.
• UV-vis absorption analyzer was applied in drainage type online recognition.• The UV-vis spectrum of four drainage types were collected and evaluated.• A convolutional neural network with multiple derivative inputs was established.• Effects of different network structures and input contents were compared. Optimizing sewage collection is important for water pollution control and wastewater treatment plants quality and efficiency improvement. Currently, the urban drainage pipeline network is upgrading to improve its classification and collection ability. However, there is a lack of efficient online monitoring and identification technology. UV-visible absorption spectrum probe is considered as a potential monitoring method due to its small size, reagent-free and fast detection. Because the performance parameters of probe like optic resolution, dynamic interval and signal-to-noise ratio are weak and high turbidity of sewage raises the noise level, it is necessary to extract shape features from the turbidity disturbed drainage spectrum for classification purposes. In this study, drainage network samples were online collected and tested, and four types were labeled according to sample sites and environment situation. Derivative spectrum were adopted to amplify the shape features, while convolutional neural network algorithm was established to conduct nonlinear spectrum classification. Influence of input and network structure on classification accuracy was compared. Original spectrum, first-order derivative spectrum and a combination of both were set to be three different inputs. Artificial neural network with or without convolutional layer were set be two different network structures. The results revealed a convolutional neural network combined with inputs of first and zero-order derivatives was proposed to have the best classification effect on domestic sewage, mixed rainwater, rainwater and industrial sewage. The recognition rate of industrial wastewater was 100%, and the recognition rate of domestic sewage and rainwater mixing system were over 90%. 相似文献
