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The commercial imidacloprid (IMD) insecticide [1-(6-chloro-3-pyridinyl) methyl-4,5-dihydro-N-nitro-1H-imidazole-2-amine] is widely used for the enhancement of crop production, but the intensive use of this insecticide has caused serious environmental problems. This work presents an electrochemical process for the removal of this insecticide using galvanostatic electrolysis at modified tantalum surface by lead dioxide film anode (Ta(PbO2)) anode. The electrolytic process was monitored by chemical oxygen demand (COD). The influence of operating parameters, such as current density, initial concentration of IMD, temperature and initial pH value was investigated. The COD decay follows a pseudo first-order kinetic and the process was under mass transport control. COD removal reach 97% when using an apparent current density of 100 mA cm−2, initial COD of 953 mg L−1 and at 25 °C after 4.5 h electrolysis time.

In recent years, the need for the rapid development of rural tourism in China has resulted in a large consumption of ecological resources, which has seriously damaged the ecological balance of travel destinations. Rural tourism in many parts of China is increasingly lacking distinctive local characteristics. It is difficult to adapt to tourists’ requirements for a deeper experience of rural travel. Some measures have been proposed to address these issues. Firstly, the connotation of “green” is reinterpreted based on relevant theoretical knowledge starting from solving the problem of rural tourism community development in China. The questionnaire method is used to screen the indicator system. An empirical analysis is carried out for actual cases. The pictures uploaded by tourists to social media are the main data source. A relatively complete and effective method system for the image research of rural tourism destinations is constructed combined with the visual analysis technology based on deep learning. A dense CNN model producing 10 output parameter combined with various factors is formed to assist the promotion by comparing the differences between the advertising pictures of the tourist destination and the pictures taken by tourists. The results show obvious consistency in the construction scene of the rural travel perception environment. Each tourist destination has its own visual characteristics, as well as differences in various sensory modality types. This research method adopts a combination of subjective and objective and comprehensively examines the scene conspicuousness, distinction, artistic quality, design quality, and time of the pictures. The formed marketing picture research method is more scientific and feasible.

The accumulation of heavy metals in soils increases rapidly due to various natural processes. Heavy metal pollution changes the geotechnical properties of soil and poses a serious threat to human health and ecosystems, so soil pollution remediation is particularly important. Phytoremediation is an environmentally friendly approach that can be used as a successful mitigation measure to restore heavy metal-contaminated soils cost-effectively. The plants extract and remove elemental contaminants or reduce their bioavailability in soil and improve the geotechnical properties. In this study, we are conducting a pot experiment with Amaranthus by adding Zn salt as a contaminant and determining the rate of removal of metal.

Flood management is the act of determining the frequency, magnitude, and duration of flood episodes based on the elements of a river basin that may be monitored. Floods endanger human lives and inflict significant property damage. It is essential for creating suitable flood risk management plans, lowering flood dangers, and evacuating people from flood-prone locations. Hydrodynamic methods for managing floods may be replaced with deep learning. Existing methods, however, concentrate on employing CNNs or RNNs to capture either the spatial or temporal flood patterns. Despite several advancements in flood control technology, less focus has been placed on minimising the damage that these systems do to the environment in order to boost their dependability and effectiveness. When the data is skewed, CNNs might overfit. We demonstrate how automated regularisation and uncertainty quantification allows Bayesian-CNN to get beyond these drawbacks. We have created a unique method to make use of the uncertainties that the Bayesian-CNN provides, which greatly improves performance on a big portion of the test data (around a 6% increase in accuracy on 77% of test data). By projecting the data into a low-dimensional space using a nonlinear dimensionality reduction approach, we also provide an entirely novel rationale for the uncertainty. This dimensionality reduction makes it possible to visualize the test data for interpretation and displays the data's structure in a low-dimensional feature space. This paper discusses and makes use of uncertainties for flood control while demonstrating the benefits of Bayesian-CNN over state-of-the-art technology. As a consequence, the Bayesian-CNN obtains 95.7% F1-score, 99.3% F1-score, 98.5% precision, and 98.3% recall.