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Constructed wetland plays an important role in the removal of micro-polluted water pollutants in urban water sources. However, the water purification capacity and influencing factors of the constructed wetlands still need to be explored. This study monitored the turbidity, ammonia nitrogen (NH3-N), dissolved oxygen (DO), and chemical oxygen demand (COD) at the water inlet and outlet of Shijiuyang Wetland in Jiaxing City from 2019 to 2021. The results showed that the turbidity and the wetland turbidity removal rate stayed high. The DO couldn’t meet national standard III for surface water during the high temperature time in summer, and NH3-N and COD are stable within national standard III. The partial regression analysis shows that the addition of chemicals is the most important factor affecting the turbidity. Temperature had the most important affect to NH3-N and DO, the higher the temperature, the lower the NH3-N value and the DO. The flow rate is the most important factor affecting the COD, the higher the flow rate, the lower the COD. Moreover, this paper proposes operation and maintenance improvement measures for the problem of excessive turbidity and DO in Shijiuyang Wetland. The research results are conducive to improving the understanding of water security in the Yangtze River Delta region.

Outcomes-Based Education (OBE) is the core concept of professional accreditation in engineering education, and is a teaching and learning approach that determines teaching strategies based on learning outcomes. In the context of new engineering, it is important to actively cultivate high quality composite new engineering talents with international competitiveness that are compatible with the emerging industries, and improving their teaching effect is important to accomplish the training objectives and graduation requirements.This paper analyzes the necessity of using virtual reality technology in the teaching system, explains the application and development of OBE concept and virtual simulation technology in the teaching system, and finally proposes specific objectives, ideas and methods for the construction of virtual simulation training teaching system for intelligent manufacturing.

The contaminated soil caused by an organophosphorus pesticide production enterprise was evaluated, in order to provide constructive suggestions for the future remediation work. This study follows the basic technical route of sampling with the method of distribution, and combined with the relevant national laws and regulations and the actual situation of the site to evaluate and analyze, and finally find out whether the characteristic pollutants of the contaminated site exceed the standard and the environmental health risk. The results show that in the selected contaminated sites, the method can be implemented and obtained clear analysis results. The characteristic pollutants such as benzene, ethiophosphorus and parathion are far beyond the national standards, and seriously endanger the life and health of the surrounding residents. Conclusions:1) In the assessment of some contaminated sites, especially those contaminated by organophosphorus pesticides, the sampling method has the advantages of uniform, extensive, accurate and low sampling cost;2) The pollutants related to the polluted site, especially the volatile organic compounds such as benzene and parathion, are much higher than the limit value, and the peculiar smell is obvious, which seriously endangers the life and health of surrounding residents, causing the attention of relevant departments and providing guiding opinions for the future restoration work. In the future, the pollutants in the polluted site will be effectively removed through the restoration work. To ensure the life and health of neighboring residents, and promote China's ecological civilization construction.

Short-term photovoltaic power forecasting is of great significance for maintaining the security and stability of the power grid and coordinating the utilization of resources. As one of the Deep Learning Methods, Recurrent Neural Network (RNN) is widely used in time series prediction but lacks the ability of parallel computing. With good prediction effect, RNN is faced with the problem of long training time. In this paper, Sliced Recurrent Neural Network (SRNN) is applied to PV power prediction to guarantee the ability of parallel computing. The research result shows that compared to other commonly used models, SRNN can greatly speed up the training of Deep Learning Network with over 4 times higher training speed of the application of PV power prediction than that of ordinary RNN structure like LSTM and GRU. The accuracy of SRNN model is also improved by 0.1102 mae, which is significantly ahead of the others, as its parallel structure causes the more efficient parameter update, thus achieving ideal effect in PV prediction.

The integrated energy system creates the possibility for the interconnection and coordination of different energy sources, and is an effective means to improve the energy use of the system, increase energy efficiency, and reduce environmental pollution. At present, the planning of distributed energy stations for integrated energy systems mostly focuses on equipment selection and equipment capacity. However, there are relatively few studies on the location of energy stations and pipeline layout planning. Firstly, this paper proposes a distributed energy station location method based on the improved p-median model, which combines the energy supply path of the energy station with the actual transportation network, and introduces the weight coefficient of multi energy load to reflect the diversity of energy demand of load. Finally, the specific solution method is given, and the rationality and feasibility of the proposed method are verified by an example.