Recovery of Outliers in Water Environment Monitoring Data

The water environment monitoring data are time sequences with outliers which depress the data quality, so outlier detection and recovery play an important role in the applications such as knowledge acquisition and prediction modelling of water environment indicators. To detect the outliers, the short-term chain comparison with the sliding window based on the time sequence characteristics is adopted. To recover outliers closer to the real data at that time, the sub-sequences are divided dynamically according to the change characteristics of the dataset, then the similarity between sub-sequences is measured by the shape distance and the outliers are recovered according to the change trend of the corresponding data in the most similar sub-sequences. The monitoring data of a water station are selected in the study. The experimental results show that the recovery method is superior to the commonly used prediction recovery method and fitting recovery method, the recovered data is smoother and the short-term trend is more obvious

Water Quality Prediction Method Based on OVMD and Spatio-Temporal Dependence

Water quality changes at one monitoring spot are not only related to local historical data but also spatially to the water quality of the adjacent spots. Additionally, the non-linear and non-stationary nature of water quality data has a significant impact on prediction results. To improve the accuracy of water quality prediction models, a comprehensive water quality prediction model has been initially established that takes into account both data complexity and spatio-temporal dependencies. The Optimal Variational Mode Decomposition (OVMD) technology is used to effectively decompose water quality data into several simple and stable time series, highlighting short-term and long-term features and enhancing the model\u27s learning ability. The component sequence and spot adjacency matrix are used as the input of Graph Convolutional Network (GCN) to extract the spatial characteristics of the data, and the spatio-temporal dependencies of water quality data at different spots are obtained by combining GCN into the neurons of Gated Recurrent Unit (GRU). The attention model is added to automatically adjust the importance of each time node to further improve the accuracy of the training model and obtain a multi-step prediction output that more closely aligns with the characteristics of water quality change. The proposed model has been validated with real monitoring data for ammonia nitrogen (NH3-N) and total phosphorus (TP), and the results show that the proposed model is better than ARIMA, GRU and GCN+GRU models in terms of prediction results and it shows obvious advantages in the benchmark comparison experiment, which can provide reliable evidence for water pollution source traceability or early warning

Colloidal toxic trace metals in urban riverine and estuarine waters of Yantai City, southern coast of North Yellow Sea

The environmental characteristics of colloidal toxic trace metals Cd, Cu and Pb in riverine and estuarine waters collected from two urban rivers of Yantai City in eastern China, the Guangdang and Xin'an Rivers, were investigated using a modified centrifugal ultrafiltration (CUF) method in conjunction with acid extraction and inductively coupled plasma mass spectrometry. The target metals in dissolved pool were divided into four CUF fractions, i.e. <1 kDa, 1-3 kDa, 3-10 kDa and 10 kDa-0.2 mu m, and the results showed that colloidal Cd, Cu and Pb were dominated by 1-10 kDa (1-3 and 3-10 kDa), 1-3 kDa and 10 kDa-0.2 lm fractions, respectively. The coagulation/flocculation of low-molecular-weight (1-10 kDa) colloidal Cd and Cu in the estuaries was obvious and strong, while the enrichment of dissolved Pb in the 10 kDa-0.2 lm fraction may be mainly related to its biogeochemical interactions with Fe-oxides, which is easy to occur in macromolecular colloids. In addition, the actual molecular weight cutoffs (MWCOs) of the three used CUF units with nominal MWCOs of 1, 3 and 10 kDa were determined to be 4.9, 8.5 and 33.9 kDa, respectively, indicating that membrane calibration is essential for explaining the actual fraction of dissolved trace metals and verifying the integrity of ultrafiltration membrane. Overall, the results in this study provide a further understanding of the heterogeneity in biogeochemical features, migration and fate of toxic trace metals in aquatic ecosystems, especially that of the river-sea mixing zone. (C) 2019 Elsevier B.V. All rights reserved

Water Quality Prediction Method Based on OVMD and Spatio-Temporal Dependence

Water quality changes at one monitoring spot are not only related to local historical data but also spatially to the water quality of the adjacent spots. Additionally, the non-linear and non-stationary nature of water quality data has a significant impact on prediction results. To improve the accuracy of water quality prediction models, a comprehensive water quality prediction model has been initially established that takes into account both data complexity and spatio-temporal dependencies. The Optimal Variational Mode Decomposition (OVMD) technology is used to effectively decompose water quality data into several simple and stable time series, highlighting short-term and long-term features and enhancing the model\u27s learning ability. The component sequence and spot adjacency matrix are used as the input of Graph Convolutional Network (GCN) to extract the spatial characteristics of the data, and the spatio-temporal dependencies of water quality data at different spots are obtained by combining GCN into the neurons of Gated Recurrent Unit (GRU). The attention model is added to automatically adjust the importance of each time node to further improve the accuracy of the training model and obtain a multi-step prediction output that more closely aligns with the characteristics of water quality change. The proposed model has been validated with real monitoring data for ammonia nitrogen (NH3-N) and total phosphorus (TP), and the results show that the proposed model is better than ARIMA, GRU and GCN+GRU models in terms of prediction results and it shows obvious advantages in the benchmark comparison experiment, which can provide reliable evidence for water pollution source traceability or early warning

Downstream Processing Strategies for Lignin-First Biorefinery

The lignin-first strategy has emerged as one of the most powerful approaches for generating novel platform chemicals from lignin by efficient depolymerization of native lignin. Because of the emergence of this novel depolymerization method and the definition of viable platform chemicals, future focus will soon shift towards innovative downstream processing strategies. Very recently, many interesting approaches have emerged that describe the production of valuable products across the whole value chain, including bulk and fine chemical building blocks, and several concrete examples have been developed for the production of polymers, pharmaceutically relevant compounds, or fuels. This Minireview provides an overview of these recent advances. After a short summary of catalytic systems for obtaining aromatic monomers, a comprehensive discussion on their separation and applications is given. This Minireview will fill the gap in biorefinery between deriving high yields of lignin monomers and tapping into their potential for making valuable consumer products.</p

Downstream Processing Strategies for Lignin-First Biorefinery

The lignin-first strategy has emerged as one of the most powerful approaches for generating novel platform chemicals from lignin by efficient depolymerization of native lignin. Because of the emergence of this novel depolymerization method and the definition of viable platform chemicals, future focus will soon shift towards innovative downstream processing strategies. Very recently, many interesting approaches have emerged that describe the production of valuable products across the whole value chain, including bulk and fine chemical building blocks, and several concrete examples have been developed for the production of polymers, pharmaceutically relevant compounds, or fuels. This Minireview provides an overview of these recent advances. After a short summary of catalytic systems for obtaining aromatic monomers, a comprehensive discussion on their separation and applications is given. This Minireview will fill the gap in biorefinery between deriving high yields of lignin monomers and tapping into their potential for making valuable consumer products.</p

Porous monolith-based magnetism-reinforced in-tube solid phase microextraction of sulfonylurea herbicides in water and soil samples.

In the present study, porous monolith-based magnetism-reinforced in-tube solid phase microextraction (MB-MR/IT-SPME) was first introduced to concentrate sulfonylurea herbicides (SUHs). To realize the effective capture of SUHs, a monolithic capillary microextraction column (MCMC) based on poly (vinylimidazole-co-ethylene dimethacrylate) polymer doped with Fe3O4 magnetic nanoparticles was in-situ synthesized in the first step. After that, the MCMC was twined with a magnetic coil which was employed to carry out variable magnetic field during adsorption and desorption procedure. Various important parameters that affecting the extraction performance were inspected in detailed. Results well indicated that exertion of magnetic field in the whole extraction procedure was in favor of the capture and release of the studied SUHs, with the extraction efficiencies increased from 36.8-58.1% to 82.6-94.5%. At the same time, the proposed MB-MR/IT-SPME was online combined to HPLC with diode array detection (HPLC/DAD) to quantify trace levels of SUHs in water and soil samples. The limits of detection (S/N = 3) for water and soil samples were in the ranges of 0.030-0.15 μg/L and 0.30-1.5 μg/kg, respectively. The relative standard deviations (RSDs) for intra- and inter-day variability were both less than 10%. Finally, the introduced approach was successfully applied to monitor the low contents of studied SUHs in environmental water and soil samples. Satisfying fortified recovery and precision were achieved

Multi - mechanism coalescence design and matrix expression of logic action sequences of the over-turn nursing robot Part I: Functions and coalescence design

In order to effectively solve the problem in over-turn of a bedridden person with the assistance of external force, a double bed face- three embedded leave over-turn nursing robot with the flexible compensation was put forward, with the abstraction of the bedridden person as an organism. This robot, on the basis of concept gesture of the person in bed and the state of the robot supporting and proving the gesture with the actions and combination of the two bed faces, held the complete function of over-turn nursing with 7 states corresponding to 5 gestures of the bedridden person obeying the fundamental requirements of safety, rapidity, and comport. The design method of "PS-MM-KD" was proposed for multi-mechanism coalescent system with related specific tasks induced from the original problems with Systems Engineering. Mechanics and Mechanisms, then applied in the concrete sub-system design followed by analysis and verification of both the scheme and the sub-systems in the design, using the Kinematics and Dynamics, implementing the gears, chain wheel, slewing mechanism, screw nut and mortise and tenon joint type clutch mechanism design successfully. Based on those above, a "two-bed face/three-leaf embedded flexible compensation nursing robot" was designed adopting to all ages, people of various kinds of body geometry. PLC, sensor and logic algorithm were used to carry out the control and operation of 7 state-5 posture sequences for realization of the automation and intelligent over-turning in safety, comfort, and convenience

Multi - mechanism coalescence design and matrix expression of logic action sequences of the over-turn nursing robot Part II: Gesture-state in sets and matrix

As is expressed in Part I, Functions and coalescence design of the over-turn nursing robot, the performance and requirements have been put forward with systematic design of several mechanisms. Here, in order to control and function well the over-turn nursing robot, the three-dimensional and five-dimensional Euclidean space with the real number were adopted in terms of sets for gesture of the bedridden person and the corresponding state of the robot, respectively. The matrix method was employed to define and describe the gestures-robot performance and its transition path. The gesture-state sequence matrix not only accurately and clearly expressed the gesture series, state sequence and their corresponding relations, but also laid a theoretical and technical foundation for the path planning from the current gesture to the target one. The control and operation of 7 states and 5 gestures were done to realize the automation and intelligent over-turning safely, comfortably and conveniently

Does Land Approval Facilitate Conservation Tillage? An Examination through the Lens of Straw- Returning Technology

Well-defined and stable property rights play a pivotal role in shaping human economic behavior by averting the tragedy of the commons. This study employs micro-survey data from Heilongjiang Province, China, to empirically investigate the impact and mechanisms of land approval on the adoption of straw returning tstraw-returning technology by farmers. Utilizing the Probit model and mediation and moderation effect testing methods, the findings reveal the following: (1) Land approval significantly promotes the adoption of straw-returning techniques by farmers, with a marginal effect of 0.288. This view is further validated through counterfactual inference constructed using the propensity score matching method. (2) Endowment effects mediate the relationship between land approval and farmers’ adoption of straw-returning technology. (3) Digital skills and farming scale negatively moderate the policy’s impact on farmers’ adoption of straw-returning technology. (4) In terms of control variables, the age of farmers and the dispersion of cultivated land have a significant negative impact on the adoption of straw-returning technology by farmers, while training related to agricultural straw-returning skills and government technology promotion significantly positively affects the use of straw-returning technology by farmers. Therefore, the clarity of land property rights helps to harness the policy effects of land approval and provides a research approach for countries with communal land ownership to implement actions for soil quality conservation