Analysis of Effluent Outfall Problems and Their Classification and Regulation Countermeasures

Effluent outfalls are an important exit for pollutants discharged from the source flowing into environmental water bodies,as well as an important guarantee for the ecological environment of natural water bodies.In response to main problems of large and diverse effluent outfalls,as well as their monitoring analysis,tracing and regulation in China,classification and regulation countermeasures were proposed based on the characteristics of effluent outfalls.It is suggested that a comprehensive management and control system should be built by improving the management and control system,upgrading monitoring techniques and strengthening social supervision and public education,so as to provide a scientific basis for the supervision and management of effluent outfalls in China and help promote the improvement of water quality and the sustainable development and utilization of water resources.

The State-of-the-Art Review on Applications of Intrusive Sensing,Image Processing Techniques,and Machine Learning Methods in Pavement Monitoring and Analysis

In modern transportation,pavement is one of the most important civil infrastructures for the movement of vehicles and pedestrians.Pavement service quality and service life are of great importance for civil engineers as they directly affect the regular service for the users.Therefore,monitoring the health status of pavement before irreversible damage occurs is essential for timely maintenance,which in turn ensures public transportation safety.Many pavement damages can be detected and analyzed by monitoring the structure dynamic responses and evaluating road surface conditions.Advanced technologies can be employed for the collection and analysis of such data,including various intrusive sensing techniques,image processing techniques,and machine learning methods.This review summarizes the state-ofthe-art of these three technologies in pavement engineering in recent years and suggests possible developments for future pavement monitoring and analysis based on these approaches.

Monitoring and analysis of nonlinear dynamic damage of transport roadway supported by composite hard rock materials in Linglong Gold Mine

The study concentrates mainly on the development of failure process incomposite rock mass. By use of acoustic emission (AE), convergence inspection, pressure monitoring,level measurement techniques and the modem signal analysis technology, as well as scan electronmicroscopy (SEM) experiment, various aspects of nonlinear dynamic damage of composite rock masssurrounding the transport roadway in Linglong gold mine are discussed. According to the monitoringresults, the stability of the rock mass can be synthetically evaluated, and the intrinsic relationbetween the damage and the characteristic parameters of acoustic emission can be determined. Thelocation of the damage of rock mass can also be detected based on the acoustic emission couplemonitoring signals. Finally, the key factors which influence the stability of the transport roadwaysupported by composite hard rock materials are found out.

Seismic Deformation and Seismic Resistance Analysis of Shapai Roller Compacted Concrete Arch Dam Based on Field Monitoring and Dynamic Finite Element Method

Shapai Roller Compacted Concrete(RCC) Arch Dam is the highest RCC arch dam of the 20th century in the world with a maximum height of 132m,and it is the only concrete arch dam near the epicentre of Wenchuan earthquake on May 12th,2008.The seismic damage to the dam and the resistance of the dam has drawn great attention.This paper analyzed the response and resistance of the dam to the seismic wave using numerical simulations with comparison to the monitored data.The field investigation after the earthquake and analysis of insitu data record showed that there was only little variation in the opening size at the dam and foundation interface,transverse joints and inducing joints before and after the earthquake.The overall stability of the dam abutment resistance body was quite good except a little relaxation was observed.The results of the dynamic finite element method(FEM) showed that the sizes of the openings obtained from the numerical modeling are comparable with the monitored values,and the change of the opening size is in millimeter range.This study revealed that Shapai arch dam exhibited high seismic resistance and overload capacity in the Wenchuan earthquake event.The comparison of the monitored and simulated results showed that the numerical method applied in this paper well simulated the seismic response of the dam.The method could be useful in the future application on the safety evaluation of RCC dams.

Tropical Cyclone Monitoring and Analysis Techniques: A Review

Tropical cyclones(TCs),including tropical depressions and different categories of typhoons,hurricanes,and cyclonic storms,mostly originate over the oceans in the absence of direct observations.Thus,detailed monitoring and analysis of TCs has always been an unsolved problem.In the recent 20 years,great changes have taken place in domestic and foreign TC monitoring techniques,imposing a significant impact on TC operations and research.Some new technologies and products gradually emerge to support operations,with improved monitoring accuracy.In this paper,the progress on TC monitoring and analysis via meteorological satellites,radars,and airplanes in China and the world is reviewed,compared,and summarized,with special focuses on multisatellite fusion observations,in situ aircraft measurements,and some unconventional observation equipment such as rockets,saildrones,and underwater gliders.On this basis,the paper points out future directions for improving TC monitoring and analysis in aid of better TC forecast and early warning.

Analysis and assessment of land desertification in Xinjiang based on RS and GIS

The land desertification in Xinjiang was monitored and analyzed based on RS and GIS techniques. Satellite data interpretation was adopted to obtain the general situation of Xinjiang’s land desertification in assistance with the sampling method and on-the-spot investigations. Related monitoring and investigations showed that Xinjiang was facing with severe wide range land desertification, and its desertified area made up 77.08% of the total monitoring area. As for land types, the desertified farmland accounted for 1.92% of the total monitoring area, desertified woodland 4%, desertified grassland 45%, and unused land 49%. Accordingly, as for desertification degrees, non-desertified land occupied 22.92%, weak desertified land 5.69%, medium-degree desertified land 16.58%, severe desertified land 33.19% and super severe desertified land 21.61%. Finally, as for inducing factors, wind-eroded desertification made up 58.23%, water-eroded desertification 8.69%, salinization desertification 6.52% and frozen-melt eroded desertification 3.64%. Xinjiang’s land desertification tended to get worse and the harnessing mission remained hard.

Monitoring result analyses of high slope of five-step ship lock in the Three Gorges Project

The construction of the double-lane five-step ship lock of the Three Gorges Project （TGP） wascommenced in 1994, the excavation of the ship lock was completed by the end of 1999, and the ship lockwas put in operation in June 2003. The side slopes of the ship lock are characterized by great height（170 m）, steepness （70 m in height of upright slope）, and great length （over 7000 m in total length）. Inassociation with the ship lock, the surrounding rocks in slope have a high potential to deform, withwhich the magnitude of deformation is restricted. Monitoring results show that the deformation of thefive-step ship lock high slopes of the TGP primarily occurred in excavation period, and deformationtended to be stable and convergent during operation period, suggesting the allowable ranges of deformation.At present, the slopes and lock chambers are stable, and the ship lock works well under normaloperation condition, enabling the social and economic benefits of the TGP. 2015 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting byElsevier B.V. All rights reserved.

Effects of modeling means on properties of monitoring models of spot welding quality

Analyzing and modeling the relation between monitoring information during welding and quality information of the joints is the foundation of monitoring resistance spot welding quality. According to the means of modeling, the known models can be divided into three large categories: single linear regression models, multiple linear regression models and multiple non linear models. By modeling the relations between dynamic resistance information and welding quality parameters with different means, this paper analyzes effects of modeling means on performances of monitoring models of resistance spot welding quality. From the test results, the following conclusions can be drawn: By comparison with two other kinds of models, artificial neural network (ANN) model can describe non linear and high coupling relationship between monitoring information and quality information more reasonably, improve performance of monitoring model remarkably, and make the estimated values of welding quality parameters more accurate and reliable.

An aligned mixture probabilistic principal component analysis for fault detection of multimode chemical processes

A novel approach named aligned mixture probabilistic principal component analysis(AMPPCA) is proposed in this study for fault detection of multimode chemical processes. In order to exploit within-mode correlations,the AMPPCA algorithm first estimates a statistical description for each operating mode by applying mixture probabilistic principal component analysis(MPPCA). As a comparison, the combined MPPCA is employed where monitoring results are softly integrated according to posterior probabilities of the test sample in each local model. For exploiting the cross-mode correlations, which may be useful but are inadvertently neglected due to separately held monitoring approaches, a global monitoring model is constructed by aligning all local models together. In this way, both within-mode and cross-mode correlations are preserved in this integrated space. Finally, the utility and feasibility of AMPPCA are demonstrated through a non-isothermal continuous stirred tank reactor and the TE benchmark process.

A novel multimode process monitoring method integrating LCGMM with modified LFDA

Complex processes often work with multiple operation regions, it is critical to develop effective monitoring approaches to ensure the safety of chemical processes. In this work, a discriminant local consistency Gaussian mixture model(DLCGMM) for multimode process monitoring is proposed for multimode process monitoring by integrating LCGMM with modified local Fisher discriminant analysis(MLFDA). Different from Fisher discriminant analysis(FDA) that aims to discover the global optimal discriminant directions, MLFDA is capable of uncovering multimodality and local structure of the data by exploiting the posterior probabilities of observations within clusters calculated from the results of LCGMM. This may enable MLFDA to capture more meaningful discriminant information hidden in the high-dimensional multimode observations comparing to FDA. Contrary to most existing multimode process monitoring approaches, DLCGMM performs LCGMM and MFLDA iteratively, and the optimal subspaces with multi-Gaussianity and the optimal discriminant projection vectors are simultaneously achieved in the framework of supervised and unsupervised learning. Furthermore, monitoring statistics are established on each cluster that represents a specific operation condition and two global Bayesian inference-based fault monitoring indexes are established by combining with all the monitoring results of all clusters. The efficiency and effectiveness of the proposed method are evaluated through UCI datasets, a simulated multimode model and the Tennessee Eastman benchmark process.

Damage detection with image processing: a comparative study

Large structures,such as bridges,highways,etc.,need to be inspected to evaluate their actual physical and functional condition,to predict future conditions,and to help decision makers allocating maintenance and rehabilitation resources.The assessment of civil infrastructure condition is carried out through information obtained by inspection and/or monitoring operations.Traditional techniques in structural health monitoring(SHM)involve visual inspection related to inspection standards that can be time-consuming data collection,expensive,labor intensive,and dangerous.To address these limitations,machine vision-based inspection procedures have increasingly been investigated within the research community.In this context,this paper proposes and compares four different computer vision procedures to identify damage by image processing:Otsu method thresholding,Markov random fields segmentation,RGB color detection technique,and K-means clustering algorithm.The first method is based on segmentation by thresholding that returns a binary image from a grayscale image.The Markov random fields technique uses a probabilistic approach to assign labels to model the spatial dependencies in image pixels.The RGB technique uses color detection to evaluate the defect extensions.Finally,K-means algorithm is based on Euclidean distance for clustering of the images.The benefits and limitations of each technique are discussed,and the challenges of using the techniques are highlighted.To show the effectiveness of the described techniques in damage detection of civil infrastructures,a case study is presented.Results show that various types of corrosion and cracks can be detected by image processing techniques making the proposed techniques a suitable tool for the prediction of the damage evolution in civil infrastructures.

Building African Ecosystem Research Network for Sustaining Local Ecosystem Goods and Services

A new form of producing and sharing knowledge has emerged as an international(United States of America,Asia,and Europe) research collaboration,known as the Long-Term Ecological Research(LTER) Network.Although Africa boasts rich biodiversity,including endemic species,it lacks the long-term initiatives to underpin sustainable biodiversity managements.At present,climate change may exacerbate hunger and poverty concerns in addition to resulting in ecosystem degradation,land use change,and other threats in Africa.Therefore,ecosystem monitoring was suggested to understanding the effects of climate change and setting strategies to mitigate these changes.This paper aimed to investigate ecosystem monitoring ground sites and address their coverage gaps in Africa to provide a foundation for optimizing the African Ecosystem Research Network(AERN) ground sites.The geographic coordinates and characteristics of ground sites-based ecosystem monitoring were collected from various networks aligned with the LTER implementation in Africa.Additionally,climatic data and biodiversity distribution maps were retrieved from various sources.These data were used to assess the size of existing ground sites and the gaps in description,ecosystems and biomes.The results reveal that there were 1089 sites established by various networks.Among these sites,30.5%,27.5%,and 28.8% had no information of area,year of establishment,current status,respectively.However,68.0% of them had an area equal to or greater than 1 km2.Sites were created progressively over the course of the years,with 68.9% being created from 2000 to 2005.To date,only 41.5% of the sites were operational.The sites were scattered across Africa,but they were concentrated in Eastern and Southern Africa.The unbalanced distribution pattern of the sites left Central and Northern Africa hardly covered,and many unique ecosystems in Central Africa were not included.To sustain these sites,the AERN should be based on operational sites,seeking secure funding by establishing multiple partnerships.

A water quality model applied for the rivers into the Qinhuangdao coastal water in the Bohai Sea, China

The water quality of all rivers into the Qinhuangdao coastal water was below the grade V in 2013. In this study, an inte- grated MIKE 1 l water quality model is applied to deal with the water environment in the rivers into the Qinhuangdao coastal water. The model is first calibrated with the field measured chemical oxygen demand （COD） concentrations. Then the transport of the COD in the rivers into the Qinhuangdao coastal water is computed based on the model in the water environmental monitoring process. Numerical results show that the COD concentration decreases dramatically in the estuaries, from which we can determine the positions of long-term monitoring stations to monitor the river pollutions into the coastal water. Furthermore, different scenarios about the inputs of the point sources and the non-point sources are simulated to discuss the model application in the water enviro- nmental control, and simplified formula are derived for assessing the water quality and the environmental management of rivers.