Pollution source identification methods and remediation technologies of groundwater: A review

Groundwater is an important source of drinking water.Groundwater pollution severely endangers drinking water safety and sustainable social development.In the case of groundwater pollution,the top priority is to identify pollution sources,and accurate information on pollution sources is the premise of efficient remediation.Then,an appropriate pollution remediation scheme should be developed according to information on pollution sources,site conditions,and economic costs.The methods for identifying pollution sources mainly include geophysical exploration,geochemistry,isotopic tracing,and numerical modeling.Among these identification methods,only the numerical modeling can recognize various information on pollution sources,while other methods can only identify a certain aspect of pollution sources.The remediation technologies of groundwater can be divided into in-situ and ex-situ remediation technologies according to the remediation location.The in-situ remediation technologies enjoy low costs and a wide remediation range,but their remediation performance is prone to be affected by environmental conditions and cause secondary pollution.The ex-situ remediation technologies boast high remediation efficiency,high processing capacity,and high treatment concentration but suffer high costs.Different methods for pollution source identification and remediation technologies are applicable to different conditions.To achieve the expected identification and remediation results,it is feasible to combine several methods and technologies according to the actual hydrogeological conditions of contaminated sites and the nature of pollutants.Additionally,detailed knowledge about the hydrogeological conditions and stratigraphic structure of the contaminated site is the basis of all work regardless of the adopted identification methods or remediation technologies.

Water source identification in mines combining LIF technology and ResNet

The problem of mine water source has always been an important hidden danger in mine safety production.The water source under the mine working face may lead to geological disasters,such as mine collapse and water disaster.The research background of mine water source identification involves many fields such as mining production,environmental protection,resource utilization and technological progress.It is a comprehensive and interdisciplinary subject,which helps to improve the safety and sustainability of mine production.Therefore,timely and accurate identification and control of mine water source is very important to ensure mine production safety.Laser-Induced Fluorescence(LIF)technology,characterized by high sensitivity,specificity,and spatial resolution,overcomes the time-consuming nature of traditional chemical methods.In this experiment,sandstone water and old air water were collected from the Huainan mining area as original samples.Five types of mixed water samples were prepared by varying their proportions,in addition to the two original water samples,resulting in a total of seven different water samples for testing.Four preprocessing methods,namely,MinMaxScaler,StandardScaler,Standard Normal Variate(SNV)transformation,and Centering Transformation(CT),were applied to preprocess the original spectral data to reduce noise and interference.CT was determined as the optimal preprocessing method based on class discrimination,data distribution,and data range.To maintain the original data features while reducing the data dimension,including the original spectral data,five sets of data were subjected to Principal Component Analysis(PCA)and Linear Discriminant Analysis(LDA)dimensionality reduction.Through comparing the clustering effect and Fisher's ratio of the first three dimensions,PCA was identified as the optimal dimensionality reduction method.Finally,two neural network models,CT+PCA+CNN and CT+PCA+ResNet,were constructed by combining Convolutional Neural Networks(CNN)and Residual Neural Networks(ResNet),respectively.When selecting the neural network models,the training time,number of iterative parameters,accuracy,and cross-entropy loss function in the classification problem were compared to determine the model best suited for water source data.The results indicated that CT+PCA+ResNet was the optimal approach for water source identification in this study.

Groundwater contaminant source identification based on iterative local update ensemble smoother

Identification of the location and intensity of groundwater pollution source contributes to the effect of pollution remediation,and is called groundwater contaminant source identification.This is a kind of typical groundwater inverse problem,and the solution is usually ill-posed.Especially considering the spatial variability of hydraulic conductivity field,the identification process is more challenging.In this paper,the solution framework of groundwater contaminant source identification is composed with groundwater pollutant transport model(MT3DMS)and a data assimilation method(Iterative local update ensemble smoother,ILUES).In addition,Karhunen-Loève expansion technique is adopted as a PCA method to realize dimension reduction.In practical problems,the geostatistical method is usually used to characterize the hydraulic conductivity field,and only the contaminant source information is inversely calculated in the identification process.In this study,the identification of contaminant source information under Kriging K-field is compared with simultaneous identification of source information and K-field.The results indicate that it is necessary to carry out simultaneous identification under heterogeneous site,and ILUES has good performance in solving high-dimensional parameter inversion problems.

Effect of observation time on source identification of diffusion in complex networks

This paper examines the effect of the observation time on source identification of a discrete-time susceptible-infectedrecovered diffusion process in a network with snapshot of partial nodes.We formulate the source identification problem as a maximum likelihood(ML)estimator and develop a statistical inference method based on Monte Carlo simulation(MCS)to estimate the source location and the initial time of diffusion.Experimental results in synthetic networks and real-world networks demonstrate evident impact of the observation time as well as the fraction of the observers on the concerned problem.

A novel approach for unlabeled samples in radiation source identification

Radiation source identification plays an important role in non-cooperative communication scene and numerous methods have been proposed in this field. Deep learning has gained popularity in a variety of computer vision tasks. Recently, it has also been successfully applied for radiation source identification.However, training deep neural networks for classification requires a large number of labeled samples, and in non-cooperative applications, it is unrealistic. This paper proposes a method for the unlabeled samples of unknown radiation source. It uses semi-supervised learning to detect unlabeled samples and label new samples automatically. It avoids retraining the neural network with parameter-transfer learning. The results show that compared with the traditional algorithms, the proposed algorithm can offer better accuracy.

Source identification of methane in groundwater in shale gas development areas:A critical review of the state of the art,prospects,and future challenges

The study aims to identify a suitable site for open and bore well in a farmhouseusing ground magnetic survey in south India.It also aims to define depth to granitoid and structural elements which traverse the selected area.Magnetic data(n=84)measured,processed and interpreted as qualitative and quantitatively.The results of total magnetic intensities indicate that the area is composed of linear magnetic lows trending NE-SW direction and circular to semi-circular causative bodies.The magnetic values ranged from-137 nT to 2345 nT with a mean of 465 nT.Reduction to equator shows significant shifting of causative bodies in the southern and northern directions.Analytical signal map shows exact boundary of granitic bodies.Cosine directional filter has brought out structural element trending NE-SW direction.Results of individual profile brought to light structurally weak zone between 90 m and 100 m in all the profile lines.Sudden decrease of magnetic values from 2042 nT to 126 nT noticed in profile line 6 between 20 m and 30 m indicates fault occurrence.Magnetic breaks obtained from these maps were visualized,interpreted and identified two suitable sites for open and bore well.Radially averaged power spectrum estimates depth of shallow and deep sources in 5 m and 50 m,respectively.Euler method has also been applied to estimate depth of granitoid and structural elements using structural indexes 0,1,2,and 3 and found depth ranges from<10 m to>90 m.Study indicates magnetic method is one of the geophysical methods suitable for groundwater exploration and site selection for open and borewells.

Source identification of aluminum in surface sediments of the Yellow Sea off the Shandong Peninsula

Surface sediment samples in the near shore area of the north Shandong Peninsula are collected for grain size and element analyses. The results indicate that the surface sediments in the study area are primarily composed of the silt-sized components similar to the Huanghe River. The total concentration of aluminum varies from 5.57% to7.37%（average（6.33 ± 0.40）%）, and its spatial distribution is mainly controlled by the grain size. Correlations between the ratio of aluminum to titanium concentration and aluminum concentration, titanium concentration and the mean grain size indicate that aluminum in the near shore surface sediments is affected majorly by the terrigenous source, and partially by the anthropogenic source. The ratios of aluminum to titanium concentrations are larger than the background value of loess matter at some stations due to the existence of excess aluminum associated with human activities. Thus, the sources of aluminum should be identified firstly when aluminum is used as an index of terrigenous matter even in the near shore area dominated by terrigenous deposits.

Source Camera Identification Algorithm Based on Multi-Scale Feature Fusion

The widespread availability of digital multimedia data has led to a new challenge in digital forensics.Traditional source camera identification algorithms usually rely on various traces in the capturing process.However,these traces have become increasingly difficult to extract due to wide availability of various image processing algorithms.Convolutional Neural Networks(CNN)-based algorithms have demonstrated good discriminative capabilities for different brands and even different models of camera devices.However,their performances is not ideal in case of distinguishing between individual devices of the same model,because cameras of the same model typically use the same optical lens,image sensor,and image processing algorithms,that result in minimal overall differences.In this paper,we propose a camera forensics algorithm based on multi-scale feature fusion to address these issues.The proposed algorithm extracts different local features from feature maps of different scales and then fuses them to obtain a comprehensive feature representation.This representation is then fed into a subsequent camera fingerprint classification network.Building upon the Swin-T network,we utilize Transformer Blocks and Graph Convolutional Network(GCN)modules to fuse multi-scale features from different stages of the backbone network.Furthermore,we conduct experiments on established datasets to demonstrate the feasibility and effectiveness of the proposed approach.

Identification of Mine Water Inrush Source Based on PCA-BP Neural Network

It is of great significance to analyze the chemical indexes of mine water and develop a rapid identification system of water source, which can quickly and accurately distinguish the causes of water inrush and identify the source of water inrush, so as to reduce casualties and economic losses and prevent and control water inrush disasters. Taking Ca2+, Mg2+, Na+ + K+, , , Cl-, pH value and TDS as discriminant indexes, the principal component analysis method was used to reduce the dimension of data, and the identification model of mine water inrush source based on PCA-BP neural network was established. 96 sets of data of different aquifers in Panxie mining area were selected for prediction analysis, and 20 sets of randomly selected data were tested, with an accuracy rate of 95%. The model can effectively reduce data redundancy, has a high recognition rate, and can accurately and quickly identify the water source of mine water inrush.

Identification and spatial patterns of coastal water pollution sources based on GIS and chemometric approach

Comprehensive and joint applications of GIS and chemometric approach were applied in identification and spatial patterns of coastal water pollution sources with a large data set （5 years （2000-2004）, 17 parameters） obtained through coastal water monitoring of Southern Water Control Zone in Hong Kong. According to cluster analysis the pollution degree was significantly different between September-next May （the 1st period） and June-August （the 2nd period）. Based on these results, four potential pollution sources, such as organic/eutrophication pollution, natural pollution, mineral/anthropic pollution and fecal pollution were identified by factor analysis/principal component analysis. Then the factor scores of each monitoring site were analyzed using inverse distance weighting method, and the results indicated degree of the influence by various potential pollution sources differed among the monitoring sites. This study indicated that hybrid approach was useful and effective for identification of coastal water pollution source and spatial patterns.

Particle-bound organic and elemental carbons for source identification of PM<0.1μm from biomass combustion

Atmospheric nanoparticles(PM<0.1μm)are a major cause of environmental problems and also affect health risk.To control and reduce these problems,sources identification of atmospheric particulates is necessary.Combustion of bituminous coal and biomass includ-ing rubber wood,palm kernel,palm fiber,rice stubble,rice straw,maize residue,sugarcane leaves and sugarcane bagasse,which are considered as sources of air quality problems in many countries,was performed.Emissions of particle-bound chemical components includ-ing organic carbon(OC),elemental carbon(EC),water-soluble ions(NH4^(+),Cl^(-),NO_(3)^(-),SO_(4)^(2-)),elements(Ca,K,Mg,Na)and heavy metals(Cd,Cr,Ni,Pb)were investigated.The results re-vealed that PM<0.1μm from all samples was dominated by the OC component(>50%)with minor contribution from EC(3%-12%).The higher fraction of carbonaceous components was found in the particulates with smaller sizes,and lignin content may relate to concentration of pyrolyzed organic carbon(PyOC)resulting in the differences of OC/EC values.PM emit-ted from burning palm fiber and rice stubble showed high values of OC/EC and also high PyOC.Non-carbonaceous components such as Cl^(-),Cr,Ca,Cd,Ni,Na and Mg may be useful as source indicators,but they did not show any correlation with the size of PM.

Study on the Algorithm of Vibration Source Identification Based on the Optical Fiber Vibration Pre-Warning System

One of the key technologies for optical fiber vibration pre-warning system （OFVWS） refers to identifying the vibration source accurately from the detected vibration signals. Because of many kinds of vibration sources and complex geological structures, the implement of identifying vibration sources presents some interesting challenges which need to be overcome in order to achieve acceptable performance. This paper mainly conducts on the time domain and frequency domain analysis of the vibration signals detected by the OFVWS and establishes attribute feature models including an energy information entropy model to identify raindrop vibration source and a fundamental frequency model to distinguish the construction machine and train or car passing by. Test results show that the design and selection of the feature model are reasonable, and the rate of identification is good.

Impact of gravitation on gaseous pollutant source identification

It is necessary to identify a gaseous pollutant source rapidly so that prompt actions can be taken, but this is one of the difficulties in the inverse problem areas. In this paper, an approach to identifying a sudden continuous emission pollutant source based on single sensor information is developed to locate a source in an enclosed space with a steady velocity field. Because the gravity has a very important influence on the gaseous pollutant transport and the source identification, its influence is analyzed theoretically and a conclusion is drawn that the velocity of fluid is a key factor to effectively help weaken the gravitational influence. Further studies for a given 2-D case by using the computational fluid dynamics （CFD） method show that when the velocity of inlet is less than one certain value, the influence of gravity on the pollutant transport is very significant, which will change the velocity field obviously. In order to quantitatively judge the practical applicability of identification approach, a synergy degree of the velocity fields before and after a source appearing is proposed as a condition for considering the influence of gravity. An experimental device simulating pollutant transmission was set up and some experiments were conducted to verify the practical application of the above studies in the actual gravitational environment. The results show that the proposed approach can successfully locate the sudden constant source when the experimental situations meet the identified conditions.

Identification of the sources of polycyclic aromatic hydrocarbons based on molecular and isotopic characterization from the Yangtze estuarine and nearby coastal areas

Samples of suspended particulate matters （SPMs）, surface sediment and road dust were collected from the Yangtze estuarine and nearby coastal areas, coastal rivers, and central Shanghai. The samples were analyzed for the presence of 16 polycyclic aromatic hydrocarbons （PAHs） in the USEPA priority-controlled list by GC-MS. The compound-specific stable carbon isotopes of the individual PAHs were also analyzed by GC-C-IRMS. The sources of PAHs in the SPMs and surface sediments in the Yangtze estuarine and nearby coastal areas were then identified using multiple source identification techniques that integrated molecular mass indices with organic compound-specific stable isotopes. The results revealed that 3-ring and 4-ring PAH compounds were dominant in the SPMs and surface sediments, which are similar to the PAH compounds found in samples from the Wusong sewage discharge outlet, Shidongkou sewage disposal plant, Huangpu River, coastal rivers and central Shanghai. Principal component analysis （PCA） integrated with molecular mass indices indicated that gasoline, diesel, coal and wood combustion and petroleum-derived residues were the main sources of PAHs in the Yangtze Estuary. The use of PAH compound-specific stable isotopes also enabled identification of the PAHs input pathways. PAHs derived from wood and coal combustion and petroleum-derived residues were input into the Yangtze Estuary and nearby coastal areas by coastal rivers, sewage discharge outlets during the dry season and urban storm water runoff during the flood season. PAHs derived from vehicle emissions primarily accumulated in road dust from urban traffic lines and the commercial district and then entered the coastal area via the northwest prevailing winds in the dry season and storm water runoff during flood season.

Optimization of Well Position and Sampling Frequency for Groundwater Monitoring and Inverse Identification of Contamination Source Conditions Using Bayes’Theorem

Coupling Bayes’Theorem with a two-dimensional(2D)groundwater solute advection-diffusion transport equation allows an inverse model to be established to identify a set of contamination source parameters including source intensity(M),release location(0 X,0 Y)and release time(0 T),based on monitoring well data.To address the issues of insufficient monitoring wells or weak correlation between monitoring data and model parameters,a monitoring well design optimization approach was developed based on the Bayesian formula and information entropy.To demonstrate how the model works,an exemplar problem with an instantaneous release of a contaminant in a confined groundwater aquifer was employed.The information entropy of the model parameters posterior distribution was used as a criterion to evaluate the monitoring data quantity index.The optimal monitoring well position and monitoring frequency were solved by the two-step Monte Carlo method and differential evolution algorithm given a known well monitoring locations and monitoring events.Based on the optimized monitoring well position and sampling frequency,the contamination source was identified by an improved Metropolis algorithm using the Latin hypercube sampling approach.The case study results show that the following parameters were obtained:1)the optimal monitoring well position(D)is at(445,200);and 2)the optimal monitoring frequency(Δt)is 7,providing that the monitoring events is set as 5 times.Employing the optimized monitoring well position and frequency,the mean errors of inverse modeling results in source parameters(M,X0,Y0,T0)were 9.20%,0.25%,0.0061%,and 0.33%,respectively.The optimized monitoring well position and sampling frequency canIt was also learnt that the improved Metropolis-Hastings algorithm(a Markov chain Monte Carlo method)can make the inverse modeling result independent of the initial sampling points and achieves an overall optimization,which significantly improved the accuracy and numerical stability of the inverse modeling results.

Exploration of the potential application of plutonium isotopes in source identification of sandstorm in the atmosphere of Beijing

Plutonium(Pu)is an anthropogenic radionuclide which mainly derived from atmospheric nuclear tests in the environment.In this study,the Pu isotopes(^(239)Pu and^(240)Pu)in aerosol samples collected during the sandstorm and non-sandstorm period were measured by accelerator mass spectrometry(AMS)and the behavior of Pu was studied.The activity concentrations of ^(239)Pu and ^(240)Pu in the aerosol samples of Beijing were ranged from 0.62 nB q/m^(3)to 99.6 n Bq/m^(3)for ^(239)Pu and 3.51 nBq/m^(3)to 60.23 n Bq/m^(3)for ^(240)Pu,respectively.^(239)Pu and ^(240)Pu concentrations exhibited a remarkable seasonal variation trend,with the higher results showed in spring,and the relatively lower concentrations in winter.The observed higher concentration of^(239)Pu and^(240)Pu detected in sandstorm samples further indicated Pu was closely related to the occurrence of sandstorms.The global fallout characteristics of^(240)Pu/^(239)Pu atom ratios(average0.20,ranging from 0.16 to 0.27)in aerosol samples indicating that global fallout was the major source of Pu in the atmosphere.Using aluminum(Al)as an indicator of soil resuspension,significant positive correlation between^(239)Pu and Al(r^(2)=0.934),240Pu and Al(^(r)2=0.525)revealed that soil resuspension was a primary source of atmospheric Pu in Beijing.These results implied that the combination of^(239)Pu,^(240)Pu and Al could be used as the potential tracer of sandstorm.

Distribution characteristics and source identification of polychlorinated dibenzo-p-dioxin and dibenzofurans,and dioxin-like polychlorinated biphenyls in the waters from River Kanzaki,running through Osaka urban area,Japan

This is the first to elucidate the distribution and sources of polychlorinated dibenzo-p-dioxins and dibenzofurans （PCDD/PCDFs）,and dioxin-like polychlorinated biphenyls in the waters from Kanzaki River,which is one of the most heavily polluted rivers in Japan.The World Health Organization （WHO）-toxic equivalent quantities （TEQs） in waters from the mainstream exceeded the Japanese environmental standard （1.0 pg-TEQ/L）.The PCDD/PCDFs were dominated by highly chlorinated DFs,which predominantly contributed to the WHO-TEQs,suggesting that the main causes would be the incineration-related wastes.To find the sources,the dioxin congener concentrations in water and sediment samples from its tributary small waterways were determined.Abnormally high WHO-TEQs were detected in a water （50 pg-TEQ/L） and a sediment sample （41,000 ng-TEQ/kg dry weight） near the industrial solid waste incinerators （ISWIs）.The PCDD/PCDF characteristics agreed well with those of the incinerator-related wastes as seen in the mainstream.These facts indicate that the dioxin pollution in the mainstream could be largely related to the industrial wastes from the ISWIs.Here,a TEQ apportionment method was used to understand the contribution of the pyrogenic sources to the WHO-TEQs.The average contribution ratios of the pyrogenic sources to WHO-TEQs were more than 80% for river waters from the mainstream,indicating that the elevated WHO-TEQs in the mainstream had been largely caused by the ISWIs.

Exposure measurement, risk assessment and source identification for exposure of traffic assistants to particle-bound PAHs in Tianjin, China

To investigate the levels of exposure to particulate-bound polycyclic aromatic hydrocarbon （PAH） and to estimate the risk these levels pose to traffic assistants （TAs） in Tianjin （a rnegacity in North China）, a measurement campaign （33 all-day exposure samples, 25 occupational-exposure samples and 10 indoor samples） was conducted to characterize the TAs＇ exposure to PAHs, assess the cancer risk and identify the potential sources of exposure. The average total exposure concentration of 14 PAHs was approximately 2871 ＋ 928 ng/rn3 （on-duty）, and 1622 ＋ 457 ng/m3 （all-day）. The indoor PAHs level was 1257 ＋ 107 ng/m3. After 8000 Monte Carlo simulations, the cancer risk resulting from exposure to PAHs was found to be approximately 1.05 x 10-4. A multivariate analysis was applied to identify the potential sources, and the results showed that, in addition to vehicle exhaust, coal combustion and cooking fumes were also another two important contributors to personal PAH exposure. The diagnostic ratios of PAH compounds agree with the source apportionment results derived from principal component analysis.

A study on the random array generation method for moving sound source identification

Based on the problem that the generating method of random array structure is inefficient, a method is proposed to generate the random target arrays by using coaxial circu- lar array in the polar coordinates in the premise that the array angular resolution of source identification is guaranteed. According to the principle of moving sound source identification, this work deduces the basic non-equidistance coaxial circular rings＇ radius, and generates target random arrays which were suitable for moving sound source identification through array partitioning, condition filtering in the polar coordinates and simulation evaluation. Finally, numerical simulation and moving car sound source identification test have been done. The analytical results show that using this method to generate random array is effective. Compared with the traditional regular arrays, the target random array has more accurate moving sound source identification performance.

On Regularization of a Source Identification Problem in a Parabolic PDE and its Finite Dimensional Analysis

We consider the inverse problem of identifying a general source term,which is a function of both time variable and the spatial variable,in a parabolic PDE from the knowledge of boundary measurements of the solution on some portion of the lateral boundary.We transform this inverse problem into a problem of solving a compact linear operator equation.For the regularization of the operator equation with noisy data,we employ the standard Tikhonov regularization,and its finite dimensional realization is done using a discretization procedure involving the space L^(2)(0,τ;L^(2)(Ω)).For illustrating the specification of an a priori source condition,we have explicitly obtained the range space of the adjoint of the operator involved in the operator equation.