Accurate identification of water sources is crucial for effective water management and safety in mining operations.However,imbalanced water sample datasets often lead to suboptimal classification accuracy.To address t...Accurate identification of water sources is crucial for effective water management and safety in mining operations.However,imbalanced water sample datasets often lead to suboptimal classification accuracy.To address this challenge,this study proposes a novel water source identification method integrating Synthetic Minority Over-Sampling Technique(SMOTE),Zebra Optimization Algorithm(ZOA),and Light Gradient Boosting Machine(LightGBM).Initially,SMOTE is utilized to synthesize samples for the minority class within the imbalanced dataset,thereby generating a balanced water sample dataset and mitigating class distribution disparities.Subsequently,an efficient water source identification model is constructed by combining ZOA with LightGBM,leveraging the strengths of both algorithms.The model’s performance is validated using a test set and compared with other common classification models.Results demonstrate that SMOTE significantly alleviates class imbalance and enhances the classification accuracy of LightGBM for minority class water samples.ZOA parameter tuning accelerates model convergence and further improves classification accuracy,optimizing the model’s overall performance.In experimental validation,the proposed SMOTE-ZOA-LightGBM model achieved an accuracy of 88.41%and a F1 score of 88.24%,outperforming six other classification models.The method proposed in this paper can accurately identify water source types,effectively addressing the issue of low classification accuracy caused by imbalanced water sample data.It provides reliable technical support and scientific basis for identifying and preventing water inrush sources in mines.展开更多
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...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.展开更多
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...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.展开更多
We considered the point source identification problems for heat equations from noisy observation data taken at the minimum number of spatially fixed measurement points.We aim to identify the unknown number of sources ...We considered the point source identification problems for heat equations from noisy observation data taken at the minimum number of spatially fixed measurement points.We aim to identify the unknown number of sources and their locations along with their strengths.In our previous work,we proved that minimum measurement points needed under the noise-free setting.In this paper,we extend the proof to cover the noisy cases over a border class of source functions.We show that if the regularization parameter is chosen properly,the problem can be transformed into a poles identification problem.A reconstruction scheme is proposed on the basis of the developed theoretical results.Numerical demonstrations in 2D and 3D conclude the paper.展开更多
In this paper,an approach is developed to optimize the quality of the training samples in the conventional Artificial Neural Network(ANN)by incorporating expert knowledge in the means of constructing expert-rule sampl...In this paper,an approach is developed to optimize the quality of the training samples in the conventional Artificial Neural Network(ANN)by incorporating expert knowledge in the means of constructing expert-rule samples from rules in an expert system,and through training by using these samples,an ANN based on expert-knowledge is further developed.The method is introduced into the field of quantitative identification of potential seismic sources on the basis of the rules in an expert system.Then it is applied to the quantitative identification of the potential seismic sources in Beijing and its adjacent area.The result indicates that the expert rule based on ANN method can well incorporate and represent the expert knowledge in the rules in an expert system,and the quality of the samples and the efficiency of training and the accuracy of the result are optimized.展开更多
In order to solve the fatigue damage identification problem of helicopter moving components, a new approach for acoustic emission （AE） source type identification based on the harmonic wavelet packet （HWPT） feature...In order to solve the fatigue damage identification problem of helicopter moving components, a new approach for acoustic emission （AE） source type identification based on the harmonic wavelet packet （HWPT） feature extraction and the hierarchy support vector machine （H-SVM） classifier is proposed. After a four-level decomposition of the HWPT, the energy feature of AE signals in different frequency bands is extracted, which overcomes the shortcomings of the traditional wavelet packet including energy leakage, and inflexible frequency band selection and different frequency resolutions on different levels. The H-SVM classifier is trained with a subset of the experimental data for known AE source types and tested using the remaining set of data. The results of pressure-off experiments on the specimens of carbon fiber materials indicate that the proposed approach can effectively implement the AE source type identification, and has a better performance in terms of computational efficiency and identification accuracy than the wavelet packet （WPT） feature extraction.展开更多
Petroleum leakage is a major groundwater contamination source,with chemical composition of water soluble fractions(WSFs)from diverse oil sources significantly impacting groundwater quality and source identification.Th...Petroleum leakage is a major groundwater contamination source,with chemical composition of water soluble fractions(WSFs)from diverse oil sources significantly impacting groundwater quality and source identification.The aim of this study was to assess impact of 15 diverse oils on groundwater quality and environmental forensics based on oil-water equilibrium experiments.Our results indicate that contamination of groundwater by gasoline and naphtha is primarily attributed to volatile hydrocarbons,while pollution from diesel,kerosene,and crude oil is predominantly from non-hydrocarbons.Rapid determination of the extent of non-hydrocarbon pollution in WSFs was achieved through a new quantitative index.Gasoline and naphtha exhibited the highest groundwater contamination potential while kerosene and light crude oils were also likely to cause groundwater contamina-tion.Although volatile hydrocarbons in the WSFs of diesel and jet fuel do not easily exceed current regulatory standards,unregulated non-hydrocarbons may pose a more severe contamination risk to groundwater.Notably,the presence of significant benzene and toluene,hydrogenation and alkylation products(e.g.,C4-C5 alkylben-zenes,alkylindenes,alkyltetralins,and dihydro-indenes),cycloalkanes in WSFs can effectively be utilized for preliminary source identification of light distillates,middle distillates,and crude oils,respectively.展开更多
Studies on the elevation gradient distribution of polycyclic aromatic hydrocarbons(PAHs)have mainly focused on high-altitude regions like the Tibetan Plateau.Investigation of the PAHs distribution in Shennongjia regio...Studies on the elevation gradient distribution of polycyclic aromatic hydrocarbons(PAHs)have mainly focused on high-altitude regions like the Tibetan Plateau.Investigation of the PAHs distribution in Shennongjia region and the effects of total organic carbon(TOC),black carbon(BC)and elevation gradient on PAHs distribution are of great practical significance for protecting the Tibetan Plateau’s environment.This study collected soil and peat samples across varying vegetation types and altitudes in Shennongjia.Results showed that theΣ16PAHs concentrations ranged from 15.44 to 199.13 ng/g in surface soil and 300.15 to 555.52 ng/g in surface peat,respectively,both dominated by low molecular weight PAHs(LMW-PAHs),and the distribution line of PAHs in the soil of Shennongjia area is 1500-1700 m.Above 1700 m,PAH concentrations increased with altitude,influenced significantly by TOC and BC,though dependence on BC was stronger than on TOC.However,the dependence of PAHs on BC is higher than TOC.Through soil-air partition coefficient(KSA)and soil fugacity,it is found that the absorption capacity of PAHs is mainly concentrated in Dajiuhu,which is equivalent to a"trap".The ILCRs induced by PAHs in the soil of Shennongjia area are within the safe range.Shennongjia serves as a barrier,preventing pollutant transport from central emission areas to the Tibetan Plateau.Thus,amid tourism development,addressing traffic-related PAH sources,promoting green energy,and controlling air pollutants in central emission areas are vital for ecological protection in Shennongjia and the Tibetan Plateau.展开更多
In order to reduce the noise and vibration of the diesel engine,it is crucial to exactly identify the engine noise source character.Based on "two-microphone" method,the sound intensity measurement of a vehic...In order to reduce the noise and vibration of the diesel engine,it is crucial to exactly identify the engine noise source character.Based on "two-microphone" method,the sound intensity measurement of a vehicle four-stroke diesel engine was carried out in a hemi-anechoic chamber.Then the sound intensity contour maps were obtained from the measurement results and the main noise components of different frequencies on all the measurement surfaces were picked out to construct contour maps.By analysizing the relationship between the characteristics of contour maps and the space distribution of the engine compartment,the major sources of the exterior radiation noise of the diesel engine were identified.The results provided a creditable basis for improving the noise performance of the engine in the next phase.展开更多
Owing to the significant reductions in streamflow and an increase in human activities in recent years,the quality of surface water in Weihe River continues to pose environmental health concerns.We utilized hydrochemis...Owing to the significant reductions in streamflow and an increase in human activities in recent years,the quality of surface water in Weihe River continues to pose environmental health concerns.We utilized hydrochemistry and nitrogen and oxygen isotopes to elucidate the status and identify sources of nitrate pollution in the south and north banks for three seasons(flood,dry,and mean-flow periods)in the Weihe River watershed.A Bayesian isotope mixing model was applied to estimate the contributions of four potential NO_(3)-sources to river pollution(manure and sewage,soil nitrogen,inorganic fertilizer,and nitrate in precipitation).The U.S.Environmental Protection Agency(USEPA)evaluation model was implemented to evaluate the health risks associated with nitrate pollution in the surface water.Nitrate pollution was most severe during the dry period because the river flow was small.Due to the influence of the topography and land use type of the Weihe River,the pollution in the main stream was greater than that of the tributaries,and the pollution of the south bank was greater than that of the north bank.During the flood and mean-flow periods,δ^(15)N and δ^(18)O were mainly distributed in the NH_(4)^(+) of the fertilizer and soil nitrogen.During the dry period,δ^(15)N and δ^(18)O were mainly distributed in domestic sewage and manure regions.According to the Stable Isotope Analysis in R(SIAR)model,manure and sewage were the major nitrate sources during the dry period(73%).However,a decrease in the contribution from domestic sewage and manure was observed during the flood period(45%)compared to the dry period,but with a significantly increased contribution from soil nitrogen(23%)and inorganic fertilizer(21%).The health risk value in the dry period was higher than that in the wet and mean flow periods,and children are more susceptible to nitrate pollution than adults.Therefore,reducing the discharge of domestic sewage and manure and improving the utilization rate of nitrogen fertilizers may be effective measures to improve water quality in the watershed.展开更多
South China is one of the regions severely suffering from acid rain in the world.However,few systematic studies of rural precipitation chemistry have been performed in comparison with the extensive studies on their ur...South China is one of the regions severely suffering from acid rain in the world.However,few systematic studies of rural precipitation chemistry have been performed in comparison with the extensive studies on their urban counterparts of this region.In order to characterize the current acid rain status and identify its possible sources in the rural area of South China,we analyzed precipitation collected event by event from a rural forested watershed in southern Anhui Province between March 2007 and February 2010.The results showed that the concentrations of major ions within precipitation in the studied rural area were significantly lower than those reported for the urban areas of the same latitude in China.Nevertheless,the precipitation acidity(with an average pH value of 4.49)and the frequency of acid rain(95%)were considerably high.The relatively high ratio of(SO_(4)^(2+)NO_(3))/(Ca^(2+)+NH_(4)^(+))was the main cause of acid rain in this rural area,as SO_(2)and NO_(x)were the main precursors of acid rain,while Ca^(2+)and NH4+acted as the dominant neutralizers to the acidity.Source identification indicated that Ca^(2+)and Mg^(2+)mainly were derived from alkaline dust,SO_(4)2,NO_(3)and NH4+originated mainly from anthropogenic sources such as industrial and agricultural activities,most Na+,Cl,K+and some of Mg^(2+)were derived from the sea.The results suggested that the major ions within precipitation in the rural area of South China were related to the meso-scale and long-range transport of particles and aerosols in the air.展开更多
Acoustic signals from diesel engines contain useful information but also include considerable noise components To extract information for condition monitoring purposes, continuous wavelet transform (CWT) is used for t...Acoustic signals from diesel engines contain useful information but also include considerable noise components To extract information for condition monitoring purposes, continuous wavelet transform (CWT) is used for the characterization of engine acoustics. This paper first reviews CWT characteristics represented by short duration transient signals. Wavelet selection and CWT are then implemented and wavelet transform is used to analyze the major sources of the engine front's exterior radiation sound. The research provides a reliable basis for engineering practice to reduce vehicle sound level. Comparison of the identification results of the measured acoustic signals with the identification results of the measured surface vibration showed good agreement.展开更多
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 disas...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.展开更多
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.Combu...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.展开更多
Rapid and accurate identification of the characteristics(source location,number,and intensity)of pollution sources is essential for emergency assessment of contamination events.Compared with single-point source iden-t...Rapid and accurate identification of the characteristics(source location,number,and intensity)of pollution sources is essential for emergency assessment of contamination events.Compared with single-point source iden-tification,the reconstruction of multiple sources is more challenging.In this study,a two-step inversion method is proposed for multi-point pollution source reconstruction from limited measurements with the number of sources unknown.The applicability of the proposed method is validated with a set of synthetic experiments correspond-ing to one-,two-,and three-point pollution sources.The results show that the number and locations of pollution sources are retrieved exactly the same as prescribed,and the source intensities are estimated with negligible errors.The algorithm exhibits good performance in single-and multi-point pollution source identification,and its accuracy and efficiency of identification do not deteriorate with the increase in the number of sources.Some limitations of the algorithm,together with its capabilities,are also discussed in this paper.展开更多
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 ...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.展开更多
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 sour...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.展开更多
This paper focused on the pollution characteristics, sources and lung cancer risk of atmospheric polycyclic aromatic hydrocarbons （PAHs） in a new urban district of Nanjing, China. Gaseous and aerosol PM2.5 （particu...This paper focused on the pollution characteristics, sources and lung cancer risk of atmospheric polycyclic aromatic hydrocarbons （PAHs） in a new urban district of Nanjing, China. Gaseous and aerosol PM2.5 （particulate matter with aerodynamic diameter smaller than 2.S μm） samples were collected in spring of 2015. Sixteen PAHs were extracted and analyzed after sampling. Firstly, arithmetic mean concentrations of PAHs and BaPeq （benzo[a]pyrene equivalent） were calculated. The mean concentrations of PAHs were 29.26 ± 14.13,18.14 ± 5.37 and 48.47 ± 16.03 ng/m3 in gas phase, particle phase and both phases, respectively. The mean concentrations of BaPeq were 0.87 ± 0.51, 2.71 ± 2.17 and 4.06 ± 2.31 ng/m3 in gas phase, particle phase and both phases, respectively. Secondly, diagnostic ratios and principal component analysis were adopted to identify the sources of PAHs and the outcomes were the same： traffic exhaust was the predominant source followed by fuel combustion and industrial process. Finally, incremental lung cancer risk （ILCR） induced by whole year inhalation exposure to PAHs for population groups of different age and gender were estimated based on a Monte Carlo simulation. ILCR values caused by particle phase PAHs were greater than those caused by gas phase PAHs. ILCR values for adults were greater than those for other age groups. ILCR values caused by total （gas ＋ particle） PAHs for diverse groups were all greater than the significant level （10-6）, indicating high potential lung cancer risk. Sensitivity analysis results showed that cancer slope factor for BaP inhalation exposure and BaPeq concentration had greater impact than body weight and inhalation rate on the ILCR.展开更多
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 id...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.展开更多
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 identi...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.展开更多
基金funding from the Natural Science Foundation of Henan Province(252300421852)the State Key Laboratory of Development and Comprehensive Utilization of Coking Coal Resources(41040220201308)+4 种基金the National Natural Science Foundation of China(41972254)the China Postdoctoral Science Foundation(2019M662494)Supported by the Key Scientific Research Projects of Higher Education Institutions of Henan Province(19A170005)the Fundamental Research Funds for the Universities of Henan Province(NSFRF200337,NSFRF200103)Key Research and Development Project of Henan Province(251111322300).
文摘Accurate identification of water sources is crucial for effective water management and safety in mining operations.However,imbalanced water sample datasets often lead to suboptimal classification accuracy.To address this challenge,this study proposes a novel water source identification method integrating Synthetic Minority Over-Sampling Technique(SMOTE),Zebra Optimization Algorithm(ZOA),and Light Gradient Boosting Machine(LightGBM).Initially,SMOTE is utilized to synthesize samples for the minority class within the imbalanced dataset,thereby generating a balanced water sample dataset and mitigating class distribution disparities.Subsequently,an efficient water source identification model is constructed by combining ZOA with LightGBM,leveraging the strengths of both algorithms.The model’s performance is validated using a test set and compared with other common classification models.Results demonstrate that SMOTE significantly alleviates class imbalance and enhances the classification accuracy of LightGBM for minority class water samples.ZOA parameter tuning accelerates model convergence and further improves classification accuracy,optimizing the model’s overall performance.In experimental validation,the proposed SMOTE-ZOA-LightGBM model achieved an accuracy of 88.41%and a F1 score of 88.24%,outperforming six other classification models.The method proposed in this paper can accurately identify water source types,effectively addressing the issue of low classification accuracy caused by imbalanced water sample data.It provides reliable technical support and scientific basis for identifying and preventing water inrush sources in mines.
基金Project supported by the National Basic Research Program (973) of China(No. 2005CB724205)China Scholarship Programs of the Ministry ofEducation of China (No. 2006100766).
文摘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.
基金National Natural Science Foundation of China, No.40801201 No.40730526+2 种基金 Special grade of the financial support from China Postdoctoral Science Foundation, No.200902224 China Postdoctoral Science Founda- tion, No.20080440605 Shanghai Postdoctoral Foundation, No.07R214120
文摘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.
文摘We considered the point source identification problems for heat equations from noisy observation data taken at the minimum number of spatially fixed measurement points.We aim to identify the unknown number of sources and their locations along with their strengths.In our previous work,we proved that minimum measurement points needed under the noise-free setting.In this paper,we extend the proof to cover the noisy cases over a border class of source functions.We show that if the regularization parameter is chosen properly,the problem can be transformed into a poles identification problem.A reconstruction scheme is proposed on the basis of the developed theoretical results.Numerical demonstrations in 2D and 3D conclude the paper.
文摘In this paper,an approach is developed to optimize the quality of the training samples in the conventional Artificial Neural Network(ANN)by incorporating expert knowledge in the means of constructing expert-rule samples from rules in an expert system,and through training by using these samples,an ANN based on expert-knowledge is further developed.The method is introduced into the field of quantitative identification of potential seismic sources on the basis of the rules in an expert system.Then it is applied to the quantitative identification of the potential seismic sources in Beijing and its adjacent area.The result indicates that the expert rule based on ANN method can well incorporate and represent the expert knowledge in the rules in an expert system,and the quality of the samples and the efficiency of training and the accuracy of the result are optimized.
基金The Natural Science Foundation of Heilongjiang Province ( No. F201018)the National Natural Science Foundation of China( No. 60901042)
文摘In order to solve the fatigue damage identification problem of helicopter moving components, a new approach for acoustic emission （AE） source type identification based on the harmonic wavelet packet （HWPT） feature extraction and the hierarchy support vector machine （H-SVM） classifier is proposed. After a four-level decomposition of the HWPT, the energy feature of AE signals in different frequency bands is extracted, which overcomes the shortcomings of the traditional wavelet packet including energy leakage, and inflexible frequency band selection and different frequency resolutions on different levels. The H-SVM classifier is trained with a subset of the experimental data for known AE source types and tested using the remaining set of data. The results of pressure-off experiments on the specimens of carbon fiber materials indicate that the proposed approach can effectively implement the AE source type identification, and has a better performance in terms of computational efficiency and identification accuracy than the wavelet packet （WPT） feature extraction.
基金supported by the National Science Foundation of China(Nos.42177042,and 42477051)the National Key R&D Program of China(No.2023YFC3708700)the Science Foundation of China University of Petroleum-Beijing(No.2462022QNXZ006).
文摘Petroleum leakage is a major groundwater contamination source,with chemical composition of water soluble fractions(WSFs)from diverse oil sources significantly impacting groundwater quality and source identification.The aim of this study was to assess impact of 15 diverse oils on groundwater quality and environmental forensics based on oil-water equilibrium experiments.Our results indicate that contamination of groundwater by gasoline and naphtha is primarily attributed to volatile hydrocarbons,while pollution from diesel,kerosene,and crude oil is predominantly from non-hydrocarbons.Rapid determination of the extent of non-hydrocarbon pollution in WSFs was achieved through a new quantitative index.Gasoline and naphtha exhibited the highest groundwater contamination potential while kerosene and light crude oils were also likely to cause groundwater contamina-tion.Although volatile hydrocarbons in the WSFs of diesel and jet fuel do not easily exceed current regulatory standards,unregulated non-hydrocarbons may pose a more severe contamination risk to groundwater.Notably,the presence of significant benzene and toluene,hydrogenation and alkylation products(e.g.,C4-C5 alkylben-zenes,alkylindenes,alkyltetralins,and dihydro-indenes),cycloalkanes in WSFs can effectively be utilized for preliminary source identification of light distillates,middle distillates,and crude oils,respectively.
基金supported by the National Natural Science Foundations of China(No.42377235)the National Key Research and Development Program of China(No.2023YFC3709803)+1 种基金the Open Research Fund of Hubei Key Laboratory of Mine Environmental Pollution Control and Remediation(No.2023XZ103)Hubei Polytechnic University talent introduction project(No.24xjz13R).
文摘Studies on the elevation gradient distribution of polycyclic aromatic hydrocarbons(PAHs)have mainly focused on high-altitude regions like the Tibetan Plateau.Investigation of the PAHs distribution in Shennongjia region and the effects of total organic carbon(TOC),black carbon(BC)and elevation gradient on PAHs distribution are of great practical significance for protecting the Tibetan Plateau’s environment.This study collected soil and peat samples across varying vegetation types and altitudes in Shennongjia.Results showed that theΣ16PAHs concentrations ranged from 15.44 to 199.13 ng/g in surface soil and 300.15 to 555.52 ng/g in surface peat,respectively,both dominated by low molecular weight PAHs(LMW-PAHs),and the distribution line of PAHs in the soil of Shennongjia area is 1500-1700 m.Above 1700 m,PAH concentrations increased with altitude,influenced significantly by TOC and BC,though dependence on BC was stronger than on TOC.However,the dependence of PAHs on BC is higher than TOC.Through soil-air partition coefficient(KSA)and soil fugacity,it is found that the absorption capacity of PAHs is mainly concentrated in Dajiuhu,which is equivalent to a"trap".The ILCRs induced by PAHs in the soil of Shennongjia area are within the safe range.Shennongjia serves as a barrier,preventing pollutant transport from central emission areas to the Tibetan Plateau.Thus,amid tourism development,addressing traffic-related PAH sources,promoting green energy,and controlling air pollutants in central emission areas are vital for ecological protection in Shennongjia and the Tibetan Plateau.
基金supported by programfor the Top Young Academic Leaders of Higher Learning Institutions of Shanxi(2009)Natural Science Foundation of Shanxi Province,China(No.2010011031-2)
文摘In order to reduce the noise and vibration of the diesel engine,it is crucial to exactly identify the engine noise source character.Based on "two-microphone" method,the sound intensity measurement of a vehicle four-stroke diesel engine was carried out in a hemi-anechoic chamber.Then the sound intensity contour maps were obtained from the measurement results and the main noise components of different frequencies on all the measurement surfaces were picked out to construct contour maps.By analysizing the relationship between the characteristics of contour maps and the space distribution of the engine compartment,the major sources of the exterior radiation noise of the diesel engine were identified.The results provided a creditable basis for improving the noise performance of the engine in the next phase.
基金supported by National Natural Science Foundation of China(Grant No.41601017)Young Talent fund of University Association for Science and Technology in Shaanxi,China(Grant No.20190702)。
文摘Owing to the significant reductions in streamflow and an increase in human activities in recent years,the quality of surface water in Weihe River continues to pose environmental health concerns.We utilized hydrochemistry and nitrogen and oxygen isotopes to elucidate the status and identify sources of nitrate pollution in the south and north banks for three seasons(flood,dry,and mean-flow periods)in the Weihe River watershed.A Bayesian isotope mixing model was applied to estimate the contributions of four potential NO_(3)-sources to river pollution(manure and sewage,soil nitrogen,inorganic fertilizer,and nitrate in precipitation).The U.S.Environmental Protection Agency(USEPA)evaluation model was implemented to evaluate the health risks associated with nitrate pollution in the surface water.Nitrate pollution was most severe during the dry period because the river flow was small.Due to the influence of the topography and land use type of the Weihe River,the pollution in the main stream was greater than that of the tributaries,and the pollution of the south bank was greater than that of the north bank.During the flood and mean-flow periods,δ^(15)N and δ^(18)O were mainly distributed in the NH_(4)^(+) of the fertilizer and soil nitrogen.During the dry period,δ^(15)N and δ^(18)O were mainly distributed in domestic sewage and manure regions.According to the Stable Isotope Analysis in R(SIAR)model,manure and sewage were the major nitrate sources during the dry period(73%).However,a decrease in the contribution from domestic sewage and manure was observed during the flood period(45%)compared to the dry period,but with a significantly increased contribution from soil nitrogen(23%)and inorganic fertilizer(21%).The health risk value in the dry period was higher than that in the wet and mean flow periods,and children are more susceptible to nitrate pollution than adults.Therefore,reducing the discharge of domestic sewage and manure and improving the utilization rate of nitrogen fertilizers may be effective measures to improve water quality in the watershed.
基金supported by the National Natural Science Foundation of China(Nos.41071141 and 40601040)the International Foundation of Science(No.C/4077-2)receivedits fund from the Institute of Soil Science,Chinese Academy of Sciences(No.ISSASIP0704)
文摘South China is one of the regions severely suffering from acid rain in the world.However,few systematic studies of rural precipitation chemistry have been performed in comparison with the extensive studies on their urban counterparts of this region.In order to characterize the current acid rain status and identify its possible sources in the rural area of South China,we analyzed precipitation collected event by event from a rural forested watershed in southern Anhui Province between March 2007 and February 2010.The results showed that the concentrations of major ions within precipitation in the studied rural area were significantly lower than those reported for the urban areas of the same latitude in China.Nevertheless,the precipitation acidity(with an average pH value of 4.49)and the frequency of acid rain(95%)were considerably high.The relatively high ratio of(SO_(4)^(2+)NO_(3))/(Ca^(2+)+NH_(4)^(+))was the main cause of acid rain in this rural area,as SO_(2)and NO_(x)were the main precursors of acid rain,while Ca^(2+)and NH4+acted as the dominant neutralizers to the acidity.Source identification indicated that Ca^(2+)and Mg^(2+)mainly were derived from alkaline dust,SO_(4)2,NO_(3)and NH4+originated mainly from anthropogenic sources such as industrial and agricultural activities,most Na+,Cl,K+and some of Mg^(2+)were derived from the sea.The results suggested that the major ions within precipitation in the rural area of South China were related to the meso-scale and long-range transport of particles and aerosols in the air.
基金Project (No. 50175078) supported by the National Natural Science Foundation of China
文摘Acoustic signals from diesel engines contain useful information but also include considerable noise components To extract information for condition monitoring purposes, continuous wavelet transform (CWT) is used for the characterization of engine acoustics. This paper first reviews CWT characteristics represented by short duration transient signals. Wavelet selection and CWT are then implemented and wavelet transform is used to analyze the major sources of the engine front's exterior radiation sound. The research provides a reliable basis for engineering practice to reduce vehicle sound level. Comparison of the identification results of the measured acoustic signals with the identification results of the measured surface vibration showed good agreement.
基金the Collaborative Innovation Center of Mine Intelligent Equipment and Technology,Anhui University of Science&Technology(CICJMITE202203)National Key R&D Program of China(2018YFC0604503)Anhui Province Postdoctoral Research Fund Funding Project(2019B350).
文摘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.
基金jointly funded by the Thailand Science Research and Innovation (TSRI) and the Electricity Generating Authority of Thailand (EGAT) under grant number RDG60D0002partially supported by the Graduate School,Prince of Songkla University,Thailandsupported by Scholarship Awards Thai PhD students under Thailand’s Education Hub for Southern Region of ASEAN Countries.
文摘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.
基金supported by the National Key R&D Program of China[Grant Nos.2017YFC1501803 and 2017YFC1502102].
文摘Rapid and accurate identification of the characteristics(source location,number,and intensity)of pollution sources is essential for emergency assessment of contamination events.Compared with single-point source iden-tification,the reconstruction of multiple sources is more challenging.In this study,a two-step inversion method is proposed for multi-point pollution source reconstruction from limited measurements with the number of sources unknown.The applicability of the proposed method is validated with a set of synthetic experiments correspond-ing to one-,two-,and three-point pollution sources.The results show that the number and locations of pollution sources are retrieved exactly the same as prescribed,and the source intensities are estimated with negligible errors.The algorithm exhibits good performance in single-and multi-point pollution source identification,and its accuracy and efficiency of identification do not deteriorate with the increase in the number of sources.Some limitations of the algorithm,together with its capabilities,are also discussed in this paper.
基金supported by the National Natural Science Foundation of China (No. 50808007)
文摘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.
基金This work was supported by Major Science and Technology Program for Water Pollution Control and Treatment(No.2015ZX07406005)Also thanks to the National Natural Science Foundation of China(No.41430643 and No.51774270)the National Key Research&Development Plan(No.2016YFC0501109).
文摘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.
基金supported by the National Natural Science Foundation of China(No.41001344)the China Postdoctoral Science Foundation Funded Project(2013M541696)+5 种基金the Jiangsu Planned Projects for Postdoctoral Research Funds(No.1301040C)the Program of Natural Science Research of Jiangsu Higher Education Institutions of China(No.13KJB610008)the Program of State Key Laboratory of Environmental Criteria and Risk Assessment,Chinese Research Academy of Environmental Sciences(No.SKLECRA2013OFP07)the Scientific Research Foundation of the High-level Personnel of Nanjing Normal University(No.2012105XGQ0102)the Program of Graduate Education Reform and Practice of Nanjing Normal University(No.1812000002A521)the Priority Academic Program Development of Jiangsu Higher Education Institutions(No.164320H116)
文摘This paper focused on the pollution characteristics, sources and lung cancer risk of atmospheric polycyclic aromatic hydrocarbons （PAHs） in a new urban district of Nanjing, China. Gaseous and aerosol PM2.5 （particulate matter with aerodynamic diameter smaller than 2.S μm） samples were collected in spring of 2015. Sixteen PAHs were extracted and analyzed after sampling. Firstly, arithmetic mean concentrations of PAHs and BaPeq （benzo[a]pyrene equivalent） were calculated. The mean concentrations of PAHs were 29.26 ± 14.13,18.14 ± 5.37 and 48.47 ± 16.03 ng/m3 in gas phase, particle phase and both phases, respectively. The mean concentrations of BaPeq were 0.87 ± 0.51, 2.71 ± 2.17 and 4.06 ± 2.31 ng/m3 in gas phase, particle phase and both phases, respectively. Secondly, diagnostic ratios and principal component analysis were adopted to identify the sources of PAHs and the outcomes were the same： traffic exhaust was the predominant source followed by fuel combustion and industrial process. Finally, incremental lung cancer risk （ILCR） induced by whole year inhalation exposure to PAHs for population groups of different age and gender were estimated based on a Monte Carlo simulation. ILCR values caused by particle phase PAHs were greater than those caused by gas phase PAHs. ILCR values for adults were greater than those for other age groups. ILCR values caused by total （gas ＋ particle） PAHs for diverse groups were all greater than the significant level （10-6）, indicating high potential lung cancer risk. Sensitivity analysis results showed that cancer slope factor for BaP inhalation exposure and BaPeq concentration had greater impact than body weight and inhalation rate on the ILCR.
基金the National Natural Science Foundation of China(Grant Nos.61673027 and 62106047)the Beijing Social Science Foundation(Grant No.21GLC042)the Humanity and Social Science Youth foundation of Ministry of Education,China(Grant No.20YJCZH228)。
文摘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.
基金funded by the National Natural Science Foundation of China(41907175)the Open Fund of Key Laboratory(WSRCR-2023-01)the project of the China Geological Survey(DD20230459).
文摘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.
