Identification Of Sources Research Articles

Innovative lake pollution profiling: unveiling pollutant sources through advanced multivariate clustering techniques.

In many developed and developing nations, lakes are the primary source of drinking water. In the current scenario, due to rapid mobilization in anthropogenic activities, lakes are becoming increasingly contaminated. Such practices not only destroy lake ecosystems but also jeopardize human health through water-borne diseases. This study employs advanced hierarchical clustering through multivariate analysis to establish a novel method for concurrently identifying significantly polluted lakes and critical pollutants. A systematic approach has been devised to generate rotating component matrices, dendrograms, monoplots, and biplots by combining R-mode and Q-mode analyses. This enables the identification of contaminant sources and their grouping. A case study analyzing five lakes in Bengaluru, India, has been conducted to demonstrate the effectiveness of the proposed methodology. Additionally, one pristine lake from Jammu & Kashmir, India, has been included to validate the findings from the aforementioned five lakes. The study explored correlations among various physical, chemical, and biological characteristics such as temperature, pH, dissolved oxygen, conductivity, nitrates, biological oxygen demand (BOD), fecal coliform (FC), and total coliform (TC). Critical contaminants forming clusters included conductivity, nitrates, BOD, TC, and FC. Factor analysis identified four primary components that collectively accounted for 85% of the overall variance. Following identification of pollution hotspots, the study recommends source-based pollution control and integrated watershed management, which could significantly reduce lake pollution levels. Continuous monitoring of lake water quality is essential for identifying actual contaminant sources. These findings provide practical recommendations for maximizing restoration efforts, enforcing regulations on pollutant sources, and improving water quality conditions to ensure sustainable development of lakes.

AXES-2MRS: A new all-sky catalogue of extended X-ray galaxy groups

Context. Understanding baryonic physics at the galaxy-group level is a prerequisite for cosmological studies of the large-scale structure. One poorly understood aspect of galaxy groups is related to the properties of their hot intragroup medium. The well-studied X-ray groups have strong cool cores by which they were selected, so expanding the selection of groups is currently an important avenue in uncovering the diversity within the galaxy group population. Aims. We present a new all-sky catalogue of X-ray-detected groups (AXES-2MRS) based on the identification of large X-ray sources found in the ROSAT All-Sky Survey (RASS) with the Two Micron Redshift Survey (2MRS) Bayesian Group Catalogue. We studied the basic properties of these galaxy groups to gain insights into the effect of different group selections on the properties. Methods. In addition to X-ray luminosity from shallow survey data of RASS, we obtained detailed X-ray properties of the groups by matching the AXES-2MRS catalogue to archival X-ray observations by XMM-Newton and complemented this by adding the published XMM-Newton results on galaxy clusters in our catalogue. We analysed temperature and density to the lowest overdensity accessible by the data, obtaining hydrostatic mass estimates at a uniform overdensity of 10 000 times the critical, M10 000, and comparing them to the velocity dispersions of the groups. We explored the relationship between X-ray and optical properties of AXES-2MRS groups through the σv−LX, σv−kT, kT−LX, σv−M, and c200−LX scaling relations. Results. We find a large spread in the central mass ( M10 000), measured by XMM-Newton, to virial mass (M200), inferred by the velocity dispersion, ratios for galaxy groups. This can either indicate that large non-thermal pressure of galaxy groups affects our X-ray mass measurements or the effect of a diversity of halo concentrations on the X-ray properties of galaxy groups. Previous catalogues based on detecting the peak of the X-ray emission preferentially sample the high-concentration groups. In contrast, our new catalogue uncovered many low-concentration groups, completely revising our understanding of X-ray groups.

A study on load sharing characteristics of the planetary gear train system with cracked gears

Load sharing characteristic is very important for the reliability of the planetary gear train. Cracked gears can make the load sharing performance become worse. Nevertheless, previous studies have not paid enough attention to the influence of cracked gears on the load sharing behavior, when compared with the impact of multiple factors under healthy condition. An 18 degrees of freedom dynamic model for the spur planetary gear train is established based on the centralized parameters theory in this paper. The influences of cracked gears on the load sharing behavior are investigated, which include the cracked sun gear, the planet gear containing cracked side meshing with the sun gear, the planet gear having cracked side meshing with the ring gear and the cracked ring gear. Additionally, the sensitivity analysis of the external and internal load sharing behavior to different cracked components is conducted. The results indicate that the load distribution becomes more uneven with the propagation of crack in the sun gear and ring gear, while the change regulation is different for each cracked planet gear. Moreover, the load sharing characteristic of the external meshing is more sensitive to the cracked sun gear and the planet gear 1, which has the crack side meshing with the ring gear. While the load sharing characteristic of the internal meshing is more sensitive to the cracked ring gear and the planet gear 3, which owns the crack side meshing with the ring gear than others. The output of this paper will be useful for the crack fault diagnose and fault source identification of the planetary gear train system.

Tracing potentially toxic elements in button mushroom cultivation and environmental implications: Insights via stable lead (Pb) isotope analysis

Elevated levels of potentially toxic elements (PTEs) have been frequently detected in substrates used for cultivating edible mushrooms. However, the distribution and accumulation pathways of PTEs within mushrooms and their cultivation substrates remain inadequately understood. This study specifically aims to clarify these aspects of button mushrooms (Agaricus bisporus). A systematic analysis was conducted on the distribution of PTEs (As, Cd, Cr, Cu, Ni, Pb, and Zn) and stable Pb isotope fingerprints across various components of the button mushroom cultivation systems, including the cultivation substrates (a mixture of wheat straw, chicken manure, soil, and additives), fruiting bodies (MR), and spent mushroom substrate (SMS). The methodology involved inductively coupled plasma mass‒spectrometry for PTE quantification and Pb isotope ratio analysis for source identification. The results demonstrated that wheat straw and additives were the main contributors to the endogenous accumulation of PTEs in MR, accounting for 35.0% and 33.2% of Pb accumulation, respectively. Furthermore, MR, wheat straw, and soil together contributed to more than 67% of the Pb loads in SMS. The study also revealed that the occurrence of PTEs in these substrates is predominantly driven by exogenous sources, particularly the significant contribution from anthropogenic activities. Overall, these findings provide valuable biogeochemical insights into the accumulation pathways and patterns of PTEs in button mushroom cultivation systems, with potential applications for cleaner food management and environmentally sustainable practices.

Nationwide Profiling and Source Identification of Organophosphate Esters in Korean Surface Waters Using Target, Suspect, and Non-Target HRMS Analysis

Organophosphate esters (OPEs) are emerging contaminants that serve as alternatives to regulated substances in aquatic environments. A nationwide large-scale assessment for OPEs, including point sources, remains insufficient. To address this issue, we aimed to investigate OPEs occurrence and novel OPEs via comprehensive target, suspect and non-target analysis. Among the 11 target OPEs, 10 were detected at sampling sites distributed evenly nationwide. The highest mean concentrations were measured for tris-(2-butoxyethyl) phosphate (TBOEP) and tris(2-chloroisopropyl) phosphate (TCIPP). The multivariate statistical analysis revealed that TBOEP and TCIPP are essential components for assessing total OPEs pollution. The systematic risk assessment results evaluated the overall risk contribution of TBOEP and the significant risk impact of 2-ethylhexyl diphenyl phosphate. Promising suspect and non-target analysis enabled frequent detection and identification of 6 antioxidant transformation products (TPs), as well as the tentative identification of 14 OPEs and TPs, including 3 di-OPEs. Based on sampling site classification, we confirmed that major OPEs are significantly discharged near point sources. We believe that this is the first attempt to assess the nationwide risk and potential sources of OPEs in Korean surface waters, providing insights that could support further prioritization and regulation efforts.

Numerical solution of source identification multi-point problem of parabolic partial differential equation with Neumann type boundary condition

We study a source identification boundary value problem for a parabolic partial differential equation with multi-point Neumann type boundary condition. Stability estimates for the solution of the overdetermined mixed BVP for multi-dimensional parabolic equation were established. The first and second order of accuracy difference schemes for the approximate solution of this problem were proposed. Stability estimates for both difference schemes were obtained. The result of numerical illustration in test example was given.

Sound Source Identification of Vehicle Noise Based on a Microphone Array With a Modified Multiple Signal Classification Algorithm

In this paper, a modified multiple signal classification (MUSIC) algorithm tailored for sound source identification (SSI) of vehicle noise is introduced and experimentally validated. A uniform planar microphone array (UPMA) is formulated for mathematical modeling, with its SSI-oriented parameters selected based on the primary frequency spectrum of vehicle noise. Simulations are conducted to compare the SSI accuracy of two conventional spatial spectra estimation (SSE) algorithms: the Capon algorithm and the MUSIC algorithm. The results demonstrate that the MUSIC algorithm, which relies on the eigenvalues of a covariance matrix to estimate signal direction, exhibits superior SSI resolution under low signal-to-noise ratio (SNR) conditions. However, it faces challenges in distinguishing between coherent or closely spaced signals. To address this, a modified MUSIC algorithm is proposed by reconstructing the covariance matrix of received signals and the SSE function. Simulation outcomes indicate that the modified MUSIC significantly outperforms the conventional version, owing to its enhanced SSI resolution. The accuracy of the SSI system, incorporating the UPMA and the modified MUSIC, is verified using a low-frequency volume source. Ultimately, the devised SSI system is successfully deployed to identify noise sources in a vehicle at different operational conditions, further validating the efficacy of the modified MUSIC. The UPMA and the modified MUSIC presented in this study have direct applicability in vehicle noise source identification and may be extended to other sound-related engineering fields for SSI purposes.

Global warming: Causes, impacts and urgent strategies for a sustainable future: A review

Global warming is defined as a rise in Earth's average temperature. As the Earth gets warmer, disasters such as droughts, hurricanes, and floods are becoming increasingly common. Most scientists, engineers, and activists express deep concern about changes in the planet's overall climate. The average air temperature above the surface of the Earth has risen by just below 1 degree Celsius, or 1.3 degrees Fahrenheit, over the past 100 years. Deforestation exacerbates the severity of global warming. The ocean, which holds about 50 times more carbon than the atmosphere, is an enormous carbon sink. However, the seas are no longer able to store carbon as effectively as they have in the past. Burning fossil fuels such as natural gas, oil, coal, and gasoline increases atmospheric carbon dioxide levels, and carbon dioxide is a major contributor to the greenhouse effect and global warming. Climate change will increase the number of people who suffer from heatwaves, floods, hurricanes, and droughts, leading to higher rates of death, illness, and injury. The threat of global warming continues to cause severe damage to the Earth's environment. Many people still do not fully understand the implications of global warming or consider it a significant problem for the future. However, global warming is already happening, and some of its devastating consequences are already being felt. It significantly impacts biodiversity and disrupts ecological balance. Due to the dangerous effects of global warming, many strategies need to be established. The report discusses global warming, outlines its causes and risks, and proposes solutions to this urgent issue. Above all, it is crucial to seriously consider alternative energy sources (biomass, wind, hydro, geothermal, and solar). One of the key strategies to counter the ever-increasing global warming is the identification and use of renewable energy sources.

Source identification problems for the neutron transport equations

In this study, the time-dependent source identification problem for the two-dimensional neutron transport equation was studied. For the approximate solution of this problem a first order of accuracy difference scheme was presented. Stability estimates for the solution of these differential and difference problems were established. Numerical results were given.

A stable difference scheme for the solution of a source identification problem for telegraph-parabolic equations

In the present paper, we construct a first order of accuracy difference scheme for the approximate solution of the inverse problem for telegraph-parabolic equations with an unknown spacewise dependent source term. The unique solvability of constructed difference scheme and the stability estimates for its solution were obtained. The proofs are based on the spectral representation of the self-adjoint positive definite operator in a Hilbert space.

Biomonitoring of exposure to metals and metalloids using toenail and fingernail sampling in individuals from artisanal gold mining areas in Mali

Artisanal gold mining can release metallic elements in the environment that can result in occupational and environmental exposures. This pilot study aimed to assess exposure to metals and metalloids from fingernail and toenail samples in artisanal gold mine workers, inhabitants of a mining and non-mining village in Mali. As it can be particularly challenging to collect and transport biological samples from remote areas, nail sample collection was tested as a potential choice for multielement biomonitoring. A convenience sampling of 315 individuals was performed equally distributed in each location group (105 per location) and stratified by populational group (male adults, female adults, and people <18 years). Toenail and fingernail samples were collected from each participant and twenty-one elements (aluminum (Al), arsenic (As), barium (Ba), beryllium (Be), cadmium (Cd), chromium (Cr), cobalt (Co), copper (Cu), gallium (Ga), iron (Fe), lithium (Li), lead (Pb), manganese (Mn), nickel (Ni), selenium (Se), silver (Ag), strontium (Sr), thallium (Th), uranium (u), vanadium (v) and zinc (Zn)) were quantified. Concentrations of 12 elements in fingernails and/or toenails were significantly higher in the mine worker group, in particular As, Co and Cu in both toenails and fingernails. In the mine worker group specifically, As concentrations in both fingernails and toenails were higher in males. Most metals also had a strong positive correlation overall. Both fingernails and toenails appear as interesting biomonitoring matrices for multielement exposure assessment with an impact of different variables, such as mining exposure and sex, on internal levels. The study also highlighted the importance of further human exposure assessment related to artisanal gold mining in Mali, including the identification of other environmental sources of exposure.

Interior noise characteristics, source identification and its quantification contribution of 400 km/h high-speed train in different operating environments

Noise level is one of the core technical indicators of high-speed trains, especially for a speed of 400 km/h. Noise source identification and its contribution quantification are key for interior noise control. This article takes a certain high-speed train running up to 400 km/h as the research object. Firstly, based on the interior noise spectrum and experimental results of source identification based on spherical harmonic function, the main source locations and energy distribution inside the train are determined. Secondly, through polynomial fitting, the correlation between vibration and noise in various areas inside and outside the train and speed is determined. Subsequently, by calculating the energy contribution through area integration, the noise contribution of each interior region is determined, and then the variation law of the noise contribution of each region with the speed is determined, and the quantitative contributions of various areas inside the train are given when the high-speed train is running at 400 km/h. Finally, the vibration and noise inside the train, aerodynamic noise on the train body surface, and noise in the bogie area in open lines and tunnel operating environments are compared and analyzed, as well as their variation laws with speed. The main noise sources inside the train under two types of operating environments are identified, and the contribution rates of noise sources in different areas inside the train are analyzed, further studying the interior noise and vibration transmission characteristics. The results show that, when the high-speed train is running at 400 km/h in open line operating environment, the significant frequency range of interior noise is 40 Hz ∼ 2000 Hz, and dominant interior noise sources are located in the roof and floor. In the tunnel operating environment, the significant frequency range of interior noise is 160 Hz ∼ 1000 Hz, and the primary sources of interior noise are predominantly located in the left window and floor. For the sidewall area, the interior noise comes mainly from the vibration of the inner sidewall when in open line operating environment, while in tunnel operating environment, the interior noise comes mainly from the aerodynamic excitation of the body surface.

Assessment of dust emissions and their controlling factors on the Hoh Xil, north-central part of the Qinghai-Tibetan plateau

Dust emissions can lead to a series of environmental hazards and are thus a global concern. However, due to the difficulties of dust source identification, there are still great uncertainties in the calculation of dust yield on a global scale. The Qinghai-Tibetan Plateau presents some challenges for understanding the region's calculation of the global dust budget. To address these issues, the Hoh Xil Plateau, a typical dust source on the Qinghai-Tibetan Plateau, was selected as the study area for analyzing dust emission processes and associated influencing factors via a robust model. The results showed that dust emissions on the Hoh Xil Plateau occurred mainly in spring and winter. The wide distribution of bare land and sandy land on the Hoh Xil Plateau is the main reason for the high dust emissions. Wind speed is the dominant factor influencing annual dust emission, while precipitation and temperature were negatively correlated with the dust emission. The recession of expansive lakes in the region resulted in an increase in sandy lake-shores, which induced a potential increase in dust emissions. In addition, the effects of human activities on dust emissions were evident. Positive and negative effects on dust emission changes coexisted on the Hoh Xil Plateau and depended on the development of the county-level economy and the implementation of ecological engineering measures. We believe that this study will clarify the dust yield of the Qinghai-Tibetan Plateau and provide valuable information for understanding the driving factors that cause variations in aeolian processes on the Qinghai-Tibetan Plateau.

Timeliness-aware rumor sources identification in community-structured dynamic online social networks

Identifying rumor sources in online social networks (OSNs) plays a crucial role in controlling the spread of rumors and mitigating their damage. However, the community structure of OSNs and timeliness of rumors increase the complexity of accurately characterizing rumor-spreading behavior, making it extremely challenging to identify the source of rumor in OSNs. Conventional studies on rumor source identification often overlook the community structure of OSNs and timeliness of rumors. This paper proposes a rumor sources identification framework that take these two aspects into consideration. First, we transfer the snapshots of a dynamic OSN into a community-structured dynamic OSN through 4 meticulously designed phases. Second, instead of considering a constant influence of rumors in traditional techniques, we introduce a time-varying dynamic propagation parameter to quantify the timeliness of rumor, and apply the propagation parameter to establish a microscopic rumor spreading model. This process addresses the issue of modeling the timeliness of rumor. Third, we adopt sensor-based observation techniques to collect the propagation information of rumor, and a hybrid sensor deployment strategy is designed to improve the efficiency of information collection. Fourth, we propose an algorithm for identifying single and multiple rumor sources in community-structured dynamic OSN, this algorithm initiates with the utilization of some criteria to analyze the gathered propagation information and estimate the suspicious communities that harbor the source of rumors. Subsequently, it applies maximum likelihood estimation method within each community to determine the ultimate source of the rumor. Experimental results on real-world and synthetic networks indicate that our method can accurately identify the real rumor sources. To the best of our knowledge, the proposed method is the first that can be used to identify rumor sources in community-structured dynamic OSNs.

A framework integrating affinity propagation algorithm and spatial bivariate analysis for enhanced identification and localisation of soil heavy metals pollution sources.

The accurate identification of pollutant sources and their spatial distribution is crucial for mitigating soil heavy metals (SHMs) pollution. However, the receptor model struggles to effectively categorize pollutant sources and pinpoint their locations and dispersion trends. We propose a novel comprehensive framework that combines a receptor model, random forest (RF), affinity propagation (AP) algorithm, and bivariate local indicator of spatial association (BLISA), to optimize the traditional approach for tracing SHMs sources in industrial regions. We apportioned SHMs sources using a receptor model combined with RF, while BLISA combined with AP methods were employed to accurately locate the source areas and identify their dispersion tendencies. The results revealed that SHMs originated from mixed sources of equipment manufacturing agglomeration and agricultural activities (59.0%), geological background (30.5%), and emissions from heavily-polluting industries (10.5%). The pollution sources of soil Cd and Pb were located near specific industries, showing characteristics of multi-site concurrent pollution diffusion influenced by their proximity to industrial sites. The spatial distribution of Cr, Cu, and Zn sources was concentrated in high-density urban industrial areas, transitioning from point to nonpoint sources, with diffusion patterns influenced by the spatial agglomeration effect of industries. Our enhanced framework accurately identifies the location of SHMs sources and their dispersion tendencies, thereby improving regional soil pollution management.

On image transformation for partial discharge source identification in vehicle cable terminals of high‐speed trains

AbstractPartial discharge (PD) detection of cable terminals is crucial for the safe operation of the traction power system in trains. However, similar PD signals in complex train‐operating environments cause difficulty to recognise the insulation defects. Therefore, a PD signal image transformation recognition method is proposed for PD detection of cable terminal defects to identify defects in cable terminals with similar PD characteristics accurately. In the proposed method, the raw PD signals are firstly transformed to images via the Gramian angular field (GAF) representation. This can reveal the discriminative characteristics embedded in the original PD signals and subsequently facilitate differentiating the PD sources, which exhibit similar characteristic in the time domain. The obtained GAF representation of PD signals (named as PD GAF images) is extracted from local and global features to train an efficient MobileVIT model, which is then utilised to identify similar types of PD sources in cable terminals. The results show that the proposed method achieves 97.5% recognition accuracy in the field experiment, which is superior to other methods.

Source-specific ecological and human health risk analysis of topsoil heavy metals in urban greenspace: a case study from Tianshui City, northwest China.

Soil contamination of heavy metals in urban greenspaces can exert detrimental impacts on ecological biodiversity and the health of inhabitants through cross-media migration-induced risks. Here, a total of 72 topsoil samples were collected from greenspaces in the popular tourist city of Tianshui, ranging from areas with parks, residential, road, industrial and educational soils. The study aimed to evaluate an integrated source-specific ecological and human health risk assessment of heavy metals. Among the analyzed heavy metals, except Cr (mean), all exceeded the local background values by 1.30-5.67-fold, and Hg, Cd, Pb and As were the metals with large CV values. The Igeo and CF results showed Hg, Cd, As and Pb exhibited significantly high pollution levels and were the primary pollution factors. The mean PLI values indicated moderate pollution in educational (2.21), industrial (2.07), and road (2.02) soils but slight pollution in park (1.84) and residential (1.39) greenspaces. The Igeo, CF, and PLI results also revealing that these heavy metals are more likely to be affected by human activity. Four primary source factors were identified based on PMF model: coal combustion (25.57%), agricultural sources (14.49%), atmospheric deposition (20.44%) and mixed sources (39.50%). In terms of ecological risk, the mean IRI values showed considerable risks in educational soils (287.52) and moderate risks in road (215.09), park (151.27) and residential (136.71) soils. And the contribution ratio of atmospheric deposition for park, residential, road, industrial and educational greenspaces were 57.72%, 65.41%, 67.69%, 59.60% and 75.76%, respectively. In terms of human health risk, the HI (below 1) and CR (below 1.00E-04) for adults from soils of all land use types was negligible. However, children have more significant non-carcinogenic and carcinogenic hazards especially in residential soils, the HI (above 1) and CR (above 1.00E-04) revealed the significance of regarding legacy As contamination from coal combustion when formulating risk mitigation strategies in this area. The proposed method for source and risk identification makes the multifaceted concerns of pollution and the different relevant risks into a concrete decision-making process, providing robust support for soil contamination control.

An ensemble optimizer with a stacking ensemble surrogate model for identification of groundwater contamination source

The application of the simulation-optimization method for groundwater contamination source identification (GCSI) encounters two main challenges: the substantial time cost of calling the simulation model, and the limitations on the accuracy of identification results due to the complexity, nonlinearity, and ill-posed nature of the inverse problem. To address these issues, we have innovatively developed an inversion framework based on ensemble learning strategies. This framework comprises a stacking ensemble model (SEM), which integrates three distinct machine learning models (Extremely Randomized Trees, Adaptive Boosting, and Bidirectional Gated Recurrent Unit), and an ensemble optimizer (E-GKSEEFO), which combines two newly proposed swarm intelligence optimizers (Genghis Khan Shark Optimizer and Electric Eel Foraging Optimizer). Specifically, the SEM serves as a surrogate model for the groundwater numerical simulation model. Compared to the original simulation model, it significantly reduces time cost while maintaining accuracy. The E-GKSEEFO, functioning as the search strategy for the optimization model, greatly enhances the accuracy of the optimization results. We have verified the performance of the SEM-E-GKSEEFO ensemble inversion framework through two hypothetical scenarios derived from an actual coal gangue pile. The results are as follows. (1) The SEM exhibits improved fitting performance compared to single machine learning models when dealing with high-dimensional nonlinear data from GCSI. (2) The E-GKSEEFO achieves significantly higher accuracy in the identification results of GCSI than individual optimizers. These findings affirm the effectiveness and superiority of the proposed SEM-E-GKSEEFO ensemble inversion framework.

Assessing water quality and human health risk near coal mines and industrial area of Singrauli, India: special emphasis on toxic elements.

Water contamination is a major environmental issue, especially in rapidly growing industrialized areas like Singrauli. This study addresses research gaps regarding the hydrochemical characterization, health risk assessment, and source identification of contaminants. Hydrochemistry shows the concentrations of Na+, Ca2+, F-, Mn, As, Mo, Sr, and Ni were above the permissible limit for drinking usage. Water quality index (WQI), heavy metal pollution (HMPI), and evaluation indices (HMEI) revealed As, Mn, Cd, Mo, Co, and Ni were the key heavy elements contributing towards aqueous media pollution in the Singrauli area. Additionally, F was also considered one of the major contaminants. In health risk assessment, the higher values of hazard quotient (HQ) for non-carcinogens were associated with Mn, As, Mo, and F; and hazard index (HI) values > 1 were found in 70% and 55% of samples for children and adults, respectively. Carcinogenic risk (CR) for human health was associated with As. CR values in 56.7% (for adults) and 61.7% (for children) of the total samples exceeded 1 × 10-4. Monte Carlo simulation was applied and highlighted the significant risk factors responsible for both carcinogenic and non-carcinogenic health impacts. 19.2%, 7.3%, and 6.9% of the simulated HQ values for adults and 30.1%, 16.9%, and 10.6% for children were above the safe limit for F, As, and Mn, respectively. Additionally, only 43.8% and 24.8% of the simulated HI for adults and children were within the safe limit. Irrespective of age groups, all the simulated values of As in CR were above 1 × 10-6; and 60% (for adults) and 77.1% (for children) of the values were above 1 × 10-4. This outcome emphasizes the urgency of pollution control measures, especially for As, F, and Mn, to safeguard public health. Moreover, a multivariate statistical approach revealed that both geogenic and anthropogenic sources were responsible for contamination. Therefore, regular monitoring, filtration, and purification are mandatory to ensure safe drinking water for human consumption.

Identification of Distorted Gamma-Ray Signature Patterns Using Digital Filtering and Auto-Associative Memory Implemented with a Hopfield Neural Network

The detection and identification of radioactive sources in search applications involve analyzing passive gamma-ray emissions from high-level radioactive materials. This process uses a mobile detector-spectrometer in a complex field test environment. Recently, the use of artificial intelligence for gamma-ray spectrum analysis has shown promising results. However, challenges persist in identifying isotopic signatures from spectral measurements that may be distorted due to source shielding, random variations in natural radioactive background, or insufficient measurement time to obtain clear spectral lines. This paper presents a novel intelligent signature recognition method that combines digital filtering techniques with an artificial Hopfield Neural Network (HNN). The HNN leverages auto-associative memory to store training sample patterns and match them with incoming gamma spectra from distorted sources. It restores the testing sources’ measurements by finding the closest matching signature patterns in the spectral library. Before HNN recognition, the measured spectrum undergoes preprocessing with a digital image filter to reduce fluctuations. Performance of the proposed method is evaluated using a set of gamma-ray spectra measured with a sodium iodide detector. The data collected include measurements from six pure samples: 241Am, 60Co, 137Cs, 192Ir, 239Pu, and 235U, which are used for training and validation (i.e. six cases). Additionally, the data set contains 24 distorted synthesized sources with various fluctuating backgrounds. Test results demonstrate the potential of the proposed method to accurately recognize the correct isotope with high precision, achieving an accuracy rate exceeding 85%. Furthermore, the proposed method exhibits superior performance compared to the conventional multiple regression fitting and simple feedforward neural network methods.