Contaminant Source Identification Research Articles

A Review on Traceability System for Fruit and Vegetables Agricultural Products Using Blockchain

This paper presents a blockchain-based traceability system designed to enhance the transparency and efficiency of the agricultural supply chain for fruits and vegetables. By leveraging blockchain technology, the system enables real-time tracking of products from farm to consumer, ensuring data integrity and reducing the risk of food fraud. Each transaction is recorded on a decentralized ledger, allowing stakeholders—farmers, distributors, retailers, and consumers—to access accurate information regarding product origins, handling processes, and quality metrics. This traceability system not only fosters consumer trust but also assists in compliance with regulatory standards and enhances food safety by enabling quick identification of contamination sources. The implementation of this solution can significantly improve supply chain management, reduce losses, and promote sustainable agricultural practices, ultimately benefiting both producers and consumers

A multi-disciplinary approach based on chemical characterization of foreshore sediments, ecotoxicity assessment and statistical analyses for environmental monitoring of marine-coastal areas

The present work aims at providing a multi-disciplinary approach for environmental monitoring in marine-coastal areas. A monitoring campaign of 13 months (October 2022–October 2023) was carried out on sandy foreshore sediments (SFSs). The SFSs were analysed for potentially toxic elements (PTEs) and rare earth elements (REEs) content determination. In the investigated area, variable contamination trends were assessed through Friedman and Nemenyi tests. Further results also indicated the usefulness of statistical data elaboration in the identification of potential contamination sources. In fact, from Spearman test, significant positive correlations (between 0.650 and 0.981) were observed among PTEs of possible anthropogenic origin (such as Co, Cr, Cu, Pb, V, and Zn). For REEs, La and Nd showed strong correlations with Ce (0.909 and 0.920, respectively). The study also integrated luminescence inhibition (Aliivibrio fischeri), algal growth inhibition (Phaeodactylum tricornutum), and embryotoxicity assessment (Paracentrotus lividus) on sediment elutriates showing varying degrees of toxicity. Also these data were analysed through statistics in order to highlight possible correlations between contaminants and observed ecotoxicological effects on the involved bioindicators. The results outline an approach useful for more comprehensive monitoring of marine areas quality and identification of suitable environmental restoration strategies.

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.

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.

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.

Driving factor, source identification, and health risk of PFAS contamination in groundwater based on the self-organizing map

The complex interactions between groundwater chemical environments and PFAS present challenges for data analysis and factor assessment of the spatial distribution and source attribution of PFAS in groundwater. This study employed spatial response analysis combining self-organizing maps (SOM), K-means clustering, Spearman correlation, positive matrix factorization (PMF) and risk quotient (RQ), to uncover the spatial characteristics, driving factors, sources, and human health risks of groundwater PFAS in the Pearl River Basin. The results indicated that the characteristics of PFAS in groundwater were classified into 16 neurons, which were further divided into 6 clusters (I—VI). This division was due to the contribution of industrial pollution (33.2 %) and domestic pollution (31.5 %) to the composition of PFAS in groundwater. In addition, the hydrochemical indicators such as pH, dissolved organic carbon (DOC), chloride (Cl−), and calcium ions (Ca2+) might also affect the distribution pattern of PFAS. The potential human health risk in the area was minimal, with cluster Ⅱ presenting the highest risk (RQ value 0.25) which is closely related to PFOA emissions from fluoropolymer industry. This study provides a theoretical basis and data support for applying of SOM to the visualization and control of PFAS contamination in groundwater.

Source identification of heavy metal contamination in beach sediments of the ancient city of Phaselis in Antalya, Türkiye

The ancient city of Phaselis, which is located along gravel, coarse sandy, and sandy beaches, is a popular area visited by thousands of domestic and foreign tourists every year, and has been selected as the study area. Sediment samples collected from 57 different locations in the ancient city of Phaselis were analyzed using an X-ray fluorescence (XRF) spectrometer, and the major, trace, and rare earth element contents of the samples were revealed. The heavy metals arsenic (As), cobalt (Co), chromium (Cr), copper (Cu), nickel (Ni), lead (Pb), tin (Sn), stronsium (Sr), and zinc (Zn) were used in the pollution index calculations. Distribution maps revealed that heavy metal concentrations reached higher levels, especially in the eastern part of the study area. Therefore, it is recommended to plant rooted macrophytes that can absorb the heavy metals Cr and Ni and perform phytoremediation of the sediment in the region.

Groundwater LNAPL Contamination Source Identification Based on Stacking Ensemble Surrogate Model

Groundwater LNAPL (Light Non-Aqueous Phase Liquid) contamination source identification (GLCSI) is essential for effective remediation and risk assessment. Addressing the GLCSI problem often involves numerous repetitive forward simulations, which are computationally expensive and time-consuming. Establishing a surrogate model for the simulation model is an effective way to overcome this challenge. However, how to obtain high-quality samples for training the surrogate model and which method should be used to develop the surrogate model with higher accuracy remain important questions to explore. To this end, this paper innovatively adopted the quasi-Monte Carlo (QMC) method to sample from the prior space of unknown variables. Then, this paper established a variety of individual machine learning surrogate models, respectively, and screened three with higher training accuracy among them as the base-learning models (BLMs). The Stacking ensemble framework was utilized to integrate the three BLMs to establish the ensemble surrogate model for the groundwater LNAPL multiphase flow numerical simulation model. Finally, a hypothetical case of groundwater LNAPL contamination was designed. After evaluating the accuracy of the Stacking ensemble surrogate model, the differential evolution Markov chain (DE-MC) algorithm was applied to jointly identify information on groundwater LNAPL contamination source and key hydrogeological parameters. The results of this study demonstrated the following: (1) Employing the QMC method to sample from the prior space resulted in more uniformly distributed and representative samples, which improved the quality of the training data. (2) The developed Stacking ensemble surrogate model had a higher accuracy than any individual surrogate model, with an average R2 of 0.995, and reduced the computational burden by 99.56% compared to the inversion process based on the simulation model. (3) The application of the DE-MC algorithm effectively solved the GLCSI problem, and the mean relative error of the identification results of unknown variables was less than 5%.

Breaking the mold of simulation-optimization: Direct forward machine learning methods for groundwater contaminant source identification

Groundwater Contaminant Source Identification (GCSI) is important for addressing environmental concerns. Currently, it is widely achieved using the Simulation/Optimization (S/O) method. However, the utilization of optimization techniques may cause high computation costs and parameter equifinality issues. We introduce two innovative GCSI methods, Direct Forward Machine Learning (DFML) and One-Hot Machine Learning (OHML), utilizing the classical Artificial Neuro Network (ANN) model in the machine learning field. Both new methods eliminate the need for an optimization algorithm in GCSI, thus reducing the construction effort and improving efficiency. The first method, DFML can directly estimate eight parameters, providing valuable insights into the contaminant location, release history, and aquifer properties. The second method, OHML can estimate the spatial probability distribution of the contaminant location through one-hot encoding, addressing uncertainties in the contaminant source location estimations realized by DFML.Evaluations demonstrate that both methods exhibit satisfying performances. DFML can estimate contaminant location and aquifer properties with high accuracy; and estimate the release history information with moderate accuracy. The OHML correctly assigns the higher contaminant probabilities to regions containing true contaminant locations. The combination of DFML and OHML offers a comprehensive framework. This study contributes valid methodologies to the GCSI field.

Multiple approaches for heavy metal contamination characterization and source identification of farmland soils in a metal mine impacted area

Heavy metal and metalloid contamination of farmland soils are closely related to human health. Soil contamination caused by small-scale mining activities has been largely neglected, especially in developing countries. This research studies the characteristics and the source of heavy metals and metalloids in farmland soils in the Bailing Cu–Zn deposit area in Northeastern China through multiple approaches. Geochemical mapping conducted by portable X-ray fluorescence (pXRF) spectrometry and elemental fractionation analysis (sequential extraction) revealed the spatial variations in elemental concentration and fractionation of the farmland soils. The elemental variations in the sediment and water samples in a stream passing by the farmland were determined by inductively coupled plasma mass spectroscopy (ICP-MS) and inductively coupled plasma atomic emission spectroscopy (ICP-AES) analysis. Results indicate elevated As, Zn, Fe, and Mn concentrations in the west bank of the farmland and the associated environmental risks caused by extremely high As concentrations (>500 mg kg−1). The upstream mining activities are identified as a dominated source of contamination in the studied farmland based on the variations of elemental concentrations and fractionations, as well as multivariate analysis results. This study revealed potential ecological risks caused by heavy metal and metalloid contamination in farmland soils in the metal mine impacted area and the stream as a primary pathway for the migration of pollutants associated with mining activities.

Identification of hydraulic conductivity and groundwater contamination sources with an Unscented Kalman Smoother

Identification of hydraulic conductivity and groundwater contamination sources with an Unscented Kalman Smoother

3D GeoRemediation: A Digital Hydrogeophysical–Chemical Clone and Virtual Hydraulic Barrier with Groundwater Circulation Wells (GCWs) for Groundwater Remediation

Identification of contamination sources and delineation of plumes in the geological environment stand as pivotal elements in reconstructing the conceptual site model (CSM) and devising remediation strategies tailored to specific physicochemical traits. This study endeavors to showcase the capabilities of a 3D digital interface, seamlessly integrating multi-source data, to elucidate site-specific contamination dynamics and steer the implementation of remediation strategies harmoniously aligned with the ethos of remediation geology. In a site historically marred by chlorinated solvent contamination, the digitization of stratigraphic, piezometric, chemical, and membrane interface probe (MIP) data underpins geomodeling endeavors and yields a meticulously crafted, data-driven CSM. The hydrogeochemical and hydrogeophysical data were interpolated to build a volumetric, digital 3D model illustrating data-driven elements. The comprehensive 3D clone adeptly delineates secondary contamination sources and renders visible the contamination plume within a georeferenced framework, mirroring the nuanced interplay of stratigraphic nuances and groundwater path. A data-centric approach to modeling facilitates the design of the first hydraulic virtual barrier leveraging groundwater circulation well (GCW) technology, its geometry finely attuned to intercept the contamination plume originating from source dissolution and aligning with preferential groundwater flow trajectories. Conventional hydrochemical monitoring and multilevel sampling substantiate the discernible reduction in chlorinated solvent concentrations across various depths within the aquifer horizon, affirming the efficacy of GCWs in their virtual barrier configuration. The findings highlight the effectiveness and limited groundwater consumption of the virtual barrier compared to the on-site pump-and-stock system. This research underscores the potency of a multi-faceted evidence-driven puzzle in conceptualizing contamination mechanisms within the geological milieu, thereby fostering the application of cutting-edge, effective, and sustainable remediation strategies.

Non-gaussian hydraulic conductivity and potential contaminant source identification: A comparison of two advanced DLPM-based inversion framework

Accurate identification of hydraulic conductivity fields (K) and contaminant source parameters is imperative for the enhanced assessment and effective remediation of polluted aquifers. Given the challenges posed by non-Gaussian distributions, high dimensionality, and ill-posed nature of groundwater inversion problems, reducing unknowns is a common strategy. Unlike conventional parameterization methods constrained by prior assumptions, this research introduces an innovative deep learning-based parameterization method (DLPM), AEdiffusion. AEdiffusion combines a Diffusion Denoising Probabilistic Model (DDPM) with a Variational Autoencoder (VAE) through a generator-refiner strategy, enabling the generation of high-dimensional K fields from low-dimensional latent representations. Additionally, this study examines the application of Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP), another advanced DLPM, in groundwater inversion. Through comparative analysis within a Data Assimilation (DA) framework, focusing on non-Gaussian K fields and three potential contaminant sources under varied data availability scenarios, this study reveals that both DLPM-based inversion frameworks are capable of identifying K fields and the true contaminant sources. Notably, the AEdiffusion-based framework excels in extracting critical information from sparse observations, delivering more stable performance, but at the cost of increased time consumption compared to WGAN-GP-based framework.

Evaluating trends in groundwater quality of coastal alluvial aquifers of Eastern India for sustainable groundwater management.

Groundwater is a precious natural element which ensures global water, food, and environmental security in the twenty-first century. Systematic monitoring, sustainable utilization, preservation and remediation are critical aspects of efficient groundwater resource management. This study deals with the analysis of spatial variability and trend in groundwater chemistry as well as identification of possible contamination sources in a coastal alluvial basin of eastern India. Pre-monsoon season data of 14 groundwater-quality variables measured in 'leaky confined' and 'confined' aquifers were analyzed for ten years (2012-2021). Mann-Kendall (M-K) test with the Sen's Slope Estimator, Spearman Rank Order Correlation (SROC) and Innovative Trend Analysis (ITA) tests were employed to assess decadal (2012-2021) trends. The analysis of the results indicated that the 'critical' water-quality parameters exceeding the acceptable limits for drinking are TDS, EC, TH, pH, Mg2+, Na+, K+, Fe2+, HCO3-, Cl- and NO3-. Weak negative correlations between rainfall and groundwater elevation for both the aquifers reveal poor rainfall recharge into the aquifers. Therefore, a reduction in groundwater abstraction and augmentation of groundwater recharge is recommended. Trend analysis results indicated that the concentrations of TH, Mg2+ and Fe2+ exhibit significant increasing trends in the 'leaky confined aquifer'. In contrast, significant rising trends in TH, Mg2+, Na+, Fe2+, HCO3- and NO3- concentrations are identified in the 'confined aquifer'. Further, the SROC test could not detect the trends in groundwater quality in most blocks and for many parameters. On the other hand, the ITA test revealed significant trends in most of the parameters of the two aquifers in almost all the blocks. Trend magnitudes of the groundwater-quality parameters based on the Sen's Slope Estimator and the ITA test vary from -63.7 to 58.65mg/L/year for TDS, -14 to 39.07mg/L/year for TH, -1.49 to 4.83mg/L/year for Mg2+, -7.14 to 22.96mg/L/year for Na+, -0.32 to 0.44mg/L/year for Fe2+, -8.33 to 20.75mg/L/year for HCO3-, -26.52 to 31.01mg/L/year for Cl- and 1.29 to 3.76mg/L/year for NO3- over the study area. The results of M-K and ITA tests were found in agreement in all the blocks for both the aquifers. Groundwater contamination in both the aquifers can be attributed to weathering, geogenic processes, mineral dissolution, seawater intrusion, poor recharge pattern and injudicious anthropogenic activities. It is strongly recommended that concerned authorities urgently formulate efficient strategies for managing groundwater quality in the 'leaky confined' and 'confined' aquifers which serve as vital sources of drinking and irrigationwater suppliesin the study area.

Source identification and assessment of heavy metal contamination in different plant species in the alpine ecosystems of Mt. Madra

Heavy metals are among important environmental pollutants due to their environmental persistence, toxicity and ability to accumulate in living organisms. The aim of the present study was to investigate the levels of aluminium (Al), boron (B), cobalt (Co), chromium (Cr), copper (Cu), and molybdenum (Mo) in plants sampled from Mt. Madra, analysed their distribution characteristics and investigated their origins using Positive Matrix Factorization (PMF) modelling. The multi-point sampling technique was used to collect samples of 26 plants from Madra Mountain at altitudes ranging from 177 m to 1347 m. Inductively-coupled plasma optical emission spectroscopy (ICP-OES) was used for the determination of metals. The metal concentrations (all in terms of mg/kg) determined by ICP-OES are the following sequences (mean ± standard deviation): Al (827.97 ± 198.95) > B (38.49 ± 2.50) > Co (0.74 ± 0.15) > Cr (1.90 ± 0.39) > Cu (7.68 ± 0.73) > Mo (0.51 ± 0.06). Based on the results obtained from the plant samples with the highest concentrations, the following metals were identified: Al (Ajuga orientalis, 4643.57 mg/kg), B (Quercus petraea, 69.25 mg/kg), Co (Ajuga orientalis, 4.23 mg/kg), Cr (Teucrium lamiifolium, 8.64 mg/kg), Cu (Ajuga orientalis, 19.75 mg/kg) and Mo (Ajuga orientalis, 1.07 mg/kg). In conclusion, the results of the source identification analysis conducted by PMF indicated that metal concentrations in the alpine zone have a primarily anthropogenic origin.

Assessment of water quality using entropy-weighted quality index, statistical methods and electrical resistivity tomography, Moti village, northern Pakistan

In this study, twenty-two water samples were collected from boreholes (BH), and streams to evaluate drinking water quality, its distribution, identification of contamination sources and apportionment for Moti village, northern Pakistan. An atomic absorption spectrophotometer (AAS) is utilized to determine the level of heavy metals in water such as arsenic (As), zinc (Zn), lead (Pb), copper (Cu), cadmium (Cd), manganese (Mn), and ferrous (Fe). Groundwater chemistry and its quantitative driving factors were further explored using multivariate statistical methods, Principal Component Analysis (PCA) and Positive Matrix Factorization (PMF) models. Finally, a total of eight electrical resistivity tomographs (ERTs) were acquired across i) the highly contaminated streams; ii) the villages far away from contaminated streams; and iii) across the freshwater stream. In the Moti village, the mean levels (mg/l) of heavy metals in water samples were 7.2465 (As), 0.4971 (Zn), 0.5056 (Pb), 0.0422 (Cu), 0.0279 (Cd), 0.1579 (Mn), and 0.9253 (Fe) that exceeded the permissible limit for drinking water (such as 0.010 for As and Pb, 3.0 for Zn, 0.003 for Cd and 0.3 for Fe) established by the World Health Organization (WHO, 2008). The average entropy weighted water quality index (EWQI) of 200, heavy metal pollution index (HPI) of 175, heavy metal evaluation index (HEI) of 1.6 values reveal inferior water quality in the study area. Human health risk assessment, consisting of hazard quotient (HQ) and hazard index (HI), exceeded the risk threshold (>1),indicating prevention of groundwater usage. Results obtained from the PCA and PMF models indicated anthropogenic sources (i.e. industrial and solid waste) responsible for the high concentration of heavy metals in the surface and groundwater. The ERTs imaged the subsurface down to about 40 m depths and show the least resistivity values (<11 Ωm) for subsurface layers that are highly contaminated. However, the ERTs revealed relatively high resistivity values for subsurface layers containing fresh or less contaminated water. Filtering and continuous monitoring of the quality of drinking water in the village are highly recommended.

Historical construction, quantitative source identification and risk assessment of heavy metals contamination in sediments from the Pearl River Estuary, South China

Historical reconstruction of heavy metals (HMs) contamination in sediments is a key for understanding the effects of anthropogenic stresses on water bodies and predicting the variation trends of environmental state. In this work, eighteen sediment cores from the Pearl River Estuary (PRE) were collected to determine concentrations and geochemical fractions of HMs. Then, their potential sources and the relative contributions during different time periods were quantitatively identified by integrating lead-210 (210Pb) radioisotope dating technique into positive matrix factorisation (PMF) method. Pollution levels and potential ecological risks (PERs) caused by HMs were accurately assessed by enrichment factors (EF) based on establishment of their geochemical baselines (GCBs) and multiparameter evaluation index (MPE). HMs concentrations generally showed a particle size- and organic matter-dependent distribution pattern. During the period of 1958–1978, HMs concentrations remained at low levels with agricultural activities and natural processes being identified as the predominant sources and averagely contributing >60%. Since the reform and opening-up in 1978, industrial and traffic factors become the primary anthropogenic sources of HMs (such as Cu, Zn, Cd, Pb, Cr, and Ni), averagely increasing from 22.1% to 28.1% and from 11.6% to 23.4%, respectively. Conversely, the contributions of agricultural and natural factors decreased from 37.0% to 28.5% and from 29.3% to 20.0%, respectively. Subsequently, implementation of environmental preservation policies was mainly responsible for the declining trend of HMs after 2010. Little enrichment of sediment Cu, Zn, Pb, Cr and Ni with EFs (0.15–1.43) was found in the PRE, whereas EFs of Cd (1.16–2.70) demonstrated a slight to moderate enrichment. MPE indices of Cu (50.7–252), Pb (52.0–147), Zn (35.5–130), Ni (19.6–71.5), Cr (14.2–68.8) and Cd (0–9.90) highlighted their potential ecological hazards due to their non-residual fractions and anthropogenic sources.

Using Enrichment Factor Approach for Source Identification of Potentially Toxic Heavy Metals along Benin-Ore-Sagamu Expressway in Nigeria

The variation in heavy metal concentration in different environment is dependent on the emission source. Source identification of heavy metal plays an important role in providing solution to its contamination in the atmosphere. Hence, the objective of this paper is to engage enrichment factor (EF) approach to evaluate the source identification of potentially toxic heavy metals along the Benin-Ore-Sagamu Expressway in Nigeria in four sampling locations (A, B, C and D) that are denoted with high traffic congestion. The source identification of Cu, Zn, Mn, Ni, Pd, Cd, Cr and Pb contamination in the study area were determined using appropriate standard methods. The EF values indicated for Cu ranged from (2.3-5.9), Zn (0.48-0.86), Mn (0.29-0.49), Ni (1.27-4.03), Pd (37012.18-59922.57), Cd (1909.11-2844.81), Cr (4.56-7.76) and Pb (0.77-4.64). The results suggest crustal source enrichment for Zn and Mn while Cu, Ni, Cr and Pb were enriched moderately. However, the EF values for Pd and Cd were found to be greater than 50, indicating extremely severe enrichment. The findings of this study provide evidence of anthropogenic impact on heavy metals pollution in the study area that is attributed to the intense vehicular traffic.

Identification of Multiple Surface Water Contamination Sources Based on UV–Vis Spectral Unmixing with Turbidity Adaptiveness

Identification of Multiple Surface Water Contamination Sources Based on UV–Vis Spectral Unmixing with Turbidity Adaptiveness

A Comparison of Inversion Methods for Surrogate‐Based Groundwater Contamination Source Identification With Varying Degrees of Model Complexity

AbstractAccurate identification of groundwater contamination sources is important for designing efficacious site remediation strategies. Currently, the methods for identifying contamination sources mainly fall into three distinct categories: simulation optimization, Bayesian inference, and data assimilation. Each method has its own advantages and disadvantages under specific site conditions. To evaluate the applicability of these methods, we chose one representative inversion algorithm from each category, namely the Improved Butterfly Optimization Algorithm (IBOA) for simulation optimization, the Ensemble Smoother with Multiple Data Assimilation (ES‐MDA) for data assimilation, and the DiffeRential Evolution Adaptive Metropolis with a Snooker Update and Sampling from a Past Archive (DREAM(ZS)) for Bayesian inference. We conducted a comprehensive evaluation of these methods' performance under different model complexities, employing a surrogate model as a substitute for the complex forward model. By addressing two distinct problems involving conservative pollutant transport and Light Non‐Aqueous Phase Liquid (LNAPL) transport with biodegradation, we employed four criteria (elapsed time, result accuracy, posterior probability distribution, and noise resistance) for evaluation. The findings unequivocally indicate that DREAM(ZS) outperforms others in terms of result accuracy and posterior probability distribution. It also adeptly navigates the interrelations among disparate unknown variables. The strength of ES‐MDA lies in its efficiency. It achieves relatively satisfactory results with a reduced computational burden. In contrast, IBOA underperforms in both test problems. In terms of resistance to noise, both DREAM(ZS) and ES‐MDA perform better than IBOA does.