Contaminant Source Identification Research Articles

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.

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.

Ecological risk assessment, source identification and spatial distribution of organic contaminants in terms of mucilage threat in streams of Çanakkale Strait Basin (Türkiye)

In this study, the spatial distributions of organic contamination stressors in water of fluvial habitats in the Çanakkale Strait (ÇS) watershed were investigated and the data were assessed in terms of human health and mucilage threat. Seven significant riverine ecosystems flowing into the ÇS were defined in the basin. Water samples were taken in the spring season (2023), when the phytoplankton communities reach their highest densities. Then they were tested for a total of 8 limnological parameters.The Nutrient Pollution Index (NPI) and Water Quality Index (WQI) were applied to assess the comprehensive quality characteristics of waters. The Hazard Quotient (HQ) and Hazard Index (HI) were applied to indicate the prospective non-carcinogenic human health risks of organic stressors. Principal Component Analysis (PCA) and Cluster Analysis (CA) were applied to categorize the investigated habitats and define the sources of investigated contamination parameters. Also, Geographic Information System (GIS) was used to make an effective assessment through visualization.The determined spatial mean values of the measured variables in ÇS watershed as follows: 18.21 °C for temperature, 8.51 mg/L for DO, 4.57 NTU for turbidity, 3.95 mg/L for suspended solids, 1.11 mg/L for NO3–N, 0.012 mg/L for NO2–N; 0.173 mg/L for PO4–P and 2.32 mg/L for BOD. It has been determined that the organic pollution loads and water temperature values of the investigated sub-basins increase from the upstream to the downstream locations and Çanakkale Stream was recorded as the riskiest fluvial habitat for the ÇS watershed. According to the results of health risk assessment indices, non-carcinogenic risks of organic pollutants would not be expected for all age groups.

Impacts of blockchain technology on food supply chains with potential food contamination

Food contamination may have a catastrophic effect on companies, causing a plunge in goodwill. Motivated by the practice that existing voluntary recall strategies still suffer from untimeliness and imprecision, we consider blockchain to provide technical support for food traceability and recall by improving supply chain traceability and transparency. We focus on a two-tier food supply chain facing potential contamination occurrences and build a piecewise deterministic differential game model. We then obtain the optimal strategies of firms and evaluate the impact of contamination events on the dynamic operations of the supply chain. The results show increased contamination likelihood and damage rate, which harm the goodwill of food and optimal decision-making. In addition, we obtain the conditions for the company to be profitable when implementing the blockchain, which is closely related to contamination source identification time, recall efficiency, damage rate, and blockchain cost. Finally, we evaluate several specific cases.

Contaminant source identification in an aquifer using a Bayesian framework with arbitrary polynomial chaos expansion

Contaminant source identification in an aquifer using a Bayesian framework with arbitrary polynomial chaos expansion

Conditional generative adversarial networks for groundwater contamination characterization and source identification

Accurate characterization of sources and spatial distribution of contamination remains crucial in groundwater survey and remediation efforts. Nevertheless, conventional geo-statistical methods, like kriging, usually produce overly smooth concentration fields that violate the transport physics. Furthermore, the identification of contaminant sources based on kriging methods may be misleading. As a deep learning approach, Generative adversarial networks (GANs) is being increasingly employed to extract patterns and insights from intricate geospatial data. The conditional variant of GANs (CGANs), can reconstruct complete spatial data from sparse point observations. In this study, we propose a deep learning framework with CGANs for the purpose of groundwater contamination characterization and source identification. In ensure compliance with the principles of transport physics, we use a numerical transport model as the underlying prior model for training the CGANs. Through the adaptation of network architecture, the generator transforms low-dimensional random variables and observation data into concentration images, while the discriminator is responsible for extracting source information from these images. The developed framework is tested through two numerical cases with channelized random hydraulic conductivity fields, which pose a challenge due to the complexity of the concentration fields. The results demonstrate that the proposed method can give accurate results for both contaminant characterization and source identification in groundwater. CGANs excel in reconstructing concentration fields compared to traditional interpolation methods such as Kriging and inverse distance weighting (IDW). Additionally, CGANs offer the capability to estimate uncertainty and exhibit a certain level of error robustness.

Spatial distribution and source identification of metal contaminants in soil and rice grain samples: a study on exploration of soil quality and risk assessment.

The present study aims to assess soil quality and potential health risks associated with soil pollution of the Batala region of Punjab, India. Physico-chemical parameters such as pH (6.69-7.43), electrical conductivity (0.17-0.33 mS/cm), and total organic carbon (1.01-5.94%) were observed to be within permissible limits. The maximum mean content (mg/kg) of heavy metals in soil was found as Fe (4060.93), Zn (444.33), Mn (278.5), Pb (23.16), Cu (21.78), Ni (20.16), Co (7.14), and Cd (1.85) which were below the prescribed limits but beyond the geochemical background limits of world soil. For rice grain samples, metal content (mg/kg) was seen as Fe (307.01) > Zn (12.41) > Mn (7.43) > Cu (4.57) and was below the permissible limits. The mean bioaccumulation factor for various metals was in the order as Zn > Cu > Fe > Mn. Single and integrated soil pollution indices revealed that among 18 sites, six were highly contaminated. The ecological risk index (Er) has shown that contamination of soil with Cd, Zn, and Ni was higher than that of other metals studied. The estimated daily intake of metal (EDI), hazard quotient (HQ), and hazard index (HI) were higher for children than those for adults. Spatial variability based on metal pollution load and soil quality was also determined using cluster analysis (CA) and principal component analysis (PCA). During CA, soil samples from 18 sites formed three statistically significant clusters based on the level of metal pollution at the specific site. PCA showed that all variables were reduced into two main components 1 and 2 with eigenvalues as 3.82 (47% variance) and 1.53 (19.7% variance), respectively.

Prediction of groundwater contamination in an open landfill area using a novel hybrid clustering-based ai model

Groundwater is a vital resource that provides drinking water to over half of the world’s population. However, groundwater contamination has become a serious issue due to human activities such as industrialization, agriculture, and improper waste disposal. The impacts of groundwater contamination can be severe, including health risks, environmental damage, and economic losses. A list of unknown groundwater contamination sources has been developed for the Wang-Tien landfill using a groundwater modeling system (GMS). Further, AI-based models have been developed which accurately predict the contamination from the sources at this site. A serious complication with most previous studies using artificial neural networks (ANN) for contamination source identification has been the large size of the neural networks. We have designed the ANN models which use three different ways of presenting inputs that are categorized by hierarchical K-means clustering. Such an implementation reduces the overall complexity of the model along with high accuracy. The predictive capability of developed models was assessed using performance indices and compared with the ANN models. The results show that the hybrid model of hierarchical K-means clustering and ANN model (HCA-ANN) is a highly accurate model for identifying pollution sources in contaminated water.

Bayesian Optimization for Contamination Source Identification in Water Distribution Networks

In the wake of the terrorist attacks of 11 September 2001, extensive research efforts have been dedicated to the development of computational algorithms for identifying contamination sources in water distribution systems (WDSs). Previous studies have extensively relied on evolutionary optimization techniques, which require the simulation of numerous contamination scenarios in order to solve the inverse-modeling contamination source identification (CSI) problem. This study presents a novel framework for CSI in WDSs using Bayesian optimization (BO) techniques. By constructing an explicit acquisition function to balance exploration with exploitation, BO requires only a few evaluations of the objective function to converge to near-optimal solutions, enabling CSI in real-time. The presented framework couples BO with EPANET to reveal the most likely contaminant injection/intrusion scenarios by minimizing the error between simulated and measured concentrations at a given number of water quality monitoring locations. The framework was tested on two benchmark WDSs under different contamination injection scenarios, and the algorithm successfully revealed the characteristics of the contamination source(s), i.e., the location, pattern, and concentration, for all scenarios. A sensitivity analysis was conducted to evaluate the performance of the framework using various BO techniques, including two different surrogate models, Gaussian Processes (GPs) and Random Forest (RF), and three different acquisition functions, namely expected improvement (EI), probability of improvement (PI), and upper confident bound (UCB). The results revealed that BO with the RF surrogate model and UCB acquisition function produced the most efficient and reliable CSI performance.

Gated graph neural networks for identifying contamination sources in water distribution systems

Contamination events in water distribution networks (WDN) pose significant threats to water supply and public health. Rapid and accurate contamination source identification (CSI) can facilitate the development of remedial measures to reduce impacts. Though many machine learning (ML) methods have been proposed for fast detection, there is a critical need for approaches capturing complex spatial dynamics in WDNs to enhance prediction accuracy. This study proposes a gated graph neural network (GGNN) for CSI in the WDN, incorporating both spatiotemporal water quality data and flow directionality between network nodes. Evaluated across various contamination scenarios, the GGNN demonstrates high prediction accuracy even with limited sensor coverage. Notably, directional connections significantly enhance the GGNN CSI accuracy, underscoring the importance of network topology and flow dynamics in ML-based WDN CSI approaches. Specifically, the method achieves a 92.27% accuracy in narrowing the contamination source to 5 points using just 2 h of sensor data. The GGNN showcases resilience under model and measurement uncertainties, reaffirming its potential for real-time implementation in practice. Moreover, our findings highlight the impact of sensor sampling frequency and measurement accuracy on CSI accuracy, offering practical insights for ML methods in water network applications.

Source identification, pollution status, and ecological risk assessment of heavy metals contamination in a highly populated unplanned industrial area (Wadi El-Qamar), Alexandria, Egypt

Source identification, pollution status, and ecological risk assessment of heavy metals contamination in a highly populated unplanned industrial area (Wadi El-Qamar), Alexandria, Egypt

Identification of fecal contamination sources of groundwater in rural areas of Vhembe District Municipality, Limpopo Province, South Africa.

Groundwater is a valuable source of drinking water worldwide, recognized as an improved drinking water source. However, on-site sanitation systems may put groundwater at risk of fecal contamination. In the present study, two approaches were used to ascertain the sources of fecal contamination in groundwater used by communities of the Vhembe District Municipality. Overall, 87.5% of boreholes (n = 70) in the wet and 72.5% in the dry season were contaminated with Escherichia coli, and septic tank (n = 18) wastewaters displayed up to 104  cfu/mL E.coli. Host-specific Bacteroidales quantitative polymerase chain reaction (qPCR) assays established the presence of human (BacHum and HF183) and animal (Cytb, BacCan, and Pig-2Bac) genetic markers in groundwater from 15.7% of boreholes (wet) and 10% of boreholes (dry). No strong associations were founded between culturable E.coli counts and the presence/absence of marker genes for all the markers except for Cytb marker, which showed a weak significant correlation (r = 0.217; p = <0.01) between E.coli and the Cytb marker under dry seasonal conditions. Human markers and Cytb were present in the household septic tank wastewater samples. Significant differences in marker genes distribution in wastewater were observed using the Chi-squared test: HF183 (p = <0.001) and BacHum (p = <0.001). Overall, no association was recorded between markers in groundwater and in wastewater for 18 households' septic tanks. A combined culturable E.coli and host-specific Bacteroidales qPCR assays remain an appropriate approach for the identification of fecal contamination of groundwater. PRACTITIONER POINTS: Households primarily used private boreholes for drinking water, as a primary source. Most households used on-site sanitation systems, including ventilated improved pit latrines and flush toilets connected to septic tanks. Escherichia coli was detected in groundwater, and the sources of fecal contamination were humans and animals (pigs, dogs, and chickens). The presence of human and animal markers in groundwater suggests that humans and animals are liable for fecal contamination. Fecal contamination in drinking water sources poses a significant concern due to pathogenic microorganisms posing potential human health risks.

Accurate identification of sludge contamination sources by classification-based PMF and machine learning with consideration of sewer network distribution differences

The application of source identification such as PMF for large-scale pollution source analysis frequently produces ambiguous outcomes. In this study, we utilized a classification-based method to accurately track key pollution sources in the sludge. In the study, we categorized the wastewater treatment plants into two groups: T1 and T2, according to the pipeline network. T1 sewage treatment plants are the main sewage plants in urban areas, covering a large area and connected to industrial wastewater treatment plants for secondary treatment. T2 sewage treatment plants are typically smaller in size and usually responsible for treating sewage in rural or township areas. The PMF analysis indicates that industrial pollution sources contribute 3.4 times more to T1 sludge than to T2 sludge, making industrial pollution the primary factor causing the disparity. The application of Random Forest and Adaboost based on pollutant concentrations for classification and fitting of sludge resulted in the identification of the main pollutants: Zn, Cu, Ni, and Cyanide, which align with characteristic pollutants from the electroplating industry. The GIS analysis shows a significant correlation between the distance of wastewater treatment plants with abnormal environmental risk and electroplating industrial parks, all within a 20 km radius. Indeed, when conducting large-scale pollution source identification studies, utilizing classification-based analysis can effectively improve the accuracy of pollution source identification, leading to more valuable analysis results.

Groundwater contaminant source identification considering unknown boundary condition based on an automated machine learning surrogate

Groundwater contamination source identification (GCSI) is a prerequisite for contamination risk evaluation and efficient groundwater contamination remediation programs. The boundary condition generally is set as known variables in previous GCSI studies. However, in many practical cases, the boundary condition is complicated and cannot be estimated accurately in advance. Setting the boundary condition as known variables may seriously deviate from the actual situation and lead to distorted identification results. And the results of GCSI are affected by multiple factors, including contaminant source information, model parameters, boundary condition, etc. Therefore, if the boundary condition is not estimated accurately, other factors will also be estimated inaccurately. This study focuses on the unknown boundary condition and proposed to identify three types of unknown variables (contaminant source information, model parameters and boundary condition) innovatively. When simulation–optimization (S-O) method is applied to GCSI, the huge computational load is usually reduced by building surrogate models. However, when building surrogate models, the researchers need to select the models and optimize the hyperparameters to make the model powerful, which can be a lengthy process. The automated machine learning (AutoML) method was used to build surrogate model, which automates the model selection and hyperparameter optimization in machine learning engineering, largely reducing human operations and saving time. The accuracy of AutoML surrogate model is compared with the surrogate model used in eXtreme Gradient Boosting method (XGBoost), random forest method (RF), extra trees regressor method (ETR) and elasticnet method (EN) respectively, which are automatically selected in AutoML engineering. The results show that the surrogate model constructed by AutoML method has the best accuracy compared with the other four methods. This study provides reliable and strong support for GCSI.

Identification of Contamination Sources and Assessment of Risk Factors Associated with the Occurrence of Escherichia coli O157:H7 on Small-scale Cow-calf Operations in Oklahoma and Louisiana

Escherichia coli O157:H7 is a human pathogen that exists as part of the commensal microflora of cattle and is shed in animal feces. Little is known about the effect of management practices on its occurrence and transmission on small-scale cow-calf operations. Identification of risk factors associated with farm practices could help implement effective measures to control E. coli O157:H7. This study quantified the risk of E. coli O157:H7 occurrence associated with cow-calf farm practices using risk modeling. Management practices of small-scale cow-calf operations in OK and LA were assessed through survey-based research. Fecal, water, sediments and water-trough-swab samples were collected to determine the incidence of E. coli O157:H7, and potential on-farm contamination sources and risk factors identified. Association between the occurrence of pathogen and farm practices was determined using two risk assessment models (I and II). Model I determined the association of E. coli O157:H7 occurrence with water source, water container, feed, cattle breed, and herd density, while Model II determined its association with farm cleanliness. For both models, logistic regression was followed using a two-step approach, univariable and multivariable analysis. In OK and LA, E. coli O157:H7 was present in 5.8% and 8.8% fecal, 4.4% and 9.4% water, 10.3% and 9.6% sediments, and 1.5% and 10.6% water-trough-swab samples, respectively. In Model I, univariable analysis identified water container and feed, whereas multivariable analysis identified feed as a significant risk factor. In Model II, the univariable analysis found cleanliness of cattle-contact areas, such as, alleyways, water-trough, chute and equipment, to be a significant risk factor. In multivariable analysis, only the cleanliness of water-trough was identified to be a significant risk factor. Results from the study could aid in the development of on-farm best management practices for the reduction of E. coli O157:H7.

Appraisal of contamination, source identification and health risk assessment of selected metals in the agricultural soil of Chakwal, Pakistan.

Contamination of metals in agricultural soil is a serious global threat but there are limited reports related to their risks in major agronomic areas. The current study is aimed to assess the distribution of selected macroelements and essential/toxic trace metals (Ca, Mg, Na, K, Sr, Li, Ag, Fe, Zn, Co, Cu, Mn, Cd, Cr, Pb and Ni) in the agricultural soil of Chakwal, Pakistan, in order to appraise their contamination status, source identification and probable human health risks. Quantification of the metals was performed by AAS employing aqua regia digestion method. Among the selected metals, dominant mean concentrations were observed for Ca (48,285mg/kg) and Fe (30,120mg/kg), followed by Mg (9171mg/kg), K (973.3mg/kg), Mn (399.0mg/kg) and Na (368.9mg/kg). The correlation study indicated strong mutual relationships among the metals as well as physicochemical properties. Multivariate analysis (PCA/CA) of the metal levels revealed their diverse anthropogenic sources in the soil. Various pollution indices indicated extremely high contamination/enrichment of Cd, followed by moderate enrichment/contamination of Ag in the soil. The HQ values for most of the metals manifested insignificant non-cancer risks. The average CR value of Cr was exceeding the safe limit (1.0E-06) for both ingestion and inhalation exposure, indicating a considerable lifelong cancer risk for the population. The results of this study will provide a better understanding related to the contamination of agricultural soil and its effects on human health and to promote effective actions to reduce the soil pollution.

An assessment of heavy metals in dust at recreational parks in Trinidad, West Indies: contamination status, source identification and health risk implications

An assessment of heavy metals in dust at recreational parks in Trinidad, West Indies: contamination status, source identification and health risk implications

Machine Learning–Based Source Identification in Sewer Networks

Motivated by the valuable epidemiological information it reveals, wastewater surveillance has received significant attention in recent years. Furthermore, monitoring the water quality in sewer systems has been shown to provide useful information to support wastewater treatment operations. Yet, a critical need still exists for developing novel approaches for rapid and efficient source identification of chemical and biological species of interest in sewer systems. A limited number of source identification approaches have been proposed in previous literature, and the majority of these approaches employed various simplifying assumptions that limit their usage in real-life applications. In this study, a machine learning–based simulation-optimization framework was developed to determine the characteristics (i.e., concentration and loading pattern) of multiple simultaneous injection sources in sewer systems. The simulation was conducted using a surrogate model in the form of a multilayer perceptron neural network, which was trained using simulation results derived from the Storm Water Management Model (SWMM). The simulation model was then coupled with a genetic algorithm to reveal the characteristics of multiple sources that reproduce the concentration patterns observed at one or more monitoring locations in the sewer system. The proposed framework was applied to a range of injection scenarios and was able to identify the characteristics of multiple simultaneous injection sources under different conditions. The results showed that the residence time plays a significant role in the identifiability of the injection source location. The proposed framework is applicable to a wide number of source identification applications, including contamination source identification and wastewater-based epidemiology.

Advancements in algal membrane bioreactors: Overcoming obstacles and harnessing potential for eliminating hazardous pollutants from wastewater

This paper offers a comprehensive analysis of algal-based membrane bioreactors (AMBRs) and their potential for removing hazardous and toxic contaminants from wastewater. Through an identification of contaminant types and sources, as well as an explanation of AMBR operating principles, this study sheds light on the promising capabilities of AMBRs in eliminating pollutants like nitrogen, phosphorus, and organic matter, while generating valuable biomass and energy. However, challenges and limitations, such as the need for process optimization and the risk of algal-bacterial imbalance, have been identified. To overcome these obstacles, strategies like mixed cultures and bioaugmentation techniques have been proposed. Furthermore, this study explores the wider applications of AMBRs beyond wastewater treatment, including the production of value-added products and the removal of emerging contaminants. The findings underscore the significance of factors such as appropriate algal-bacterial consortia selection, hydraulic and organic loading rate optimization, and environmental factor control for the success of AMBRs. A comprehensive understanding of these challenges and opportunities can pave the way for more efficient and effective wastewater treatment processes, which are crucial for safeguarding public health and the environment.