Contaminant Source Identification Research Articles

Contamination Characteristics, Source Identification, and Source-Specific Ecological-Health Risks of Heavy Metal(Loid)S in Groundwater of an Arid Oasis Region in Northwest China

Contamination Characteristics, Source Identification, and Source-Specific Ecological-Health Risks of Heavy Metal(Loid)S in Groundwater of an Arid Oasis Region in Northwest China

An ensemble-surrogate assisted cooperative particle swarm optimisation algorithm for water contamination source identification

An ensemble-surrogate assisted cooperative particle swarm optimisation algorithm for water contamination source identification

An ensemble-surrogate assisted cooperative particle swarm optimisation algorithm for water contamination source identification

An ensemble-surrogate assisted cooperative particle swarm optimisation algorithm for water contamination source identification

Simultaneous identification of groundwater contaminant source and simulation model parameters based on an ensemble Kalman filter – Adaptive step length ant colony optimization algorithm

The simulation–optimization (SO) method is the most widely used method for groundwater contaminant source identification. However, the heuristic optimization algorithms in the SO method often rely on the selection of the initial point (the initial estimate of each variable to be solved). If the initial point is far from the actual value, the convergence speed of the algorithm will be slow, and it will easily converge prematurely. It is challenging to obtain the accurate identification results of the contaminant source. Therefore, providing a good initial point for the optimization algorithm is critical. The ensemble Kalman filter (EnKF) algorithm has the advantages of a simple calculation process and fast running speed. However, when the inverse problem has a strong nonlinearity, its identification accuracy still needs improvement. This study combined the EnKF and adaptive step length ant colony optimization (ASACO) algorithms to construct an EnKF-ASACO algorithm. This was used to identify pollution source characteristics and simulation model parameters simultaneously. First, the EnKF algorithm was adopted to provide a good initial point for the optimization algorithm. On this basis, the adaptive step length search strategy was used to improve the ant colony optimization (ACO) algorithm, and the ASACO algorithm was obtained. As a result, the step length changed adaptively to avoid falling into the local optimum. At the same time, to reduce the substantial computational load caused by repeatedly calling the numerical simulation model, the Kriging method was used to establish a surrogate model of the simulation model. Finally, the identification results of the EnKF-ASACO algorithm were compared and analyzed with those of the EnKF, previous ACO, and ASACO algorithms to verify the effectiveness of the proposed new algorithm. The results show that compared with the three single algorithms, the EnKF-ASACO algorithm constructed in this study can speed up the convergence speed and significantly improve the search efficiency and identification accuracy. Additionally, the EnKF-ASACO algorithm was also applied in the scenario with measurement errors, and its robustness and reliability were further verified.

Groundwater contamination source identification using improved differential evolution Markov chain algorithm.

The groundwater contamination source identification (GCSI) can provide important bases for the design of pollution remediation plans. The Bayesian theory is commonly used in the GCSI problem. Usually, we use the Markov chain Monte Carlo (MCMC) method to realize the Bayesian framework. However, due to the ill-posed nature of the GCSI and the system model's complexity, the conventional MCMC algorithm is time-consuming and has low accuracy. In this study, we proposed an adaptive mutation differential evolution Markov chain (AM-DEMC) algorithm. In this algorithm, the Kent mapping chaotic sequence method, combined with differential evolution (DE) algorithm, was used to generate the initial population. In the iteration process, we introduced a hybrid mutation strategy to generate the candidate vectors. Moreover, we adaptively adjust the essential parameter F of the AM-DEMC algorithm according to the individual fitness value. For further improving the efficiency of solving the GCSI problem, the Kriging method was used to establish a surrogate model to avoid the enormous computational load associated with the numerical simulation model. Finally, a hypothetical groundwater contamination case was given to verify the effectiveness of the AM-DEMC algorithm. The results indicated that the proposed AM-DEMC algorithm successfully identified the contamination sources' characteristics and simulation model's parameters. It also exhibited stronger search-ability and higher accuracy than the MCMC and DE-MC algorithms.

Source identification of surface water pollution using multivariate statistics combined with physicochemical and socioeconomic parameters

Accurate identification of potential contamination sources of river water is a basis for effective pollution control and sustainable water management. Pollution source identification based on physicochemical-parameters-only method may lead to uncertainty and subjectivity. In this study along with hydrochemistry parameters (HPs), socioeconomic parameters (SPs) were considered as an auxiliary in multivariate statistics to achieve a comprehensive assessment on pollution sources with accurate estimates of source identification and apportionment. Fifteen physicochemical parameters were combined with twelve socioeconomic parameters in multivariate statistics to quantitatively assess potential pollution sources and their contributions to river water pollution. Multivariate statistics in the study included regression analysis, principal component analysis (PCA), and absolute principal component score-multiple linear regression (APCS-MLR). Regression analysis between hydro-chemical parameters and socioeconomic parameters indicated that industrial and population growths were the main factors related to ammonium nitrogen (NH4+-N), total nitrogen (TN) contamination, while total phosphorus (TP) was more correlated with domestic discharge and poultry breeding. Based on the results of PCA, four latent factors were extracted for hydrochemistry parameters (HPs) and socioeconomics parameters (SPs), accounting for 68.59% and 82.40% of the total variance, respectively. With integrating the PCA results of the two parameter groups, pollution sources were ranked as industrial effluents > rural wastewater > municipal sewage > phytoplankton growth and agricultural cultivation. Source apportionment in APCS-MLR revealed that industrial wastewater and rural wastewater averagely contributed 35.68% and 25.08% of pollution, respectively, followed by municipal sewage (18.73%) and phytoplankton pollution (15.13%) with relatively small percentage of unrecognized source. It is concluded that socioeconomic parameters assisting hydrochemistry parameters in multivariate statistics can improve the accuracy and certainty of pollution source identification, supporting decision makers to formulate strategies on protection of river water quality.

Evaluating backward probability model under various hydrogeologic and hydrologic conditions

Contaminant source identification improves the understanding of contaminant source characteristics including location and release time, which can lead to more effective remediation and water resources management plans. The backward probability model can provide probabilities of source locations and release times under various contaminant properties and hydrogeologic conditions. The backward probability model has been applied to numerous synthetic and real contamination sites for locating possible contaminant sources, but it is also important to evaluate the reliability of the backward probability model through rigorous verification analyses. Here, we present a model verification framework for the backward probability model using a stepwise approach from simple to complex model settings: comparison with previous studies, transient saturated flow under various hydrogeologic conditions, and transient variably-saturated flow conditions. As a simple condition, one-dimensional homogeneous problems under steady-state and transient flow conditions were verified by comparing with previous studies. Model verifications with complex conditions were conducted by comparing forward and backward probability simulation results. The verification results demonstrate that the backward probability model performs well for homogeneous problems. For heterogeneous problems, the backward probability model results in slightly different backward travel times due to differences in solute decay and boundary conditions assigned for both forward and backward probability simulations, but the backward travel time at the maximum probability can be reproduced well.

Identification of Metal Contamination Sources and Evaluation of the Anthropogenic Effects in Soils near Traffic-Related Facilities.

Traffic-related facilities typically have much lower metal emissions than other sources; however, they can be numerous and widespread as well. Subdividing pollution sources is necessary to assess soil contamination characteristics and identify sources according to the contamination cause. Anthropogenic contamination by metals was quantitatively determined using contamination factor (Cf) and evaluated using multivariate analysis. More than half of the concentrations for Zn, Pb, and Cu in soils were higher than that in the natural background (NB). Cf of metals was, in decreasing order, Zn > Pb = Cu > Ni = As. Zn, Pb, and Cu were identified as anthropogenic contaminants in correlation analysis. Principal component analysis showed that the two main contamination causes were coarse particles from the maintenance or crushing activities of vehicles and nonexhaust/exhaust emissions. Clusters were classified according to those two anthropogenic and lithogenic causes and included Group I (Zn, Pb, and Cu in garages, auto repair shops, and auto salvage yards), Group II (Zn, Pb, and Cu in parking lots, driving schools, and roadsides), and Group III (As and Ni with high lithogenic properties). Anthropogenic input and sources of soil contamination by metals in traffic-related facilities were appropriately estimated through the combination of Cf and multivariate analysis.

Contaminant Source Identification from Finite Sensor Data: Perron–Frobenius Operator and Bayesian Inference

Sensors in the built environment ensure safety and comfort by tracking contaminants in the occupied space. In the event of contaminant release, it is important to use the limited sensor data to rapidly and accurately identify the release location of the contaminant. Identification of the release location will enable subsequent remediation as well as evacuation decision-making. In previous work, we used an operator theoretic approach—based on the Perron–Frobenius (PF) operator—to estimate the contaminant concentration distribution in the domain given a finite amount of streaming sensor data. In the current work, the approach is extended to identify the most probable contaminant release location. The release location identification is framed as a Bayesian inference problem. The Bayesian inference approach requires considering multiple release location scenarios, which is done efficiently using the discrete PF operator. The discrete PF operator provides a fast, effective and accurate model for contaminant transport modeling. The utility of our PF-based Bayesian inference methodology is illustrated using single-point release scenarios in both two and three-dimensional cases. The method provides a fast, accurate, and efficient framework for real-time identification of contaminant source location.

Contaminant Source Identification in Aquifers: A Critical View

Forty years and 157 papers later, research on contaminant source identification has grown exponentially in number but seems to be stalled concerning advancement towards the problem solution and its field application. This paper presents a historical evolution of the subject, highlighting its major advances. It also shows how the subject has grown in sophistication regarding the solution of the core problem (the source identification), forgetting that, from a practical point of view, such identification is worthless unless it is accompanied by a joint identification of the other uncertain parameters that characterize flow and transport in aquifers.

A differential evolutionary Markov chain algorithm with ensemble smoother initial point selection for the identification of groundwater contaminant sources

Groundwater contaminant source identification (GCSI) can provide support for the confirmation of responsibility and the remediation of pollution. This study has developed an innovative combination algorithm to recognize source properties and model parameters in groundwater contamination simultaneously. The research idea is based on Bayes theory and combines an ensemble smoother (ES) algorithm, differential evolutionary Markov chain (DEMC) algorithm, and adaptive kriging surrogate model (AKSM). Poor selection of initial estimates for unknown variables will slow down the convergence rate. Therefore, the initial points are not generated completely randomly, but are partially obtained by the ES algorithm. After the initial points have been determined, the DEMC algorithm is used to recognize source properties and model parameters in groundwater contamination. To improve the efficiency of the DEMC algorithm, the updating formula was adjusted by introducing information about the optimal chain into the iteration. However, the inversion process is time-consuming because both the ES and DEMC algorithms need to run the original simulation model frequently. To solve this problem, an AKSM was established for the original simulation model, which greatly accelerated the inversion process. Different hypothetical cases with different complexities were used to illustrate the validity of the combination algorithm. The identification results implied that the combination algorithm had not only faster convergence, but also higher accuracy. These improvements were more evident in the second case and third case. This implies that the proposed method will play a greater role with increasing problem complexity.

Graph convolutional networks based contamination source identification across water distribution networks

Water distribution Networks (WDNs) are one of the most important infrastructures for modern society. Due to accidental or malicious reasons, water contamination incidents have been repeatedly reported all over the world, which not only disrupt the water supply but also endanger public health. To ensure the safety of WDNs, water quality sensors are deployed across the WDNs for real-time contamination detection and source identification. In the literature, various methods have been employed to improve the performance of contamination source identification (CSI) and recent studies show that there is a great potential to tackle the CSI problem by deep learning models. The success of deep learning based CSI methods often requires a large size of training samples being collected. In real-world situations, the number of contamination events occurring in a single WDN is rather small, especially for a newly built WDN. However, the existing CSI methods in the literature mostly focus on the study of training and applying models on the same WDNs and the knowledge of CSI gained from one WDN cannot be reused by a different WDN. To these ends, based on the application of graph convolutional networks, this paper provides a solution for cross-network CSI that can transfer the CSI knowledge learned from one WDN to a different WDN. Empirically, based on a benchmark WDN in the task of contamination source identification, we show that the proposed cross-network CSI method can achieve comparable accuracy even trained on a different WDN.

Using the backward probability method in contaminant source identification with a finite-duration source loading in a river.

Violation of industries in discharging their effluents into rivers leads to river pollution, which endangers the environment and human health. Appropriate tools are needed to deal with violations and protect rivers. The backward probability method (BPM) is one of the most recommended tools identifying the release time and location of the pollutant source. However, the BPM generally was developed for groundwater and spill injection. Since most industries inject their effluents with a constant rate for a finite duration, the use of prevailing models will have some errors. In this study, a numerical model was developed that could simulate a source with either a finite-duration or spill injection. This model is verified for two hypothetical cases and one real case. The results show that the model can accurately identify the release time and location of the pollutant source.

Contamination level, source identification and health risk evaluation of potentially toxic elements (PTEs) in groundwater of an industrial city in eastern India.

The present investigation explores the spatial and seasonal variations in potentially toxic element (PTEs) concentrations and contamination level assessment of groundwater samples in and around the Asansol industrial city, eastern India. The representative samples of groundwater from 24 different locations were analyzed for pH, electrical conductivity (EC), total dissolved solids (TDS), total hardness (TH) and PTEs, e.g., Pb, Cu, Cd, Zn, Fe and Cr for pre-monsoon and post-monsoon. The pH level of examined groundwater samples is under the desirable limit with few exceptions (S5, S11 and S16 in pre-monsoon and S12 in post-monsoon). The recorded values for Pb, Cd, Fe and Cr in many sampling stations found higher than the prescribed limits of Indian standards (IS 10500: 2012) in both the seasons. The mean contamination factor (Cf) for PTEs in the groundwater is in the order of Cr > Fe > Cd > Pb > Cu > Zn and Fe > Cr > Cd > Pb > Cu > Zn, with mean contamination index (Cd) value of 2.83 and 2.72 in pre-monsoon and post-monsoon season, respectively, indicating moderate level of contamination in the examined area. Geospatial depiction of HPI values shows high level of contamination during pre-monsoon (58.3% sampling sites) and post-monsoon (45.8% sampling sites) in majority of sampling sites. Further, application of multivariate statistical analysis ascertains that the PTEs in groundwater are majorly derived from anthropogenic activities such as opencast mining, thermal power plants, iron and steel industries, sponge iron and other metallurgical industries, and leachate from urban and industrial wastes along with limited contribution from geogenic and lithogenic sources. The health risk assessment demonstrates that the non-carcinogenic risk (due to PTEs) in adults is in the sequence of Cr > Cd > Pb > Fe > Cu > Zn, while for children the order is Cr > Pb > Cd > Fe > Cu > Zn for both the seasons. The results also reveal higher chance of occurrence of carcinogenic risk due to Cr (ILCR > 1.0E-04) for children and adults in both the seasons.

Integrative Survey of 68 Non-overlapping Upstate New York Watersheds Reveals Stream Features Associated With Aquatic Fecal Contamination

Aquatic fecal contamination poses human health risks by introducing pathogens in water that may be used for recreation, consumption, or agriculture. Identifying fecal contaminant sources, as well as the factors that affect their transport, storage, and decay, is essential for protecting human health. However, identifying these factors is often difficult when using fecal indicator bacteria (FIB) because FIB levels in surface water are often the product of multiple contaminant sources. In contrast, microbial source-tracking (MST) techniques allow not only the identification of predominant contaminant sources but also the quantification of factors affecting the transport, storage, and decay of fecal contaminants from specific hosts. We visited 68 streams in the Finger Lakes region of Upstate New York, United States, between April and October 2018 and collected water quality data (i.e., Escherichia coli, MST markers, and physical–chemical parameters) and weather and land-use data, as well as data on other stream features (e.g., stream bed composition), to identify factors that were associated with fecal contamination at a regional scale. We then applied both generalized linear mixed models and conditional inference trees to identify factors and combinations of factors that were significantly associated with human and ruminant fecal contamination. We found that human contaminants were more likely to be identified when the developed area within the 60 m stream buffer exceeded 3.4%, the total developed area in the watershed exceeded 41%, or if stormwater outfalls were present immediately upstream of the sampling site. When these features were not present, human MST markers were more likely to be found when rainfall during the preceding day exceeded 1.5 cm. The presence of upstream campgrounds was also significantly associated with human MST marker detection. In addition to rainfall and water quality parameters associated with rainfall (e.g., turbidity), the minimum distance to upstream cattle operations, the proportion of the 60 m buffer used for cropland, and the presence of submerged aquatic vegetation at the sampling site were all associated based on univariable regression with elevated levels of ruminant markers. The identification of specific features associated with host-specific fecal contaminants may support the development of broader recommendations or policies aimed at reducing levels of aquatic fecal contamination.

Characterization, geostatistical modeling and health risk assessment of potentially toxic elements in groundwater resources of northeastern Iran

Study regionNortheastern Iran. Study focusIn northeastern Iran, water needed for municipal and agricultural activities mainly comes from groundwater resources. However, it is subject to substantial anthropogenic and geogenic contamination. We characterize the sources of groundwater contamination by employing an integrated approach that can be applied to the identification of large-scale contamination sources in other regions. An existing dataset of georeferenced water quality parameters from 676 locations in northeast of Iran was analyzed to investigate the geochemical properties of groundwater. Gridding of the parameters graphically illustrates the areas affected by high concentrations of As, Cl−, Cr, Fe, Mg2+, Na+, NO3−, Se, and SO42-. We then identified potential anthropogenic and geogenic contamination sources by employing random forest (RF) regression modeling. New hydrological insights for the regionRandom forest (RF) models show that the major ions, As, Cr, Fe, and Se content of groundwater are mainly determined by geology in the study area. Modeling also links groundwater NO3− contamination with sewage discharge into aquifers as well as the application of nitrogenous and animal-waste fertilizers. Areas of high salinity result from evaporate deposits and irrigation return flow. Medium to high non-carcinogenic health risk is found in areas with high concentrations of geogenic As and Cr in groundwater. Our approach can be applied elsewhere to analyze regional groundwater quality and associated health risks as well as identify potential sources of contamination.

Groundwater contamination sources identification based on the Long-Short Term Memory network

When simulation–optimization (S/O) is used for groundwater contamination source identification (GCSI), to reduce the calculation load and time generated by calling the simulation model, a surrogate model is often used instead of the simulation model. However, when the conversion relationship between the simulation model inputs and outputs is complex and the model is highly nonlinear, the commonly used approach of building a surrogate model may lose its advantage. This study use and check a deep learning method with the long-short term memory (LSTM) network which has great potential for characterizing the input–output conversion relationship of complex nonlinear numerical simulations, to a surrogate model of the simulation model. The accuracy of the surrogate model developed by the LSTM method was compared with that of commonly used surrogate models developed by the Kriging method, the radial basis function network method, and the kernel extreme learning machine method. The surrogate model with the highest accuracy was linked to the optimization model, and the optimization model was solved to identify the contamination source information. Results show that compared with the other three methods, the surrogate model constructed by the LSTM method had the best accuracy and generalization performance. LSTM is therefore an effective method of building a surrogate model. Linking the LSTM surrogate model to an optimization model and then solving the optimization model can save approximately 99% of the computing load and time otherwise required.

Observability‐Based Sensor Placement Improves Contaminant Tracing in River Networks

AbstractThis study presents a new methodology for identifying near‐optimal sensor locations for contaminant source tracing in river networks. We define an optimal sensor placement as one that enables the best overall reconstruction of contaminant concentrations from observed data. To establish a physical basis for the problem, we first derive a linear time‐invariant (LTI) model for riverine contaminant transport using the one‐dimensional advection‐reaction‐diffusion equation. We then formulate an optimization problem to find the sensor placement that maximizes the observability of the modeled system and identify two heuristics for efficiently achieving this goal. By evaluating each sensor placement strategy on its ability to reconstruct initial contaminant loads from observed outputs, we find that the best sensor placement is obtained by maximizing the rank of the LTI system's Observability Gramian. This sensor placement strategy enables the best overall reconstruction of both magnitudes and distributions of nonpoint‐source contaminants. Our methodology will enable researchers to build sensor networks that better interpolate pollutant loads in ungauged locations, improve contaminant source identification, and inform more effective pollution control strategies.

Case Study:Identification of Microbial Distribution and Contamination Source by PDCA Cycle at a Boiled Noodles Factory

Boiled noodles are considered to be one of the most perishable foods due to their high moisture content and high water activity. Thus, the hygiene control measures based on HACCP manuals has been recommended in the noodle manufacturing industry. However, there were several cases in which post-packaged products manufactured at the Boiled noodles factory of small-to-medium size company detected a viable cell count higher than their voluntary standards. To identify the source of microbial contamination, an investigation based on the Plan-Do-Check-Act (PDCA) cycle was conducted. The results showed that the bacteria causing the contamination were environmental bacteria. Secondary contamination occurred during the cooling process after sterilization. Airborne environmental bacteria and oxygen may have been introduced into the rinsing and cooling water tank by the strong water flow during the rinsing and cooling process, inducing growth of microorganisms in the cooling water and contaminating the final product. This is a new finding, as such occurrence was not listed in the HACCP manual and should be contributed to plan HACCP system.

Assessment and source identification of heavy metal contamination of groundwater using geospatial technology in Gadilam River basin, Tamil Nadu, India

The purpose of this study is to determine the groundwater quality of the Gadilam River basin on the basis of Geographic Information Technology (GIS) taxonomy evaluation and risk assessment and statistical analysis. The Gadilam River originates from the district of Kallakurichi and flows into the district of Viluppuram finally confluence at Bay of Bengal in the district of Cuddalore. Groundwater supplies are serving the entire drinking needs of the residents of the Gadilam River basin, and pollution of its toxic elements has been raised in recent years. A total of 120 groundwater samples, excluding reserved forest areas, were obtained from the Gadilam River basin. 50 samples of the Archaean formation, 34 samples of the Quaternary formation, 35 additional Tertiary formation samples and one Cretaceous formation sample. During the monsoon season (Nov. 2018), groundwater samples were taken, which is after the Gaja cyclone’s diastral effects. At the time of the sample acquisition, pH, EC and TDS were measured using the Elico field kit. Using the Atomic Absorption Spectrometer, the heavy metals Zn, Cr, Cu, Pb, Co, Fe, Mn and Ni were analyzed. The spatial distribution maps of the GIS shows that in all the samples the concentrations of chromium, lead, cobalt, nickel are high (above the permitted limit). In 93.40%of the area, the manganese concentrations are higher, dropping under the unauthorized cap for human use in compliance with WHO requirements. In addition, statistical analysis indicates that groundwater pollution of the roots of Pb and Cr is from foreign sources and geogenic origin cannot be avoided. The country's rocks and industrial effluents can trigger Zn and Co. The anthropogenic operations may be due to Cu. The taxonomy diagram showing all groundwater samples is classified as ‘near neutral high metal.’ Industrial waste, household sewage, has boosted the emission of heavy metals. Groundwater risk assessment study of heavy metals using SMCE approach reveals that dense residential area in the Archaean formation, lineaments intersection with river portion and sea shore area in Quaternary formation and nearby SIPCOT industry and Lignite mining area in Tertiary formation is noted in the ‘Extreme risk dangerous’ areas.