Application of Unknown Groundwater Pollution Source Release History Estimation Methodology to Distributed Sources Incorporating Surface-Groundwater Interactions

Sources of contamination of groundwater are often difficult to characterize. However, it is essential for effective remediation of polluted groundwater resources. This study demonstrates an application of the linked simulation-optimization based methodology to estimate the release history from spatially distributed sources of pollution at an illustrative abandoned mine-site. In linked simulation-optimization approaches a numerical groundwater flow and transport simulation model is linked to the optimization model. In this study, topographic and geologic characteristics of the abandoned mine-site were simulated using a three-dimensional (3D) numerical groundwater flow model. Transport of contaminant in the groundwater was simulated using a 3D transient advective-dispersive contaminant transport model. Adsorption or chemical reaction of the contaminant was not considered in the contaminant transport model. Adaptive simulated annealing (ASA) was employed for solving the optimization problem. An optimization algorithm generates the candidate solutions corresponding to various unknown groundwater source characteristics. The candidate solutions are used as input in the numerical groundwater transport simulation model to generate the concentration of pollutant in the study area. This information is used to calculate the objective function value, which is utilized by the optimization algorithm to improve the candidate solution. This process continues until an optimal solution is obtained. Optimal solutions obtained in this study show that the linked simulation-optimization based methodology is potentially applicable for the characterization of spatially distributed pollutant sources, typically present at abandoned mine-sites.     

Groundwater vulnerability and risk mapping using GIS, modeling and a fuzzy logic tool

A groundwater vulnerability and risk mapping assessment, based on a source-pathway-receptor approach, is presented for an urban coastal aquifer in northeastern Brazil. A modified version of the DRASTIC methodology was used to map the intrinsic and specific groundwater vulnerability of a 292 km(2) study area. A fuzzy hierarchy methodology was adopted to evaluate the potential contaminant source index, including diffuse and point sources. Numerical modeling was performed for delineation of well capture zones, using MODFLOW and MODPATH. The integration of these elements provided the mechanism to assess groundwater pollution risks and identify areas that must be prioritized in terms of groundwater monitoring and restriction on use. A groundwater quality index based on nitrate and chloride concentrations was calculated, which had a positive correlation with the specific vulnerability index.     

Stochastic analysis to assess the spatial distribution of groundwater nitrate concentrations in the Po catchment (Italy)

A large database including temporal trends of physical, ecological and socio-economic data was developed within the EUROCAT project. The aim was to estimate the nutrient fluxes for different socio-economic scenarios at catchment and coastal zone level of the Po catchment (Northern Italy) with reference to the Water Quality Objectives reported in the Water Framework Directive (WFD 2000/60/CE) and also in Italian legislation. Emission data derived from different sources at national, regional and local levels are referred to point and non-point sources. While non-point (diffuse) sources are simply integrated into the nutrient flux model, point sources are irregularly distributed. Intensive farming activity in the Po valley is one of the main Pressure factors Driving groundwater pollution in the catchment, therefore understanding the spatial variability of groundwater nitrate concentrations is a critical issue to be considered in developing a Water Quality Management Plan. In order to use the scattered point source data as input in our biogeochemical and transport models, it was necessary to predict their values and associated uncertainty at unsampled locations. This study reports the spatial distribution and uncertainty of groundwater nitrate concentration at a test site of the Po watershed using a probabilistic approach. Our approach was based on geostatistical sequential Gaussian simulation used to yield a series of stochastic images characterized by equally probable spatial distributions of the nitrate concentration across the area. Post-processing of many simulations allowed the mapping of contaminated and uncontaminated areas and provided a model for the uncertainty in the spatial distribution of nitrate concentrations.     

Support vector machine―an alternative to artificial neuron network for water quality forecasting in an agricultural nonpoint source polluted river?

Water quality forecasting in agricultural drainage river basins is difficult because of the complicated nonpoint source (NPS) pollution transport processes and river self-purification processes involved in highly nonlinear problems. Artificial neural network (ANN) and support vector model (SVM) were developed to predict total nitrogen (TN) and total phosphorus (TP) concentrations for any location of the river polluted by agricultural NPS pollution in eastern China. River flow, water temperature, flow travel time, rainfall, dissolved oxygen, and upstream TN or TP concentrations were selected as initial inputs of the two models. Monthly, bimonthly, and trimonthly datasets were selected to train the two models, respectively, and the same monthly dataset which had not been used for training was chosen to test the models in order to compare their generalization performance. Trial and error analysis and genetic algorisms (GA) were employed to optimize the parameters of ANN and SVM models, respectively. The results indicated that the proposed SVM models performed better generalization ability due to avoiding the occurrence of overtraining and optimizing fewer parameters based on structural risk minimization (SRM) principle. Furthermore, both TN and TP SVM models trained by trimonthly datasets achieved greater forecasting accuracy than corresponding ANN models. Thus, SVM models will be a powerful alternative method because it is an efficient and economic tool to accurately predict water quality with low risk. The sensitivity analyses of two models indicated that decreasing upstream input concentrations during the dry season and NPS emission along the reach during average or flood season should be an effective way to improve Changle River water quality. If the necessary water quality and hydrology data and even trimonthly data are available, the SVM methodology developed here can easily be applied to other NPS-polluted rivers.     

Artificial neural network for the identification of unknown air pollution sources

Artificial neural networks (ANN), whose performances to deal with pattern recognition problems is well known, are proposed to identify air pollution sources. The problem that is addressed is the apportionment of a small number of sources from a data set of ambient concentrations of a given pollutant. Three layers feed-forward ANN trained with a back-propagation algorithm are selected. A test case is built, based on a Gaussian dispersion model. A subset of hourly meteorological conditions and measured concentrations constitute the input patterns to the network that is wired to recover relevant emission parameters of unknown sources as outputs. The rest of the model data are corrupted adding noise to some meteorological parameters and we test the effectiveness of the method to recover the correct answer. The ANN is applied to a realistic case where 24 h SO₂ concentrations were previously measured. Some of the limitations of the ANN approach, together with its capabilities, are discussed in this paper.     

Identification of Pollution Source of Cadmium in Soil. Application of Material Flow Analysis and A Case Study in Taiwan (11 pp)

Background Since the 1970s, at least 200 hectares (ha) of farmland has been polluted by the heavy metal cadmium (Cd).Consequently, the Cd pollution has led to contaminate the rice production and caused acute social panic. According to the recent investigation results performed by the Taiwan Environmental Protection Administration (TEPA), it is indicated that most of the Cd pollution incidents in Taiwan resulted from the wastewater discharge of stearate Cd factories. To prevent the Cd pollution incidents from spreading, the TEPA has either forced these factories to close down or assisted them in improving their production processes since the 1980s. Unfortunately, accidental incidents of Cd pollution still emerge in an endless stream, despite the strict governmental controls placed on these questionable factories. Whether this pollution has resulted from undetected or hidden pollution sources stemming from two decades ago or comes from some new source, will be an outstanding issue. Therefore, this study attempts to identify the pollution sources of Cd in soil in Taiwan as well as to find the solution to the above-mentioned, outstanding issue by way of a methodology termed Material Flow Analysis (MFA). Method logy. The MFA has proved to be a useful tool on providing quantitative information of the flow of substances through an economic to an environmental system. Based upon the supply-and-demand theory of MFA, researchers have successfully conducted an overview of the use of materials in many industries, the construction industry being one of these. Therefore, this study tries to establish a set of analytical processes by way of MFA for identifying the pollution source of Cd in soil in Taiwan. In addition, the spirit of Life Cycle Analysis (LCA) technique was also employed to identify the materials, and products should be ignored as a crucial pollution source in this study. Results and Discussion According to the MFA methodology applied in this study and on the basis of related studies performed by Taiwanese governmental authorities, we arrive at the following analysis results: (1) the total amount of Cd from the economic perspective of material and product flow was approximately 441.2 tons; (2) the wastewater directly discharged into irrigation water can be concluded to be the major pollution route of Cd in farmland soil in Taiwan; (3) material plastic stabilizer (cadmium oxide, CdO), Zn-Pd compounds and Cu compounds should be the crucial pollution sources to contaminate environment through the route of wastewater in Cd flow analysis; (4) the crucial pollution sources to contaminate environment through the route of wastewater in Cd flow analysis were five factories, Coin, Jili, Taiwan Dye, Guangzheng and Mingguan, and they were all categorized as stearate Cd industries; (5) the typical source of the Cd pollution in soil in Changhua County through the pollution route of wastewater should be the metal surfacing process industries. Conclusions This study proved that MFA can be a good tool for identifying Cd flow as well as for recognizing the crux of the problem concerning incidents of Cd pollution. This study led to the conclusion that the causal relationship between farmland pollution caused by Cd and stearate Cd factories in Taiwan seemed quite close by way of MFA methodology. In addition, this study also found that the wastewater discharged from a single metal surfacing process factory will not cause remarkable farmland pollution. However, the wastewater simultaneously discharged from a group of pollution factories can result in a significant pollution incident. Recommendations and Outlook This case study is only a small contribution to the understanding of the toxic material flow related to Cd in the environment. This study recommends that Taiwanese governmental authorities should not deal with problems on an ad hoc basis, but should instead deal with Cd pollution problems overall employing control measures. Finally, the more accurate information or data we can collect, the more reliable results we can identify. Therefore, the quality and quantity of related data used in this MFA model should be closely scrutinized in order to ensure the most correct and comprehensive investigation on the toxic material flow.     

Description of hydrogeologic heterogeneity and evaluation of radionuclide transport at an underground nuclear test

Realistic models of contaminant transport in groundwater demand detailed characterization of the spatial distribution of subsurface hydraulic properties, while at the same time programmatic constraints may limit collection of pertinent hydraulic data. Fortunately, alternate forms of data can be used to improve characterization of spatial variability. We utilize a methodology that augments sparse hydraulic information (hard data) with more widely available hydrogeologic information to generate equiprobable maps of hydrogeologic properties that incorporate patterns of connected permeable zones. Geophysical and lithologic logs are used to identify hydrogeologic categories and to condition stochastic simulations using Sequential Indicator Simulation (SIS). The resulting maps are populated with hydraulic conductivity values using field data and Sequential Gaussian Simulation (SGS). Maps of subsurface hydrogeologic heterogeneity are generated for the purpose of examining groundwater flow and transport processes at the Faultless underground nuclear test, Central Nevada Test Area (CNTA), through large-scale, three-dimensional numerical modeling. The maps provide the basis for simulation of groundwater flow, while transport of radionuclides from the nuclear cavity is modeled using particle tracking methods. Sensitivity analyses focus on model parameters that are most likely to reduce the long travel times observed in the base case. The methods employed in this study have improved our understanding of the spatial distribution of preferential flowpaths at this site and provided the critical foundation on which to build models of groundwater flow and transport. The results emphasize that the impacts of uncertainty in hydraulic and chemical parameters are dependent on the radioactive decay of specific species, with rapid decay magnifying the effects of parameters that change travel time.     

Use of selected chemical markers in combination with a multiple regression model to assess the contribution of domesticated animal sources of fecal pollution in the environment

Human and animal wastes are major sources of environmental pollution. Reliable methods of identifying waste sources are necessary to specify the types and locations of measures that best prevent and mitigate pollution. This investigation demonstrates the use of chemical markers (fecal sterols and bile acids) to identify selected sources of fecal pollution in the environment. Fecal sterols and bile acids were determined for pig, horse, cow, and chicken feces (10-26 feces samples for each animal). Concentrations of major fecal sterols (coprostanol, epicoprostanol, cholesterol, cholestanol, stigmastanol, and stigmasterol) and bile acids (lithocholic acid, deoxycholic acid, cholic acid, chenodeoxycholic acid, ursodeoxycholic acid, and hyodeoxycholic acid) were determined using a gas chromatography and mass spectrometer (GC-MS) technique. The fecal sterol and bile acid concentration data were used to estimate parameters of a multiple linear regression model for fecal source identification. The regression model was calibrated using 75% of the available data validated against the remaining 25% of the data points in a jackknife process that was repeated 15 times. The regression results were very favorable in the validation data set, with an overall coefficient of determination between predicted and actual fecal source of 0.971. To check the potential of the proposed model, it was applied on a set of simulated runoff data in predicting the specific animal sources. Almost 100% accuracy was obtained between the actual and predicted fecal sources. While additional work using polluted water (as opposed to fresh fecal samples) as well as multiple pollution sources are needed, results of this study clearly indicate the potential of this model to be useful in identifying the individual sources of fecal pollution.     

Source characteristics of hazardous Chihuahuan Desert dust outbreaks

The Chihuahuan Desert region is an important contributor to atmospheric dust loading and transport in North America; however, specific dust sources in this region are poorly characterized. Major dust events frequently are characterized by multiple dust plumes developing nearly simultaneously over a large region. Remote sensing data were used to identify the source locations and associated land cover for the most extreme dust events in the Chihuahuan Desert since 2002. Analysis of infrared channels utilizing brightness temperature differences was used to analyze data from geostationary and polar-orbiting satellites, from which dust sources were determined and located. This methodology was applied to the five dust events in the region that resulted in “hazardous” PM₁₀ levels in Texas per the USEPA’s Air Quality Index. Source locations determined from satellite images were used in conjunction with LANDSAT data and Google Earth? images to determine the corresponding land-surface features. Agricultural lands, playas, and their edges are pointed out as focus areas for dust emission, at least during the most intense events. The 130 dust plume initiation sites were relatively uniformly spaced over the landscape, not clumped into a few “hotspots,” suggesting the role of spatiotemporally random meteorological factors in determining major points of emission within and between dust storms. These findings provide an initial characterization of Chihuahuan Desert dust source locations and establish a baseline for continued research in determining potential locations for future dust outbreaks in the southwestern U.S. and northwestern Mexico.     

Source contributions to fine particulate matter in an urban atmosphere

This paper proposes a practical method for estimating source attribution by using a three-step methodology. The main objective of this study is to explore the use of the three-step methodology for quantifying the source impacts of 24-h PM2.5 particles at an urban site in Seoul, Korea. 12-h PM2.5 samples were collected and analyzed for their elemental composition by ICP-AES/ICP-MS/AAS to generate the source composition profiles. In order to assess the daily average PM2.5 source impacts, 24-h PM2.5 and polycyclic aromatic hydrocarbons (PAH) ambient samples were simultaneously collected at the same site. The PM2.5 particle samples were then analyzed for trace elements. Ionic and carbonaceous species concentrations were measured by ICP-AES/ICP-MS/AAS, IC, and a selective thermal MnO2 oxidation method. The 12-h PM2.5 chemical data was used to estimate possible source signatures using the principal component analysis (PCA) and the absolute principal component scores method followed by the multiple linear regression analysis. The 24-h PM2.5 source categories were extracted with a combination of PM2.5 and some PAH chemical data using the PCA, and their quantitative source contributions were estimated by chemical mass balance (CMB) receptor model using the estimated source profiles and those in the literature. The results of PM2.5 source apportionment using the 12-h derived source composition profiles show that the CMB performance indices; chi2, R2, and percent of mass accounted for are 2.3%, 0.97%, and 100.7%, which are within the target range specified. According to the average PM2.5 source contribution estimate results, motor vehicle exhaust was the major contributor at the sampling site, contributing 26% on average of measured PM2.5 mass (41.8 microg m-3), followed by secondary sulfate (23%) and nitrate (16%), refuse incineration (15%), soil dust (13%), field burning (4%), oil combustion (2.7%), and marine aerosol (1.3%). It can be concluded that quantitative source attribution to PM2.5 in an urban area where source profiles have not been developed can be estimated using the proposed three-step methodology approach.     

Spatial modeling of factor analysis scores

The use of mosses as biomonitors operates as an indicator of their concentration in the environment, becoming a methodology which provides a significant interpretation in terms of environmental quality. The different types of pollution are variables that can not be measured directly in the environment - latent variables. Therefore, we propose the use of factor analysis to estimate these variables in order to use them for spatial modelling. On the contrary, the main aim of the commonly used principal components analysis method is to explain the variability of observed variables and it does not permit to explicitly identify the different types of environmental contamination. We propose to model the concentration of each heavy metal as a linear combination of its main sources of pollution, similar to the case of multiple regression where these latent variables are identified as covariates, though these not being observed. Moreover, through the use of geostatistical methodologies, we suggest to obtain maps of predicted values for the different sources of pollution. With this, we summarize the information acquired from the concentration measurements of the various heavy metals, and make possible to easily determine the locations that suffer from a particular source of pollution.     

Impacts of Meteorological Factors on PM10: Artificial Neural Networks (ANN) and Multiple Linear Regression (MLR) Approaches

In this study, prediction capacities of multi-linear regression (MLR) and artificial neural networks (ANN) onto coarse particulate matter (PM₁₀) concentrations were investigated. Different meteorological factors on particulate pollution were chosen for operating variables in the model analyses. Two different regions (urban and industrial) were identified in the region of Kocaeli, Turkey. All data sets were obtained from air quality monitoring network of the Ministry of Environment and Urban Planning, and 120 data sets were used in the MLR and ANN models. Regression equations explained the effects of the meteorological factors in MLR analyses. In the ANN model, backpropagation network with two hidden layers has achieved the best prediction efficiency. Determination coefficients and error values were examined for each model. ANN models displayed more accurate results compared to MLR.     

Estimation of the exchange of sulphur pollution over the Balkan region in 1995–2000

The Bulgarian dispersion model 'Eulerian Model for Air Pollution' (EMAP) is used to estimate the sulphur pollution over the Balkan region for the period 1995–2000. A subdomain of the European Monitoring and Evaluation Programme (EMEP) grid is chosen containing 12 countries. The computational grid in this domain has a space step of 25 km, twice as fine as the EMEP grid. The former operational DWD 'Europa-Model' is used as meteorological driver. The source input is the official EMEP emission data. Monthly calculations are made having the last moment fields from the previous month as initial conditions for the next one. The boundary conditions are set to zero, so the influence of other European sources is not accounted for in this study. According to the EMEP methodology, multiple runs are made setting every time the sources of various countries to zero. The impact of every country in the pollution of all others is estimated.     

Investigating hydrochemistry of groundwater in Indo-Gangetic alluvial plain using multivariate chemometric approaches

Groundwater hydrochemistry of an urban industrial region in Indo-Gangetic plains of north India was investigated. Groundwater samples were collected both from the industrial and non-industrial areas of Kanpur. The hydrochemical data were analyzed using various water quality indices and nonparametric statistical methods. Principal components analysis (PCA) was performed to identify the factors responsible for groundwater contamination. Ensemble learning-based decision treeboost (DTB) models were constructed to develop discriminating and regression functions to differentiate the groundwater hydrochemistry of the three different areas, to identify the responsible factors, and to predict the groundwater quality using selected measured variables. The results indicated non-normal distribution and wide variability of water quality variables in all the study areas, suggesting for nonhomogenous distribution of sources in the region. PCA results showed contaminants of industrial origin dominating in the region. DBT classification model identified pH, redox potential, total-Cr, and λ ₂₅₄ as the discriminating variables in water quality of the three areas with the average accuracy of 99.51 % in complete data. The regression model predicted the groundwater chemical oxygen demand values exhibiting high correlation with measured values (0.962 in training; 0.918 in test) and the respective low root mean-squared error of 2.24 and 2.01 in training and test arrays. The statistical and chemometric approaches used here suggest that groundwater hydrochemistry differs in the three areas and is dominated by different variables. The proposed methods can be used as effective tools in groundwater management.     

Characterizing sources of nitrate leaching from an irrigated dairy farm in Merced County, California

Dairy farms comprise a complex landscape of groundwater pollution sources. The objective of our work is to develop a method to quantify nitrate leaching to shallow groundwater from different management units at dairy farms. Total nitrate loads are determined by the sequential calibration of a sub-regional scale and a farm-scale three-dimensional groundwater flow and transport model using observations at different spatial scales. These observations include local measurements of groundwater heads and nitrate concentrations in an extensive monitoring well network, providing data at a scale of a few meters and measurements of discharge rates and nitrate concentrations in a tile-drain network, providing data integrated across multiple farms. The various measurement scales are different from the spatial scales of the calibration parameters, which are the recharge and nitrogen leaching rates from individual management units. The calibration procedure offers a conceptual framework for using field measurements at different spatial scales to estimate recharge N concentrations at the management unit scale. It provides a map of spatially varying dairy farming impact on groundwater nitrogen. The method is applied to a dairy farm located in a relatively vulnerable hydrogeologic region in California. Potential sources within the dairy farm are divided into three categories, representing different manure management units: animal exercise yards and feeding areas (corrals), liquid manure holding ponds, and manure irrigated forage fields. Estimated average nitrogen leaching is 872 kg/ha/year, 807 kg/ha/year and 486 kg/ha/year for corrals, ponds and fields respectively. Results are applied to evaluate the accuracy of nitrogen mass balances often used by regulatory agencies to assess groundwater impacts. Calibrated leaching rates compare favorably to field and farm scale nitrogen mass balances. These data and interpretations provide a basis for developing improved management strategies.     

Land Use-related Chemical Composition of Street Sediments in Beijing

BACKGROUND: More than 10 million people are currently living in Beijing. This city faces severe anthropogenic air pollution caused by an intense vehicle increase (11% per year in China), coal combusting power plants, heavy industry, huge numbers of household and restaurant cookers, and domestic heating stoves. Additionally, each year dust storms are carrying particulate matter from the deserts of Gobi and Takla Makan towards Beijing, especially in spring. Other geogenic sources of particulate matter which contribute to the air pollution are bare soils, coal heaps and construction sites occurring in and around Beijing. Streets function as receptor surfaces for atmospheric dusts. Thus, street sediments consist of particles of different chemical compositions from many different sources, such as traffic, road side soils and industry. METHODS: Distributions and concentrations of various chemical elements in street sediments were investigated along a rural-urban transect in Beijing, China. Chemical elements were determined with X-ray fluorescence analysis. Factor analysis was used to extract most important element sources contributing to particulate pollution along a main arterial route of the Chinese capital. RESULTS AND DISCUSSION: The statistical evaluation of the data by factor analysis identifies three main anthropogenic sources responsible for the contamination of Beijing street sediments. The first source is a steel factory in the western part of Beijing. From this source, Mn, Fe, and Ti were emitted into the atmosphere through chimneys and by wind from coal heaps used as the primary energy source for the factory. The second source is a combination of traffic, domestic heating and some small factories in the center of Beijing discharging Cu, Pb, Zn and Sn. Calcium and Cr characterize a third anthropogenic element source of construction materials such as concrete and mortar. Beside the anthropogenic contamination, some elements like Y, Zr, Nb, Ce, and Rb are mainly derived from natural soils and from the deserts. This is supported by mineral phase analysis, which showed a clear imprint of material in road dusts coming from the West-China deserts. CONCLUSIONS: Our results clearly show that the chemical composition of urban road dusts can be used to identify distinct sources responsible for their contamination. The study demonstrates that the chemistry of road dusts is an important monitor to assess the contamination in the urban environment. Chemical composition of street sediments in Beijing comprises the information of different sources of atmospheric particles. RECOMMENDATIONS AND OUTLOOK: This study is only a small contribution to the understanding of substance fluxes related to Beijing's dust. More effort is required to assess Beijing's dust fluxes, since the dust harms the living quality of the inhabitants. Especially the measurable superimposing of long scale transported dust from dry regions with the anthropogenic polluted urban dust makes investigations of Beijing's dust scientifically valuable.     

Determination of the origin of groundwater nitrate at an air weapons range using the dual isotope approach

Nitrate is one of the most common contaminants in shallow groundwater, and many sources may contribute to the nitrate load within an aquifer. Groundwater nitrate plumes have been detected at several ammunition production sites. However, the presence of multiple potential sources and the lack of existing isotopic data concerning explosive degradation-induced nitrate constitute a limitation when it comes to linking both types of contaminants. On military training ranges, high nitrate concentrations in groundwater were reported for the first time as part of the hydrogeological characterization of the Cold Lake Air Weapons Range (CLAWR), Alberta, Canada. Explosives degradation is thought to be the main source of nitrate contamination at CLAWR, as no other major source is present. Isotopic analyses of N and O in nitrate were performed on groundwater samples from the unconfined and confined aquifers; the dual isotopic analysis approach was used in order to increase the chances of identifying the source of nitrate. The isotopic ratios for the groundwater samples with low nitrate concentration suggested a natural origin with a strong contribution of anthropogenic atmospheric NOx. For the samples with nitrate concentration above the expected background level the isotopic ratios did not correspond to any source documented in the literature. Dissolved RDX samples were degraded in the laboratory and results showed that all reproduced degradation processes released nitrate with a strong fractionation. Laboratory isotopic values for RDX-derived NO(3)(-) produced a trend of high delta(18)O-low delta(15)N to low delta(18)O-high delta(15)N, and groundwater samples with nitrate concentrations above the expected background level appeared along this trend. Our results thus point toward a characteristic field of isotopic ratios for nitrate being derived from the degradation of RDX.     

Quantification of groundwater contamination in an urban area using integral pumping tests

In this paper, the integral groundwater investigation method is used for the quantification of PCE and TCE mass flow rates at an industrialized urban area in Linz, Austria. In this approach, pumping wells positioned along control planes perpendicular to the groundwater flow direction are operated for a time period on the order of days and sampled for contaminants. The concentration time series of the contaminants measured during operation of the pumping wells are then used to determine contaminant mass flow rates, mean concentrations and the plume shapes and positions at the control planes. The three control planes used in Linz were positioned downstream of a number of potential source zones, which are distributed over the field site. By use of the integral investigation method, it was possible to identify active contaminant sources, quantify the individual source strength in terms of mass flow rates at the control planes and estimate the contaminant plume position relative to the control planes. The source zones emitting the highest PCE and TCE mass flow rates could be determined, representing the areas where additional investigation and remediation activities will be needed. Additionally, large parts of the area investigated could be excluded from further investigation and remediation activities.     

Mathematical Model for Pollution Source Identification in Rivers

Release of pollution into rivers is required to be handled with special consideration to environmental standards. For this purpose, it is essential to specify the contribution of each pollution source in contamination of water resources. In this study, a mathematical model is proposed for determining locations and concentration release histories of polluting point sources using measured downstream river concentrations via an inverse problem framework. The inverse solution is based on the integral equation obtained from applying the Green's function method on the one-dimensional advection-dispersion contaminant transport equation. Discretization of this integral equation results in a linear, over-determined and ill-posed system of algebraic equations that are solved by using the Tikhonov regularization method. Several examples and some real field data are investigated to illustrate the abilities of the proposed model. Results imply that the proposed method is effective and can identify the pollution sources in rivers with acceptable accuracy.     

Surrogate Model-Based Simulation-Optimization Approach for Groundwater Source Identification Problems

This study investigates and discusses a time-efficient technology that contains a surrogate model within a simulation-optimization model to identify the characteristics of groundwater pollutant sources. In the proposed surrogate model, Latin hypercube sampling (a stratified sampling approach) and artificial neural network (commencing at the stress period when the concentration is within a certain range, and ending at the peak time) were utilized to reduce workload and costly computing time. The results of a comparison between the proposed surrogate model and the common artificial neural network model and non-surrogate model indicated that the proposed model is a time-efficient technology which could be used to solve groundwater source identification problems.