Multi-stage optimal design for groundwater remediation: A hybrid bi-level programming approach

This paper presents the development of a hybrid bi-level programming approach for supporting multi-stage groundwater remediation design. To investigate remediation performances, a subsurface model was employed to simulate contaminant transport. A mixed-integer nonlinear optimization model was formulated in order to evaluate different remediation strategies. Multivariate relationships based on a filtered stepwise clustering analysis were developed to facilitate the incorporation of a simulation model within a nonlinear optimization framework. By using the developed statistical relationships, predictions needed for calculating the objective function value can be quickly obtained during the search process. The main advantage of the developed approach is that the remediation strategy can be adjusted from stage to stage, which makes the optimization more realistic. The proposed approach was examined through its application to a real-world aquifer remediation case in western Canada. The optimization results based on this application can help the decision makers to comprehensively evaluate remediation performance.     

A coupled simulation-optimization approach for groundwater remediation design under uncertainty: An application to a petroleum-contaminated site

This study provides a coupled simulation–optimization approach for optimal design of petroleum-contaminated groundwater remediation under uncertainty. Compared to the previous approaches, it has the advantages of: (1) addressing the stochasticity of the modeling parameters in simulating the flow and transport of NAPLs in groundwater, (2) providing a direct and response-rapid bridge between remediation strategies (pumping rates) and remediation performance (contaminant concentrations) through the created proxy models, (3) alleviating the computational cost in searching for optimal solutions, and (4) giving confidence levels for the obtained optimal remediation strategies. The approach is applied to a practical site in Canada for demonstrating its performance. The results show that mitigating the effects of uncertainty on optimal remediation strategies (through enhancing the confidence level) would lead to the rise of remediation cost due to the increase in the total pumping rate.     

A statistical approach for estimating uncertainty in dispersion modeling: An example of application in southwestern USA

A method based on a statistical approach of estimating uncertainty in simulating the transport and dispersion of atmospheric pollutants is developed using observations and modeling results from a tracer experiment in the complex terrain of the southwestern USA. The method takes into account the compensating nature of the error components by representing all terms, except dispersion error and variance of stochastic processes. Dispersion error and the variance of the stochastic error are estimated using the maximum likelihood estimation technique applied to the equation for the fractional error. Mesoscale Model 5 (MM5) and a Lagrangian random particle dispersion model with three optional turbulence parameterizations were used as a test bed for method application. Modeled concentrations compared well with the measurements (correlation coefficients on the order of 0.8). The effects of changing two structural components (the turbulence parameterization and the model grid vertical resolution) on the magnitude of the dispersion error also were examined. The expected normalized dispersion error appears to be quite large (up to a factor of three) among model runs with various turbulence schemes. Tests with increased vertical resolution of the atmospheric model (MM5) improved most of the dispersion model statistical performance measures, but to a lesser extent compared to selection of a turbulence parameterization. Method results confirm that structural components of the dispersion model, namely turbulence parameterizations, have the most influence on the expected dispersion error.     

The green-agile supplier selection problem for the medical devices: a hybrid fuzzy decision-making approach

Environmental Science and Pollution Research - The supplier selection problem (SSP) is known as one of the major issues in the supply chain management area. In this field, the literature shows that...     

A GIS-based approach for geospatial modeling of groundwater vulnerability and pollution risk mapping in Bou-Areg and Gareb aquifers,northeastern Morocco

Environmental Science and Pollution Research - Groundwater resources are the main supply of freshwater for human activities. Nevertheless, during the last 50 years, groundwater has become very...     

An integrated multivariate statistical approach for the evaluation of spatial variations in groundwater quality near an unlined landfill

Environmental Science and Pollution Research - Groundwater is a major resource for water supply in Canada, and 43 of 68 Saskatchewan municipalities rely on groundwater or combined groundwater and...     

Characterization and optimization of long-term controlled release system for groundwater remediation: a generalized modeling approach

A well-based reactive barrier system using controlled-release KMnO4 has been recently developed as a long-term in situ treatment option for plumes of dense and non-aqueous phase liquids in groundwater. In order to take advantage of the merits of controlled release systems (CRS) in environmental remediation, the release behavior needs to be optimized for the hydrologic and environmental conditions of target treatment zone. Where release systems are expected to be operated over long times, like for the reactive barriers, it may only be practical to describe the long-term behavior numerically. We developed a numerical model capable of describing release characteristics associated with variable forms and structures of long-term CRS. Sensitivity analyses and illustrative simulations showed that the release kinetics and durations would be constrained by changes in agent solubility, bulk diffusion coefficients, or structures of the release devices. The generality of the numerical model was demonstrated through simulations for CRS with monolithic and double-layered matrices. The generalized model was then used for actual design and analyses of an encapsulated-matrix CRS, which can yield constant release kinetics for several years. A well-based reactive barrier system (4.05 x 10(3)m3) using the encapsulated-matrix CRS can release approximately 1.65 kg of active agent (here MnO4(-)) daily over the next 6.6 yr, creating prolonged reaction zone in the subsurface. The generalized model-based, target-specific approach using the long-term CRS could provide practical tool for improving the efficacy of advanced in situ remediation schemes such as in situ chemical oxidation, bioremediation, or in situ redox manipulation. Development of techniques for adjusting the bulk diffusion coefficients of the release matrices and facilitating the lateral spreading of the released agent is warranted.     

Site selection for waste vegetable oil and waste battery collection boxes: a GIS-based hybrid hesitant fuzzy decision-making approach

Environmental Science and Pollution Research - The amount of municipal solid waste (MSW) has been increasing rapidly in the urban centres of developing countries during the last few decades;...     

A generalized fuzzy linear programming approach for environmental management problem under uncertainty

In this study, a generalized fuzzy linear programming (GFLP) method was developed to deal with uncertainties expressed as fuzzy sets that exist in the constraints and objective function. A stepwise interactive algorithm (SIA) was advanced to solve GFLP model and generate solutions expressed as fuzzy sets. To demonstrate its application, the developed GFLP method was applied to a regional sulfur dioxide (SO2) control planning model to identify effective SO2 mitigation polices with a minimized system performance cost under uncertainty. The results were obtained to represent the amount of SO2 allocated to different control measures from different sources. Compared with the conventional interval-parameter linear programming (ILP) approach, the solutions obtained through GFLP were expressed as fuzzy sets, which can provide intervals for the decision variables and objective function, as well as related possibilities. Therefore, the decision makers can make a tradeoff between model stability and the plausibility based on solutions obtained through GFLP and then identify desired policies for SO2-emission control under uncertainty.     

A semiparametric statistical approach for forecasting SO2 and NO x concentrations

This study develops a new semiparametric statistical approach for urban air quality forecasting. Compared to conventional approaches, the semiparametric approach allows the model users to benefit from the positive aspects and alleviate the negative ones of parametric and nonparametric approaches. Two advantages of the approach lie in (1) the interpretation of the data set being easily decoded and used by the model and (2) its capability in dependence on prior assumption. To illustrate the performance of the proposed approach, three semiparametric regression models (i.e., linear-, quadratic-, and interactive-based semiparametric regression) are applied to an air quality forecasting problem in the city of Xiamen, China, and satisfactory training and prediction performance are obtained. The three models are also compared to three parametric and two nonparametric regression models. The results indicate that the predictive accuracy of semiparametric regression models is higher than those obtained from the parametric and stepwise cluster analysis models. However, the proposed three semiparametric regression models could be much favored, since they can be achieved more easily and rapidly than the artificial neural network model.     

A regional flux-based risk assessment approach for multiple contaminated sites on groundwater bodies

In the context of the Water Framework Directive (EP and CEU, 2000), management plans have to be set up to monitor and to maintain water quality in groundwater bodies in the EU. In heavily industrialized and urbanized areas, the cumulative effect of multiple contaminant sources is likely and has to be evaluated. In order to propose adequate measures, the calculated risk should be based on criteria reflecting the risk of groundwater quality deterioration, in a cumulative manner and at the scale of the entire groundwater body. An integrated GIS- and flux-based risk assessment approach for groundwater bodies is described, with a regional scale indicator for evaluating the quality status of the groundwater body. It is based on the SEQ-ESO currently used in the Walloon Region of Belgium which defines, for different water uses and for a detailed list of groundwater contaminants, a set of threshold values reflecting the levels of water quality and degradation with respect to each contaminant. The methodology is illustrated with first results at a regional scale on a groundwater body-scale application to a contaminated alluvial aquifer which has been classified to be at risk of not reaching a good quality status by 2015. These first results show that contaminants resulting from old industrial activities in that area are likely to contribute significantly to the degradation of groundwater quality. However, further investigations are required on the evaluation of the actual polluting pressures before any definitive conclusion be established.     

A statistical approach to the regional use of critical loads

The premise of this paper is that: (1) effects of spatial heterogeneity of watershed response to acid deposition must be considered when models are used to set abatement policies, and (2) the evaluation of critical chemical values is a better measure off the effects of abatement policies than the comparison of deposition values to critical loads. The authors used Monte Carlo methods to apply a site-specific version of the RAINS-Lake-Model to a regional data set from The Netherlands. Statistical methods were then used to identify the important parameters affecting the spatial and temporal response, i.e. a change in pH, of watersheds to acid deposition and a subset of sensitive and insensitive watersheds were derived. The results show that the failure to subset a region into sensitive and insensitive zones may result in an erroneous estimation of the effect of abatement policies based on critical loads alone.     

A statistical approach applied to trace metal data from biomonitoring studies

This work underlines the importance of the statistical procedures necessary for the significant assessment of analytical data of heavy metals derived from marine organisms (biomonitors) collected at five stations in Ustica at the Southern Tyrrhenian Sea. The statistical method described indicates that, where the differences among the averages of Pb, Cu and Zn concentrations in the molluscs of the sites analysed tested with ANOVA are significant, it is necessary to conduct multiple comparison tests in order to determine which average is significantly different from the other. Using the method described, it is possible to indicate which site or sites is/are more contaminated in comparison with the other sites. This way it is possible to make a preliminary evaluation of the basal levels of trace metals present in the reference marine ecosystem under study.     

Material selection for landfill leachate piping by using a grey target decision-making approach

Landfill leachate, due to its quantity and inherent risk, is generally collected and transported by piping system for advanced treatment. During the piping, the pipe materials may react with leachate, resulting in corrosion and scaling. In order to reduce possible failures and mitigate the associated consequences, this study provides an indicator system for material selection to aid the pipe system design. The material functional, economic, and environmental attributes are incorporated into the indicator system, to perform a precise selection of commercial drainage pipe materials, thus improving empirically oriented selection. Four common drainage pipe materials including high-density polyethylene (HDPE), polyvinyl chloride (PVC), galvanized steel, and seamless steel are taken as the material alternatives for the selection. Based upon their experimental data, a grey target decision-making framework is employed to perform the priority ranking of the materials. The results indicate that HDPE has the best performance, followed by PVC, galvanized steel, and seamless steel. This study discusses the validity of the selection results and the applicability of the proposed method, to provide insight into  leachate piping system design.

     

Pulsed gas injection: A minimum effort approach for enhanced natural attenuation of chlorobenzene in contaminated groundwater

Chlorobenzene-contaminated groundwater was used to assess pulsed gas sparging as a minimum effort aeration strategy to enhance intrinsic natural attenuation. In contrast to existing biosparging operations, oxygen was supplied at minimum rate by reducing the gas injection frequency to 0.33 day^?1. Field tests in a model aquifer were conducted in a 12 m long reactor, filled with indigenous aquifer material and continuously recharged with polluted groundwater over 3 years. The closed arrangement allowed yield balances, cost accounting as well as the investigation of spatial distributions of parameters which are sensitive to the biodegradation process. Depending on the injection frequency and on the gas chosen for injection (pure oxygen or air) oxygen-deficient conditions prevailed in the aquifer. Despite the limiting availability of dissolved oxygen in the groundwater, chlorobenzene degradation under oxygen-deficient conditions proved to be more effective than under conditions with dissolved oxygen being available in high concentrations.     

Tools based on multivariate statistical analysis for classification of soil and groundwater in Apulian agricultural sites

In this paper, the results obtained from multivariate statistical techniques such as PCA (Principal component analysis) and LDA (Linear discriminant analysis) applied to a wide soil data set are presented. The results have been compared with those obtained on a groundwater data set, whose samples were collected together with soil ones, within the project “Improvement of the Regional Agro-meteorological Monitoring Network (2004–2007)”. LDA, applied to soil data, has allowed to distinguish the geographical origin of the sample from either one of the two macroaeras: Bari and Foggia provinces vs Brindisi, Lecce e Taranto provinces, with a percentage of correct prediction in cross validation of 87%. In the case of the groundwater data set, the best classification was obtained when the samples were grouped into three macroareas: Foggia province, Bari province and Brindisi, Lecce and Taranto provinces, by reaching a percentage of correct predictions in cross validation of 84%. The obtained information can be very useful in supporting soil and water resource management, such as the reduction of water consumption and the reduction of energy and chemical (nutrients and pesticides) inputs in agriculture.     

Exposure assessment of pesticides in a shallow groundwater of the Tagus vulnerable zone (Portugal): a multivariate statistical approach (JCA)

Purpose

To assess groundwater exposure to pesticides, in agricultural areas of ??Ribatejo?? region (Portugal), and the influence of some key factors in that exposure, field, laboratory and modelling studies were carried out.

Methods

The study was performed in maize, potato, sugar beet, tomato and vegetables agricultural areas, located in a shallow aquifer, with pesticides use and, in most cases, with irrigation practices. Pesticides used in the studied agricultural areas and having leaching potential were selected, being considered also other pesticides included in priority lists, defined in Europe. Evaluation of groundwater exposure to pesticides was carried out by successively: (1) groundwater sampling in seven campaigns over the period 2004?C2006; (2) pesticide analysis [including isolation and concentration from the groundwater samples and further determination by gas chromatography?Cmass spectrometry (GC?CMS) of 14 herbicides, four insecticides and two metabolites]; and (3) analysis and discussion of the results by applying joint correspondence analysis (JCA).

Results

From the 20 pesticides and metabolites selected for the study, 11 were found in groundwater. Pesticides and metabolites most frequently detected were atrazine, alachlor, metolachlor, desethylatrazine, ethofumesate, ??-endosulfan, metribuzine, lindane and ??-endosulfan. The results showed that groundwater exposure to pesticides is influenced by local factors??either environmental or agricultural, as precipitation, soil, geology, crops and irrigation practices. Spring and autumn were more associated with the detection of pesticides being more likely to observe mixtures of these compounds in a groundwater sample in these transition seasons.

Conclusions

This work evidences the importance of models, which evaluate pesticides environmental behaviour, namely their water contamination potential (as Mackay multicompartimental fugacity model) and, specially, groundwater contamination potential (as GUS and Bacci and Gaggi leaching indices), in pesticide selection. Moreover, it reveals the importance to adapt proper statistical methods according to level of left-censored data. Using JCA was still possible to establish relations between pesticides and their temporal trend in a case study where there were more than 80% of data censored. This study will contribute to the Tagus river basin management plan with information on the patterns of pesticide occurrence in the alluvial aquifer system.     

A hybrid modeling approach to resolve pollutant concentrations in an urban area

A modeling tool that can resolve contributions from individual sources to the urban environment is critical for air-toxics exposure assessments. Air toxics are often chemically reactive and may have background concentrations originated from distant sources. Grid models are the best-suited tools to handle the regional features of these chemicals. However, these models are not designed to resolve pollutant concentrations on local scales. Moreover, for many species of interest, having reaction time scales that are longer than the travel time across an urban area, chemical reactions can be ignored in describing local dispersion from strong individual sources making Lagrangian and plume-dispersion models practical. In this study, we test the feasibility of developing an urban hybrid simulation system. In this combination, the Community Multi-scale Air Quality model (CMAQ) provides the regional background concentrations and urban-scale photochemistry, and local models such as Hybrid Single Particle Lagrangian Integrated Trajectory model (HYSPLIT) and AMS/EPA Regulatory Model (AERMOD) provide the more spatially resolved concentrations due to local emission sources. In the initial application, the HYSPLIT, AERMOD, and CMAQ models are used in combination to calculate high-resolution benzene concentrations in the Houston area. The study period is from 18 August to 4 September of 2000. The Mesoscale Model 5 (MM5) is used to create meteorological fields with a horizontal resolution of 1×1 km². In another variation to this approach, multiple HYSPLIT simulations are used to create a concentration ensemble to estimate the contribution to the concentration variability from point sources. HYSPLIT simulations are used to model two sources of concentration variability; one due to variability created by different particle trajectory pathways in the turbulent atmosphere and the other due to different flow regimes that might be introduced when using gridded data to represent meteorological data fields. The ensemble mean concentrations determined by HYSPLIT plus the concentrations estimated by AERMOD are added to the CMAQ calculated background to estimate the total mean benzene concentration. These estimated hourly mean concentrations are also compared with available field measurements.     

A statistical model for predicting carbon monoxidelevels

This paper presents a statistical model that is able to predict carbon monoxide (CO) concentrations as a function of meteorological conditions and various air quality parameters. The experimental work was conducted in an urban atmosphere, where the emissions from cars are prevalent. A mobile air pollution monitoring laboratory was used to collect data, which were divided into two groups: a development group and a testing group. Only the development dataset was used for developing the model. The model was determined by using a stepwise multiple regression modelling procedure. Thirteen independent variables were selected as inputs: non-methane hydrocarbon (NMHC), methane (CH₄), suspended dust, carbon dioxide (CO₂), nitrogen oxide (NO), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ozone (O₃), wind speed, wind direction, temperature, relative humidity and solar energy. It was found that NO has the most effect on the predicted CO concentration. The contribution of NO to the CO concentration variations was 91.3%. Adding in the terms for NO₂), NMHC and CH₄ improved the model by only a further 2.3%. The derived model was shown to be statistically significant, and model predictions and experimental observations were shown to be consistent.     

Improvement of sampling strategies for randomly distributed hotspots in soil applying a computerized simulation considering the concept of uncertainty

Introduction  

The pollution of soil and environment as a result of human activity is a major problem. Nowadays, the determination of local contaminations is of interest for environmental remediation. These hotspots can have various toxic effects on plants, animals, humans, and the whole ecological system. However, economical and juridical consequences are also possible, e.g., high costs for remediation measures.