Groundwater pollution source apportionment using principal component analysis in a multiple land-use area in southwestern China

Identification of different pollution sources in groundwater is challenging, especially in areas with diverse land uses and receiving multiple inputs. In this study, principal component analysis (PCA) was combined with geographic information system (GIS) to explore the spatial and temporal variation of groundwater quality and to identify the sources of pollution and main factors governing the quality of groundwater in a multiple land-use area in southwestern China. Groundwater samples collected from 26 wells in 2012 and 38 wells in 2018 were analyzed for 13 water quality parameters. The PCA results showed that the hydro-geochemical process was the predominant factor determining groundwater quality, followed by agricultural activities, domestic sewage discharges, and industrial sewage discharges. Agriculture expansion from 2012 to 2018 resulted in increased apportionment of agricultural pollution. In contrast, economic restructure and infrastructure improvement reduced the contributions of domestic sewage and industrial pollution. Anthropogenic activities were found the major causes of elevated nitrogen concentrations (NO₃^?, NO₂^?, NH₄⁺) in groundwater, highlighting the necessity of controlling N sources through effective fertilizer managements in agricultural areas and reducing sewage discharges in urban areas. The applications of GIS and PCA successfully identified the sources of pollutants and major factors driving the variations of groundwater quality in tested years.

     

Redundancy analysis for characterizing the groundwater quality in coastal industrial areas

Groundwater quality in coastal area has been an issue of interest because of excessive groundwater extraction for human use, for example, industrialization, irrigation, which can lead to saltwater intrusion. The study develops an integrated data analysis procedure based on multivariate statistics principal component analysis (PCA), hierarchical cluster analysis (HCA) and redundancy analysis (RDA), to determine the effects of key environmental conditions on the formulation of groundwater pollutants. This proposed method was demonstrated by analyzing groundwater quality monitoring data collected between 2011 and 2014 from four coastal industrial areas in Changhua county of Taiwan, namely Chuansing, Xianxi, Lukang and Fangyuan industrial parks. First, different environmental conditions in each industrial region were explored by PCA. The spatial hierarchy and spatial distribution of pollutant categories were then identified using HCA with the kriging method. Finally, the effect of environmental conditions on constitutive pollutants were identified with RDA. The three environmental patterns identified from the analytical results in Chuansing, Lukang and Xianxi were the salination factor (including conductivity and general hardness (GH)), water level and redox condition (including dissolved oxygen and oxidation–reduction potential). Fangyuan industrial park had only two patterns, namely salination (including conductivity and GH) and oxygen content (including DO and depth). The pollutant category indicated high concentrations of all pollutants in Chuansing and Fangyuan, and higher concentration of SO₄^2?, TDS, Cl^? in Xianxi, and of NH₃-N, Mn, Fe and TOC in Lukang. According to RDA results, salination caused the high concentrations of NH₃N, Cl^?, TDS in Chuansing, and of Cl^?, TDS and SO₄^2? in Xianxi and Lukang. Additionally, salination caused high concentrations of Fe in both Lukang and Fangyuan industrial parks in combination with those three pollutants. The redox condition was linked to high content of NO₃^? in Chuansing and Lukang, and of TOC in Xianxi. In Fangyuan industrial park, NO₃^? was also linked to high oxygen concentration. In summary, the combination of PCA, HCA and RDA enables the analysis of monitoring data to support policy decision-making.     

Hydrochemical characteristics and identification of groundwater pollution sources in tropical savanna

Groundwater pollution of the watershed is mainly influenced by the multifaceted interactions of natural and anthropogenic processes. In this study, classic chemical and multivariate statistical methods were utilized to assess the groundwater quality and ascertain the potential contamination sources affecting the groundwater quality of Galma sub-watershed in a tropical savanna. For this purpose, the data set of 18 groundwater quality variables covering 57 different sampling boreholes (BH) was used. The groundwater samples essentially contained the cations in the following order of dominance: Ca²⁺ ?>?Na⁺ ?>?Mg²⁺ ?>?K⁺. However, the anions had HCO₃^–?>?Cl^–?>?SO₄^–2?>?NO₃^– respectively. The hydrochemical facies classified the groundwater types of the sub-watershed into mixed Ca–Mg–Cl type of water, which means no cations and anions exceeds 50%. The second dominant water type was Ca–Cl. The Mg–HCO₃ water type was found in BH 9, and Na–Cl water type in BH 29 of the studied area. The weathering of the basement rocks was responsible for the concentrations of these ions in the groundwater chemistry of the sub-watershed. Hierarchical cluster analysis (HCA) grouped the groundwater samples (boreholes) into five clusters that are statistically significant regarding the similarities of groundwater quality characteristics. The principal component analysis (PCA) extracted two major principal components explained around 65% of the variance and suggested the natural and anthropogenic processes especially the agricultural pollutants including synthetic fertilizers, and leaching of agricultural waste as the main factors affecting the groundwater quality. The integrated method proved to be efficient and robust for groundwater quality evaluation, as it guaranteed the precise assessment of groundwater chemistry in the sub-watershed of the tropical savanna. The findings of this investigation could be useful to the policy makers for developing effective groundwater management plans for the groundwater resources and protection of the sub-watershed.

     

Groundwater quality assessment and pollution source apportionment in an intensely exploited region of northern China

Deterioration in groundwater quality has attracted wide social interest in China. In this study, groundwater quality was monitored during December 2014 at 115 sites in the Hutuo River alluvial-pluvial fan region of northern China. Results showed that 21.7% of NO₃ ^? and 51.3% of total hardness samples exceeded grade III of the national quality standards for Chinese groundwater. In addition, results of gray relationship analysis (GRA) show that 64.3, 10.4, 21.7, and 3.6% of samples were within the I, II, IV, and V grades of groundwater in the Hutuo River region, respectively. The poor water quality in the study region is due to intense anthropogenic activities as well as aquifer vulnerability to contamination. Results of principal component analysis (PCA) revealed three major factors: (1) domestic wastewater and agricultural runoff pollution (anthropogenic activities), (2) water-rock interactions (natural processes), and (3) industrial wastewater pollution (anthropogenic activities). Using PCA and absolute principal component scores-multivariate linear regression (APCS-MLR), results show that domestic wastewater and agricultural runoff are the main sources of groundwater pollution in the Hutuo River alluvial-pluvial fan area. Thus, the most appropriate methods to prevent groundwater quality degradation are to improve capacities for wastewater treatment and to optimize fertilization strategies.     

Groundwater quality in Imphal West district, Manipur, India, with multivariate statistical analysis of data

The aim of this paper was to analyze the groundwater quality of Imphal West district, Manipur, India, and assess its suitability for drinking, domestic, and agricultural use. Eighteen physico-chemical variables were analyzed in groundwater from 30 different hand-operated tube wells in urban, suburban, and rural areas in two seasons. The data were subjected to uni-, bi-, and multivariate statistical analysis, the latter comprising cluster analysis (CA), principal component analysis (PCA), and factor analysis (FA). Arsenic concentrations exceed the Indian standard in 23.3 % and the WHO limit in 73.3 % of the groundwater sources with only 26.7 % in the acceptable range. Several variables like iron, chloride, sodium, sulfate, total dissolved solids, and turbidity are also beyond their desirable limits for drinking water in a number of sites. Sodium concentrations and sodium absorption ratio (SAR) are both high to render the water from the majority of the sources unsuitable for agricultural use. Multivariate statistical techniques, especially varimax rotation of PCA data helped to bring to focus the hidden yet important variables and understand their roles in influencing groundwater quality. Widespread arsenic contamination and high sodium concentration of groundwater pose formidable constraints towards its exploitation for drinking and other domestic and agricultural use in the study area, although urban anthropogenic impacts are not yet pronounced.     

Multivariate analysis of Almendares River waters.

The Almendares River is the most important surface water body of the Cuban capital, Havana. In the present work, the environmental quality of waters was studied as a function of the following 14 variables: content of calcium, cadmium, iron, potassium, magnesium, manganese, sodium, nickel, zinc, chlorine, bicarbonate, and sulfate; pH; and electric conductivity parameters, which were reduced to three new variables by means of principal component analysis (PCA). The content of metal increased in waters sampled at stations located near garbage dumps and decreased inside the Ejercito Rebelde dam. The variation of the river water environmental quality with rainy and dry seasons and the differentiation of samples in three groups along the river course were obtained by PCA and corroborated by discriminant analysis. Applied statistical techniques showed their ability for environmental interpretation of limited experimental data.     

Spatial variability of arsenic and chromium in the soil water at a former wood preserving site

Contamination of industrial sites by wood preservatives such as chromated copper arsenate (CCA) may pose a serious threat to groundwater quality. The objective of this study was to characterise the spatial variability of As and Cr concentrations in the solid phase and in the soil water at a former wood impregnation plant and to reveal the fundamental transport processes. The soil was sampled down to a depth of 2m. The soil water was extracted in situ from the vadose zone over a period of 10 months at depths of 1 and 1.5m, using large horizontally installed suction tubes. Groundwater was sampled from a depth of 4.5m. Results showed that arsenic and chromium had accumulated in the upper region of the profile and exhibited a high spatial variability (As: 21-621 mg kg(-1); Cr: 74-2872 mg kg(-1)). Concentrations in the soil water were high (mean As 167 microg L(-1); Cr: 62 microg L(-1)) and also showed a distinct spatial variability, covering concentration ranges up to three orders of magnitude. The variability was caused by the severe water-repellency of the surface soil, induced by the concurrent application of creosote wood preservatives, which leads to strong preferential flow as evident from a dye experiment. In contrast to soil water concentrations, only low As concentrations (<12 microg L(-1)) were detected in the groundwater. High Cr concentrations in the groundwater (approx. 300 microg L(-1)), however, illustrated the pronounced mobility of chromium. Our study shows that at sites with a heterogeneous flow system in the vadose zone a disparity between flux-averaged and volume-averaged concentrations may occur, and sampling of soil water might not be adequate for assessing groundwater concentrations. In these cases long-term monitoring of the groundwater appears to be the best strategy for a groundwater risk assessment.     

Assessment and rationalization of water quality monitoring network: a multivariate statistical approach to the Kabbini River (India)

The establishment of an efficient surface water quality monitoring (WQM) network is a critical component in the assessment, restoration and protection of river water quality. A periodic evaluation of monitoring network is mandatory to ensure effective data collection and possible redesigning of existing network in a river catchment. In this study, the efficacy and appropriateness of existing water quality monitoring network in the Kabbini River basin of Kerala, India is presented. Significant multivariate statistical techniques like principal component analysis (PCA) and principal factor analysis (PFA) have been employed to evaluate the efficiency of the surface water quality monitoring network with monitoring stations as the evaluated variables for the interpretation of complex data matrix of the river basin. The main objective is to identify significant monitoring stations that must essentially be included in assessing annual and seasonal variations of river water quality. Moreover, the significance of seasonal redesign of the monitoring network was also investigated to capture valuable information on water quality from the network. Results identified few monitoring stations as insignificant in explaining the annual variance of the dataset. Moreover, the seasonal redesign of the monitoring network through a multivariate statistical framework was found to capture valuable information from the system, thus making the network more efficient. Cluster analysis (CA) classified the sampling sites into different groups based on similarity in water quality characteristics. The PCA/PFA identified significant latent factors standing for different pollution sources such as organic pollution, industrial pollution, diffuse pollution and faecal contamination. Thus, the present study illustrates that various multivariate statistical techniques can be effectively employed in sustainable management of water resources. Highlights ? The effectiveness of existing river water quality monitoring network is assessed ? Significance of seasonal redesign of the monitoring network is demonstrated ? Rationalization of water quality parameters is performed in a statistical framework     

Parametrization of groundwater quality of the Quaternary aquifer in N’Djamena (Chad), Lake Chad Basin: application of numerical and multivariate analyses

The urban groundwater of the Quaternary aquifer of the Lake Chad basin in N’Djamena has been subject to many hydrochemical studies. However, the results are often not presented in a way that enables water quality managers to make an appropriate decisions, which restrict development and poverty reduction efforts. The objective of the present study was to contribute the improved management of the local groundwater resources. A total of 85 groundwater samples were interpreted using hydrochemical techniques associated with integrated numerical indices and multivariate statistical analysis. The hydrochemical results coupled with the relative residence time of water have shown that the chemical composition of these waters is linked to geogenic and anthropogenic factors and to their proximity to the Chari-Logone rivers. These investigations showed that the groundwater quality in N’Djamena is characterized by a high spatial variability. This study also assessed the suitability of groundwater for user needs and identified areas which are more/less favorable for a specific use. The evaluation of water quality and its suitability for human consumption is also a problem of optimizing data acquisition strategy, and this study used the correlation between water quality index (WQI) and electrical conductivity (EC) to orientate future data acquisition strategies. This parametrization can assist the decision makers and water management professionals in evaluating groundwater availability and setting up a robust water quality management plan in areas with similar hydrogeological and climatic conditions.

     

Contaminated site remedial investigation and feasibility removal of chlorinated volatile organic compounds from groundwater by activated carbon fiber adsorption

Groundwater contaminated by dense, non-aqueous phase liquids (DNAPLs) such as chlorinated solvents has become a serious problem in some regions of Taiwan. The sources of these contaminants are due to industrial discharges. These chlorinated volatile organic compounds (VOCs) have been proven to be carcinogenic to humans. The groundwater is used for domestic drinking water supply in some cities of Taiwan and the severely contaminated groundwater has to be treated in order to meet the requirement of drinking water standards. This study covers two areas of work. In the first part, polluted groundwater samples were collected from the contaminated site and analytical results indicated measurable concentrations of 12 representative chlorinated VOCs in water samples. The primary VOCs detected included trichloroethene (TCE), tetrachloroethene (PCE), 1,1,2-trichloroethane (1,1,2-TCA), and 1,1-dichloroethene (1,1-DCE). Second, to remove VOCs groundwater was treated using adsorption on activated carbon fiber (ACF). This involved pumping groundwater through vessels containing ACF. Most VOCs, including TCE, PCE, 1,1,2-TCA, and DCE, were readily adsorbed onto ACF and are removed from the water stream. Our study showed that the technology was able to significantly reduce chlorinated VOCs concentrations in groundwater.     

Assessment of Aquifer Vulnerability in an Agricultural Area in Spain Using the DRASTIC Model

The assessment of aquifer vulnerability is a very important task, especially in agricultural areas because the quality and availability of groundwater affects both the sustainability of agriculture and the quality of life. In this study, an integrated approach is considered, with the use of the generic and agricultural DRASTIC models as well as a geographic information system (GIS), to assess groundwater vulnerability in the agricultural area of Barrax, in the province of Albacete, in Spain. Seven parameters—depth to water, net recharge, aquifer media, soil media, topography, impact of vadose zone media, and hydraulic conductivity of the aquifer (DRASTIC)—have been considered as weighted layers to enable an accurate groundwater risk mapping. The results of the generic DRASTIC model indicated very low vulnerability to contamination for Barrax groundwater due to limited urban and industrial development in the wider area. However, agricultural activities impose pressure to groundwater resources and the results of the agricultural DRASTIC model show that 6.86% of the study area is characterized by very high, 2.29% by high, 47.28% by medium, 38.28% by low, and the remaining 5.29% by no vulnerability to groundwater contamination. The distribution of nitrate concentration in groundwater in the area under study is quite well correlated with the agricultural DRASTIC vulnerability index. Sensitivity analysis was also performed to acknowledge statistical uncertainty in the estimation of each parameter used, to assess its impact, and thus to identify the most critical parameters that require further investigation. Depth to water and impact of vadose zone are the parameters that had the most noticeable impact on the generic DRASTIC vulnerability index followed by the soil media and topography. In contrast, the agricultural DRASTIC method is more sensitive to the removal of the depth to water parameter followed by the topography and the soil media parameters.     

Characterizing and locating air pollution sources in a complex industrial district using optical remote sensing technology and multivariate statistical modeling

Emissions of volatile organic compounds (VOCs) are most frequent environmental nuisance complaints in urban areas, especially where industrial districts are nearby. Unfortunately, identifying the responsible emission sources of VOCs is essentially a difficult task. In this study, we proposed a dynamic approach to gradually confine the location of potential VOC emission sources in an industrial complex, by combining multi-path open-path Fourier transform infrared spectrometry (OP-FTIR) measurement and the statistical method of principal component analysis (PCA). Close-cell FTIR was further used to verify the VOC emission source by measuring emitted VOCs from selected exhaust stacks at factories in the confined areas. Multiple open-path monitoring lines were deployed during a 3-month monitoring campaign in a complex industrial district. The emission patterns were identified and locations of emissions were confined by the wind data collected simultaneously. N,N-Dimethyl formamide (DMF), 2-butanone, toluene, and ethyl acetate with mean concentrations of 80.0?±?1.8, 34.5?±?0.8, 103.7?±?2.8, and 26.6?±?0.7 ppbv, respectively, were identified as the major VOC mixture at all times of the day around the receptor site. As the toxic air pollutant, the concentrations of DMF in air samples were found exceeding the ambient standard despite the path-average effect of OP-FTIR upon concentration levels. The PCA data identified three major emission sources, including PU coating, chemical packaging, and lithographic printing industries. Applying instrumental measurement and statistical modeling, this study has established a systematic approach for locating emission sources. Statistical modeling (PCA) plays an important role in reducing dimensionality of a large measured dataset and identifying underlying emission sources. Instrumental measurement, however, helps verify the outcomes of the statistical modeling. The field study has demonstrated the feasibility of using multi-path OP-FTIR measurement. The wind data incorporating with the statistical modeling (PCA) may successfully identify the major emission source in a complex industrial district.     

Influence of soil hydrological pathways on stream aluminium chemistry at Llyn Brianne, mid-wales

Storm runoff in afforested catchments at Llyn Brianne is acidic and Al-bearing. At baseflows, stream water is well-buffered with low Al levels. This paper presents the results of a study into how hydrological pathways account for these variations in stream-water chemistry. The investigation was carried out in the LI1 catchment; a 0.4-ha subcatchment covered by stagnohumic gley soils was monitored between October 1988 and September 1989. An instrumented hill-slope was established to identify the hydrological pathways that control the hydrochemistry of storm runoff draining from the subcatchment. Perched watertables developed in the surface horizons of the soil during storm episodes and produced lateral flow above the impeding subsoil. This near-surface flow path was responsible for generating acid, Al-rich storm runoff. Some water drained vertically through the soil profile into the underlying slope drift; seepage from groundwater in the drift sustained baseflows. Buffering reactions in the groundwater zone reduced the acidity and Al levels of baseflows. These hydrochemical characteristics are likely to be representative of other areas of stagnohumic gley soils, which cover 19% of the LI1 catchment: these soils may therefore provide a substantial source of acid, Al-bearing storm runoff in LI1 and similar afforested catchments.     

Occurrence of psychoactive compounds and their metabolites in groundwater downgradient of a decommissioned sewage farm in Berlin (Germany)

Purpose

Psychoactive compounds??meprobamate, pyrithyldione, primidone, and its metabolites, phenobarbital, and phenylethylmalonamide??were detected in groundwater within the catchment area of a drinking water treatment plant located downgradient of a former sewage farm in Berlin, Germany. The aim of this study was to investigate the distribution of the psychoactive compounds in anoxic groundwater and to assess the risk of drinking water contamination. Groundwater age was determined to achieve a better understanding of present hydrogeological conditions.

Methods

A large number of observation and production wells were sampled. Samples were analyzed using solid-phase extraction and ultrahigh-performance liquid chromatography?Ctandem mass spectrometry. Groundwater age was estimated using the helium?Ctritium (³He?C³H) dating method.

Results

Concentrations of psychoactive compounds up to 1???g/L were encountered in the contamination plume. Generally, concentrations of phenobarbital and meprobamate were the highest. Elevated concentrations of the analytes were also detected in raw water from abstraction wells located approximately 2.5?km downgradient of the former sewage farm. Concentrations in the final drinking water were below the limit of quantification owing to dilution. The age of shallow groundwater samples ranged from years to a decade, whereas groundwater was up to four decades old at 40?m below ground. Concentrations of the compounds increased with groundwater age.

Conclusions

Elevated concentrations of psychoactive drugs indicate a strong persistence of these compounds in the environment under anoxic aquifer conditions. Results suggest that the heritage of sewage irrigation will affect raw water quality in the area for decades. Therefore, further monitoring of raw and final drinking water is recommended to ensure that contaminant concentrations remain below the health-based precautionary value.     

Characterization of shallow groundwater quality in the Lower St. Johns River Basin: a case study

Characterization of groundwater quality allows the evaluation of groundwater pollution and provides information for better management of groundwater resources. This study characterized the shallow groundwater quality and its spatial and seasonal variations in the Lower St. Johns River Basin, Florida, USA, under agricultural, forest, wastewater, and residential land uses using field measurements and two-dimensional kriging analysis. Comparison of the concentrations of groundwater quality constituents against the US EPA’s water quality criteria showed that the maximum nitrate/nitrite (NO _x ) and arsenic (As) concentrations exceeded the EPA’s drinking water standard limits, while the maximum Cl, SO ₄ ^2?? , and Mn concentrations exceeded the EPA’s national secondary drinking water regulations. In general, high kriging estimated groundwater NH ₄ ⁺ concentrations were found around the agricultural areas, while high kriging estimated groundwater NO _x concentrations were observed in the residential areas with a high density of septic tank distribution. Our study further revealed that more areas were found with high estimated NO _x concentrations in summer than in spring. This occurred partially because of more NO _x leaching into the shallow groundwater due to the wetter summer and partially because of faster nitrification rate due to the higher temperature in summer. Large extent and high kriging estimated total phosphorus concentrations were found in the residential areas. Overall, the groundwater Na and Mg concentration distributions were relatively more even in summer than in spring. Higher kriging estimated groundwater As concentrations were found around the agricultural areas, which exceeded the EPA’s drinking water standard limit. Very small variations in groundwater dissolved organic carbon concentrations were observed between spring and summer. This study demonstrated that the concentrations of groundwater quality constituents varied from location to location, and impacts of land uses on groundwater quality variation were profound.     

Extreme learning machines: a new approach for modeling dissolved oxygen (DO) concentration with and without water quality variables as predictors

In this paper, several extreme learning machine (ELM) models, including standard extreme learning machine with sigmoid activation function (S-ELM), extreme learning machine with radial basis activation function (R-ELM), online sequential extreme learning machine (OS-ELM), and optimally pruned extreme learning machine (OP-ELM), are newly applied for predicting dissolved oxygen concentration with and without water quality variables as predictors. Firstly, using data from eight United States Geological Survey (USGS) stations located in different rivers basins, USA, the S-ELM, R-ELM, OS-ELM, and OP-ELM were compared against the measured dissolved oxygen (DO) using four water quality variables, water temperature, specific conductance, turbidity, and pH, as predictors. For each station, we used data measured at an hourly time step for a period of 4 years. The dataset was divided into a training set (70%) and a validation set (30%). We selected several combinations of the water quality variables as inputs for each ELM model and six different scenarios were compared. Secondly, an attempt was made to predict DO concentration without water quality variables. To achieve this goal, we used the year numbers, 2008, 2009, etc., month numbers from (1) to (12), day numbers from (1) to (31) and hour numbers from (00:00) to (24:00) as predictors. Thirdly, the best ELM models were trained using validation dataset and tested with the training dataset. The performances of the four ELM models were evaluated using four statistical indices: the coefficient of correlation (R), the Nash-Sutcliffe efficiency (NSE), the root mean squared error (RMSE), and the mean absolute error (MAE). Results obtained from the eight stations indicated that: (i) the best results were obtained by the S-ELM, R-ELM, OS-ELM, and OP-ELM models having four water quality variables as predictors; (ii) out of eight stations, the OP-ELM performed better than the other three ELM models at seven stations while the R-ELM performed the best at one station. The OS-ELM models performed the worst and provided the lowest accuracy; (iii) for predicting DO without water quality variables, the R-ELM performed the best at seven stations followed by the S-ELM in the second place and the OP-ELM performed the worst with low accuracy; (iv) for the final application where training ELM models with validation dataset and testing with training dataset, the OP-ELM provided the best accuracy using water quality variables and the R-ELM performed the best at all eight stations without water quality variables. Fourthly, and finally, we compared the results obtained from different ELM models with those obtained using multiple linear regression (MLR) and multilayer perceptron neural network (MLPNN). Results obtained using MLPNN and MLR models reveal that: (i) using water quality variables as predictors, the MLR performed the worst and provided the lowest accuracy in all stations; (ii) MLPNN was ranked in the second place at two stations, in the third place at four stations, and finally, in the fourth place at two stations, (iii) for predicting DO without water quality variables, MLPNN is ranked in the second place at five stations, and ranked in the third, fourth, and fifth places in the remaining three stations, while MLR was ranked in the last place with very low accuracy at all stations. Overall, the results suggest that the ELM is more effective than the MLPNN and MLR for modelling DO concentration in river ecosystems.     

Dynamic factor analysis of groundwater quality trends in an agricultural area adjacent to Everglades National Park

The extensive eastern boundary of Everglades National Park (ENP) in south Florida (USA) is subject to one of the most expensive and ambitious environmental restoration projects in history. Understanding and predicting the water quality interactions between the shallow aquifer and surface water is a key component in meeting current environmental regulations and fine-tuning ENP wetland restoration while still maintaining flood protection for the adjacent developed areas. Dynamic factor analysis (DFA), a recent technique for the study of multivariate non-stationary time-series, was applied to study fluctuations in groundwater quality in the area. More than two years of hydrological and water quality time series (rainfall; water table depth; and soil, ground and surface water concentrations of N-NO3-, N-NH4+, P-PO4(3-), Total P, F-and Cl-) from a small agricultural watershed adjacent to the ENP were selected for the study. The unexplained variability required for determining the concentration of each chemical in the 16 wells was greatly reduced by including in the analysis some of the observed time series as explanatory variables (rainfall, water table depth, and soil and canal water chemical concentration). DFA results showed that groundwater concentration of three of the agrochemical species studied (N-NO3-, P-PO4(3-)and Total P) were affected by the same explanatory variables (water table depth, enriched topsoil, and occurrence of a leaching rainfall event, in order of decreasing relative importance). This indicates that leaching by rainfall is the main mechanism explaining concentration peaks in groundwater. In the case of N-NH4+, in addition to leaching, groundwater concentration is governed by lateral exchange with canals. F-and Cl- are mainly affected by periods of dilution by rainfall recharge, and by exchange with the canals. The unstructured nature of the common trends found suggests that these are related to the complex spatially and temporally varying land use patterns in the watershed. The results indicate that peak concentrations of agrochemicals in groundwater could be reduced by improving fertilization practices (by splitting and modifying timing of applications) and by operating the regional canal system to maintain the water table low, especially during the rainy periods.     

Groundwater quality assessment of one former industrial site in Belgium using a TRIAD-like approach

Contaminated industrial sites are important sources of pollution and may result in ecotoxicological effects on terrestrial, aquatic and groundwater ecosystems. An effect-based approach to evaluate and assess pollution-induced degradation due to contaminated groundwater was carried out in this study. The new concept, referred to as “Groundwater Quality TRIAD-like” (GwQT) approach, is adapted from classical TRIAD approaches. GwQT is based on measurements of chemical concentrations, laboratory toxicity tests and physico-chemical analyses. These components are combined in the GwQT using qualitative and quantitative (using zero to one subindices) integration approaches. The TRIAD approach is applied for the first time on groundwater from one former industrial site located in Belgium. This approach will allow the classification of sites into categories according to the degree of contaminant-induced degradation. This new concept is a starting point for groundwater characterization and is open for improvement and adjustment.     

Multivariant statistical analysis of PCDD/FS in sewage sludges from different areas of the Valencian Community (Spain)

The levels of polychlorinated dibenzo-p-dioxins (PCDDs) and dibenzofurans (PCDFs) in 31 sewage sludges from different wastewater treatment plants corresponding to rural, urban and industrial areas in the Valencian Community (Spain) were analysed. Values of 5.1-346.2 ng I-TEQ kg(-1) (dry weight) were detected for sewage sludge with the highest value in one sample from an industrial area. Therefore the majority of the samples did not exceed the limit proposed (100 ng I-TEQ kg(-1)) by the [EU, 2000. Working document on sludge, 3rd draft. Brussels. Available from: http://europa.eu.int/comm/environment/waste/sludge/sludge_en.pdf] for use in agriculture. The dominant congeners for each family of compounds were 1,2,3,4,6,7,8-HpCDD and OCDD from PCDDs, and 1,2,3,4,6,7,8-HpCDF and OCDF from PCDFs. The total concentrations of PCDD/Fs were evaluated statistically through SPPS 11.0 for Windows. The principal component analysis (PCA) was used to extract two PCs as a linear combination of the original variables, one of them associated to urban+highly industrial areas and the other one to urban+low industrial areas. The linear regression method was applied and an efficient correlation was obtained between the total I-TEQ values for each sample and two of most abundant congeners (OCDF and OCDD). This expression was obtained with the results of the 31 samples analysed and a variety of data from other authors. Furthermore, several bilateral correlations between the different congeners completed the statistical analysis.     

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.