Chemometric data analysis of pollutants in wastewater—a case study

Cookie composition analysis by Fourier transform near infrared spectroscopy coupled to chemometric analysis

The consumption ofcookies is ever growing and during the COVID-19 pandemic reached record consumption values and it is imperative to guarantee the quality and safety of the products.Fourier transform near infrared (FT-NIR) spectroscopy, combined with chemometric techniques, provides a promising solution in that regard, due to its speed and simple sample preparation. The objective of this study was to investigate the possibilities of using FT-NIR to predict lipids, carbohydrates, fibers, proteins, salt and energy contents, as well as to identify cookies type and main cereals present in a batch of 120 commercially acquired samples. The prediction models were performed using ordinary least squares (OLS), partial least squares (PLS), and PLS based classification models including discriminant analysis (PLS-DA), k-nearest neighbors (PLS-kNN) and naïve Bayes (PLS-NB). The best prediction models allowed for good accuracies, with correlation coefficients higher than 0.9 for all studied nutritional parameters. PLS-kNN methodology was able to identify all 5 main cereals (wheat, integral wheat, oat, corn and rice) as well as the 14 types of cookies based on the nutritional contents. The developed methods were able to accurately identify the cookies type and composition, confirming the proposed methodology as a fast, reliable, environmentally friendly and non-destructive alternative to standard analytical methods.

A review of iron use and recycling in municipal wastewater treatment plants and a novel applicable integrated process

Chemical methods are expected to play an increasingly important role in carbon-neutral municipal wastewater treatment plants. This paper briefly summarises the enhancement effects of using iron salts in wastewater and sludge treatment processes. The costs and environmental concerns associated with the widespread use of iron salts have also been highlighted. Fortunately, the iron recovery from iron-rich sludge provides an opportunity to solve these problems. Existing iron recovery methods, including direct acidification and thermal treatment, are summarised and show that acidification treatment of FeS digestate from the anaerobic digestion-sulfate reduction process can increase the iron and sulphur recycling efficiency. Therefore, a novel applicable integrated process based on iron use and recycling is proposed, and it reduces the iron salts dosage to 4.2 mg/L and sludge amount by 80%. Current experimental research and economic analysis of iron recycling show that this process has broad application prospects in resource recovery and sludge reduction.

Human Health Risk Assessment of Trace Elements in Tap Water and the Factors Influencing Its Value

(1) Background: The influence of tap water fittings construction and internal pipe-work on the release of heavy metals was investigated. (2) Methods: A statistical approach was applied for the examination of the chemistry of tap water in five different cities in southern Poland. In total, 500 samples were collected (from 100 to 101 samples in each city). The sampling protocol included information on the construction of the water supply network and the physicochemical parameters of measured tap water. (3) Results: The statistical analysis allowed to extract the crucial factors that affect the concentrations of trace elements in tap water. Age of connection, age of tap, age of pipe-work as well as material of connection, material of pipe-work and material of appliance reveal the most significant variability of concentrations observed for As, Al, Cd, Cu, Fe, Mn, Pb, and Zn. Calculated cancer risks (CRs) decrease with the following order of analysed elements Ni > Cd > Cr > As = Pb and can be associated with the factors that affect the appearance of such elements in tap water. The hazard index (HI) was evaluated as negligible in 59.1% of the sampling points and low in 40.1% for adults. For children, a high risk was observed in 0.2%, medium in 9.0%, negligible in 0.4%, and low for the rest of the analysed samples.

Current trends and advances in analytical techniques for the characterization and quantification of biologically recalcitrant organic species in sludge and wastewater: A review

The study of organic matter in wastewater is a major regulatory and environmental issue and requires new developments to identify non-biodegradable refractory compounds, produced mainly by thermal treatments. Recent advances linking physicochemical properties to spectroscopic analyzes (UV, Fluorescence, IR) have shown that the refractory property is favored by several physicochemical parameters: weight, hydrophobicity, aromaticity and chemical functions. Currently, the most effective developments for the quantification of refractory compounds are obtained with hyphenated methods, based on steric separation of the macromolecular species by steric exclusion chromatography (SEC)/PDA/Fluorescence systems. Hyphenated techniques using High Resolution Mass Spectrometry (HRMS), ultra-high-resolution mass spectrometry with Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS) and NMR have been developed to analyze macromolecules in wastewater with minor sample preparation procedures. A particular class has been identified, the melanoidins, generated by Maillard reactions between sugars, amino acids, peptides and proteins present in wastewater and sludge, but low molecular weight compounds formed as intermediates, such as ketones, aldehydes, pyrazines, pyridines or furans, are also recalcitrant and are complex to identify in the complex matrices. The lack of available standards for the study of these compounds requires the use of specific techniques and data processing. Advances in chemometrics are obtained in the development of molecular or physicochemical indices resulting from the data generated by the analytical detectors, such as aromaticity calculated by SUVA₂₅₄ and determined by UV, fluorescence, molar mass, H/C ratio or structural studies (measuring the amount of unsaturated carbon) given by hyphenated techniques with SEC. It is clear that nitrogen compounds are widely involved in refractoriness. New trends in nitrogen containing compounds characterization follow two axes: through SEC/PDA/Fluorescence and HRMS/NMR techniques with or without separation. Other techniques widely used in food or marine science are also being imported to this study, as it can be seen in the use of "omics" methods, high-performance thin layer chromatography (HPTLC) and chromatography at the critical condition, rounding out the important developments around SEC. While improving the performance of stationary phases is one of the challenges, it results in a fundamental understanding of the retention mechanisms that today provide us with more information on the structures identified. The main objective of this review is to present the spectroscopic and physicochemical techniques used to qualify and characterize refractoriness with a specific focus on chemometric approaches.

Assessment of the Bulgarian Wastewater Treatment Plants' Impact on the Receiving Water Bodies

Deterioration of water quality is a major problem world widely according to many international non-governmental organizations (NGO). As one of the European Union (EU) countries, Bulgaria is also obliged by EU legislation to maintain best practices in assessing surface water quality and the efficiency of wastewater treatment processes. For these reasons studies were undertaken to utilize ecotoxicological (Microtox^®, Phytotoxkit F^TM, Daphtoxkit F^TM), instrumental (to determine pH, electrical conductivity (EC), chemical oxygen demand, total suspended solids (TSS), total nitrogen (N) and phosphorus (P), chlorides, sulphates, Cr, Co, Cu, Cd, Ba, V, Mn, Fe, Ni, Zn, Se, Pb), as well as advanced chemometric methods (partial least squares-discriminant analysis (PLS-DA)) in data evaluation to comprehensively assess wastewater treatment plants' (WWTPs) effluents and surface waters quality around 21 major Bulgarian cities. The PLS-DA classification model for the physicochemical parameters gave excellent discrimination between WWTP effluents and surface waters with 93.65% correct predictions (with significant contribution of EC, TSS, P, N, Cl, Fe, Zn, and Se). The classification model based on ecotoxicological data identifies the plant test endpoints as having a greater impact on the classification model efficiency than bacterial, or crustaceans' endpoints studied.

Use of chemometric analyses to assess biological wastewater treatment plants by protozoa and metazoa monitoring

Protozoa and metazoa biota communities in biological wastewater treatment plants (WWTP) are known to be dependent of both the plant type (oxidation ditch, trickling filter, conventional activated sludge, among others) and the working operational conditions (incoming effluent characteristics, toxics presence, organic load, aeration, hydraulic and sludge retention times, nitrification occurrence, etc.). Thus, for analogous WWTP operating in equivalent operating conditions, similar protozoa and metazoa communities can be found. Indeed, the protozoa and metazoa biota monitoring can be considered a quite useful tool for assessing the functioning of biological WWTP. Furthermore, the use of chemometric techniques in WWTP monitoring is becoming widespread to enlighten interrelationships within the plant, especially when a large collection of data can be obtained. In the current study, the protozoa and metazoa communities of three different types of WWTP, comprising one oxidation ditch, four trickling filters, and three conventional activated sludge plants, were monitored. For that purpose, metazoa, as well as the main protozoa groups (flagellates, free-swimming, crawling and sessile ciliates, and testate amoeba) were determined in terms of contents and relative abundance. The collected data was further processed by chemometric techniques, such as cross-correlation, principal components, multivariate ANOVA, and decision trees analyses, allowing to successfully identify, and characterize, the different studied WWTP, and thus, being able to help monitoring and diagnosing operational problems.

Multivariate Chemometric Analysis of Membrane Fouling Patterns in Biofilm Ceramic Membrane Bioreactor

Membrane fouling highly limits the development of Membrane bioreactor technology (MBR), which is among the key solutions to water scarcity. The current study deals with the determination of the fouling propensity of filtered biomass in a pilot-scale biofilm membrane bioreactor to enable the prediction of fouling intensity. The system was designed to treat domestic wastewater with the application of ceramic microfiltration membranes. Partial least squares regression analysis of the data obtained during the long-term operation of the biofilm-MBR (BF-MBR) system demonstrated that Mixed liquor suspended solids (MLSS), diluted sludge volume index (DSVI), chemical oxygen demand (COD), and their slopes are the most significant for the estimation and prediction of fouling intensity, while normalized permeability and its slope were found to be the most reliable fouling indicators. Three models were derived depending on the applied operating conditions, which enabled an accurate prediction of the fouling intensities in the system. The results will help to prevent severe membrane fouling via the change of operating conditions to prolong the effective lifetime of the membrane modules and to save energy and resources for the maintenance of the system.

Tracing the sources of lead (Pb) in Brunei Bay, Borneo by using integrated spectrometry ICP-MS and chemometric techniques

The present study aims to define the possible sources that contribute to the level of Pb into the Brunei Bay, Borneo. The cluster analysis has classified the bay into the northern part with heavy and agriculture-related industries; the southern area with a moderate rural human settlement as well as the southwestern area with a more pristine environment and a low level of human settlement. The score plot of spatial discriminant analysis verified a significant influence of the river system toward the estuary, whereas the temporal discriminant analysis has discriminated the seasonal changes. In comparison to elsewhere, the stable Pb isotopic ratios in Brunei Bay showed a fingerprint similar to coal-related sources and of aerosol input. Briefly, even though Pb in the Brunei Bay ecosystem proved to be at a low level, the stable Pb isotopic ratios showed that human and industrial activities are slowly contributing Pb into the bay ecosystem.

Temporal performance assessment of wastewater treatment plants by using multivariate statistical analysis

For most water treatment plants, a significant number of performance data variables are attained on a time series basis. Due to the interconnectedness of the variables, it is often difficult to assess over-arching trends and quantify operational performance. The objective of this study was to establish simple and reliable predictive models to correlate target variables with specific measured parameters. This study presents a multivariate analysis of the physicochemical parameters of municipal wastewater. Fifteen quality and quantity parameters were analyzed using data recorded from 2010 to 2016. To determine the overall quality condition of raw and treated wastewater, a Wastewater Quality Index (WWQI) was developed. The index summarizes a large amount of measured quality parameters into a single water quality term by considering pre-established quality limitation standards. To identify treatment process performance, the interdependencies between the variables were determined by using Principal Component Analysis (PCA). The five extracted components from the 15 variables accounted for 75.25% of total dataset information and adequately represented the organic, nutrient, oxygen demanding, and ion activity loadings of influent and effluent streams. The study also utilized the model to predict quality parameters such as Biological Oxygen Demand (BOD), Total Phosphorus (TP), and WWQI. High accuracies ranging from 71% to 97% were achieved for fitting the models with the training dataset and relative prediction percentage errors less than 9% were achieved for the testing dataset. The presented techniques and procedures in this paper provide an assessment framework for the wastewater treatment monitoring programs.

Improving biodegradation potential of domestic wastewater by manipulating the size distribution of organic matter

Carbon source is a critical constraint on nutrient removal in domestic wastewater treatment. However, the functions of particulate organic matter (POM) and some organics with high molecular weight (HMW) are overlooked in the conventional process, as they cannot be directly assimilated into cells during microbial metabolism. This further aggravates the problem of carbon source shortage and thus affects the effluent quality. Therefore, to better characterize organic matter (OM) based MW distribution, microfiltration/ultrafiltration/nanofiltration (MF/UF/NF) membranes were used in parallel to fractionate OM, which obtained seven fractions. Hydrolysis acidification (HA) was adopted to manipulate the MW distribution of dissolved organic matter (DOM) and further explore the correlation between molecular size and biodegradability. Results showed that HA pretreatment of wastewater not only promoted transformation from POM to DOM, but also boosted biodegradability. After 8hr of HA, the concentration of dissolved organic carbon (DOC) increased by 65%, from the initial value of 20.25 to 33.48mg/L, and the biodegradability index (BOD5 (biochemical oxygen demand)/SCOD (soluble chemical oxygen demand)) increased from 0.52 to 0.74. Using MW distribution analysis and composition optimization, a new understanding on the characteristics of organics in wastewater was obtained, which is of importance to solving low C/N wastewater treatment in engineering practice.

Microbial-based evaluation of foaming events in full-scale wastewater treatment plants by microscopy survey and quantitative image analysis

Activated sludge systems are prone to be affected by foaming occurrences causing the sludge to rise in the reactor and affecting the wastewater treatment plant (WWTP) performance. Nonetheless, there is currently a knowledge gap hindering the development of foaming events prediction tools that may be fulfilled by the quantitative monitoring of AS systems biota and sludge characteristics. As such, the present study focuses on the assessment of foaming events in full-scale WWTPs, by quantitative protozoa, metazoa, filamentous bacteria, and sludge characteristics analysis, further used to enlighten the inner relationships between these parameters. In the current study, a conventional activated sludge system (CAS) and an oxidation ditch (OD) were surveyed throughout a period of 2 and 3 months, respectively, regarding their biota and sludge characteristics. The biota community was monitored by microscopic observation, and a new filamentous bacteria index was developed to quantify their occurrence. Sludge characteristics (aggregated and filamentous biomass contents and aggregate size) were determined by quantitative image analysis (QIA). The obtained data was then processed by principal components analysis (PCA), cross-correlation analysis, and decision trees to assess the foaming occurrences, and enlighten the inner relationships. It was found that such events were best assessed by the combined use of the relative abundance of testate amoeba and nocardioform filamentous index, presenting a 92.9 % success rate for overall foaming events, and 87.5 and 100 %, respectively, for persistent and mild events.

Principal factor and hierarchical cluster analyses for the performance assessment of an urban wastewater treatment plant in the Southeast of Spain

Process performance and operation of wastewater treatment plants (WWTP) are carried out to ensure their compliance with legislative requirements imposed by European Union. Because a high amount of variables are daily measured, a coherent and structured approach of such a system is required to understand its inherent behavior and performance efficiency. In this sense, both principal factor analysis (PFA) and hierarchical cluster analysis (HCA) are multivariate techniques that have been widely applied to extract and structure information for different purposes. In this paper, both statistical tools are applied in an urban WWTP situated in the Southeast of Spain, a zone with special characteristics related to the geochemical background composition of water and an important use of fertilizers. Four main factors were extracted in association with nutrients, the ionic component, the organic load to the WWTP, and the efficiency of the whole process. HCA allowed distinguish between influent and effluent parameters, although a deeper examination resulted in a dendrogram with groupings similar to those previously reported for PFA.

Assessment of metal species in river Ganga sediment at Varanasi, India using sequential extraction procedure and SEM-EDS

Aim of the present study was to assess impact of urban drains over river water and sediments by physico-chemical and metal analysis. Metal speciation (Sequential Extraction Procedure) and elemental composition analysis (SEM-EDS) was used to quantify metal pollution load in river sediments. Metal speciation analysis showed dominance of available and labile fractions of all heavy metals (Cr, Ni, Cu, Zn, Cd and Pb) except Mn and Fe which were dominant in residual forms. Cluster analysis (CA), Principal Components Analysis (PCA) and Partial Least Square Regression (PLSR) were applied as source receptor modeling for pollutants. Results classified river stretch into three zones i.e. moderately, severely and extremely polluted, on the basis of pollutant concentration released from anthropogenic sources. SEM-EDS study revealed the elemental composition percentage in river sediments. Pollution Load Index (PLI) varied from 1.8 (S1)-3.9 (S15). The Geo accumulation index (GAI) was found highest for Cd (6.88-8.97) and Pb (2.41-3.24).

Application of chemometric analysis and self Organizing Map-Artificial Neural Network as source receptor modeling for metal speciation in river sediment

Present study deals with the river Ganga water quality and its impact on metal speciation in its sediments. Concentration of physico-chemical parameters was highest in summer season followed by winter and lowest in rainy season. Metal speciation study in river sediments revealed that exchangeable, reducible and oxidizable fractions were dominant in all the studied metals (Cr, Ni, Cu, Zn, Cd, Pb) except Mn and Fe. High pollution load index (1.64-3.89) recommends urgent need of mitigation measures. Self-organizing Map-Artificial Neural Network (SOM-ANN) was applied to the data set for the prediction of major point sources of pollution in the river Ganga.

Evaluation of ecological risk of metal contamination in river Gomti, India: a biomonitoring approach

The aim of this study was to evaluate the extent of heavy metal pollution in river Gomti and associated ecological risk. River water, sediments and locally abundant mollusk (Viviparus (V.) bengalensis) were sampled from six different sites and analyzed for seven metals: Cadmium (Cd), Chromium (Cr), Copper (Cu), Manganese (Mn), Nickel (Ni), Lead (Pb) and Zinc (Zn). Mean metal concentrations (mg/l) in river water were 0.024 for Cd, 0.063 for Cr, 0.022 for Cr, 0.029 for Mn, 0.044 for Ni, 0.018 for Pb and 0.067 for Zn. In river sediments, the concentrations (mg/kg dry wt) were 5.0 for Cd, 16.2 for Cr, 23.2 for Cr, 203.2 for Mn, 23.9 for Ni, 46.2 for Pb and 76.3 for Zn, while in V. bengalensis mean metal concentrations (mg/kg, dry wt) were 0.57 for Cd, 12.0 for Cr, 30.7 for Cu, 29.9 for Mn, 8.8 for Ni, 3.6 for Pb and 48.3 for Zn. Results indicated elevated concentrations of Cu, Zn and Mn in V. bengalensis as compared to other non-essential elements. Potential ecological risk (RI) in sediments showed high to very high metal contamination. Cluster analysis indicated that Pb, Zn, Cd and Ni in sediments may have anthropogenic sources. The findings thus suggest heavy metal contamination of river water and sediments have reached alarming levels, which is well corroborated by elevated level of metal accumulation in V. bengalensis.

Source apportionment of wastewater pollutants using multivariate analyses

A faster and cost-effective methodology has been developed to estimate the spatial and seasonal variations in wastewater quality and apportion the influencing sources through multivariate statistical techniques, cluster analysis and principal component analysis (PCA). Partially treated or untreated wastewater is released into the river from various industrial and domestic sources, which poses a serious threat to human health. Wastewater samples were collected from five stations along the river bank. PCA performed on overall wastewater samples revealed that in present study all the five sampling stations were influenced by sewage and industrial effluents mixed together. However, the pollutant levels were significantly different in the three groups of wastewater samples, which were confirmed by univariate analysis of principal component (PC) scores. Based on wastewater similarities, cluster analysis identified three groups (central, upstream and downstream) of sampling stations, which further confirmed univariate analysis of PCs scores. Spatial variations in wastewater quality reveled that the highest pollutant concentration was noted for group 1 and lowest for group 2. Seasonal variations in the wastewater quality revealed that highest values of pollutants were observed in low flow and lowest in high flow. Results of the present study obtained through multivariate analyses may be used to classify wastewater and identify the influencing sources of pollutants. The present study may be useful in reducing 11 % of the cost in future investigations. Thus, in future quality estimation of the representative wastewater samples would be faster as well as cost-effective approach.

River water quality assessment using environmentric techniques: case study of Jakara River Basin

Jakara River Basin has been extensively studied to assess the overall water quality and to identify the major variables responsible for water quality variations in the basin. A total of 27 sampling points were selected in the riverine network of the Upper Jakara River Basin. Water samples were collected in triplicate and analyzed for physicochemical variables. Pearson product-moment correlation analysis was conducted to evaluate the relationship of water quality parameters and revealed a significant relationship between salinity, conductivity with dissolved solids (DS) and 5-day biochemical oxygen demand (BOD5), chemical oxygen demand (COD), and nitrogen in form of ammonia (NH4). Partial correlation analysis (r p) results showed that there is a strong relationship between salinity and turbidity (r p=0.930, p=0.001) and BOD5 and COD (r p=0.839, p=0.001) controlling for the linear effects of conductivity and NH4, respectively. Principal component analysis and or factor analysis was used to investigate the origin of each water quality parameter in the Jakara Basin and identified three major factors explaining 68.11 % of the total variance in water quality. The major variations are related to anthropogenic activities (irrigation agricultural, construction activities, clearing of land, and domestic waste disposal) and natural processes (erosion of river bank and runoff). Discriminant analysis (DA) was applied on the dataset to maximize the similarities between group relative to within-group variance of the parameters. DA provided better results with great discriminatory ability using eight variables (DO, BOD5, COD, SS, NH4, conductivity, salinity, and DS) as the most statistically significantly responsible for surface water quality variation in the area. The present study, however, makes several noteworthy contributions to the existing knowledge on the spatial variations of surface water quality and is believed to serve as a baseline data for further studies. Future research should therefore concentrate on the investigation of temporal variations of water quality in the basin.

Metal fractionation in soils and assessment of environmental contamination in Vallecamonica, Italy

Metal contamination was investigated in soils of the Vallecamonica, an area in the northern part of the Brescia province (Italy), where ferroalloy industries were active for a century until 2001. The extent in which emissions from ferroalloy plants affected metal concentration in soils is not known in this area. In this study, the geogenic and/or anthropogenic origin of metals in soils were estimated. A modified Community Bureau of Reference sequential chemical extraction method followed by inductively coupled plasma optical emission spectroscopy (ICP-OES) analyses were employed to evaluate the potential bioavailability of Al, Cd, Mn, Fe, Cr, Zn, and Pb in soils. Principal component analysis (PCA) was used to assess the relationships among metal sources in soil samples from different locations. This approach allowed distinguishing of different loadings and mobility of metals in soils collected in different areas. Results showed high concentrations and readily extractability of Mn in the Vallecamonica soils, which may suggest potential bioavailability for organisms and may create an environmental risk and potential health risk of human exposure.

Monitoring and modelling to support wastewater system management in developing mega-cities

Urban drainage system models can be useful to assess and manage system performance and to plan its development. However, due to data and computational costs, sophisticated, high-resolution contemporary models of the sewer system may not be applicable. This constraint is particularly marked in developing country mega-cities where catchments can be large, data tend to be scarce, and there are many unknowns, for example regarding sources, losses and wrong connections. This paper presents work undertaken over the last 7 years to develop a suitable monitoring and modelling framework to support operation and development of the wastewater system of Bogotá (Colombia). Components of the framework covered here are: (a) the flow and water quality database, (b) a wastewater pollution load generator, and (c) a semi-distributed sewer network model, which aims at a complexity that matches the information available from the previous two components. Results from a catchment within Bogotá, area 150 km(2) and with 2.5 million inhabitants, show that the model outputs capture the scale and dynamics of the observed concentrations and loads at various points on the sewer system. However uncertainty is high because much of variability of observed dry weather flow profiles is apparently random. Against this variability, the effects of in-sewer processes were not identifiable except where backwaters caused particularly high retention times. Hence the work has resulted in an operational model with a scientifically justified, yet useful, level of complexity for Bogotá. More generally, the work demonstrates the value of monitoring and modelling programmes, including having modellers actively involved in monitoring specification and operations; and the insights into suitable level of model complexity that may be gained by uncertainty and sensitivity analysis.

A comparative chemometric study for water quality expertise of the Athenian water reservoirs

The aim of the present study is to compare the application of unsupervised and supervised pattern recognition techniques for the quality assessment and classification of the reservoirs used as the source for the domestic and industrial water supply of the city of Athens, Greece. A new optimization strategy for sampling, monitoring, and water management is proposed. During the period of October 2006 to April 2007, 89 samples were collected from the three water reservoirs (Iliki, Mornos, and Marathon), and 13 parameters (metals and metalloids) were analytically determined. Generally, all the elements were found to fluctuate at very low levels, especially for Mornos that comprises the main water reservoir of Athens. Iliki and Marathon showed relatively elevated values, compared to Mornos, but below the legislative limits. Multivariate unsupervised statistical techniques, such as factor analysis/principal components analysis, and cluster analysis and supervised ones, like discriminant analysis and classification trees, were applied to the data set, and their classification abilities were compared. All the chemometric techniques successfully revealed the critical variables and described the similarities and dissimilarities among the sampling points, emphasizing the individual characteristics in every sample and revealing the sources of elements in the region. New data from posterior samplings (November and December 2007) were used for the validation of the supervised techniques. Finally, water management strategies were proposed concerning the sampling points and representative parameters.

Physicochemical properties and trace organic compounds in a dairy processor's aerobic bioreactor

Wastewater samples were taken from an aerobic bioreactor, operated by a dairy processor in southeastern Australia to reduce nutrient and pollutant loads. Samples were taken over a two-year period, to determine whether trace organic compounds or physicochemical analyses of the wastewater could be used to discriminate the water taken before, during and after processing of the wastewater in the bioreactor. Multivariate analyses of the physicochemical data suggested that nitrate, pH and total dissolved nitrogen best described the infeed wastewater entering the bioreactor, while organic and particulate phosphorus concentrations where predominantly responsible for describing the composition of the content of the bioreactor. Gas chromatography-mass spectrometry data of organic compounds within the wastewater samples were also analysed via multivariate analyses. The analyses found that the compound 4-nitrophenol was associated with ammonia concentrations and mixed liquor wastewater. Therefore, 4-nitrophenol may possibly be used to act as an indicator of anaerobicity in aerobic bioreactors.

Support vector machines in water quality management

Support vector classification (SVC) and regression (SVR) models were constructed and applied to the surface water quality data to optimize the monitoring program. The data set comprised of 1500 water samples representing 10 different sites monitored for 15 years. The objectives of the study were to classify the sampling sites (spatial) and months (temporal) to group the similar ones in terms of water quality with a view to reduce their number; and to develop a suitable SVR model for predicting the biochemical oxygen demand (BOD) of water using a set of variables. The spatial and temporal SVC models rendered grouping of 10 monitoring sites and 12 sampling months into the clusters of 3 each with misclassification rates of 12.39% and 17.61% in training, 17.70% and 26.38% in validation, and 14.86% and 31.41% in test sets, respectively. The SVR model predicted water BOD values in training, validation, and test sets with reasonably high correlation (0.952, 0.909, and 0.907) with the measured values, and low root mean squared errors of 1.53, 1.44, and 1.32, respectively. The values of the performance criteria parameters suggested for the adequacy of the constructed models and their good predictive capabilities. The SVC model achieved a data reduction of 92.5% for redesigning the future monitoring program and the SVR model provided a tool for the prediction of the water BOD using set of a few measurable variables. The performance of the nonlinear models (SVM, KDA, KPLS) was comparable and these performed relatively better than the corresponding linear methods (DA, PLS) of classification and regression modeling.

Temporal variations of membrane foulants in the process of using flat-sheet membrane for simultaneous thickening and digestion of waste activated sludge

Membrane foulants were extracted at different operation time in simultaneous sludge thickening and digestion reactors using flat-sheet membranes. Temporal variations of foulants were analyzed by three-dimensional excitation-emission matrix (EEM) fluorescence spectroscopy, gel filtration chromatography (GFC), particle size distribution (PSD) and attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopy. Results showed that during the first 4days fouling was mainly assigned to internal membrane foulants (IMFs), and afterwards external membrane foulants (EMFs) increased dramatically. EEM analysis showed that both IMFs and EMFs changed during the operation. Cluster analysis demonstrated that the characteristics of IMFs were relatively similar; however, both quantity and properties of EMFs were changed. GFC analysis showed that EMFs contained more molecules with large molecular weight compared to IMFs. PSD analysis illuminated that particle size of EMFs gradually increased and was larger than that of IMFs. ATR-FTIR analysis indicated that the foulants on membranes consisted of polysaccharides and proteins.

Pretreatment of wastewater: optimal coagulant selection using Partial Order Scaling Analysis (POSA)

Jar-test is a well-known tool for chemical selection for physical-chemical wastewater treatment. Jar test results show the treatment efficiency in terms of suspended matter and organic matter removal. However, in spite of having all these results, coagulant selection is not an easy task because one coagulant can remove efficiently the suspended solids but at the same time increase the conductivity. This makes the final selection of coagulants very dependent on the relative importance assigned to each measured parameter. In this paper, the use of Partial Order Scaling Analysis (POSA) and multi-criteria decision analysis is proposed to help the selection of the coagulant and its concentration in a sequencing batch reactor (SBR). Therefore, starting from the parameters fixed by the jar-test results, these techniques will allow to weight these parameters, according to the judgments of wastewater experts, and to establish priorities among coagulants. An evaluation of two commonly used coagulation/flocculation aids (Alum and Ferric Chloride) was conducted and based on jar tests and POSA model, Ferric Chloride (100 ppm) was the best choice. The results obtained show that POSA and multi-criteria techniques are useful tools to select the optimal chemicals for the physical-technical treatment.

Sewage sludge reduction and system optimization in a catalytic ozonation process

The main objective of this study was to suggest a feasible, effective process for the reduction of sewage sludge using ozone oxidation catalysed by metal ion. A series of lab-scale experiments was conducted to select a suitable catalyst and its proper dose to achieve optimum sludge reduction. Using a central composite design under response surface methodology (RSM), system optimization with respect to sludge reduction and cost-effectiveness was performed by varying the independent parameters: dosages of ozone and ions. Five metal ions, Mn2+, Fe2+, Zn2+, CU2+, and Al3+1, were tested, and the manganese ion showed the highest sludge reduction, as measured by a decrease in total suspended solids. The ozone/Mn combination achieved approximately twice as much sludge reduction as the ozonation alone. Furthermore, the Mn dose of 10 mg/g-TS (total solids) resulted in the highest sludge reduction efficiency among the different doses, which ranged from 0 to 20 mg-Mn/g-TS. The predicted efficiency of sewage sludge reduction using the RSM was found to agree well with the experimental results, and the statistical analyses predicted optimum ranges for the doses of ozone and Mn ions, taking into account the overall cost for sewage sludge treatment.

Chemometric approach to validating faecal sterols as source tracer for faecal contamination in water

Faecal sterols detection is a promising method for identifying sources of faecal pollution. In this study, faecal contamination in water samples from point source (sewage treatment plants, chicken farms, quail farms and horse stables) was extracted using the solid phase extraction (SPE) technique. Faecal sterols (coprostanol, cholesterol, stigmasterol, beta-sitosterol and stigmastanol) were selected as parameters to differentiate the source of faecal pollution. The results indicated that coprostanol, cholesterol and beta-sitosterol were the most significant parameters that can be used as source tracers for faecal contamination. Chemometric techniques, such as cluster analysis, principal component analysis and discriminant analysis were applied to the data set on faecal contamination in water from various pollution sources in order to validate the faecal sterols' profiles. Cluster analysis generated three clusters: coprostanol was in cluster 1, cholesterol and beta-sitosterol formed cluster 2, while cluster 3 contained stigmasterol and stigmastanol. Discriminant analysis suggested that coprostanol, cholesterol and beta-sitosterol were the most significant parameters to discriminate between the faecal pollution source. The use of chemometric techniques provides useful and promising indicators in tracing the source of faecal contamination.

Vertical distribution of heavy metals in grain size fractions in sedimentary rocks: Mosina-Krajkowo water well field, Poland

The paper presents the results of heavy metals determination in samples of sedimentary rocks from the Mosina-Krajkowo water well field (Poland). The concentration of heavy metals was analysed by type of rock (sand, gravel, warp, silt, till, and clay). Variation of heavy metal concentrations with depth was studied taking into account the age series of the rocks (fluvial sediments of the modern Warta River valley, sediments of the Baltic Glaciation, tills of the Middle-Polish Glaciation, sediments of the Masovian Interglacial (Holstein), tills of the Poznań series) and granulometric fractions. The grain sizes considered included: >2.0, 2.0-1.0, 1.0-0.5, 0.5-0.25, 0.25-0.1, 0.1-0.063, and <0.063 mm. The concentrations of the heavy metals studied were found to change with the type of rock, age series, and granulometric fraction. The levels of the metals were determined by the technique of atomic absorption spectrometry with flame atomisation (F-AAS) and inductively coupled plasma-atomic emission spectrometry (ICP-AES).

Multivariate modeling of chromium-induced oxidative stress and biochemical changes in plants of Pistia stratiotes L

Biochemical changes in the plants of Pistia stratiotes L., a free floating macrophyte exposed to different concentrations of hexavalent chromium (0, 10, 40, 60, 80 and 160 microM) for 48, 96 and 144 h were studied. Chromium-induced oxidative stress in macrophyte was investigated using the multivariate modeling approaches. Cluster analysis rendered two fairly distinct clusters (roots and shoots) of similar characteristics in terms of their biochemical responses. Discriminant analysis identified ascorbate peroxidase (APX) as discriminating variable between the root and shoot tissues. Principal components analysis results suggested that malondialdehyde (MDA), superoxide dismutase (SOD), APX, non-protein thiols (NP-SH), cysteine, ascorbic acid, and Cr-accumulation are dominant in root tissues, whereas, protein and guaiacol peroxidase (GPX) in shoots of the plant. Discriminant partial least squares analysis results further confirmed that MDA, SOD, NP-SH, cysteine, GPX, APX, ascorbic acid and Cr-accumulation dominated in the root tissues, while protein in the shoot. Three-way analysis helped in visualizing simultaneous influence of metal concentration and exposure duration on biochemical variables in plant tissues. The multivariate approaches, thus, allowed for the interpretation of the induced biochemical changes in the plant tissues exposed to chromium, which otherwise using the conventional approaches is difficult.

Laser-induced breakdown spectroscopy for detection of explosives residues: a review of recent advances, challenges, and future prospects

In this review we discuss the application of laser-induced breakdown spectroscopy (LIBS) to the problem of detection of residues of explosives. Research in this area presented in open literature is reviewed. Both laboratory and field-tested standoff LIBS instruments have been used to detect explosive materials. Recent advances in instrumentation and data analysis techniques are discussed, including the use of double-pulse LIBS to reduce air entrainment in the analytical plasma and the application of advanced chemometric techniques such as partial least-squares discriminant analysis to discriminate between residues of explosives and non-explosives on various surfaces. A number of challenges associated with detection of explosives residues using LIBS have been identified, along with their possible solutions. Several groups have investigated methods for improving the sensitivity and selectivity of LIBS for detection of explosives, including the use of femtosecond-pulse lasers, supplemental enhancement of the laser-induced plasma emission, and complementary orthogonal techniques. Despite the associated challenges, researchers have demonstrated the tremendous potential of LIBS for real-time detection of explosives residues at standoff distances.

Iron-induced oxidative stress in a macrophyte: a chemometric approach

Iron-induced oxidative stress in plants of Bacopa monnieri L., a macrophyte with medicinal value, was investigated using the chemometric approach. Cluster analysis (CA) rendered two distinct clusters of roots and shoots. Discriminant analysis (DA) identified discriminating variables (NP-SH and APX) between the root and shoot tissues. Principal component analysis (PCA) results suggested that protein, superoxide dismutase (SOD), ascorbic acid, proline, and Fe uptake are dominant in root tissues, whereas malondialdehyde (MDA), guaiacol peroxidase (POD), cysteine, and non-protein thiol (NP-SH) in shoot of the stress plant. Discriminant partial-least squares (DPLS) results further confirmed that SOD and ascorbic acid contents dominated in root tissues, while NP-SH, cysteine, POD, ascorbate peroxidase (APX), and MDA in shoot. MDA and NP-SH were identified as most pronounced variables in plant during the highest exposure time. The chemometric approach allowed for the interpretation of the induced biochemical changes in plant tissues exposed to iron.

Impact of pesticides used in agriculture and vineyards to surface and groundwater quality (North Spain)

An environmental monitoring program was carried out to determine the impact of eight pesticides on the surface and groundwater quality of agricultural areas within the Ebro, Duero and Miño river basins. Three triazines and their desethyl degradation products, metolachlor and metalaxyl, were monitored during 18 months in 63 sites. Solid-phase extraction (SPE) using OASIS HLB 60 mg cartridges and gas chromatography-mass spectrometry (GC-EI-MS) provided good analytical quality parameters and limits of quantification of 0.01 microg/L. Environmental data were assessed using descriptive statistical analysis and multivariate data analysis with principal component analysis (PCA) to elucidate the relevant contamination patterns and provide a description of their seasonal trends, according to the pesticide application timing. Duero was the site with the highest frequency of detection and highest concentration levels, followed by the Ebro and Miño basins. The frequency of detection of the studied compounds, considering all surface and groundwater samples, was atrazine>desethylatrazine>simazine>desethylsimazine>metolachlor>desethylterbuthylazine>terbuthylazine>metalaxyl. Over all results, and taking into consideration the European Union (EU) maximum residual limit of pesticides in groundwater, only 12% of the results exceeded the 0.1 microg/L limit. However, sporadic high levels up to 2.46 mug/L in groundwater and 0.63 microg/L in surface water were detected. PCA permitted to state that Duero and Ebro river basins were especially affected by a contamination pattern dominated by atrazine, the Ebro river basin being occasionally affected by a contamination pattern dominated by simazine. Only trace levels were rarely detected in the Miño river basin. Groundwater levels were higher than surface water levels for the studied pesticides.

Multivariate analysis of selected metals in agricultural soil receiving UASB treated tannery effluent at Jajmau, Kanpur (India)

Tannery effluent and soils samples were collected from 12 different sites of an agricultural area receiving treated tannery wastewater near Kanpur city (India). The samples were analyzed for heavy metals (Fe, Cr, Zn, Mn, Cu, Ni and Pb) content with a view to assess the impact of industrial wastewater on agricultural soils. The results revealed elevated levels of Fe and Cr in agricultural soils irrigated with treated tannery effluents. Cluster analysis of tannery effluent and soil datasets yielded two groups of the metals and demonstrated their relationship in each media. Principal component analysis performed on two datasets yielded two significant factors each for the effluents and soils, suggested tanneries as the probable sources of metals in the soils.

Hydrochemistry of wet atmospheric precipitation over an urban area in northern indo-gangetic plains

Rain water samples were collected to study the chemical composition of wet atmospheric precipitation (first event) over the Lucknow city in the northern Indo-gangetic alluvial plains. The samples were collected in the month of July, 2005 from different sites. The wet precipitation samples were analyzed for pH, EC, major ions (HCO(-3), Cl(-), SO4(2-), NO(-3), PO4(-3), F(-), Na(+), K(+), Ca(2+), Mg(2+), NH4+) and heavy metals (As, Cd, Al, Co, Cr, Cu, Fe, Mn, Mo, Ni, Hg, Pb, Se, Sn, Ti, V, Zn). The pH values of wet precipitation samples ranged between 6.5 and 8.7. The analysis of linear regression applied to the set of studied variables and computation of neutralization factors showed that neutralization occurred in precipitation samples and Ca(2+) had the maximum neutralization capacity. It was found that Cl(-), [Formula: see text], Ca(2+), Mg(2+), Na(+) and K(+) in the precipitation samples originated mainly from crustal/anthropogenic sources in the region. On an average Fe, and Al accounted for >72% of the total concentration of trace metals in the wet precipitation samples followed by Zn (>10%). Enrichment factors calculated for heavy metals over reference background level in seawater and Earth's crust showed relatively higher enrichment of Zn. The principal component analysis (PCA) identified the possible sources of ionic species and heavy metals in the wet precipitation samples.

Exploring groundwater hydrochemistry of alluvial aquifers using multi-way modeling

A three-way data set pertaining to hydrochemistry of the groundwater of north Indo-Gangetic alluvial plains was analyzed using three-way component analysis method with the purpose of extracting the information on spatial and temporal variation trends in groundwater composition. Three-way data modeling was performed using PARAFAC and Tucker3 models. The models were tested for their stability and goodness of optimal fit using core consistency diagnostic and split-half analysis. Although, a two-component PARAFAC model, explaining 50.47% of data variance, yielded 100% core consistency, it failed to qualify the validation test. Tucker3 model (3, 3, 1) captured 55.18% of the data variance and yielded simple diagonal core with three significant elements, explaining 100% of the core variability. Interpretation of the information obtained through Tucker3 model revealed that the groundwater quality in Khar watershed is mainly dominated by water hardness and related variables, whereas, water composition of the dug wells is dominated by alkalinity and carbonate/bicarbonates. Moreover, shallow groundwater sources in the region are contaminated with nitrate derived from fertilizers application in the region. The shallow aquifers are relatively more contaminated during the post-monsoon season.

Multi-way modeling of hydro-chemical data of an alluvial river system--a case study

A large data set pertaining to water quality of an alluvial river was analyzed using multi-way data analysis methods with a view to extract the hidden information, spatial and temporal variation trends in the river water quality. Four-way data (8 monitoring sites x 22 water quality variables x 10 monitoring years x 12 sampling months) analysis was performed using PARAFAC and Tucker3 models. A two component PARAFAC model, although explained 35.1% of the data variance, could not fit to the data set. Tucker3 model of optimum complexity (2,3,1,3) explaining 39.7% of the data variance, allowed interpretation of the data information in four modes. The model explained spatial and temporal variation trends in terms of water quality variables during the study period and revealed that sampling sites in mid-stretch of the river were dominated mainly by the variables of anthropogenic origin. The results delineated the mid stretch of the river as critical from pollution point of view and also identified summer months as having high influence on river water quality in this stretch. The information regarding spatial and temporal variations in water quality generated by the four-way modeling of data would be useful in developing long-term water resources management strategies in the river basin.

Chemometrical investigation of the presence and distribution of organochlorine and polyaromatic compounds in sediments of the Ebro River Basin

A multivariate statistical data analysis, using principal component analysis, of historical data from 1996 to 2003 concerning the concentration of different polycyclic aromatic hydrocarbons and organochlorine compounds in sediment samples from different sampling sites of the Ebro River Basin was performed under the UE funded project AQUATERRA. Three major contamination sources were identified and their composition and distribution profiles were resolved. The first contamination profile was mostly loaded by polycyclic aromatic hydrocarbons, the second contamination profile was loaded by some organochlorine compounds and the third contamination profile was more specifically loaded by naphthalene. Samples from the different geographical regions of the Ebro River Basin were grouped according to the contamination described by these three major profiles.

NMR spectroscopy study of freshwater humic material in light of supramolecular assembly

The structural similarity-dissimilarity of several humic-type derivatives, separated from a strongly colored freshwater sample by different sorbing solid techniques, tangential-flow ultrafiltration (UF), and large-scale preparative high-performance size-exclusion chromatography (HPSEC), were in detail studied with one-dimensional liquid 1H and solid-state 13C nuclear magnetic resonance (NMR) spectroscopy, especially in light of the native humic-type dissolved organic matter (DOM-HM). The results support the applicability of functional cross-linked poly(vinylpyrrolidone) (PVP) or diethylaminoethyl-cellulose (DEAE) sorbents in concentrating representative integrated wholes of aquatic humic-type material along with a conventional nonionic XAD-8/DAX-8 (polymethyl methacrylates) technique. Apart from the fact that the acidification of the original humic water before a separation procedure seems not to be so destructive to the original structural composition of the DOM-HM as expected, the refinement of aquatic humic solutes, independent of the selected sorbing solid technique, will cause structural changes in the separated humic complexes in comparison with the situation predominating in the original starting material. Tangential-flow ultrafiltration (UF) proved an overpowering reliability to concentrate the aquatic DOM-HM. Most fundamental is the combined outcome of different HPSEC experiments and determined structural functionalities which indicate that almost all original DOM-HM solutes are aggregated mixtures consisting of structurally similar associations possessing various molecular size ranges, which can be separated from their integrated whole as nearly homogeneous and uniform species. This finding permits a reasonable starting point to go on working with more advanced multidimensional NMR techniques in resolving the uncertainty about supramolecular assembly of dissolved humic material. The tested conformity between the obtained molecular NMR descriptors and the corresponding previously collected FT-IR parameters was acceptable thus speaking for the fact that the less sensitive FT-IR spectroscopy can also provide valuable information on the structural and functional properties of heterogeneous humic-type mixtures.