Sediment-quality assessment by intelligent data analysis

Multi-year monitoring of atmospheric dust fall as a sink for lead in an agro-industrial and petrochemical city of Argentina. Geo-accumulation and ecological risk assessment

A multi-year monitoring data set of potentially harmful elements (PHEs), which are present in the chemical composition of atmospheric settleable particulate matter (SPM) in the urban, industrial and port areas in Bahía Blanca, was studied in order to assess potential ecological risk. The selected PHEs were metal elements of local and regional environmental importance (Cd, Cr, Cu, Ni, Pb, and Zn). Seventeen sampling campaigns were carried out between April 2013 and September 2019. After the microwave-assisted acid digestion of samples, the total contents of the PHEs were determined by ICP-OES. The annual dry deposition rate, the indexes associated with the potential ecological risk (RI) and the degree of geo-accumulation (Igeo) of each PHE were calculated. The results indicated that: (a) there are 3 groups (I, II, III) of PHEs with differentiated concentration levels, ranked I (Pb > Zn > Cu) > II (Cr ≈ Ni) > III (Cd) (p < 0.01) in all the studied areas; (b) the median of the total deposition rate was 1 mg cm-2. month-1 with a significant relative contribution of Pb; (c) a considerable increase in geo-accumulation of Pb indicated that SPM was functioning as a sink for Pb, and also reflected a significant progressive increase in the potential ecological risk in all sites (p < 0.01); and (d) there were chemometrically identified potential sources of Pb, Cu and Zn emissions that would be associated mainly to the resuspension of dust from geogenic, industrial and urban origin, and to a lesser extent, to other gaseous emissions of the industrial sector. This work highlights three major aspects of environmental assessment: (a) the value of continuous monitoring as an important tool to detect long-term trends; (b) the importance of the role of dust fall as a useful environmental indicator of lead geo-accumulation; and (c) the great utility of geo-accumulation and potential ecological risk indices as rapid quantitative assessment tools of environmental pollution.

Determination of the botanical origin of honeybee honeys based on the analysis of their selected physicochemical parameters coupled with chemometric assays

The aim of this paper was to study the select physicochemical parameters of 58 honey samples of 4 different botanical origins (buckwheat, linden, rape and acacia) using multivariate methods in order to classify honeys according to the botanical origin. Five standard physicochemical parameters were determined according to the international legislation: water content, electrical conductivity, total ash content, free acidity and pH. The results obtained were mostly in agreement with international regulations. Then, the results obtained were analysed by principal components analysis and cluster analysis. The chemometric analysis of results of determinations of the physicochemical parameters demonstrated such markers as electrical conductivity and ash content (i.e. parameters linked with minerals content) to be the most reliable markers in determining the botanical origin of linden and buckwheat honeys. Unfortunately, they appear insufficient for reliable identification of acacia and rapeseed honeys.

Chemometric approach to evaluate element distribution in muscle, liver and fish bone of roach (Rutilus rutilus), silver bream (Blicca bjoerkna) and crucian carp (Carassius carassius) from Swarzędzkie Lake (Poland) using ICP-MS and FIAS-CVAAS techniques

The content of elements in fish tissues and organs from Swarzędzkie Lake was investigated in order to evaluate the possible risk associated with their consumption by animals as well as humans. Samples of muscle, liver and fish bone of three fish species; roach (Rutilus rutilus), silver bream (Blicca bjoerkna) and crucian carp (Carassius carassius) were collected from seine catches undertaken as part of the biomanipulation of Swarzędzkie Lake. Element concentration (Al, As, Cd, Co, Cr, Cu, Hg, Ni, Pb, Zn) was determined by inductively coupled plasma mass spectrometry (ICP-MS), with the exception of Hg where the flow injection analysis system cold vapour atomic absorption spectrometry (FIAS-CVAAS) was applied. The study indicated a large variation in the occurrence of the investigated elements in different parts of the fish body. The highest content of Al and Zn was stated in all fish organs for each fish species. The majority of the applied statistical and chemometric methods (e.g., PCA, CA) refer to roach since we had a large number of data for this species. The obtained results were assessed in terms of their accuracy and precision using certified reference material of Fish Muscle ERM BB422.

Assessment by self-organizing maps of element release from sediments in contact with acidified seawater in laboratory leaching test conditions

Carbon capture and storage (CCS) is gaining interest as a significant global option to reduce emissions of CO2. CCS development requires an assessment of the potential risks associated with CO2 leakages from storage sites. Laboratory leaching tests have proved to be a useful tool to study the potential mobilization of metals from contaminated sediment in a decreased-pH environment that mimics such a leakage event. This work employs a self-organizing map (SOM) tool to interpret and analyze the release of dissolved organic carbon (DOC), As, Cd, Cr, Cu, Ni, Pb, and Zn from equilibrium, column, and pH-dependent leaching tests. In these tests, acidified seawater is used for simulating different CO2 leakage scenarios. Classification was carried out detailing the mobilization of contaminants for environments of varying pH, liquid-to-solid ratio, and type of contact of the laboratory leaching tests. Component planes in the SOMs allow visualization of the results and the determination of the worst case of element release. The pH-dependent leaching test with initial addition of either base or acid was found to mobilize the highest concentrations of metals.

Assessment of heavy metals mobility and toxicity in contaminated sediments by sequential extraction and a battery of bioassays

The aim of this study was to assess heavy metals mobility and toxicity in sediments collected from a dam reservoir in the conditions of intensive human impact by using chemical fractionation and a battery of bioassays. In the studies, the test organisms were exposed to substances dissolved in water (Microtox, Phytotestkit) as well to substances absorbed on the surface of solid particles (Phytotoxkit, Ostracodtoxkit F). The studies showed that sediments from the Rybnik reservoir are toxic, but the tested organisms showed different sensitivity to heavy metals occurring in the bottom sediments. The sediment samples were classified as toxic and very toxic. Moreover, the studies showed a higher toxicity in solid phases and whole sediment than in pore water. The lowest sensitivity was observed in H. incongruens (solid phases) and V. fischeri (pore water, whole sediment). The studies revealed that the toxicity of the sediments is caused mainly by heavy metal forms associated with the solid phase of the sediments. The studies did not confirm the metals occurring in fraction I (exchangeable) to be bioavailable and toxic to living organisms because most correlations between the metal concentration in fraction I and the response of the organisms were negative. The highest mobility from the bottom sediments was found in zinc, average mobility--in copper, cadmium and nickel, and low mobility--in chromium and lead. Organic matter is likely to be the most important factor controlling metal distribution and mobility in the studied sediments.

Melissopalynological Characterization of North Algerian Honeys

A pollen analysis of Algerian honey was conducted on a total of 10 honey samples. The samples were prepared using the methodology described by Louveaux et al., that was then further adapted by Ohe et al. The samples were subsequently observed using light microscopy. A total of 36 pollen taxa were discovered and could be identified in the analyzed honey samples. Seventy percent of the studied samples belonged to the group ofmonofloral honeys represented by Eucalyptus globulus, Thymus vulgaris, Citrus sp. and Lavandula angustifolia. Multifloral honeys comprised 30% of the honey samples, with pollen grains of Lavandula stoechas (28.49%) standing out as the most prevalent. Based on cluster analysis, two different groups of honey were observed according to different pollen types found in the samples. The identified pollen spectrum of honey confirmed their botanical origin.

Pattern recognition and classification of sediments according to their metal content using chemometric tools. A case study: the estuary of Nerbioi-Ibaizabal River (Bilbao, Basque Country)

Chemometrics are increasingly used in environmental monitoring studies, but are still far from being accepted as routine tools by field specialists. The multivariate character of usually highly correlated environmental data recommends the use of advanced chemometrics as part of the analytical methodology in order to get information on the basic structure of data. In this work, we have applied a battery of non-supervised (Principal Component Analysis (PCA)) and supervised (k-Nearest Neighbour (k-NN), Soft Independent Modelling of Class Analogies (SIMCA), Linear Discriminant Analysis (LDA) and Artificial Neural Networks (ANNs)) multivariate techniques on a specific environmental dataset. The dataset consists on the concentration of 14 elements (Al, As, Cd, Co, Cr, Cu, Fe, Mg, Mn, Ni, Pb, Sn, V and Zn) in 95 sediments collected at eight different locations of the estuary of the Nerbioi-Ibaizabal River (Bilbao, Basque Country) during 12 sampling campaigns conducted every 3 months between 2005 and 2008. The study aims to present a simple methodology of general applicability which may result in a flexible and practical tool to assess chemical pollution in sediments of a given specific site.

Spatiotemporal dynamics of spring and stream water chemistry in a high-mountain area

The present study deals with the application of the self-organizing map (SOM) technique in the exploration of spatiotemporal dynamics of spring and stream water samples collected in the Chochołowski Stream Basin located in the Tatra Mountains (Poland). The SOM-based classification helped to uncover relationships between physical and chemical parameters of water samples and factors determining the quality of water in the studied high-mountain area. In the upper part of the Chochołowski Stream Basin, located on the top of the crystalline core of the Tatras, concentrations of the majority of ionic substances were the lowest due to limited leaching. Significantly higher concentration of ionic substances was detected in spring and stream samples draining sedimentary rocks. The influence of karst-type springs on the quality of stream water was also demonstrated.

Environmetric approaches for lake pollution assessment

The application of multivariate statistical methods to high mountain lake monitoring data has offered some important conclusions about the importance of environmetric approaches in lake water pollution assessment. Various methods like cluster analysis and principal components analysis were used for classification and projection of the data set from a large number of lakes from Rila Mountain in Bulgaria. Additionally, self-organizing maps of Kohonen were constructed in order to solve some classification tasks. An effort was made to relate the maps with the input data in order to detect classification patterns in the data set. Thus, discrimination chemical parameters for each pattern (cluster) identified were found, which enables better interpretation of the pollution situation. A methodology for application of a combination of different environmetric methods is suggested as a pathway to interpret high mountain lake water monitoring data.

Heavy metal induced ecological risk in the city of Urumqi, NW China

A total of 169 samples of road dust collected in the city of Urumqi, capital of the Xinjiang Uygur Autonomous Region in northwest China, were analyzed by method of inductively coupled plasma-mass spectrometry for 10 elements (i.e., Cd, Cr, Cu, Ni, Pb, Mn, Be, Co, Zn, and U). The possible sources of metals are identified with multivariate analysis such as correlation analysis, principal component analysis, and cluster analysis. Besides, enrichment factors are used to quantitatively evaluate the influences of human activities on heavy metal concentrations. Moreover, the potential ecological risk index is applied to evaluating the ecological risk of heavy metal pollutants. The results indicate that: (1) the concentrations of the heavy metals involved were much higher in urban areas than the background values, except those of Co and U. Mn, U, and Co are mainly of natural origin; Cu, Pb, Zn, and Cr are mainly of traffic sources and are partly of industrial sources; Ni and Be are mainly the results of industrial activities, such as machine shops, firepower plants, tire and rubber factories, cement factories, and textile mills and are partly of the traffic sources; (2) with high "toxic-response" factor and high concentration, Cd has more serious influences on the environment than other heavy metals. Therefore, commercial and industrial areas are usually characterized by higher potential ecological risk when compared with residential areas and new developing urban areas. The results of this study could be helpful for the management of environment in industrial areas.

Test of the relationships between the content of heavy metals in sewage sludge and source of their pollution by chemometric methods

The content of various metals (Cd, Cr, Cu, Ni, Pb and Zn) in sewage sludge was analysed by ICP-OES technique. The study was performed on 14 samples from the Wielkopolska region and 4 from the neighbouring provinces. The results were used to perform chemometric analysis. Two chemometric methods were used to test the relationships between the content of heavy metals in sewage sludge and the sources of their pollution. The application of cluster analysis displayed important information about the identification of similar locations of sewage sludge sampling stations. This chemometric method showed that all the monitoring locations are grouped into three main clusters. Separated clusters present similarities between locations of the sewage treatment plants, which have the same kind of industrial plants in their catchment area. Principal component analysis enabled interpretation of the complex relationships between determined elements. Application of principal component analysis to the whole data set helped to distinguish only two sewage sludge stations (Ostrow Wlkp. and Poznan-Kozieglowy) that could be interpreted, each in different principal component thereby suggesting that element's concentration differ considerably. The interpretation of relationships between the rest of the stations was possible by performing PCA for the second time, but on the reduced data set (two above-mentioned stations were excluded). It distinguished two groups: (1) Gniezno, Srem, Kalisz, Inowrocław and Sroda Wlkp, and (2) Gostyn, Gniezno and Kalisz, which differ with regard to element's concentration.

Assessment of Self-Organizing Map artificial neural networks for the classification of sediment quality

The application of mathematical tools in initial steps of sediment quality assessment frameworks can be useful to provide an integrated interpretation of multiple measured variables. This study reveals that the Self-Organizing Map (SOM) artificial neural network can be an effective tool for the integration of multiple physical, chemical and ecotoxicological variables in order to classify different sites under study according to their similar sediment quality. Sediment samples from 40 sites of 3 estuaries of Cantabria (Spain) were classified with respect to 13 physical, chemical and toxicological variables using the SOM. Results obtained with the SOM, when compared to those of traditional multivariate statistical techniques commonly used in the field of sediment quality (principal component analysis (PCA) and hierarchical cluster analysis (HCA)), provided a more useful classification for further assessment steps. Especially, the powerful visualization tools of the SOM, which offer more information and in an easier way than HCA and PCA, facilitate the task of establishing an order of priority between the distinguished groups of sites depending on their need for further investigations or remediation actions in subsequent management steps.

Spatial distribution of major and trace elements in the water of Swarzedzkie Lake (Poland)

In order to characterize the contamination of the Swarzedzkie Lake and generate elemental distribution patterns 18 elements have been determined in samples of water, taken just above the bottom sediments. Analyses were carried out using two instrumental techniques - ICP-OES and ICP-MS. Two main sources of contamination were identified - diffuse pollution from agriculture and stormwater from the town. Three separate groups of elements were distinguished using cluster analysis and two groups using principal component analysis. These three groups are characterized as geogenic elements (Ag, As, Mg and Sb), elements supplied by the agricultural catchment (Ca, Mn, Sr, Na, K and Al) and elements originated from the urban contamination (Ni, Cr, Cu, Fe, V, Ba, Zn and Pb). PCA differentiated the above group of urban elements from the rest, originated from diffuse sources, with exception of alluminium, which was situated between two distinguished groups.