Application of Pattern Recognition and Computer Vision Tools to Improve the Morphological Analysis of Microplastic Items in Biological Samples.

Since, in many routine analytical laboratories, a stereomicroscope coupled with a digital camera is not equipped with advanced software enabling automatic detection of features of observed objects, in the present study, a procedure of feature detection using open-source software was proposed and validated. Within the framework of applying microscopic expertise coupled with image analysis, a set of digital images of microplastic (MP) items identified in organs of fish was used to determine shape descriptors (such as length, width, item area, etc.). The edge points required to compute shape characteristics were set manually in digital images acquired by the camera coupled with a binocular, and respective values were computed via the use of built-in MotiConnect software. As an alternative, a new approach consisting of digital image thresholding, binarization, the use of connected-component labeling, and the computation of shape descriptors on a pixel level via using the functions available in an OpenCV library or self-written in C++ was proposed. Overall, 74.4% of the images were suitable for thresholding without any additional pretreatment. A significant correlation was obtained between the shape descriptors computed by the software and computed using the proposed approach. The range of correlation coefficients at a very high level of significance, according to the pair of correlated measures, was higher than 0.69. The length of fibers can be satisfactorily approximated using a value of half the length of the outer perimeter (r higher than 0.75). Compactness and circularity significantly differ for particles and fibers.

Microplastics in Commercial Fishes and By-Catch from Selected FAO Major Fishing Areas of the Southern Baltic Sea.

According to recent world wide studies, microplastics (MPs) have been found in many fish species; however, the majority of research has focused only on the gastrointestinal tract, neglecting edible organs. This study aimed to assess the presence of microplastics in the non-edible (gills, digestive tract) and edible organs (liver) of three commercial fish species and twoby-catch species from the southern Baltic Sea. Fish (Clupea harengus, Gadus morhua, Platichthy sflesus, Taurulus baublis, Cyclopterus lumpus) were caught in 108 and 103 FAO Fishing Zones belonging to the Polish fishing zone. The abundanceof MPs ranged from 1 to 12 items per fish, with an average of 4.09 items. MPs were observed in different organs, such as the liver, gills, and digestive tract of all five tested species. MPs recognized as fibers were the most abundant. Other shapes of polymers found in fish organs were pellets and particles of larger plastic pieces. The dominant color of the MPs was blue, but there were also red, black, transparent, yellow, green, and white items found. According to dimensions, dominant MPs were between 0.1 and 0.5 mm in size. The chemical characterization of polymers accomplished by the use of Fourier Transform Infrared (FT-IR) Spectroscopy demonstrated the abundance of cellophane, polyamide, polyethylene, polypropylene, polyethylene terephthalate, polyvinyl propionate, polyacrylonitrile, and polyester.

Exploring Soil Pollution Patterns Using Self-Organizing Maps.

The geochemical composition of bedrock is the key feature determining elemental concentrations in soil, followed by anthropogenic factors that have less impact. Concerning the latter, harmful effects on the trophic chain are increasingly affecting people living in and around urban areas. In the study area of the present survey, the municipalities of Cosenza and Rende (Calabria, southern Italy), topsoil were collected and analysed for 25 elements by inductively coupled plasma mass spectrometry (ICP-MS) in order to discriminate the different possible sources of elemental concentrations and define soil quality status. Statistical and geostatistical methods were applied to monitoring the concentrations of major oxides and minor elements, while the Self-Organizing Maps (SOM) algorithm was used for unsupervised grouping. Results show that seven clusters were identified-(I) Cr, Co, Fe, V, Ti, Al; (II) Ni, Na; (III) Y, Zr, Rb; (IV) Si, Mg, Ba; (V) Nb, Ce, La; (VI) Sr, P, Ca; (VII) As, Zn, Pb-according to soil elemental associations, which are controlled by chemical and mineralogical factors of the study area parent material and by soil-forming processes, but with some exceptions linked to anthropogenic input.

Urban BTEX Spatiotemporal Exposure Assessment by Chemometric Expertise.

Normative regulations on benzene in fuels and urban management strategies are expected to improve air quality. The present study deals with the application of self-organizing maps (SOMs) in order to explore the spatiotemporal variations of benzene, toluene, ethylbenzene, and xylene levels in an urban atmosphere. Temperature, wind speed, and concentration values of these four volatile organic compounds were measured after passive sampling at 21 different sampling sites located in the city of Trieste (Italy) in the framework of a multi-year long-term monitoring program. SOM helps in defining pollution patterns and changes in the urban context, showing clear improvements for what concerns benzene, toluene, ethylbenzene, and xylene concentrations in air for the 2001-2008 timeframe.

The effect of metals accumulated in reed (Phragmites australis) on the structure of periphyton.

Studies on trace elements in reed stands and limiting effect of the reed substrate on the periphyton structure were performed in various aquatic ecosystems of Greece during the summer and autumn of 2006. The analysed factors were concentrations of chemical elements (cadmium, lead, zinc, chromium, nickel, copper, cobalt, iron, manganese, potassium, sodium, calcium, magnesium) in reed shoots as well as the density of zooperiphyton and phytoperiphyton taxa. The relationships between metal concentrations and periphyton structure were determined with the use of the multivariate methods Canonical Correspondence Analysis (CCA), Detrended Correspondence Analysis (DCA) and RDA (Redundancy Analysis). The results showed that bioaccumulation of lead and cadmium in the reed had the most negative influence on zooperiphyton species, while low concentrations of alkali metals favoured the occurrence of Cyclopoida, Cladocera (Chydorus sp.) and Oligochaeta (Neis sp.). A considerable resistance to toxic heavy metals characterised Cyanophyta representatives and, partly, colonial Bacillariophyta. High concentrations of alkali metals supported the presence of unicellular Bacillariophyta but diminished the densities of colonial Bacillariophyta and Chlorophyta of the genus Scenedesmus.

Soil Contamination Interpretation by the Use of Monitoring Data Analysis.

The presented study deals with the interpretation of soil quality monitoring data using hierarchical cluster analysis (HCA) and principal components analysis (PCA). Both statistical methods contributed to the correct data classification and projection of the surface (0-20 cm) and subsurface (20-40 cm) soil layers of 36 sampling sites in the region of Burgas, Bulgaria. Clustering of the variables led to formation of four significant clusters corresponding to possible sources defining the soil quality like agricultural activity, industrial impact, fertilizing, etc. Two major clusters were found to explain the sampling site locations according to soil composition-one cluster for coastal and mountain sites and another-for typical rural and industrial sites. Analogous results were obtained by the use of PCA. The advantage of the latter was the opportunity to offer more quantitative interpretation of the role of identified soil quality sources by the level of explained total variance. The score plots and the dendrogram of the sampling sites indicated a relative spatial homogeneity according to geographical location and soil layer depth. The high-risk areas and pollution profiles were detected and visualized using surface maps based on Kriging algorithm.

Influence of anthropogenic transformations of river bed on plant and macrozoobenthos communities.

This study describes the influence of urban area on plant communities and benthic invertebrates inhabiting the Slupia River (northern Poland). Ten plant communities and 37 macrozoobenthos taxa were determined during four seasonal samplings at 25 sampling sites (October 2005 and January, April, and August 2006). The obtained data set was statistically evaluated in order to reveal the influence of anthropogenic transformations on the investigated communities against the background of other abiotic factors. Multivariate regression tree (MRT) method was used for vegetation, while for benthic fauna, both MRT and artificial neural network (ANN) methods were applied. The following explanatory variables were used: season, water temperature, and salinity; location of a sampling site; degree of human impact on the riverbed; microhabitat; and substrate type. MRT analyses showed significant differences in plant community structure depending on the location of a sampling site, indicating the influence of anthropogenic pressure, while macrozoobenthos composition differed significantly only between seasons. The overall ANN model proved the importance of type and location of a sampling site for the approximation of benthic fauna density. Additionally, influence of the explanatory variables on the consecutive macrozoobenthos taxa was analyzed on the basis of separate ANN.