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

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 Occurrence in Two Mountainous Rivers in the Lowland Area—A Case Study of the Central Pomeranian Region, Poland

Because of the increasing worldwide awareness concerning the occurrence of microplastics (MPs) in aquatic ecosystems, our goal was to analyze for the first time the quality and abundance of MPs and assess their seasonal variation in two unique rivers flowing through the low-land area in northern Poland. Their uniqueness is due to the fact they flow through landscape parks and urbanized zones, possess mountainous characteristics, and are aquatic habitats for sea trout (Salmo trutta m. trutta) and salmon (Salmo salar). In this pioneering study, the morphological types, geometric dimensions, and color of MP particles were analyzed by the use of an optical microscope. MPs particles were detected in 62.5% of the river water samples, while the average abundance was 3.6–4.2 items per sample. In terms of general seasonality, the sum of MPs items found in investigated river water samples decreased in the following order: spring (75 items) > summer (64 items) > autumn (52 items). Neither the total MPs abundance nor any morphological MPs types were statistically different between rivers according to single seasons. The quantity of MPs present in the river water was higher downstream of the wastewater treatment plant studied, which confirms that treated sewage effluent is a key source of MPs in an aquatic environment. Among the morphological types, fragments were prevalent among granules and fibers, while their average length not exceeding 1.0 mm enabled them to be classified as small. MPs were classified into nine colors, however, the bright colors were dominating only in the case of granule. In the case of the fragments and fibers, the dominating colors were transparent, white, blue, and black. Fourier transform infrared spectroscopy was performed on a small sample of microplastics (21.0%) due to their small size. Polymers containing polyethylene, polyvinyl chloride, polypropylene, polyester, and polystyrene were identified

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

6Normative 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.nonemixedA. M. Astel; L. Giorgini; A. Mistaro; I. Pellegrini; S. Cozzutto; P. BarbieriA. M., Astel; L., Giorgini; A., Mistaro; I., Pellegrini; Cozzutto, Sergio; Barbieri, Pierluig