The pollen morphology of many collections of taxa of the tribe Nigelleae from the family Ranunculaceae which occur worldwide is presented in this study. A total of 88 specimens from 21 taxa, some of which were recently proposed, belonging to the genera Komaroffia, Garidella, and Nigella of Nigelleae were examined using light microscopy (LM) and scanning electron microscopy (SEM). In the tribe, the pollen type is mostly trizonocolpate, but in many taxa and specimens, both trizonocolpate and non-trizonocolpate types occur together. The pollen grains are small to medium (25–53.75 μm × 20–55 μm) in size and oblate to prolate in shape. The exine pattern at the mesocolpium in all the taxa investigated is similar: micro-echinate in LM and micro-echinate-punctate in SEM. The colpus membrane in Komaroffia and Nigella is micro-echinate in both LM and SEM. In Garidella, it is micro-echinate in LM but echinate (spinulose) in SEM. In this study, multivariate analyses, principal component analysis (PCA), and unweighted pair group method with arithmetic mean (UPGMA), were used to evaluate relationships between the genera and species within the tribe with respect to pollen morphology. PCA results show three main groups in the tribe: Garidella, Komaroffia, and Nigella. Moreover, the UPGMA tree also chiefly supports generic segregation into the smaller genera. An overall synthesis of the pollen characteristics of the three genera is provided and discussed.

The aim of this study was the application of the geo-accumulation index and geostatistical methods to the assessment of forest soil contamination with heavy metals in the Babia Góra National Park (BGNP). For the study, 59 sample plots were selected to reflect all soil units (soil subtypes) in the studied area and take into account various forms of terrain. The content of organic carbon and total nitrogen, pH, hydrolytic acidity, the base cations and heavy metals content were determined in the soil samples. The geo-accumulation index (Igeo) was calculated, enabling estimation of the degree of soil pollution. The tested soils are characterized by strong contamination with heavy metals, especially with lead. The concentration of heavy metals in the surface horizons of the tested soils exceeds allowable concentration. The content of heavy metals was related to the content of soil organic matter, soil acidity and altitude. Higher altitudes are dominated by coniferous tree stands, which are accompanied by acidic, poorly decomposed organic horizons. Our study has confirmed the impact of pollutants transported from industrial areas on the amount of heavy metals in soils of the BGNP.

Popular statistical techniques, such as Spearman's rank correlation matrix, principal component analysis (PCA) and multiple linear regression analysis were applied to analyze a large set of water quality data of the Rybnik Reservoir generated during semiannual monitoring. Water samples collected at 9 sampling sites located along the main axis of the reservoir were tested for 14 selected parameters: concentrations of co-occurring elements, ions and physicochemical parameters. The aim of this study was to estimate the impact of those parameters on inorganic arsenic occurrence in Rybnik Reservoir water by means of multivariate statistical methods. The spatial distribution of arsenic in Rybnik Power Station reservoir was also included. Inorganic arsenic As(III), As(V) concentrations were determined by hydride generation method (HG-AAS) using SpectrAA 880 spectrophotometer (Varian) coupled with a VGA-77 system for hydride generation and ECT-60 electrothermal furnace. Spearman's rank correlation matrix was used in order to find existing correlations between total inorganic arsenic (AsTot) and other parameters. The results of this analysis suggest that As was positively correlated with PO43-; Fe and TDS. PCA confirmed these observations. Principal component analysis resulted in three PC's explaining 57% of the total variance. Loading values for each component indicate that the processes responsible for As release and distribution in Rybnik Reservoir water were: leaching from bottom sediments together with other elements like Cu, Cd, Cr, Pb, Zn, Ni, Ca (PC1) and co-precipitation with PO43-, Fe and Mn (PC3) regulated by physicochemical properties like T and pH (PC2). Finally, multiple linear regression model has been developed. This model incorporates only 8 (T, pH, PO43-, Fe, Mn, Cr, Cu, TDS) out of initial 14 variables, as the independent predictors of total As contamination level. This study illustrates the usefulness of multivariate statistical techniques for analysis and interpretation of complex environmental data sets.

Aluminium slag waste is a residue from aluminium recycling activities, classified as hazardous waste so its disposal into the environment without processing can cause environmental problems, including groundwater pollution. There are 90 illegal dumping areas for aluminium slag waste spread in the Sumobito District, Jombang Regency. This study aims to evaluate the quality of shallow groundwater surrounding aluminium slag disposal in the Sumobito District for drinking water. The methods applied an integrated water quality index ( WQI) and heavy metal pollution index ( HPI), multivariate analysis (principal component analysis (PCA) and hierarchical clustering analysis (HCA)), and geospatial analysis for assessing groundwater quality. The field campaign conducted 40 groundwater samples of the dug wells for measuring the groundwater level and 30 of them were analysed for the chemical contents. The results showed that some locations exceeded the quality standards for total dissolved solids ( TDS), electrical conductivity (EC), and Al ²⁺. The WQI shows that 7% of dug well samples are in poor drinking water condition, 73% are in good condition, and 20% are in excellent condition. The level of heavy metal contamination based on HPI is below the standard limit, but 13.3% of the water samples are classified as high contamination. The multivariate analysis shows that anthropogenic factors and natural sources/geogenic factors contributed to shallow groundwater quality in the study area. The geospatial map shows that the distribution of poor groundwater quality is in the northern area, following the direction of groundwater flow, and is a downstream area of aluminium slag waste contaminants.