The paper discusses the use of multiclustering statistical analysis in the assessment of domestic wastewater filtration effectiveness. Calculations included data collected over four months of experiments with using waste as filling material of vertical flow filters for domestic sewage treatment. The effectiveness of pollutants removal was analysed in case of me-chanically shredded waste in the form of PET flakes, PUR foam trims, shredded rubber tires and wadding. The organic compounds (CODcr, BOD5) removal, suspend solids, biogens (as NH4+, PO43– ions) and oxygen saturation changing com-pared with sand filling was analysed. Multiclustering statistical analysis allowed to divide pollutants removal efficiency of analysed materials into 3 clusters, depending on the hydraulic loading. The first group consisted in quality parameters of treated sewage: the highest reduction of BOD5 and NH4-N. It included the values of quality parameters and indicators for the filtrates obtained at the lowest hydraulic load from columns filled with 60 cm of rubber tires or sand. The second group comprised the results for fillings containing foam, PET and rubber tires (the other hydraulic loads).It featured the highest reduction of total suspended solids and PO43–. Removal of easily biodegradable organic compounds was at a similar level in both cluster groups. The filter filled with polyester waste (wadding), which was as effective as 30 cm layer of sand, and the filters filled with 60 cm of sand working at the highest hydraulic load. Third group showed the lowest values of parameters and indicators for analysed filtrates.
The article presents the results of research, the aim of which was to determine the qualitative and quantitative structure of the causes of accidents that were a result of falling from scaffolding. An original methodology for the classification of accidents with regards to their causes was developed and was based on cluster analysis. An example of using the proposed methodology is provided. 187 post-accident protocols of occupational accidents involving construction scaffolding, which occurred between 2010 and 2017 in selected Polish voivodeships, were analyzed. Afterwards, the matrix of accident causes, for which the calculations were made, was created. Five subsets of accidents were obtained and the accidents were classified to a subset with similar causes.
The construction site and its elements create circumstances that are conducive to the formation of risks to work safety during the execution of works. Analysis indicates the critical importance of these factors in the set of characteristics that describe the causes of accidents in the construction industry. This paper attempts to analyse the characteristics of the construction site to indicate their importance in defining the circumstances of an accident at work. The research was carried out on the basis of data from the register kept by the District Labour Inspectorate in Krakow, Poland. Main substantive tasks include isolating patterns of accidents on site and identifying those of the analysed characteristics that are important in defining these patterns. In terms of methodology, the paper presents a method of analysing data resources by using means of conceptual grouping in the form of cluster analysis.
The aim of this article is to evaluate the quality of the Danube River in its course through Serbia as well as to demonstrate the possibilities for using three statistical methods: Principal Component Analysis (PCA), Factor Analysis (FA) and Cluster Analysis (CA) in the surface water quality management. Given that the Danube is an important trans-boundary river, thorough water quality monitoring by sampling at different distances during shorter and longer periods of time is not only ecological, but also a political issue. Monitoring was carried out at monthly intervals from January to December 2011, at 17 sampling sites. The obtained data set was treated by multivariate techniques in order, firstly, to identify the similarities and differences between sampling periods and locations, secondly, to recognize variables that affect the temporal and spatial water quality changes and thirdly, to present the anthropogenic impact on water quality parameters.
The paper discusses possible applications of the percolation theory in analysis of the microstructure images of polycrystalline materials.
Until now, practical use of this theory in metallographic studies has been an almost unprecedented practice. Observation of structures so
intricate with the help of this tool is far from the current field of its application. Due to the complexity of the problem itself, modern
computer programmes related with the image processing and analysis have been used. To enable practical implementation of the task
previously established, an original software has been created. Based on cluster analysis, it is used for the determination of percolation
phenomena in the examined materials. For comparative testing, two two-phase materials composed of phases of the same type (ADI
matrix and duplex stainless steel) were chosen. Both materials have an austenitic - ferritic structure. The result of metallographic image
analysis using a proprietary PERKOLACJA.EXE computer programme was the determination of the content of individual phases within
the examined area and of the number of clusters formed by these phases. The outcome of the study is statistical information, which
explains and helps in better understanding of the planar images and real spatial arrangement of the examined material structure. The results
obtained are expected to assist future determination of the effect that the internal structure of two-phase materials may have on a
relationship between the spatial structure and mechanical properties.
The Shatt Al Arab River (SAAR) is a major source of raw water for most water treatment plants (WTP’s) located along with it in Basrah province. This study aims to determine the effects of different variables on water quality of the SAAR, using multivariate statistical analysis. Seventeen variables were measured in nine WTP’s during 2017, these sites are Al Hussain (1), Awaissan (2), Al Abass (3), Al Garma (4), Mhaigran (5), Al Asmaee (6), Al Jubaila (7), Al Baradia (8), Al Lebani (9). The dataset is treated using principal component analysis (PCA) / factor analysis (FA), cluster analysis (CA) to the most important factors affecting water quality, sources of contamination and the suitability of water for drinking and irrigation. Three factors are responsible for the data structure representing 88.86% of the total variance in the dataset. CA shows three different groups of similarity between the sampling stations, in which station 5 (Mhaigran) is more contami-nated than others, while station 3 (Al Abass) and 6 (Al Asmaee) are less contaminated. Electrical conductivity (EC) and sodium adsorption ratio (SAR) are plotted on Richard diagram. It is shown that the samples of water of Mhaigran are locat-ed in the class of C4-S3 of very high salinity and sodium, water samples of Al Abass station, are located in the class of C3-S1 of high salinity and low sodium, and others are located in the class of C4-S2 of high salinity and medium sodium. Generally, the results of most water quality parameters reveal that SAAR is not within the permissible levels of drinking and irrigation.