School of Chemistry, Faculty of Science, University of Witwatersrand, Johannesburg, South Africa; Department of Environmental Chemistry and Bioanalytics, Faculty of Chemistry, Nicholas Copernicus University, Torun, Poland; Climatology Research Group, Faculty of Science, University of Witwatersrand, Johannesburg, South Africa; Department of Environmental Chemistry and Bioanalytics, Faculty of Chemistry, Nicholas Copernicus University, Gagarina 7, 87-100 Torún, Poland
Kowalkowski, T., School of Chemistry, Faculty of Science, University of Witwatersrand, Johannesburg, South Africa, Department of Environmental Chemistry and Bioanalytics, Faculty of Chemistry, Nicholas Copernicus University, Torun, Poland, Department of Environmental Chemistry and Bioanalytics, Faculty of Chemistry, Nicholas Copernicus University, Gagarina 7, 87-100 Torún, Poland; Piketh, S.J., Climatology Research Group, Faculty of Science, University of Witwatersrand, Johannesburg, South Africa; Cukrowska, E.M., School of Chemistry, Faculty of Science, University of Witwatersrand, Johannesburg, South Africa
The aim of this study is re-evaluation of the data collected during Ben Macdhui High Altitude Trace Gas and Aerosols Transport Experiment (BHATTEX) to identify the dominant species of aerosols and their interactions in the atmosphere. Particularly, investigations of seasonal variations, the origin of sulphates and the formation of the ammonia sulphate were essential topics of this study. Such analyses were done by application of unique combination of supervised and unsupervised learning classification methods. Namely discriminant function analysis (DFA) for simple identification of parameters and principal component analysis (PCA) for the further investigations on hidden structure of data has been applied. The analysis shows that sulphate concentration and C,N isotopic ratios can classify winter and summer patterns of data. Differences between sulphate concentrations in summer and winter samples indicated by second PCA component were probably related to meteorological conditions over that region. The relations between anthropogenic compounds and ammonia or sulphate were much more stronger in summer samples than in winter ones, that is related with seasonal transport of that particles from industry to the investigated regions. The hypothesis of competitive reaction between sulphates, nitrates and ammonia ions has been also proven by application of simple regression analysis. Moreover the analysis of correlations coefficients shows that those relations are independent on seasons. Copyright © Taylor & Francis Group, LLC.
Air pollution; Ammonia; Discriminant analysis; Principal component analysis; Unsupervised learning; Ammonia sulphate; Discriminant function analysis (DFA); Seasonal variations; Sulphates; Aerosols; ammonia; nitrate; sulfate; aerosol; article; atmosphere; chemical reaction; concentration (parameters); correlation coefficient; discriminant analysis; meteorology; principal component analysis; regression analysis; seasonal variation; South Africa; statistical analysis; structure analysis; summer; winter; Aerosols; Air Pollutants; Altitude; Environmental Monitoring; Geography; Models, Statistical; Seasons; South Africa
