Chemometric exploration of the abundance of trace metals and ions in desalinated and bottled drinking water in Kuwait.

Chemometric exploration of desalinated and bottled water in Kuwait was employed to interpret the spatial variation in the physicochemical parameters. The data set consisted of the concentrations of principal macronutrient elements, ions, trace elements, temperature, pH, electrolytic conductivity, and total dissolved solids measured in indoor, outdoor, and bottled water samples. Quantitative assessment of the Cd, Hg, and Sb contents revealed rare cases of elevated concentrations; however, these concentrations were always below international health agency standards. Two general clusters of similar parameters were discovered in the variables mode and were associated with "natural" water characteristics or "conditions" of the pipeline system. We found that an increase in temperature facilitates the leaching of metals from the metallic equipment in the system. Spatial variation in the water quality was discovered, which indicates that residential areas fed from the Az-Zoor plant are supplied with water that contains lower concentrations of Ca, Cr, Mg, Mo, Ni, Na, TDS, and SO4 (2-) than the desalinated water produced and fed from the Doha plant. However, on the basis of the aluminum concentration in the water, cement mortar lining is assumed to be prevalent in the pipeline systems of the Mubarak Al-Kabeer, Ahmadi, Umm Al-Haiman, and Sorra areas.

Self-Organizing Maps: An AI Tool for Identifying Unexpected Source Signatures in Non-Target Screening Analysis of Urban Wastewater by HPLC-HRMS.

(1) Background: Monitoring effluent in water treatment plants has a key role in identifying potential pollutants that might be released into the environment. A non-target analysis approach can be used for identifying unknown substances and source-specific multipollutant signatures. (2) Methods: Urban and industrial wastewater effluent were analyzed by HPLC-HRMS for non-target analysis. The anomalous infiltration of industrial wastewater into urban wastewater was investigated by analyzing the mass spectra data of "unknown common" compounds using principal component analysis (PCA) and the Self-Organizing Map (SOM) AI tool. The outcomes of the models were compared. (3) Results: The outlier detection was more straightforward in the SOM model than in the PCA one. The differences among the samples could not be completely perceived in the PCA model. Moreover, since PCA involves the calculation of new variables based on the original experimental ones, it is not possible to reconstruct a chromatogram that displays the recurring patterns in the urban WTP samples. This can be achieved using the SOM outcomes. (4) Conclusions: When comparing a large number of samples, the SOM AI tool is highly efficient in terms of calculation, visualization, and identifying outliers. Interpreting PCA visualization and outlier detection becomes challenging when dealing with a large sample size.

Multivariate statistics as means of tracking atmospheric pollution trends in Western Poland.

This study was carried out over a period of 4 years (2002-2005) at 2 sites located in western Poland differing as regards to human impact by analysis of chemical composition of bulk precipitation. The aim of the study was to determine the sources of pollutions and assess their quantitative contribution to the bulk precipitation composition and to analyse long term-changes in the chemical quality of precipitation. Based on this information the possible transboundary impacts of pollution were also determined. The samples were characterized by determining the values of pH, electrolytic conductivity and concentration levels of Cl(-), F(-), SO(4)(2-), NO(3)(-), Na(+), K(+), Mg(2+), Ca(2+) and NH(4)(+). Analytical measurements were connected with application of principal component regression (PCR) and time series analysis (TS). Based on PCR results three major sources of pollutants in central part of Poland have been identified and quantitatively assessed as follows: "combined" (Poznan - 31%, WNP - 32%), "soil-particulates" (Poznan - 2%, WNP - 26%), "anthropogenic-fossil fuels" (Poznan - 43%, WNP - 23%). Time series analysis enabled discovering 12-month time cycle for NO(3)(-), NH(4)(+), Cl(-), F(-) and SO(4)(2-) in average monthly concentration values in bulk precipitation collected in Wielkopolski National Park. Seasonal variation in the emission of precursors of NO(3)(-) and NH(4)(+) was caused by changes in intensity of fertilizer application in agriculture and automobile exhaust emissions. Decreasing trend was visible for sulphates, nitrates, chlorides and fluorides which is an important indication of the acid rain reduction in the ecologically protected area and in Poznan.