Assessment of the sequential principal component analysis chemometric tool to identify the soluble atmospheric pollutants in rainwater

Abstract

In this study a new method of principal component (PC) analysis, sequential PC analysis (SPCA), is proposed and assessed on real samples. The aim was to identify the atmospheric emission sources of soluble compounds in rainwater samples, and the sample collection was performed with an automatic sampler. Anions and cations were separated and quantified by ion chromatography, whereas trace metals and metalloids were determined by inductively coupled plasma mass spectrometry. SPCA results showed eight interfering PCs and ten significant PCs. The interfering cases originated from different atmospheric sources, such as resuspended crustal particles, marine aerosols, urban traffic and a fertilizer factory. The significant PCs explained 84.6% of the total variance; 28.1% accounted for the main contribution, which was resuspended industrial soil from a fertilizer factory containing NO - 2 , NH + 4 , NO - 3 , SO 2- 4 , F-, Al, K+, Mn, Sb and Ca2+ as indicators of the fertilizer factory. Another important source (15.0%) was found for Na+, Mg2+, K+, Cl- and SO 2- 4 , which represents the marine influence from south and southwest directions. Emissions of Ba2+, Pb, Sr2+, Sb and Mo, which represent a traffic source deposited in soils, were identified as another abundant contribution (12.1%) to the rainwater composition. Other important contributions to the rainwater samples that were identified through SPCA included the following: different urban emissions (Cu, As, Cd, Zn, Mo and Co, 18.1%), emissions from vegetation (HCOO-, 7.7%) and emissions from industrial combustion processes (Ni, V 15.6%). The application of SPCA proved to be a useful tool to identify the complete information on rainwater samples as indicators of urban air pollution in a city influenced mainly by vehicle traffic emissions and resuspended polluted soils.