doiSerbia

Ambient air particles: The use of ion chromatography and multivariate techniques in the analysis of water-soluble substances

Todorović Žaklina N. (Anahem Laboratory, Belgrade, Serbia)
Radulović Jelena M. (Anahem Laboratory, Belgrade, Serbia)
Sredović-Ignjatović Ivana D. (University of Belgrade, Faculty of Agriculture, Belgrade, Serbia)
Ignjatović Ljubiša M. (University of Belgrade, Faculty of Physical Chemistry, Belgrade, Serbia), ljignjatovic@ffh.bg.ac.rs
Onjia Antonije E. (University of Belgrade, Faculty of Technology and Metallurgy, Belgrade, Serbia)

Seventeen water-soluble substances (of sodium, ammonium, potassium, magnesium, calcium, formate, methanesulfonate, glyoxylate, chloride, nitrite, nitrate, glutarate, succinate, malate, malonate, sulfate and oxalate) in 94 samples of particle matter in the ambient air, collected over ten months, in a suburb of Belgrade (Serbia), were determined by ion chromatography. To apportion the sources of the air pollution, the log-transformed data were processed by applying multivariate techniques. Principal component and factor analysis identified three main factors controlling the data variability: stationary combustion processes with the highest loadings of oxalate, malonate and malate; landfill emission and secondary inorganic aerosol characterized by high levels of ammonium, nitrate and sulfate; a contribution of mineral dust composed of magnesium, calcium and chloride. The hierarchical cluster analysis pointed out a differentiation of the samples into five groups belonging to different variables inputs. For the classification of ambient air samples using nine selected ions, the recognition ability of linear discriminant analysis, k-nearest neighbors, and soft independent modeling of class analogy were 87.0, 94.6, and 97.8 %, respectively. Time-series analysis showed that the traffic emission is more pronounced in winter in contrast to the mineral dust influence, while the effect of waste combustion exhibits no trend.

Keywords: organic acids, pollution sources, PCA, emission factors, time-series

Project of the Serbian Ministry of Education, Science and Technological Development, Grant no. 451-03-68/2020-14/200135