Effects of transport patterns on chemical composition of sequential rain samples: trajectory clustering and principal component analysis approach

The chemical composition and long-range transportation (LRT) of rain events were assessed in this study. For this purpose, a fully automated wet-only sequential sampler was used to differentiate between washout and rainout processes. The chemical composition of elements (Al, As, Ca, Cd, Co, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, Pb, V, and Zn) and ions (F-, Cl-, NO3-, SO4-2, and NH4+) were quantified in 172 rainwater samples. Cluster analysis (CA) statistical approach was used to classify the back trajectories of the rain events. The CA revealed a seven-cluster solution which provided better explanations for the effects of possible source regions on the receptor site. Consequently, principal component analysis (PCA) was conducted on the normalized cluster-based mean concentrations of the chemical species in order to statistically identify the similarities among the clusters. In conclusion, there were four main sources which strongly affected the chemical composition of precipitation in the study area namely: (i) anthropogenic pollutants from Southwestern and Eastern Europe, (ii) Saharan dust intrusion from Northern Africa, (iii) resuspension of crustal material from nearby regions, and (iv) marine aerosols from Mediterranean and the Black Sea. The proposed methodology combining trajectory cluster analysis, chemical analysis, and principal component analysis was satisfactory to identify the source regions of the trajectories carrying the observed pollutants to the study area.