Abstract. Primary biological aerosol particles (PBAPs) play an important
role in the interaction between biosphere, atmosphere, and climate, affecting
cloud and precipitation formation processes. The presence of pollen, plant
fragments, spores, bacteria, algae, and viruses in PBAPs is well known. In
order to explore the complex interrelationships between airborne and
particulate chemical tracers (amino acids, saccharides), gene copy numbers
(16S and 18S for bacteria and fungi, respectively), gas phase chemistry, and
the particle size distribution, 84 size-segregated aerosol samples from four
particle size fractions (< 1.0, 1.0–2.5, 2.5–10, and > 10 µm) were collected at the SMEAR II station,
Finland, in autumn 2017. The gene copy numbers and size distributions of
bacteria, Pseudomonas, and fungi in biogenic aerosols were determined by DNA extraction
and amplification. In addition, free amino acids (19) and saccharides (8)
were analysed in aerosol samples by hydrophilic interaction liquid
chromatography–mass spectrometry (HILIC-MS). Different machine learning
(ML) approaches, such as cluster analysis, discriminant analysis, neural
network analysis, and multiple linear regression (MLR), were used for the clarification
of several aspects related to the composition of biogenic aerosols. Clear
variations in composition as a function of the particle
size were observed. In most cases, the highest concentration values and gene copy numbers (in
the case of microbes) were observed for 2.5–10 µm particles, followed
by > 10, 1–2.5, and < 1.0 µm particles.
In addition, different variables related to the air and soil temperature,
the UV radiation, and the amount of water in the soil affected the
composition of biogenic aerosols. In terms of interpreting the results, MLR provided the greatest improvement over classical
statistical approaches such as Pearson correlation among the ML approaches considered. In all cases, the
explained variance was over 91 %. The great variability of the samples
hindered the clarification of common patterns when evaluating the
relation between the presence of microbes and the chemical composition of
biogenic aerosols. Finally, positive correlations were observed between
gas-phase VOCs (such as acetone, toluene, methanol, and 2-methyl-3-buten-2-ol)
and the gene copy numbers of microbes in biogenic aerosols.
Enantioselectivity-Evaluation of Chiral Copper(II) Complexes Coordinated by Novel Chiral Tetradentate Ligands for Free Amino Acids by Mass Spectrometry Coupled With the Isotopically Labeled Enantiomer Method
