The PM2.5-bound polycyclic aromatic hydrocarbon behavior in indoor and outdoor environments, part I: Emission sources

The previous research, aimed at exploring the relationships between the indoor and outdoor air quality, has evidenced that outdoor PM2.5-bound polycyclic aromatic hydrocarbons (PAH) levels exhibit significant daily and seasonal variations which does not necessary corresponds with PAH indoor dynamics. For the purpose of this study, a three-month measurement campaign was performed simultaneously at indoor and outdoor sampling sites of a university building in an urban area of Belgrade (Serbia), during which the concentrations of O-3, CO, SO2, NOx, radon, PM2.5 and particle constituents including trace metals (As, Cd, Cr, Mn, Ni and Pb), ions (Cl- , Na+, Mg2+, Ca2+, K+, NO3-, SO42- and NH4+) and 16 US EPA priority PAHs were determined. Additionally, the analysis included 31 meteorological parameters, out of which 24 were obtained from Global Data Assimilation System (GDAS1) database. The Unmix and PAH diagnostic ratios analysis resolved the source profiles for both indoor and outdoor environment, which are comparable in terms of their apportionments and pollutant shares, although it should be emphasized that ratio-implied solutions should be taken with caution since these values do not reflect emission sources only. The highest contributions to air quality were attributed to sources identified as coal combustion and related pyrogenic processes. Noticeable correlations were observed between 5- and 6-ring high molecular weight PAHs, but, except for CO, no significant linear dependencies with other investigated variables were identified. The PAH level predictions in the indoor and outdoor environment was performed by using machine learning XGBoost method.

Automated summary

This study investigated the relationships between indoor and outdoor air quality in an urban area of Belgrade through a three-month measurement campaign at a university building. The highest contributions to air quality were attributed to sources identified as coal combustion and related pyrogenic processes. Machine learning XGBoost method was used to predict PAH levels in the indoor and outdoor environment.