Chemical characteristics and sources of nitrogen-containing organic compounds at a regional site in the North China Plain during the transition period of autumn and winter

Organic nitrogen constitutes a significant fraction of the nitrogen budget in particulate matter (PM). However, the composition and sources of nitrogen-containing organic compounds (NOCs) in PM remain unclear currently in North China Plain (NCP), China. Rare local or regional studies on NOCs were conducted. In this study, ambient fine particles (PM_2.5) were collected in Xianghe, a regional background site in NCP, from 26 October to 26 December 2017. The insights from this study include NOC molecule identification, concentration level, and NOC sources and origins. Specifically, we have identified and quantified >90 NOC species, with urea being the most abundant, accounting for 39.7 ± 4.7% of the total NOC followed by free amino acids (FAAs; 21.9 ± 1.5%), cyclic NOCs (15.3 ± 4.5%), amines (14.8 ± 1.5%), alkyl amides (5.8 ± 0.5%), isocyanates (1.7 ± 0.2%), and nitriles (1.1 ± 0.2%). The time series of FAAs was well correlated (r = 0.51–0.68, p < 0.01) with the organic marker of levoglucosan and was moderately correlated with O_x (r = 0.29–0.41, p < 0.01), suggesting biomass burning and secondary formation were important FAAs sources. We also show that amines can be oxidized and/or reacted by aqueous-phase processing to form secondary aerosols, which are further enhanced by the involvement of iron in the catalytic process. Using the receptor model of positive matrix factorization (PMF), six factors were identified including coal combustion, crustal sources, biomass burning, industry-related sources, traffic emissions, and secondary aerosols. Source apportionment of NOC shows biomass burning was the dominant factor, accounting for 31.8% of the total NOCs. This study provides a unique dataset of NOCs at this regional background site in the NCP, with the insights of NOC chemical composition and sources gained in this study being important for future NOC modeling as well as NOC health effects studies.