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Research Article

Source appointment of nitrogen in PM2.5 based on bulk δ15N signatures and a Bayesian isotope mixing model

, , , , , , , , & show all
Article: 1299672 | Received 20 Jan 2017, Accepted 20 Feb 2017, Published online: 03 Apr 2017
 

Abstract

Nitrogen isotope (δ15N) has been employed to differentiate major sources of atmospheric N. However, it remains a challenge to quantify contributions of multiple sources based on δ15N values of the N mixture in atmospheric samples. This study measured δ15N of bulk N in PM2.5 at an urban site of Beijing during a severe haze episode of 22–30 January 2013 and a background site of Qinghai, north-western China from 6 September to 15 October 2013, then applied a Bayesian isotope mixing model (SIAR, Stable Isotope Analysis in R) to analyse the N sources. At Beijing site, δ15N values of PM2.5 (−4.1‰ to +13.5‰, +2.8 ± 6.4‰) were distributed within the range of major anthropogenic sources (including NH3 and NO2 from coal combustion, vehicle exhausts and domestic wastes/sewage). At Menyuan site, δ15N values of PM2.5 (+8.0‰ to +27.9‰, +18.5 ± 5.8‰) were significantly higher than that of potential sources (including NH3 and NO2 from biomass burning, animal wastes, soil N cycle, fertilizer application and dust N). High molar ratios of to and/or in PM2.5 at the background site suggested that the equilibrium of NH3 ↔  caused apparent 15N enrichments in ammonium. Results of the SIAR model showed that 39 and 32% of bulk N in PM2.5 of Beijing site were contributed from N emissions of coal combustion and vehicle exhausts, respectively, whereas N in PM2.5 at Menyuan site was derived mainly from N emissions of biomass burning (46%) and NH3 volatilization (34%). These results revealed that the stoichiometry between NH3 and acidic gases plays an important role in controlling the bulk δ15N signatures of PM2.5 and emissions of reactive N from coal combustion and urban transportation should be strictly controlled to advert the risk of haze episodes in Beijing.

Acknowledgements

All the financial support from fund and research support from the staff of CRAES are gratefully acknowledged.