Rayleigh based concept to track NOx emission sources in urban areas of China

Despite the implementation of some effective measures to control emissions of nitrogen oxides (NOx = NO + NO2) in recent years, the ambient NOx concentration in urban cities of China remains high. Therefore, a quantitative understanding of NOx emission sources is critical to developing effective mediation policies. In the present study, the dual isotopic compositions of nitrate (p-NO3) in fine-particle aerosol (PM2.5) collected daily at a regional scale (Beijing-Tianjin-Shijiazhuang) were measured to better constrain the NOx emission sources. The specific focus was on a typical haze episode that occurred simultaneously in the three urban cities (October 22-29, 2017). It was found that the nitrogen isotopic values of nitrate in PM2.5(hereafter as delta N-15-NO3) ranged widely from -3.1%, to + 11.4%0, with a mean value of 3. 5 +/- 3.7%0. Furthermore, a negative relationship between the delta N-15-NO3 values and the corresponded pNO(3) concentrations during the haze period was observed. This implied the preferential formation of N-15-enriched NO3 into a fine-particle aerosol. Taking a different approach to previous publications, the Rayleigh fractionation model was used to characterize the initial isotopic signatures of ambient NOx. After accounting for the delta(15) difference between p-NO3 and the source NOx, the estimated initial delta N-15-NO x ranged from -20%, to 0%0, which indicated a cosniderable contribution of non-fossil fuel emissions. The individual contributions of potential sources were further quantified using the Bayesian mixing model, revealing that NOx from coal or natural gas combustion, vehicle exhausts, biomass burning, and the microbial activity contributed 17.9 +/- 11.4%, 29.4 +/- 19.6%, 29.1 +/- 18.9% and 23.5 +/- 12.7% to NO3 in PM2.5, respectively. These results highlighted that tightening controls of gaseous NOx emissions from non-fossil sources may represent an opportunity to mitigate PM2.5 pollution in urban cities of China. (C) 2019 Elsevier B.V. All rights reserved.