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[Display omitted] Aerosol ammonium (NH 4 +), mainly produced from the reactions of ammonia (NH 3) with acids in the atmosphere, has significant impacts on air pollution, radiative forcing, and human health. Understanding the source and formation mechanism of NH 4 + can provide scientific insights into air quality improvements. However, the sources of NH 3 in urban areas are not well understood, and few studies focus on NH 3 /NH 4 + at different heights within the atmospheric boundary layer, which hinders a comprehensive understanding of aerosol NH 4 +. In this study, we perform both field observation and modeling studies (the Community Multiscale Air Quality, CMAQ) to investigate regional NH 3 emission sources and vertically resolved NH 4 + formation mechanisms during the winter in Beijing. Both stable nitrogen isotope analyses and CMAQ model suggest that combustion-related NH 3 emissions, including fossil fuel sources, NH 3 slip, and biomass burning, are important sources of aerosol NH 4 + with more than 60% contribution occurring on heavily polluted days. In contrast, volatilization-related NH 3 sources (livestock breeding, N-fertilizer application, and human waste) are dominant on clean days. Combustion-related NH 3 is mostly local from Beijing, and biomass burning is likely an important NH 3 source (∼15%–20%) that was previously overlooked. More effective control strategies such as the two-product (e.g., reducing both SO 2 and NH 3) control policy should be considered to improve air quality. [ABSTRACT FROM AUTHOR]

Identifying the sources of increased ammonia (NH 3) in the atmosphere has important implications for air quality, ecosystems, and climate. While analysis of the nitrogen isotopic composition (δ15N) can aid in the apportionment of NH 3 emission sources, the widely-used passive collection methods have not been verified for their ability to accurately characterize δ15N-NH 3. In this study, three types of passive samplers (ALPHA, Analyst, and Radiello) were employed and collocated with a denuder based active sampler (DELTA, serves as a reference method) to compare and contrast their ability in collecting atmospheric NH 3 for subsequent concentration and δ15N-NH 3 analysis in urban Beijing during the summer of 2018. The results showed that NH 3 concentrations determined using passive collection samplers that included ALPHA (n = 11), Analyst (n = 12), and Radiello (n = 7) samplers were 13.4%, 14.4%, and 27.5% lower than that of the active sampler (n = 12), respectively. The low NH 3 concentration bias in the passive collection samplers was suggested to be the result of inaccurate effective sampling rates due to incorrect mass transfer correction factors for the environmental conditions of this study. The δ15N-NH 3 values did not show significant differences between passive samplers, with weekly means of −31.0 ± 1.0‰ (n = 4), −28.4 ± 5.5‰ (n = 4), and −29.1 ± 0.9‰ (n = 4) for the ALPHA, Analyst, and Radiello, respectively. Notably, the overall mean δ15N-NH 3 values collected by these passive samplers (−29.5 ± 3.2‰, n = 12) was significantly lower than that of the active sampler (−14.1 ± 1.6‰, n = 4), with a difference of 15.4 ± 3.5‰. The large underestimation of δ15N-NH 3 values by passive samplers can be explained by the diffusive isotope fractionation of NH 3 isotopologues, which is driven by relative mass differences of 14NH 3 and 15NH 3. Overall, these findings highlight a potential low bias in NH 3 concentrations and a substantial uncertainty in previous source interpretation using δ15N-NH 3 as previous studies have almost exclusively characterized δ15N-NH 3 using passive samplers. We suggest that the denuder-based active sampler should be used in the future for characterizing δ15N-NH 3 whether at the source or in an atmospheric environment with mixed sources. This work also has important implications for characterizing δ15N of other gaseous nitrogen compounds. Unlabelled Image • Nitrogen isotopic composition of ammonia (δ15N-NH 3) aids in source apportionment. • Conventional collection methods for δ15N-NH 3 were almost based on passive samplers. • δ15N-NH 3 obtained by passive and active samplers were respective −29.5‰ and −14.1‰. • The low bias of 15.4‰ by passive δ15N-NH 3 was due to diffusive isotope fractionation. • Source apportionment based on passive δ15N-NH 3 might mislead mitigation strategy. [ABSTRACT FROM AUTHOR]