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The concentration of atmospheric ammonia (NH 3 ) in urban Beijing substantially decreased during the COVID-19 lockdown (24 January to 3 March 2020), likely due to the reduced human activities. However, quantifying the impact of anthropogenic interventions on NH 3 dynamics is challenging, as both meteorology and chemistry mask the real changes in observed NH 3 concentrations. Here, we applied machine learning techniques based on random forest models to decouple the impacts of meteorology and emission changes on the gaseous NH 3 and ammonium aerosol (NH 4 ) concentrations in Beijing during the lockdown. Our results showed that the meteorological conditions were unfavorable during the lockdown and tended to cause an increase of 8.4 % in the NH + ) concentrations in Beijing during the lockdown. Our results showed that the meteorological conditions were unfavorable during the lockdown and tended to cause an increase of 8.4 % in the NH 3 concentration. In addition, significant reductions in NO x and SO 2 emissions could also elevate NH 3 concentrations by favoring NH 3 gas-phase partitioning. However, the observed NH 3 concentration significantly decreased by 35.9 % during the lockdown, indicating a significant reduction in emissions or enhanced chemical sinks. Rapid gas-to-particle conversion was indeed found during the lockdown. Thus, the observed reduced NH 3 concentrations could be partially explained by the enhanced transformation into NH 4 + . Therefore, the sum of NH 3 and NH 4 + (collectively, NH x ) is a more reliable tracer than NH 3 or NH 4 emissions in Beijing due to the lockdown measures, which was less of a decrease than that based on NH + alone to estimate the changes in NH 3 emissions. Compared to that under the scenario without lockdowns, the NH x concentration decreased by 26.4 %. We considered that this decrease represents the real decrease in NH 3 emissions in Beijing due to the lockdown measures, which was less of a decrease than that based on NH 3 only (35.9 %). This study highlights the importance of considering chemical sinks in the atmosphere when applying machine learning techniques to link the concentrations of reactive species with their emissions., Competing Interests: Declaration of competing interest The authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)