Abstract
Although combustion is considered a common source of ammonia (NH3) in the atmosphere, field measurements quantifying such emissions of NH3 are still lacking. In this study, online measurements of NH3 were performed by a cavity ring-down spectrometer, in the cold season at a rural site in Xianghe on the North China Plain. We found that the NH3 concentrations were mostly below 65 ppb during the study period. However, from 18 to 21 November 2017, a close burn event (~100 m) increased the NH3 concentrations to 145.6 ± 139.9 ppb. Using a machine-learning technique, we quantified that this burn event caused a significant increase in NH3 concentrations by 411%, compared with the scenario without the burn event. In addition, the ratio of ∆NH3/∆CO during the burn period was 0.016, which fell in the range of biomass burning. Future investigations are needed to evaluate the impacts of the NH3 combustion sources on air quality, ecosystems, and climate in the context of increasing burn events worldwide.
Highlights
As an important alkaline gas in the atmosphere, atmospheric ammonia (NH3 ) has a crucial influence on atmospheric chemistry and the nitrogen cycle [1,2]
We found that the NH3 concentrations were mostly below 65 ppb during the study period, with the exception of 18–21 November, when a burn event occurred
The NH3 concentrations in this study were comparable to the urban observations (28.5 ± 11.6 ppb) in autumn in Beijing [36]
Summary
As an important alkaline gas in the atmosphere, atmospheric ammonia (NH3 ) has a crucial influence on atmospheric chemistry and the nitrogen cycle [1,2]. Livestock waste and nitrogen fertilization are considered the most important sources of NH3 emissions on a global or regional scale [13,14], NH3 is emitted into into the atmosphere during the fuel combustion process through pyrolysis [15] Biomass burning, such as forest fires, plays a critical role in NH3 emissions in rural areas. China is a global hotspot of atmospheric NH emissions, with an annual increasing occur in the cold season in this region. Combine a novel machine-learning techniqueand based on theimproving random forest (RF) algorithm, to quantify the impact of burn events Such an understanding could be beneficial for. 3 emissions and further improving air quality in the future
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