Data‐Driven Compound Identification in Atmospheric Mass Spectrometry

Aerosol particles found in the atmosphere affect the climate and worsen air quality. To mitigate these adverse impacts, aerosol particle formation and aerosol chemistry in the atmosphere need to be better mapped out and understood. Currently, mass spectrometry is the single most important analytical technique in atmospheric chemistry and is used to track and identify compounds and processes. Large amounts of data are collected in each measurement of current time‐of‐flight and orbitrap mass spectrometers using modern rapid data acquisition practices. However, compound identification remains a major bottleneck during data analysis due to lacking reference libraries and analysis tools. Data‐driven compound identification approaches could alleviate the problem, yet remain rare to non‐existent in atmospheric science. In this perspective, the authors review the current state of data‐driven compound identification with mass spectrometry in atmospheric science and discuss current challenges and possible future steps toward a digital era for atmospheric mass spectrometry. Enhancing the understanding of atmospheric processes necessitates fast and precise atmospheric compound identification. Mass spectrometry enables this in field and laboratory settings. Digitizing identification protocols may expedite new compound discovery, but success relies on having access to relevant reference standards. Initiating a digital era in atmospheric mass spectrometry requires establishing a collaborative, open data infrastructure and concurrently developing data‐driven methods.