Towards data-driven mass spectrometry in atmospheric science

TL;DR

This paper reviews the current state and challenges of applying data-driven methods to mass spectrometry in atmospheric science, aiming to improve compound identification and understanding of aerosols.

Contribution

It provides a comprehensive overview of data-driven compound identification approaches and discusses future directions for digital mass spectrometry in atmospheric research.

Findings

Mass spectrometry is crucial for atmospheric chemistry analysis.

Compound identification is hindered by limited reference libraries.

Data-driven approaches could significantly improve identification accuracy.

Abstract

Aerosols found in the atmosphere affect the climate and worsen air quality. To mitigate these adverse impacts, aerosol 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. Vast 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 as 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, we review the current state of data-driven compound identification with mass spectrometry in atmospheric science, and discuss current challenges and possible future steps towards a digital mass spectrometry era in atmospheric science.