Year: 2023
Type: article
Abstract: 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.
Source: Advanced Science
Language: English
Subfield: Spectroscopy
Field: Chemistry
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $5,720
Awards: AI technologies for interaction prediction in biomedicine, G2054136870, G3484815789, Virtual laboratory for molecular level atmospheric transformations, G434337789, Autoxidation and Autoignition of Anthropogenic Volatile Organic Compounds (AAAVOC), Machine learning for digItal diagnostics of antimicrobial resistance, Machine Learning for Systems Pharmacology
OpenAlex ID: W4389732247