Abstract

Fine particulate-matter is an important component of air pollution that impacts health and climate, and which delivers anthropogenic contaminants to remote global regions. The complex composition of organic molecules in atmospheric particulates is poorly constrained, but has important implications for understanding pollutant sources, climate-aerosol interactions, and health risks of air pollution exposure. Here, comprehensive nontarget high-resolution mass spectrometry was combined with in silico structural prediction to achieve greater molecular-level insight for fine particulate samples (n = 40) collected at a remote receptor site in the Maldives during January to April 2018. Spectral database matching identified 0.5% of 60,030 molecular features observed, while a conservative computational workflow enabled structural annotation of 17% of organic structures among the remaining molecular dark matter. Compared to clean air from the southern Indian Ocean, molecular structures from highly-polluted regions were dominated by organic nitrogen compounds, many with computed physicochemical properties of high toxicological and climate relevance. We conclude that combining nontarget analysis with computational mass spectrometry can advance molecular-level understanding of the sources and impacts of polluted air.

Highlights

  • Fine particulate-matter is an important component of air pollution that impacts health and climate, and which delivers anthropogenic contaminants to remote global regions

  • High-resolution mass spectrometry (HRMS) is an established instrumental technique that can reveal the molecular complexity of PM2.5 organic compounds, most substances remain uncharacterized beyond assignment of molecular formula or the presence of certain functional groups[15,16,17,18,19,20,21,22]

  • Each feature is defined by a retention time (Rt) in the chromatographic dimension for GC and liquid chromatography (LC), and for GC analyses a mass spectrum dimension corresponding to mass-to-charge ratio (m/z) with a base-peak ion and deconvoluted MS1 spectrum (EI and negative chemical ionization (NCI)), and for LC analyses by a precursor MS1 and corresponding deconvoluted data-independent (DIA) MS2 spectrum

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Summary

Introduction

Fine particulate-matter is an important component of air pollution that impacts health and climate, and which delivers anthropogenic contaminants to remote global regions. Organic molecules can be a major mass fraction of total PM14, a comprehensive molecular characterization of PM2.5 could contribute to improved understanding of global air pollution sources, climate impacts, and health effects. Highthroughput workflows for batch-processing of full-scan HRMS chromatographic data (i.e. MS1) and the associated fragmentation spectra (MS2) are applied to explore the chemical structures of ‘molecular dark matter’ in biological systems[23,24,25,26] Such methods have yet to be applied in atmospheric research, but could open new molecular-level windows for studies of air pollution.

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