Atmospheric organic aerosols: online molecular characterization and environmental impacts

Abstract

Organic aerosols (OA) play critical roles in atmospheric chemistry, air quality, climate forcing, and public health. However, their chemical complexity, comprising thousands of compounds with a wide range of volatilities, functionalities, and oxidation states, poses substantial challenges for comprehensive characterization and impact assessment. Advances in high-resolution mass spectrometry, particularly when coupled with specialized inlets such as the Filter Inlet for Gases and Aerosols (FIGAERO) and Extractive Electrospray Ionization (EESI), have enabled real-time molecular-level analysis of both gas- and particle-phase organics. These developments have substantially improved insights into OA composition, physicochemical properties, sources, and formation pathways. This review critically assesses recent progress in widely used analytical techniques for molecular characterization of OA and their applications in ambient air, emission sources, and indoor environments. Parameterizations of key OA properties, including volatility, viscosity, and hygroscopicity based on molecular data are summarized. Recent findings on secondary organic aerosol (SOA) formation mechanisms, including homogeneous oxidation, heterogeneous processing, and gas-particle partitioning, are discussed. In addition, the review highlights molecular-marker-based advances in source apportionment and examines the role of OA in new particle formation and its implications for climate and health. Finally, future research directions to improve molecular-level understanding of OA and its environmental impacts are proposed.

Introduction

Organic aerosols (OA) are complex mixtures of organic compounds suspended in the atmosphere, originating from both natural processes and human activities¹. These aerosols play a central role in atmospheric chemistry, air quality, and climate by influencing key processes such as cloud formation, heterogeneous reactions, and radiative forcing (Fig. 1)^{2, 3–4}. OA impacts the Earth’s radiation budget by scattering and absorbing solar radiation and by acting as cloud condensation nuclei (CCN), thereby modifying cloud microphysical properties and radiative transfer^5,6. For example, increased OA concentrations in boreal forests are associated with enhanced CCN abundance, which can alter cloud dynamics and precipitation patterns, ultimately contributing to a net cooling effect^7,8. Similarly, secondary organic aerosols (SOA) formed from highly oxygenated organic molecules (HOMs) have been shown to increase CCN concentrations by ~10%, with an associated direct aerosol radiative forcing of –0.10 W m^–2 9,. Furthermore, biogenic SOA, especially from isoprene, can serve as depositional ice-nucleating particles (INPs) in the upper troposphere, thereby influencing cirrus cloud formation and properties⁹.

Fig. 1 [Images not available. See PDF.]

Life cycle of atmospheric organic aerosols and impacts.

OA also poses substantial risks to human health. Epidemiological and toxicological studies have demonstrated strong associations between OA exposure and increased incidences of respiratory and cardiovascular diseases^10,11. The health impacts are influenced by factors such as chemical composition, particle size distribution, and exposure duration. For example, Pye et al.¹⁰ found that SOA is strongly correlated with county-level cardiorespiratory death rates, with SOA being associated with a mortality rate 6.5 times higher than PM_2.5. Daellenbach et al.¹¹ further identified oxidative potential, an indicator of aerosol toxicity, as primarily associated with anthropogenic fine-mode SOA from sources like residential biomass burning. These findings highlight the importance of detailed molecular-level characterization in assessing the toxicity and health risks of OA, particularly for compounds such as polycyclic aromatic hydrocarbons (PAHs), which are known to be highly toxic and carcinogenic.

The molecular composition of OA is highly complex, with a broad range of molecular weights, volatilities, oxidation states, and hygroscopicity. Emissions from different sources contribute distinct molecular fingerprints to OA. Biogenic emissions from vegetation release volatile organic compounds (VOCs) such as isoprene, monoterpenes, and sesquiterpenes¹², which undergo oxidation by atmospheric oxidants, such as hydroxyl radical (OH), nitrate radical (NO₃), and ozone (O₃) to form SOA. Anthropogenic sources, including vehicular exhaust, industrial activities, and biomass burning, also emit a wide range of organic species, further complicating OA composition. Additionally, interactions between biogenic VOCs and anthropogenic emissions such as sulfur dioxide (SO₂) and nitrogen oxides (NO_x) can significantly enhance OA formation¹³, particularly through the formation of organic nitrates and sulfates^{14, 15–16}.

Traditional analytical approaches, such as offline filter-based sampling combined with chromatographic separation, have been complemented and, in many cases, replaced by advanced mass spectrometry techniques that enable rapid, high-resolution molecular characterization of OA^{17, 18–19}. High-resolution mass spectrometry (HRMS) coupled with chromatographic methods, such as gas chromatography (GC) and liquid chromatography (LC) enables the identification and quantification of individual organic compounds in complex aerosol mixtures^20,21. More recently, the development of real-time techniques such as proton-transfer reaction mass spectrometry²² and chemical ionization mass spectrometry (CIMS)²³, has revolutionized OA research by enabling in situ measurements of gas- and particle-phase organics with high temporal resolution and sensitivity. These techniques are particularly valuable for detecting short-lived intermediates and highly reactive compounds, thereby advancing our understanding of the dynamic chemical processes that govern OA formation, transformation, and fate.

In this review, we provide a critical synthesis of recent advances in the molecular-level understanding of OA. We focus particularly on the role of the most widely used online and semi-continuous mass spectrometry techniques in elucidating OA composition, volatility, sources, and their impacts on climate and human health, as well as SOA formation mechanisms. We summarize key findings from field measurements, laboratory experiments, and modeling studies, and highlight current challenges and future research priorities aiming at bridging knowledge gaps and enhancing predictive capabilities in OA science.

Measurement techniques for molecular composition of OA

Online measurements of particle‑phase OA molecular composition generally employ either semi‑continuous sampling or real‑time direct‑inlet analysis. In the former, particles are collected over 30 min to 1 h via specialized inlets, e.g., Thermal Desorption Aerosol GC/MS (TAG), Filter Inlet for Gases and Aerosols (FIGAERO), or Micro‑Orifice Volatilization Impactor (MOVI) that optimize capture efficiency and minimize gas–particle cross‑contamination. The collected aerosol particles are then thermally desorbed and introduced into a gas chromatograph or mass spectrometer for detailed chemical characterization. In the latter approach, particle‑phase organics are vaporized dynamically by direct thermal evaporation, and continuously introduced into a mass spectrometer through inlets such as Chemical Analysis of Aerosol Online (CHARON), Extractive Electrospray Ionization (EESI), or Vaporization Inlet for Aerosols (VIA), enabling measurements of molecular species in seconds. A summary of the advantages and limitations of each inlet is presented in Table 1.

Table 1. Overview of six inlets used for molecular-level analysis of OA

Instruments	TR	MDL	Capabilities and advantages	Limitations
TAG-GC202	30–60 min	0.03–0.57 ng m–3	Measure non-polar or polar compounds that can be derivatized; hourly in-situ molecular-level identification and quantification of via thermal desorption; humidified impaction minimizes particle bounce; offers high reproducibility, linear response, and enables source apportionment without filter-handling artifacts.	Limited transfer efficiency for underivatized polar compounds; volatile organics lost during water-purge step; hourly analysis restricts real-time resolution; derivatization often needed for oxygenated species, adding complexity and limiting the number of species analyzed; semi-volatile range exclusion affects comprehensiveness.
MOVI-CIMS32	10– 60 min	0.8– 4.4 ng m–3	Measure low to moderate oxygenated organic compounds; near-ambient pressure collection minimizes semi-volatile losses; semi-continuous gas- and particle-phase measurements via thermal desorption; offers linear response and high reproducibility for quantifying aerosol composition and volatility.	Brass surface interactions may introduce thermal desorption artifacts; particle bounce lowers collection efficiency for solid aerosols; hourly analysis limits temporal resolution; complementary methods often required for comprehensive characterization.
FIGAERO-CIMS33	20–30 min	0.06– 0.9 ng m–3	Measure low to moderate oxygenated organic compounds; simultaneous gas- and particle-phase measurements with high molecular specificity; Teflon filter ensures near-unity collection efficiency down to 10 nm; thermal desorption process is highly reproducible, supporting volatility-resolved aerosol analysis.	Thermal decomposition of larger or liable molecules can bias particle-phase composition and volatility estimates. High filter blanks from gas-phase adsorption limit sensitivity, while the hourly desorption cycles constrain the time resolution for particle-phase measurements.
CHARON-PTR40	1 s	10–20 ng m–3	Measure non to low oxygenated organic compounds; real-time, high-time-resolution measurements without offline sampling artifacts.	Low transmission efficiency for particles <100 nm; potential thermal decomposition and fragmentation; risk of particle deposition artifacts; humidity-related partitioning biases and size-dependent measurement errors.
EESI-CIMS192	1–5 s	1–10 ng m–3	Measure oxygenated organic compounds; real-time, near-molecular OA analysis with minimal thermal decomposition or fragmentation; insensitive to matrix effects and inorganic content; high time resolution; spectra accurately represent true particle-phase composition.	Incomplete detection of non-oxygenated species, and organosulfates; highly compound-dependent sensitivity (up to 30× variation); humidity-induced signal suppression; and spectral interferences from solvent impurities.
VIA-NO3-CIMS47	1 s	<1 ng m–3	Measure moderate to high oxygenated organic compounds; real-time measurements without pre-collection; ultra-short residence time (~0.1 s) reduces thermal decomposition; high transmission efficiency for particles; broadly compatible with multiple mass spectrometers.	High temperatures risk decomposition; significant wall losses for HOMs; model-based corrections introduce uncertainties; slow cooling in thermogram mode; performance is sensitive to flow rates and tube configurations.

TR denotes time resolution, and MDL refers to method detection limit.

The TAG system is designed for in-situ, hourly measurements of speciated organic compounds in atmospheric aerosols^24,25. TAG integrates humidification and inertial impaction for aerosol collection, followed by thermal desorption and transfer to a gas chromatography (GC) column for separation. Detection is achieved using both a quadrupole mass spectrometer (MS) and a flame ionization detector (FID). This automated process allows for continuous speciation with high temporal resolution, overcoming limitations of traditional filter-based GC/MS analyses. When coupled with time-of-flight mass spectrometry (TOFMS), TAG’s capabilities are further enhanced, offering high data acquisition rates and mass spectral library matches for compound identification. Although intercomparisons during the Southern Oxidant Aerosol Study (SOAS) in 2013 revealed systematic differences beyond estimated errors²⁶, TAG represents a significant advancement in short-timescale OA speciation, with applications in studying SOA formation²⁷ and indoor dynamics of SVOCs^{28, 29, 30–31}.

The MOVI coupled with CIMS, developed by Yatavelli and Thornton³², features a multi-jet design with 100 nozzles to collect particles efficiently. After impaction, the particles are volatilized by heating, and the resulting organic matter is carried into CIMS for detection and quantification. Despite its promise, MOVI-CIMS has limited use due to artifacts associated with the impaction process, such as the adsorption of semi- and intermediate VOCs (S/IVOCs) onto the impaction surface.

The FIGAERO offers a versatile platform for analyzing organic species in both gas and particle phases^33,34. By drawing ambient air through a Teflon filter for aerosol collection and a separate inlet for gas-phase analysis, FIGAERO minimizes cross-contamination. The particles undergo temperature-programmed thermal desorption, enabling identification and quantification based on their volatility and thermal stability. When coupled with CIMS, FIGAERO offers high sensitivity and selectivity, providing valuable insights into the physicochemical properties of atmospheric organic material. Quasi real-time composition measurement with concurrent volatility information makes FIGAERO a powerful tool for atmospheric research, enhancing source apportionment, partitioning theories, and OA evolution studies in diverse environments^{35, 36, 37–38}. However, the volatilities estimated from the measured elemental formulas often yielded higher values compared to those derived from thermograms due to thermal decomposition³⁹.

The CHARON inlet, designed by Eichler et al.⁴⁰, is a modular system that facilitates the real-time chemical characterization of semi-volatile submicron particulate matter. Integrated with low-pressure gas analyzers like PTR-ToF-MS, CHARON enables simultaneous analysis of gas-phase and particulate-phase organics. The system includes a gas-phase denuder for efficient removal of gas-phase analytes and an aerodynamic lens for particle collimation. Through laboratory and atmospheric experiments, CHARON has successfully quantified organic compounds in submicron particles, detecting SOA formation from ozonolysis of various terpenes and real plant emissions^41,42. Despite its effectiveness, fragmentation during ionization may bias quantification and interpretation.

The EESI is an innovative method for real-time online analysis of organic compounds in aerosol particles^43,44. EESI uses a specially designed aerosol injector to interface with the ESI source. Solvent droplets generated in the ESI probe extract and ionize organic analytes from aerosol particles, with the resulting ions analyzed by mass spectrometry. The EESI source is optimized for sensitivity and signal stability, allowing for real-time, high-time-resolution measurements of aerosol chemistry with minimal fragmentation of the organic compounds, making it a powerful tool for studying atmospheric aerosols and their chemical transformations^45,46.

The recently developed VIA is a highly sensitive tool for assessing the molecular composition of particulate matter⁴⁷. VIA works by first removing gaseous compounds from the air sample with an activated carbon denuder, then rapidly heating the particles to cause evaporation. The resulting vapors are directed to the NO₃-CIMS for detection. VIA’s short residence time in the heating region, typically around 0.1 seconds, minimizes thermal decomposition and enhances the detection of reactive compounds. This technique can handle a wide range of compounds, especially highly oxidized organic species generated from ozonolysis of α-pinene, making it suitable for studying the molecular composition of atmospheric particles^48,49. Similar to FIGAERO, the VIA system’s high evaporation temperatures may also lead to thermal decomposition, wall losses, and fragmentation of larger molecules, which can impact the accuracy and sensitivity of detecting organic aerosol components.

Molecular composition of ambient OA

FIGAERO has been widely used for online molecular characterization of OA, revealing that highly functionalized organic nitrates significantly contribute to nighttime OA mass, especially from monoterpene and isoprene precursors. Studies in the U.S., Germany, and China indicate strong diurnal trends and highlight anthropogenic influences on biogenic SOA formation. In a rural forested environment in the southeast United States, measurements identified a series of low-volatility, highly functionalized organic nitrates that accounted for 3% and 8% of total submicron OA mass during the day and night, respectively⁵⁰. Molecular composition analysis revealed that these organic nitrates contained between six and eight oxygen atoms per carbon number group. More importantly, the mass of monoterpene-derived SOA increased with enhanced nitrogen oxide processing, indicating anthropogenic influences on biogenic SOA formation⁵¹. A strong diurnal trend was observed in the abundance of organic nitrates (ONs), with higher mass loadings at night compared to the day, consistent with the emission patterns of biogenic hydrocarbon precursors like isoprene and monoterpenes. This pattern is consistent with another study showing higher contributions of particulate organic nitrates at night³⁵. While the monoterpene-derived C10 groups exhibited higher mass during the night, isoprene-derived C5 groups showed the opposite diurnal trends. Research in southwest Germany also found larger contributions of organonitrates to OA mass at night⁵², with ON compounds highly functionalized, containing four to twelve oxygen atoms, contributing 18–25% to the mass increase of newly formed particles after sunset, suggesting their role in nighttime new particle formation (NPF). In a rural site in China during winter, Salvador et al.⁵³ measured nitro-aromatic compounds (NACs), finding that the mass concentrations of 16 NACs reached 1 μg m^–3, though they accounted for less than 2% of OA. Daytime peaks of gas-phase NACs suggested that photochemical production was the major source.

Huang et al.⁵⁴ summarized the chemical compositions of oxygenated OA (OOA) in different rural, urban, and mountain environments across Europe, South America, North America, and India. OOA was dominated by CHO compounds at all locations (62-87%), with rural sites exhibiting the highest contributions. This is attributed to the presence of highly oxidized, acid-like compounds in aged OA, leading to high oxygen-to-carbon (O:C) ratios. The composition of biogenic volatile organic compounds (BVOCs) also influenced OA composition, even in areas with similarly low OA concentrations. For example, rural Alabama showed comparable contributions of C5 and C8–C10 compounds, with isoprene emissions dominant, while rural Hyytiälä had a higher proportion of C8–C10 compounds due to predominant monoterpenes⁵⁵. These results suggest that terpene-derived OA compositions are more prevalent in rural and mountain background areas.

Urban sites such as Karlsruhe, Germany, and Delhi, India, were characterized by higher contributions of larger aromatic compounds, likely due to the oxidation of polycyclic aromatic hydrocarbons (PAHs) emitted from vehicles⁵⁶. As a result, C11–C18 compounds increased in OA. In areas influenced by intense biomass burning in autumn and winter, such as urban regions of China and Germany, C6 compounds, especially levoglucosan, were significantly enhanced^57,58. In Guangzhou, southern China, Ye et al.³⁸ found that biomass burning and secondary production both contributed to particle-phase oxidized organic nitrogen in urban polluted areas. OA in these regions was characterized by compounds containing two to five oxygen atoms, including organic acids, monosaccharide-derived compounds, oxygenated aromatic compounds, and oxidation products of BVOCs. Among these, monocarboxylic and dicarboxylic acids dominated the total OA mass.

The deployment of other inlets, such as TAG, CHARON, EESI, and VIA, has significantly advanced our understanding of OA molecular composition across diverse environments. For instance, Wang et al.⁵⁹ used TAG to measure SOA tracers at a suburban site over a four-month period in Hong Kong. Their study found distinct seasonal variations in SOA tracers, with higher concentrations during summer and fall episodes, indicating enhanced SOA formation during these periods. The CHARON-PTR-MS system has been utilized in airborne studies to measure the chemical composition of submicrometer atmospheric particles in real-time, including measurements of fresh smoke plumes from the Lions Fire in the Sierra Nevada⁶⁰ and particle plumes from a petroleum refinery near Bakersfield, California⁶¹. Comparison of the measured particle-phase fractions of 152 organic species with model predictions using instantaneous equilibrium partitioning theory showed that while the model accurately captured the partitioning of more oxidized compounds, significant discrepancies were found for less oxidized compounds, likely due to fragmentation during the PTR ionization process. (Fig. 2).

Fig. 2 Inlet systems for molecular characterization of OA. [Images not available. See PDF.]

The figure shows a history of the development of TAG²⁴, MOVI³², FIGAERO³³, CHARON⁴¹, EESI¹⁹², and VIA⁴⁷.

In contrast, VIA is predominantly used for measuring gas- and particle-phase compositions of HOMs⁴⁸. For example, particle-phase HOMs from α-pinene ozonolysis exhibited enhanced C16–C19 dimers not observed in the gas phase, suggesting particle-phase chemistry. The particle-phase HOMs showed slightly lower average O:C ratios compared to the gas phase, with C₁₇H₂₆O_z compounds dominating the particle-phase HOM mass spectra. VIA–NO₃-CIMS has also effectively detected organic nitrates, with measurements correlating well with those from AMS, indicating its promise for particle-phase HOM measurements. VIA–NO₃-CIMS has also been used to measure hundreds of particulate HOMs from organic aerosol generated from different precursors⁴⁹, showing that the volatility of organic compounds decreases with increasing m/z ratio and oxygenation level. OA was found to consist of both monomers and oligomeric compounds.

The applications of FIGAERO-CIMS, EESI-Orbitrap, and EESI-CIMS for the offline analysis of OA molecular composition have also been demonstrated. Cai et al.⁶² analyzed filter samples collected in urban Beijing using FIGAERO-CIMS, showing its capability to identify over 900 organic compounds with high signal-to-noise ratios, repeatability, and linear signal response to filter loadings. Zheng et al.⁶³ identified key precursors and chemical processes involved in SOA formation during severe haze episodes in Beijing, emphasizing the importance of reducing nitrogen oxides and nitrates for SOA control and highlighting the need for further research on the formation of highly oxygenated long-chain molecules in polluted urban environments. The EESI offline analysis of molecular composition has predominantly been conducted in Europe^{64, 65–66}. Daellenbach et al.⁶⁴ conducted a comprehensive study of OA molecular composition in an urban environment in Zurich, Switzerland, focusing on specific source spectra from wood-burning emissions and biogenic SOA. Their study found strong seasonality in OA composition, with primary and aged wood-burning emissions dominating winter samples (characterized by CHON compounds like C₆H₅O₄N and C₇H₇O₄N) and summer samples being predominantly impacted by oxygenated compounds, highlighting the importance of biogenic precursors in SOA formation.

Overall, molecular analysis has demonstrated the molecular diversity of atmospheric OA worldwide. As summarized in Fig. 3, both online and offline measurements consistently show that CHO and CHON compounds dominate OA globally, accounting for 44–79% and 16–42% of identified species, respectively. However, their relative contributions exhibit strong regional and seasonal variability. In addition, high-resolution mass spectrometry techniques (e.g., Orbitrap, Nano-DESI) demonstrate varying sensitivities to specific compound classes. For example, CHOS compounds contribute 18–25% of OA in many locations but are notably absent in Houston datasets. These results highlight the importance of considering instrumental and methodological differences when comparing OA molecular composition across platforms.

Fig. 3 Organic aerosol composition of formula categories worldwide. [Images not available. See PDF.]

The pie charts with green inner circles are offline analysis from high resolution mass spectrometry, and the others are from online measurements of FIGAERO-CIMS or EESI-CIMS^{37,52,193, 194, 195, 196, 197, 198–199}. Su and Win refer to the seasons of summer and winter, and Dry and Wet refer to the dry and wet seasons. The figure was created using IGOR Pro 9.

Molecular composition of primary OA

Emissions from vehicle exhaust, biomass burning, and cooking are major sources of primary organic aerosol (POA) and serve as important precursors for SOA formation through various oxidation processes. Le Breton et al.⁶⁷ analyzed the OA composition of freshly emitted OA from buses using FIGAERO-CIMS. Their results revealed substantial differences in OA chemical compositions depending on fuel type, including diesel, compressed natural gas (CNG), and rapeseed methyl ester (RME). Across all fuel types, lubrication oil-related species, such as malonic, malic, succinic, and propionic acids, were dominant, originating from the oxidation and fragmentation of larger hydrocarbon chains. In contrast, OA from RME combustion was notably enriched in fatty acids, contributing 0.7 mg kg^–1 to the total emission factor (2.7 mg kg^–1), while such species were nearly absent in emissions from diesel and CNG. The most abundant fatty acids identified were oleic, linoleic, palmitic, and stearic acids, which together accounted for 1.5% of the total fresh OA mass.

The OA compositions from biomass burning and cooking activities differ significantly from those emitted by vehicles. In biomass burning, levoglucosan is a well-established tracer, while guaiacol- and syringol-derived methoxyphenols are widely recognized products of lignin pyrolysis⁶⁸. Oxidation products, such as nitro-phenols, are also prevalent with nitrocatechol and methyl nitrocatechol accounting for up to 85% of total particulate phenols in the Western United States. In a study of cooking emissions, Masoud et al.⁶⁹ identified 169 organic compounds, with CHO compounds being dominant, including oxidized C3, C6, C10, and C18 species. Among these, oleic acid (C₁₈H₃₄O₂) and linoleic acid (C₁₈H₃₂O₂) were the most abundant fatty acids. A wide range of marker compounds, such as histidine (C₆H₉N₃O₂), likely originated from protein-rich foods like beef and beans, and isomaltol (C₆H₆O₃), a product of sugar caramelization. In contrast, the CHON group contributed a smaller fraction (12-19%) to the OA mass, a value determined more by food nitrogen content than by ambient NO_x levels. These CHON compounds were primarily C5 species with 1-4 oxygen atoms.

Molecular composition of indoor OA

Indoor air pollution has received growing attention over the past decade, as people typically spend 80–90% of their time indoors^{70, 71–72}. With the advancement of real-time mass spectrometry techniques, tools such as TAG and EESI have been widely used to study VOCs and particulate OA, which are major contributors to indoor air pollution. Indoor TAG measurements have shown that the gas-particle partitioning of SVOCs is strongly influenced by environmental factors, including indoor temperature, particle mass concentration, and the chemical composition of airborne particles^{29, 30–31}. Higher volatility SVOCs (e.g., C13–C23 alkanes) were primarily found in the gas phase and exhibit strong temperature dependence. In contrast, lower volatility SVOCs (C25 to C31 alkanes) correlate with particle mass, indicating enhanced partitioning to the particle phase.

Kristensen et al.³⁰ demonstrated that common indoor activities such as cooking and candle burning significantly modify SVOC partitioning by increasing particle emissions, which enhance gas-to-particle transfer and alter the chemical composition of indoor aerosols, potentially increasing inhalation exposure. TAG data further emphasize the importance of both primary particle emissions and secondary processes (e.g., surface emissions and oxidation) in shaping indoor SVOC dynamics²⁸. EESI-MS has also been effectively utilized in indoor studies. EESI-MS has also proven effective for high-resolution indoor measurements. Its first indoor deployment identified over 200 aerosol-phase molecular species with 5-second time resolution⁷³. Dominant compounds included fatty acids, carbohydrates, and phthalates, while high-molecular-weight, low-volatility siloxanes were also detected, though they contributed minimally to total aerosol mass.

Volatility and viscosity characteristics of OA

With the increasing application of HRMS, molecular formula-based methods for predicting volatility have been widely developed over the past decade. When the chemical structure of a compound is known, volatility can be estimated using functional group-contribution models that predict vapor pressure⁷⁴. To extend this approach to ambient OA, Donahue et al.⁷⁵ developed a simplified method that relates the saturation mass concentration (C₀) of pure organic compounds to their elemental composition, specifically log₁₀C₀ = f (n_C, n_C). This two-dimensional volatility basis set (2-D VBS) constrains OA within C₀ – O:C space. Building on this framework, Daumit et al.⁷⁶ incorporated hydrogen content (H:C ratio) to further improve volatility predictions, especially for low-volatility OOA (LV-OOA), using molecular insights derived from factor analysis of AMS data.

Li et al.⁷⁷ expanded this parameterization to include nitrogen and sulfur atoms (n_N, n_S), i.e., log₁₀C₀ = f (n_C, n_O, n_N, n_S,), thereby enabling volatility estimation of a wider range of organic species based on HRMS data. As shown in Fig. 4, the “molecular corridor” representation of ambient nitrogen- and sulfur-containing compounds illustrates that volatility inversely correlates with molar mass and is strongly constrained by functional group composition⁷⁷. This approach has been applied across diverse OA sources, including brown carbon in biomass burning⁷⁸, third-hand smoke VOCs⁷⁹, microplastic particles⁸⁰, and gaseous emissions from biomass combustion^81,82. It also supports the assessment of volatility across different ambient and experimental conditions^{83, 84, 85, 86–87}.

Fig. 4 Molecular corridors of organic compounds and OA. [Images not available. See PDF.]

Molecular corridors of a saturation mass concentration (C₀) versus molar mass (M) for different types of organic compounds identified from ambient observations, and b for OA observed in indoor air, outdoor air, and atmospheric water⁷⁷. The data points are color-coded by O:C ratios. Reprinted with permission from Li et al.⁷⁷.

In addition to these general formula-based approaches, specific methods have been developed for compound classes, such as HOMs^88,89, and low-volatile compounds⁹⁰. These advances have significantly enhanced the use of HRMS for characterizing OA volatility. For individual molecules, volatility estimates from formula-based methods, such as the modified Daumit-Li approach, generally agree well with those from structure-based models, showing minimal bias⁹¹. However, challenges remain in extending these predictions to bulk OA. Difficulties include converting peak intensities from mass spectra into quantitative molecular concentrations and accurately accounting for activity coefficients in complex OA mixtures^75,92,93.

Viscosity is a critical physical property of OA, influencing key aerosol processes such as gas–particle partitioning, new particle formation, and heterogeneous reactions^94,95. Accurate viscosity predictions require detailed molecular structural information^{96, 97–98}, which is often lacking for ambient SOA, thereby complicating the estimation of phase state and diffusion kinetics. Shiraiwa et al.⁹⁹ developed a parameterization for estimating the glass transition temperature (T_g) based on molar mass (M) and atomic O:C ratios for CH and CHO compounds with M < 450 g mol^–1. Given the strong correlation between T_g and volatility, subsequent studies extended this approach to nitrogen- and sulfur-containing organics^100,101, enabling broader applicability across chemically complex systems.

When the elemental composition of SOA particles is determined through HRMS, these T_g parameterizations, along with estimates of water uptake and ambient temperature, can be incorporated into the Gordon-Taylor and Vogel-Tammann-Fulcher equations to predict the viscosity of SOA-water mixtures¹⁰². For example, Smith et al.¹⁰³ showed that SOA from aphid-stressed Scots pine trees exhibited higher viscosity than from healthy trees, consistent with poke-flow measurements. Similar approaches have yielded accurate viscosity predictions for SOA derived from precursors such as α-pinene, isoprene, toluene¹⁰⁰, β-caryophyllene¹⁰⁴, and diesel vapor¹⁰⁵, across varying relative humidities. However, viscosity estimates based solely on elemental ratios may fail for complex mixtures containing unsaturated or conjugated structures, such as kitchen organic films¹⁰⁶. These findings highlight the need to incorporate additional structural parameters, e.g., double bond equivalence for improved prediction of OA phase state and dynamics.

Hygroscopicity of OA

Recent studies have increasingly emphasized the influence of molecular composition, particularly oxidation state, functional groups, and molecular weight, on the hygroscopic behavior of OA. Organic matter isolated from water-soluble fractions exhibits hygroscopicity comparable to SOA, though it remains less hygroscopic than highly soluble organic acids, with hygroscopic growth factors (HGF) ranging from 1.08 to 1.17 at 90% RH¹⁰⁷. In contrast, SOA formed from various precursors displays relatively low hygroscopicity, with HGF values ranging from 1.01 to 1.16 at 85% RH and lacking evidence of deliquescence or efflorescence transitions^108,109. A key advance in recent years has been the recognition that specific chemical features, such as polar functional groups, molecular size, and degree of oxidation, critically govern water uptake behavior¹¹⁰. Consequently, methods have been developed to predict water activity based on measured hygroscopicity, with findings indicating that uncertainties in critical dry diameters can substantially impact cloud droplet number concentrations¹¹¹.

The hygroscopicity of OA is often correlated with oxidation state, particularly as indicated by the O:C ratio and f₄₄ (the fraction of m/z 44 in OA composition)¹¹². Higher oxidation states, associated with increased O:C and f₄₄ values, generally enhance water affinity due to the increased polarity and solubility of oxidized organic species. For instance, McMurry et al.¹¹³ determined a hygroscopic parameter (κ) of 0.22 ± 0.04 for OOA, and proposed a simple linear relationship between κ_org and O:C, i.e., κ_org = (0.29 ± 0.05) × (O:C). Building on this, Massoli et al.¹¹⁴ showed that both HGF at 90% RH (HGF90%) and CCN-derived κ (κ_org,CCN) increased with O:C, though HGF90% followed a linear trend while κ_org,CCN exhibited nonlinearity, suggesting that additional compositional factors modulate hygroscopicity. Duplissy et al.¹¹⁵ also reported a strong correlation between the hygroscopicity of SOA and f₄₄, i.e., κ_org = 2.2 × f₄₄ − 0.13. Mei et al.¹¹⁶ further found that κ_org showed a more consistent relationship with f₄₄ than with O:C across field and chamber studies, highlighting the utility of f₄₄ as a proxy for OA hygroscopicity despite interstudy variability. However, some other studies also reported weak correlation between κ_org and O:C or f₄₄ especially when considering SOA from different precursors^117,118, demonstrating the importance of molecular composition such as molecular weight, rather than only elemental composition in affecting hygroscopicity¹¹⁷.

Homogeneous formation of SOA

Homogeneous formation of SOA involves gas-phase oxidation of biogenic and anthropogenic VOCs, producing complex mixtures of monomers, dimers, and HOMs. Molecular-level analysis reveals that oxidation pathways involving OH, O₃, and NO₃ radicals generate multigenerational products with decreasing volatility and increasing oxidation state. These studies also highlight the critical roles of radical–radical reactions, RO and RO₂ autoxidation, and the influence of temperature, NO_x levels, and VOC mixtures in shaping SOA composition and yields. For example, Isaacman-VanWertz et al.¹¹⁹ investigated the multigenerational oxidation of α-pinene using various mass spectrometers, and found that the oxidation of organic compounds yields a complex mixture of products, including gas- and particle-phase organic species, and inorganic carbon-containing species. Over time, the chemical composition evolves markedly, from intermediate-volatility products and the parent α-pinene to lower-volatility, longer-lived species with broader ranges of oxidation states and reduced reactivity. In a complementary study, Zhang et al.¹²⁰ observed distinct responses of α-pinene-derived monomers and dimers to different oxidants (ozone vs. OH), temperature, and relative humidity. These findings suggest that gas-phase radical combination reactions and condensed-phase rearrangement of labile molecules are key mechanisms explaining the newly characterized features of α-pinene SOA.

The oxidation of biogenic VOCs by NO₃ also plays a critical role in determining SOA formation pathways, chemical composition, and volatility¹²¹. For example, simultaneous oxidation of α-pinene and limonene was found to enhance SOA formation from α-pinene by ~50%, while reducing limonene SOA yields by ~20% compared to individual oxidation. These non-linear effects underscore the need to incorporate multi-precursor interactions into models for accurate SOA prediction. However, McFiggans et al.¹²² showed that the presence of isoprene, along with CO and CH₄, can suppress SOA yields from monoterpenes due to both product scavenging and OH radical depletion.

A major focus of recent research is the gas-phase formation of low-volatility compounds, particularly HOMs. Bianchi et al.¹²³ reviewed the key formation pathways of HOMs, and subsequent work has refined our mechanistic understanding. For instance, Iyer et al.¹²⁴ used quantum chemical calculations to show that the excess energy released during α-pinene ozonolysis facilitates the formation of oxidation intermediates without steric constraints, enabling rapid generation of products with up to eight oxygen atoms. This is considered a key pathway for atmospheric SOA formation (Fig. 5). Shen et al.¹²⁵ highlighted the crucial role of alkoxy radicals in HOM formation during the OH oxidation of α-pinene, especially under high-NO conditions where RO forms via RO₂ + NO reactions. This mechanism has been validated by other studies, reinforcing the central role of RO radicals in the formation of HOMs^126,127.

Fig. 5 Ozonolysis reaction of α-pinene. [Images not available. See PDF.]

Reprinted with permission from Iyer et al.¹²⁴.

To better understand the mechanisms of autoxidation, Meder et al.¹²⁸ employed selective deuteration combined with CI-Orbitrap mass spectrometry to identify intramolecular hydrogen shifts in RO₂ radicals formed during α-pinene ozonolysis. They suggested that this approach could be broadly applied to investigate H-shift pathways in other atmospheric oxidation systems. In a related study, Berndt¹²⁹ quantified the yields of initial closed-shell products in α-pinene ozonolysis and assessed the impact of NO on the formation of highly oxidized RO₂ and HOMs. Their results indicate that NO suppresses HOM formation more effectively during α-pinene ozonolysis than in OH-initiated α-pinene oxidation. Additionally, Shi et al.¹³⁰ proposed a new accretion reaction pathway between RO₂ and α-pinene, supported by quantum chemical calculations.

The influence of NO, seed aerosol composition, and water vapor on HOM formation has also been investigated through a combination of laboratory experiments and quantum chemical modeling^{131, 132–133}. In complex atmospheric VOC mixtures, the presence of other organic vapors such as isoprene has been shown to modulate dimer formation in monoterpene oxidation via a “product scavenging” mechanism. This process favors the formation of smaller dimers through reactions between C₅-RO₂ and C₁₀-RO₂, thereby suppressing new particle formation relative to pure monoterpene systems^122,134. Temperature exerts a strong influence on HOM distribution as well^{135, 136–137}. For instance, Simon et al.¹³⁵ observed that colder conditions reduce HOM yields and lower the oxidation states of the products, while paradoxically enhancing NPF rates. Consistent with these findings, Quéléver et al.¹³⁷ reported a ~50-fold reduction in HOM molar yields when experiments were conducted at 0 °C compared to 20 °C.

Heterogeneous formation of SOA

Heterogeneous SOA formation typically begins with the adsorption of gas-phase molecules onto particle surfaces, followed by surface-mediated chemical reactions that yield secondary organic products such as carboxylic acids and oligomers. These processes are often studied using OH radicals and ozone as oxidants. For example, Loisel et al.¹³⁸ found that photooxidation of vanillin enhanced the formation of carboxylic acids, hydroxylated compounds, and oligomers, accompanied by subtle changes in the absorption spectrum pH. Zhao et al.¹³⁹ identified five structural isomeric diacids (C₆H₁₀O₄) from OH-oxidized OA, demonstrating that site-specific oxidation mechanisms strongly influence product distributions via functionalization, fragmentation, and oligomerization. Zahardis et al.¹⁴⁰ observed the formation of polymers and cyclic oxygenates, including secondary ozonides and geminal diperoxides from the heterogeneous ozonolysis of oleic acid, attributing these products to reactions involving Criegee intermediates.

Aqueous-phase reactions are increasingly recognized as critical pathways for SOA formation^{141, 142, 143–144}. These processes involve the uptake and dissolution of gaseous precursors and their oxidation products into cloud and fog droplets, where aqueous-phase chemistry leads to the formation of low-volatility compounds^145,146. Although previous work has often focused on single-component precursors such as glycolaldehyde, levoglucosan, phenol, or vanillin^{147, 148, 149–150}, recent efforts emphasize the need to examine more complex mixtures from real-world emissions. Lamkaddam et al.¹⁵¹ developed a wetted-wall flow reactor (WFR) to simulate the aqueous-phase processing of isoprene oxidation products (iOP), finding that 50–70% of iOP partitioned into the aqueous phase and underwent rapid OH oxidation, with a molar SOA yield of 0.45 after evaporation. Similarly, Wang et al.¹⁵² used the WFR to examine aqueous-phase SOA (aqSOA) formation from residential wood burning, finding that 19% of the identified compounds exhibited moderate solubility, enabling cloud-phase partitioning. Despite these advances, molecular-level understanding of aqSOA formation remains limited. Applying extractive electrospray ionization (EESI) to haze events in Beijing, Tong et al.¹⁵³ showed that aqueous-phase chemistry can dominate under high-NO_x and high-RH conditions, contributing up to 53.7% of total SOA mass.

Gas-particle partitioning of OA

The gas-to-particle partitioning of OA is governed by ambient temperature, relative humidity, aerosol composition, and critically, the volatility or vapor pressure of the constituent organic compounds^{154, 155–156}. Simultaneous measurements of gas- and particle-phase species using advanced inlets such as FIGAERO, are essential for elucidating partitioning behavior and understanding its role in SOA formation. Yatavelli et al.¹⁵⁷ found that the partitioning of organic acids is primarily influenced by carbon number and oxygen-containing functional groups. They also observed a rapid response to temperature changes (within 1–2 hours), indicating limited kinetic constraints. Peng et al.¹⁵⁸ found that for oxidized compounds like C_xH_yO₄, measured particle-phase fractions (F_p) agree reasonably well with model predictions, while less oxidized compounds exhibited discrepancies of several orders of magnitude, demonstrating the need for refined experimental setups and improved understanding of potential fragmentation processes.

Shen et al.¹⁵⁹ also reported that observed concentrations of particulate carbonyls in Beijing significantly exceeded theoretical estimates. This discrepancy was similarly observed for species like phthalic acid and pinonaldehyde¹⁶⁰. In a study of 640 organic acids in a boreal forest of Hyytiälä, Finland, Lutz et al.¹⁶¹ found that aerosol composition markedly influenced partitioning behavior. At low sulfate concentrations (<0.4 μg m^–3), the particle/gas ratios increased linearly with organic mass following Raoult’s law. However, under higher sulfate conditions, this relationship broke down, likely due to shifts in equilibrium or kinetic limitations, consistent with observations by Masoud et al.⁶⁹ who attributed scattered F_p values at lower m/z to varying functional group properties.

In general, F_p increases as compound volatility decreases, in agreement with absorptive partitioning theory. For example, pinonic acid (C₁₀H₁₆O₃) remains predominantly in the gas phase (F_p < 7%), while the more oxidized pinic acid (C₉H₁₄O₄) partitions mainly to the particle phase in forested environments^26,161. Additionally, higher oxidation states correlate positively with particle-phase fractions due to lower volatility. Although F_p tends to rise with total OA concentration⁶⁹, Liang et al.¹⁶² found that biomass burning tracers such as levoglucosan and nitrocatechols largely persist in the particle phase, even under relatively low OA mass loadings.

Source apportionment of OA

The identification and quantification of OA sources have significantly advanced over the past decade, driven by the molecular-level characterization of hundreds of organic compounds. In early studies using TAG, Zhang et al.¹⁶³ developed a bin-PMF technique that enabled rapid integration of chromatographically separated mass spectral data into factor analysis, complementing AMS measurements and enhancing source apportionment capabilities. Since then, TAG has been widely applied in megacities such as Hong Kong and Shanghai for high-time-resolution source apportionment^{59,164, 165–166}. For example, Li et al.¹⁶⁷ conducted a detailed PM_2.5 source apportionment in Shanghai using hourly TAG measurements of molecular markers, including anhydrosugars, fatty acids, aromatic acids, PAHs, and stearic acid. Their molecular marker–PMF analysis resolved eight primary sources, including vehicle exhaust, industrial emissions, tire wear, residual oil combustion, dust, coal combustion, biomass burning, and cooking¹⁶⁵. Incorporating molecular tracers also allowed further refinement, identifying three distinct organic-dominated sources: biomass burning, cooking, and SOA^166,168.

The EESI-TOF, with its high time resolution and minimal fragmentation, provides real-time molecular-level insights into OA source dynamics and atmospheric transformations. In Delhi, Kumar et al.¹⁶⁹ used EESI-TOF to identify biomass burning and cooking as key primary OA sources (Fig. 6), with SOA accounting for 40% of total OA, predominantly from photochemical processing of aromatic precursors during daytime. A complementary study by Tong et al.¹⁷⁰ combined EESI-TOF and AMS data to resolve OA factors linked to biogenic and wood-burning SOA, as well as primary emissions from cigarette smoke, cooking, and traffic. In Zurich, wintertime EESI-TOF analyses revealed that >70% of SOA originated from biomass burning⁶⁵. Offline PMF analysis of one year of EESI-TOF filter data further resolved seven OA factors, explaining 58% of total OA mass, with SOA being dominant⁶⁶.

Fig. 6 Source apportionment of OA from FIGAERO-CIMS and EESI-CIMS measurements worldwide^{35,65,169,171,193,200,201}. [Images not available. See PDF.]

COA, BBOA, and HOA represent cooking, biomass burning, and hydrocarbon-like OA, respectively. LO, MO, LA, and MA refer to less oxidized, more oxidized, less aged, and more aged OA, respectively. AM and MRN denote morning, while PM and AFTN represent afternoon; NT and NGT correspond to night-time. ONRich indicates organic nitrogen-rich, and NSOA refers to nitrogen-containing SOA; MT, ISOP, and IEPOX refer to monoterpene, isoprene, and isoprene epoxydiols. The figure was created using IGOR Pro 9.

FIGAERO-CIMS measurements have also been used effectively for source apportionment. Mehra et al.¹⁷¹ first applied PMF to online FIGAERO-CIMS data collected during APHH-Beijing summer campaign, resolving eight OA factors representing distinct sources and atmospheric processes (Fig. 6). Regional transport from the North China Plain contributed 7–14% to OA, with aromatics driving SOA formation (13%), and biogenic emissions (isoprene, monoterpenes, sesquiterpenes) contributing 13–18%, particularly in the afternoon and night. Despite these advances, challenges remain in the consistent definition and interpretation of OA factors, even with rich molecular data. As shown in Fig. 6, factors are often defined based on diurnal chemical processes or source subtypes (e.g., biomass burning vs. BB-derived SOA), complicating comparability across studies. Future efforts should aim to standardize PMF applications and harmonize source classification schemes to improve the robustness of OA source apportionment.

Roles of OA in new particle formation

The growth of newly formed atmospheric particles is essential for their activation as CCN and is often limited by the condensation of organic vapors^172,173. Over the past decade, molecular-level analyses of organic vapors and OA has significantly advanced our understanding of NPF and subsequent growth. Tröstl et al.¹⁷⁴ demonstrated that organic vapors with extremely low volatilities, particularly HOMs, play a dominant role in the initial growth of particles up to ~2 nm. Beyond this size, LVOCs contribute increasingly to growth as the influence of the Kelvin effect diminishes. Mohr et al.⁸⁸ provided direct molecular evidence that the volatility distribution of organic vapors, particularly monoterpene oxidation products (C7–C9 with 9–10 oxygen atoms), is sufficient to explain nanoparticle growth from 3 to 50 nm without invoking particle-phase chemistry.

The mechanisms of NPF have been extensively investigated through laboratory studies, particularly in the CERN CLOUD chamber, where sulfuric acid–ammonia and sulfuric acid–amine nucleation pathways are recognized as dominant under many conditions¹⁷⁵. However, field observations, especially in the free troposphere, increasingly highlight the importance of organic-dominated nucleation. Bianchi et al.¹⁷⁶ showed that in the free troposphere, NPF is primarily driven by neutral condensation of HOMs rather than ion-induced nucleation. Zhao et al.¹⁷⁷ further reported that in the upper troposphere over the Amazon (above ~13 km), pure organic nucleation, driven by biogenic isoprene emissions, dominates, facilitated by low temperatures that reduce organic vapor volatility. Recent aircraft studies confirmed that isoprene-derived organonitrates with extremely low volatility are key drivers of particle nucleation under these conditions, with observed nucleation occurring at temperatures between −30 °C and −50 °C without the need for sulfuric acid or other vapors^178,179. These findings demonstrate the critical role of molecular composition in understanding and modeling NPF processes across different atmospheric regions.

Climate and health impacts of OA

OA plays a critical role in the Earth’s climate system by scattering solar radiation and acting as CCN. The ability of OA to serve as CCN depends on its chemical composition, hygroscopicity, phase state, and particle size. Gordon et al.¹⁸⁰ found that biogenic particle formation, particularly from α-pinene oxidation products, substantially influenced CCN concentrations in the preindustrial atmosphere, with an estimated global mean radiative forcing ranging from 0.22 W m⁻² to −0.60 W m⁻². Yli-Juuti et al.⁷ reported that rising temperatures in boreal forests increased both OA loadings and CCN concentrations. Recently, unexpected nighttime hotspots of isoprene emissions over tropical regions were linked to seasonal OA production in the upper troposphere, impacting cloud formation and precipitation¹⁸¹. Furthermore, isoprene-derived SOA have been identified as effective depositional ice-nucleating particles (INPs) in the upper troposphere, influencing cirrus cloud formation¹⁸². These findings highlight the importance of incorporating molecular-level aerosol properties into climate models to improve predictions of their radiative and microphysical effects.

In terms of public health, PM_2.5 is a major environmental risk factor globally, with the highest mortality rates often observed in regions with substantial anthropogenic contributions^183,184. Pye et al.¹⁰ found that SOA exhibits a stronger association with cardiorespiratory mortality than PM_2.5 as a whole, highlighting the need to account for SOA in air quality and health impact assessments. Environmental conditions, such as temperature and humidity, also modulate OA toxicity. Mu et al.¹⁸⁵ showed that low temperature and humidity significantly extend the atmospheric lifetime of carcinogenic PAHs, especially benzo(a)pyrene (BaP), enhancing their vertical transport into the free troposphere. Ridley et al.³ observed a nationwide decline in OA concentrations in the U.S. across all seasons, largely due to reductions in anthropogenic emissions from vehicles and residential combustion. This decrease was linked to an estimated 84,000 additional lives saved between 2000 and 2010. Daellenbach et al.¹¹ further demonstrated that OA oxidative potential is predominantly driven by anthropogenic sources, particularly fine-mode SOA from residential biomass burning. These results suggest that health-based mitigation strategies should prioritize specific OA sources rather than focusing solely on total particulate mass.

Future perspectives

OA comprising thousands of individual chemical species plays a central role in both climate change and public health. However, comprehensive characterization of OA remains challenging due to their broad distributions in volatility, functionality, solubility, and oxidation state. While recent advances in online analytical techniques, particularly mass spectrometry, have significantly improved our capacity to analyze OA at the molecular level, key uncertainties persist, especially for semi-volatile and low-volatility organic compounds. Continued progress in instrumentation, laboratory experiments, field campaigns, and modeling frameworks is therefore essential to deepen our understanding of OA composition and behavior under diverse atmospheric conditions.

Future developments in analytical instrumentation will be central to advancing OA research. High-resolution mass spectrometers with faster response times and improved sensitivity will enhance the real-time identification and quantification of complex mixtures. For example, the recently developed high-resolution Orbitrap mass spectrometer coupled with Atmospheric Pressure Chemical Ionization (APCI-Orbitrap-MS) enables real-time measurements of ambient OA with a temporal resolution of 1 second and a high mass resolving power (R = 120,000)¹⁸⁶. Innovations in soft ionization techniques will reduce molecular fragmentation and improve detection of thermally labile compounds. The integration of ion mobility spectrometry^187,188 or chromatography with mass spectrometry will provide both molecular formulae and structural insights, complementing existing online chemical ionization approaches. These advancements will extend the range of detectable species, including highly oxidized and trace-level compounds, thereby refining our understanding of OA sources, evolution, and reactivity.

Efforts should also be directed toward linking molecular composition data with toxicological effects to better understand the health impacts of OA. Identifying specific toxic constituents and elucidating their biological mechanisms of action is essential for risk assessment. Xiao et al.¹⁸⁹ found that anthropogenic organic aerosols in Europe were predominantly formed through second-generation oxidation, and that the resulting changes in molecular composition significantly influenced the oxidative potential of OA and its associated health impacts. Particular attention should also be given to indoor environments, where individuals spend most of their time. Studies focused on indoor OA sources, including cooking, cleaning products, and building materials, as well as their transformations, will help mitigate indoor air pollution and associated health risks.

Globally, long-term field campaigns and continuous molecular-level monitoring are essential for characterizing the spatial and temporal variability of OA. This includes comprehensive observations across various atmospheric layers, from the surface to the free and upper troposphere, where OA, particularly biogenic SOA, play a critical role in NPF^176,177,190 and the formation of cirrus clouds and ice-nucleating particles^182,191. Establishing coordinated global observation networks equipped with advanced molecular analytical platforms, such as high-resolution mass spectrometry, will generate essential datasets across these atmospheric layers. These data are critical for improving the representation of OA in regional and global climate models, particularly for understanding aerosol-cloud-climate interactions, and their impact on global climate systems.

Finally, molecular-level characterization will enhance OA source apportionment techniques. By applying advanced mass spectrometry and chemometric tools, future studies can generate detailed molecular source profiles that improve attribution of ambient OA to specific emission sources. This information is crucial for developing targeted and effective air quality management strategies. Linking molecular markers to source categories will support evidence-based policy-making aimed at reducing the environmental and health burdens associated with OA.

Acknowledgements

This work is supported by the National Natural Science Foundation of China (Nos. 42330605, 42377101, 42075110, 42305113), the National Key Research and Development Program of China (No. 2024YFC3711900), Shanghai Pilot Program for Basic Research−Fudan University 21TQ1400100 (22TQ010), Shanghai International Science and Technology Partnership Project (No. 21230780200), and the State Key Laboratory of Atmospheric Environment and Extreme Meteorology (2024ZD02). We thank Shuhui Xue, Jianghe Xiong, and Siqi Tang for providing valuable materials and discussions.

Author contributions

Y.S. and D.Z. conceptualized the study and led the writing. All authors contributed to the writing, discussions, and revisions of the manuscript.

Data availability

No datasets were generated or analyzed during the current study.

Competing interests

The authors declare no competing interests.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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