Particle Size Distribution Dynamics Can Help Constrain the Phase State of Secondary Organic Aerosol

Molecular composition of fresh and aged aerosols from residential wood combustion and gasoline car with modern emission mitigation technology

Emissions from road traffic and residential heating contribute to urban air pollution. Advances in emission reduction technologies may alter the composition of emissions and affect their fate during atmospheric processing. Here, emissions of a gasoline car and a wood stove, both equipped with modern emission mitigation technology, were photochemically aged in an oxidation flow reactor to the equivalent of one to five days of photochemical aging. Fresh and aged exhausts were analyzed by ultrahigh resolution mass spectrometry. The gasoline car equipped with a three-way catalyst and a gasoline particle filter emitted minor primary fine particulate matter (PM2.5), but aging led to formation of particulate low-volatile, oxygenated and highly nitrogen-containing compounds, formed from volatile organic compounds (VOCs) and gases incl. NOx, SO2, and NH3. Reduction of the particle concentration was also observed for the application of an electrostatic precipitator with residential wood combustion but with no significant effect on the chemical composition of PM2.5. Comparing the effect of short and medium photochemical exposures on PM2.5 of both emission sources indicates a similar trend for formation of new organic compounds with increased carbon oxidation state and nitrogen content. The overall bulk compositions of the studied emission exhausts became more similar by aging, with many newly formed elemental compositions being shared. However, the presence of particulate matter in wood combustion results in differences in the molecular properties of secondary particles, as some compounds were preserved during aging.

Volatility Basis Set Distributions and Viscosity of Organic Aerosol Mixtures: Insights from Chemical Characterization Using Temperature-Programmed Desorption-Direct Analysis in Real-Time High-Resolution Mass Spectrometry

Quantitative assessment of gas-particle partitioning of individual components within complex atmospheric organic aerosol (OA) mixtures is critical for predicting and comprehending the formation and evolution of OA particles in the atmosphere. This investigation leverages previously documented data obtained through a temperature-programmed desorption-direct analysis in real-time, high-resolution mass spectrometry (TPD-DART-HRMS) platform. This methodology facilitates the bottom-up construction of volatility basis set (VBS) distributions for constituents found in three biogenic secondary organic aerosol (SOA) mixtures produced through the ozonolysis of α-pinene, limonene, and ocimene. The apparent enthalpies (ΔH*, kJ mol-1) and saturation mass concentrations (CT*, μg·m-3) of individual SOA components, determined as a function of temperature (T, K), facilitated an assessment of changes in VBS distributions and gas-particle partitioning with respect to T and atmospheric total organic mass loadings (tOM, μg·m-3). The VBS distributions reveal distinct differences in volatilities among monomers, dimers, and trimers, categorized into separate volatility bins. At the ambient temperature of T = 298 K, only monomers efficiently partition between gas and particle phases across a broad range of atmospherically relevant tOM values of 1-100 μg·m-3. Partitioning of dimers and trimers becomes notable only at T > 360 K and T > 420 K, respectively. The viscosity of SOA mixtures is assessed using a bottom-up calculation approach, incorporating the input of elemental formulas, ΔH*, CT*, and particle-phase mass fractions of the SOA components. Through this approach, we are able to accurately estimate the variations in SOA viscosity that result from the evaporation of its components. These variations are, in turn, influenced by atmospherically relevant changes in tOM and T. Comparison of the calculated SOA viscosity and diffusivity values with literature reported experimental results shows close agreement, thereby validating the employed calculation approach. These findings underscore the significant potential for TPD-DART-HRMS measurements in enabling the untargeted analysis of organic molecules within OA mixtures. This approach facilitates quantitative assessment of their gas-particle partitioning and allows for the estimation of their viscosity and condensed-phase diffusion, thereby contributing valuable insights to atmospheric models.

Fostering a Holistic Understanding of the Full Volatility Spectrum of Organic Compounds from Benzene Series Precursors through Mechanistic Modeling

A comprehensive understanding of the full volatility spectrum of organic oxidation products from the benzene series precursors is important to quantify the air quality and climate effects of secondary organic aerosol (SOA) and new particle formation (NPF). However, current models fail to capture the full volatility spectrum due to the absence of important reaction pathways. Here, we develop a novel unified model framework, the integrated two-dimensional volatility basis set (I2D-VBS), to simulate the full volatility spectrum of products from benzene series precursors by simultaneously representing first-generational oxidation, multigenerational aging, autoxidation, dimerization, nitrate formation, etc. The model successfully reproduces the volatility and O/C distributions of oxygenated organic molecules (OOMs) as well as the concentrations and the O/C of SOA over wide-ranging experimental conditions. In typical urban environments, autoxidation and multigenerational oxidation are the two main pathways for the formation of OOMs and SOA with similar contributions, but autoxidation contributes more to low-volatility products. NOx can reduce about two-thirds of OOMs and SOA, and most of the extremely low-volatility products compared to clean conditions, by suppressing dimerization and autoxidation. The I2D-VBS facilitates a holistic understanding of full volatility product formation, which helps fill the large gap in the predictions of organic NPF, particle growth, and SOA formation.

Modeling the Formation of Organic Compounds across Full Volatility Ranges and Their Contribution to Nanoparticle Growth in a Polluted Atmosphere

Nanoparticle growth influences atmospheric particles' climatic effects, and it is largely driven by low-volatility organic vapors. However, the magnitude and mechanism of organics' contribution to nanoparticle growth in polluted environments remain unclear because current observations and models cannot capture organics across full volatility ranges or track their formation chemistry. Here, we develop a mechanistic model that characterizes the full volatility spectrum of organic vapors and their contributions to nanoparticle growth by coupling advanced organic oxidation modeling and kinetic gas-particle partitioning. The model is applied to Nanjing, a typical polluted city, and it effectively captures the volatility distribution of low-volatility organics (with saturation vapor concentrations <0.3 μg/m3), thus accurately reproducing growth rates (GRs), with a 4.91% normalized mean bias. Simulations indicate that as particles grow from 4 to 40 nm, the relative fractions of GRs attributable to organics increase from 59 to 86%, with the remaining contribution from H2SO4 and its clusters. Aromatics contribute much to condensable organic vapors (∼37%), especially low-volatility vapors (∼61%), thus contributing the most to GRs (32-46%) as 4-40 nm particles grow. Alkanes also contribute 19-35% of GRs, while biogenic volatile organic compounds contribute minimally (<13%). Our model helps assess the climatic impacts of particles and predict future changes.

Secondary Organic Aerosol Formation from Volatile Chemical Product Emissions: Model Parameters and Contributions to Anthropogenic Aerosol

Volatile chemical products (VCP) are an increasingly important source of hydrocarbon and oxygenated volatile organic compound (OVOC) emissions to the atmosphere, and these emissions are likely to play an important role as anthropogenic precursors for secondary organic aerosol (SOA). While the SOA from VCP hydrocarbons is often accounted for in models, the formation, evolution, and properties of SOA from VCP OVOCs remain uncertain. We use environmental chamber data and a kinetic model to develop SOA parameters for 10 OVOCs representing glycols, glycol ethers, esters, oxygenated aromatics, and amines. Model simulations suggest that the SOA mass yields for these OVOCs are of the same magnitude as widely studied SOA precursors (e.g., long-chain alkanes, monoterpenes, and single-ring aromatics), and these yields exhibit a linear correlation with the carbon number of the precursor. When combined with emissions inventories for two megacities in the United States (US) and a US-wide inventory, we find that VCP VOCs react with OH to form 0.8-2.5× as much SOA, by mass, as mobile sources. Hydrocarbons (terpenes, branched and cyclic alkanes) and OVOCs (terpenoids, glycols, glycol ethers) make up 60-75 and 25-40% of the SOA arising from VCP use, respectively. This work contributes to the growing body of knowledge focused on studying VCP VOC contributions to urban air pollution.

Assessment of sample freezing as a preservation technique for analysing the molecular composition of dissolved organic matter in aquatic systems

Dissolved organic matter (DOM) is widely studied in environmental and biogeochemical sciences, but is susceptible to chemical and biological degradation during sample transport and storage. Samples taken in remote regions, aboard ships, or in large numbers need to be preserved for later analysis without changing DOM composition. Here we compare high-resolution mass spectra of solid phase extractable DOM before and after freezing at -20 °C. We found that freezing increases compositional dissimilarity in DOM by between 0 to 18.2% (median = 2.7% across 7 sites) when comparing replicates that were frozen versus unfrozen, i.e., processed immediately after sampling, as compared with differences between unfrozen replicates. The effects of freezing primarily consisted of a poorer detection limit, but were smaller than other sample preparation and analysis steps, such as solid phase extraction and variable ionisation efficiency. Freezing samples for either 21 or 95 days led to similar and only slight changes in DOM composition, albeit with more variation for the latter. Therefore, we conclude that sample freezing on these time scales should not impede scientific study of aquatic DOM and can be used where it makes logistical sense, such as for large spatial surveys or study of archived samples.

Overview of ICARUS-A Curated, Open Access, Online Repository for Atmospheric Simulation Chamber Data

Atmospheric simulation chambers continue to be indispensable tools for research in the atmospheric sciences. Insights from chamber studies are integrated into atmospheric chemical transport models, which are used for science-informed policy decisions. However, a centralized data management and access infrastructure for their scientific products had not been available in the United States and many parts of the world. ICARUS (Integrated Chamber Atmospheric data Repository for Unified Science) is an open access, searchable, web-based infrastructure for storing, sharing, discovering, and utilizing atmospheric chamber data [https://icarus.ucdavis.edu]. ICARUS has two parts: a data intake portal and a search and discovery portal. Data in ICARUS are curated, uniform, interactive, indexed on popular search engines, mirrored by other repositories, version-tracked, vocabulary-controlled, and citable. ICARUS hosts both legacy data and new data in compliance with open access data mandates. Targeted data discovery is available based on key experimental parameters, including organic reactants and mixtures that are managed using the PubChem chemical database, oxidant information, nitrogen oxide (NOx) content, alkylperoxy radical (RO₂) fate, seed particle information, environmental conditions, and reaction categories. A discipline-specific repository such as ICARUS with high amounts of metadata works to support the evaluation and revision of atmospheric model mechanisms, intercomparison of data and models, and the development of new model frameworks that can have more predictive power in the current and future atmosphere. The open accessibility and interactive nature of ICARUS data may also be useful for teaching, data mining, and training machine learning models.

Vapors Are Lost to Walls, Not to Particles on the Wall: Artifact-Corrected Parameters from Chamber Experiments and Implications for Global Secondary Organic Aerosol

Atmospheric models of secondary organic aerosol (OA) (SOA) typically rely on parameters derived from environmental chambers. Chambers are subject to experimental artifacts, including losses of (1) particles to the walls (PWL), (2) vapors to the particles on the wall (V2PWL), and (3) vapors to the wall directly (VWL). We present a method for deriving artifact-corrected SOA parameters and translating these to volatility basis set (VBS) parameters for use in chemical transport models (CTMs). Our process involves combining a box model that accounts for chamber artifacts (Statistical Oxidation Model with a TwO-Moment Aerosol Sectional model (SOM-TOMAS)) with a pseudo-atmospheric simulation to develop VBS parameters that are fit across a range of OA mass concentrations. We found that VWL led to the highest percentage change in chamber SOA mass yields (high NO_x: 36-680%; low NO_x: 55-250%), followed by PWL (high NO_x: 8-39%; low NO_x: 10-37%), while the effects of V2PWL are negligible. In contrast to earlier work that assumed that V2PWL was a meaningful loss pathway, we show that V2PWL is an unimportant SOA loss pathway and can be ignored when analyzing chamber data. Using our updated VBS parameters, we found that not accounting for VWL may lead surface-level OA to be underestimated by 24% (0.25 μg m^-3) as a global average or up to 130% (9.0 μg m^-3) in regions of high biogenic or anthropogenic activity. Finally, we found that accurately accounting for PWL and VWL improves model-measurement agreement for fine mode aerosol mass concentrations (PM_2.5) in the GEOS-Chem model.

Comparison of Phase States of PM2.5 over Megacities, Seoul and Beijing, and Their Implications on Particle Size Distribution

Although the particle phase state is an important property, there is scant information on it, especially, for real-world aerosols. To explore the phase state of fine mode aerosols (PM_2.5) in two megacities, Seoul and Beijing, we collected PM_2.5 filter samples daily from Dec 2020 to Jan 2021. Using optical microscopy combined with the poke-and-flow technique, the phase states of the bulk of PM_2.5 as a function of relative humidity (RH) were determined and compared to the ambient RH ranges in the two cities. PM_2.5 was found to be liquid to semisolid in Seoul but mostly semisolid to solid in Beijing. The liquid state was dominant on polluted days, while a semisolid state was dominant on clean days in Seoul. These findings can be explained by the aerosol liquid water content related to the chemical compositions of the aerosols at ambient RH; the water content of PM_2.5 was much higher in Seoul than in Beijing. Furthermore, the overall phase states of PM_2.5 observed in Seoul and Beijing were interrelated with the particle size distribution. The results of this study aid in a better understanding of the fundamental physical properties of aerosols and in examining how these are linked to PM_2.5 in polluted urban atmospheres.

Process-Level Modeling Can Simultaneously Explain Secondary Organic Aerosol Evolution in Chambers and Flow Reactors

Secondary organic aerosol (SOA) data gathered in environmental chambers (ECs) have been used extensively to develop parameters to represent SOA formation and evolution. The EC-based parameters are usually constrained to less than one day of photochemical aging but extrapolated to predict SOA aging over much longer timescales in atmospheric models. Recently, SOA has been increasingly studied in oxidation flow reactors (OFRs) over aging timescales of one to multiple days. However, these OFR data have been rarely used to validate or update the EC-based parameters. The simultaneous use of EC and OFR data is challenging because the processes relevant to SOA formation and evolution proceed over very different timescales, and both reactor types exhibit distinct experimental artifacts. In this work, we show that a kinetic SOA chemistry and microphysics model that accounts for various processes, including wall losses, aerosol phase state, heterogeneous oxidation, oligomerization, and new particle formation, can simultaneously explain SOA evolution in EC and OFR experiments, using a single consistent set of SOA parameters. With α-pinene as an example, we first developed parameters by fitting the model output to the measured SOA mass concentration and oxygen-to-carbon (O:C) ratio from an EC experiment (<1 day of aging). We then used these parameters to simulate SOA formation in OFR experiments and found that the model overestimated SOA formation (by a factor of 3-16) over photochemical ages ranging from 0.4 to 13 days, when excluding the abovementioned processes. By comprehensively accounting for these processes, the model was able to explain the observed evolution in SOA mass, composition (i.e., O:C), and size distribution in the OFR experiments. This work suggests that EC and OFR SOA data can be modeled consistently, and a synergistic use of EC and OFR data can aid in developing more refined SOA parameters for use in atmospheric models.

Rapid growth of anthropogenic organic nanoparticles greatly alters cloud life cycle in the Amazon rainforest

Aerosol-cloud interactions remain uncertain in assessing climate change. While anthropogenic activities produce copious aerosol nanoparticles smaller than 10 nanometers, they are too small to act as efficient cloud condensation nuclei (CCN). The mechanisms responsible for particle growth to CCN-relevant sizes are poorly understood. Here, we present aircraft observations of rapid growth of anthropogenic nanoparticles downwind of an isolated metropolis in the Amazon rainforest. Model analysis reveals that the sustained particle growth to CCN sizes is predominantly caused by particle-phase diffusion-limited partitioning of semivolatile oxidation products of biogenic hydrocarbons. Cloud-resolving numerical simulations show that the enhanced CCN concentrations in the urban plume substantially alter the formation of shallow convective clouds, suppress precipitation, and enhance the transition to deep convective clouds. The proposed nanoparticle growth mechanism, expressly enabled by the abundantly formed semivolatile organics, suggests an appreciable impact of anthropogenic aerosols on cloud life cycle in previously unpolluted forests of the world.

Heterogeneous Nucleation Drives Particle Size Segregation in Sequential Ozone and Nitrate Radical Oxidation of Catechol

Secondary organic aerosol formation via condensation of organic vapors onto existing aerosol transforms the chemical composition and size distribution of ambient aerosol, with implications for air quality and Earth's radiative balance. Gas-to-particle conversion is generally thought to occur on a continuum between equilibrium-driven partitioning of semivolatile molecules to the pre-existing mass size distribution and kinetic-driven condensation of low volatility molecules to the pre-existing surface area size distribution. However, we offer experimental evidence in contrast to this framework. When catechol is sequentially oxidized by O₃ and NO₃ in the presence of (NH₄)₂SO₄ seed particles with a single size mode, we observe a bimodal organic aerosol mass size distribution with two size modes of distinct chemical composition with nitrocatechol from NO₃ oxidation preferentially condensing onto the large end of the pre-existing size distribution (∼750 nm). A size-resolved chemistry and microphysics model reproduces the evolution of the two distinct organic aerosol size modes─heterogeneous nucleation to an independent, nitrocatechol-rich aerosol phase.

Humidity Dependence of the Condensational Growth of α-Pinene Secondary Organic Aerosol Particles

The influence of relative humidity (RH) on the condensational growth of organic aerosol particles remains incompletely understood. Herein, the RH dependence was investigated via a series of experiments for α-pinene ozonolysis in a continuously mixed flow chamber in which recurring cycles of particle growth occurred every 7 to 8 h at a given RH. In 5 h, the mean increase in the particle mode diameter was 15 nm at 0% RH and 110 nm at 75% RH. The corresponding particle growth coefficients, representing a combination of the thermodynamic driving force and the kinetic resistance to mass transfer, increased from 0.35 to 2.3 nm² s^-1. The chemical composition, characterized by O:C and H:C atomic ratios of 0.52 and 1.48, respectively, and determined by mass spectrometry, did not depend on RH. The Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) was applied to reproduce the observed size- and RH-dependent particle growth by optimizing the diffusivities D_b within the particles of the condensing molecules. The D_b values increased from 5 α^-1 × 10^-16 at 0% RH to 2 α^-1 × 10^-12 cm^-2 s^-1 at 75% RH for mass accommodation coefficients α of 0.1 to 1.0, highlighting the importance of particle-phase properties in modeling the growth of atmospheric aerosol particles.