The Accessory Olfactory System: Innately Specialized or Microcosm of Mammalian Circuitry?

In mammals, the accessory olfactory system is a distinct circuit that has received attention for its role in detecting and responding to pheromones. While the neuroscientific investigation of this system is comparatively new, recent advances and its compact size have made it an attractive model for developing an end-to-end understanding of such questions as regulation of essential behaviors, plasticity, and individual recognition. Recent discoveries have indicated a need to reevaluate our conception of this system, suggesting that ( a) physical principles-rather than biological necessity-play an underappreciated role in its raison d'être and that ( b) the anatomy of downstream projections is not dominated by unique specializations but instead consists of an abbreviated cortical/basal ganglia motif reminiscent of other sensorimotor systems. These observations suggest that the accessory olfactory system distinguishes itself primarily by the physicochemical properties of its ligands, but its architecture is otherwise a microcosm of mammalian neurocircuitry.

Different learning aberrations relate to delusion-like beliefs with different contents.

The prediction error account of delusions has had success. However, its explanation of delusions with different contents has been lacking. Persecutory delusions and paranoia are the common unfounded beliefs that others have harmful intentions towards us. Other delusions include believing that one's thoughts or actions are under external control, or that events in the world have specific personal meaning. We compare learning on two different cognitive tasks, probabilistic reversal learning (PRL) and Kamin blocking, that have relationships to paranoid and non-paranoid delusion-like beliefs, respectively. We find that Clinical High-Risk status alone does not result in different behavioral results on the PRL task but that an individual's level of paranoia is associated with excessive switching behavior. During the Kamin blocking task, paranoid individuals learned inappropriately about the blocked cue. However, they also had decreased learning about the control cue, suggesting more general learning impairments. Non-paranoid delusion-like belief conviction (but not paranoia) was associated with aberrant learning about the blocked cue but intact learning about the control cue, suggesting specific impairments in learning related to cue combination. We fit task-specific computational models separately to behavioral data to explore how latent parameters vary within individuals between tasks, and how they can explain symptom-specific effects. We find that paranoia is associated with low learning rates on the PRL task as well as the blocking task. Non-paranoid delusion-like belief conviction was instead related to parameters controlling the degree and direction of similarity between cue updating during simultaneous cue presentation. These results suggest that paranoia and other delusion-like beliefs involve dissociable deficits in learning and belief updating, which - given the transdiagnostic status of paranoia - may have differential utility in predicting psychosis.

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.

Obscured Contribution of Oxygenated Intermediate-Volatility Organic Compounds to Secondary Organic Aerosol Formation from Gasoline Vehicle Emissions.

Secondary organic aerosol (SOA) formation from gasoline vehicles spanning a wide range of emission types was investigated using an oxidation flow reactor (OFR) by conducting chassis dynamometer tests. Aided by advanced mass spectrometric techniques, SOA precursors, including volatile organic compounds (VOCs) and intermediate/semivolatile organic compounds (I/SVOCs), were comprehensively characterized. The reconstructed SOA produced from the speciated VOCs and I/SVOCs can explain 69% of the SOA measured downstream of an OFR upon 0.5-3 days' OH exposure. While VOCs can only explain 10% of total SOA production, the contribution from I/SVOCs is 59%, with oxygenated I/SVOCs (O-I/SVOCs) taking up 20% of that contribution. O-I/SVOCs (e.g., benzylic or aliphatic aldehydes and ketones), as an obscured source, account for 16% of total nonmethane organic gas (NMOG) emission. More importantly, with the improvement in emission standards, the NMOG is effectively mitigated by 35% from China 4 to China 6, which is predominantly attributed to the decrease of VOCs. Real-time measurements of different NMOG components as well as SOA production further reveal that the current emission control measures, such as advances in engine and three-way catalytic converter (TWC) techniques, are effective in reducing the "light" SOA precursors (i.e., single-ring aromatics) but not for the I/SVOC emissions. Our results also highlight greater effects of O-I/SVOCs to SOA formation than previously observed and the urgent need for further investigation into their origins, i.e., incomplete combustion, lubricating oil, etc., which requires improvements in real-time molecular-level characterization of I/SVOC molecules and in turn will benefit the future design of control measures.

Molecular-Level Insights into the Relationship between Volatility of Organic Aerosol Constituents and PM2.5 Air Pollution Levels: A Study with Ultrahigh-Resolution Mass Spectrometry.

Volatility of organic aerosols (OAs) significantly influences new particle formation and the occurrence of particulate air pollution. However, the relationship between the volatility of OA and the level of particulate air pollution (i.e., particulate matter concentration) is not well understood. In this study, we compared the chemical composition (identified by an ultrahigh-resolution Orbitrap mass spectrometer) and volatility (estimated based on a predeveloped parametrization method) of OAs in urban PM2.5 (particulate matter with aerodynamic diameter ≤ 2.5 µm) samples from seven German and Chinese cities, where the PM2.5 concentration ranged from a light (14 µg m-3) to heavy (319 µg m-3) pollution level. A large fraction (71-98%) of compounds in PM2.5 samples were attributable to intermediate-volatility organic compounds (IVOCs) and semivolatile organic compounds (SVOCs). The fraction of low-volatility organic compounds (LVOCs) and extremely low-volatility organic compounds (ELVOCs) decreased from clean (28%) to heavily polluted urban regions (2%), while that of IVOCs increased from 34 to 62%. We found that the average peak area-weighted volatility of organic compounds in different cities showed a logarithmic correlation with the average PM2.5 concentration, indicating that the volatility of urban OAs increases with the increase of air pollution level. Our results provide new insights into the relationship between OA volatility and PM pollution levels and deepen the understanding of urban air pollutant evolution.

New Insights on the Formation of Nucleation Mode Particles in a Coastal City Based on a Machine Learning Approach.

Atmospheric particles have profound implications for the global climate and human health. Among them, ultrafine particles dominate in terms of the number concentration and exhibit enhanced toxic effects as a result of their large total surface area. Therefore, understanding the driving factors behind ultrafine particle behavior is crucial. Machine learning (ML) provides a promising approach for handling complex relationships. In this study, three ML models were constructed on the basis of field observations to simulate the particle number concentration of nucleation mode (PNCN). All three models exhibited robust PNCN reproduction (R2 > 0.80), with the random forest (RF) model excelling on the test data (R2 = 0.89). Multiple methods of feature importance analysis revealed that ultraviolet (UV), H2SO4, low-volatility oxygenated organic molecules (LOOMs), temperature, and O3 were the primary factors influencing PNCN. Bivariate partial dependency plots (PDPs) indicated that during nighttime and overcast conditions, the presence of H2SO4 and LOOMs may play a crucial role in influencing PNCN. Additionally, integrating additional detailed information related to emissions or meteorology would further enhance the model performance. This pilot study shows that ML can be a novel approach for simulating atmospheric pollutants and contributes to a better understanding of the formation and growth mechanisms of nucleation mode particles.

Enhanced Light Absorption and Elevated Viscosity of Atmospheric Brown Carbon through Evaporation of Volatile Components.

Samples of brown carbon (BrC) material were collected from smoke emissions originating from wood pyrolysis experiments, serving as a proxy for BrC representative of biomass burning emissions. The acquired samples, referred to as "pyrolysis oil (PO1)," underwent subsequent processing by thermal evaporation of their volatile compounds, resulting in a set of three additional samples with volume reduction factors of 1.33, 2, and 3, denoted as PO1.33, PO2, and PO3. The chemical compositions of these POx samples and their BrC chromophore features were analyzed using a high-performance liquid chromatography instrument coupled with a photodiode array detector and a high-resolution mass spectrometer. The investigation revealed a noteworthy twofold enhancement of BrC light absorption observed for the progression of PO1 to PO3 samples, assessed across the spectral range of 300-500 nm. Concurrently, a decrease in the absorption Ångstrom exponent (AAE) from 11 to 7 was observed, indicating a weaker spectral dependence. The relative enhancement of BrC absorption at longer wavelengths was more significant, as exemplified by the increased mass absorption coefficient (MAC) measured at 405 nm from 0.1 to 0.5 m2/g. Molecular characterization further supports this darkening trend, manifesting as a depletion of small oxygenated, less absorbing monoaromatic compounds and the retention of relatively large, less polar, more absorbing constituents. Noteworthy alterations of the PO1 to PO3 mixtures included a reduction in the saturation vapor pressure of their components and an increase in viscosity. These changes were quantified by the mean values shifting from approximately 1.8 × 103 µg/m3 to 2.3 µg/m3 and from ∼103 Pa·s to ∼106 Pa·s, respectively. These results provide quantitative insights into the extent of BrC aerosol darkening during atmospheric aging through nonreactive evaporation. This new understanding will inform the refinement of atmospheric and chemical transport models.

On explosive boiling of a multicomponent Leidenfrost drop.

The gasification of multicomponent fuel drops is relevant in various energy-related technologies. An interesting phenomenon associated with this process is the self-induced explosion of the drop, producing a multitude of smaller secondary droplets, which promotes overall fuel atomization and, consequently, improves the combustion efficiency and reduces emissions of liquid-fueled engines. Here, we study a unique explosive gasification process of a tricomponent droplet consisting of water, ethanol, and oil ("ouzo"), by high-speed monitoring of the entire gasification event taking place in the well-controlled, levitated Leidenfrost state over a superheated plate. It is observed that the preferential evaporation of the most volatile component, ethanol, triggers nucleation of the oil microdroplets/nanodroplets in the remaining drop, which, consequently, becomes an opaque oil-in-water microemulsion. The tiny oil droplets subsequently coalesce into a large one, which, in turn, wraps around the remnant water. Because of the encapsulating oil layer, the droplet can no longer produce enough vapor for its levitation, and, thus, falls and contacts the superheated surface. The direct thermal contact leads to vapor bubble formation inside the drop and consequently drop explosion in the final stage.

Fruit bats adjust their decision-making process according to environmental dynamics.

One of the main functions of behavioral plasticity lies in the ability to contend with dynamic environments. Indeed, while numerous studies have shown that animals adapt their behavior to the environment, how they adapt their latent learning and decision strategies to changes in the environment is less understood. Here, we used a controlled experiment to examine the bats' ability to adjust their decision strategy according to the environmental dynamics. Twenty-five Egyptian fruit bats were placed individually in either a stable or a volatile environment for four consecutive nights. In the stable environment, two feeders offered food, each with a different reward probability (0.2 vs. 0.8) that remained fixed over two nights and were then switched, while in the volatile environment, the positions of the more and the less rewarding feeders were changed every hour. We then fit two alternative commonly used models namely, reinforcement learning and win-stay-lose-shift strategies to the bats' behavior. We found that while the bats adapted their decision-making strategy to the environmental dynamics, they seemed to be limited in their responses based on natural priors. Namely, when the environment had changed slowly, at a rate that is natural for these bats, they seemed to rely on reinforcement learning and their performance was nearly optimal, but when the experimental environment changed much faster than in the natural environment, the bats stopped learning and switched to a random decision-making strategy. Together, these findings exemplify both the bats' decision-making plasticity as well as its natural limitations.

Why Some Nonelderly Adult Medicaid Enrollees Appear Ineligible Based on Their Annual Income.

CONTEXT: Recent studies have highlighted Medicaid enrollment among middle- and higher-income populations and questioned whether the program is reaching those for whom it is intended. METHODS: Medicaid enrollment and income in 2017 are measured using administrative tax data, monthly income is measured using survey data, and Medicaid enrollment pathways are identified in administrative data. FINDINGS: Among 38.8 million nonelderly adults in Medicaid at any point in 2017, 24.4 million had annual income below their state's typical eligibility threshold, and 14.4 million (37%) had income above the threshold. Among those above the threshold, 3.5 million enrolled through a pathway allowing higher income (pregnant women, the "medically needy", and others); we also estimate that over 12 million had at least one month with income below the threshold and roughly 4 million had at least five months with income below the eligibility threshold. CONCLUSION: Pathways allowing higher income account for one-quarter of enrollees with annual incomes above typical thresholds. Among low-income adults, month-to-month variation in income is common and can account for most or all of the remaining enrollees with annual incomes above typical thresholds. A complete accounting of eligibility status would require merged data on income, Medicaid enrollment, and family structure.

Atomic Layer Deposition of CeO2 Film with a Novel Heteroleptic Ce(III) Complex.

In this paper, four heteroleptic Ce(III) complexes, including Ce(thd)3-phen (thd = 2,2,6,6-tetramethyl-3,5-heptanedione, phen = 1, 10-phenanthroline (1), Ce(thd)3-MEDA (MEDA = N-Methylethylenediamine (2), Ce(thd)3-MOMA (MOMA = N-(2-Methoxyethyl)methylamine (3), and Ce(thd)3-DMDE (DMDE = N,N″-dimethyl ethanol amine (4), were synthesized and characterized with 1H-NMR, elemental analysis, and X-ray single-crystal diffraction. The thermogravimetric analysis and vapor pressure results indicated that the complexing ability of a nitrogen-containing bidentate ligand with a cerium ion was stronger than that of a mixed oxygen-nitrogen-containing bidentate ligand. Complex 2 was selected as an ALD precursor to deposit a CeO2 film on a SiO2/Si (100) wafer. The self-limited deposition results demonstrated that complex 2 was a potential ALD precursor.

Prediction of volatility and seasonality vegetation by using the GARCH and Holt-Winters models.

Seasonality and volatility of vegetation in the ecosystem are associated with climatic sensitivity, which can have severe consequences for the environment as well as on the social and economic well-being of the nation. Monitoring and forecasting vegetation growth patterns in ecosystems significantly rely on remotely sensed vegetation indices, such as Normalized Difference Vegetation Index (NDVI). A novel integration of the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and the Holt-Winters (H-W) models was used to simulate the seasonality and volatility of the three different agro-climatic zones in Jharkhand, India: the central north-eastern, eastern, and south-eastern agro-climatic zones. MODIS Terra Vegetation Indices NDVI data MOD13Q1, from 2001 to 2021, was used to create NDVI time series volatility and seasonality modeled by the GARCH and the H-W models, respectively. GARCH-based Exponential GARCH (EGARCH) [1,1] and Standard GARCH (SGARCH) [1,1] models were used to check the volatility of vegetation growth in three different agro-climatic zones of Jharkhand. The SGARCH [1,1] and EGARCH [1,1] models for the western agro-climatic zone experienced the best indicator as it has maximum likelihood and minimal Schwarz-Bayesian criterion and Akaike information criterion. The seasonality results showed that the additive H-W model showed better results in the eastern agro-climatic zone with the optimized values of MAE (16.49), MAPE (0.49), NSE (0.86), RMSE (0.49), and R2 (0.82) followed by the south-eastern and central north-eastern agro-climatic zones. By utilizing the H-W and GARCH models, the finding demonstrates that vegetation orientation and monitoring seasonality can be predicted using NDVI.

Stochastic Volatility Models with Skewness Selection.

This paper expands traditional stochastic volatility models by allowing for time-varying skewness without imposing it. While dynamic asymmetry may capture the likely direction of future asset returns, it comes at the risk of leading to overparameterization. Our proposed approach mitigates this concern by leveraging sparsity-inducing priors to automatically select the skewness parameter as dynamic, static or zero in a data-driven framework. We consider two empirical applications. First, in a bond yield application, dynamic skewness captures interest rate cycles of monetary easing and tightening and is partially explained by central banks' mandates. In a currency modeling framework, our model indicates no skewness in the carry factor after accounting for stochastic volatility. This supports the idea of carry crashes resulting from volatility surges instead of dynamic skewness.

Investigating the effect of volatility on the hygroscopicities of acetate nanoparticle aerosols by surface plasmon resonance microscopy.

Under high relative humidity (RH) conditions, the release of volatile components (such as acetate) has a significant impact on the aerosol hygroscopicity. In this work, one surface plasmon resonance microscopy (SPRM) measurement system was introduced to determine the hygroscopic growth factors (GFs) of three acetate aerosols separately or mixed with glucose at different RHs. For Ca(CH3COO)2 or Mg(CH3COO)2 aerosols, the hygroscopic growth trend of each time was lower than that of the previous time in three cyclic humidification from 70% RH to 90% RH, which may be due to the volatility of acetic acid leading to the formation of insoluble hydroxide (Ca(OH)2 or Mg(OH)2) under high RH conditions. Then the third calculated GF (using the Zdanovskii-Stokes-Robinson method) for Ca(CH3COO)2 or Mg(CH3COO)2 in bicomponent aerosols with 1:1 mass ratio were 3.20% or 5.33% lower than that of the first calculated GF at 90% RH. The calculated results also showed that the hygroscopicity change of bicomponent aerosol was negatively correlated with glucose content, especially when the mass ratio of Mg(CH3COO)2 to glucose was 1:2, the GF at 90% RH only decreased by 4.67% after three cyclic humidification. Inductively coupled plasma atomic emission spectrum (ICP-AES) based measurements also indicated that the changes of Mg2+concentration in bicomponent was lower than that of the single-component. The results of this study reveal thatduring the efflorescence transitions of atmospheric nanoparticles, the organic acids diffusion rate may be inhibited by the coating effect of neutral organic components, and the particles aging cycle will be prolonged.

Anxiety attenuates learning advantages conferred by statistical stability and induces loss of volatility-attuning in brain activity.

Anxiety can alter an individual's perception of their external sensory environment. Previous studies suggest that anxiety can increase the magnitude of neural responses to unexpected (or surprising) stimuli. Additionally, surprise responses are reported to be boosted during stable compared to volatile environments. Few studies, however, have examined how learning is impacted by both threat and volatility. To investigate these effects, we used threat-of-shock to transiently increase subjective anxiety in healthy adults while they performed an auditory oddball task under stable and volatile environments and while undergoing functional Magnetic Resonance Imaging (fMRI) scanning. We then used Bayesian Model Selection (BMS) mapping to identify the brain areas where different models of anxiety displayed the highest evidence. Behaviourally, we found that threat-of-shock eliminated the accuracy advantage conferred by environmental stability over volatility. Neurally, we found that threat-of-shock led to attenuation and loss of volatility-attuning of brain activity evoked by surprising sounds across most subcortical and limbic regions including the thalamus, basal ganglia, claustrum, insula, anterior cingulate, hippocampal gyrus and the superior temporal gyrus. Taken together, our findings suggest that threat eliminates learning advantages conferred by statistical stability compared to volatility. Thus, we propose that anxiety disrupts behavioural adaptation to environmental statistics, and that multiple subcortical and limbic regions are implicated in this process.

Expecting the unexpected: a review of learning under uncertainty across development.

Many of our decisions take place under uncertainty. To successfully navigate the environment, individuals need to estimate the degree of uncertainty and adapt their behaviors accordingly by learning from experiences. However, uncertainty is a broad construct and distinct types of uncertainty may differentially influence our learning. We provide a semi-systematic review to illustrate cognitive and neurobiological processes involved in learning under two types of uncertainty: learning in environments with stochastic outcomes, and with volatile outcomes. We specifically reviewed studies (N = 26 studies) that included an adolescent population, because adolescence is a period in life characterized by heightened exploration and learning, as well as heightened uncertainty due to experiencing many new, often social, environments. Until now, reviews have not comprehensively compared learning under distinct types of uncertainties in this age range. Our main findings show that although the overall developmental patterns were mixed, most studies indicate that learning from stochastic outcomes, as indicated by increased accuracy in performance, improved with age. We also found that adolescents tended to have an advantage compared with adults and children when learning from volatile outcomes. We discuss potential mechanisms explaining these age-related differences and conclude by outlining future research directions.

Aberrant uncertainty processing is linked to psychotic-like experiences, autistic traits, and is reflected in pupil dilation during probabilistic learning.

Aberrant belief updating due to misestimation of uncertainty and an increased perception of the world as volatile (i.e., unstable) has been found in autism and psychotic disorders. Pupil dilation tracks events that warrant belief updating, likely reflecting the adjustment of neural gain. However, whether subclinical autistic or psychotic symptoms affect this adjustment and how they relate to learning in volatile environments remains to be unraveled. We investigated the relationship between behavioral and pupillometric markers of subjective volatility (i.e., experience of the world as unstable), autistic traits, and psychotic-like experiences in 52 neurotypical adults with a probabilistic reversal learning task. Computational modeling revealed that participants with higher psychotic-like experience scores overestimated volatility in low-volatile task periods. This was not the case for participants scoring high on autistic-like traits, who instead showed a diminished adaptation of choice-switching behavior in response to risk. Pupillometric data indicated that individuals with higher autistic- or psychotic-like trait and experience scores differentiated less between events that warrant belief updating and those that do not when volatility was high. These findings are in line with misestimation of uncertainty accounts of psychosis and autism spectrum disorders and indicate that aberrancies are already present at the subclinical level.

How to measure mast seeding?

The periodic production of large seed crops, or masting, is a widespread phenomenon in perennial plants. This behavior can enhance the reproductive efficiency of plants, leading to increased fitness, and produce ripple effects on food webs. While variability from year to year is a defining characteristic of masting, the methods used to quantify this variability are highly debated. The commonly used coefficient of variation lacks the ability to account for the serial dependence in mast data and can be influenced by zeros, making it a less suitable choice for various applications based on individual-level observations, such as phenotypic selection, heritability, and climate change studies, which rely on individual-plant-level datasets that often contain numerous zeros. To address these limitations, we present three case studies and introduce volatility and periodicity, which account for the variance in the frequency domain by emphasizing the significance of long intervals in masting. By utilizing examples of Sorbus aucuparia, Pinus pinea, Quercus robur, Quercus pubescens, and Fagus sylvatica, we demonstrate how volatility captures the effects of variance at both high and low frequencies, even in the presence of zeros, leading to improved ecological interpretations of the results. The growing availability of long-term, individual-plant datasets promises significant advancements in the field, but requires appropriate tools for analysis, which the new metrics provide.

Impact of Biomass Burning Organic Aerosol Volatility on Smoke Concentrations Downwind of Fires.

Biomass burning particulate matter (BBPM) affects regional air quality and global climate, with impacts expected to continue to grow over the coming years. We show that studies of North American fires have a systematic altitude dependence in measured BBPM normalized excess mixing ratio (NEMR; ΔPM/ΔCO), with airborne and high-altitude studies showing a factor of 2 higher NEMR than ground-based measurements. We report direct airborne measurements of BBPM volatility that partially explain the difference in the BBPM NEMR observed across platforms. We find that when heated to 40-45 °C in an airborne thermal denuder, 19% of lofted smoke PM1 evaporates. Thermal denuder measurements are consistent with evaporation observed when a single smoke plume was sampled across a range of temperatures as the plume descended from 4 to 2 km altitude. We also demonstrate that chemical aging of smoke and differences in PM emission factors can not fully explain the platform-dependent differences. When the measured PM volatility is applied to output from the High Resolution Rapid Refresh Smoke regional model, we predict a lower PM NEMR at the surface compared to the lofted smoke measured by aircraft. These results emphasize the significant role that gas-particle partitioning plays in determining the air quality impacts of wildfire smoke.

Volatility Parametrization of Low-Volatile Components of Ambient Organic Aerosols Based on Molecular Formulas.

Evaluating the volatility of organic compounds based solely on their molecular formulas would avoid tough demands in deriving molecular structures. Here, we deployed an iodide-adduct Long Time-of-Flight Chemical Ionization Mass Spectrometry (LToF-CIMS) combined with a Filter Inlet for Gases and AEROsols (FIGAERO) to investigate molecular formulas and thermograms of organic compounds on ambient particulate samples collected in the summer of 2021 in a suburban site of Shanghai and to estimate saturation vapor pressures of low- and semivolatile components of ambient organic aerosols. Then, a hierarchical cluster analysis and a subsequent classification of obtained clusters by similarity calculation were applied to the measured data set of molecular formulas and saturation vapor pressures of organic aerosols with at least a 2/3 appearance frequency, together with a similar data set collected at a rural site in the Beijing-Tianjin-Hebei region during the winter of 2018 (Ren et al., 2018), to classify all compounds into multiple groups. For each group of compounds, parametrizations between volatility and elemental composition were derived, and then relationships between each group of parameters and the mean O:C were established to achieve a volatility-molecular formula parametrization with the O:C as a key input. Statistical comparison of estimated volatilities of low-volatile organic compounds shows a much better performance of our parametrization than previous molecular formula-based ones.