Global organic and inorganic aerosol hygroscopicity and its effect on radiative forcing

The climate effects of atmospheric aerosol particles serving as cloud condensation nuclei (CCN) depend on chemical composition and hygroscopicity, which are highly variable on spatial and temporal scales. Here we present global CCN measurements, covering diverse environments from pristine to highly polluted conditions. We show that the effective aerosol hygroscopicity, κ, can be derived accurately from the fine aerosol mass fractions of organic particulate matter (ϵorg) and inorganic ions (ϵinorg) through a linear combination, κ = ϵorg ⋅ κorg + ϵinorg ⋅ κinorg. In spite of the chemical complexity of organic matter, its hygroscopicity is well captured and represented by a global average value of κorg = 0.12 ± 0.02 with κinorg = 0.63 ± 0.01 as the corresponding value for inorganic ions. By showing that the sensitivity of global climate forcing to changes in κorg and κinorg is small, we constrain a critically important aspect of global climate modelling.

Lidar and Radar Signal Simulation: Stability Assessment of the Aerosol–Cloud Interaction Index

Aerosol–cloud interactions (ACI) are in the spotlight of atmospheric science since the limited knowledge about these processes produces large uncertainties in climate predictions. These interactions can be quantified by the aerosol–cloud interaction index (ACI index), which establishes a relationship between aerosol and cloud microphysics. The experimental determination of the ACI index through a synergistic combination of lidar and cloud radar is still quite challenging due to the difficulties in disentangling the aerosol influence on cloud formation from other processes and in retrieving aerosol-particle and cloud microphysics from remote sensing measurements. For a better understanding of the ACI and to evaluate the optimal experimental conditions for the measurement of these processes, a Lidar and Radar Signal Simulator (LARSS) is presented. LARSS simulate vertically-resolved lidar and cloud-radar signals during the formation process of a convective cloud, from the aerosol hygroscopic enhancement to the condensation droplet growth. Through LARSS simulations, it is observed a dependence of the ACI index with height, associated with the increase in number (ACINd) and effective radius (ACIreff) of the droplets with altitude. Furthermore, ACINd and ACIreff for several aerosol types (such as ammonium sulfate, biomass burning, and dust) are estimated using LARSS, presenting different values as a function of the aerosol model. Minimum ACINd values are obtained when the activation of new droplets stops, while ACIreff reaches its maximum values several meters above. These simulations are carried out considering standard atmospheric conditions, with a relative humidity of 30% at the surface, reaching the supersaturation of the air mass at 3500 m. To assess the stability of the ACI index, a sensitivity study using LARSS is performed. It is obtained that the dry modal aerosol radius presents a strong influence on the ACI index fluctuations of 18% cause an ACI variability of 30% while the updraft velocity within the cloud and the wet modal aerosol radius have a weaker impact. LARSS ACI index uncertainty is obtained through the Monte Carlo technique, obtaining ACIreff uncertainty below 16% for the uncertainty of all LARSS input parameters of 10%. Finally, a new ACI index is introduced in this study, called the remote-sensing ACI index (ACIRs), to simplify the quantification of the ACI processes with remote sensors. This new index presents a linear relationship with the ACIreff, which depends on the Angstrom exponent. The use of ACIRs to derive ACIreff presents the advantage that it is possible to quantify the aerosol–cloud interaction without the need to perform microphysical inversion retrievals, thus reducing the uncertainty sources.

Imaging atmospheric aerosol particles from a UAV with digital holography

The lack of quantitative characterization of aerosol particles and their loading in the atmosphere is one of the greatest uncertainties in climate-change science. Improved instrumentation capable of determining the size and shape of aerosol particles is needed in efforts to reduce this uncertainty. We describe a new instrument carried by an unmanned aerial vehicle (UAV) that images free-floating aerosol particles in the atmosphere. Using digital holography, the instrument obtains the images in a non-contact manner, resolving particles larger than ten micrometers in size in a sensing volume of approximately three cubic centimeters. The instrument, called the holographic aerosol particle imager (HAPI), has the unique ability to image multiple particles freely entering its sensing volume from any direction via a single measurement. The construction of HAPI consists of 3D printed polymer structures that enable a sufficiently low size and weight that it may be flown on a commercial-grade UAV. Examples from field trials of HAPI show images of freshly emitted tree pollen and mineral dust.

Characterization of Aerosol Hygroscopicity Over the Northeast Pacific Ocean: Impacts on Prediction of CCN and Stratocumulus Cloud Droplet Number Concentrations

During the Marine Aerosol Cloud and Wildfire Study (MACAWS) in June and July of 2018, aerosol composition and cloud condensation nuclei (CCN) properties were measured over the N.E. Pacific to characterize the influence of aerosol hygroscopicity on predictions of ambient CCN and stratocumulus cloud droplet number concentrations (CDNC). Three vertical regions were characterized, corresponding to the marine boundary layer (MBL), an above-cloud organic aerosol layer (AC-OAL), and the free troposphere (FT) above the AC-OAL. The aerosol hygroscopicity parameter (κ) was calculated from CCN measurements (κ CCN) and bulk aerosol mass spectrometer (AMS) measurements (κ AMS). Within the MBL, measured hygroscopicities varied between values typical of both continental environments (~0.2) and remote marine locations (~0.7). For most flights, CCN closure was achieved within 20% in the MBL. For five of the seven flights, assuming a constant aerosol size distribution produced similar or better CCN closure than assuming a constant "marine" hygroscopicity (κ = 0.72). An aerosol-cloud parcel model was used to characterize the sensitivity of predicted stratocumulus CDNC to aerosol hygroscopicity, size distribution properties, and updraft velocity. Average CDNC sensitivity to accumulation mode aerosol hygroscopicity is 39% as large as the sensitivity to the geometric median diameter in this environment. Simulations suggest CDNC sensitivity to hygroscopicity is largest in marine stratocumulus with low updraft velocities (<0.2 m s-1), where accumulation mode particles are most relevant to CDNC, and in marine stratocumulus or cumulus with large updraft velocities (>0.6 m s-1), where hygroscopic properties of the Aitken mode dominate hygroscopicity sensitivity.

Predicting cloud condensation nuclei number concentration based on conventional measurements of aerosol properties in the North China Plain

Cloud condensation nuclei (CCN) play an important role in the formation and evolution of cloud droplets. However, the dataset of global CCN number concentration (N_CCN) is still scarce due to the lack of direct CCN measurements, hindering an accurate evaluation of its climate effects. Alternative approaches to determine N_CCN have thus been proposed to calculate N_CCN based on measurements of other aerosol properties, such as particle number size distribution, bulk aerosol chemical composition and aerosol optical properties. To better understand the interaction between haze pollution and climate, we performed direct CCN measurements in the winter of 2018 at the Gucheng site, a typical polluted suburban site in North China Plain (NCP). The results show that the average CCN concentrations were 3.81 × 10³ cm^-3, 5.35 × 10³ cm^-3, 9.74 × 10³ cm^-3, 1.27 × 10⁴ cm^-3, 1.44 × 10⁴ cm^-3 at measured supersaturation levels of 0.114%, 0.148%, 0.273%, 0.492% and 0.864%, respectively. Based on these observational data, we have further investigated two methods of calculating N_CCN from: (1) bulk aerosol chemical composition and particle number size distribution; (2) bulk aerosol chemical composition and aerosol optical properties. Our results showed that both methods could well reproduce the observed concentration (R² > 0.88) and variability of N_CCN with a 9% to 23% difference in the mean value. Further error analysis shows that the estimated N_CCN tends to be underestimated by about 20% during the daytime while overestimated by <10% at night compared with the measured N_CCN. These results provide quantitative instructions for the N_CCN prediction based on conventional aerosol measurements in the NCP.

A global view on the effect of water uptake on aerosol particle light scattering

A reference dataset of multi-wavelength particle light scattering and hemispheric backscattering coefficients for different relative humidities (RH) between RH = 30 and 95% and wavelengths between λ = 450 nm and 700 nm is described in this work. Tandem-humidified nephelometer measurements from 26 ground-based sites around the globe, covering multiple aerosol types, have been re-analysed and harmonized into a single dataset. The dataset includes multi-annual measurements from long-term monitoring sites as well as short-term field campaign data. The result is a unique collection of RH-dependent aerosol light scattering properties, presented as a function of size cut. This dataset is important for climate and atmospheric model-measurement inter-comparisons, as a means to improve model performance, and may be useful for satellite and remote sensing evaluation using surface-based, in-situ measurements.

Design Type(s)	spectral data collection and processing objective • data integration objective • time series design
Measurement Type(s)	light scattering
Technology Type(s)	Nephelometry
Factor Type(s)	geographic location • instrument • Environment • temporal_interval
Sample Characteristic(s)	United States of America • climate system • Canada • The Netherlands • Greece • Germany • Portuguese Republic • South Korea • China • United Kingdom • Finland • Switzerland • Maldives Archipelago • Brazil • Republic of Ireland • Niger • India • Kingdom of Spain • Kingdom of Norway

Machine-accessible metadata file describing the reported data (ISA-Tab format)

Evaluation of global simulations of aerosol particle and cloud condensation nuclei number, with implications for cloud droplet formation

A total of 16 global chemistry transport models and general circulation models have participated in this study; 14 models have been evaluated with regard to their ability to reproduce the near-surface observed number concentration of aerosol particles and cloud condensation nuclei (CCN), as well as derived cloud droplet number concentration (CDNC). Model results for the period 2011-2015 are compared with aerosol measurements (aerosol particle number, CCN and aerosol particle composition in the submicron fraction) from nine surface stations located in Europe and Japan. The evaluation focuses on the ability of models to simulate the average across time state in diverse environments and on the seasonal and short-term variability in the aerosol properties. There is no single model that systematically performs best across all environments represented by the observations. Models tend to underestimate the observed aerosol particle and CCN number concentrations, with average normalized mean bias (NMB) of all models and for all stations, where data are available, of -24% and -35% for particles with dry diameters > 50 and > 120nm, as well as -36% and -34% for CCN at supersaturations of 0.2% and 1.0%, respectively. However, they seem to behave differently for particles activating at very low supersaturations (< 0.1 %) than at higher ones. A total of 15 models have been used to produce ensemble annual median distributions of relevant parameters. The model diversity (defined as the ratio of standard deviation to mean) is up to about 3 for simulated N3 (number concentration of particles with dry diameters larger than 3 nm) and up to about 1 for simulated CCN in the extra-polar regions. A global mean reduction of a factor of about 2 is found in the model diversity for CCN at a supersaturation of 0.2% (CCN0.2) compared to that for N3, maximizing over regions where new particle formation is important. An additional model has been used to investigate potential causes of model diversity in CCN and bias compared to the observations by performing a perturbed parameter ensemble (PPE) accounting for uncertainties in 26 aerosol-related model input parameters. This PPE suggests that biogenic secondary organic aerosol formation and the hygroscopic properties of the organic material are likely to be the major sources of CCN uncertainty in summer, with dry deposition and cloud processing being dominant in winter. Models capture the relative amplitude of the seasonal variability of the aerosol particle number concentration for all studied particle sizes with available observations (dry diameters larger than 50, 80 and 120 nm). The short-term persistence time (on the order of a few days) of CCN concentrations, which is a measure of aerosol dynamic behavior in the models, is underestimated on average by the models by 40% during winter and 20% in summer. In contrast to the large spread in simulated aerosol particle and CCN number concentrations, the CDNC derived from simulated CCN spectra is less diverse and in better agreement with CDNC estimates consistently derived from the observations (average NMB -13% and -22% for updraft velocities 0.3 and 0.6 ms-1, respectively). In addition, simulated CDNC is in slightly better agreement with observationally derived values at lower than at higher updraft velocities (index of agreement 0.64 vs. 0.65). The reduced spread of CDNC compared to that of CCN is attributed to the sublinear response of CDNC to aerosol particle number variations and the negative correlation between the sensitivities of CDNC to aerosol particle number concentration (∂N d/∂N a) and to updraft velocity (∂N d/∂w). Overall, we find that while CCN is controlled by both aerosol particle number and composition, CDNC is sensitive to CCN at low and moderate CCN concentrations and to the updraft velocity when CCN levels are high. Discrepancies are found in sensitivities ∂N d/∂N a and ∂N d/∂w; models may be predisposed to be too "aerosol sensitive" or "aerosol insensitive" in aerosol-cloud-climate interaction studies, even if they may capture average droplet numbers well. This is a subtle but profound finding that only the sensitivities can clearly reveal and may explain inter-model biases on the aerosol indirect effect.

Prediction of size-resolved number concentration of cloud condensation nuclei and long-term measurements of their activation characteristics

Atmospheric aerosol particles acting as cloud condensation nuclei (CCN) are key elements in the hydrological cycle and climate. To improve our understanding of the activation characteristics of CCN and to obtain accurate predictions of their concentrations, a long-term field campaign was carried out in the Yangtze River Delta, China. The results indicated that the CCN were easier to activate in this relatively polluted rural station than in clean (e.g., the Amazon region) or dusty (e.g., Kanpur-spring) locations, but were harder to activate than in more polluted urban areas (e.g., Beijing). An improved method, using two additional parameters—the maximum activation fraction and the degree of heterogeneity, is proposed to predict the accurate, size-resolved concentration of CCN. The value ranges and prediction uncertainties of these parameters were evaluated. The CCN predicted using this improved method with size-resolved chemical compositions under an assumption that all particles were internally mixed showed the best agreement with the long-term field measurements.

Collocated observations of cloud condensation nuclei, particle size distributions, and chemical composition

Cloud condensation nuclei (CCN) number concentrations alongside with submicrometer particle number size distributions and particle chemical composition have been measured at atmospheric observatories of the Aerosols, Clouds, and Trace gases Research InfraStructure (ACTRIS) as well as other international sites over multiple years. Here, harmonized data records from 11 observatories are summarized, spanning 98,677 instrument hours for CCN data, 157,880 for particle number size distributions, and 70,817 for chemical composition data. The observatories represent nine different environments, e.g., Arctic, Atlantic, Pacific and Mediterranean maritime, boreal forest, or high alpine atmospheric conditions. This is a unique collection of aerosol particle properties most relevant for studying aerosol-cloud interactions which constitute the largest uncertainty in anthropogenic radiative forcing of the climate. The dataset is appropriate for comprehensive aerosol characterization (e.g., closure studies of CCN), model-measurement intercomparison and satellite retrieval method evaluation, among others. Data have been acquired and processed following international recommendations for quality assurance and have undergone multiple stages of quality assessment.

Optical Properties of Secondary Organic Aerosol from cis-3-Hexenol and cis-3-Hexenyl Acetate: Effect of Chemical Composition, Humidity, and Phase

Atmospheric aerosols play an important role in Earth's radiative balance directly, by scattering and absorbing radiation, and indirectly, by acting as cloud condensation nuclei (CCN). Atmospheric aerosol is dominated by secondary organic aerosol (SOA) formed by the oxidation of biogenic volatile organic compounds (BVOCs). Green leaf volatiles (GLVs) are a class of BVOCs that contribute to SOA, yet their role in the Earth's radiative budget is poorly understood. In this work we measured the scattering efficiency (at 450, 525, and 635 nm), absorption efficiency (between 190 and 900 nm), particle phase, bulk chemical properties (O:C, H:C), and molecular-level composition of SOA formed from the ozonolysis of two GLVs: cis-3-hexenol (HXL) and cis-3-hexenyl acetate (CHA). Both HXL and CHA produced SOA that was weakly absorbing, yet CHA-SOA was a more efficient absorber than HXL-SOA. The scatter efficiency of SOA from both systems was wavelength-dependent, with the stronger dependence exhibited by HXL-SOA, likely due to differences in particle size. HXL-SOA formed under both dry (10% RH) and wet (70% RH) conditions had the same bulk chemical properties (O:C), yet significantly different optical properties, which was attributed to differences in molecular-level composition. We have found that SOA derived from green leaf volatiles has the potential to affect the Earth's radiative budget, and also that bulk chemical properties can be insufficient to predict SOA optical properties.

Characterization and parameterization of aerosol cloud condensation nuclei activation under different pollution conditions

To better understand the cloud condensation nuclei (CCN) activation capacity of aerosol particles in different pollution conditions, a long-term field experiment was carried out at a regional GAW (Global Atmosphere Watch) station in the Yangtze River Delta area of China. The homogeneity of aerosol particles was the highest in clean weather, with the highest active fraction of all the weather types. For pollution with the same visibility, the residual aerosol particles in higher relative humidity weather conditions were more externally mixed and heterogeneous, with a lower hygroscopic capacity. The hygroscopic capacity (κ) of organic aerosols can be classified into 0.1 and 0.2 in different weather types. The particles at ~150 nm were easily activated in haze weather conditions. For CCN predictions, the bulk chemical composition method was closer to observations at low supersaturations (≤0.1%), whereas when the supersaturation was ≥0.2%, the size-resolved chemical composition method was more accurate. As for the mixing state of the aerosol particles, in haze, heavy haze, and severe haze weather conditions CCN predictions based on the internal mixing assumption were robust, whereas for other weather conditions, predictions based on the external mixing assumption were more accurate.

The Impact of Aerosol Particle Mixing State on the Hygroscopicity of Sea Spray Aerosol

Aerosol particles influence global climate by determining cloud droplet number concentrations, brightness, and lifetime. Primary aerosol particles, such as those produced from breaking waves in the ocean, display large particle-particle variability in chemical composition, morphology, and physical phase state, all of which affect the ability of individual particles to accommodate water and grow into cloud droplets. Despite such diversity in molecular composition, there is a paucity of methods available to assess how particle-particle variability in chemistry translates to corresponding differences in aerosol hygroscopicity. Here, an approach has been developed that allows for characterization of the distribution of aerosol hygroscopicity within a chemically complex population of atmospheric particles. This methodology, when applied to the interpretation of nascent sea spray aerosol, provides a quantitative framework for connecting results obtained using molecular mimics generated in the laboratory with chemically complex ambient aerosol. We show that nascent sea spray aerosol, generated in situ in the Atlantic Ocean, displays a broad distribution of particle hygroscopicities, indicative of a correspondingly broad distribution of particle chemical compositions. Molecular mimics of sea spray aerosol organic material were used in the laboratory to assess the volume fractions and molecular functionality required to suppress sea spray aerosol hygroscopicity to the extent indicated by field observations. We show that proper accounting for the distribution and diversity in particle hygroscopicity and composition are important to the assessment of particle impacts on clouds and global climate.

On the competition among aerosol number, size and composition in predicting CCN variability: a multi-annual field study in an urbanized desert

A 2-year data set of measured CCN (cloud condensation nuclei) concentrations at 0.2 % supersaturation is combined with aerosol size distribution and aerosol composition data to probe the effects of aerosol number concentrations, size distribution and composition on CCN patterns. Data were collected over a period of 2 years (2012-2014) in central Tucson, Arizona: a significant urban area surrounded by a sparsely populated desert. Average CCN concentrations are typically lowest in spring (233 cm^-3), highest in winter (430 cm^-3) and have a secondary peak during the North American monsoon season (July to September; 372 cm^-3). There is significant variability outside of seasonal patterns, with extreme concentrations (1 and 99 % levels) ranging from 56 to 1945 cm^-3 as measured during the winter, the season with highest variability. Modeled CCN concentrations based on fixed chemical composition achieve better closure in winter, with size and number alone able to predict 82% of the variance in CCN concentration. Changes in aerosol chemical composition are typically aligned with changes in size and aerosol number, such that hygroscopicity can be parameterized even though it is still variable. In summer, models based on fixed chemical composition explain at best only 41% (pre-monsoon) and 36% (monsoon) of the variance. This is attributed to the effects of secondary organic aerosol (SOA) production, the competition between new particle formation and condensational growth, the complex interaction of meteorology, regional and local emissions and multi-phase chemistry during the North American monsoon. Chemical composition is found to be an important factor for improving predictability in spring and on longer timescales in winter. Parameterized models typically exhibit improved predictive skill when there are strong relationships between CCN concentrations and the prevailing meteorology and dominant aerosol physicochemical processes, suggesting that similar findings could be possible in other locations with comparable climates and geography.

Airborne single particle mass spectrometers (SPLAT II & miniSPLAT) and new software for data visualization and analysis in a geo-spatial context

Understanding the effect of aerosols on climate requires knowledge of the size and chemical composition of individual aerosol particles-two fundamental properties that determine an aerosol's optical properties and ability to serve as cloud condensation or ice nuclei. Here we present our aircraft-compatible single particle mass spectrometers, SPLAT II and its new, miniaturized version, miniSPLAT that measure in-situ and in real-time the size and chemical composition of individual aerosol particles with extremely high sensitivity, temporal resolution, and sizing precision on the order of a monolayer. Although miniSPLAT's size, weight, and power consumption are significantly smaller, its performance is on par with SPLAT II. Both instruments operate in dual data acquisition mode to measure, in addition to single particle size and composition, particle number concentrations, size distributions, density, and asphericity with high temporal resolution. We also present ND-Scope, our newly developed interactive visual analytics software package. ND-Scope is designed to explore and visualize the vast amount of complex, multidimensional data acquired by our single particle mass spectrometers, along with other aerosol and cloud characterization instruments on-board aircraft. We demonstrate that ND-Scope makes it possible to visualize the relationships between different observables and to view the data in a geo-spatial context, using the interactive and fully coupled Google Earth and Parallel Coordinates displays. Here we illustrate the utility of ND-Scope to visualize the spatial distribution of atmospheric particles of different compositions, and explore the relationship between individual particle compositions and their activity as cloud condensation nuclei.

Mass spectrometry of atmospheric aerosols--recent developments and applications. Part II: On-line mass spectrometry techniques

Many of the significant advances in our understanding of atmospheric particles can be attributed to the application of mass spectrometry. Mass spectrometry provides high sensitivity with fast response time to probe chemically complex particles. This review focuses on recent developments and applications in the field of mass spectrometry of atmospheric aerosols. In Part II of this two-part review, we concentrate on real-time mass spectrometry techniques, which provide high time resolution for insight into brief events and diurnal changes while eliminating the potential artifacts acquired during long-term filter sampling. In particular, real-time mass spectrometry has been shown recently to provide the ability to probe the chemical composition of ambient individual particles <30 nm in diameter to further our understanding of how particles are formed through nucleation in the atmosphere. Further, transportable real-time mass spectrometry techniques are now used frequently on ground-, ship-, and aircraft-based studies around the globe to further our understanding of the spatial distribution of atmospheric aerosols. In addition, coupling aerosol mass spectrometry techniques with other measurements in series has allowed the in situ determination of chemically resolved particle effective density, refractive index, volatility, and cloud activation properties.

Molecular dynamics studies to understand the mechanism of heat accommodation in homogeneous condensing flow of carbon dioxide

Using molecular dynamics (MD), we have studied the mechanism of heat accommodation between carbon dioxide clusters and monomers for temperatures and cluster size conditions that exist in homogeneous condensing supersonic expansion plumes. The work was motivated by our meso-scale direct simulation Monte Carlo and Bhatnagar-Gross-Krook based condensation simulations where we found that the heat accommodation model plays a key role in the near-field of the nozzle expansion particularly as the degree of condensation increases [R. Kumar, Z. Li, and D. Levin, Phys. Fluids 23, 052001 (2011)]. The heat released by nucleation and condensation and the heat removed by cluster evaporation can be transferred or removed from either the kinetic or translational modes of the carbon dioxide monomers. The molecular dynamics results show that the time required for gas-cluster interactions to establish an equilibrium from an initial state of non-equilibrium is less than the time step used in meso-scale analyses [R. Kumar, Z. Li, and D. Levin, Phys. Fluids 23, 052001 (2011)]. Therefore, the good agreement obtained between the measured cluster and gas number density and gas temperature profiles with the meso-scale modeling using the second energy exchange mechanism is not fortuitous but is physically based. Our MD simulations also showed that a dynamic equilibrium is established by the gas-cluster interactions in which condensation and evaporation processes take place constantly to and from a cluster.

Simulation of homogeneous condensation of small polyatomic systems in high pressure supersonic nozzle flows using Bhatnagar-Gross-Krook model

In the present work, we have simulated the homogeneous condensation of carbon dioxide and ethanol using the Bhatnagar-Gross-Krook based approach. In an earlier work of Gallagher-Rogers et al. [J. Thermophys. Heat Transfer 22, 695 (2008)], it was found that it was not possible to simulate condensation experiments of Wegener et al. [Phys. Fluids 15, 1869 (1972)] using the direct simulation Monte Carlo method. Therefore, in this work, we have used the statistical Bhatnagar-Gross-Krook approach, which was found to be numerically more efficient than direct simulation Monte Carlo method in our previous studies [Kumar et al., AIAA J. 48, 1531 (2010)], to model homogeneous condensation of two small polyatomic systems, carbon dioxide and ethanol. A new weighting scheme is developed in the Bhatnagar-Gross-Krook framework to reduce the computational load associated with the study of homogeneous condensation flows. The solutions obtained by the use of the new scheme are compared with those obtained by the baseline Bhatnagar-Gross-Krook condensation model (without the species weighting scheme) for the condensing flow of carbon dioxide in the stagnation pressure range of 1-5 bars. Use of the new weighting scheme in the present work makes the simulation of homogeneous condensation of ethanol possible. We obtain good agreement between our simulated predictions for homogeneous condensation of ethanol and experiments in terms of the point of condensation onset and the distribution of mass fraction of ethanol condensed along the nozzle centerline.