Hygroscopic Growth Research Articles

Time series trends and correlations of aerosol optical depth and cloud parameters over Addis Ababa

Aerosols are microscopic liquid or solid particles that enter the atmosphere through natural and man-made processes. Time series trends and correlations of aerosol optical depth and cloud parameters over Addis Ababa have become the subject of scientific research because of the importance of clouds in controlling climate. In this study, we have examined the temporal variations (time series analysis) in aerosol particles over Addis Ababa and the impact of these variations on various optical properties of clouds using Moderate Resolution Imaging Spectroradiometer (MODIS) data from January 2003 to December 2018. The maximum aerosol optical depth (AOD) was found during the summer period, with minimum values in winter. During summer, the air temperature is high, which leads to abundant atmospheric water vapor, facilitating the hygroscopic growth of aerosols. We then analyzed the relationships between AOD and other cloud parameters, namely, water vapor (WV), cloud fraction (CF), cloud top temperature (CTT), and cloud top pressure (CTP), to provide a better understanding of aerosol–cloud interactions. The correlation between AOD and CF and WV was positive for Addis Ababa, while there was a negative correlation with CTT and CTP.

Multiwavelength Raman lidar system for profiling the CCN number concentrations

Cloud condensation nuclei (CCN) play an important role in the research of cloud microphysical and aerosol–cloud interactions. This study employs a multiwavelength Raman lidar for measuring CCN concentration. First, the multiwavelength Raman lidar was used to measure the atmospheric relative humidity profile, and the combination of relative humidity and the aerosol backscattering coefficient was used to retrieve the hygroscopic growth factor. By fitting the hygroscopic growth factor using the κkappa parameter model, the hygroscopic parameter κkappa that characterizes the hygroscopicity of aerosols was obtained. Then, the critical activation radius of aerosols was derived using the κ–Köhler theory and hygroscopicity parameter κkappa. Finally, the CCN number concentration was obtained by combining with the aerosol particle size distribution. Experiments were conducted to verify the feasibility of the multiwavelength Raman lidar. Results showed that the effective detection range of the lidar is approximately 0–4 km. The error of the temperature measured by the lidar at the height of 0.3–3.8 km is approximately ±1K. When the relative humidity change is 0.77–0.87, the range of the hygroscopic growth factor change is 1.06–1.10, the hygroscopic parameter γ is 0.065, and the hygroscopic parameter κkappa is 0.009. The CCN numbers concentration decreases with height but increases closer to the cloud. The multiwavelength Raman lidar is an important tool for detection of cloud microphysical and aerosol–cloud interactions and could have scientific importance and research value, both for improved understanding of the formation of clouds and precipitation and for enhanced accuracy of weather modification.

Predicting hygroscopic growth of organosulfur aerosol particles using COSMOtherm

Abstract. Organosulfur (OS) compounds are important sulfur species in atmospheric aerosol particles, due to the reduction of global inorganic sulfur emissions. Understanding the physicochemical properties, such as hygroscopicity, of OS compounds is important for predicting future aerosol–cloud–climate interactions. However, their hygroscopicity is not yet well understood due to the scarcity of authentic standards. In this work, we investigated a group of OS compounds with short carbon chains (C1–C5) and oxygen-containing functional groups in the form of sodium, potassium, or ammonium salts and their mixtures with ammonium sulfate. The hygroscopic growth factors (HGFs) of these OS compounds have been experimentally studied. Here, the HGFs were calculated from mass fraction of water that was computed using the conductor-like screening model for real solvents (COSMO-RS). A good agreement was found between the model-estimated and experimental HGFs for the studied OS compounds. This quantum-chemistry-based approach for HGF estimation will open up the possibility of investigating the hygroscopicity of other OS compounds present in the atmosphere.

Vertical Distribution of Water Vapor During Haze Processes in Northeast China Based on Raman Lidar Measurements

Haze refers to an atmospheric phenomenon with extremely low visibility, which has significant impacts on human health and safety. Water vapor alters the scattering properties of atmospheric particulate matter, thus affecting visibility. A comprehensive analysis of the role of water vapor in haze formation is of great scientific significance for forecasting severe pollution weather events. This study investigates the distribution characteristics and variations of water vapor during haze weather in Changchun City (44°N, 125.5°E) in autumn and winter seasons, aiming to reveal the relationship between haze and atmospheric water vapor content. Analysis of observational results for a period of two months (October to November 2023) from a three-wavelength Raman lidar deployed at the site reveals that atmospheric water vapor content is mainly concentrated below 5 km, accounting for 64% to 99% of the total water vapor below 10 km. Furthermore, water vapor content in air pollution exhibits distinct stratification characteristics with altitude, especially within the height range of 1–3 km, where significant water vapor variation layers exist, showing spatial consistency with inversion layers. Statistical analysis of haze events at the site indicates a high correlation between the concentration variations of PM2.5 and PM10 and the variations in average water vapor mixing ratio (WVMR). During haze episodes, the average WVMR within 3 km altitude is 3–4 times higher than that during clear weather. Analysis of spatiotemporal height maps of aerosols and water vapor during a typical haze event suggests that the relative stability of the atmospheric boundary layer may hinder the vertical transport and diffusion of aerosols. This, in turn, could lead to a sharp increase in aerosol extinction coefficients through hygroscopic growth, thereby possibly exacerbating haze processes. These observational findings indicate that water vapor might play a significant role in haze formation, emphasizing the potential importance of observing the vertical distribution of water vapor for better simulation and prediction of haze events.

Hysteresis in Water Content of Ultrafine Glassy Organic Aerosol Particles

AbstractWater content of aerosol particles is atmospherically important. Water content of organic aerosol (OA) particles has been estimated by assuming thermodynamic equilibrium. Here, we discovered that the hysteresis phenomenon occurred to water content of glassy ultrafine OA particles, demonstrating that thermodynamically non‐equilibrium states need to be considered. Hygroscopic growth for monodisperse ultrafine particles (sucrose and glucose) was investigated for the temperature range from 252 to 296 K. Hysteresis was not observable at 296 K, consistent with literature data. However, hysteresis in water content was observed at sub‐273 K. The lowest relative humidity (RH), at which hygroscopic growth of particles did not depend on exposure history to water vapor, was defined as threshold RH. Threshold RH for 100 nm particles was approximately the same as the glass transition points when hysteresis clearly happened, demonstrating that water diffusion in a highly viscous matrix of organic aerosols was the key to the phenomenon. Employment of a kinetic multi‐layer model quantitatively predicted threshold RH as a function of temperature, exposure time, and the particle size. Considering the temperature and RH range of Earth's atmosphere, we hypothesize that hysteresis in water content for organic aerosols ubiquitously occurs in the upper troposphere, impacting chemical aging and cloud formation processes.

Hygroscopic growth and activation changed submicron aerosol composition and properties in the North China Plain

Abstract. Aerosol hygroscopic growth and activation under high-relative-humidity (RH) conditions significantly influence the physicochemical properties of submicron aerosols (PM1). However, this process remains poorly characterized due to limited measurements. To address this gap, we deployed an advanced aerosol–fog sampling system that automatically switched between PM1, PM2.5 and total suspended particulate (TSP) inlets at a rural site in the North China Plain in the cold season. The results revealed that aerosol swelling due to water vapor uptake influenced aerosol sampling under high-RH conditions by shifting the cut-off size of impactors. At subsaturated high RH (> 90 %), over 25 % of aerosol mass with dry diameters below 1 µm resided in supermicron ranges, while in supersaturated foggy conditions, more than 70 % of submicron aerosol migrated to supermicron ranges. Hygroscopic growth and activation particularly affected highly hydrophilic inorganic salts, shifting a significant number of submicron sulfate and nitrate particles to supermicron ranges, with 27 %–33 % at 95 % ≤ RH ≤ 99 % and more than 78 % under supersaturated foggy conditions. Moreover, more than 10 % of submicron biomass burning organic aerosols grew beyond 2.5 µm during fog events, while fossil-fuel-related organic aerosol (FFOA) remained dominantly in submicron ranges, suggesting inefficient aqueous conversion of FFOA. The two secondary organic aerosol (SOA) factors (OOA1 and OOA2) behaved differently under supersaturated conditions, with OOA2 exhibiting a higher activated fraction despite a lower oxygen / carbon ratio. A substantial increase in organic nitrate and organosulfur mass concentrations in activated droplets during fog events suggested aqueous conversions and formations of brown carbon with potential radiative impacts. Overall, our study highlights remarkably different cloud and fog processing behaviors between primary and secondary aerosols, which would benefit a better understanding of aerosol–cloud interactions under distinct atmospheric conditions.

Innovative Air-Preconditioning Method for Accurate Particulate Matter Sensing in Humid Environments.

Smart cities rely on a network of sensors to gather real-time data on various environmental factors, including air quality. This paper addresses the challenges of improving the accuracy of low-cost particulate matter sensors (LCPMSs) which can be compromised by environmental conditions, such as high humidity, which is common in many urban areas. Such weather conditions often lead to the overestimation of particle counts due to hygroscopic particle growth, resulting in a potential public concern, although most of the detected particles consist of just water. The paper presents an innovative design for an indicative air-quality measuring station that integrates the particulate matter sensor with a preconditioning subsystem designed to mitigate the impact of humidity. The preconditioning subsystem works by heating the incoming air, effectively reducing the relative humidity and preventing the hygroscopic growth of particles before they reach the sensor. To validate the effectiveness of this approach, parallel measurements were conducted using both preconditioned and non-preconditioned sensors over a period of 19 weeks. The data were analyzed to compare the performance of the sensors in terms of accuracy for PM1, PM2.5, and PM10 particles. The results demonstrated a significant improvement in measurement accuracy for the preconditioned sensor, especially in environments with high relative humidity. When the conditions were too severe and both sensors started measuring incorrect values, the preconditioned sensor-measured values were closer to the actual values. Also, the period of measuring incorrect values was shorter with the preconditioned sensor. The results suggest that the implementation of air preconditioning subsystems in LCPMSs deployed in smart cities can provide a cost-effective solution to overcome humidity-related inaccuracies, thereby improving the overall quality of measured air pollution data.

Estimating visibility and understanding factors influencing its variations at Bangkok airport using machine learning and a game theory-based approach.

In this study, six individual machine learning (ML) models and a stacked ensemble model (SEM) were used for daytime visibility estimation at Bangkok airport during the dry season (November-April) for 2017-2022. The individual ML models are random forest, adaptive boosting, gradient boosting, extreme gradient boosting, light gradient boosting machine, and cat boosting. The SEM was developed by the combination of outputs from the individual models. Furthermore, the impact of factors affecting visibility was examined using the Shapley Additive exPlanation (SHAP) method, an interpretable ML technique inspired by the game theory-based approach. The predictor variables include different air pollutants, meteorological variables, and time-related variables. The light gradient boosting machine model is identified as the most effective individual ML model. On an hourly time scale, it showed the best performance across three out of four metrics with the ρ = 0.86, MB = 0, ME = 0.48km (second lowest), and RMSE = 0.8km. On a daily time scale, the model performed the best for all evaluation metrics with ρ = 0.92, MB = 0.0km, ME = 0.3km, and RMSE = 0.43km. The SEM outperformed all the individual models across three out of four metrics on an hourly time scale with ρ = 0.88, MB = 0.0km, (second lowest), and RMSE = 0.75km. On the daily scale, it performed the best with ρ = 0.93, MB = 0.02km, ME = 0.27km, and RMSE = 0.4km. The seasonal average original (VISorig) and meteorologically normalized visibility (VISnorm) decrease from 2017 to 2021 but increase in 2022. The rate of decrease in VISorig is double than rate of decrease in VISnorm which suggests the effect of meteorology visibility degradation. The SHAP analysis identified relative humidity (RH), PM2.5, PM10, day of the season year (i.e., Julian day) (JD), and O3 as the most important variables affecting visibility. At low RH, visibility is not sensitive to changes in RH. However, beyond a threshold, a negative correlation between RH and visibility is found potentially due to the hygroscopic growth of aerosols. The dependence of the Shapley values of PM2.5 and PM10 on RH and the change in average visibilities under different RH intervals also suggest the effect of hygroscopic growth of aerosol on visibility. A negative relationship has been identified between visibility and both PM2.5 and PM10. Visibility is positively correlated with O3 at lower to moderate concentrations, with diminishing impact at very high concentrations. The JD is strongly negatively related to visibility during winter while weakly associated positively later in summer. Findings from this research suggest the feasibility of employing machine learning techniques for predicting visibility and understanding the factors influencing its fluctuations.

Probing the capacity of a spatiotemporal deep learning model for short-term PM2.5 forecasts in a coastal urban area

Accurate forecast of fine particulate matter (PM2.5) is crucial for city air pollution control, yet remains challenging due to the complex urban atmospheric chemical and physical processes. Recently deep learning has been routinely applied for better urban PM2.5 forecasts. However, their capacity to represent the spatiotemporal urban atmospheric processes remains underexplored, especially compared with traditional approaches such as chemistry-transport models (CTMs) and shallow statistical methods other than deep learning. Here we probe such urban-scale representation capacity of a spatiotemporal deep learning (STDL) model for 24-hour short-term PM2.5 forecasts at six urban stations in Rizhao, a coastal city in China. Compared with two operational CTMs and three statistical models, the STDL model shows its superiority with improvements in all five evaluation metrics, notably in root mean square error (RMSE) for forecasts at lead times within 12 h with reductions of 49.8 % and 47.8 % respectively. This demonstrates the STDL model's capacity to represent nonlinear small-scale phenomena such as street-level emissions and urban meteorology that are in general not well represented in either CTMs or shallow statistical models. This gain of small-scale representation in forecast performance decreases at increasing lead times, leading to similar RMSEs to the statistical methods (linear shallow representations) at about 12 h and to the CTMs (mesoscale representations) at 24 h. The STDL model performs especially well in winter, when complex urban physical and chemical processes dominate the frequent severe air pollution, and in moisture conditions fostering hygroscopic growth of particles. The DL-based PM2.5 forecasts align with observed trends under various humidity and wind conditions. Such investigation into the potential and limitations of deep learning representation for urban PM2.5 forecasting could hopefully inspire further fusion of distinct representations from CTMs and deep networks to break the conventional limits of short-term PM2.5 forecasts.

Seasonal simulation and source apportionment of SO42− with integration of major highlighted chemical pathways in WRF-Chem model in the NCP

Particulate sulfate (SO42−) is the major component of fine particles in the North China Plain (NCP). It plays an essential role in the entire atmosphere and climate system. Accurately reproducing the SO42− levels is challenging for chemical transport model. Here, the major highlighted multiple phase SO42− formation pathways are integrated into the WRF-Chem model and seasonal model performance of SO42− are assessed by observed SO42− at multiple sampling sites located in the NCP. The results show that the integration of these SO42− formation pathways obviously narrow the gap between simulation and observation in autumn, winter, and summer at three sampling sites in the NCP, for high RH conditions facilitate the hygroscopic growth of PM2.5 and promote the multiple phase reaction to form SO42−. The underestimation in autumn, winter, and summer may ascribe to the missed SO42− source from photo-induced chemical route and missed hygroscopicity of organic aerosols. The obviously discrepancy between simulation and observation in spring may ascribe to the large underestimation of SO2 levels and lack consideration of dust scheme. Source apportionment results show that the gas phase reaction played a vital role in the formation of SO42− in each season. The contribution of aqueous phase SO2 oxidation by H2O2 is higher in autumn and summer due to simultaneously high RH levels and stronger photochemical reaction activity. The Mn(II) catalytic SO2 oxidation pathway at the particle interface is important in autumn and make greater contribution to SO42− formation during winter severe haze events under extremely high RH levels.

Hygroscopic Properties of Water-Soluble Counterpart of Ultrafine Particles from Agriculture Crop-Residue Burning in Patiala, Northwestern India

To determine the link between hygroscopicity and the constituent chemical composition of real biomass-burning atmospheric particles, we collected and analyzed aerosols during wheat-straw (April–May), rice-straw (October–November), and no-burning periods (August–September) in 2008 and 2009 in Patiala, Punjab. A hygroscopicity tandem differential mobility analyzer (HTDMA) system was used to measure hygroscopicity at ~5 to ~95% relative humidity (RH) of aerosolized 100 nm particles generated from the water extracts of PM0.4 burning and no-burning aerosol samples. The chemical analyses of the extracts show that organic carbon and water-soluble inorganic-ion concentrations are 2 to 3 times higher in crop-residue burning aerosol samples compared to no-burning aerosols, suggesting the substantial contribution of biomass burning to the carbonaceous aerosols at the sampling site. We observed that aerosolized 100 nm particles collected during the crop-residue burning period show higher and more variable hygroscopic growth factor (g(RH)) ranging from 1.21 to 1.68 at 85% RH, compared to no-burning samples (1.27 to 1.33). Interestingly, crop-residue burning particles also show considerable shrinkage in their size (i.e., g(RH) < 1) at lower RH (<50%) in the dehumidification mode. The increased level of major inorganic ions in biomass-burning period aerosols is a possible reason for higher g(RH) as well as the observed particle shrinkage. Overall, the measured g(RH), together with the correlation observed between aerosol water content and ionic-species volume fraction, and the study of the abundance of individual constituent ionic species suggests that inorganic salts and their proportion in aerosol particles primarily governed the aerosol hygroscopicity.

Aerosol interference with open-path eddy covariance measurement in a lake environment

Open-path eddy covariance (OPEC) is widely used for measuring trace gas fluxes between the surface and the atmosphere. At a lake in Eastern China (Lake Taihu), the CO2 flux measured with OPEC was often negative at night (with values as low as −22.1 μmol m−2 s−1) and was coherent across the whole lake, as if the lake were a large sink of atmospheric CO2. The purpose of this study is to investigate the cause of this negative flux phenomenon. In addition to OPEC, we also used closed-path eddy covariance (CPEC) and the transfer coefficient (TC) method to measure the flux. The results show that the persistent negative CO2 flux phenomenon was observed with OPEC but not with CPEC or TC. Because air drawn into the CPEC analyzer was filtered but the OPEC analyzer was influenced by aerosol contamination, the most logical explanation was that particles deposited on the optical lens of the OPEC analyzer changed its cross-sensitivity to water vapor. The direct evidence of this interference was a strong positive correlation between the OPEC analyzer's signal strength and the CO2 mixing ratio observed at 10 Hz. We suggest that it is possible to perform post-field correction to this negative flux bias using the 10 Hz signal strength data. In comparison, an OPEC system at a nearby land site did not experience aerosol interference due to low water vapor flux at night and lack of hygroscopic growth of particles on the optical lens in low humidity conditions. The type of aerosol interference reported here may also occur in high humidity and high pollution conditions elsewhere.

Innovative aerosol hygroscopic growth study from Mie–Raman–fluorescence lidar and microwave radiometer synergy

Abstract. This study focuses on the characterization of aerosol hygroscopicity using remote sensing techniques. We employ a Mie–Raman–fluorescence lidar (Lille Lidar for Atmospheric Study, LILAS), developed at the ATOLL platform, Laboratoire d'Optique Atmosphérique, Lille, France, in combination with the RPG-HATPRO-G5 microwave radiometer to enable continuous aerosol and water vapor monitoring. We identify hygroscopic growth cases when an aerosol layer exhibits an increase in both aerosol backscattering coefficient and relative humidity. By examining the fluorescence backscattering coefficient, which remains unaffected by the presence of water vapor, the potential temperature, and the absolute humidity, we verify the homogeneity of the aerosol layer. Consequently, the change in the backscattering coefficient is solely attributed to water uptake. The Hänel theory is employed to describe the evolution of the backscattering coefficient with relative humidity and introduces a hygroscopic coefficient, γ, which depends on the aerosol type. The particularity of this method revolves around the use of the fluorescence which is employed to take into account and correct the aerosol concentration variations in the layer. Case studies conducted on 29 July and 9 March 2021 examine, respectively, an urban and a smoke aerosol layer. For the urban case, γ is estimated as 0.47 ± 0.03 at 532 nm; as for the smoke case, the estimation of γ is 0.5 ± 0.3. These values align with those reported in the literature for urban and smoke particles. Our findings highlight the efficiency of the Mie–Raman–fluorescence lidar and microwave radiometer synergy in characterizing aerosol hygroscopicity. The results contribute to advance our understanding of atmospheric processes, aerosol–cloud interactions, and climate modeling.

Developing a climatological simplification of aerosols to enter the cloud microphysics of a global climate model

Abstract. Aerosol particles influence cloud formation and properties. Hence climate models that aim for a physical representation of the climate system include aerosol modules. In order to represent more and more processes and aerosol species, their representation has grown increasingly detailed. However, depending on one's modelling purpose, the increased model complexity may not be beneficial, for example because it hinders understanding of model behaviour. Hence we develop a simplification in the form of a climatology of aerosol concentrations. In one approach, the climatology prescribes properties important for cloud droplet and ice crystal formation, the gateways for aerosols to enter the model cloud microphysics scheme. Another approach prescribes aerosol mass and number concentrations in general. Both climatologies are derived from full ECHAM-HAM simulations and can serve to replace the HAM aerosol module and thus drastically simplify the aerosol treatment. The first simplification reduces computational model time by roughly 65 %. However, the naive mean climatological treatment needs improvement to give results that are satisfyingly close to the full model. We find that mean cloud condensation nuclei (CCN) concentrations yield an underestimation of cloud droplet number concentration (CDNC) in the Southern Ocean, which we can reduce by allowing only CCN at cloud base (which have experienced hygroscopic growth in these conditions) to enter the climatology. This highlights the value of the simplification approach in pointing to unexpected model behaviour and providing a new perspective for its study and model development.

Additive Water Uptake of the Mixtures of Urban Atmospheric HULIS and Ammonium Sulfate

AbstractThe hygroscopicity of atmospheric aerosols affect the global climate and human health. However, the hygroscopic behavior of mixtures of inorganic salts and organics in atmospheric aerosols have not been well characterized for ambient complex organic mixtures. Here, the hygroscopic growth of humic‐like substances (HULIS), which represent the complexity of organic aerosol compounds, from urban aerosols and their mixtures with ammonium sulfate were investigated with measurements from a hygroscopicity tandem differential mobility analyzer and model analyses. The Zdanovskii–Stokes–Robinson (ZSR) relationship, which assumes that the water uptake of each component is additive, was examined for the mixtures. The dependence of the hygroscopicity parameters (κ) of the mixtures on the volume fraction of HULIS is generally represented by the ZSR relationship with the deviations of κ on average of 16% (4%–27% in different mixing ratios), and 0.03 (0.02–0.04) on absolute basis. An assumption of asphericity for the dry particles reduced the deviations (14%). Approximations for the surface tension depression by surfactants and surface‐bulk partitioning were predicted to account for the deviations to a small extent. Furthermore, a thermodynamic model for simplified compound systems showed that the non‐ideality of the solutions potentially explained the deviations from the ZSR relationship. The results of this study support the use of the ZSR relationship for organic‒inorganic mixtures as a practical approximation in atmospheric models, provide a guide for the uncertainty associated with this approximation, and suggest directions for future studies.

In silico investigation of inhalation condition impacts on hygroscopic growth and deposition of salbutamol sulphate in human airways

The objective of this study is to explore the transport, size growth, and deposition of Salbutamol Sulphate (SS) using Computational Fluid Dynamics (CFD). A CT-based realistic model of human airways from the oral cavity to the 5th generation of the lung was utilized as the computational domain. Four Test Cases (TC) with varying temperature and relative humidity (RH) under two inspiratory waveforms were considered to completely evaluate the impact of inhalation conditions on particle growth. Salbutamol Sulphate (SS) is a β2-adrenergic agonist and has been extensively used for asthma treatment. A monodispersed distribution of SS particles with an initial diameter of 167 nm was considered at the mouth inlet based on pharmaceutical data. Results indicated that inhalation of saturated/supersaturated air (RH>100%) leads to significant hygroscopic growth of SS particles with a factor of 10. In addition, the deposition efficiency of SS particles under the Quick and Deep (QD) inhalation profile was enhanced as the flow temperature and humidity increased. However, the implementation of Slow and Deep (SD) inspiratory waveform revealed that the same particle size growth is achieved in the respiratory system with lower deposition efficiency in the mouth-throat (less than 3%) and tracheobronchial airway (less than 2.18%). For the escaped particles form the right lung, in the SD waveform under TC 3, the maximum particle size distribution was for 600 nm particles with 25% probability. In the left lung, 30% of the particles were increased up to 950 nm in size. For the QD waveform in TC 3 and TC4, the most frequent particles were 800 nm with 36% probability. This holds practical significance in the context of deep lung delivery for asthmatic patients with enhanced deposition efficiency and large particle size. The findings of the present study can contribute to the development of targeted drug delivery strategies for the treatment of pulmonary diseases using hygroscopic dry powder formulations.

Improved simulations of biomass burning aerosol optical properties and lifetimes in the NASA GEOS Model during the ORACLES-I campaign

Abstract. In order to improve aerosol representation in the NASA Goddard Earth Observing System (GEOS) model, we evaluated simulations of the transport and properties of aerosols from southern African biomass burning sources that were observed during the first deployment of the NASA ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) field campaign in September 2016. An example case study of 24 September was analyzed in detail, during which aircraft-based in situ and remote sensing observations showed the presence of a multi-layered smoke plume structure with significant vertical variation in single scattering albedo (SSA). Our baseline GEOS simulations were not able to represent the observed SSA variation or the observed organic aerosol-to-black-carbon ratio (OA : BC). Analyzing the simulated smoke age suggests that the higher-altitude, less absorbing smoke plume was younger (∼4 d), while the lower-altitude and more absorbing smoke plume was older (∼7 d). We hypothesize a chemical or microphysical loss process exists to explain the change in aerosol absorption as the smoke plume ages, and we apply a simple loss rate to the model hydrophilic biomass burning OA to simulate this process. We also utilized the ORACLES airborne observations to better constrain the simulation of aerosol optical properties, adjusting the assumed particle size, hygroscopic growth, and absorption. Our final GEOS model simulation with additional OA loss and updated optics showed better performance in simulating aerosol optical depth (AOD) and SSA compared to independent ground- and space-based retrievals for the entire month of September 2016, including the Ozone Monitoring Instrument (OMI) Aerosol Index. In terms of radiative implications of our model adjustments, the final GEOS simulation suggested a decreased atmospheric warming of about 10 % (∼2 W m−2) over the southeastern Atlantic region and above the stratocumulus cloud decks compared to the model baseline simulations. These results improve the representation of the smoke age, transport, and optical properties in Earth system models.

Characteristics of Daytime‐And‐Nighttime AOD Differences Over China: A Perspective From CALIOP Satellite Observations and GEOS‐Chem Model Simulations

AbstractWe use the GEOS‐Chem chemistry transport model to quantify the factors in the diel discrepancy of Aerosol Optical Depth (AOD) retrieved from Cloud‐Aerosol Lidar with Orthogonal Polarization (CALIOP) satellite observations over eastern China. The GEOS‐Chem simulation reveals that the AOD below 1 km is 58.5% larger at night than during the daytime, which is comparable to the counterpart of 41.3% from CALIOP (v4.2). Model sensitivity simulations show that the diurnal variation in wind barely impacts the AOD difference between daytime and nighttime, and the increase in AOD at nighttime is primarily caused by the lower temperature at nighttime compared to daytime. Further simulations demonstrate that the low temperature at night increases AOD primarily by increasing relative humidity, and hence particle hygroscopic growth, while the effect of temperature on chemical rate barely influences AOD. CALIOP also observes that the absolute difference in AOD above 1 km between nighttime and daytime is 0.105, while the counterpart in GEOS‐Chem simulations is −0.031. This contrast can be partly explained by the factor that the percentage of valid CALIOP retrievals below 5 km is 15%–20% greater at nighttime than in the daytime due to the CALIOP detection limit. Removing the detection limit impact decreases the difference in the CALIOP AOD above 1 km between nighttime and daytime to 0.073.

Size-resolved water-soluble organic carbon and its significant contribution to aerosol liquid water

Size-segregated aerosols collected in Beijing from 2021 to 2022 were used to investigate the contribution of organic aerosols to the aerosol liquid water content (ALWC), the influencing factors of ALWC, and the concentrations and size distribution characteristics of water-soluble organic carbon (WSOC) after clean air actions. The results showed that the concentration of WSOC in particulate matter (PM)1.8 was 3.52 ± 2.43 μg/m3 during the sampling period. Obvious changes were observed in the size distribution of WSOC after clean air actions, which may be attributed to the enhancement of atmospheric oxidation capacity and the decrease in PM concentration. The contribution of organic aerosols to the ALWC in fine PM was 18.1 % during the sampling period, which was more significant at lower particles concentration and smaller particle size ranges. The ambient relative humidity (RH) and the ratio of NO3−/SO42− had an apparent influence on ALWC. The continuous increase in the nitrate proportion significantly reduced the deliquescence point of the aerosols, making them prone to hygroscopic growth at lower RH. Analysis of the relation among nitrogen oxidation ratio (sulfur oxidation ratio), ALWC and PM1.8 mass concentrations suggests that organic matter has a significant effect on the formation of secondary inorganic aerosols in the initial phase of pollution formation and plays a crucial role in aerosol pollution formation in Beijing. These results are conducive to understanding the formation mechanism of aerosols and provide scientific data and theoretical support for the formulation of more effective emission-reduction measures.

The Impact of Indoor Air Humidity on the Infiltration of Ambient Particles

Ambient particles contribute to occupant exposure as they infiltrate indoor environments through building envelope cracks, impacting indoor air quality. This study investigates the impact of indoor air humidity on the infiltration of ambient particles, highlighting humidity’s crucial role in influencing particle dynamics in indoor environments. Employing a controlled chamber system, we conducted experiments to quantify the infiltration of size-resolved particles under varying relative humidity (RH) conditions. Both the total number and the mass concentration of particles increased with RH in the experimental chamber. The smallest particles (0.3–0.4 μm) experienced reduced infiltration at higher RH levels due to hygroscopic growth, while intermediate-sized particles showed increased infiltration, resulted from coagulation effects. Large particles (>1.0 μm) demonstrated reduced infiltration factors, caused by lower penetration and higher deposition rates, with minimal impact from RH changes. Our findings reveal that RH influences particle hygroscopic growth, deposition rate, and coagulation process, thereby affecting indoor particle size distribution and concentration.