ACS ES&T Air最新文献

{"title":"Importance of New Particle Formation for Climate and Air Quality","authors":"Markku Kulmala*, ","doi":"10.1021/acsestair.5c0009510.1021/acsestair.5c00095","DOIUrl":"https://doi.org/10.1021/acsestair.5c00095https://doi.org/10.1021/acsestair.5c00095","url":null,"abstract":"","PeriodicalId":100014,"journal":{"name":"ACS ES&T Air","volume":"2 5","pages":"710–712 710–712"},"PeriodicalIF":0.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsestair.5c00095","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143921359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Molecular Properties of Brown Carbon Aerosol from Biomass Burning of Wildland Fuels at the Fire Sciences Laboratory","authors":"Lisa Azzarello,&nbsp;Rebecca A. Washenfelder,&nbsp;Caroline C. Womack,&nbsp;Alessandro Franchin,&nbsp;Ann M. Middlebrook and Cora J. Young*,&nbsp;","doi":"10.1021/acsestair.4c0026410.1021/acsestair.4c00264","DOIUrl":"https://doi.org/10.1021/acsestair.4c00264https://doi.org/10.1021/acsestair.4c00264","url":null,"abstract":"<p >Wildfires are a large and increasing source of absorbing organic aerosol (brown carbon) in North America, with a direct impact on the global radiative budget. Brown carbon from wildfires is a complex and poorly characterized mixture of compounds with varying composition, volatility, and reactivity. We conducted controlled burns of western United States fuels at the Missoula Fire Sciences Laboratory during the FIREX FireLab 2016 study. We measured water-soluble brown carbon absorption, total aerosol absorption, and aerosol composition with a shared thermally denuded inlet at temperatures between ambient and 250 °C. We simultaneously collected filter samples at ambient temperature and 250 °C for detailed analysis of molecular size, hydrophobicity, and octanol–water partitioning coefficient log(<i>K</i>_<i>OW</i>) using chromatographic separation techniques with wavelength-resolved absorption detection. For the controlled burns, ∼92% of the water-soluble brown carbon at 365 nm consisted of low-volatility organic compounds with log(<i>C</i>_<i>sat</i>) values between (−5.1 ± 2) to (0.4 ± 2) and oxygen-to-carbon ratios between 0.0–1.1. Species with molecular mass &lt;500 Da contributed 82 ± 13% of the absorption at 365 nm, while species &gt;500 Da contributed only 6.2 ± 3.7%. Thermodenuder temperatures of 250 °C were equivalent to log(<i>C</i>_<i>sat</i>) &lt; −9 with observed oxygen-to-carbon ratios of 1.2 ± 0.3. We found that while only ∼6% of water-soluble brown carbon at 365 nm persisted at these temperatures, approximately 50% of the total absorption in the offline samples remained, with an increased contribution by molecules &gt;500 Da of 15 ± 12%. HPLC analysis showed that the compounds removed at 250 °C had log(<i>K</i>_<i>OW</i>) values between 2.9 ± 0.7 and 3.5 ± 0.7 and contained aliphatic, aromatic, hydroxyl, and carbonyl functional groups.</p>","PeriodicalId":100014,"journal":{"name":"ACS ES&T Air","volume":"2 5","pages":"759–772 759–772"},"PeriodicalIF":0.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143921423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Importance of New Particle Formation for Climate and Air Quality.","authors":"Markku Kulmala","doi":"10.1021/acsestair.5c00095","DOIUrl":"https://doi.org/10.1021/acsestair.5c00095","url":null,"abstract":"","PeriodicalId":100014,"journal":{"name":"ACS ES&T Air","volume":"2 5","pages":"710-712"},"PeriodicalIF":0.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12070401/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144081903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Differentiation of Oxygenated Organic Aerosol: A Machine Learning Approach to Distinguish Local and Transboundary Pollution.","authors":"Lu Lei, Wei Xu, Chunshui Lin, Baihua Chen, Kirsten N Fossum, Darius Ceburnis, Colin O'Dowd, Jurgita Ovadnevaite","doi":"10.1021/acsestair.4c00331","DOIUrl":"https://doi.org/10.1021/acsestair.4c00331","url":null,"abstract":"<p><p>Accurate source apportionment of particulate matter (PM), especially of organic aerosol (OA), is crucial for targeted mitigation efforts. Positive Matrix Factorization (PMF) is powerful in source attribution of primary OA (POA); however, it often struggles to differentiate sources of oxygenated OA (OOA) due to their similar chemical profiles. In this study, a support vector regression machine learning (ML) model was developed to enhance the OOA source apportionment in Dublin from 2016 to 2023. Rolling PMF analysis identified four POA factors and differentiated OOA into less- and more-oxidized (LO-OOA and MO-OOA), highlighting the significant role of the OOA (47-74% of total OA). The ML model further distinguished locally produced OOA (LO-OOA_local and MO-OOA_local) from transboundary transport OOA and exhibited robust performance across different pollution scenarios. The relative importance analysis revealed that LO-OOA_local was more impacted by fossil fuel emissions like hydrocarbon-like OA (20%) and coal (14%), whereas MO-OOA_local was most influenced by LO-OOA (17%), providing insights into their sources and formation mechanisms. During a mixed pollution episode, the results show that despite the significant contribution of transboundary transport, local heating emissions were more critical sources of OA, with local OA accounting for 68% of total OA and reaching 78% during heating hours. These findings highlight the ongoing need to reduce local emissions to achieve cleaner air in Dublin. The ML model's ability to quantitatively separate local and transboundary OOA offers invaluable insights for future air quality regulations.</p>","PeriodicalId":100014,"journal":{"name":"ACS ES&T Air","volume":"2 5","pages":"891-902"},"PeriodicalIF":0.0,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12070415/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144083136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Prototypical Oil and Gas Sites to Model Methane Emissions in Colorado’s Denver-Julesburg Basin Using a Mechanistic Emission Estimation Tool","authors":"Winrose Mollel*,&nbsp;Daniel Zimmerle,&nbsp;Arthur Santos and Anna Hodshire,&nbsp;","doi":"10.1021/acsestair.4c0016810.1021/acsestair.4c00168","DOIUrl":"https://doi.org/10.1021/acsestair.4c00168https://doi.org/10.1021/acsestair.4c00168","url":null,"abstract":"<p >Traditional bottom-up (BU) methods estimate methane emissions from oil and gas facilities by multiplying activity data with emission factors. Top-down (TD) methods measure methane emissions from all sources across an entire site and often use BU estimates to attribute emissions specifically to natural gas, often through ethane-to-methane ratio analysis. However, traditional BU methods do not adequately account for variations in throughput and failure conditions, which can significantly impact gas composition and emission rates. The Mechanistic Air Emissions Simulator (MAES) is a computer model developed to estimate methane and other hydrocarbon emissions from oil and gas facilities. MAES employs two distinct modeling approaches: mechanistic models and traditional methods. MAES uses mechanistic models to estimate emissions based on fluid flow through equipment and equipment states, offering a detailed, process-oriented emissions representation. In contrast, the traditional methods utilized by MAES estimate emissions by applying activity data multiplied by emission factor distributions, offering a statistical approach grounded in empirical data. This study applies MAES to assess emission impacts on three vintages of production wellpads in the Denver-Julesburg (DJ) basin: a wellpad with two stages of separation (“old” facility), one with three stages of separation (“current” facility), and a tankless wellpad (“future” facility). The study found that increased throughput led to higher methane emissions in older wellpads but not in tankless future facilities. Additionally, MAES also showed that failure conditions, like stuck dump valves, increased emission rates and affected the ethane-to-methane ratio, which could vary by 2.15 times depending on facility configuration. These findings underscore the importance of incorporating variability in facility operations into emissions estimates to improve accuracy and guide effective mitigation strategies.</p><p >Traditional inventory methods do not capture how variations in throughput and failure conditions impact gas composition and emission rates.</p>","PeriodicalId":100014,"journal":{"name":"ACS ES&T Air","volume":"2 5","pages":"723–735 723–735"},"PeriodicalIF":0.0,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsestair.4c00168","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143921356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Differentiation of Oxygenated Organic Aerosol: A Machine Learning Approach to Distinguish Local and Transboundary Pollution","authors":"Lu Lei,&nbsp;Wei Xu*,&nbsp;Chunshui Lin,&nbsp;Baihua Chen,&nbsp;Kirsten N. Fossum,&nbsp;Darius Ceburnis,&nbsp;Colin O’Dowd and Jurgita Ovadnevaite*,&nbsp;","doi":"10.1021/acsestair.4c0033110.1021/acsestair.4c00331","DOIUrl":"https://doi.org/10.1021/acsestair.4c00331https://doi.org/10.1021/acsestair.4c00331","url":null,"abstract":"<p >Accurate source apportionment of particulate matter (PM), especially of organic aerosol (OA), is crucial for targeted mitigation efforts. Positive Matrix Factorization (PMF) is powerful in source attribution of primary OA (POA); however, it often struggles to differentiate sources of oxygenated OA (OOA) due to their similar chemical profiles. In this study, a support vector regression machine learning (ML) model was developed to enhance the OOA source apportionment in Dublin from 2016 to 2023. Rolling PMF analysis identified four POA factors and differentiated OOA into less- and more-oxidized (LO-OOA and MO-OOA), highlighting the significant role of the OOA (47–74% of total OA). The ML model further distinguished locally produced OOA (LO-OOA_local and MO-OOA_local) from transboundary transport OOA and exhibited robust performance across different pollution scenarios. The relative importance analysis revealed that LO-OOA_local was more impacted by fossil fuel emissions like hydrocarbon-like OA (20%) and coal (14%), whereas MO-OOA_local was most influenced by LO-OOA (17%), providing insights into their sources and formation mechanisms. During a mixed pollution episode, the results show that despite the significant contribution of transboundary transport, local heating emissions were more critical sources of OA, with local OA accounting for 68% of total OA and reaching 78% during heating hours. These findings highlight the ongoing need to reduce local emissions to achieve cleaner air in Dublin. The ML model’s ability to quantitatively separate local and transboundary OOA offers invaluable insights for future air quality regulations.</p><p >Machine learning is applied to distinguish local and transboundary contributions to oxygenated organic aerosol in Dublin, providing quantitative insights into air quality regulations.</p>","PeriodicalId":100014,"journal":{"name":"ACS ES&T Air","volume":"2 5","pages":"891–902 891–902"},"PeriodicalIF":0.0,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsestair.4c00331","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143921338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating Simulations and Observations: A Foundation Model for Estimating the Aerosol Mixing State Index","authors":"Fei Jiang,&nbsp;Zhonghua Zheng*,&nbsp;Hugh Coe,&nbsp;Robert M. Healy,&nbsp;Laurent Poulain,&nbsp;Valérie Gros,&nbsp;Hao Zhang,&nbsp;Weijun Li,&nbsp;Dantong Liu,&nbsp;Matthew West,&nbsp;David Topping* and Nicole Riemer*,&nbsp;","doi":"10.1021/acsestair.4c0032910.1021/acsestair.4c00329","DOIUrl":"https://doi.org/10.1021/acsestair.4c00329https://doi.org/10.1021/acsestair.4c00329","url":null,"abstract":"<p >Accurately predicting aerosol mixing states in real-world environments is crucial for understanding their impacts on climate change and human health. However, observational data inherently exhibit spatiotemporal gaps, and high costs and equipment requirements further exacerbate these limitations, particularly for in situ measurements. While particle-resolved models can simulate individual particle composition and size changes and serve as benchmarks, they face challenges in real-world applications due to a combination of factors. One of the major challenges is the limited availability of detailed input data (e.g., emission inventories) that accurately reflect actual environmental conditions. In this study, we frame the emulation of aerosol simulation as a general task and treat the estimation of real-world mixing states as a downstream task. We developed a foundation model pretrained on particle-resolved simulations and fine-tuned it using observational data from the field campaign. The fine-tuned model consistently outperformed baseline models, showing greater stability and robustness across various data sets. Permutation feature importance and sensitivity analyses revealed that aerosol species concentrations were the most critical factors for the foundation model. This approach, which involves pretraining on particle-resolved simulations and fine-tuning on limited observational data, offers a viable solution to challenges posed by limited observational data.</p><p >Both observational data and simulations face limitations in real-world aerosol mixing state estimations. This study employs a foundation model combining simulations and observations, enhancing temporal variation estimations with implications for climate and human health impacts.</p>","PeriodicalId":100014,"journal":{"name":"ACS ES&T Air","volume":"2 5","pages":"877–890 877–890"},"PeriodicalIF":0.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsestair.4c00329","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143921223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Synthesized Size-Resolved Lignin Nanoparticles to Investigate the Atmospheric Ice Nucleation of Biomass Burning Organic Aerosols","authors":"Anna T. Zeleny,&nbsp;Jingqian Chen,&nbsp;Paul Bieber,&nbsp;Orlando J. Rojas and Nadine Borduas-Dedekind*,&nbsp;","doi":"10.1021/acsestair.4c0022310.1021/acsestair.4c00223","DOIUrl":"https://doi.org/10.1021/acsestair.4c00223https://doi.org/10.1021/acsestair.4c00223","url":null,"abstract":"<p >Biomass burning organic aerosols (BBOA) released from wildfires impact the formation, lifetime, and optical depth of mixed-phase clouds through heterogeneous ice nucleation. However, the underlying physicochemical mechanism of how organic matter, such as BBOA, promotes ice nucleation remains difficult to predict. Here, we investigated the ice-nucleating ability of lignin, a major component of BBOA, by synthesizing lignin nanoparticles (LNPs) from three different plant sources, namely, a conifer (softwood, sw), an angiosperm (hardwood, hw), and grass (g). First, we used a precipitation technique to make polydispersed LNP suspensions with acetone and water as antisolvents. Transmission electron microscopy (TEM) images indicated that the LNP samples were spherical and, notably, that the surface of grass LNP appeared floccose compared to the other two LNP types. Using our custom-built drop Freezing Ice Nuclei Counter (FINC), we found that LNPs from softwood (LNP_sw) were the most ice-active with a median ice nucleation temperature, <i>T</i>₅₀, of −15.6 °C at a concentration of 0.2 mg/mL. ³¹P NMR suggested that LNP_sw had the lowest number of hydroxyl groups, indicating that the functional groups present at the surface of the nanoparticles may be impacting the ice nucleation ability of LNPs. We then separated LNP_sw by size with cascade centrifugation to create three distinct size bins of particles with mean diameters of 79, 154, and 279 nm. Nanoparticle tracking analysis (NTA) was used to quantify the size, surface area, and particle number of these size-resolved LNP_sw. Despite their different sizes, all size-resolved LNP_sw suspensions at 0.2 mg/mL were ice active at the same temperature, with <i>T</i>₅₀ values ranging from −14.9 to −15.9 °C. Remarkably, solubilized lignin, which did not undergo the nanoprecipitation procedure, froze in the same temperature range. Thus, the conversion of solubilized lignin into nanoparticles did not improve the ice nucleation ability of softwood lignin. We reconcile these results with a proposed role of the aggregation of lignin, as nanoparticles or dissolved, which facilitates the ice nucleation of aqueous droplets of lignin. Overall, the chemical composition and the ability of nonproteinaceous organic matter to aggregate may govern its ice nucleating ability. These findings help us understand how BBOA nucleate ice and impacts the formation and phase of clouds in the atmosphere.</p>","PeriodicalId":100014,"journal":{"name":"ACS ES&T Air","volume":"2 5","pages":"746–758 746–758"},"PeriodicalIF":0.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143921217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating the Impact of Aviation Activity on Fine Particulate Matter, Black Carbon, and Ultrafine Particles Using Flight Track Data at the Ottawa International Airport","authors":"Ben Nikkel,&nbsp;Kieran Aston,&nbsp;Ryan Kulka,&nbsp;Mathieu Rouleau,&nbsp;Shayesta Seenundun,&nbsp;Paul J. Villeneuve and Keith Van Ryswyk*,&nbsp;","doi":"10.1021/acsestair.4c0028810.1021/acsestair.4c00288","DOIUrl":"https://doi.org/10.1021/acsestair.4c00288https://doi.org/10.1021/acsestair.4c00288","url":null,"abstract":"<p >Ambient particulate matter pollution has been found to increase in concentration downwind from airports. This relationship is mostly evident for particles in the ultrafine size range. Furthermore, epidemiological research has found that those who live near airports have increased risks of premature mortality, decreased lung function, and adverse birth outcomes. Previous exposure studies of airport emissions have been based in urban centers, making it difficult to selectively measure airport emissions without the contribution of other related sources. Our aim was to characterize the relationships between air pollutant particle measures (ultrafine particles [UFP], fine particulate matter [PM_2.5], black carbon [BC]) and air traffic (landings and take-offs [LTO]) at the Ottawa International Airport [YOW]. A monitoring site was established in greenspace approximately 600 m east of YOW and away from roadways and urban development. Air pollutant particles were measured continuously from June 2022 to January 2023. Flight track data was used to derive hourly LTO counts. Analyses of source directionality showed that UFP concentrations were higher when downwind from the airport. Further, when wind speeds were less than 20 km/h, UFP and LTO showed similar diurnal trends. No evidence of these associations was evident for PM_2.5 and BC. After selecting for airport wind directions and wind speeds less than 20 km/h, linear regression models showed each additional takeoff led to a 10–13% increase in the 50th to 99th UFP concentration percentiles. Our findings support policies designed to reduce potential health impacts of airport emissions on the exposed community.</p><p >Nine months of hourly monitoring of UFP, BC, and PM_2.5 in an urban greenspace 600 meters from Ottawa International Airport showed that, when downwind of the airport, UFP concentrations rose, tracked the diurnal pattern of aviation activity, and increased with each additional hourly takeoff. In contrast, no such trends were observed for PM_2.5 or BC.</p>","PeriodicalId":100014,"journal":{"name":"ACS ES&T Air","volume":"2 5","pages":"786–796 786–796"},"PeriodicalIF":0.0,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/epdf/10.1021/acsestair.4c00288","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143921254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Machine Learning Approach for Predicting the Pure-Component Surface Tension of Atmospherically Relevant Organic Compounds.","authors":"Ryan Schmedding, Mees Franssen, Andreas Zuend","doi":"10.1021/acsestair.4c00291","DOIUrl":"https://doi.org/10.1021/acsestair.4c00291","url":null,"abstract":"<p><p>Atmospheric aerosols are complex mixtures of highly functionalized organic compounds, water, inorganic electrolytes, metals, and carbonaceous species. The surface properties of atmospheric aerosol particles can influence several of their chemical and physical impacts, including their hygroscopic growth, aerosol-cloud interactions, and heterogeneous chemical reactions. The effects of the various compounds within a particle on its surface tension depend in part on the pure-component surface tensions. For many of the myriad of organic compounds of interest, experimental pure-component surface tension data at tropospheric temperatures are lacking, thus, requiring the development and application of property estimation methods. In this work, a compiled database of experimental pure-component surface tension data, covering a wide range of organic compound classes and temperatures, is used to train four different types of machine learning models to predict the temperature-dependent pure-component surface tensions of atmospherically relevant organic compounds. The trained models process input information about the temperature and the molecular structure of an organic compound, initially in the form of a Simplified Molecular Input Line Entry System (SMILES) string, to enable predictions. Our quantitative model assessment shows that extreme gradient-boosted descent along with Molecular ACCess System (MACCS) key descriptors of molecular structure provided the best balance of derived input complexity and model performance, resulting in a root-mean-square error (RMSE) of ∼1 mJ m^-2 in pure-component surface tension. Additionally, a simplified model based on molar mass, elemental ratios, and temperature as inputs was developed for use in applications for which molecular structure information is incomplete (RMSE of ∼2 mJ m^-2). We demonstrate that including predicted pure-component surface tension values in thermodynamically rigorous bulk-surface partitioning calculations may substantially modify the critical supersaturations necessary for aerosol activation into cloud droplets.</p>","PeriodicalId":100014,"journal":{"name":"ACS ES&T Air","volume":"2 5","pages":"808-823"},"PeriodicalIF":0.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12071373/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144083135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}