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":"","authors":"Michael S. Taylor Jr.,&nbsp;Devon N. Higgins,&nbsp;Justin M. Krasnomowitz and Murray V. Johnston*,&nbsp;","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":100014,"journal":{"name":"ACS ES&T Air","volume":"2 4","pages":"XXX-XXX XXX-XXX"},"PeriodicalIF":0.0,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/pdf/10.1021/acsestair.4c00339","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144372015","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":"","authors":"Felipe A. Rivera-Adorno,&nbsp;Jay M. Tomlin,&nbsp;Nurun Nahar Lata,&nbsp;Lisa Azzarello,&nbsp;Michael A. Robinson,&nbsp;Rebecca A. Washenfelder,&nbsp;Alessandro Franchin,&nbsp;Ann M. Middlebrook,&nbsp;Swarup China,&nbsp;Steven S. Brown,&nbsp;Cora J. Young,&nbsp;Matthew Fraund,&nbsp;Ryan C. Moffet and Alexander Laskin*,&nbsp;","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":100014,"journal":{"name":"ACS ES&T Air","volume":"2 4","pages":"XXX-XXX XXX-XXX"},"PeriodicalIF":0.0,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/pdf/10.1021/acsestair.4c00242","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144372023","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":"","authors":"Peeyush Khare,&nbsp;Jo Machesky,&nbsp;Leah Williams,&nbsp;Mackenzie Humes,&nbsp;Edward C. Fortner,&nbsp;Manjula Canagaratna,&nbsp;Jordan E. Krechmer,&nbsp;Andrew T. Lambe,&nbsp;Albert A. Presto and Drew R. Gentner*,&nbsp;","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":100014,"journal":{"name":"ACS ES&T Air","volume":"2 4","pages":"XXX-XXX XXX-XXX"},"PeriodicalIF":0.0,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/pdf/10.1021/acsestair.4c00193","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144345741","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":"","authors":"Jiayun Zhao,&nbsp;Sahir Gagan,&nbsp;Molly P. Frauenheim,&nbsp;Sining Niu,&nbsp;Bianca Aridjis-Olivos,&nbsp;Jason D. Surratt,&nbsp;Zhenfa Zhang,&nbsp;Avram Gold,&nbsp;Renyi Zhang* and Yue Zhang*,&nbsp;","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":100014,"journal":{"name":"ACS ES&T Air","volume":"2 4","pages":"XXX-XXX XXX-XXX"},"PeriodicalIF":0.0,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://pubs.acs.org/doi/pdf/10.1021/acsestair.4c00367","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144345736","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}