Pablo Lichtig , Julián Gelman Constantin , Melisa Diaz Resquin , Facundo Baraldo Victorica , Diego Alessandrello , Darío Gómez , Cristina Rössler , Marcelo de Oto , Ramiro Espada Guerrero , Héctor Bajano , Facundo Bajano , Jorge Herrera-Murillo , Laura Dawidowski
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PM<sub>2.5</sub> concentrations ranged from 4.2 <span><math><mi>μ</mi></math></span>g m<sup>−3</sup> to 51.4 <span><math><mi>μ</mi></math></span>g m<sup>−3</sup>, with a mean of 17.5 <span><math><mi>μ</mi></math></span>g m<sup>−3</sup>, and maxima during biomass burning (BB) events. Samples were classified according to the presence or absence of BB events affecting the area, and mass reconstruction was performed. Optimal OM/OC ratios were determined to be 2.5 (BB-samples) and 1.9 (non-BB samples), being OM <span><math><mo>∼</mo></math></span> <!--> <!-->65% and <span><math><mo>∼</mo></math></span> <!--> <!-->54%, respectively. On average, other components accounted for <span><math><mo>∼</mo></math></span> <!--> <!-->14% geological minerals <span><math><mo>></mo></math></span> <span><math><mo>∼</mo></math></span> <!--> <!-->10% inorganic ions <span><math><mo>></mo></math></span> <span><math><mo>∼</mo></math></span> <!--> <!-->6% elemental carbon <span><math><mo>></mo></math></span> <span><math><mo>∼</mo></math></span> <!--> <!-->3% sea salt <span><math><mo>></mo></math></span> <span><math><mo>∼</mo></math></span> <!--> <!-->2% non crustal K. Source contributions were further studied using Positive Matrix Factorization. Open biomass burning was the main contributor to PM<sub>2.5</sub> (28.4%) and total carbon (25.7%), highlighting the significance of long-range pollutant transport. The temporal variability of this factor aligns with fire events identified using fire location, back-trajectory analysis, and aerosol classification schemes. The remaining factors found were: SOA + soil + road dust (17.7%), mobile sources powered by low sulfur (15.8%) and high sulfur fuels (11.1%), construction + grills (12.1%), agriculture (9.3%) and thermal power plants + industry (5.6%). This study provides relevant information for air quality management, highlighting knowledge gaps on primary and secondary sources affecting the site.</div></div>","PeriodicalId":250,"journal":{"name":"Atmospheric Environment","volume":"353 ","pages":"Article 121236"},"PeriodicalIF":4.2000,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Comprehensive chemical profile and source apportionment of PM2.5 in Buenos Aires: Insights from the southernmost megalopolis\",\"authors\":\"Pablo Lichtig , Julián Gelman Constantin , Melisa Diaz Resquin , Facundo Baraldo Victorica , Diego Alessandrello , Darío Gómez , Cristina Rössler , Marcelo de Oto , Ramiro Espada Guerrero , Héctor Bajano , Facundo Bajano , Jorge Herrera-Murillo , Laura Dawidowski\",\"doi\":\"10.1016/j.atmosenv.2025.121236\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Understanding aerosol composition is essential for identifying sources and assessing impacts. We analyzed the chemical profile of 100 24-hour PM<sub>2.5</sub> samples and used this data for mass reconstruction and source apportionment. Samples were collected between April 2019 and March 2020 at a site located in Buenos Aires, Argentina. PM<sub>2.5</sub> concentrations ranged from 4.2 <span><math><mi>μ</mi></math></span>g m<sup>−3</sup> to 51.4 <span><math><mi>μ</mi></math></span>g m<sup>−3</sup>, with a mean of 17.5 <span><math><mi>μ</mi></math></span>g m<sup>−3</sup>, and maxima during biomass burning (BB) events. Samples were classified according to the presence or absence of BB events affecting the area, and mass reconstruction was performed. Optimal OM/OC ratios were determined to be 2.5 (BB-samples) and 1.9 (non-BB samples), being OM <span><math><mo>∼</mo></math></span> <!--> <!-->65% and <span><math><mo>∼</mo></math></span> <!--> <!-->54%, respectively. 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引用次数: 0
摘要
了解气溶胶成分对于确定来源和评估影响至关重要。我们分析了100个24小时PM2.5样本的化学特征,并利用这些数据进行了质量重建和来源分配。样本于2019年4月至2020年3月在阿根廷布宜诺斯艾利斯的一个地点收集。PM2.5浓度范围为4.2 ~ 51.4 μ m - 3,平均值为17.5 μ m - 3,在生物质燃烧(BB)期间达到最大值。根据是否存在影响该区域的BB事件对样本进行分类,并进行大量重建。确定最佳OM/OC比为2.5 (bb样品)和1.9(非bb样品),分别为OM ~ 65%和~ 54%。平均而言,其他组分占地质矿物的约14% >;~ 10%无机离子>;~ 6%单质碳>;~ 3%海盐>;利用正矩阵分解法进一步研究了源的贡献。露天生物质燃烧是PM2.5(28.4%)和总碳(25.7%)的主要来源,凸显了污染物远距离迁移的重要性。该因子的时间变异性与利用火灾位置、反轨迹分析和气溶胶分类方案确定的火灾事件相一致。其余因素为:SOA +土壤+道路粉尘(17.7%),低硫燃料(15.8%)和高硫燃料(11.1%)驱动的移动源,建筑+烧烤(12.1%),农业(9.3%)和火力发电厂+工业(5.6%)。这项研究为空气质量管理提供了相关信息,突出了影响场地的主要和次要来源的知识差距。
Comprehensive chemical profile and source apportionment of PM2.5 in Buenos Aires: Insights from the southernmost megalopolis
Understanding aerosol composition is essential for identifying sources and assessing impacts. We analyzed the chemical profile of 100 24-hour PM2.5 samples and used this data for mass reconstruction and source apportionment. Samples were collected between April 2019 and March 2020 at a site located in Buenos Aires, Argentina. PM2.5 concentrations ranged from 4.2 g m−3 to 51.4 g m−3, with a mean of 17.5 g m−3, and maxima during biomass burning (BB) events. Samples were classified according to the presence or absence of BB events affecting the area, and mass reconstruction was performed. Optimal OM/OC ratios were determined to be 2.5 (BB-samples) and 1.9 (non-BB samples), being OM 65% and 54%, respectively. On average, other components accounted for 14% geological minerals 10% inorganic ions 6% elemental carbon 3% sea salt 2% non crustal K. Source contributions were further studied using Positive Matrix Factorization. Open biomass burning was the main contributor to PM2.5 (28.4%) and total carbon (25.7%), highlighting the significance of long-range pollutant transport. The temporal variability of this factor aligns with fire events identified using fire location, back-trajectory analysis, and aerosol classification schemes. The remaining factors found were: SOA + soil + road dust (17.7%), mobile sources powered by low sulfur (15.8%) and high sulfur fuels (11.1%), construction + grills (12.1%), agriculture (9.3%) and thermal power plants + industry (5.6%). This study provides relevant information for air quality management, highlighting knowledge gaps on primary and secondary sources affecting the site.
期刊介绍:
Atmospheric Environment has an open access mirror journal Atmospheric Environment: X, sharing the same aims and scope, editorial team, submission system and rigorous peer review.
Atmospheric Environment is the international journal for scientists in different disciplines related to atmospheric composition and its impacts. The journal publishes scientific articles with atmospheric relevance of emissions and depositions of gaseous and particulate compounds, chemical processes and physical effects in the atmosphere, as well as impacts of the changing atmospheric composition on human health, air quality, climate change, and ecosystems.