Lei Lu, Xu Wei, Lin Chunshui, Chen Baihua, Fossum Kirsten N, Ceburnis Darius, O'Dowd Colin, Ovadnevaite Jurgita
School of Natural Sciences, Ryan Institute's Centre for Climate & Air Pollution Studies, University of Galway, Galway, H91 CF50 Ireland.
Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, 361021 China.
ACS EST Air. 2025 Apr 15;2(5):891-902. doi: 10.1021/acsestair.4c00331. eCollection 2025 May 9.
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 and MO-OOA) from transboundary transport OOA and exhibited robust performance across different pollution scenarios. The relative importance analysis revealed that LO-OOA was more impacted by fossil fuel emissions like hydrocarbon-like OA (20%) and coal (14%), whereas MO-OOA 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.
准确地对颗粒物（PM），尤其是有机气溶胶（OA）进行源解析，对于有针对性的减排工作至关重要。正定矩阵因子分解（PMF）在一次有机气溶胶（POA）的源解析方面很强大；然而，由于氧化有机气溶胶（OOA）的化学特征相似，它往往难以区分其来源。在本研究中，开发了一种支持向量回归机器学习（ML）模型，以加强对2016年至2023年都柏林OOA的源解析。滚动PMF分析确定了四个POA因子，并将OOA分为氧化程度较低和较高的（低氧化态OOA和高氧化态OOA），突出了OOA（占总OA的47 - 74%）的重要作用。ML模型进一步区分了本地产生的OOA（低氧化态OOA和高氧化态OOA）和跨境传输的OOA，并在不同污染情景下表现出强大的性能。相对重要性分析表明，低氧化态OOA受化石燃料排放的影响更大，如类烃有机气溶胶（20%）和煤炭（14%），而高氧化态OOA受低氧化态OOA的影响最大（17%），这为它们的来源和形成机制提供了见解。在一次混合污染事件中，结果表明，尽管跨境传输有重大贡献，但本地供暖排放是OA的更关键来源，本地OA占总OA的68%，在供暖时段达到78%。这些发现凸显了持续减少本地排放以在都柏林实现更清洁空气的必要性。ML模型定量分离本地和跨境OOA的能力为未来的空气质量法规提供了宝贵的见解。
