Source apportionment (SA) techniques allocate the measured ambient pollutants with their potential source origin; thus, they are a powerful tool for designing air pollution mitigation strategies. Positive Matrix Factorization (PMF) is one of the most widely used SA approaches, and its multi-time resolution (MTR) methodology, which enables mixing different instrument data in their original time resolution, was the focus of this study. One year of co-located measurements in Barcelona, Spain, of non-refractory submicronic particulate matter (NR-PM₁), black carbon (BC) and metals were obtained by a Q-ACSM (Aerodyne Research Inc.), an aethalometer (Aerosol d.o.o.) and fine offline quartz-fibre filters, respectively. These data were combined in a MTR PMF analysis preserving the high time resolution (30 minutes for the NR-PM₁ and BC, and 24h every 4th day for the offline samples). The MTR-PMF outcomes were assessed varying the time resolution of the high-resolution data subset and exploring the error weightings of both subsets. The time resolution assessment revealed that averaging the high-resolution data was disadvantageous in terms of model residuals and environmental interpretability. The MTR-PMF resolved eight PM₁ sources: ammonium sulphate + heavy oil combustion (25%), ammonium nitrate + ammonium chloride (17%), aged secondary organic aerosol (SOA) (16%), traffic (14%), biomass burning (9%), fresh SOA (8%), cooking-like organic aerosol (5%), and industry (4%). The MTR-PMF technique identified two more sources relative to the 24h base case data subset using the same species and four more with respect to the pseudo-conventional approach mimicking offline PMF, indicating that the combination of both high and low TR data is significantly beneficial for SA. Besides the higher number of sources, the MTR-PMF technique has enabled some sources disentanglement compared to the pseudo-conventional and base case PMF as well as the characterisation of their intra-day patterns.

源解析（SA）技术将测量到的环境污染物及其潜在来源进行分配；因此，它们是设计空气污染缓解战略的有力工具。正矩阵分解 (PMF) 是最广泛使用的 SA 方法之一，其多时间分辨率 (MTR) 方法是本研究的重点，该方法能够以原始时间分辨率混合不同的仪器数据。在西班牙巴塞罗那对非难熔亚微米颗粒物 (NR-PM ₁)、黑碳 (BC) 和金属分别通过 Q-ACSM (Aerodyne Research Inc.)、气体浓度计 (Aerosol doo) 和精细离线石英纤维过滤器获得。这些数据在 MTR PMF 分析中组合，保留了高时间分辨率（NR-PM ₁和 BC 为 30 分钟，离线样本每 4 天 24 小时）。通过改变高分辨率数据子集的时间分辨率并探索两个子集的误差权重来评估 MTR-PMF 结果。时间分辨率评估表明，对高分辨率数据进行平均在模型残差和环境可解释性方面是不利的。MTR-PMF 解决了 8 个 PM ₁来源：硫酸铵+重油燃烧(25%)、硝酸铵+氯化铵(17%)、老化二次有机气溶胶(SOA)(16%)、交通(14%)、生物质燃烧(9%)、新鲜SOA （8%）、烹饪类有机气溶胶（5%）和工业（4%）。MTR-PMF 技术相对于使用相同物种的 24 小时基本案例数据子集确定了另外两个来源，相对于模仿离线 PMF 的伪传统方法又确定了四个来源，表明高和低 TR 数据的组合是显着有益的对于南澳。除了源数量较多之外，与伪传统和基本情况 PMF 相比，MTR-PMF 技术还能够解开一些源，并能够表征其日内模式。