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Real-time Source Apportionment of Organic Aerosols in Three European Cities
GANG CHEN, Francesco Canonaco, Jay G. Slowik, Kaspar R. Daellenbach, Iasonas Stavroulas, Nikolaos Mihalopoulos, Evangelos Gerasopoulos, Jean-Eudes Petit, Olivier Favez, Urs Baltensperger, André S. H. Prévôt, Paul Scherrer Institute
Abstract Number: 386
Working Group: Source Apportionment
Abstract
Currently, European countries are still suffering from poor air quality: 70% of air quality monitoring stations within Europe exceed the annual PM2.5 value of the WHO guidelines (10 μg/m3). Considering organic aerosol (OA) is one of the major air pollutants, the knowledge of OA sources is crucial for policymakers to design effective mitigation strategies.
Positive matrix factorization (PMF) on Aerosol Mass Spectrometer (AMS) data is still the most common technique to conduct source apportionment (SA) analyses. However, conventional PMF suffers mostly from rotational ambiguity and a high degree of subjectiveness from the analyst, which prevents from providing high-quality information in near real-time. To overcome these disadvantages, we propose a novel and innovative real-time SA methodology. In addition to running PMF (rolling PMF) for a short time window in the order of 1-2 weeks with a small step (one day or few hours), we assess source loading in real-time for the latest scan by performing chemical mass balance (CMB).
To test this concept of real-time SA technique, we will conduct rolling PMF analyses by following a standardized protocol on three ACSM datasets collected in Athens, Paris, and Zurich. Then, we will compare CMB results and rolling results in many aspects (time series, relative contributions, and factor profiles) to investigate the performance of the real-time approach. So far, we found that it showed a good agreement (R2 >0.94) between the time series of resolved factors from CMB and rolling results using Athens ACSM data.
This project's success will minimize user interactions and subjective judgments and provide up-to-date, trustable information for policymakers and air quality modellers. Finally, reliable SA results in real-time pave the way for air quality forecast, especially relevant for safeguarding public health in megacities.