Abstract

The Aerosol Time-of-Flight Mass Spectrometer (ATOFMS) is one of few instruments able to measure the size and mass spectra of individual airborne particles with high temporal resolution. Data analysis is challenging and for the first time, Positive Matrix Factorization (PMF) was directly applied to single particle mass spectra to a regional ATOFMS dataset collected at Harwell, UK. The analysis allowed the extraction of 10 factors, their time-series and size distributions. Different inorganic, EC and OC components were separated: fresh EC (factor EC-) from aged EC (factor EC+), different organic families, such as oxidised organic aerosol, aromatic aerosol, fresh N-containing organic aerosol and oxidised N-containing organic aerosol (factors NH4 -OOA, OC-Arom, OC-CHNO and CNO -COOH respectively), and inorganic factors such as nitrate (factor NIT ), potassium (factor K), sulphate (factor SUL ) and sodium chloride (factor NaCl). A comparison was conducted between PMF and two methods commonly used in ATOFMS data treatment, K-means cluster analysis and the ART -2a artificial neural network. Relationships were also examined between the estimated volumes of ATOFMS PMF factors and species concentrations measured independently by GRAEGOR and AMS instruments, showing generally moderate to strong correlations. The optimized approach was then applied to a dataset collected in a six-lane road within an urban canyon in central London, UK in which the PMF analysis was able to separate the contribution of marine, background, local and vehicular traffic-related factors.