Multivariate statistics for large data sets: applications to individual aerosol particles

Saharan dust contribution to PM₁₀, PM₂.₅ and PM₁ in urban and suburban areas of Rome: a comparison between single-particle SEM-EDS analysis and whole-sample PIXE analysis

From February 15th to April 15th 2009, a period characterised by two episodes of Saharan dust outbreaks in Italy, particulate matter (PM) samples were collected at two stations (urban and suburban) in Rome. Some samples were selected and analysed using the SEM-EDS technique to characterise PM, focussing especially on the mineral contribution. Samples were representative both of days affected by Saharan dust episodes and days without this contribution. Cluster analysis allowed the attribution of each of about 67,000 analysed particles to one of the seven main statistical groups based on their composition. Characteristics of the particulate components identified using SEM-EDS analysis were verified by PIXE analysis carried out on filters collected in a suburban area. Ultimately, the contribution of crustal particles was revealed to be consistently high, highlighting the importance of local and regional mineral contributions, as well as those of Saharan origin. Therefore, quantifying all mineral contributions to resuspended particulate could lead to significant reductions of the PM level also on days not influenced by Saharan dust, thus limiting conditions when PM₁₀ daily limit value (DLV) established by European legislation is exceeded.

Chemically-assigned classification of aerosol mass spectra

An Algorithm for Discriminant Analysis of Mass Spectra--ADAMS--was created that classified aerosol mass spectra into dominant chemically-assigned classes, and grouped rare cases in an outlier class. ADAMS was trained with ambient particulate matter (PM) mass spectra, and then validated through classification tests on known spectra with random noise added, various standard chemicals, and salt-spiked polystyrene latex microspheres. The classification results showed that ADAMS gave a reasonable chemical description of the particle populations. In contrast to adaptive resonance theory (ART-2a) classification, ADAMS could be trained to be advantageously sensitive or insensitive to selected chemical markers. Application of ADAMS to Toronto ambient PM and diesel PM (NIST 2975) demonstrated that these samples could be well described, with a low proportion of the cases falling into the outlier class. Such an algorithm may find application for source-receptor modeling of aerosol mass spectra.

Random amplified polymorphic DNA and restriction enzyme analysis of PCR amplified rDNA in taxonomy: two identification techniques for food-borne yeasts

The random amplified polymorphic DNA (RAPD) assay and the restriction enzyme analysis of PCR amplified rDNA are compared for the identification of the common spoilage yeasts Zygosaccharomyces bailii, Z. rouxii, Saccharomyces cerevisiae, Candida valida and C. lipolytica. Both techniques proved to be adequate tools for yeast identification. Since the RAPD does provide less stable patterns than restriction enzyme analysis of PCR amplified rDNA, and a large amount of data had to be compared without data reduction, Principal Component Analysis (PCA) was applied successfully for clustering the RAPD patterns. The success of PCA is highly influenced by the primer used in RAPD and the amount of reference samples. A large amount of reference samples improves the performance of clustering in PCA. The primer of choice was shown to be important with respect to the discriminatory power of the RAPD method. Some primers used enabled discrimination on the subspecies level. The results collected with both typing methods justify the conclusion that the present typing system can be applied for taxonomical purposes.