Exhaust and non-exhaust contributions from road transport to PM10 at a Southern European traffic site.

It is a well - established fact that road traffic is one of the main contributors to ambient levels of airborne particulate matter (APM). This study was carried out at a traffic site in which the PM10 levels are monitored all year round. A trend analysis of these levels revealed that over a decade there was no discernible trend, with the PM10 concentrations normally hovering around the EU limit values. In 2018, one of these limit values was exceeded. The contribution of traffic at the site was therefore investigated through a chemical speciation of 209 PM10 samples collected throughout this year. The speciation data were used in a source apportionment exercise in which the output of the PMF model was further refined using the lesser-known, constraint weighted non - negative matrix factorization (CW - NMF) model. This technique enabled the isolation of two factors clearly related to traffic, which were labelled as "exhaust contribution" (responsible for 3.4% of the PM10), "tire/brake wear contribution" (contributing 17% of the PM10). Additionally, a factor including both traffic resuspended dust and crustal material was also isolated and labelled "road dust/crustal" factor. The two contributors to the factor jointly contribute 18% to the PM10 and the contribution of the traffic resuspended dust was estimated at 7.3%. The traffic resuspended component of this factor together with the "tire/brake wear contribution" jointly make up the non-exhaust contribution of traffic - derived dust. Consonant with what has been known for quite some time, the exhaust fraction is the minor component of traffic PM10. It is therefore, clear that policies aimed at controlling traffic derived PM10 pollution at the receptor will have a minimal effect unless the non - exhaust emissions are adequately controlled.

Investigating the plausibility of a PMF source apportionment solution derived using a small dataset: A case study from a receptor in a rural site in Apulia - South East Italy.

Results of a methodological study on the use of Positive Matrix Factorization (PMF) with smaller datasets are being reported in this work. This study is based on 29 PM10 and 33 PM2.5 samples from a receptor in a rural setup in Apulia (Southern Italy). Running PMF on the two size fractions separately resulted in the model not functioning correctly. We therefore, augmented the size of the dataset by aggregating the PM10 and PM2.5 data. The 5-factor solution obtained for the aggregated data was fairly rotationally stable, and was further refined by the rotational tools included in USEPA PMF version 5. These refinements include the imposition of constraints on the solution, based on our knowledge of the chemical composition of the aerosol sources affecting the receptor. Additionally, the uncertainties associated with this solution were fully characterised using the improved error estimation techniques in this version of PMF. Five factors in all, were isolated by PMF: ammonium sulfate, marine aerosol, mixed carbonaceous aerosol, crustal/Saharan dust and total traffic. The results obtained by PMF were further tested inter alia, by comparing them to those obtained by two other receptor modelling techniques: Constrained Weighted Non-negative Matrix Factorization (CW - NMF) and Chemical Mass Balance (CMB). The results of these tests suggest that the solution obtained by PMF, is valid, indicating that for this particular airshed PMF managed to extract most of the information about the aerosol sources affecting the receptor - even from a dataset with a limited number of samples.

Estimation of the contributions of the sources driving PM2.5 levels in a Central Mediterranean coastal town.

Receptor modelling techniques are widely used in order to identify the main natural and anthropogenic processes driving aerosol levels at a receptor. In this work, Positive Matrix Factorization (PMF) was used to apportion PM2.5 levels at a traffic site (Msida) located in a coastal town. 180 filters collected throughout a yearly sampling campaign conducted in 2016, were chemically characterized by light absorbance analysis, x-ray fluorescence and ion chromatography in order to determine the concentrations of black carbon, 17 elements and 5 ions, respectively. The resulting chemical data base was used in conjunction with PMF in order to identify the 7 components affecting the PM2.5 levels at the receptor site. Six of these sources are considered to be typical of the atmospheric composition of coastal traffic sites: traffic (27.3%), ammonium sulfate (23.6%), Saharan dust (15%), aged sea salt (12.7%), shipping (5%) and fresh sea salt (4.6%). This is the first time that such a study was carried out in Malta and helps in understanding the aerosol pollution climate of the Central Mediterranean, which is still relatively understudied when compared to the Eastern and Western Mediterranean. Furthermore, we have isolated a factor exclusive to Malta: the fireworks component, which is responsible for 2.9% of the PM2.5 and which has health implications due to its chemical composition. The results of this work should also serve to guide the policy makers in achieving the necessary emission reductions in order to achieve the WHO guideline for PM2.5 by 2020.