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London, like many major cities, has a noted air pollution problem, and a better understanding of the sources of airborne particles in the different size fractions will facilitate the implementation and effectiveness of control strategies to reduce air pollution. Thus, the trace elemental composition of the fine and coarse fraction were analysed at hourly time resolution at urban background (North Kensington, NK) and roadside (Marylebone Road, MR) sites within central London. Unlike previous work, the current study focuses on measurements during the summer providing a snapshot of contributing sources, utilising the high time resolution to improve source identification. Roadside enrichment was observed for a large number of elements associated with traffic emissions (Al, S, Ca, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn, As, Rb and Zr), while those elements that are typically from more regional sources (e.g. Na, Cl, S and K) were not found to have an appreciable increment. Positive Matrix Factorization (PMF) was applied for the source apportionment of the particle mass at both sites with similar sources being identified, including sea salt, airborne soil, traffic emissions, secondary inorganic aerosols and a Zn-Pb source. In the fine fraction, traffic emissions was the largest contributing source at MR (31.9%), whereas it was incorporated within an “urban background” source at NK, which had contributions from wood smoke, vehicle emissions and secondary particles. Regional sources were the major contributors to the coarse fraction at both sites. Secondary inorganic aerosols (which contained influences from shipping emissions and coal combustion) source factors accounted for around 33% of the PM10 at NK and were found to have the highest contributions from regional sources, including from the European mainland. Exhaust and non-exhaust sources both contribute appreciably to PM10 levels at the MR site, highlighting the continuing importance of vehicle-related air pollutants at roadside.

This report contains a guide and a European harmonised protocol prepared within the framework of the Working Group 3 on source apportionment of the Forum for Air Quality Modelling in Europe (FAIRMODE). It has been initiated as a JRC initiative for the harmonisation of source apportionment with receptor models, in collaboration with FAIRMODE as well as with the European networks in the field of air quality measurements (AQUILA), and then further with the European COLOSSAL (Chemical On-Line cOmpoSition and Source Apportionment of fine aerosol) COST action. The JRC initiative also included a review of the methodologies used in Europe for source identification and intercomparison exercises for the quantitative assessment of the performance of source apportionment models. The document, drafted and then revised by a group of international experts, is organised following the logical sequence of steps to be carried out in a source apportionment study. Sections with increasing levels of complexity make it accessible to readers with different degrees of familiarity with this topic, from air quality managers to air pollution experts and modellers. It has been conceived as a reference document that includes tutorials, technical recommendations and check lists.

This report contains a guide and a European harmonised protocol prepared within the framework of the Working Group 3 on source apportionment of the Forum for Air Quality Modelling in Europe (FAIRMODE). It has been initiated as a JRC initiative for the harmonisation of source apportionment with receptor models, in collaboration with FAIRMODE as well as with the European networks in the field of air quality measurements (AQUILA), and then further with the European COLOSSAL (Chemical On-Line cOmpoSition and Source Apportionment of fine aerosol) COST action. The JRC initiative also included a review of the methodologies used in Europe for source identification and intercomparison exercises for the quantitative assessment of the performance of source apportionment models. The document, drafted and then revised by a group of international experts, is organised following the logical sequence of steps to be carried out in a source apportionment study. Sections with increasing levels of complexity make it accessible to readers with different degrees of familiarity with this topic, from air quality managers to air pollution experts and modellers. It has been conceived as a reference document that includes tutorials, technical recommendations and check lists., JRC.C.5-Air and Climate

In this study, the performance of the source apportionment model applications were evaluated by comparing the model results provided by 44 participants adopting a methodology based on performance indicators: z-scores and RMSEu, with pre-established acceptability criteria. Involving models based on completely different and independent input data, such as receptor models (RMs) and chemical transport models (CTMs), provided a unique opportunity to cross-validate them. In addition, comparing the modelled source chemical profiles, with those measured directly at the source contributed to corroborate the chemical profile of the tested model results. The most used RM was EPA- PMF5. RMs showed very good performance for the overall dataset (91% of z-scores accepted) and more difficulties are observed with SCE time series (72% of RMSEu accepted). Industry resulted the most problematic source for RMs due to the high variability among participants. Also the results obtained with CTMs were quite comparable to their ensemble reference using all models for the overall average (>92% of successful z-scores) while the comparability of the time series is more problematic (between 58% and 77% of the candidates’ RMSEu are accepted). In the CTM models a gap was observed between the sum of source contributions and the gravimetric PM10 mass likely due to PM underestimation in the base case. Interestingly, when only the tagged species CTM results were used in the reference, the differences between the two CTM approaches (brute force and tagged species) were evident. In this case the percentage of candidates passing the z-score and RMSEu tests were only 50% and 86%, respectively. CTMs showed good comparability with RMs for the overall dataset (83% of the z-scores accepted), more differences were observed when dealing with the time series of the single source categories. In this case the share of successful RMSEu was in the range 25% - 34%., JRC.C.5-Air and Climate