Source apportionment of PM2.5 before and after COVID-19 lockdown in an urban-industrial area of the Lisbon metropolitan area, Portugal

The lockdowns held due to the COVID-19 pandemic conducted to changes in air quality. This study aimed to understand the variability of PM2.5 levels and composition in an urban-industrial area of the Lisbon Metropolitan Area and to identify the contribution of the different sources. The composition of PM2.5 was assessed for 24 elements (by PIXE), secondary inorganic ions and black carbon. The PM2.5 mean concentration for the period (December 2019 to November 2020) was 13 ± 11 μg.m−3. The most abundant species in PM2.5 were BC (19.9%), SO42− (15.4%), NO3− (11.6%) and NH4+ (5.3%). The impact of the restrictions imposed by the COVID-19 pandemic on the PM levels was found by comparison with the previous six years. The concentrations of all the PM2.5 components, except Al, Ba, Ca, Si and SO42−, were significantly higher in the winter/pre-confinement than in post-confinement period. A total of seven sources were identified by Positive Matrix Factorisation (PMF): soil, secondary sulphate, fuel-oil combustion, sea, vehicle non-exhaust, vehicle exhaust, and industry. Sources were greatly influenced by the restrictions imposed by the COVID-19 pandemic, with vehicle exhaust showing the sharpest decrease. Secondary sulphate predominated in summer/post-confinement. PM2.5 levels and composition also varied with the types of air mass trajectories.info:eu-repo/semantics/publishedVersio

Results of the first European Source Apportionment intercomparison for Receptor and Chemical Transport Models

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 Climat

Spatial Distribution of Air Pollution, Hotspots and Sources in an Urban-Industrial Area in the Lisbon Metropolitan Area, Portugal—A Biomonitoring Approach

This study aimed to understand the influence of industries (including steelworks, lime factories, and industry of metal waste management and treatment) on the air quality of the urban-industrial area of Seixal (Portugal), where the local population has often expressed concerns regarding the air quality. The adopted strategy was based on biomonitoring of air pollution using transplanted lichens distributed over a grid to cover the study area. Moreover, the study was conducted during the first period of national lockdown due to COVID-19, whereas local industries kept their normal working schedule. Using a set of different statistical analysis approaches (such as enrichment and contamination factors, Spearman correlations, and evaluation of spatial patterns) to the chemical content of the exposed transplanted lichens, it was possible to assess hotspots of air pollution and to identify five sources affecting the local air quality: (i) a soil source of natural origin (based on Al, Si, and Ti), (ii) a soil source of natural and anthropogenic origins (based on Fe and Mg), (iii) a source from the local industrial activity, namely steelworks (based on Co, Cr, Mn, Pb, and Zn); (iv) a source from the road traffic (based on Cr, Cu, and Zn), and (v) a source of biomass burning (based on Br and K). The impact of the industries located in the study area on the local air quality was identified (namely, the steelworks), confirming the concerns of the local population. This valuable information is essential to improve future planning and optimize the assessment of particulate matter levels by reference methods, which will allow a quantitative analysis of the issue, based on national and European legislation, and to define the quantitative contribution of pollution sources and to design target mitigation measures to improve local air quality

Sources and geographic origin of particulate matter in urban areas of the Danube macro-region: the cases of Zagreb (Croatia), Budapest (Hungary) and Sofia (Bulgaria)

The contribution of main PM pollution sources and their geographic origin in three urban sites of the Danube macro-region (Zagreb, Budapest and Sofia) were determined by combining receptor and Lagrangian models. The source contribution estimates were obtained with the Positive Matrix Factorization (PMF) receptor model and the results were further examined using local wind data and backward trajectories obtained with FLEXPART. Potential Source Contribution Function (PSCF) analysis was applied to identify the geographical source areas for the PM sources subject to long-range transport. Gas-to-particle transformation processes and primary emissions from biomass burning are the most important contributors to PM in the studied sites followed by re-suspension of soil (crustal material) and traffic. These four sources can be considered typical of the Danube macro-region because they were identified in all the studied locations. Long-range transport was observed of: a) sulphate-enriched aged aerosols, deriving from SO2 emissions in combustion processes in the Balkans and Eastern Europe and b) dust from the Saharan and Karakum deserts. The study highlights that PM pollution in the studied urban areas of the Danube macro-region is the result of both local sources and long-range transport from both EU and no-EU areas.JRC.C.5-Air and Climat

Ambient particulate matter source apportionment using receptor modelling in European and Central Asia urban areas

This work presents the results of a PM2.5 source apportionment study conducted in urban background sites from 16 European and Asian countries. For some Eastern Europe and Central Asia cities this was the first time that quantitative information on pollution source contributions to ambient particulate matter (PM) has been performed. More than 2200 filters were sampled and analyzed by X-Ray Fluorescence (XRF), Particle-Induced X-Ray Emission (PIXE), and Inductively Coupled Plasma Mass Spectrometry (ICP-MS) to measure the concentrations of chemical elements in fine particles. Samples were also analyzed for the contents of black carbon, elemental carbon, organic carbon, and water-soluble ions. The Positive Matrix Factorization receptor model (EPA PMF 5.0) was used to characterize similarities and heterogeneities in PM2.5 sources and respective contributions in the cities that the number of collected samples exceeded 75. At the end source apportionment was performed in 11 out of the 16 participating cities. Nine major sources were identified to have contributed to PM2.5: biomass burning, secondary sulfates, traffic, fuel oil combustion, industry, coal combustion, soil, salt and “other sources”. From the averages of sources contributions, considering 11 cities 16% of PM2.5 was attributed to biomass burning, 15% to secondary sulfates, 13% to traffic, 12% to soil, 8.0% to fuel oil combustion, 5.5% to coal combustion, 1.9% to salt, 0.8% to industry emissions, 5.1% to “other sources” and 23% to unaccounted mass. Characteristic seasonal patterns were identified for each PM2.5 source. Biomass burning in all cities, coal combustion in Krakow/POL, and oil combustion in Belgrade/SRB and Banja Luka/BIH increased in Winter due to the impact of domestic heating, whereas in most cities secondary sulfates reached higher levels in Summer as a consequence of the enhanced photochemical activity. During high pollution days the largest sources of fine particles were biomass burning, traffic and secondary sulfates.JRC.C.5-Air and Climat