The performance and the uncertainty of receptor models (RMs) were assessed in intercomparison exercises employing real-world and synthetic input datasets. To that end, the results obtained by different practitioners using ten different RMs were compared with a reference. In order to explain the differences in the performances and uncertainties of the different approaches, the apportioned mass, the number of sources, the chemical profiles, the contribution-to-species and the time trends of the sources were all evaluated using the methodology described in Belis et al. (2015). In this study, 87% of the 344 source contribution estimates (SCEs) reported by participants in 47 different source apportionment model results met the 50% standard uncertainty quality objective established for the performance test. In addition, 68% of the SCE uncertainties reported in the results were coherent with the analytical uncertainties in the input data. The most used models, EPA-PMF v.3, PMF2 and EPA-CMB 8.2, presented quite satisfactory performances in the estimation of SCEs while unconstrained models, that do not account for the uncertainty in the input data (e.g. APCS and FA-MLRA), showed below average performance. Sources with well-defined chemical profiles and seasonal time trends, that make appreciable contributions (>10%), were those better quantified by the models while those with contributions to the PM mass close to 1% represented a challenge. The results of the assessment indicate that RMs are capable of estimating the contribution of the major pollution source categories over a given time window with a level of accuracy that is in line with the needs of air quality management.JRC.H.2-Air and Climat

In the last two decades, the incorporation of green roofs and living walls in buildings has increased significantly worldwide because of their benefits such as building energy savings, promoting biodiversity, controlling water run-off, mitigating urban heat island effect, improving indoor and urban air quality, and connecting people with nature. However, few studies have quantified the impact of green roofs (GRs) and living walls (LWs) on mitigating air pollution, especially in semiarid climates where airborne particle matter (PM) levels are high. Therefore, the aim of this paper is quantifying the dry deposition of PM₁₀ and PM_2.5 by several vegetation species commonly used in GRs and LWs in semiarid climates. Five species (Pitosporumtobira, Lavandulaangustifolia, Lampranthusspectabillis, Sedumalbum, and Sedumreflexum) for GRs and four species (Apteniacordiflora, Erigeronkarvinskianus, Sedumpalmeri, and Sedumspuriump.) for LWs were tested in an experimental facility—through washing, filtering, and weighing—to quantify the dry deposition of PM_2.5 and PM₁₀ on vegetation leaves as well as PM captured by the leaf wax. The main result is that a significant amount of PM is deposited on the typical vegetation used in GRs and LWs in semiarid climates. However, large differences in PM dry deposition were found among species, ranging from 0.09 μg/cm²∙h⁻¹ to 1.32 μg/cm²∙h⁻¹ for PM_2.5, 0.48 μg/cm²∙h⁻¹ to 4.7 μg/cm²∙h⁻¹ for PM₁₀ and 0.41 μg/cm²∙h⁻¹ to 25.6 μg/cm²∙h⁻¹ for leaf wax. The species that showed the highest potential to capture PM were S. album, S. reflexum, S. palmeri, and L. spectabillis. This study shows this green infrastructures can contribute to mitigate air pollution, thus GRs and LWs have the potential for being included in decontamination plans.