Search

Abstract A methodology to detect the weekly cycle impact of the particulate matter (PM), and PM sources on the near surface temperature and wind speed is discussed in the paper. Chemically-speciated PM10 and PM2.5 samples are analyzed to detect the weekly cycle of both the PM mass concentrations and the PM sources identified by the Positive Matrix Factorization technique. The average percent departure (APD) of the PM mass concentration from the mean value calculated for each day of the week shows that a positive (higher values during midweek) and a negative (higher values during weekend) weekly cycle characterizes the PM10 and PM2.5 mass concentrations in Autumn-Winter (AW, September–February) and Spring-Summer (SS, March–August), respectively. The westerly transport of pollution seems to have a role on the negative PM weekly cycle found in SS. The analysis of the six identified aerosol sources indicates that in SS the mixed anthropogenic and the reacted dust sources likely impact the PM10 negative weekly cycle and that the mixed anthropogenic source likely impacts the PM2.5 negative weekly cycle. The mixed anthropogenic and soil dust sources likely affect in AW the positive weekly cycle of the PM10 mass concentration. Both sources in addition to the reacted dust source seem to affect the PM2.5 mass concentration in AW. The APD analysis of the temperature (T) and wind speed (WS) at the surface from measurements co-located in space and time with the PM ones reveals that the WS and T values are characterized by a negative weekly cycle in AW. Conversely, in SS, the WS-APD value decreases on Sunday and the T-APD values increase in the second half of the week. These last results likely give evidence of the PM impact on the near-surface temperature and wind speed at the study site. Graphical abstract Unlabelled Image Highlights • Weekly cycles of PM10 and PM2.5 mass concentrations and seasonal dependence • Weekly cycles of temperature and wind speed at the surface and seasonal dependence • Chemical speciation of PM10 and PM2.5 samples • Aerosol source identification by Positive Matrix Factorization • Aerosol source impact on the weekly cycles of PM10 and PM2.5 mass concentrations [ABSTRACT FROM AUTHOR]

From January to December 2010, surface snow samples were collected with monthly resolution at the Concordia station (75°06’S, 123°20’E), on the Antarctic plateau, and analysed for major and trace elements in both dissolved and particulate (i.e. insoluble particles, >0.45 μm) phase. Additional surface snow samples were collected with daily resolution, for the determination of sea-salt sodium and not-sea-salt calcium, in order to support the discussion on the seasonal variations of trace elements. Concentrations of alkaline and alkaline-earth elements were higher in winter (April–October) than in summer (November–March) by a factor of 1.2–3.3, in agreement with the higher concentration of sea-salt atmospheric particles reaching the Antarctic plateau during the winter. Similarly, trace elements were generally higher in winter by a factor of 1.2–1.5, whereas Al and Fe did not show any significant seasonal trend. Partitioning between dissolved and particulate phases did not change with the sampling period, but it depended only on the element: alkaline and alkaline-earth elements, as well as Co, Cu, Mn, Pb and Zn were for the most part (>80%) in the dissolved phase, whereas Al and Fe were mainly associated with the particulate phase (>80%) and Cd, Cr, V were nearly equally distributed between the phases. Finally, the estimated marine and crustal enrichment factors indicated that Cd, Cr, Cu, Pb and Zn have a dominant anthropogenic origin, with a possible contribution from the Concordia station activities. [ABSTRACT FROM AUTHOR]

In this study, the information potential of a two-step sequential extraction procedure was evaluated. For this purpose, first of all the elemental composition of Arctic PM10 samples collected in Ny-Ålesund (Svalbard Islands) from 28 February 2015 to 21 October 2015 was investigated. Enrichment Factors, Principal Component Analysis and Hierarchical Cluster Analysis were performed to identify PM10 sources and to understand the effects of short- and long-range transport processes. The investigation of the potential source areas was also aided by taking into account back-trajectories. Then, the sequential extraction procedure was applied to some of the samples in order to obtain more information on these sources. This approach allowed us to establish that most of the elements prevalently having an anthropogenic origin not only were present in higher concentrations, but they were also more easily extractable in late winter and early spring. This confirms the common statement that the anthropogenic portion of the elements present in a sample is generally loosely bound to the particulate matter structure, and so it is more easily extractable and releasable on the Arctic snowpack. Moreover, in the samples collected in late winter and early spring, even the elements prevalently having a crustal origin were more easily extractable, probably due to the particle size selection occurred during the long-range transport. [ABSTRACT FROM AUTHOR]

An extensive field campaign was carried out in Florence (Tuscany) to investigate the PM2.5 composition and to identify its sources. The scientific objective of this study is providing a reliable source apportionment, which is mandatory for the application of effective mitigation actions. Particulate matter (PM) was collected for one year, simultaneously in a traffic site, in an urban background, and in a regional background site. While the use of two filter types (quartz and Teflon) allowed obtaining a comprehensive chemical characterization (elemental and organic carbon, ions, elements) by the application of different analytical techniques, the location of the three sampling sites allowed getting a better separation among local, urban, regional and transboundary sources. During shorter periods, the aerosol was also collected by means of a streaker sampler and PIXE (Particle Induced X-ray Emission) analysis of these samples allowed the assessment of hourly resolution elemental time trends. Positive matrix factorisation (PMF) identified seven main sources: traffic, biomass burning, secondary sulphate, secondary nitrates, urban dust, Saharan dust and marine aerosol. Traffic mass concentration contributions were found to be strong only at the traffic site (~8 μg·m−3, 33% of PM2.5). Biomass burning turned out to be an important PM2.5 source in Florence (~4 μg·m−3), with very similar weights in both city sites while at the regional background site its weight was negligible. Secondary sulphate is an important PM2.5 source on a regional scale, with comparable values in all three sites (~3.5 μg·m−3). On average, the contribution of the "natural" components (e.g., mineral dust and marine aerosols) to PM2.5 is moderate (~1 μg·m−3) except during Saharan dust intrusions where this contribution is higher (detected simultaneously in all three sites). High-time resolution data confirmed and reinforced these results. [ABSTRACT FROM AUTHOR]

The Arctic is an important natural laboratory that is extremely sensitive to climatic changes and its monitoring is, therefore, of great importance. Due to the environmental extremes it is often hard to deploy sensors and observations are limited to a few sparse observation points limiting the spatial and temporal coverage of the Arctic measurement. Given these constraints the possibility of deploying a rugged network of low-cost sensors remains an interesting and convenient option. The present work validates for the first time a low-cost sensor array (AIRQino) for monitoring basic meteorological parameters and atmospheric composition in the Arctic (air temperature, relative humidity, particulate matter, and CO2). AIRQino was deployed for one year in the Svalbard archipelago and its outputs compared with reference sensors. Results show good agreement with the reference meteorological parameters (air temperature (T) and relative humidity (RH)) with correlation coefficients above 0.8 and small absolute errors (≈1 °C for temperature and ≈6% for RH). Particulate matter (PM) low-cost sensors show a good linearity (r2 ≈ 0.8) and small absolute errors for both PM2.5 and PM10 (≈1 µg m−3 for PM2.5 and ≈3 µg m−3 for PM10), while overall accuracy is impacted both by the unknown composition of the local aerosol, and by high humidity conditions likely generating hygroscopic effects. CO2 exhibits a satisfying agreement with r2 around 0.70 and an absolute error of ≈23 mg m−3. Overall these results, coupled with an excellent data coverage and scarce need of maintenance make the AIRQino or similar devices integrations an interesting tool for future extended sensor networks also in the Arctic environment. [ABSTRACT FROM AUTHOR]