Search

Emissions of atmospheric pollutants from ships and harbour activities are a growing concern at international level given their potential impacts on air quality and climate. These close-to-land emissions have potential impact on local communities in terms of air quality and health. Recent studies show that the impact of maritime traffic to atmospheric particulate matter concentrations in several coastal urban areas is comparable with the impact of road traffic of a medium size town. However, several different approaches have been used for these estimates making difficult a direct comparison of results. In this work, an integrated approach based on emission inventories and dedicated measurement campaigns has been applied to give a comparable estimate of the impact of maritime traffic to PM2.5 and particle number concentrations in three major harbours of the Adriatic/Ionian Seas. The influences of local meteorology and of the logistic layout of the harbours are discussed., {"references":["M. Zhao, Y. Zhang, W. Maa, Q. Fu, X. Yang, C. Li, B. Zhou, Q. Yua, L.\nChen, \"Characteristics and ship traffic source identification of air\npollutants in China's largest port,\" Atmospheric Environment, Vol. 64,\npp. 277–286, 2013.","V. Eyring, I.S.A. Isaksen, T. Berntsen, W.J. Collins, J.J. Corbett, O.\nEndresen, R.G. Grainger, J. Moldanova, H. Schlager, D.S. Stevenson,\n\"Transport impacts on atmosphere and climate: shipping.\", Atmospheric\nEnvironment, Vol. 44, pp. 4735–4771, 2010.","H. Saxe, T. Larsen, \"Air pollution from ships in three Danish ports\",\nAtmospheric Environment, Vol. 38, pp. 4057–4067, 2004.","A.J. Dore, M. Vieno, Y.S. Tang, U. Dragosits, A. Dosio, K.J. Weston,\nM.A. Sutton, A. McCreddin, \"Modelling the atmospheric transport and\ndeposition of sulphur and nitrogen over the United Kingdom and\nassessment of the influence of SO2 emissions from international\nshipping\", Atmospheric Environment, Vol. 41, pp. 2355–2367, 2007.","S.B. Dalsøren, M.S. Eide, Ø. Endresen, A. Mjelde, G. Gravir, I.S.A.\nIsaksen, \"Update on emissions and environmental impacts from the\ninternational fleet of ships: the contribution from major ship types and\nports\", Atmospheric Chemistry and Physics, Vol. 9, pp. 2171–2194,\n2009.","C. Trozzi, R. Vaccaro, L. Nicolo, \"Air pollutants emissions estimate\nfrom maritime traffic in the Italian harbours of Venice and Piombino\",\nScience of the Total Environment, Vol. 169, pp. 257–263, 1995.","C. Gariazzo, V. Papaleo, A. Pelliccioni, G. Calori, P. Radice, G.\nTinarelli, \"Application of a Lagrangian particle model to assess the\nimpact of harbour, industrial and urban activities on air quality in the\nTaranto area, Italy\", Atmospheric Environment, Vol. 41, pp. 6432-6444,\n2007.","E. Marmer, F. Dentener, J. Van Aardenne, F. Cavalli, E. Vignati, K.\nVelchev, J. Hjorth, F. Boersma, G. Vinken, N. Mihalopoulos, F. Raes,\n\"What can we learn about ship emission inventories from measurements\nof air pollutants over the Mediterranean Sea?\", Atmospheric Chemistry\nand Physics, Vol. 9, pp. 6815–6831, 2009.","D. Cesari, A. Genga, P. Ielpo, M. Siciliano, G. Mascolo, F.M. Grasso,\nD. Contini, \"Source apportionment of PM2.5 in the harbour–industrial\narea of Brindisi (Italy): Identification and estimation of the contribution\nof in-port ship emissions\", Science of the Total Environment, Vol. 497,\npp. 392-400, 2014.\n[10] M. C. Minguillon, M. Arhami, J. J. Schauer, C. Sioutas,\"Seasonal and\nSpatial Variations of Sources of Fine and Quasi-Ultrafine Particulate\nMatter in Neighborhoods Near the Los Angeles-Long Beach Harbor\",\nAtmospheric Environment, Vol. 42 (32), pp. 7317–7328, 2008.\n[11] A. M. Stortini, A. Freda, D. Cesari, W. R. L. Cairns, D. Contini, C.\nBarbante, F. Prodi, P. Cescon, A. Gambaro, \"An evaluation of the\nPM2.5 trace elemental composition in the Venice Lagoon area and an\nanalysis of the possible sources\", Atmospheric Environment, Vol. 43,\npp. 6296-6304, 2009.\n[12] M. Viana, F. Amato, A. Alastuey, X. Querol, T. Moreno, S. G. Dos\nSantos, M. D. Herce, R. Fernandez-Patier, \"Chemical tracers of\nparticulate emissions from commercial shipping\", Environmental\nScience & Technology, Vol. 43, pp. 7472-7477, 2009.\n[13] M. Pandolfi, Y. Gonzalez-Castanedo, A. Alastuey, J. de la Rosa, E.\nMantilla, A. Sanchez de la Campa, X. Querol, J. Pey, F. Amato, T.\nMoreno, \"Source apportionment of PM10 and PM2.5 at multiple sites in\nthe strait of Gibraltar by PMF: impact of shipping emissions\",\nEnvironmental Science and Pollution Research, Vol. 28, pp. 260-269,\n2011.\n[14] M. D. Gibson, J. R. Pierce, D. Waugh, J. S. Kuchta, L. Chisholm, T. J.\nDuck, J. T. Hopper, S. Beauchamp, G. H. King, J. E. Franklin, W. R.\nLeaitch, A. J. Wheeler, Z. Li, G. A. Gagnon, P. I. Palmer, \"Identifying\nthe sources driving observed PM2.5 temporal variability over Halifax,\nNova Scotia, during BORTAS-B\", Atmospheric Chemistry and Physics,\nVol. 13, pp. 7199-7213, 2013.\n[15] C. Samara, D. Voutsa, \"Size distribution of airborne particulate matter\nand associated heavy metals in the roadside environment\",\nChemosphere, Vol. 59, pp. 1197–1206, 2005.\n[16] G. Lu, J. R. Brook, M. R. Alfarra, K. Anlauf, W. R. Leaitch, S. Sharma,\nD. Wang, D. R. Worsnop, L. Phinney, \"Identification and\ncharacterisation of inland ship plumes over Vancouver, BC\".\nAtmospheric Environment, Vol. 40, pp. 2767-2782, 2006.\n[17] R. M. Healy, S. Hellebust, I. Kourtchev, A. Allanic, I. P. O'Connor, J.\nM. Bell, D. A. Healy, J. R. Sodeau, J. C. Wenger, \"Source\napportionment of PM2.5 in Cork Harbour, Ireland using a combination\nof single particle mass spectrometry and quantitative semi-continuous\nmeasurements\", Atmospheric Chemistry and Physics, Vol. 10, pp. 9593-\n9613, 2010.\n[18] D. Contini, A. Gambaro, F. Belosi, S. De Pieri, W. R. L. Cairns, A.\nDonateo, E. Zanotto, M. Citron, \"The direct influence of ship traffic on\natmospheric PM2.5, PM10 and PAH in Venice\", Journal of\nEnvironmental Management, Vol. 92, pp. 2119-2129, 2011.\n[19] A. Donateo, E. Gregoris, A. Gambaro, E. Merico, R. Giua, A. Nocioni,\nD. Contini, \"Contribution of harbour activities and ship traffic to PM2.5,\nparticle number concentrations and PAHs in a port city of the\nMediterranean Sea (Italy)\", Environmental Science and Pollution\nResearch, Vol. 21, pp. 9415-9429, 2014.\n[20] M. Viana, P. Hammingh, A. Colette, X. Querol, B. Degraeuwe, I. de\nVlieger, J. Van Aardenne, \"Impact of maritime transport emissions on\ncoastal air quality in Europe\", Atmospheric Environment, Vol. 90, pp.\n96–105, 2014. [21] A. Donateo, D. Contini, F. Belosi, A. Gambaro, G. Santachiara, D.\nCesari, F. Prodi, \"Characterization of PM2.5 concentrations and\nturbulent fluxes on a island in the Venice lagoon using high temporal\nresolution measurements\", Meteorology Zeitschrift, Vol. 21, pp. 385-\n398, 2012.\n[22] ARPAV, \"Metodi e modelli per l'indagine scientifica su emissioni e\nconcentrazioni in atmosfera: risultati principali per la laguna di\nVenezia\", APICE Project Report, www.apice-project.eu, Febbraio 2013.\n[23] A. Poupkou, N. Liora, A. Karagiannidis, T. Giannaros, C. Giannaros, D.\nMelas, A Argiriou, \"Maritime Sector Emissions Contribution to the\nParticulate Matter Pollution in a Mediterranean City-Port: A Modeling\nApproach\", in Air Pollution Modeling and its Application XXIII,\nSpringer Proceedings in Complexity, pp. 307-310, 2014.\n[24] R. Giua, A. Nocioni, T. Pastore, S. Spagnolo, \"The ARPA Puglia tools\nwithin the project CESAPO: a contribute to the study of atmospheric\npollution in Brindisi port\", In Proceedings of the Workshop\n\"Characterisation of atmospheric pollution in harbor areas\"\n(www.cesapo.upatras.gr), pp. 51-57, 2013.\n[25] A. Donateo, D. Contini, F. Belosi, \"Real time measurements of PM2.5\nconcentrations and vertical turbulent fluxes using an optical detector\",\nAtmospheric Environment, Vol. 40, pp. 1346-1360, 2006.\n[26] R.M. Healy, I.P. O'Connor, S. Hellebust, A. Allanic, J.R. Sodeau, J.C.\nWenger JC, \"Characterisation of single particles from in-port ship\nemissions\", Atmospheric Environment, Vol. 43, pp. 6408-6414, 2009.\n[27] C. Reche, M. Viana, T. Moreno, X. Querol, A. Alastuey, J. Pey, M.\nPandolfi, A. Prévôt, C. Mohr, A. Richard, B. Artiñano, F.J. Gomez-\nMoreno, N. Cots, \"Peculiarities in atmospheric particle number and sizeresolved\nspeciation in an urban area in the western Mediterranean:\nresults from the DAURE campaign\", Atmospheric Environment, Vol.\n45, pp. 5282-5293, 2011.\n[28] D. Contini, A. Gambaro, A. Donateo, P. Cescon, D. Cesari, E. Merico,\nF. Belosi, M. Citron, \"Inter-annual trend of the primary contribution of\nship emissions to PM2.5 concentrations in Venice (Italy): Efficiency of\nemissions mitigation strategies\", Atmospheric Environment, Vol. 102,\npp. 183-190, 2015."]}

T06A195P

Atti curati da F. Forget, M.A. Lopez-Valverde, M.C. Desjean, J.P. Huot, F. Lefevre, S. Lebonnois, S.R. Lewis, E. Millour, P.L. Read e R.J. Wilson

Airborne particulate matter (PM) is studied because of its effects on human health and climate change. PM long-term characterisation allows identifying trends and evaluating the outcomes of environmental protection policies. This work is aimed to study the inter-annual variability of PM2.5 and PM10 concentrations and chemical composition in an urban background site (Italy). A dataset of daily PM2.5 and PM10 was collected in the period 2016-2017, including the content of OC, EC, major water-soluble ions, main metals, and compared to a similar dataset collected in the period 2013-2014. Oxidative potential using DTT assay (dithiothreitol) was evaluated and expressed in DTTV as 0.39nmol/min·m3 in PM10 and 0.29 in PM2.5nmol/min·m3. PM source apportionment was computed using the EPA PMF5.0 model and source contributions compared with those of a previous dataset collected between 2013 and 2014. Multi linear regression analysis identified which source contributed (p&nbsp

Long and short-term exposure to atmospheric particulate matter (PM) has detrimental effects on human health. The effective mechanisms leading to PM toxicity are still not fully understood, even if it is known that physical-chemical properties, strongly influenced by sources and atmospheric processes, are known to play an important role. In this work, PM10 samples were collected, at an urban background site in southern Italy, to determine cytotoxicity (using MTT test on A549 cells), genotoxicity (using the comet assay), and intracellular oxidative stress on A549 cells exposed for 24 h to aqueous extracts of PM10 samples. Organic carbon (OC) and elemental carbon (EC) content of PM10 and acellular determination of oxidative potential with DTT assay were performed to compare results of acellular and cellular biological assays. Cellular (OSGCV and MTTV) and acellular (OPDTTV) outcomes, normalized in volume, are well correlated (statistically significant results) with carbon content suggesting that combustion sources play an important role in determining cellular oxidative stress and cytotoxicity of PM10. Even if the number of data is limited, genotoxicity results are well correlated (Pearson r &gt, 0.95) with OSGCV and MTTV, and a weaker, but statistically significant correlation was observed with OPDTTV. OSGCV is well correlated with the cell mortality observed with the MTTV test and a lower, but still statistically significant correlation is observed between MTTV and OPDDTV. A statistically significant correlation was found between OPDTTV and OSGCV results. When the outcomes of the cellular and acellular assay are compared normalized in mass (i.e., intrinsic values), the correlations become significantly weaker suggesting that the different sources acting on the site produces particulate matter with different toxicological potential influencing differently the biological tests studied.

Atmospheric particlulate matter (PM) is an heterogeneuous and dynamic mixture of particles with different origins, and its harmful effects are more strongly linked to its size and chemical composition than to its mass. The capability of some chemical compounds, present in airborne particulate matter, to generate reactive oxygen species (ROS) is known as “oxidative potential” (OP), and it represents a useful indicator of PM toxicity. The high production of ROS causes oxidative damages to cells that ultimately can lead to different chronic diseases. Although numerous studies were performed in the last years, however the biological mechanisms of toxicity and their relation with the chemical and physical properties of particulate matter are still largely unknown and futher research is need. In this work the oxidative potential of the water-soluble fraction of PM2.5 and PM10, collected at an urban background site in Southern Italy was investigated. The analysis of the OP were performed using the dithiothreitol (DTT) assay, a surrogate for cellular antioxidants, which analyses the rate of DTT depletion catalysed by chemical species present in the PM. Organic and Elemental carbon content was determined on all the collected samples in order to study the correlation between carbonaceous fractions and oxidative potential as well as to quantify the contribution of the combustion sources. DTT activity normalised by sampled air volume (OPV) and normalised by collected aerosol mass (OPM) were investigated. Results showed a seasonal trend in PM concentrations, in both size fractions, with larger concentrations during the cold seasons compared to the warm seasons. The same trend was also observed in carbonaceous faraction. OPV showed a similar trend with values larger in PM10 compared to PM2.5 as well as greater OPV values was observed in high carbon content samples. OPM was instead larger for PM2.5 than PM10. Also, OPV was well correlated both with mass concentration of PM and carbonaceous fractions, while no significant correlation has been observed with OPM

Exposure to atmospheric particulate matter (PM) leads to adverse health effects and the exact mechanisms of toxicity are still not completely understood. Recent studies suggested that a large number of PM health effects could be due to the oxidative potential (OP) of ambient particles leading to high concentrations of reactive oxygen species (ROS). However, information about the OP of natural sources are scarce and no data is available regarding the OP during Saharan dust outbreaks (SDO) in Mediterranean regions. This work uses the a-cellular DTT (dithiothreitol) assay to evaluate OP of the water-soluble fraction of PM2. 5 and PM10 collected at an urban background site in Southern Italy

The Fourier transform infrared (FTIR) spectroscopy allows the measurement of absorption peaks, due to vibrational transitions of individual chemical bonds, leading to the identification and quantification of main functional groups in a complex system. A number of investigations reporting the FTIR spectra of airborne aerosol samples have appeared in the last years (Coury and Dillner, 2009). In the attenuated total reflectance (ATR) mode, the sample is placed directly on a transparent crystal with a high refractive index, through which the IR beam is passed. The spectrum is then measured detecting the attenuation of the light reflected within the crystal and delivered to the detector. The path length depends solely on the penetration depth of the evanescent wave beyond the interface; therefore, the technique is suitable for analysing thin solid films with high sensitivity and repeatability. In this work, we have employed the ATR-FTIR technique for analysing thin solid films of particulate matter (PM) components, extracted from filter-deposited aerosol material, by solubilisation in deionized water. The goal was the identification of functional groups, soluble in aqueous biological fluids, able to influence the toxicological potential of airborne particles, in order to elucidate the relationships between PM chemical characteristics and human health effects.

Organic (OC) and elemental carbon (EC), inorganic ions (Cl−, NO3−, SO42−, Na+, NH4+, K+, Ca2+), methanesulfonate (MSA−) and metals (Al, Fe, Pb, Mn, Ba, V) were monitored in PM1 and PM2.5 samples collected at a suburban site in south-eastern Italy, to contribute to the characterisation of fine particles in the Central Mediterranean. Mean mass concentrations are 13 µg/m3 and 22 µg/m3 in PM1 and PM2.5, respectively. OC, EC, SO42−, NH4+, NO3−, K+ and Ca2+ are predominant components and account for 54% and 56% of the PM1 and PM2.5 mass, respectively. OC, EC, SO42−, NH4+, K+ and Ca2+ concentrations lie in the range of the corresponding ones measured in PM1 and PM2.5 samples collected at suburban/urban Mediterranean sites. NO3− and trace element concentrations lie in the range of the corresponding ones measured in PM1 and PM2.5 samples collected at remote/background Mediterranean sites. The biogenic nss-SO42− accounts for ~5% and 4% of nss-SO42− in PM1 and PM2.5, respectively. The seasonal trend of the component...

Water soluble ions, methanesulfonate, organic and elemental carbon, and metals in PM2.5 and PM1 samples were analysed by Positive Matrix Factorization to identify and quantify major sources of fine particles at a Central Mediterranean site. The cluster analysis of four-day back trajectories was used to determine the dependence of PM2.5 and PM1 levels and composition on air-flows. The cluster analysis has identified six, six, and seven distinct air-flow types arriving at 500, 1500, and 3000 m above sea level (asl), respectively. Slow-west (Wslow) and north-eastern (NE) flows at 500 and 1500 m asl were the most frequent and were associated with the highest PM2.5 and PM1 concentrations. The PM concentrations from combustion sources including biomass burning were at their maximum under north-western (NW) flows. Similarly, the ammonium sulphate source was enhanced under Wslow and NE flows. South-eastern Mediterranean Sea air-flows were associated with the highest PM2.5 concentrations due to the heavy-oil-combustion source and the highest PM2.5 and PM1 concentrations due to the secondary marine source. PM2.5 concentrations due to the reacted dust and traffic source and PM1 concentrations due to the nitrate with reacted dust and mixed anthropogenic source showed no clear dependence on air-flows. This work highlights the different impact of aerosol sources on PM2.5 and PM1 fractions, being PM1 more adequate to control anthropogenic emissions from combustion sources.

Aerosol optical thicknesses (AOTs) by the MODerate Resolution Imaging Spetroradiometer (MODIS) on-board Aqua and Terra satellites, and ground-based measurements of PM10 mass concentrations, collected over three years (2006–2008) at two suburban sites which are 20 km apart, are correlated to assess the use of satellite data for regional air quality studies over Southeastern Italy, in the central Mediterranean. Due to the geographical location, this area is affected by local and long-range transported marine, desert (from Sahara), and anthropogenic (from continental Europe) aerosols. 24-hour averaged PM10 mass concentrations span the 1.6–152 µg/m 3 range. Yearly means of PM10 mass concentrations decrease from 2006 to 2008 and vary within the 26–36 µg/m 3 range. Daily mean values of MODIS AOTs vary up to 0.8 at 550 nm, while yearly means span the 0.15–0.17 range. A first assessment of the regression relationship between daily averaged PM10 mass concentrations and MODIS-AOTs shows that linear correlation coefficients ( R ) vary within the 0.20–0.35 range and are affected by the sampling year and the site location. The PM10-AOT correlation becomes stronger (0.34 ≤ R ≤ 0.57) when the analysis is restricted to clear-sky MODIS measurements. The cloud screening procedure adopted within the AERONET network is used in this study to select clear-sky MODIS measurements, since it allows obtaining larger R values than the ones obtained using the cloud fraction MODIS product to select clear-sky MODIS measurements. Using three years of clear-sky measurements to estimate PM10 mass concentrations from MODIS-AOTs, the empirical relation we have found is: PM10 ( m g/m 3 ) = 25 ( m g/m 3 ) + 65 ( m g/m 3 ) × AOT. Over 80% of the differences between the measured and satellite estimated PM10 mass concentrations over the three years are within ±1 standard deviation of the yearly means. The differences between yearly means of calculated and measured mass concentrations that are close to zero in 2006, increase up to 4 m g/m 3 at one siteand 8 m g/m 3 at the other site in 2008. The PM10 mass concentration decrease from 2006 to 2008 contributes to this last result. Our results demonstrate the potential of MODIS data for deriving indirect estimates of PM10 over Southeastern Italy. It is also shown that a stronger relationship between PM10 and MODIS-AOTs is obtained when the AOT is divided by the product of the mixing layer height with the ground wind speed and the analysis restricted to clear sky MODIS measurements. However, we have found that the stronger correlation (0.52 ≤ R ≤ 0.66) does not allow a significant improvement of MODIS-based-estimates of PM10 mass concentrations.