A. Dinoi

Application of MODIS Products for Air Quality Studies Over Southeastern Italy

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.

Seasonal variability of PM2.5 and PM10 composition and sources in an urban background site in Southern Italy

Comparison of fine and coarse fractions in terms of sources and dynamics is scarce in southeast Mediterranean countries; differences are relevant because of the importance of natural sources like sea spray and Saharan dust advection, because most of the monitoring networks are limited to PM10. In this work, the main seasonal variabilities of sources and processes involving fine and coarse PM (particulate matter) were studied at the Environmental-Climate Observatory of Lecce (Southern Italy). Simultaneous PM2.5 and PM10 samples were collected between July 2013 and July 2014 and chemically analysed to determine concentrations of several species: OC (organic carbon) and EC (elemental carbon) via thermo-optical analysis, 9 major ions via IC, and 23 metals via ICP-MS. Data was processed through mass closure analysis and Positive Matrix Factorization (PMF) receptor model characterizing seasonal variabilities of nine sources contributions. Organic and inorganic secondary aerosol accounts for 43% of PM2.5 and 12% of PM2.5-10 with small seasonal changes. SIA (secondary inorganic aerosol) seasonal pattern is opposite to that of SOC (secondary organic carbon). SOC is larger during the cold period, sulphate (the major contributor to SIA) is larger during summer. Two forms of nitrate were identified: NaNO3, correlated with chloride depletion and aging of sea-spray, mainly present in PM2.5-10; NH4NO3 more abundant in PM2.5. Biomass burning is a relevant source with larger contribution during autumn and winter because of the influence of domestic heating, however, is not negligible in spring and summer, because of the contributions of fires and agricultural practices. Mass closure analysis and PMF results identify two soil sources: crustal associated to long range transport and carbonates associated to local resuspended dust. Both sources contributes to the coarse fraction and have different dynamics with crustal source contributing mainly in high winds from SE conditions and carbonates during high winds from North direction.

Chemical composition of PM1 and PM2.5 at a suburban site in southern Italy

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 components partitioning and the interspecies correlation analysis in PM1 and PM2.5-1 indicated that the PM1 and PM2.5-1 components depend on season and are likely not controlled by similar sources, and/or similar generation processes, and/or similar transport patterns. The sulfur and nitrogen oxidation ratios were calculated to contribute to the understanding of the seasonal dependence of nitrate and sulfate concentrations in PM1 and PM2.5-1. The mass closure analysis showed that organic matter (OM), EC, and nitrate mass percentages are larger in autumn–winter. NH4+, nss-SO42−, and crustal matter mass percentages are larger in spring–summer. Finally, the ratio of the crustal matter in PM1 to that in PM2.5-1, which is 0.2 and 0.3 in spring–summer and autumn–winter, respectively, and the higher (OM+EC) contribution in PM1 than in PM2.5-1 led to the conclusion that PM1 would be a better indicator for fine-anthropogenic particles than PM2.5.

Comparison of black carbon concentrations retrieved by AERONET with surface measurements

Lidars are ideally placed to investigate the effects of aerosol and cloud on the climate system due to their unprecedented vertical and temporal resolution. Dozens of techniques have been developed in recent decades to retrieve the extinction and backscatter of atmospheric particulates in a variety of conditions. These methods, though often very successful, are fairly ad hoc in their construction, utilising a wide variety of approximations and assumptions that makes comparing the resulting data products with independent measurements difficult and their implementation in climate modelling virtually impossible. As with its application to satellite retrievals, the methods of non-linear regression can improve this situation by providing a mathematical framework in which the various approximations, estimates of experimental error, and any additional knowledge of the atmosphere can be clearly defined and included in a mathematically ‘optimal’ retrieval method, providing rigorously derived error estimates. In addition to making it easier for scientists outside of the lidar field to understand and utilise lidar data, it also simplifies the process of moving beyond extinction and backscatter coefficients and retrieving microphysical properties of aerosols and cloud particles. Such methods have been applied to a prototype Raman lidar system. A technique to estimate the lidar’s overlap function using an analytic model of the optical system and a simple extinction profile has been developed. This is used to calibrate the system such that a retrieval of the profile extinction and backscatter coefficients can be performed using the elastic and nitrogen Raman backscatter signals.