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Chemical mass balance modeling (CMB) was applied to determine the PM10 sources and their contributions. PM10 samples were collected in Lecce (40.338N, 18.108E, a town of South Italy), during two monitoring campaigns performed on July 2005 and February 2006. Nine source profiles and average mass concentration of the following chemical parameters: elemental carbon (EC), organic carbon (OC), chlorine (Cl-), nitrate (NO3-), sulfate (SO42-), sodium (Na+), ammonium (NH4+), potassium (K+), magnesium (Mg2+), calcium (Ca2+), aluminum (Al), silicon (Si), titanium (Ti), vanadium (V), manganese (Mn), iron (Fe), copper (Cu), lead (Pb), and zinc (Zn) were used to run the CMB model. The results obtained by application of CMB8.2 are shown. The contributions to PM10 show that dominant contributor was traffic with 37% followed by petroleum industry with 19% and field burning with 16%. Minor source contributions were marine aerosol (1%), ammonium sulfate production (4%), ammonium nitrate production (11%), oil-fired power plant (0.1%), gypsum handling (10%) and crustal (2%). Moreover, the Absolute Principal Component Scores (APCS) model was applied to the PM10 samples collected in order to find a correlation between the two source profile sets. With APCS model five source profiles were found and a good correlation (correlation coefficient bigger than 0.8) between crustal, marine, industrial profiles of CMB model and the corresponding ones of APCS model was found.

The human activities in their various aspects cause a change in the natural air quality. This change results more marked in very populated and in high industrialized areas. Some pollutants emitted are typical of a particular activity. Each source of pollution is identified by its profile in the composition of the emissions in the environment. Multivariate receptor models can be used in order to apportion pollutants to the different sources assessing the contribution of each source to the total pollution. This paper deals with the application of Absolute Principal Component Scores (APCS) receptor model to data obtained from the automatic network of air quality monitoring in the city of Bari (South Italy). The parameters monitored by automatic networks, as bihourly values, are PM10, NOx, CO, Benzene, Toluene, Xilene. The data shown in this paper concerning 1 month almost of sampling in different monitoring stations of Bari Municipality during the period of time from January 2005 to April 2006. Moreover preliminary results obtained applying the APCS model to daily PM2.5 samples collected during SITECOS PRIN project are shown. The results concerning data collected in corso Cavour (Bari) during the month of October 2005. The results obtained by APCS receptor model seem to suggest a poor contribution of the “vehicular traffic source” and a relevant contribution of the “secondary particulate source” to particulate matter concentrations.

A method for the determination of Cu(II), Ni(II), Zn(II), Co(II), Fe(II), and Pd(II) at ppb/subppb levels by ion chromatography was developed, improving a previous work of the same authors. In order to lower the detection limits, the direct injection of a large sample volume (5 mL) and 4‐(2‐pyridylazo) resorcinol solution at pH 6, with hexadecylpyridinium chloride as the post‐column reagent were used. The obtained calibration curves were linear (for each metal R2≥0.99) with good reproducibility; the detection limits for Cu(II), Ni(II), Zn(II), Co(II), Fe(II), and Pd(II) were 1.1, 0.46, 39, 0.18, 4.5, and 1.7 ppb, respectively.