[Algorithm for assessment of exposure to asbestos]. / Proposta di algoritmo per la valutazione stimata dell'esposizione lavorativa ad amianto.

There is no universally approved method in the scientific literature to identify subjects exposed to asbestos and divide them in classes according to intensity of exposure. The aim of our work is to study and develope an algorithm based on the findings of occupational anamnestical information provided by a large group of workers. The algorithm allows to discriminate, in a probabilistic way, the risk of exposure by the attribution of a code for each worker (ELSA Code--work estimated exposure to asbestos). The ELSA code has been obtained through a synthesis of information that the international scientific literature identifies as the most predictive for the onset of asbestos-related abnormalities. Four dimensions are analyzed and described: 1) present and/or past occupation; 2) type of materials and equipment used in performing working activity; 3) environment where these activities are carried out; 4) period of time when activities are performed. Although it is possible to have informations in a subjective manner, the decisional procedure is objective and is based on the systematic evaluation of asbestos exposure. From the combination of the four identified dimensions it is possible to have 108 ELSA codes divided in three typological profiles of estimated risk of exposure. The application of the algorithm offers some advantages compared to other methods used for identifying individuals exposed to asbestos: 1) it can be computed both in case of present and past exposure to asbestos; 2) the classification of workers exposed to asbestos using ELSA code is more detailed than the one we have obtained with Job Exposure Matrix (JEM) because the ELSA Code takes in account other indicators of risk besides those considered in the JEM. This algorithm was developed for a project sponsored by the Italian Armed Forces and is also adaptable to other work conditions for in which it could be necessary to assess risk for asbestos exposure.

Removal and degradation of aromatic compounds from a highly polluted site by coupling soil washing with photocatalysis.

The possible application of two environmental remediation technologies - soil washing and photocatalysis - to remove and decompose various aromatic pollutants present in excavated soils of a contaminated industrial site has been investigated. Aqueous solutions containing the non-ionic surfactant Brij 35 were used to extract the contaminants from the soil samples. The photocatalytic treatment of the obtained washing wastes, performed in the presence of TiO(2) suspensions irradiated with simulated sunlight, showed a slow abatement of the toxic compounds due to the relevant concentrations of organics in the waste. A neat improvement of the process performances, obtained by operating in the presence of added potassium peroxydisulfate, suggests a feasible treatment route.

Ionic strength dependence of formation constants-XVIII. The hydrolysis of iron(III) in aqueous KNO(3) solutions.

The hydrolysis of iron(III) was studied potentiometrically at different ionic strengths in KNO(3) aqueous solutions, at 25 degrees C, to determine the dependence of hydrolysis constants on ionic strength (nitrate media), to check the existence of nitrate-ferric ion interactions, and to confirm the formation of high polymeric species. Under the experimental conditions 0.03 I (KNO(3)) 1M, 0.3 C 12 mM, the species Fe(OH)(2+), Fe(2)(OH)(4+)(2), Fe(OH)(+)(2) and Fe(12)(OH)(2+)(34) were found, and the hydrolysis constants log beta(11) = 2.20, log beta(12) = -2.91, log beta(22) = -5.7, log beta(12,34) = -48.9 (I = 0M) were calculated. The ionic strength dependence of hydrolysis constants is quite close to that found for several protonation and metal complex formation constants reported elsewhere.

Preconcentration and selective metal ion separation using chelating micelles.

Chelating aggregates consisting of Triton X100 host micelles and hydrophobic derivatives of PAN have been examined as suitable candidates for preconcentration and selective separation of transition metal ions through micellar-enhanced ultrafiltration. The effective accumulation in the surfactant-rich retentate of nickel(II), copper(II), cobalt(II), manganese(II) and zinc(II), present at trace levels in aqueous samples, has been achieved by operating at pH ca. 6 with a ligand having a binding constant to the host micelles higher than 2000 l./mol. The efficient separation of micelle-bound metal chelates from unreactive ions has been assessed, together with the feasibility of selective enrichment and purification of the investigated metal ions present in mixtures through a multistage process.

Microwave-assisted extraction of polycyclic aromatic hydrocarbons from marine sediments using nonionic surfactant solutions.

Microwave-assisted micellar extraction (MAME) has been tested for the recovery of polycyclic aromatic hydrocarbons (PAHs) present in samples of marine sediments. An aqueous solution of the nonionic surfactant polyoxyethylene(23)dodecyl ether (Brij 35) was employed as the extracting medium. The proposed approach showed recovery efficiencies comparable to those afforded by the Soxhlet technique with organic solvents, but a neat reduction of the extraction times and a better reproducibility were observed. A MAME-based protocol was successfully applied for the analysis of a certified sample.

Characterization of ligand sites on natural sediment particles.

Organic and inorganic ligand sites on sediment particles were alkalimetrically titrated using a glass electrode as indicating device. Data obtained for suspensions containing known masses of sediment were used to calculate the concentration of surface ligand sites and their stability constants for complex formation with proton and copper(II) ion. The relationship between the concentration of ligand sites and the concentrations of metals (Cd, Cr, Cu, Fe, Mn, Pb, and Zn) and of C, N, and S was used to try to discriminate between the contributions of organic and inorganic components to the total ligand capacity of the sediment. The reliability of the chemical model deduced from potentiometric data was checked by comparing calculated values for aqueous copper(II) as a function of pH with values experimentally determined via atomic absorption spectrometry. The procedure proposed might contribute to the modeling of sediment-contaminant interaction, providing information on the nature of the ligands involved.