A PM10 chemically characterised nation-wide dataset for Italy. Geographical influence on urban air pollution and source apportionment

: Urban textures of the Italian cities are peculiarly shaped by the local geography generating similarities among cities placed in different regions but comparable topographical districts. This suggested the following scientific question: can such different topographies generate significant differences on the PM10 chemical composition at Italian urban sites that share similar geography despite being in different regions? To investigate whether such communalities can be found and are applicable at Country-scale, we propose here a novel methodological approach. A dataset comprising season-averages of PM10 mass concentration and chemical composition data was built, covering the decade 2005-2016 and referring to urban sites only (21 cities). Statistical analyses, estimation of missing data, identification of latent clusters and source apportionment modelling by Positive Matrix Factorization (PMF) were performed on this unique dataset. The first original result is the demonstration that a dataset with atypical time resolution can be successfully exploited as an input matrix for PMF obtaining Country-scale representative chemical profiles, whose physical consistency has been assessed by different tests of modelling performance. Secondly, this dataset can be considered a reference repository of season averages of chemical species over the Italian territory and the chemical profiles obtained by PMF for urban Italian agglomerations could contribute to emission repositories. These findings indicate that our approach is powerful, and it could be further employed with datasets typically available in the air pollution monitoring networks

Pancreatic hyperamylasemia during acute gastroenteritis: incidence and clinical relevance

BACKGROUND: Many case reports of acute pancreatitis have been reported but, up to now, pancreatic abnormalities during acute gastroenteritis have not been studied prospectively. OBJECTIVES: To evaluate the incidence and the clinical significance of hyperamylasemia in 507 consecutive adult patients with acute gastroenteritis. METHODS: The clinical significance of hyperamylasemia, related predisposing factors and severity of gastroenteritis were assessed. RESULTS: Hyperamylasemia was detected in 10.2 % of patients studied. Although amylasemia was found over four times the normal values in three cases, the clinical features of acute pancreatitis were recorded in only one case (0.1%). Hyperamylasemia was more likely (17%) where a microorganism could be identified in the stools (p < 0.01). Among patients with positive stool samples, Salmonella spp. and in particular S. enteritidis, was the microorganism most frequently associated with hyperamylasemia [17/84 (20.2 %) and 10/45 (22.2%), respectively], followed by Rotavirus, Clostridium difficile and Campylobacter spp. Patients with hyperamylasemia had more severe gastroenteritis with an increased incidence of fever (80 % vs 50.6 %, O.R. 3.0; P < 0.01), dehydration (18% vs 8.5%; O.R. 2.5; P < 0.05), and a higher mean number of evacuations per day (9.2 vs 7.5; P < 0.05) than those with amylasemia in the normal range. Hyperamylasemia was significantly associated with cholelithiasis, (30.0 % vs 10.7%, O.R. 3.5; P < 0.01) and chronic gastritis or duodenal ulceration (22.0 % vs 10.2%, O.R. 2.4, P < 0.05). CONCLUSIONS: Hyperamylasemia is relatively frequent, and is associated with severe gastroenteritis. However, acute pancreatitis in the setting of acute gastroenteritis, is a rare event

Long-term efficacy of dual nucleoside reverse transcriptase inhibitor antiretroviral therapy in HIV-1 infection.

Long-term efficacy of dual nucleoside reverse transcriptase inhibitor antiretroviral therapy in HIV-1 infection

Apportioning PM1 in a contrasting receptor site in the Mediterranean region: Aerosol sources with an updated sulfur speciation

A multi-parametric experimental campaign was performed in Agri Valley (Basilicata, southern Italy) from July 2017 to January 2018. The investigated area, though basically rural and devoted to agricultural activities, hosts a huge onshore oil reservoir, i.e. Centro Olio Val d'Agri (COVA), bringing substantial environmental modifications and impacts to the district landscape. Daily concentrations of PM1 aerosol samples, Equivalent Black Carbon and number size distributions were evaluated. Chemical aerosol speciation based on elemental and ion analyses were carried out and source apportionment by Positive Matrix Factorization (PMF) was applied to reconstruct PM1 source profile. The most significant emission sources found are torches from the oil treatment facility (37 % w/w), an unresolved factor constituted by soil resuspension, Saharan dust, and biomass burning (24 % w/w), ammonium sulphate (23 % w/w), emissions from the oil desulfurization (Claus process) (13 % w/w), and traffic +road dust (3 % w/w). SEM analysis on PM1 single particles allowed to confirm the finding from PMF including the occurrence of elemental sulfur associated with the Claus process. The novelty of the present study consists in the identification of this latter fingerprint

A PM10 chemically characterized nation-wide dataset for Italy. Geographical influence on urban air pollution and source apportionment

Urban textures of the Italian cities are peculiarly shaped by the local geography generating similarities among cities placed in different regions but comparable topographical districts. This suggested the following scientific question: can different topographies generate significant differences on the PM10 chemical composition at Italian urban sites that share similar geography despite being in different regions? To investigate whether such communalities can be found and are applicable at Country-scale, we propose here a novel methodological approach. A dataset comprising season-averages of PM10 mass concentration and chemical composition data was built, covering the decade 2005–2016 and referring to urban sites only (21 cities). Statistical analyses, estimation of missing data, identification of latent clusters and source apportionment modeling by Positive Matrix Factorization (PMF) were performed on this unique dataset. The first original result is the demonstration that a dataset with atypical time resolution can be successfully exploited as an input matrix for PMF obtaining Country-scale representative chemical profiles, whose physical consistency has been assessed by different tests of modeling performance. Secondly, this dataset can be considered a reference repository of season averages of chemical species over the Italian territory and the chemical profiles obtained by PMF for urban Italian agglomerations could contribute to emission repositories. These findings indicate that our approach is powerful, and it could be further employed with datasets typically available in the air pollution monitoring networks