ABSTRACT
The study of spatial distribution of the indoor radon has assumed in the last years a lot of interest. The geostatistical techniques turn out to be particularly promising. The present work presents the results of a study where around 4000 indoor radon data from Veneto, Friuli Venezia-Giulia and Alto Adige, collected during the sampling campaigns performed in dwellings and in schools, have been analyzed. After the definition of the common data set, the study of the spatial distribution of the phenomenon has been performed by examining the experimental variograms. Declustering techniques have been applied. Predictive maps were defined by using simulation techniques; they allow to determine the probabilities of exceeding defined concentration levels, the 'radon-prone' areas. Systematic results regarding the validation of these maps are reported. This methodological study indicates how it is possible to understand the geographical variability of the phenomenon, trying to find out correlations among indoor radon, geological characteristics (i.e. lithology, morphology, tectonics, soil gas) and building-specific features, which can significantly influence radon concentrations.
Subject(s)
Air Pollution, Indoor/analysis , Air Pollution, Indoor/statistics & numerical data , Air Pollution/analysis , Air Pollution/statistics & numerical data , Data Interpretation, Statistical , Radiation Monitoring/statistics & numerical data , Radon/analysis , Italy , Radiation DosageABSTRACT
Having a reliable forecasting tool is necessary to correctly identify radon prone areas, especially in cases where the variable of interest is the indoor radon concentration. An appropriate characterisation of the features of the buildings becomes fundamental. In this work, the results obtained (in global and local scale) using the following approaches for estimating the concentration of indoor radon at locations that were not sampled were compared: geostatistical model, based on ordinary kriging, and machine learning (ML) technique. In the first case, algorithms designed for the specific and fine treatment (by modelling the variographic structure) of the spatial component of the phenomenon were used, whereas in the second case a model that can also exploit information linked to other variables that characterise each single dwelling in which the measure was conducted was used. For locations having large errors, the ML approach provides better results, due to the information related to 'soil contact' and 'building material'.
Subject(s)
Air Pollution, Indoor/analysis , Air Pollution, Indoor/statistics & numerical data , Air Pollution/analysis , Air Pollution/statistics & numerical data , Artificial Intelligence , Radiation Monitoring/statistics & numerical data , Radon/analysis , Italy , Radiation DosageABSTRACT
In this paper a modern statistical technique of multivariate analysis is applied to an indoor radon concentration data base. Several parameters are more or less significant in determining the radon concentration inside a building. The elaboration of the information available on South Tyrol makes it possible both to identify the statistically significant variables and to build up a statistical model that allows us to forecast the radon concentration in dwellings, when the values of the same variables involved are given. The results confirm the complexity of the phenomenon.
Subject(s)
Air Pollutants, Radioactive/analysis , Air Pollution, Indoor/analysis , Algorithms , Geographic Information Systems , Radiation Monitoring/methods , Radon/analysis , Risk Assessment/methods , Computer Simulation , Forecasting/methods , Italy/epidemiology , Models, Statistical , Multivariate Analysis , Radiation Dosage , Radiometry/methods , Risk Factors , Topography, Medical/methodsABSTRACT
In this paper two different procedures for radon mapping have been compared on the same database referring to indoor radon records of South Tyrol. The first procedure is based on descriptive statistics applied to administrative units while the second one implies a more complicated statistical analysis applied to a regular grid: it involves floor level normalization of radon data, filling and smoothing algorithms for the territory cells.
Subject(s)
Air Pollutants, Radioactive/analysis , Air Pollution, Indoor/analysis , Geographic Information Systems , Radiation Monitoring/methods , Radiometry/methods , Radon/analysis , Risk Assessment/methods , Algorithms , Computer Simulation , Forecasting/methods , Italy/epidemiology , Models, Statistical , Multivariate Analysis , Radiation Dosage , Reproducibility of Results , Risk Factors , Sensitivity and Specificity , Topography, Medical/methodsABSTRACT
The PERS (soil radon exhalation potential) project was promoted by ANPA (Italian Environmental Protection Agency) together with the Università Cattolica del Sacro Cuore of Rome: the aim was to produce a geographic information system allowing the discovery of regions with different radon exhalation potential starting from some territorial knowledge. Some environmental measurements were carried out within this project in selected areas in South-Tyrol and Veneto. The measurement of radon in springwater and groundwater as well as in soil gas plays a decisive role for the validation of the algorithm for computing the PERS. Along with technical aspects, a possible use of the PERS method by the Regional Environmental Protection Agencies and by other agencies is discussed with the scope of identifying radon prone areas, as stated in the Italian 'Decreto Legislativo' 26 May 2000, n. 241. Moreover the forecasting power of PERS regarding indoor radon concentration is analysed.
Subject(s)
Radon/analysis , Soil Pollutants, Radioactive/analysis , Geography , Humans , Italy , Radiation Monitoring/instrumentation , Radiation Monitoring/methods , RiskABSTRACT
This paper describes the isolation, identification and analgesic activity of a new biflavonoid from Rheedia gardneriana leaves, which correspond to I3-naringenin-II8-4'-OMe-eriodictyol (GB-2a-II-4'-OMe) (1), with a methoxyl group in position 4 of ring-II. Its structure was determined by spectroscopic data and confirmed by an alkaline hydrolysis. Its analgesic effect was evaluated in a writhing test and a formalin test in mice. It was found that this compound exhibits potent and dose-related analgesic action in both experimental models, with ID50's values of 4.5 micromol/kg against the writhing test and 8.2 and 6.8 micromol/kg against the first and second phase of the formalin test, respectively. It was several times more potent than some well-known analgesic drugs used as reference.
Subject(s)
Analgesics/chemistry , Biflavonoids , Flavonoids/chemistry , Pain/drug therapy , Plants, Medicinal/chemistry , Acetaminophen/pharmacology , Acetic Acid , Analgesics/isolation & purification , Analgesics/pharmacology , Animals , Aspirin/pharmacology , Dipyrone/pharmacology , Flavanones , Flavonoids/isolation & purification , Flavonoids/pharmacology , Formaldehyde , Indomethacin/pharmacology , Male , Mice , Models, Molecular , Molecular Conformation , Molecular Structure , Pain/chemically induced , Pain/physiopathology , Plant LeavesABSTRACT
Some phytoconstituents present in the R. Gardneriana Pl. Tr. leaves, a Brazilian medicinal plant, were isolated by column chromatography. Their preliminary analgesic effects were evaluated against formalin-induced pain in mice. The results demonstrated that four biflavonoids, identified as volkensiflavone, GB-2 a, fukugetin and fukugeside are the main active components of the ethyl acetate fraction. Such compounds exhibited significative analgesic activity in relation to the second phase (inflammatory pain) of the formalin test, suggesting that they are the compounds responsible for the pharmacological effects previously observed for the hydroalcoholic extract of this plant.
