1,721,173 research outputs found
Spatio-temporal analysis of PM10 concentrations in the Taranto area
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Major PM10 source location by a spatial multivariate receptor model
No full textIn this paper, we present a multivariate receptor model for identifying the spatial location of major PM10 pollution sources. We build on a mixed multiplicative log-normal factor model adjusting the source contributions for meteorological covariates and for temporal correlation and considering source profiles as compositional Gaussian random fields, to account for the variability induced by the spatial distribution of the monitoring sites. Taking a Bayesian approach to estimation, the proposed model is implemented and used to analyze average daily PM10 concentration measurements from 13 monitoring sites in Taranto, Italy, for the period April-December 2005. Three major sources of pollution are identified and characterized in terms of their spatial and temporal behavior and in relation to meteorologic data.
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Two Approaches to Imputation and Adjustment of Air Quality Data from a Composite Monitoring Network
Full text linkAn analysis of air quality data is provided for the municipal area of Taranto characterized by high environmental risks, due to the massive presence of industrial sites with elevated environmental impact activities. The present study is focused on particulate matter as measured by PM10 concentrations. Preliminary analysis involved addressing several data problems, mainly: (i) an imputation techniques were considered to cope with the large number of missing data, due to both different working periods for groups of monitoring stations and occasional malfunction of PM10 sensors; (ii) due to the use of different validation techniques for each of the three monitoring networks, a calibration procedure was devised to allow for data comparability. Missing data imputation and calibration were addressed by three alternative procedures sharing a leave-one-out type mechanism and based on {\it ad hoc} exploratory tools and on the recursive Bayesian estimation and prediction of spatial linear mixed effects models. The three procedures are introduced by motivating issues and compared in terms of performance
A multivariate approach to the analysis of air quality in a high environmental risk area
No full textThis study analyzes air quality data in the Taranto municipal area. This is a high environmental risk region being characterized by the massive presence of industrial sites with elevated environmental impact activities. We focus on three pollutants formed by combustion processes and related to meteorological conditions, namely PM10, SO2 and NO2. Preliminary analysis involved addressing several data problems. First of all an imputation technique was considered to cope with the large number of missing data. Missing data imputation was addressed by a leave-one-out procedure based on the recursive Bayesian estimation and prediction of spatial linear mixed effects models enriched by a time-recursive prior structure. Secondly a unique daily weather database at the city level was obtained combining data from 3 stations, characterized by gaps and unreliable measurements. Spatio-temporal modeling of the multivariate normalized daily pollution data was then performed within a Bayesian hierarchical framework, including time varying weather covariates and a semi-parametric spatial covariance structure. Daily estimates of the pollutants’ concentration surfaces allow to identify areas of higher concentration (hot spots), possibly related to specific anthropic activities.
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Ad hoc exploratory tools for managing air quality data from a composite monitoring network
No full textISBN-978-88-8422-673-
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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