1,721,172 research outputs found
Random Walk
No full textRandom walks (RW’s) appeared in the mathematical and statistical literature in 1905 when KarlPearson, in a letter to the journal Nature, introduced the name for the first time. They are a simple kind of stochasticprocesses and describe the random movements of an object in a set of possible positions. RW’s are Markov processesas the conditional distribution of a future state given the present and the past depends only on the present state. Asa consequence the classification of Markov chains as irreducible, recurrent and periodic can be applied to characterizetheir limiting behavior. An important role in this regard is played by asymptotic results in probability theory concerningsums of i.i.d. random variables, such as the laws of large numbers and the central limit theorem. At present a largenumber of papers in environmental sciences make explicit or implicit use of RW based models. Their applicationhas mainly to do with studies of animal movements and microscopic motility and of particle diffusion in fluids. Theimplicit use of RW models arises when computational algorithms or complex, possibly hierarchical, statistical modelsare employed
Identifying sources of PMIO pollution in the Taranto area
No full textAirquality management strategies can be improved by the identification of the major sources contributing to air pollution. Here a new multivariate receptor model is presented. The model accounts explicitly for data spatial variation
Energy-efficiency optimization of the biomass pelleting process by using statistical indicators
No full textBiomass pelleting process strongly depends on a number of variables hard to be
simultaneously controlled. This paper suggests a method to ensure pellets moisture optimization and
process energy saving. An experimental testbed was arranged in order to validate the performance
of the proposed strategy. It is based on a closed-loop control system that regulates material moisture
and flow rate, but its robustness is affected by the control-loop delay (the actuator delay is about 10
minutes) and by the random arrangement of the pellets inside the cooler that strongly affects product
moisture (the measurement errors are not negligible). To overcome those problems, a robust
statistical approach was adopted to reach the best tradeoff between estimation accuracy and
computational effort. It was derived by the well known Random Close Packing model and statistical
estimator. Experimental results prove the effectiveness of the proposed approach that provides
moisture errors less than 7.2% with a continuous limitation of energy consumption.
The present work is part of Idea75’s project - SEI Smart supervisor for Energy efficiency
optimization of Industrial processes - funded by Regione - PO FESR 2007-2013, Asse I, Linea di
Intervento 1.1. Azione 1.1.3 - Aiuti alle piccole imprese innovative di nuova costituzione
Accounting for misreporting in spatial zero-Inflated Poisson models
No full textA bayesian modeling approach based on the zero-inflated Poisson (ZIP) framework is proposed
to accommodate for the possible presence of misreporting and spatial dependencies in count
data with excess of zeros. Structured spatial effects are introduced in the model specification to capture correlation and heterogeneity of counts while controlling for possible sources of measurement error. To evaluate the model performance, a simulation study is carried out under configurations that allow for alternative structures of the random effects (i.e. structured and unstructured spatial effects). Bayesian Markov Chain Monte Carlo (MCMC) inference is implemented using the NIMBLE package in R
A Poisson model for overdispersed spatial counts with misreporting
No full textWe propose a Poisson model for zero-inflated spatial counts contaminated
by measurement error.We accommodate the excess of zeroes in the counts, consider the possible under/over reporting of the response and account for the neighboring structure of spatial areal units. Bayesian inferences are provided by MCMC implementation through the R package NIMBLE. The modeling approach is proposed to investigate the relation between the counts of wildfire occurrences in municipal areas and several potential socio-economic and environmental-driven factors, considering
two neighboring regions in southern Italy (Apulia and Basilicata). Multiple
sources of data with different spatial support are used and data were pre-processed in order to re-conduct the analysis to the municipal units. Results suggest the appropriateness of the approach and provide some insights on the features of wildfire occurrences
Spatio-temporal analysis of the PM10 concentration over the Taranto area
No full textIn this paper, an analysis of air quality data is provided for the municipal area of Taranto (southern Italy) characterized by high environmental risks as formally decreed by the Italian government in the 1990s with two administrative measures. This is due to the massive presence of industrial sites with elevated environmental impact activities along the NW boundary of the city conurbation. The aforementioned activities have effects on the environment and on public health, as a number of epidemiological researches concerning this area reconfirm. The present study is focused on particulate matter as measured by PM10 concentrations at 13 monitoring stations, equipped with analogous instruments based on the Beta absorption technology, either reporting hourly, two-hourly, or daily measurements. Daily estimates of the PM10 concentration surfaces are obtained in order to identify areas of higher concentration (hot spots), possibly related to specific anthropic activities. Preliminary analysis involved addressing several data problems: (1) due to the use of two different validation techniques, a calibration procedure was devised to allow for data comparability; (2) imputation techniques were considered to cope with the large number of missing data, due to both different working periods and occasional malfunctions of PM10 sensors; and (3) reliable weather covariates (wind speed and direction, pressure, temperature, etc.) were obtained and considered within the analysis. Spatiotemporal modelling was addressed by a Bayesian kriging-based model proposed by Le and Zidek (2006) characterized by the use of time varying covariates and a semiparametric covariance structure. Advantages and disadvantages of the model are highlighted and assessed in terms of fit and performance. Estimated daily PM10 concentration surfaces are suitable for the interpretation of time trends and for identifying concentration peaks within the urban area
A multivariate circular-linear hidden Markov model and site-specific assessment of wind predictions by an atmospheric simulation system
No full textWinds from the North-West quadrant and lack of precipitation are known
to lead to an increase of PM10 concentrations in the city of Taranto. In 2012 the
Apulia Government prescribed a reduction of industrial emissions by 10% every
time such meteorological conditions are forecasted 72 hours in advance. Wind
prediction is addressed using the Weather Research and Forecasting (WRF) atmospheric
simulation system by the Regional Environmental Protection Agency
(ARPA Puglia). We investigate the ability of the WRF system to properly predict
the local wind speed and direction allowing different performances for unknown
weather regimes. Observed and WRF-predicted wind speed and direction at a relevant
location are jointly modeled as a 4-dimensional time series with a finite number
of states (wind regimes) characterized by homogeneous distributional behavior. Observed
and simulated wind data are made of two circular (direction) and two linear
(speed) variables, then the 4-dimensional time series is jointly modeled by a mixture
of projected-skew normal distributions with time-dependent states, where the
temporal evolution of the state membership follows a first order Markov process.
Parameter estimates are obtained by a Bayesian MCMC-based method and results
provide useful insights on wind regimes corresponding to different performances of
WRF predictions
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 particulate
matter, sulphur dioxide, and nitrogen dioxide. 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 (LME) models enriched by a time-recursive prior structure. Secondly, a unique daily
weather database at the city level was obtained combining data from three 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 semiparametric
spatial covariance structure. Daily estimates of the pollutants’ concentration surfaces allow us to
identify areas of higher concentration (hot spots), possibly related to specific anthropic activities
Major PM10 source location by a spatial multivariate receptor model
No full textWe present a multivariate receptor model for identifying the spatial location of major PM10 pollution sources through the concentrations at multiple monitoring stations. 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 hierarchical 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 meteorological data
- …
