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The use of indicator Kriging permits to derive,
at each unsampled location, the conditional cu-
mulative distribution function ccdf which models
the uncertainty about the unknown value (Van
Meirvenne & Goovaerts, 2001). Uncertainty
assessment represents a preliminary step in the
decision-making process, such as delineation of
hazardous areas. In fact, many environmental
investigations are aimed at making important deci-
sions, as for example declaring an area potentially
contaminated. In this context, the Indicator Kriging
can be applied in the study of environmental phe-
nomena for the construction of maps of exceeding
the fixed threshold also in presence of coexistence
of a few random 'hot spots' of large concentrations
within a background of data below the detection
limit (censored observations).
This helps those responsible for the decisional
process to delimit the vulnerable areas on the basis
of the knowledge of the uncertainty associated
to the examined phenomenon. This approach
lends itself well also to the analysis of qualita-
tive variables, allowing integration with data of
quantitative nature through a soft indicator coding
of observations.
An alternative technique of spatial modeling,
particularly adapt in those applications in which
more importance is attached to global statistics
rather than to local accuracy, is the stochastic simu-
lation. The statistics obtained from postprocessing
of a huge number of simulated images permits to
evaluate both the uncertainty in the estimation and
the consequences that this uncertainty can imply
in the decision making. Therefore the principal
purpose of the next study will be that of proving
the feasibility and utility of the application of
stochastic simulation to evaluate the probability
that a pollutant exceeds a concentration value
retained critical for environmental safety and/or
human health.
Agenzia per la protezione dell'ambiente e per i
servizi tecnici (APAT), (2008).
Criteri metod-
ologici per l'applicazione dell'analisi assoluta
di rischio ai siti contaminati, revisione2
. Rome:
APAT. Retrieved from http://apat.gov.it
Beretta, G. P., Colombo, F., & Pranzini, G. (1995).
Progettazione e ottimizzazione di una rete di
monitoraggio delle acque sotterranee nella media
valle del F. Arno (Toscana) mediante l'uso della
teoria delle variabili regionalizzate.
2° Convegno
nazionale sulla protezione e gestione delle acque
sotterranee: metodologie, tecnologie e obiettivi
,
May 17-19, Nonantola, Modena, Italy.
Castrignanò, A. Cherubini Claudia, Di Mucci,
G., & Molinari, M. (2004). The application of
spatial modelization of the variability for the in-
terpretation of a case regarding arsenic pollution
in groundwater.
COST 629 workshop “Integrated
methods for assessing water quality,
” October
21-22, Louvain-la-Neuve, Belgium.
Castrignanò, A. Cherubini Claudia, Dima, L.,
Giasi, C. I., & Musci, F. (2007). The application of
multivariate geostatistical techniques for the study
of natural attenuation processes of chlorinated
compounds. In
Heat Transfer, Thermal Engin-
nering and Environment.
WSEAS Press.
Castrignanò, A. (2008). Introduction to Spatial
Data Processing.
International Short Course on
Introduction to Spatial Data Processing,
Uni-
versity of Zagreb Faculty of Agriculture April
21-24, 2008.
Castrignanò, A., & Buttafuoco, G. (2004). Geo-
statistical Stochastic Simulation of Soil Water
Content in a Forested Area of South Italy.
Bio-
systems Engineering
,
87
, 257-266. doi:10.1016/j.
biosystemseng.2003.11.002
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