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Stoch Environ Res Risk Assess (2013) 27:12691280 DOI 10.1007/s00477-012-0662-1
ORIGINAL PAPER
Simultaneous identication of the pollutant release history and the source location in groundwater by meansof a geostatistical approach
Ilaria Butera Maria Giovanna Tanda
Andrea Zanini
Published online: 13 November 2012 Springer-Verlag Berlin Heidelberg 2012
Abstract This paper describes an innovative procedure that is able to simultaneously identify the release history and the source location of a pollutant injection in a groundwater aquifer (simultaneous release function and source location identication, SRSI). The methodology follows a geostatistical approach: it develops starting from a data set and a reliable numerical ow and transport model of the aquifer. Observations can be concentration data detected at a given time in multiple locations or a time series of concentration measurements collected at multiple locations. The methodology requires a preliminary delineation of a probably source area and results in the identication of both the sub-area where the pollutant injection has most likely originated, and in the contaminant release history. Some weak hypotheses have to be dened about the statistical structure of the unknown release function such as the probability density function and correlation structure. Three case studies are discussed concerning two-dimensional, conned aquifers with strongly non-uniform ow elds. A transfer function approach has been adopted for the numerical denition of the sensitivity matrix and the recent step input function procedure has been successfully applied.
Keywords Geostatistical approach Transfer function
Source detection Non uniform ow Groundwater
List of symbolsC(x,t) Concentration at point x and time t t Times Timex Position in the domainx0 Source locationu Velocity tensorD Dispersion tensor
r Nabla operator
F(t) Concentration of the water injected at the source as function of time t
F0 Constant and known mass rate input function f(x,t) Transfer function at position x and time tm Number of observationsn Number of unknownsz m 9 1 Observationss n 9 1 Unknownss(t) Unknown release functionp Number of unknown coefcientsh(s) m 9 1 Vector that describes the transport process v m 9 1 Measurement errorsR m 9 m Error covariance matrixH m 9 n Sensitivity matrixT Sampling timeX n 9 p Matrix, mean of the unknown processb p 9 1 Unknown coefcientsQ(h) n 9 n Matrix, covariance of the unknown processh Structural parameters of the covariance functionr2s Variance...