Spatio-temporal statistical models for river monitoring networks.
When introducing new wastewater treatment plants (WWTP), investors and policy makers often want to know if there indeed is a beneficial effect of the installation of a WWTP on the river water quality. Such an effect can be established in time as well as in space. Since both temporal and spatial components affect the output of a monitoring network, their dependence structure has to be modelled. River water quality data typically come from a river monitoring network for which the spatial dependence structure is unidirectional. Thus the traditional spatio-temporal models are not appropriate, as they cannot take advantage of this directional information. In this paper, a state-space model is presented in which the spatial dependence of the state variable is represented by a directed acyclic graph, and the temporal dependence by a first-order autoregressive process. The state-space model is extended with a linear model for the mean to estimate the effect of the activation of a WWTP on the dissolved oxygen concentration downstream.
Department of Applied Mathematics, Biometrics and Process Control, Ghent University, Coupure Links 653, B-9000 Gent, Belgium. Lieven.Clement@ugent.be
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Clement
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Thas
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Vanrolleghem
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Ottoy
D003625:Data Collection; D004784:Environmental Monitoring / Q000706:statistics & numerical data*; D005544:Forecasting; D008962:Models, Theoretical; D010100:Oxygen / Q000032:analysis; D011786:Quality Control; D045483:Rivers; D014865:Waste Disposal, Fluid; D014867:Water / Q000737:chemistry
D014867:Water; D010100:Oxygen