The Use of Neural Networks, Principal Component Analysis and Universal Process Modeling for the Interpretation of Environmental Data.
In recent years, numerous applications of computer-based methods to environmental chemistry have been developed. These include the use of principal component analysis (PCA), soft independent modelling of class analogy (SIMCA), geographical information systems (GIS), neural networks and expert systems (Natusch et al, 1983; Breen and Robinson, 1985; James 1993). The use of these techniques has been driven by the need to convert complex environmental analytical data into useful information. Regulatory efforts, clean-up strategies, monitoring programs and other environmental efforts all rely on the successful conversion of analytical data into a form that contains relevant information necessary to make decisions. Among others, analytical measurements are used to evaluate loadings of toxic chemicals into ecosystems, the effectiveness of remediation efforts and in assessing drinking water treatment standards.
Unfortunately, differing analytical methodologies, varying degrees of quality control in the analytical process, and the complexity of environmental data have all challenged the environmental scientists ability to adequately translate data into environmentally useful information. This is illustrated by the fact that there can be greater than 65% relative standard deviation in the amount of specific contaminants reported by laboratories when the contaminants are at the parts per billion (ng/1) level (Garfield, 1991). These types of problems have led to situations where entire data sets, covering years of analysis, have been declared useless (Bennoit, 1994). The first step in interpreting environmental data, therefore, is to ensure that the analytical variability is much less than the environmental variability being measured. This can only be done if laboratories adhere to strict quality control principles. Computational tools can then successfully be used to detect trends associated with changing environmental conditions.
The Niagara River Toxics Management Plan is a program established by Environment Canada, the U.S. Environmental Protection Agency Region 11, the Ontario Ministry of the Environment and the New York State Department of Environmental Conservation. The plan has, as one of its stated goals, to achieve a significant reduction of toxic contaminants in the Niagara River and to reduce the inputs of specific toxic chemicals from point and non-point sources by 50% by 1996 (Williams et al., 1994). Associated with this plan is an upstream/downstream monitoring program designed to specifically measure target organic compounds. The analytical procedures used to support this monitoring program are prescribed by the Niagara River Analytical Protocol and contain specific guidelines controlling the analytical methodologies and associated quality control procedures used to generate analytical data (Analytical Protocol Group of River Monitoring Committee, 1992). Because this program has been in place since 1987 and because of its associated monitoring program which has a rigorous analytical component, the data generated from this program is of suitable quality for analysis by specific chemometric methods. This chapter describes the use of neural networks (NN), PCA and universal process modelling (UPM) for the evaluation of analytical data generated from three locations along the Niagara River (Figure 9.1). This project had four specific goals:
1. To use NN, PCA and UPM techniques to detect variations in the levels of target organic compounds over time between specific locations along the Niagara River.
2. To use NN, PCA and UPM techniques to identify the source of water swnples collected from locations along the Niagara River.
3. To use UPM techniques to detect variations in the levels of target organic compounds over time within specific locations along the Niagara River.
4. To evaluate the use of NN, PCA and UPM techniques as tools for identifying non-target contaminants using a broad spectrum analytical approach.
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