3.1. Database structure

The database was constructed using Microsoft Access software which offers a number of advantages. It is an extremely powerful relational database tool with excellent application development. In addition, it is both widely used by other organisations, such as EUROSTAT, and is able to support a large number of other database formats. Access, therefore, offers the potential for a great degree of compatibility with other databases that are already established.

The database is designed to minimise duplication of data and retain flexibility to allow interrogation for a wide range of queries. Figure 3.1 outlines the major component data tables held within the database. Four tables hold the information relating to monitoring required for each directive, these are labelled sampling, core, frequency and analysis. The information contained in these is as follows:

  • Sampling: This table contains general information such as directive name, reporting frequency and other general comments;
  • Core: This contains a list of determinands measured for each directive and indicates the type of water sampled along with which specific matrix is involved. This table is the key table in the database structure and provides all of the linking fields for relating different aspects of data;
  • Frequency: Holds data related to frequency and sampling strategy for a determinand on a determinand basis;
  • Analysis: Holds data related to methods of analysis associated with each determinand and covers all aspects of analytical method and allows comparison of methods of pre-treatment, limits of detection and AQC procedures.

In the four main tables described above data have been entered as faithfully as possible to that given in the directives (English versions). This reproduction of directive requirements has associated problems, small differences in the words used between one directive and another will mean that the database is incapable of accurately resolving commonalties. To solve these problems the database holds several look-up tables which attempt to standardise aspects of the data contained. A clear illustration of this comes from the determinand look-up table.

Determinand described by directive Standardised term for determinand
Phenol Phenol
Phenols Phenol
Phenolic compounds Phenol

In the above example phenol has been described by different directives in slightly different ways, the introduction of a standardised term allows the database to readily assess commonality. By careful consideration look-up tables can be constructed to assess commonality at any level. For example, the determinand look-up table has been further extended by grouping determinands into broad categories.

Determinand described by directive Standardised term for determinand Broad determinand type
Total ammonium Ammonia Nutrient
Non-ionised ammonia Ammonia Nutrient
Ammonia Ammonia Nutrient
Nitrites Nitrite Nutrient
Nitrite Nitrite Nutrient

This allows comparison between directives in terms of the types of determinand that are required for monitoring.

Figure 3.1 Schematic representation of the major elements of the database structure

Information from the directives was categorised in the database in terms of:

1. Water type (using the seven categories stated in the directives):

  • surface water (i.e. all types of surface water, namely fresh surface water, coastal and estuarine waters);
  • fresh surface water (i.e. only fresh water, namely rivers, lakes and reservoirs);
  • coastal waters;
  • estuaries;
  • groundwater;
  • freshwater (i.e. fresh surface water and groundwater); or,
  • salt water (i.e. coastal and estuarine waters).

2. Matrix:

  • water;
  • sediment; or,
  • biota.

3. Determinand category (quantity and quality):

  • physical (river flow, groundwater level, abstraction rate);
  • physicochemical (pH, salinity, alkalinity, temperature, turbidity, suspended solids);
  • organic pollution indicators (dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), organic carbon, redox potential);
  • metals;
  • synthetic organic compounds (e.g. organochlorines, organophosphorus, PAHs);
  • nutrients (nitrogen, phosphorus);
  • microbiological (total coliforms, faecal coliforms, faecal streptococci, viruses);
  • biological (invertebrates, fish tissue, shellfish, algae);
  • radioactivity (gross alpha and beta radioactivity, specific radionuclides);
  • chemical (other than physicochemical, organic, nutrients or metals); or,
  • aesthetic (foam, mineral oils, surfactants).

4. Sampling

  • methodology (e.g. on-line, discrete, composite);
  • frequency; or,
  • location and density.

5. Analysis

  • storage and pre-treatment;
  • methods;
  • performance; or,
  • specified quality control procedures.

6. Reporting

  • data treatment;
  • data storage;
  • compliance assessment of data against, for example, relevant standards and other requirements; or,
  • data availability, exchange and reporting.

An identical database was constructed for the international agreements. This allowed comparison of directives and international agreements at any level of detail either, within the databases, or through other packages such as Microsoft Excel.

3.2. Data analysis

Much of the analysis of the database has been undertaken using graphical-descriptions of the relationships between the monitoring requirements in the various directives. This has the aim of reducing the complexity of the multivariate information to a low-dimensional picture of how the directives may interrelate. The two techniques exploited here are hierarchical clustering and non-parametric multi-dimensional scaling (MDS). Each of these methods start explicitly from a triangular matrix of similarity coefficients computed between every pair of directives. This coefficient is a simple algebraic measure of how close the directives are, for example if two directives required monitoring of exactly the same determinands they would be 100% similar at this level.

Following the calculation of the similarity matrix, the results can be represented by a dendogram, linking the samples in hierarchical groups on the basis of the similarity between each cluster, or using MDS which attempts to place directives on a map. Both of these methods give a visual representation of "closeness" of the monitoring requirements of any combination of directives, Figure 3.2 outlines the stages involved the multivariate analysis.

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Figure 3.2 Stages in multivariate analysis (adapted from Clarke and Warwick 1994)

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