During the last few decades environmental studies have been performed on many occasions. Sometimes they just consist of collecting data for “accidents” occurring randomly but they can also be world-wide concerted action, aiming to monitor pollution and involving several laboratories. The author describes the scientific background to these environmental studies in order to explain the concepts that can be used to implement consistent databases which permit meaningful statistical processing and as correct as possible decision-making.To start with, it is useful to establish a classification system for the various kinds of data (factual, referential and documentary). This classification is substantial for providing a rigorous structuring of data. This will considerably reduce the possible lack of data consistency and simplify the later statistical processing. According to the modern theory of the entity-relationship model, data can be modelled before being stored in a database. This modelling confers a fairly flexible structuring capability for data retrieval and validation. The basic theory of entity-relationship modelling is presented and illustrated for two typical environmental studies. The importance of designing efficient coding systems is largely addressed as it must be the result of several considerations: simplicity, accuracy and convenience.All described concepts can be implemented in a Laboratory Information Management System (LIMS). Moreover, it appears that good automation of laboratory management is based on the definitions of standard procedures which relate to Quality Assurance and Good Laboratory Practices (GLP). Quality Assurance can be considered as a general policy based on international standards. But practical applications vary from one organisation to another. Its fundamental goal consists in bringing proofs of the correct functioning of the laboratory. This goal is complementary to the data modelling.Modern Relational Database Management System (RDBMS) software contains many utilities that renders the practical development of LIMS easier. All that was described before, in general terms, must be adapted to the specific study that is planned. It is then recommended to write a document that will clearly indicate the goal and the frame work of the study. It can be totally or partially used as a requirement document for the software that will manage the data.Statistical processing for analytical chemistry is faced with many problems where chemometrics is involved. It is usual to consider separately univariate and multivariate statistical methods. Univariate methods can be very poor when dealing with large numbers of variables. Some guidelines are presented for selecting multivariate methods according to the nature of the information, in order to avoid any misinterpretation of the results. When dealing with large data sets, several variables types may be encountered: it can then be necessary to transform some variables in order to obtain an homogeneous data array.Two examples of multivariate environmental data processing are presented. The first consists of a study of soil pollution by heavy metals. As a preliminary step, non-linear regression was used in order to predict soil pollution around one source and delimit the hazardous area. For a restricted area this gives consistent results. Thereafter, multiple linear regression was applied in order to estimate the relative influence of different pollution sources on the total study area.The second example deals with the problem of mercury pollution of rivers in the Alsace region. An original multivariate method is presented, called Multiple Correspondence Factorial Analysis. The double weighing technique applied allows a simultaneous comparison of different qualitative pollution mechanisms: sampling point location along the river, fish species, fish weight and sampling year. The last chapter gives general guidelines on commercially available statistical software.The goal of this work is to demonstrate that, when the data processing phase arrives, it is evident that the fulfillment of an environmental study must be concerted. Poor conclusions are extracted from poor data and it is compulsory to orient data collection by strong initial hypothesis. Computerisation of data, if correctly done, is the best way to reach this goal.
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- Data Management in Relation to Statistical Processing and Quality Control
- Springer Berlin Heidelberg
Systemische Notwendigkeit zur Weiterentwicklung von Hybridnetzen