SCOPE 5 - Environmental Impact Assessment

A 1.1 INTRODUCTION 

Data are sets of observations of environmental elements, indicators or properties. Data may be in numerical form or may be qualitative perceptions of beauty, odour, cloudiness, etc. Scientists are accustomed to reserving judgement on environmental questions until they have adequate data. When preparing are EIA, however, the assessor must often make predictions based on incomplete and sometimes irrelevant data sets. He also has an obligation to advise on the need for supplementary monitoring programmes. Sometimes, of course, an over abundance of data is available; but in undigested form, this flood of information would only confuse the readers of are EIA. The task of the assessor is therefore to select those observations that are relevant and sufficiently accurate for the problem under study. The selection process should be done in an objective manner, if possible. 

The notes that follow outline some of the problems that are commonly encountered in obtaining, assessing, and presenting data pertinent to environmental impact studies.

A l.2 THE NATURE OF AND NEEDS FOR DATA

Many individuals and agencies are generating and using data. Individuals tend to be 'discipline oriented', while agencies are 'mission oriented'. In either case, the data may seem to be deficient for use in broad interdisciplinary environmental studies. In many instances, however, the deficiency is imagined and reflects the fact that individuals are often only vaguely aware of available sources of data in other disciplines. The assessor often overlooks rich sources of information resident in experienced individuals or organizations. The public, for example, is seldom invited to contribute its views about values, needs, and wants.

There are two philosophies of data collection:

1. The accounting theory assumes that the subsequent use of data is independent of collection methods. An accountant believes that it is possible to collect data in some neutral sense, and that any subsequent manipulation can be justified if it contributes to the understanding of a problem.

2. The statistical theory insists on the essential interdependence between the ways in which data are collected and the methods of analysis which are appropriate for these data. The collection methods (including such questions as the population sampled, the sampling units, and the scales of measurement) limit the range of analysis methods that may be employed. Alternatively, if we wish to use particular analysis methods, we must select appropriate methods of data collection.

Much of the discussion on data collection and data banks assumes acceptance of the accounting theory of data manipulation. In contrast, most, if not all, of the available methods for handling numerical data assume the statistical theory of data collection, management, and manipulation.

The data sets available at the outset of an impact assessment are mostly of the first type. However, the assessor and his staff will be guided to a certain extent in their selection of data sets by their knowledge of the physical, biological, social. and/or econornic systems they are studying. Conversely, however, the data sources available within a region will influence the nature of the perceptual models used in the assessment. Where there are few data, the analysis will not include much detail.

Supplementary data collected during the impact assessment should preferably be of the second type. The data should be sufficient to enable the prediction of an impact to be made within specified confidence limits. The amount to be collected, the frequency, precision, accuracy, and type are dependent upon the known variability of the element in space and time. Where the variability is unknown, it must be determined by a pilot study.

A l.3 SOURCES OF ERRORS IN DATA 

Errors in field data include those resulting from the instrument and those introduced by the observer. Unless the instrumentation is very specialized, the measured value is rarely the same as the true value. However, standardized observational procedures tend to minimize errors to the point that many data can be used directly without concern as to quality. They also tend to ensure that data biases are similar from one location or time to another, so that the data, if not accurate, are at least comparable.

A l.4 DATA INTERPRETATION

Having selected some environmental data sets, the assessor should next try to determine their information content, i.e., he should search for patterns, trends. correlations, etc., and test for statistical significance.

Here the example of aliasing can be given. (See Figure A1.1.) When observations of a variable are made at discrete time or space intervals, interpolation between data points can be misleading. In the simplified example given in Figure A1.1 , even the sign of the interpolated value could be wrong occasionally.

A l.5 DIFFICULTIES DUE TO INTERDISCIPLINARY MISUNDERSTANDINGS

The interdisciplinary nature of environmental assessments challenges the assessor and his staff. Even within the natural sciences, specialists in different fields may use in a phrase in quite different ways (e.g., the use of the word 'plasma' by physicists and  by biologists). Even greater difficulties occur when natural and social scientists attempt to communicate with one another.

Inevitably, the varied nature of environmental problems leads scientists to use all. information which does not fall within their sphere of specialization. Time and other constraints may cause them to do this without due regard for the accuracy and representativeness of the data.

A typical example is the incorrect use of wind information by nonspecialists.  Winds are greatly affected by local topography, and the assumption that published values measured at a single site are representative of a large area, while sometimes valid, often leads to serious errors in predicting the pollution patterns surrounding a power station or the wind loadings on a building. 

Equally glaring errors have been made with other kinds of environmental measurements: pesticides in soils, SO₂ in the atmosphere, and heavy metals in water, for example, whenever recognition has not been given to the space and time  variations of the constituents. To avoid these problems, local specialists should be consulted during an impact assessment since they have the best knowledge of the available data and of their reliability. In particular, advice should be sought on the following four points:

1. The statistical population represented by the sample;

2. The methods of data collection employed in their acquisition;

3. The extent to which the data have been subjected to smoothing or editing procedures;

4. The transposability of the data.

A l.6 DATA BANKS

Data banks and retrieval systems speed up the impact assessment process by optimizing the use of existing data and by helping to eliminate wasteful redundancy. These systems work well if they have been designed carefully and if they are well managed. However, the limitations of data banks should be appreciated. The development of a very large, all-inclusive system could lead to a morass of data, sometimes with large amounts never being used. Furthermore, the data within such a system may contain hidden traps, since the observing procedures (changes of equipment, siting, etc.) are rarely documented. A lack of an updating procedure is a related impediment.

The discipline-based data systems that have been developed for national purposes ( e.g., for aviation weather forecasting) provide large sources of quality controlled data. However, the observing sites (e.g., at airports) may not always be representative of the proposed development site. In addition, because the acquisition of environmental data is undertaken by a variety of governmental departments, organizations, and individuals, there may be data gaps and incompatibilities amongst systems. For example, socio-economic information about population and the location of industries may often be difficult to find in an environmentally oriented government department. A data system is needed wherein information from these diverse sources can be put readily at the disposal of the assessor in the desired form. Special attention must also be given to the ways in which data are stored so that they may be recalled in sub-sets convenient for comparison and modelling.

A l.7 DATA PRESENTATION

Data may be presented directly or in summarized form such as on maps and graphs. However, since the eye responds in different ways to different geometric forms and arrays as well as colours, a scientifically correct diagram may sometimes be misleading. Care is therefore required to ensure that the interpretative materials convey exactly what is intended.

Large data sets are sometimes reduced to small sets with the aid of empirical or physical models. Dimensional analysis, for example, often permits several variables to be collapsed to a single new parameter (see, e.g., Munn, 1970, pp. 157-161).In this connection, it is important to note that empirical models cannot be extrapolated with assurance to new situations.

A l.8 EXCHANGE OF INFORMATION

The mere fact that information exists does not ensure availability to prospective users. Communication links between an major interest groups must therefore be established. At an early stage of an impact assessment, these groups and their respective needs must be identified as a basis for developing:

1. inventories of relevant data sources;

2. procedures for exchanging data amongst users.

A l.9 SOME OPERATIONAL PRINCIPLES OF DATA ACQUISITION, ANALYSIS AND PROCESSING

The principles which must be followed by the assessor and his staff concerning data, their acquisition, analysis, and processing should include the following:

1. Standardization of units, sampling methods, criteria, classification and referral systems, cartographic scales, and projections is essential;
2. Because information required for environmental impact assessments is often widely scattered, the need for data storage and retrieval capabilities is particularly important; 
3. The data bases used by the assessor should always be clearly identified in terms of quantity, quality, and character to ensure that they are not misemployed or misinterpreted by the reviewer or some other party;
4. Data should be consistent with respect to sampling and averaging times, time lags, and measurement locations;
5. Statistical tests should be carried out to ascertain the significance, errors, frequency distributions, and other characteristics of data which are used as a basis for subsequent analysis;
6. The methods of data synthesis and also physical constraints to data use, (e.g., threshold effects) should be clearly identified;
7. The conclusions which are presented should not imply more than can be deduced with confidence from the data and other knowledge;
8. The precision and accuracy demanded for the resolution of problems should be clearly defined prior to establishing supplementary data networks (excessive accuracy may be quite wasteful);
9. Empirical relationships may not be transposable. Extreme caution must be  employed in using relationships which were not developed for the project; their validity must be established by pilot programmes; 
10. Finally, new technology should not be overlooked. New systems and sensors, which are being developed at a steadily increasing rate, may greatly facilitate supplementary data acquisition.

A 1.10 SUMMARY

The environment is highly variable in time and space. For this reason, and because available environmental data are often widely scattered, incomplete, and incompatible, the selection of data sets is a difficult but not impossible task. The most important criterion here is relevance: only data that are necessary to assess environmental impacts should be chosen. A secondary consideration is efficiency: where there are alternative sources of acceptable data, the sets that can be organized meaningfully in minimum time and with least cost should be selected. A third criterion is related to the fact that, in many cases, some kinds of data sets are over-abundant while others are almost non-existent. While a least-common-denominator approach is not recommended, the assessor should avoid the temptation to carry out a precise analysis of a small component of the total assessment, merely to demonstrate his virtuosity .Unfortunately, too , there has been a tendency to exclude qualitative data because they do not fit preconceived quantitative models.

A related consideration concerns measurement standards, which are needed to ensure internal consistency and reproducibility. These standards must be made explicit to guide the non-specialist, to avoid mischievous biasing of results, and to facilitate subsequent review. Ideally, a prediction method must include, as part of its procedure, the documentation of measurement standards.

Reference

Munn, R. E. (1970) Biometeorological Methods. Academic Press: New York 336 pp.