The application of Geographical Information Systems to determine ...

Environmental Impact Assessment Review 21 (2001) 511 – 535 www.elsevier.com/locate/eiar

Feature article

The application of Geographical Information Systems to determine environmental impact significance Paula Antunes*, Rui Santos, Luı´s Jordaõ Ecological Economics and Management Centre, Department of Environmental Sciences and Engineering, Faculty of Sciences and Technology, New University of Lisbon, Quinta da Torre, 2829-516 Caparica, Portugal Received 1 June 2000; received in revised form 1 June 2001; accepted 1 June 2001

Abstract This paper presents a new methodology for impact assessment — SIAM (Spatial Impact Assessment Methodology) — which is based on the assumption that the importance of environmental impacts is dependent, among other things, on the spatial distribution of the effects and of the affected environment. The information generated by the use of Geographical Information Systems (GIS) in impact identification and prediction stages of Environmental Impact Assessment (EIA) is used in the assessment of impact significance by the computation of a set of impact indices. For each environmental component (e.g., air pollution, water resources, biological resources), impact indices are calculated based on the spatial distribution of impacts. A case study of impact evaluation of a proposed highway in Central Portugal illustrates the application of the methodology and shows its capabilities to be adapted to the particular characteristics of a given EIA problem. D 2001 Elsevier Science Inc. All rights reserved. Keywords: Environmental Impact Assessment; Significance of impacts; Spatial indices; Geographical Information Systems (GIS)

* Corresponding author. Tel.: +351-21-295-4464. E-mail addresses: [email protected] (P. Antunes), [email protected] (R. Santos), [email protected] (L. Jordaõ). 0195-9255/01/$ – see front matter D 2001 Elsevier Science Inc. All rights reserved. PII: S 0 1 9 5 - 9 2 5 5 ( 0 1 ) 0 0 0 9 0 - 7

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P. Antunes et al. / Environmental Impact Assessment Review 21 (2001) 511–535

1. Introduction The Environmental Impact Assessment (EIA) process generally requires the completion of several stages, namely (Antunes et al., 1996): 1. project definition and characterisation of environmental baseline information; 2. scoping and impact identification; 3. impact prediction; 4. impact evaluation; 5. impact mitigation and compensation, and design of monitoring systems. Although all EIA stages are essential for the overall objective of impact assessment and environmental planning, impact evaluation is particularly important because the results obtained in the previous stages are handled to determine the significance of environmental impacts. This allows the establishment of comparisons among alternatives and environmental components (e.g., air, water, and soil) in order to support decision-making about project acceptance and the need for mitigation or compensation. Duinker and Beanlands (1986) state that the matter of the significance of human-induced perturbations in the natural environment constitutes the very heart of EIA. The significance of environmental impacts is largely dependent on the spatial distribution of the effects of the proposed action and of the affected receptors. The choice of the level of analysis (i.e., the extension of the study area considered in the EIA) to adopt for the assessment can also have a decisive influence on the evaluation results. However, in current EIA practice, this spatial dimension of impacts is often ignored or hidden in the overall decision-making process. This paper presents a new methodology for impact assessment — SIAM (Spatial Impact Assessment Methodology) — which aims to improve the evaluation of impact significance by considering explicitly the spatial dimension of the impacts. Therefore, it relies on the evaluation of the spatial significance of impacts, using information generated within the EIA process, with the support of Geographical Information Systems (GIS). The evaluation of impacts significance has always a subjective dimension, arising from the integration of the values, experiences, and knowledge of the different actors that perform the evaluation. Although subjectivity can never be eliminated, the results of an evaluation may become more credible, if they are obtained by the application of an a priori-defined methodology, with clearly stated assessment criteria, making full use of the information generated in the previous EIA stages. This is the major objective of SIAM. The paper begins with a brief overview of current use of GIS for EIA. This is followed by the presentation of the proposed methodology. A case study dealing with the evaluation of environmental impacts of a proposed highway in Portugal is presented to illustrate in detail the practical application of the methodology.


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2. The use of GIS in EIA GIS are tools for collecting, storing, retrieving at will, transforming, and displaying spatial data for a particular set of purposes (Burrough and McDonnell, 1998). Given the spatial nature of many environmental impacts, GIS can have a wide application in all EIA stages, acting as an integrative framework for the entire process, from the generation, storage, and display of the thematic information relative to the vulnerability/sensitivity of the affected resources, to impact prediction and finally their evaluation for decision support (Antunes et al., 1996). Eedy (1995) stresses the advantages of the use of GIS in EIA, namely for data management, overlay and analysis, trend analysis, as sources of data sets for mathematical impact models, habitat and aesthetic analysis, and public consultation. According to a survey undertaken by Joaõ and Fonseca (1996), GIS were used for all EIA stages. The most frequent use was for the presentation of results, followed by analysis/modelling and data preparation. GIS have also been used for the presentation of environmental baseline information and project description, through the preparation of thematic maps for the several environmental descriptors. Also, the overlay of baseline information maps with project layouts is frequently used for impact identification (Joaõ and Fonseca, 1996). The prediction of the magnitude of impacts is often undertaken by the application of simulation models (Fedra, 1993). The obtained result will most often be a map of the value of a given environmental descriptor (e.g., concentration of an air pollutant) at any location within the study area. The extension of environmental impacts can therefore be estimated from the spatial distribution of environmental quality values predicted for each alternative. GIS also enable the use of a set of simple operations (such as overlay, classification, interpolation, aggregation of spatial information) that can generate additional information to support impact prediction. For instance, impacts on occupation of agricultural soils, disturbance of ecologically sensitive areas, water courses disruption, and accessibility changes can be directly predicted from information stored in the GIS by overlaying the project layout with thematic data. The use of GIS to support tasks besides presentation of results has been less explored. However, there are already some examples of simplified impact evaluation and visualisation methodologies based on the use of GIS. Most of these approaches are aimed at producing impact maps, for the spatial identification and evaluation of impacts, through the overlay of baseline information with project characteristics and effects. For example, GIS have been used for the assessment of impacts in specific environmental components, namely for the evaluation of landscape impacts, where GIS are used to generate views from particular points of the scenery for the project alternatives, to perform visibility analysis for structures such as electricity poles, or to evaluate effects of alternative routes for high tension lines (Davidson, 1992). Schaller (1992) uses a GIS to produce a visual representation, named

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‘‘annoyance mountain,’’ which combines population data with the noise levels expected with the installation of a new airport in Munich. A similar approach is presented in Szergo (1994), where the influence calculation technique is applied to generate impact maps by the overlay of an external influence (such as air pollution) with population data in an urban area. Sankoh (1996) and Sankoh et al. (1993) applied two EIA methods (ecological risks and utility values analysis) to generate space resistance maps, which allow the identification of the route alternatives that present minimum conflict with the environment. Rivas et al. (1994) present a methodology for the evaluation of impacts of land-use plans, based on the computation of impact indices obtained by the overlay of the proposed land uses with thematic maps. Smit and Spaling (1995) refer several studies where GIS have been applied for the evaluation of cumulative effects through time series analysis. The importance and potential of using GIS to adopt a spatial approach to economic valuation of the environment, namely through the preparation of ‘‘economic value maps’’ has also recently been acknowledged (Eade and Moran, 1996; Martinho et al., 1998). This type of approach has a very strong potential to increase the practical application of cost – benefit analysis, and therefore to enhance the role of economic valuation on environmental impacts assessment. Bateman et al. (1995) describe preliminary results for woodland recreation demand valuation and Wang (1996) and Martinho et al. (1998) for the assessment of recreation suitability. Although the methodologies and applications described above present valuable contributions to the use of GIS within EIA, in this paper it is considered that they can play a more important role, acting as a framework to support the development of all tasks. Particularly, for impact evaluation, the information generated by the use of GIS in previous EIA stages can be used more thoroughly in the assessment of impact significance, contributing to increase the credibility of the evaluation, and therefore to improve the effectiveness of the whole EIA process.

3. SIAM 3.1. Conceptual model In the EIA evaluation stage, the information previously generated is interpreted and integrated to support decision-making. The joint interpretation of that information, and the comparison of alternatives, is difficult, since the results of impact prediction are presented in different units for the several environmental components. In EIA practice, a common impact measurement scale is usually defined to overcome this difficulty (for instance, from 5 — very significant negative impact — to +5 — very significant positive impact). Environmental impacts can be measured considering two aspects: magnitude and significance. Theoretically, the impact magnitude is related with the differ-


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ence in environmental quality, or in the state of a resource, between the with and without project situations, while significance relates with the importance that is given (by the experts or by the public) to that difference (see for instance Kennedy and Ross, 1992; Duinker and Beanlands, 1986). Although from a conceptual point of view, the difference between magnitude and significance can be established, there is a great difficulty to separate these two factors to evaluate impacts in a measurement scale. Usually, when an environmental impact scale is defined, the impacts are classified on that scale by experts participating in the EIA, considering simultaneously factors such as magnitude, extension of the affected area, importance/sensitivity of the resources, time frame of the impacts, and affected population. In practice, the integration and evaluation of all these factors is often performed mentally by the experts, without making explicit the rules and criteria used for that classification, being therefore difficult to validate or replicate the obtained results. The spatial extension of the impact, measured by the affected area, is one of the factors that is usually taken into account in this evaluation and that is frequently ‘‘lost’’ in the integration procedure. This means that, for instance, impacts that are felt at a local scale are not clearly distinguished from impacts occurring at a regional or national scale in the decision-making process. In fact, when this aggregation is performed, one of two possible errors may occur: (1) local impacts are completely absorbed by impacts at a larger scale, and are neglected in the evaluation, or (2) small scale impacts are given the same weight as higher scale impacts, introducing a bias in the evaluation. The later can lead to a situation where, for instance, all alternative locations for a project with positive impacts at the regional or national level (such as the installation of an industrial waste treatment facility) are rejected due to the significant negative impacts at a local scale. SIAM was developed to overcome some of these problems. The proposed methodology can be used to provide to the decision-maker a set of impact indices (i.e., aggregate measures of impact). For each environmental component considered (e.g., air quality, noise disturbance), impact indices are calculated at different spatial levels of analysis (e.g., local, regional, national, global). The impact indices are obtained through the aggregation of impact indicators, which are the measures of the severity of the impact, generally referred to number of people, or amount of area or resources affected by a given environmental quality value. The computation procedure adopted in SIAM can be implemented using the information stored in a GIS. A raster data format is more suited to the operations required for the application of the methodology (basically reclassification and map overlay). Although SIAM is particularly focused on impact evaluation, it requires that previous EIA stages be performed in a specific way, in order to fulfil the information requirements for its application. In this sense, SIAM can be described

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Fig. 1. Steps in the application of SIAM.

as a framework for the implementation of the overall EIA process, as described in Fig. 1 and in the following sections. The application of the proposed methodology is made in three steps: 1. scoping and impact identification, including selection of environmental components and indicators to be used and definition of the levels of analysis and corresponding study areas; 2. prediction of impacts and classification of the environmental descriptors; 3. computation of the impact indicators and indices. In the following sections, the development of these steps in SIAM is presented. 3.2. Scoping and impact identification 3.2.1. Environmental components and impact indicators In the scoping stage, the environmental components (e.g., air quality, ecosystems, landscape) to be studied should be identified. In order to evaluate the impact on each component, it is necessary to identify the receptors considered important, or valued, relative to that component, and to describe the impact pathways affecting those receptors. For instance, in the case of impacts caused by air pollution, one has to define the receptors affected (people, a particular species, or a given ecological process) and to study the pathway through which that pollution affects those receptors, using environmental descriptors (e.g., NOx concentrations).


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Impact indicators are used to measure the impacts on each component. Therefore, for each component, at least one impact indicator should be defined. The indicators, defined in the scoping stage of EIA, should address all issues considered relevant for impact assessment, namely by professionals, stakeholders, and the general public. The impact indicators to be used depend on the project type and on the particular characteristics of the affected area. Table 1 shows examples of possible impact indicators. 3.2.2. Definition of the study areas The area that is considered in the application of SIAM, and therefore for the computation of the impact indices, is referred herein as ‘‘study area.’’ The definition of the study area is a choice that is always made in EIA, but which is seldom the object of detailed analysis and for which no standard methods are presented in the literature. However, the selection of the study area can influence significantly the results of the EIA process. According to Mostert (1996), the preferred study area depends on one’s subjective perspective. Proponents and competent authorities usually favour relatively small study areas, while citizens and environmental NGOs usually favour a larger study area. Table 1 Examples of impact indicators Environmental component

Impact indicators (examples)

Air quality

area with pollutant concentration in each air quality class number of people affected in each class area of sensitive ecosystems affected area affected in each class of noise level number of people by noise level class uses/sensitive species affected by noise level class number of surface water flows disrupted volume (length)/water quality class number of affected people/uses vulnerable/sensitive species affected area/groundwater recharge rate area/groundwater contamination risk class occupied area in each soil quality class rate of soil loss by erosion area affected by contamination risk affected area of natural value (classified areas, biotopes) number of protected species affected fragmentation and changes in habitat size travel time (accessibility) employment agricultural production forestry production mineral resources affected

Noise

Surface water

Groundwater Soil

Biological populations, communities, and habitats

Socioeconomic impacts

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Impact evaluation in SIAM is performed considering different levels of spatial incidence of the impacts. Therefore, several study areas, of increasing dimension, are defined for each project alternative. The following levels of analysis can be defined, to which different study areas are associated: Project corresponding to the analysis of the impacts in the area directly affected by the project Local the immediate neighbourhood more directly affected by the project Supralocal the municipality(ies) where the project is implemented, or the area of a small catchment, for instance Subregional the group of municipalities affected by the project, or to the watershed where the project is integrated Regional the region where the project is located National impact analysis at the national level International the evaluation of effects in neighbouring countries Global evaluation of impacts on a global scale For a given project, the levels of analysis, and the corresponding study areas, must be defined, according with its specific characteristics and dimension. The shape of the study areas is defined according with the nature of the project. For instance, for the smaller spatial scales (project, local, supralocal), the study areas for a point structure (factory, power plant, waste incinerator) can be defined as circles of increasing radius with the level of analysis. For linear infrastructures (e.g., highway, railway, power lines), study areas can be defined as corridors of increasing width. The shapes of the study areas for larger scales of analysis (e.g., regional, national) are not so dependent on the project characteristics, but are usually determined by administrative or environmental boundaries. 3.3. Prediction and classification of the environmental descriptors SIAM assumes that the results of the prediction stage are presented as maps depicting the expected spatial distribution of environmental descriptors for each project alternative. ‘‘Environmental descriptors’’ is used in this paper to refer the characteristics of the environment that can be affected by a proposed project (e.g., NOx concentrations, land use). The results obtained in impact prediction need to


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be classified, in a common evaluation scale, using criteria based, for instance, in environmental quality regulations, resource evaluation guidelines, dose – response relationships, known effects, published literature, or by the experts. At this stage, SIAM assumes that a series of value functions, or classification criteria, are defined. These value functions relate the units used to measure environmental descriptors (e.g., in micrograms per cubic meter for NOx ground concentration) with an environmental quality or environmental desirability level (see, for instance, O’Bannion, 1980; Beinat, 1997). Applying these value functions, maps can be generated representing the spatial distribution of the environmental quality values. Although some subjectivity can be introduced in the analysis with this classification step, the fact that the value functions are explicit allows their validation. Also, the remaining steps of the methodology can be performed with different classification rules (or different thresholds for the classes), in order to perform a sensitivity analysis on the results. 3.4. Computation of impact indicators and indices The impact indicators are obtained by the overlay of the baseline information stored in the GIS, namely about the sensitivity of the affected areas or the importance of the resources involved, and the spatial environmental quality patterns associated to each project alternative, obtained in the previous stage. Impact indicators are calculated, for each study area, by the evaluation of the extension (of soil or ecosystems) or number of receptors affected by each environmental quality class. For each alternative, a set of impact indices is calculated aggregating the impact indicators for the environmental components considered (e.g., impact index for air quality, water resources, and biological resources). For each spatial level of analysis (i.e., for each study area), the indices are computed by comparison of the indicator values obtained for the with and without project situations. To illustrate the computation of impact indices in SIAM, the case of impacts on the environmental component ‘‘air pollution’’ can be considered. A single environmental descriptor is taken into account (NOx concentrations), adopting as impact indicators the affected area and the number of people in each air quality class. In this case, considering only one environmental component, one descriptor and two impact indicators, the impact index of alternative j, for a given study area is given by: n n X X Ia;i;j Ia;i;0 Ip;i;j Ip;i;0 EIj ¼ wa Qi Qi þ wp ITa ITp i¼1 i¼1

ð1Þ

where wa+wp=1 and wa and wp are the weights given to the indicators area and population, respectively; Qi is the value of class i in the environmental quality scale, for the environmental descriptor NOx; Ia,i,0 is the area affected

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by environmental quality class i, without the project, within the study area; Ia,i,j is the area affected by environmental quality class i, for alternative j within the study area; ITa is the total area of study; Ip,i,0 is the population in areas affected by environmental quality class i, without the project within the study area; Ip,i,j is the population in areas affected by environmental quality class i, for alternative j within the study area; ITp is the total population in the study area; and n is the number of classes considered in the adopted environmental quality scale. The IT values refer to the total extension or total population in the study area, and, in principle, remain the same for the with and without project situation, unless it is expected that the project will cause significant population movements. If more environmental descriptors were used, e.g., two or more air pollutants considered, the number of impact indicators to be used should increase, considering, for example, population affected by different concentrations of each pollutant. In general, the environmental impact index of alternative j in a selected environmental component for a given study area can be obtained using the equation: " !# m n X X Ik;i; j Ik;i;0 EIj ¼ wk Qi;k ; 8j 2 J ð2Þ ITk i¼1 k¼1 where

m P

wk ¼ 1 and EIj is the environmental impact index of alternative j for

k¼1

the considered environmental component in the study area; m is the number of impact indicators considered in the environmental component; wk is the weight given to the impact indicator k; n is the number of classes considered in the adopted environmental quality scale; Qi,k is the value of class i in the environmental quality scale for impact indicator k; Ik,i, j is the value (area, population, species) of indicator k, classified with environmental quality i, for alternative j, within the study area (e.g., area with NOx concentration within a given interval); Ik,i,0 is the value (area, population, species) of indicator k, classified with environmental quality i, for the situation without project, within the study area; ITk is the total value of the indicator k within the study area (e.g., total area, number of people); and J is the alternatives set. Impact indices are calculated by the difference between the average environmental quality within the study area for alternative j and the corresponding value for the without project situation. Since several indicators can be used within a given component, Eq. (2) assumes that the impact index is given by the weighted average of the impact on the several indicators. The weight assigned to each indicator should be defined by the experts involved in the EIA, or by the participants in the scoping stage. The use of a weighted average to aggregate the impact indicators (as in Eqs. (1) and (2)) can lead to the balancing of opposite effects among indicators, which means that, for instance, a significant negative impact in one indicator can be


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compensated by a positive impact in another indicator leading to an overall low impact. If the decision-maker intends to assume a more conservative attitude, and avoid impact compensation, other operators (such as minimum) should be used in this aggregation of indicators. In this case, the least favourable indicator determines the environmental impact in each component, and the following equation is applied: ( EIj ¼ min

n X i¼1

Qi;k

) Ik;i; j Ik;i;0 ; ITk

8j 2 J

ð3Þ

Eqs. (2) or (3) performs the spatial aggregation of impacts, within a given study area. This is achieved by the computation of an average spatial impact. Therefore, the index is calculated by the combination of the impacts in all the cells that are included in the study area. A value for the impact of each alternative on a given component, for the different study areas, is obtained, giving a measure of the spatial extension of the impact, and allowing a distinction between those impacts that are felt only at a local level, from those that are significant at broader areas of analysis. In general, when impact indices are calculated at increasing spatial scales, the impact value decreases as the study area is expanded, since the magnitude of the effects of the proposed action diminishes with the increase in the distance to the project location. However, in some cases, widening the study area can lead to the inclusion of sensitive areas, more important resources, or increase uniqueness (for example, including endemic species), thus increasing the impact value. Eqs. (2) and (3) assume that the adopted environmental quality scale is an increasing scale, which means that desirability increases with the increase in the Qi,k values. When a decreasing scale is adopted (i.e., a scale where higher Qi,k values are considered worse), environmental impact is given by the difference between baseline and project situation, and therefore the signs of Eqs. (2) and (3) should be inverted. The impact indices obtained by the application of this methodology integrate the magnitude of the effects (expressed by the maps of environmental quality, the extension of the affected area, or habitat) and their significance (considered by the environmental sensitivity, population distribution, and resource importance maps). Applying this methodology, it is therefore possible to calculate a value of impact of each alternative on the several environmental components, for each spatial level of analysis, which can be used in a multicriteria decision framework. The aggregation between components is much more sensitive to the judgement and values of the evaluators, since it implies the weighting or comparison among components, and therefore this step is not performed in SIAM. If the different components are aggregated into a single value for each alternative, the trade-offs between them would be hidden. The objective of SIAM is precisely the opposite: to use the information generated in previous EIA stages to unveil the major trade-

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offs between the different alternatives, considering the environmental components studied and the different levels of analysis.

4. Case study The application of SIAM was tested in the context of a case study based on the EIA of a highway in Central Portugal, the IC7 between Venda de Galizes and Covilha˜ (see Fig. 2). This highway was devised to replace an existing road (EN 230) adjacent to the Serra da Estrela Natural Park, crossing a region of low human population density. Agriculture and forestry are the major land uses in the area. The application of the methodology is illustrated for a segment, between Venda de Galizes and Vide, of 17.2 km length, for which four alternative routes were studied (Alternatives 1, 2, 3, and 1A, see Fig. 2). Alternative 1 follows generally the route of the existing road, with an increase in width and an improvement of the curves radius, in order to improve safety and allow higher traffic speed. Alternative 2 is based on a new route alignment, on the opposite bank of the Alvoco River, where human occupation is much lower. Alternative 3 is very similar to Alternative 1, only with differences in the corridor chosen to go round the urban settlements. Alternative 1A is a combination of Alternatives 1 and 2, following Alternative 1 until Kilometer 7.5 and Alternative 2 from then until Vide.

Fig. 2. Location of the proposed highway and alternatives.


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For the development of this case study, a geo-referenced database was built including elevation data, land use, protected areas, and areas of ecological interest (according with the Natura 2000 European network), meteorological, population, and project data (corridor location and estimated traffic). The GIS software IDRISI was used, considering cells of 3030 m2 (the resolution associated with Landsat Satellite Thematic Data). The impact indices computations were performed in Excel with data files exported from IDRISI. The environmental components presented in this case study are air quality, noise, and disturbance of ecosystems. 4.1. Air quality The application of SIAM follows the steps described in Section 3 and illustrated in Fig. 3. 4.1.1. Scoping The indicators affected area and population were selected to measure the impacts on air quality, which were evaluated for three study areas: 1. project study area, defined as a 250-m width buffer along the highway (250 m for each side of the road axis); 2. local scale, considering a 500-m buffer relative to the road axis; 3. supralocal scale, defined as a 2.5-km buffer. The choice of the distances adopted was related with the nature of the effects on air quality and noise (which decrease sharply with the distance to the road axis) and the need to consider a wider buffer to allow the assessment of impacts on ecological systems. Since the project refers to the replacement of an existing road, the study areas for a given spatial scale of analysis also comprise a buffer of the same width (250, 500, and 2500 m) along the existing road, whose traffic will be almost eliminated due to the construction of the new highway. Therefore, the study area defined for each spatial scale is the result of the sum of the two buffers considered: one corresponding to the alternative under evaluation and another corresponding to the formerly existing road. 4.1.2. Prediction and classification of the environmental descriptors The impacts on air quality were evaluated based on a simulation of the dispersion of major traffic related air pollutants (NOx, CO, HC, suspended particles, and lead) in selected road sections, using the air pollution dispersion model for highways CALINE 4, developed by the California State Department of Transportation (Projectope/Geometral, 1993). Only the environmental descriptor NOx was considered in this case study for illustrative purposes. A map of expected concentrations for each alternative was generated by interpolation of the results obtained in contiguous sections of the road.

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Table 2 Classification of NOx concentration values NOx concentration (mg/m3)

Value

> 200 50 – 200 < 50

10 4 0

The NOx concentration values were classified in a 0 to 10 pollution scale, where 0 represents the lower levels of pollution and 10 the highest, based on Portuguese air quality regulations (Law by Decree Nos. 352/90 and 286/93). Table 2 shows the value function used for the classification. 4.1.3. Computation of the impact indicators and indices Air pollution maps, classified in the adopted scale, and population density maps were used to compute the impact indicators for the several study areas. Appendix A shows the data obtained from the GIS, which were used for the computation of the impact indicators for each alternative. The values for the without project case differ among alternatives, due to the integration of a different buffer for each alternative. Table 3 shows the computation of the impact index for air pollution for the different spatial study areas, using the data included in Appendix A. Columns 2 to 5 are obtained by the computation of the average air pollution value obtained for each alternative, with and without the project. For instance, the first value in column 2 is calculated by the sum of the product of the number of people in each class by the value of the class divided by the total population in that study area. Eq. (2) was used to calculate the environmental impact index, obtained by the difference between the without and with project values. A weight of 0.65 was assigned to the indicator affected population and 0.35 to affected area. The analysis of the results shows that Alternatives 2 and 1A are the most favourable from this point of view. Alternative 3 is the least preferred. This is explained mainly by the fact that the corridor for Alternative 2 (and also for part of Alternative 1A) is located in an area with lower human population density than the existing road, having therefore a positive impact for the indicator affected population. This result is generally in agreement with the conclusions obtained in the EIA study. 4.2. Noise The indicators selected to measure noise impacts were also affected area and population. The study areas considered were the same as those considered for air quality. The magnitude of the impacts was evaluated based on a simulation of

Fig. 3. Application of SIAM for air pollution.

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Table 3 Environmental impact index for air pollution

Alternative

Affected population

Affected area

Without project

With project

Without project

With project

Impact index

250-m buffer 1 3.74 1A 3.25 2 2.52 3 3.21

4.00 3.09 2.42 4.13

3.75 2.86 2.45 3.29

4.00 3.16 3.04 4.40

0.25 0.00 0.14 0.99

500-m buffer 1 2.22 1A 2.06 2 1.90 3 2.06

2.62 2.32 1.85 2.80

2.00 1.72 1.57 1.86

2.37 2.24 1.99 2.67

0.39 0.36 0.11 0.76

2500-m buffer 1 0.69 1A 0.68 2 0.68 3 0.69

0.82 0.77 0.66 0.93

0.46 0.44 0.43 0.45

0.54 0.58 0.55 0.64

0.11 0.10 0.03 0.23

expected noise levels in selected road sections, based on the traffic forecasts for the new road. These values of noise level were classified in the 0 to 10 scale using the value function presented in Table 4. The values used for the classification were extracted from Portuguese Legislation (Law by Decree Nos. 251/87, 292/89, and 72/92 — General Noise Regulation) and allow the distinction between very noisy, noisy, and quiet places. The indicators affected population and area in the several noise classes were used to measure the impact. Table 5 shows the results obtained for the considered study areas, applying the data included in Appendix A. The analysis of the results shows that Alternatives 1A and 2 are also preferred in terms of noise. As expected, the impacts are generally reduced with the increase in the study area, since noise (and also air pollution) is a dispersive phenomenon, which is attenuated with an increase in the distance to the source. 4.3. Affected ecosystems For this illustrative case study, only the impacts on ecosystems due to occupation by the road platform were considered. Other impacts such as Table 4 Classification of noise levels Noise levels, dB(A)

Value

> 65 55 – 65 < 55

10 4 0


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Table 5 Environmental impact index for noise

Alternative

Affected population

Affected area

Without project

With project

Without project

With project

Impact index

250-m buffer 1 1.25 1A 1.09 2 0.84 3 1.07

2.13 1.62 1.21 1.75

1.26 0.96 0.82 1.10

2.12 1.67 1.41 1.86

0.87 0.60 0.44 0.70

500-m buffer 1 0.74 1A 0.69 2 0.64 3 0.69

1.26 1.03 0.91 1.13

0.67 1.72 0.53 0.62

1.13 2.24 0.91 1.05

0.50 0.37 0.31 0.43

2500-m buffer 1 0.23 1A 0.23 2 0.23 3 0.23

0.39 0.34 0.33 0.38

0.15 0.44 0.15 0.15

0.26 0.58 0.25 0.25

0.14 0.11 0.10 0.13

disturbance, contamination, and habitat fragmentation are not included in this case study due to lack of data in an adequate format in the original Environmental Impact Statement (EIS). The study areas considered were the corridor (which corresponds to the area directly occupied by the road) and the 250-, 500-, and 2500-m buffers studied for the other components. However, since the former road will remain, and therefore there will be no change in the existing situation in this corridor for this component, the study area for ecosystems did not include the buffer corresponding to the existing road. Data regarding the types of ecosystem in the several study areas were extracted from the CORINE LANDCOVER Database for Portugal (CNIG —

Table 6 Classification of the landcover classes CORINE landcover class

Description

Value

7 9 8 4 and 10 1 11 5 2, 3, and 6

riparian vegetation hardwood forests Arbutus unedo brushwood brushwood pine tree forests agricultural areas eucalyptus forests constructed areas

10 9 8 6 3 2 1 0

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Table 7 Environmental impact index for ecosystems Affected ecosystems Alternative

Without project

With project

Impact index

Corridor 1 1A 2 3

4.60 4.53 5.00 3.79

0.00 0.00 0.00 0.00

4.60 4.53 5.00 3.79

250-m buffer 1 1A 2 3

4.36 4.52 4.82 4.09

4.06 4.23 4.49 3.84

0.30 0.29 0.33 0.25

500-m buffer 1 1A 2 3

4.52 4.60 4.56 4.46

4.36 4.45 4.39 4.33

0.16 0.16 0.18 0.13

2500-m buffer 1 1A 2 3

4.53 4.62 4.73 4.58

4.49 4.59 4.69 4.55

0.04 0.03 0.04 0.03

National Centre for Geographical Information). The area of each type occupied by a given alternative was estimated by the overlay of that data with project information (see Appendix A). A value in the 0 – 10 scale was assigned to the different CORINE classes of land use according with their ecological relevance, applying the value function presented in Table 6. Table 7 shows the results obtained for the impacts on ecosystems due to occupation. These results show that Alternative 3, whose road alignment is closer to urban areas, is the most desirable in this perspective. Alternative 2, which implies the destruction of ecosystems with higher naturalness and conservation value, is the worse. 4.4. Discussion of the results The results obtained in this case study are generally consistent with those presented in the EIS that was used as a basis. However, in the case of noise, the expert in the EIA, based on the detailed analysis of the situations along the corridor, concluded that Alternative 1 was preferred to Alternative 2, which was


529

not confirmed by the results obtained with SIAM. This disagreement may be partially explained by the fact that the expert in the EIS gave more weight to the situations of households that were not previously subject to noise and that would be significantly affected by Alternative 2, than to the positive impacts related with the deviation of traffic from existing urban areas. The application of SIAM guarantees consistency in the application of the criteria established for evaluating impact significance, which is not always the case when significance assessment is based only on expert judgement. Another advantage of using a procedure like the one proposed in this paper for the evaluation of impacts is related with the fact that the results can be tested and reproduced by someone different from those who performed the study. The loss of the detailed information in the aggregation process inherent to the computation of indices, is always a drawback that can be pointed to index based methodologies (Antunes and Caˆmara, 1992). However, they provide an aggregate picture of the impacts, which is essential for decision-making, namely for the analysis of trade-offs among alternatives. SIAM is mainly aimed at performing an aggregation of impacts in the spatial dimension. The problem of loss of information in this spatial aggregation is handled herein by the computation of impact indices for different study areas, allowing the distinction between impacts at the several spatial scales.

5. Conclusions Although the costs associated with the development of a GIS database are sometimes pointed to as an obstacle to their wider use in EIA, these costs are almost irrelevant when compared with the project costs and the costs associated with impact mitigation measures (Ventura et al., 1988). On the other hand, the benefits obtained from a better decision-making can be very significant. The work presented in this paper attempts to improve the effectiveness of EIA by the application of a methodology to evaluate the spatial significance of impacts, SIAM. The major feature of SIAM is the consideration of different spatial levels of analysis and the acknowledgement of the fact that impacts at different spatial scales, and in different environmental components, should not be aggregated. Within a given scale of analysis, SIAM performs the spatial aggregation of impacts, and aggregates the impact indicators considered in each environmental component studied. This aggregation is essential to reduce the amount of information provided to the decision-maker and to stress the existing trade-offs between impacts at the several spatial scales and between valued environmental components. Aggregation of impacts among components is not performed, since it is considered that the differences between alternatives should be clearly given to the decision-maker.

530


Also, SIAM can contribute to increase the objectivity of the evaluation of impacts through the definition and application of clearly defined rules, based on technically grounded criteria, for the classification of the results obtained in the impact prediction stage. SIAM can be applied to the EIA of any proposed project where the spatial distribution of the impacts is relevant (which is the case of almost all projects with environmental impacts). The general framework described in this paper can be adapted to each particular situation, defining, for instance, the spatial scales of analysis to be studied, the shape and size of the different study areas, the value functions for classification of descriptors values, the impact indicators, and the aggregation procedure to be used. SIAM allows the assessment of cumulative effects in space. The evaluation of cumulative effects in time, requires some adaptation of the methodology. In the current version, such effects can be handled, for example, by the computation of environmental impact indices for different time horizons, or using a map depicting the accumulation of effects in time as the data source for computation of the indices. This issue can be the subject of future developments of SIAM. The application of this methodology can contribute to a more effective use of the information generated in all EIA stages for impact evaluation, as shown in the case study presented, and to increase the benefits that can be derived from a wider application of GIS.

Acknowledgments The work presented in this paper was partially supported by JNICT/STRIDE Project STRD/AMB/67/93. The authors wish to thank the helpful comments and suggestions of several colleagues and reviewers on previous versions of the manuscript.

Appendix A A.1. Alternative 1 Baseline (without project) Pollution class 250 m 500 m 2500 m Population affected by noise (number of people) 10 43 43 43 4 99 99 99 0 519 973 3421 Total 661 1116 3563

With project 250 m 500 m

2500 m

43 244 374 661

43 244 3276 3563

43 244 829 1116


531

Population affected by air pollution (NOx ) (number of people) 10 0 0 0 0 0 4 618 618 618 661 732 0 43 497 2945 0 384 Total 661 1116 3563 661 1116

0 732 2832 3563

Area affected by noise (ha) 10 30 4 68 0 354 Total 451

30 68 3605 3702

30 164 257 451

30 164 653 847

30 164 3508 3702

Area affected by air pollution (NOx ) (ha) 10 0 0 0 4 423 423 423 0 28 425 3279 Total 451 847 3702

0 451 0 451

0 502 345 847

0 502 3199 3702

CORINE class Value Corridor Affected ecosystems (ha) 1 3 7 2 0 0 3 0 0 4 6 1 5 1 2 6 0 1 7 10 1 8 8 0 9 9 9 10 6 0 11 2 8 Total 30

30 68 750 847

250 m

500 m

2500 m

Corridor

250 m

500 m

2500 m

87 0 0 12 33 4 22 0 114 0 178 451

186 0 0 15 51 4 45 10 218 0 318 847

910 0 0 128 89 4 110 62 968 0 1430 3702

0 0 30 0 0 0 0 0 0 0 0 30

80 0 30 11 31 3 21 0 105 0 170 451

179 0 30 13 49 3 45 10 209 0 310 847

903 0 30 126 87 3 110 62 959 0 1422 3702

A.2. Alternative 1A Baseline (without project) Pollution class 250 m 500 m 2500 m Population affected by noise (number of people) 10 43 43 43 4 99 99 99 0 619 1060 3479 Total 762 1202 3621


2500 m

36 219 507 762

36 219 3366 3621

36 219 947 1202

532


Population affected by air pollution ( NOx ) (number of people) 10 0 0 0 0 0 4 618 618 618 587 698 0 143 584 3003 174 504 Total 762 1202 3621 762 1202

0 698 2923 3621


30 68 3733 3830

29 176 388 592

29 176 780 984

29 176 3626 3830


0 467 125 592

0 552 432 984

0 552 3278 3830


30 68 887 984

250 m

500 m

2500 m

Corridor

250 m

500 m

2500 m

71 0 0 8 20 4 22 0 123 0 193 442

160 0 0 13 45 4 47 7 229 0 332 838

910 0 0 100 90 4 115 62 1042 0 1412 3735

0 0 29 0 0 0 0 0 0 0 0 29

70 0 29 8 18 3 21 0 114 0 179 442

159 0 29 13 44 3 46 7 220 0 318 838

909 0 29 99 89 3 113 62 1033 0 1398 3735

A.3. Alternative 2 Baseline (without project) Pollution class 250 m 500 m 2500 m Population affected by noise (number of people) 10 43 43 43 4 99 99 99 0 840 1158 3479 Total 983 1300 3621


2500 m

33 214 736 983

33 214 3374 3621

33 214 1053 1300


533


86 386 3150 3621

Area affected by noise (ha) 10 30 30 4 68 68 0 594 977 Total 691 1075

30 68 3793 3890

28 174 489 691

28 174 873 1075

28 174 3688 3890


71 346 273 691

71 355 648 1075

71 355 3464 3890


250 m

500 m

2500 m

Corridor

250 m

500 m

2500 m

56 0 0 8 19 2 36 4 113 0 175 412

126 0 0 16 45 4 44 15 205 0 331 786

850 0 0 102 87 4 114 62 1063 0 1344 3626

0 0 28 0 0 0 0 0 0 0 0 28

55 0 28 8 17 2 32 4 105 0 161 412

125 0 28 16 43 4 41 15 197 0 317 786

849 0 28 102 86 4 110 62 1055 0 1331 3626

A.4. Alternative 3 Baseline (without project) Pollution class 250 m 500 m 2500 m Population affected by noise (number of people) 10 43 43 43 4 99 99 99 0 629 1057 3457 Total 771 1200 3600


2500 m

40 238 494 771

40 238 3322 3600

40 238 922 1200

534



105 578 2917 3600


30 68 3664 3762

29 166 319 514

29 166 712 907

29 166 3567 3762


76 375 63 514

76 415 416 907

76 415 3271 3762


30 68 810 907

250 m

500 m

2500 m

Corridor

250 m

500 m

2500 m

62 0 0 12 32 4 17 0 101 0 210 439

172 0 0 14 50 4 38 10 217 0 329 834

901 0 0 119 86 4 112 62 1001 0 1418 3703

0 0 29 0 0 0 0 0 0 0 0 29

58 0 29 11 30 3 17 0 95 0 196 439

167 0 29 12 48 3 38 10 211 0 315 834

897 0 29 118 84 3 111 62 995 0 1404 3703

References Antunes P, Caˆmara A. HyperAIA — an integrated system for Environmental Impact Assessment. J Environ Manage 1992;35:93 – 111. Antunes P, Santos R, Jordaõ L, Gonçalves P, Videira N. A GIS-based decision support system for Environmental Impact Assessment. Proc IAIA’96 Conf, Estoril, Portugal, 1996. pp. 451 – 6. Bateman IJ, Brainard JS, Lovett AA. Modelling woodland recreation demand using Geographical Information Systems: a benefit transfer study. CSERGE Working Paper GEC 95-06, 1995.


535

Beinat E. Value functions for environmental management. Dordrecht: Kluwer Academic Publishers, 1997. Burrough PA, McDonnell R. Principles of Geographical Information Systems. Oxford: Clarendon Press, 1998. Davidson DA. The evaluation of land resources. London: Longman Scientific and Technical, 1992. Duinker PN, Beanlands GE. The significance of environmental impacts: an exploration of the concept. Environ Manage 1986;10(1):1 – 10. Eade J, Moran D. Spatial economic valuation: benefits transfer using Geographical Information Systems. J Environ Manage 1996;48:97 – 110. Eedy W. The use of GIS in environmental assessment. Impact Assess 1995;13:199 – 206.. Fedra K. GIS and environmental modelling. In: Goodchild MF, Parks BO, Steyaert LT, editors. Environmental modeling with GIS. Oxford: Oxford Univ. Press, 1993. pp. 35 – 50. Joaõ E, Fonseca A. The role of GIS in improving environmental assessment effectiveness: theory vs. practice. Impact Assess 1996;14:371 – 87. Kennedy AJ, Ross WA. An approach to integrate impact scoping with Environmental Impact Assessment. Environ Manage 1992;16:475 – 84. Martinho S, Santos R, Antunes P, Jordaõ L. Using natural assets attributes to produce economic value maps. Paper presented at the Fifth Biennial Meeting of the International Society for Ecological Economics, Santiago, Chile, 1998. Mostert E. Subjective Environmental Impact Assessment: causes, problems and solutions. Impact Assess 1996;14:191 – 213. O’Bannion K. Use of value functions in environmental decisions. Environ Manage 1980;4(1):3 – 6. Projectope/Geometral. Estudo de Impacte Ambiental. Estudo Pre´vio do Lanço Venda de Galizes/ Covilha˜ — Itinera´rio Complementar 7. Relato´rio Sıńtese e Anexos. Lisboa, 1993. Rivas V, Gonzalez A, Fischer DW, Cendrero A. An approach to environmental assessment within land-use planning process: northern Spanish experiences. J Environ Plann Manage 1994;37: 305 – 22. Sankoh OA. An evaluation of the analysis of ecological risks method in Environmental Impact Assessment. Environ Impact Assess Rev 1996;16:183 – 8. Sankoh O, Bonner D, Determann T, Gersten J, Gropp A, Hoelters I, Krueger N, Lehmann U, Marty C, Strauss K, Wegner H. Finding and assessing route alternatives. J Environ Manage 1993;38: 323 – 34. Schaller J. GIS helps measure impact of new Munich airport. GIS Eur 1992, June. Smit B, Spaling H. Methods for cumulative effects assessment. Environ Impact Assess Rev 1995;15:81 – 106. Szergo J. Estimating and mapping the impact of environmental influences on a dynamic population: II. Proc EGIS’94 Conf, Paris, pp. 997 – 1006. Ventura SJ, Nieman BJ, Moyer DD. A multipurpose land information system for rural resource planing. J Soil Water Conserv 1988;43:230 – 43. Wang HL. A method to assess the allocation suitability of recreational activities: an economic approach. Environ Manage 1996;20:85 – 193.

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