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INDICATORS OF ENVIRONMENTAL PERFORMANCE FOR RAILWAY OPERATIONS

Maria Ins FaUniversidade Federal do Esprito Santo,

Vitria, Esprito Santo, Brasil, [email protected]

Eduardo Fausto Kuster CidMestrando Universidade Federal do Esprito Santo,


Eliana ZandonadeUniversidade Federal do Esprito Santo,


ABSTRACT

This article presents the definition and evaluation of indicators of environmental performance for Brazilian railway

operations. The methodology comprised a survey using a questionnaire, to obtain the degree of importance of 25indicators, and the application of the statistical method Factorial Analysis, to determine and analyse the interrelation

among the indicators. The questionnaire was applied to 82 specialists in transport and environment, aiming to obtain the

degree of importance of each indicator according to a range of values varying between 1 to 10. The operational aspects

covered by the selected indicators were particulate matters; noise; waste; falling material on the rail track; accidentsinvolving hazardous materials, accidents involving humans or wild animals; and oil spills. The data analysis showed that

2 indicators were considered of high importance, 3 of low importance and 20 of medium importance. The highest

importance was given to the indicators concerning train accidents involving hazardous materials classes 1 to 4 andclasses 5 to 9. The indicator related to noise generated by locomotive engines presented the lowest standard deviation,

while the largest variation of 2.9 was found for accidents involving people in railway terminals. As a result of the

aggregation of variables given by the Factorial Analysis Method, the following categories of indicators for the

environmental performance of railway operations were defined: particulate matters, water and soil contamination,

railway infrastructure, and accidents in the railway.

KEY WORDS: Railway indicators, environmental indicators, operational indicators
mailto:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]:[email protected]


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1. INTRODUCTION

Environmental impacts are a growing concern of international financial agencies. Transport

infrastructure requires expensive investment as concerns both the amount of capital needed as well as

the quantity of natural resources involved and exploited. Transport operation may also interfere in

nature, particularly on situations of poor managerial approach and old and inefficient equipment,vehicles, and infrastructure. In this aspect environmental indicators may play an important role, since

they can provide performance information regarding the way transport operators and enterprisesinteract with the environment. Indicators may provide a guideline to support environmental studies in

licensing new constructions or expansion of the transportation network, as well as on the design and

implementation of environmental planning systems. According to Fedez e Vtora (1993) apudSEAMA et al.(1998), indicators should express the level of environmental quality and may provide

a tool for mitigation and control of environment impacts, monitor environmental programs, and

predict risk situations.

Research on indicators concerned to natural resources, like sustainable environmental of forests and

water, are quite well explored in the studies by Padovan (2001), Pedroni and Camino (2001), IBGE(2002), among others. On the specific domain of transport, few works were found. Galves and Av

(1999) present a study in which indicators aim to represent the level of environmental impacts for

roadways. Regarding indicators for railway performance, Vandermeulen et al. (2003) report a

research carried out within the Brite/Duram project Ravel. Such a project consists on a Internet-based tool which allows an assessment of railway vehicles by means of the following set of

environmental performance indicators: amount of prohibited and restricted materials; component

complexity; cradle-to-gate material index, degree of inventoried materials; existence ofdisposable/recycling manuals; fraction of recyclable materials; fraction of renewable materials;

fraction of reused components; marking of selected material and component groups; possible

hazardous waste rate; possible material recycling rate; suppliers with environmental managementsystem; system mass; and total energy consumption. It was developed a method to measure the Eco-

efficiency of rail vehicles, and a guideline concerning international standards criteria to be

considered in commercial contracts.

The indicators defined here aim evaluating the environmental performance of railway operations,

and differ from those of the Brite/Duram Ravel project both in the focus and in the methodology

used. The focus is in the rail operation rather than the vehicle itself, and the method the FactorialAnalysis used to establish the indicators hierarchy according to their degree of importance. Santos

and Pivello (1999) highlight the need for systematisation, qualification, quantification and ranking

the hierarchy of indicators for environmental planning. The authors also stress the need for usingwell-known and recognised methods to cope with subjective aspects of assessing environmental

impacts.

Given the importance of indicators and the relatively few studies on this field for Brazilian railways,

this paper aims to define and evaluate indicators of the environmental performance for railway

operations. The remainder of this article is structured as follows. Section 2 approaches the

methodology used, followed by the data analysis and the main results in section 3. Some conclusionsand issues for further research are given in section 4.


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2. METHODOLOGY

The methodology comprised a survey, using a questionnaire, and the application of Factorial

Analysis method. A questionnaire was applied to 82 specialists in transport and environment, aiming

to collect their valuation of environmental indicators for the Brazilian railway operations. We used

the same sample chosen by Cid (2004) in the main institutions and enterprises which deal with thissubject in Brazil. The author stressed the relatively few population of specialists in such a domain.

The questionnaire comprised the definition of 25 indicators for which was asked the degree of

importance according to the following range of values: 1 to 3 = no importance; 4 to 5 = low

importance; 6 to 7 = medium importance; 8 to 10 = high importance. A total of 72% of answers wereobtained.

The operational aspects covered by the selected indicators were particulate matters; noise; waste;falling material on the rail track; accidents involving hazardous materials, accidents involving

humans or wild animals; and oil spills. Two different groups regard emissions. One is the particulate

matters due to cargo handling (grains, minerals, etc). The other group of indicators is related tolocomotive emissions, expressed by particulate in general, Carbon Monoxide (CO), Nitrogen Oxides

(NOx,), Sulfur Oxides (SOx) and Hydrocarbons (HC). It is important to highlight that locomotives are

mobile sources of pollution and that railways have linear infrastructure. The combination of these 2

elements favours the emissions dispersion in open areas, while in confined areas, like in tunnel andcanyons, concentration of particulate matters is likely to occur. The definition of an indicator, which

contemplates such a variety of landscape details, is not a simple task, so it was very difficult to

predict the tendency of the questionnaire answers for such indicators.

The definition of indicators for noise was based on the Brazilian regulations ABNT NBR 10151 and

ABNT NBR 10152. Two different indicators were defined, one concerning the level of locomotivenoise (engine, piston, etc) and the other the level of noise generated by the rolling system like the

wheels friction, irregularities in the infrastructure, connections, steel railway sleeper, among others.

The environmental landscape interferes in the level of nuisance and the answers may reflect suchvariability. A generic definition for the noise indicator was chosen due to the large spectrum of

environmental situations and consequent difficulties to measure the level of noise on bridges,

tunnels, open air, urban areas, and rail stations. Nevertheless, relatively high importance was initially

predicted in the questionnaire answers due the impact of noise indicators on humans health as wellas on animals.

The Brazilian regulation NBR 10004/87 was considered in the definition of three waste indicators:(1) the total amount of annual disposal waste in relation to the total waste generated in rail vehicles

maintenance and in rail stations (metal scrap, oil, wood, wrap paper, etc); (2) annual percentage of

recycled or reused waste that is generated in rail vehicles maintenance and in rail stations (metalscrap, oil, wood, wrap paper, etc); and (3) annual amount of waste generated in passengers wagons in

relation to the number of passengers (napkins, remaining of food; plastic cups and bags, etc). Given

the level of waste risk, a high level of importance was expected to indicator (1), while low

importance to indicator (2) and (3).

For materials which may fall in the track during train movement, five indicators were defined: (1)

average amount of general cargo that annually falls in the rail track in relation to the average amount


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of general cargo transported (metal scrap, iron tubes, rocks, glasses, industrial equipment, cars,

among others); (2) average amount of pallets or containers that annually falls in the rail track inrelation to the average amount of such a cargo transported; (3) average amount of grains, minerals,

fertilisers, etc that annually falls in the rail track in relation to the average amount of this kind of

cargo transported; (4) average amount of grains, minerals, fertilisers, etc, per railway kilometre, that

annually falls in the rail track exclusively due to rail accidents; (5) average liquid in bulk thatannually falls in the railway, in relation to the total amount of liquid in bulk that was transported in

that year (chemicals, petrol, acids, etc). Given the potential risk of environmental damages of liquidin bulk in contact with water springs and the soil, the prediction was answers that would favour high

importance to the latter indicator.

The group of indicators concerning accidents with hazardous materials was divided into two: one for

material classes 1 to 4 (explosives, inflammables, spontaneous combustion, compressed gas, among

others), and the other for material classes 5 to 9 (oxidant substances, peroxides; toxic substances,infectious substances; radioactive materials; corrosives; hazardous substances in general). One would

expect high importance for both indicators giving the severe damage caused by this kind of material

when in contact with the environment.

Two indicators were defined for the sewage generated by railway operations. The first represents the

annual average volume of sewage collected in train toilets in relation to the annual average number

of travellers. The second is the average annual volume of sewage from stations, terminals, and trainmaintenance buildings that receives a cleaning treatment before being returned to nature. Medium

importance was expected for these 2 indicators.

Three indicators concerning accidents to humans and animals were defined: (1) annual number of

animals injured or killed by trains in relation to the annual train frequency; (2) annual number of

people and autos crashed by trains in railway crossings in relation to the annual train frequency; (3)annual number of people injured or killed by trains in railway terminals in relation to the annual train

frequency. Despite the relatively high number of accidents involving both people and animals in

Brazilian railways, one could predict that the answers would pinpoint low importance to theseindicators.

For oil spills, two indicators were defined: (1) average annual oil leaking from locomotive engines,

in litres per kilometre; and (2) emission of oil from locomotive chimney in litres per kilometre. Sincethis waste is officially classified as hazardous, class III, it was likely that the specialists would

consider the indicators of oil spills of high importance in their answers to the survey.

The methodology also comprised the application of Factorial Analysis in the evaluation of

environmental performance for Brazilian railway operations, according to the indicators degree of

importance given by specialists.

This method was successfully applied by Dalla (2003) to determine the hierarchy of environmental

indicators regarding managerial aspects of coffee plantation. Hair et al. (1998) define Factorial

Analysis as a class of multivariate statistical methods which aims to determine/analyse theinterrelation among a large number of variables, by using a sequence of factors which are derived by

variables correlation. Such a technique reduces the amount of variables into a small number of

factors and, consequently, easy the data analysis. Gomez (1993) recognises that this implies some


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information loss, however variables with similar behaviour, or similar meaning, will be aggregated

into the same factor. The relation between the original variable and the factor is given by a weightrepresented by the correlation coefficient. So, the weights vary between -1 and +1, and a high value

means that the variable is highly significant. When the correlation is very close to zero or equal to

zero then there is no factor. When there is correlation among the variables factors are likely to

appear.

Next section presents the data analysis carried out and the main results obtained in this study.

3. RESULTS

Table 1 shows the indicators hierarchy given by the mean value obtained in the questionnaire

answers. The data analysis showed that among the 25 indicators, 2 of them were considered of high

importance, 3 of low importance and 20 of medium importance. The highest importance was givento the indicator concerning train accidents involving hazardous materials classes 1 to 4 (mean of 8.6

and standard deviation of 2.2), followed by the indicator of hazardous materials classes 5 to 9 (mean

of 8.4 and standard deviation of 2.4). Low importance was given to the indicators annual averagevolume of sewage collected in train toilets in relation to the annual average number of travellers,annual number of animals injured or killed by trains in relation to the annual train frequency and

average amount of pallets or containers that annually falls in the rail track in relation to the average

amount of such a cargo transported. The others indicators were considered of medium importance.

The lowest standard deviation of 1.5 was shown by the indicator of noise generated by locomotive

engines, while the largest variation of 2.9 was found for accidents involving people in railwayterminals. Surprisingly, medium importance was given to the indicator of liquid in bulk that falls in

the rail track, with a mean of 7.8 and 2.3 standard deviation. The lowest mean value of 5.5 was givento the indicator regarding pallets and containers that fall in the track during the trip.

The low level of importance given to the indicator sewage collected in train toilets contradicted ourinitial estimation of medium importance. Other estimations that were not fulfilled were related to

indicators of particulate matters and noise.

The range of marks attributed to the indicators varied between the maximum of 10, for highimportance, and the minimum of 1, for no importance. Among the answers, many indicators received

the lowest mark. For particulate matters due to cargo handling, the minimum value was 4 what

means that no single respondent considered this indicator of no importance.

As a result of the aggregation of variables given by the Factorial Analysis Method, the following

categories of indicators for the environmental performance of railway operations could be defined:particulate matters, water and soil contamination, railway infrastructure, and accidents in the railway.

The accumulated variance explained in the first six factors was 78,99%, as shown in table 2.

The first factor comprises the group of best correlated indicators according to the level of importance

given in the questionnaire answers. Seven indicators regarding emissions (noise and particulate

matters) were grouped in this factor: noise due to both locomotive engine and weel-rail friction; and


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the following emissions from locomotive chimney - particulate matters; Carbon Monoxide (CO),

Nitrogen Oxides(NOx,), Sulphur Oxides (SOx) and Hydrocarbons (HC).

It is important to highlight that the Brazilian Law n 9.605 (Brasil,1998) and official guidelines for

railways project and operation (SEAMA, 2002) do not specify the kind of impact caused by railway

operation. They rather approach this subject in general terms.

The second factor includes the following 5 indicators: accidents with hazardous materials classes 1 to4 and 5 to 9, oil leaking from locomotive engines, liquid in bulk that falls in the rail track, and

amount of grains, minerals, fertilisers, etc that falls exclusively due to rail accidents. They are closely

related to contamination of soil and water, particularly in the case of train crash involving dangerouscargo. As concerns the legislation, the Brazilian Law n 9.605 (Brasil,1998) establishes penalties for

inadequate operation that affects the environment. The same Law regulates the safe operation to

prevent such problems. However, it fails to consider contamination of soil and water, and approachesthe impacts in general terms. Such omission is also in the scope of the study by Menezes (2000) .

The indicators included in the third factor are general cargo that falls in the rail track; amount ofgrains, minerals, fertilisers, etc that falls in the rail track during transportation; animals injured or

killed by trains; and oily emissions from locomotive chimney.

Factor 4 comprises a group of 5 indicators that can be related to railway infra-structure: disposalwaste; recycled or reused waste generated in rail vehicle maintenance and stations; waste collected in

passenger wagons; particulate matter due to cargo handling, and sewage collected in train toilets. It is

interesting to pinpoint the correlation found in both kind of wastes, the one for final disposal and theother which is recycled or reused. The legislation broadly approach the environmental impact caused

by waste and omits the details regarding the kind of pollutant.

Indicators of accidents involving people, pallets or containers that falls on the rail track were grouped

in factor 5. Surprisingly, the questionnaire answers concerning the level of importance of accidents

presented low correlation. Factor 6 comprises a single indicator sewage that receives cleaningtreatment before going back to nature. This last factor express an indicator of low correlation with

the others and a possible reason for this is the misunderstanding/ misinterpretation of the question.

4. CONCLUSIONS

The method used in this study supported the definition and evaluation of indicators of environmental

performance for Brazilian railway operations. According to the answers obtained in the survey,indicators related to accidents involving hazardous materials classes 1 to 4 and 5 to 9 had the highest

level of importance, while the ones concerning accidents involving wild animals, pallets andcontainers that fall in the track during the trip, and sewage collected in train toilets received thelowest importance.

The Factorial Analysis gave 6 groups of indicators, based on the level of correlation among them.

The group of indicators that presented the highest correlation concerns those of air pollution, given

by noise and particulate and gas emissions. Some results fail to attend our predictions, particularly on


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the indicators of liquid in bulk that fall in the rail track due to accidents and rail accidents involving

people crash.

The definition and evaluation of indicators obtained in this study may support the development of

Environment Management Systems for railways. Further studies are required in order to provide the

Brazilian Government and the Railway Enterprises of better managerial tools. In this sense, thefollowing suggestions may apply:

- To validate the indicators defined in this study using case studies;

- To carry on similar research aiming to define and evaluate other indicators of environmentalperformance of railway operations;

- To develop guidelines for railways projects and operation based on indicators of environmentalperformance, giving support to the actual Brazilian legislation that uses generic and broad terms.

ACKNOWLEDGEMENTSThe authors would like to acknowledge the financial support given by the Brazilian Government on the development of

this research through the CNPq project 500030/02-2.

REFERENCESBrasil (1998) Lein 9.605, de 12 de fevereiro de 1998.

Cid, E.F.K. (2004) Estabelecimento e hierarquizao de indicadores de desempenho ambiental de operaes ferrovirias.MSc Thesis, Universidade Federal do Esprito Santo, Vitria, Brasil.

Dalla, C.F.M. (2003) Indicadores para Avaliao do Desempenho Ambiental na Cafeicultura Capixaba. MSc Thesis,Universidade Federal do Esprito Santo, Vitria, Brasil.

Galves, M.L. and A.M. Av (1999) Investigao do Passivo Ambiental de Rodovias por Meio de Indicadores de

Impacto.A Varivel Ambiental em Obras Rodovirias, Seminrio Nacional, Foz do Iguau, Brasil.

Gmez, F.C. (1993) Tcnicas estadsticas multivariantes com resolucin de ejeros practicos mediante los paquetes

estadsticos SPSS e PROGSTAD, Universidad de Deusto, Bilbao, Espanha.

Hair, J.F., R.E. Anderson, R.L. Tatham and W.C. Black (1998)Multivariate Data Analysis, 5th. Edition, Prentice-Hall.

IBGE (2002) Indicadores de Desenvolvimento Sustentvel, Rio de Janeiro, Brasil.

Menezes A (2000) Gesto ambiental do transporte ferrovirio. MSc Thesis, Instituto Militar de Engenharia, Rio de

Janeiro, Brasil.

Padovan, M.P. (2001) Formulacion de um Estandar y um Procedimento para la Certificacion del Manejo de Areas

Protegidas. MSc Thesis CATIE, Turrialba, Costa Rica.

Pedroni, L. and R. Camino (2001) Um Marco Lgico para la Formulacin de Estndares de Manejo Florestal

Sostenible. Turrialba, Costa Rica.

Santos, R. and V. Pivello (1999)Princpios de Planejamento Ambiental. Apostila da disciplina Planejamento Ambiental,Universidade Estadual de Campinas, So Paulo, Brasil.

SEAMA, IAP e GTZ (1998) Curso de Avaliao de Impactos ambientais. Vitria, Brasil.


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SEAMA (2002) Termos de Referncia. Vitria, Esprito Santo, Brasil

Vandermeulen B., W. Dewulf, J.Duflou, A. Ander and T. Zimmermann (2003) The use of indicators for environmentalassessment within the railway business: the RAVEL workbench prototype, a web-based tool. Journal of Cleaner

Production11, 779-785, Elsevier.


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Table 1: Indicators hierarchy given by the average value of theirs importanceINDICATORS MEAN STANDARD

DEVIATION

VARIANCE

COEFFICIENT

I HIGH IMPORTANCE

Accidents involving hazardous materials classes 1 to 4 8.56 2.23 26.07

Accidents involving hazardous materials classes 5 to 9 8.43 2.38 28.3

II MEDIUM IMPORTANCELiquid in bulk that falls in the rail track 7.86 2.33 29.69

Waste for final disposition 7.69 1.98 25.82

Noise caused by the locomotive engine 7.61 1.21 15.88

Waste to be recycled or reused 7.56 2.11 27.88

Accidents involving people and cars 7.51 2.61 34.83

Oil leaking from locomotive engines 7.40 1.96 26.55

Carbon Monoxide from locomotive chimney 7.39 1.88 25.54

Particulate matters due to cargo handling 7.39 1.76 23.81

Hydrocarbons from locomotive chimney 7.27 1.89 26.05

Sulphur Oxides from locomotive chimney 7.15 2.17 30.40

Nitrogen Oxides from locomotive chimney 7.13 2.12 29.85

Noise generated by the rolling system 7.03 1.91 27.22

Grains, minerals, fertilisers that falls exclusively due to train accident 7.01 2.39 34.06

Particulate matters from locomotive chimney 6.97 2.25 32.38

Sewage that receives cleaning treatment 6.77 2.34 34.60

Oil spills from locomotive chimney 6.72 2.24 33.45

Grains, minerals, fertilisers that fall in the rail track 6.71 2.15 32.10

Accidents caused to humans in stations 6.49 2.91 44.91

General cargo that falls in the rail track 6.28 2.43 38.84

Waste collected in passenger wagons 6,12 2,24 36.58

III LOW IMPORTANCE

Sewage collected in train toilets 5.866 2.16 36.84

Accidents caused to wild animals 5.632 2.22 39.58

Pallets or containers that fall in the rail track 5.455 2.75 50.43


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Table 2: Variance explained in the Factorial Analysisinitial eigenvalues extraction sums squared loading rotation sums squared loadingindicator

total % variance total % variance total % cumulative variance

1 9.15 36.60 9.15 36.60 5.6 22.39

2 3.35 13.39 3.35 13.39 3.9 37.92

3 2.43 9.72 2.43 9.72 3.4 51.27

4 2.26 9.02 2.26 9.02 2.8 62.345 1.42 5.67 1.42 5.67 2.6 72.83

6 1.15 4.58 1.15 4.58 1.5 78.99

7 0.82 3.29

8 0.79 3.15

9 0.62 2.47

10 0.52 2.09

11 0.44 1.78

12 0.37 1.47

13 0.33 1.33

14 0.28 1.11

15 0.24 0.94

16 0.22 0.86

17 0.18 0.7218 0.14 0.57

19 8.5E-2 0.34

20 7.4E2 0.30

21 6 E-2 0.24

22 3.5E-2 0.14

23 2.9E-2 0.12

24 1.4E-2 5.4E-2

25 1.1E-2 4.3E-2

Extraction Method: Principal Component Analysis

xiii panam- eua 2004

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