Ildiko Tulbure 1 , Daniela Ciolea 2, *
1
University “1 Decembrie 1918”, Str. Gabriel Bethlen Nr. 5, RO-510009 Alba Iulia
2
University of Petrosani, Str. Universitatii Nr. 20, RO-332006 Petrosani, Romania
* cdipentrucdi@yahoo.com
Abstract
At latest starting with the Conference for Environment in Stockholm in 1972 it became clear that human technological and economic activities are not bringing only positive results, but there are negative aspects as well. The economic industrial developments have impacts on the environmental field as well. The regional environmental quality can be assessed by taking into account some aspects, as air, water and soil quality. In most of the cases admissible values are established, regarding the pollution degree caused by one or another factor.
In this paper two methods for air quality assessment on regional level are presented. One method is the so-called
I
PG
– method , that is the method of the global pollution index. Depending on the deviation from the normal state, so-called quality ranking classes are defined, which can be used for graphical design of some specific diagrams.
The second method is consisting of using the Air Pollution Index, API . This indicator is considering the concentrations of several pollutants in one place and some weighting coefficients for each considered pollutant.
A fuzzy logic based method for calculating the weighting coefficients will be presented, which offers the possibility of considering knowledge from different fields.
Keywords: environmental assessment, sustainable development, air quality, fuzzy logic, environmental indicators
AIMS AND BACKGROUND
The role of environmental aspects for the concept of sustainable development
The world began to realize the dangers and undesired effects of human activities, especially industrial ones, especially afterr the Conference for Environment in Stockholm in 1972 and it was understood that besides wanted effects of technological progress, undesired and negative effects can appear. After this time the environmental awareness in the Western world began changing [6]. It was clear that the created problems are very serious and need to be solved.
Worldwide began discussions on political, scientifical and social levels starting with the ‘70 years in order to find solutions for the regional and global environmental problems.
The Brundtland Report of the World Council on Environment and Development represented a result of the worldwide political discussions. Nowadays we confront us with a series of global

problems known as the „trilemma“ of our society: world population growth, growth of the energy and natural resources consumption as well as environmental pollution [6], pointed out for the first time in the first report to the Club of Rome „The Limits of the Growth“ [10]
(Figure 1). The concept of sustainable development was for the first time defined in the
Brundtland Report [12] and accepted as a possible solution for the global complex ecological, economical and social problems. Many actions after this time emphasize that the evolution of technical, social and ecological systems has to be analysed in synergetic relation [16].
Economic
Sustainability
Global (Environmental) Problems
Ecologic
Sustainability
Sustainable
Development
Social
Sustainability
Operationalization
Sustainable Development
Indicators SDI
Environmental
Aspects
Figure 1: Environmental aspects in the context of operationalizing sustainable development.
The tranformation of the concept of sustainable development in political measures and controlling instruments needs his operationalization [6, 15]. From Figure 1 is following that it is necessary to evaluate the economic, ecological and social situation of each country when the sustainability of a country is evaluated. Indicators have an important role in this context.
Economic indicators show something about the economic level of the country, social ones about the human relations, but environmental ones are pointing out the environmental quality and how environmental friendly are industrial processes in the analysed country.
Sometimes environmental indicators have to be newly defined and often used not only for operationalisation of sustainable development but also for environmental monitoring and for gaining information about the environmental quality in the considered region.

Environmental indicators
These indicators permit to formulate quantitatively the proposed objectives and goals in order to obtain sustainable development and serve as an instrument which helps to understand better the possible effects by introducing certain measures [13].
In the last time there is a consent among political economists and engineers that the gross national product do not represent a measure for the life quality of a nation. It gives informations about national economies, but it do not consider many parameters which influence the life quality, as for instance: environmental pollution, irreversible use of fossil fuels, social aspects etc. That means, GNP can not be used alone as a sustainable development indicator (SDI).
A lot of attempts to define indicators for sustainable development are known in the whole world. In the development of SDIs three directions are to be observed:
 defining one single aggregated indicator;
 defining a set of indicators for measuring sustainable development;
 defining partial aggregated indicators for each component of sustainable development, for the ecological, economical and social one.
To illustrate the first direction, as aggregated indicators the Index of Sustainable
Economic Welfare (ISEW), the Human Development Index (HDI) and the Ecological
National Product (ENP) can be named [4]. The second direction is represented by a system of indicators as that in the German region Baden-Württemberg developed by the Academy for
Technology Assessment, Stuttgart [14] or the indicators system for the town Jacksonville [4].
The third possibility is given by the modular design of the SDI (figure 2). Each of the three significant components of the concept of sustainable development is described by an indicator (Figure 2). In order to gain one single SDI the aggregation problem of the three components has to be solved. There are different aggregation methods, for instance calculating mean values. In Germany we had established an aggregation method based on fuzzy logic, which assures a transparent aggregation and eliminates the negative aspects of other methods [16]. As mentioned, to build a sustainable development indicator means first of all to define its components. One of the components is represented by indicators for environmental aspects, as for instance indicators for air, water or soil quality.
Environmental indicators are used for characterising and monitoring different environmental systems. By environmental systems two aspects are from interest. One aspect concerns the description of the state of a system at a certain time. The other aspect is referring

Indicators for economic aspects
Environmental indicators
Aggregation
SDI
Indicators for social aspects
Figure 2: Aggregation levels for a sustainable development indicator.
to the dynamic behavior of the system (Figure 3). Depending on the analysis limits, environmental systems can be approached on different levels, for instance on local, regional, national or global level.
State analysis
Local level
Regional level
Environmental
Systems
Indicators
National level
Dynamic behaviour
Global level
Figure 3: The role of indicators by describing environmental systems.
Worldwide there are many preoccupations to define environmental indicators. Well known is the OECD-model, so called pressure-state-response model [2]. Mitchell gives in
[11] a structural classification of environmental indicators in specific, composite an d key indicators. Going into details a lot of indicators for air, water or soil pollution were defined and are used nowadays in many countries (see for details [13], [16]). For instance in Germany the LBI index for air quality is used and published every week by the VDI-Journal (Journal of the German Engineers Association). When studying all these used indicators one can observe that many of them integrate coefficients, which are not transparently defined or assume that impacts of different pollutants are equivalent to each other.

In order to minimize these deficiencies methods of soft-computing have to be used.
Beside neuronal networks and genetic algorithms especially fuzzy logic offers, as mentioned, possibilities to use new methods for defining indicators by its potential to integrate complexity in the systematic and exact mathematical approach.
Air quality assessment by using environmental indicators
In the following two methods for air quality assessment on regional level will be presented, one based on the global pollution index, I
PG
, and the other one based on the air pollution index, API.
Global Pollution Index, I
PG
In the context of the method of the global pollution index, I
PG
, an air quality assessment in a graphical form is carried out [7, 13].
The Global Pollution Index, I
PG
, is represented by the ratio between the surface corresponding to the ideal state (S i
) and the surface corresponding to the real state (S r
) [3, 7].
I
PG

S
S i r
(1)
The regional environmental quality at a certain time can be assessed by considering the air, water and soil quality, population health status, registered plants and animals deficit.
Depending on the existing air pollution degree or on the existing air quality, as mentioned in
Table 1, for the environmental factor ”air” so-called ”goods classes” are awarded.
The goods classes are supporting the diagram graphical design as being a simulation method of the synergic effects produced by different air pollutants (SO2, NO2, CO2, CO).
The resulting geometric figure is represented by a regular figure (rectangle, in this case), as represented in Figure 4, but can be a triangle, a pentagon et al., depending on the number of the analysed pollutants.
S r
S i
Figure 4: Global Pollution Index, I
PG
, for air, by considering 4 pollutants.

Table 1: Marks for the air quality ranking
Goods class, Nb
Air class Effects on the population Effects on the vegetation, visibility and on the materials
10
9
8
7
6
5
4
3
2
1
Normal quality air
Natural population health status
Clean air, level 1
Without effects
Equilibrium natural status
Without effects
Clean air, level 2
Without traceable effects
Affected air, level 1
Affected air, level 2
Death rate increase bronchitis and lung cancer through
Increased frequency of breathing diseases and lung diseases with hospitalisation
Without factual traceable effects
Plants negative influence, partial sheets damage
Plants acute affect, moderate till strong, by synergistic action through O
3
or NO x
Polluted air, level 1
Increased death rate by emphasising the symptons at persons with lung diseases
Reduced visibility till 6-8 km
Increase of daily death rate Steel corrosion Polluted air, level 2
Degraded air, level 1
Grave increase of daily death rate Harmful effects on vegetation
Degraded air, level 2
Terminal effects on medium time exposure
Harmful effects on plants and pastures
Infected air Terminal effects on short time exposure
Emergence of dusty landscape
The ideal state is graphically represented by these geometric regular figures, their inscribed circles radii, through tops, are diveded in 10 goods class marks, the numbering starting at the circle center. By unifying the points, that are emphasising the values corresponding to the real state, an irregular geometric figure is obtained, having a smaller surface than that of the regular geometric figure representing the ideal state. Figure 4 is pointing out the explained methodology.
The Global Pollution Index, I
PG
, is conventionally defined on

1, 6

, as follows:

I
PG
= 1 – natural environment, non-impacted by human activities;

1 < I
PG
≤ 2 – natural environment, impacted by human activities in admissible limits

2 < I
PG
≤ 3 – natural environment, impacted by human activities, causing an inconvenient state of different creatures;

3 < I
PG
≤ 4 – natural environment, impacted by human activities, causing troubles to different creatures;


4 < I
PG
≤ 6 – natural environment, strong impacted by human activities, being dangerous to different creatures;

I
PG
> 6 – natural degraded environment, inappropriately to different creatures.
Air Pollution Index, API
The air pollution index (API) is used in order to carry out an air quality assessment, by characterizing the air pollution by considering different pollutants in the air. The air pollution index, API, to be calculated with the following relation [15]:

1 n
 i

1 w i
 i n


1
C

C i
(2) where: C real,i
- values of pollutants concentrations at a certain place and time [ppm or mg/m
3
];
C ref,i
- reference values: admissible values of pollutants concentrations [ppm or mg/m
3
] (along with German standards of TA Luft); w i
- weighting coefficients.
When API = 1 means that all pollutants concentrations have reached their limits
API > 1 means that all concentrations are above the limits and
API < 1 means that all concentrations are below the limits.
The establishment of the weighting coefficients is a complex problem because a big amount of implicit knowledge from different features has to be integrated in this process.
Anyway, the usage of weighting coefficients is the expression of the conviction that the importance of different pollutants emissions is not equal. A possibility is given by using a fuzzy logic based method [16, 17]. Such an application has been developed [8, 9, 16] and will be shown shortly in the following.
A transparent way to establish weighting coefficients for pollutants is the proposed method based on fuzzy logic [9, 15]. The basic criteria, shown in figure 5, which determine the weighting coefficients, are: impact on health, impact on ecosphere and emitted quantity .
Impact on health
Impact on ecosphere
Emitted quantity
FUZZY
BLOCK
Weighting coefficient
Figure 5: Aggregation levels for the weighting coefficients.

Linguistic variables
The chosen basic input criteria defined on the interval [0, 1] are formulated as linguistic variables each with the three linguistic terms small, medium and high (fig. 6).
1

0.5 small mediu m high
0
0 0.5 1
Figure 6: Linguistic terms and membership function for the linguistic variables impact on health , impact on ecosphere and emitted quantity .
The weighting coefficient as output variable has seven linguistic terms, the three mentioned above and in addition very very small, very small, very high and very very high
[16]. The connection between the linguistic terms is given by the rulebase. Table 2 shows the principial rulebase, wherefrom one can obtain the whole rulebase (27 rules in this case).
The weighting coefficients for API calculated with the following input values for the basic criteria are given in Table 3. To establish the input values knowledge regarding the impact on health and on ecosphere of the considered pollutants was taken into account [1].
The input values corresponding to the basic criterion quantity are related to the reference values and given as relative values in the interval [0, 1]. The reference values are represented by emissions of CO
2
for each country [15, 16].
Table 2: Rulebase structure with linguistic terms for the weighting coefficients of the Air
Pollution Index (API) [15].
Inputs Output
Health Ecosphere Quantity Weighting coef. small small small medium high high high small small high small very very small medium very small medium medium medium medium medium medium high medium high small medium high very high very very high

Table 3: Inputs of the basic criteria for the weighting coefficients for API.
CO
2
NO x
SO
2
CO
Impact on Impact on Emitted quantity Weight. health ecosphere RO coef.
0.05 0.95 1.0 0.67
0.7
0.7
0.9
0.6
0.5
0.05
0.0036
0.0073
0.082
0.46
0.42
0.33
EXPERIMENTAL
Air quality assessment by using the Global Pollution Index
In the following the Global Pollution Index, I
PG
, corresponding to the environmental factor air , will be calculated , by considering following pollutants: SO
2
, NO
2
, CO
2
si CO in the residential area Colonie from the city of Petrosani, as presented in Fig. 4 (see also [3]).
The goods class marks for the ideal state equals 10 for all indicators and for the real state following goods class marks have been obtained, as shown in Fig. 4:
Nb,NO2 = 9
Nb,SO2 = 8,5
Nb,CO2 = 7
Nb,CO = 8
In order to apply relation (1) the surface corresponding to the ideal state (S i
) and the surface corresponding to the real state (S r
) are calculated, getting at the end following values:
Si = 200 m 2
Sr =131,7 m
2
.
With these values, the Global Pollution Index can be calculated:
I
PG
= 1,51.
It is to be observed that 1 < I
PG
≤ 2 . From this calculations there is following that the air in this natural environment, in the residential area Colonie from the city of Petrosani, is impacted by human activities in admissible limits.
Air quality assessment by using the Air Pollution Index
In order to emphasize the working ways of the two presented methods, especially of fuzzy logic, the proposed method has been applied for the same residential area from the city of Petrosani, as for the Global Pollution Index, I
PG
. By considering a consumption of q = 10 kg/day fossil fuels in a household, the pollutants emissions were calculated, see Table 4 [3].

Table 4: Pollutants emissions in the residential area Colonie from the city of Petrosani [3] q [kg/day] E
CO2
[kg/day] E
CO
[kg/day] E
SO2
[kg/day] E
NO2
[kg/day]
10 9280 3977 141.2 196.8
Knowing the pollutants emissions it is possible to calculate the pollutants concentrations in a certain height, h , by using the Gauss-relation [1, 15]: c i
( x , y , z )

2
   u
Q
  y
  z
 e
 y
2
2
 y
2 


 e

 
2
2
 z
2  e

 z
 h

2
2
 z
2



(3) where: c i
(x,y,z) – pollutants concentrations in a place having the coordinates (x,y,z) (kg/m
3
);
Q – pollutant emission (kg/s); h – height of the pollutants source (m); u – average wind velocity (m/s);
 y
,
 z
- difusion parameters, that are calculated related to the distance x, (m);
For the concrete situation of the polluting sources from the mentioned residential area, the concentrations of the pollutants CO
2
, SO
2
, NO
2
, and CO are calculated by using the relation: c i

10
6
3600

24

2
 
 u
 
Q i y
  z
 e

2 y
2
 y
2  e

 z
 h

2
2
 z
2  e

 z
 h

2
2
 z
2
(4) where: c i
– pollutant concentration (mg/m
3
);
Q i
– emission of the pollutant i (kg/zi) i = CO
2
, SO
2
, NO
2
, CO h – polluting source height (m) u – average wind velocity (m/s)
 y
,
 z
- difusion parameters, depending on the distance x (m) [15].
Calculations have been made for different distances from the polluting source. The main wind direction is considered the x-direction. Obtained results are given in Table 5 [3].
These are compared to the reference values from standards, these values being given in Table
6. It is to be mentioned that for CO
2
, there is no reference value mentioned in environmental standards, that is why it will be assumed as a reference value for the concentration of CO
2
the present existing value in the world, c
CO2
= 360 ppm , this means actually c
CO2
= 707 mg/m
3
.

Table 5: Pollutants immissions for the residential area Colonie of the city Petrosani and resulting API
Nr. crt.
3
4
5
1
2
8
9
6
7 x [m]
200
300
400
5 0 0
600
700
800
900
1000
CO
2
6.11
3.00
1.82
1.26
0.896
0.684
0.542
0.441
0.367
CO
2.617
1.287
0.779
0.541
0.384
0.293
0.232
0.189
0.157
c [mg/m 3 ]
SO
2
0.093
0.046
0.028
0.019
0.014
0.010
0.008
0.006
0.005
NO
2
0.130
0.064
0.037
0.027
0.019
0.015
0.012
0.009
0.008
API
0.634
0.312
0.185
0.131
0.093
0.071
0.057
0.044
0.038
Table 6: Maximum admissible value for different pollutants concentration (STAS 12574 - 87)
Admissible daily concentration c adm
[mg/m 3 ]
CO
2
NO x
RESULTS AND DISCUSSION
SO
2
CO
-
0.1
0.25
2.0
From the presented results in Table 5 it is to be observed that for the pollutant CO, having the admissible concentration c
COadm
= 2.0 mg/m
3
as presented in Table 6, for the distance of x =
200 m the concentration value is c
CO
= 2,617 mg/m
3
, this means c
CO
> c
COadm
. Going into details the limit value is exceeded with the value:
∆c
CO
= c
CO
-c
COadm
= 0.617 mg/m
3
On the other side for the pollutant NO
2
, having the admissible concentration c
NO2adm
=
0.1 mg/m 3 as also presented in Table 6, for the distance of x = 200 m the concentration value is c
NO2
= 0,130 mg/m
3
, this means c
NO2
> c
NO2adm
. Going into details the limit value is exceeded with the value:
∆c
NO2
= c
NO2
-c
NO2adm
= 0.030 mg/m 3
From Table 5 follows that API

1 in all presented situations, this means that the pollution degree in the analysed region is in acceptable ranges.

CONCLUSIONS
Environmental monitoring is one of the requirements related to environmental pollution control. The monitorization can be realized by using specific technical devices for measurements. The results can be communicated outside by using environmental indicators.
For carrying out an environmental assessment on regional level, the air quality assessment is playing an important role, in this paper two methods have been presented. One method, based on the global pollution index, I
PG
, is carrying out an air quality assessment in a graphical form. The graphical method based on IPG is offering a global vision regarding the air quality in a region at a certain time. The other one, based on the air pollution index, API, which is defined as the sum of the weightened relative pollutants concentrations, is making an air quality assessment by mathematical calculations. The weighting coefficients have been established by using a fuzzy logic based method. The fuzzy logic application presented in this paper show the manifold possibilities of fuzzy logic based methods to solve environmental problems. It can be used especially for transparent assessment, when the available knowledge is diffuse, unstructured and disorderly, as in the most cases when approaching subjects regarding environmental indicators. The two presented methods permit comparing different regions from the point of view of the air pollution, the relevant condition is that the same pollutants are considered in the analysed regions, so that the comparison among regions can be carried out by calculating the same indicators for the different considered regions.
REFERENCES
[1] M. BANK: Basiswissen Umwelttechnik
; 2. Auflage, Vogel Buchverlag, Würzburg, 2000.
[2] BUND / MISEREOR (Ed.):
Zukunftsfähiges Deutschland;
Birkhäuser, Basel, 1996.
[3] D. I. CIOLEA: Metode de monitorizare şi evaluare a calităţii aerului pentru zona Colonie din municipiul Petroşani (Air Quality Monitoring and Evaluation methods for the residential area Colonie from the city of Petrosani. Master's degree dissertation, University of Petrosani,
Petrosani, 2004.
[4] W. VAN DIEREN (Ed.): Taking Nature into Account; Springer, New York, 1995.
[5] I. DUMITRESCU: Poluarea mediului
, Editura Focus, Petroşani, 2002.
[6] M. F. JISCHA: Herausforderung Zukunft . New Edition. SpringerSpektrum, Berlin
Heidelberg, 2014.
[7] K. M. LEE: A Weighting Method for the Korean Eco-Indicator ; in: The International
Journal of LCA, ecomed publishers, Landsberg, Vol. 4, 1999, p.161-165.

[8] B. LUDWIG: Methoden zur Modellbildung in der Technikbewertung , doctorate thesis, TU
Clausthal, 1995, see also CUTEC-Schriftenreihe Nr. 18, Papierflieger, Clausthal-Zellerfeld.
[9] B. LUDWIG, I. TULBURE: Ein fuzzy-unterstützter Ansatz zur Erfassung und Bewertung von Umweltinformationen am Beispiel der Luftverunreinigung (A fuzzy based method to evaluate environmental information regarding air pollution). In: C. Ranze e.a. (Ed.):
Intelligente Methoden zur Verarbeitung von Umweltinformationen. Metropolis Verlag.
Marburg, 1996, pp. 181-192.
[10] D. and D. MEADOWS: The Limits to Growth; Universe Book, New York, 1972.
[11] G. MITCHELL: Problems and Fundamentals of Sustainable Development Indicators ; in:
Sustainable Development, Vol. 4, Number 1, March 1996, pp. 1-11.
[12] Our Common Future, The Brundtland Report to the World Commission on Environment and Development; Oxford Univ. Press, Oxford, 1987.
[13] W. R. OTT: Environmental Indices – Theory and Practice ; Ann Arbor Science,
Michigan, 1978.
[14] G. PFISTER, O. RENN: Ein Indikatorensystem zur Messung der nachhaltigen
Entwicklung in Baden-Württemberg; Arbeitsbericht Nr. 64 der Akademie für
Technikfolgenabschätzung in Baden-Württemberg, Stuttgart, 1996.
[15] I. TULBURE: Integrative Modellierung zur Beschreibung von Transformations prozessen . Habilitationsschrift, TU Clausthal. VDI-Fortschrittsberichte, Reihe 16, Nr. 154,
VDI-Verlag, Düsseldorf, 2003.
[16] I. TULBURE: Technikbewertung .Vorlesungsskript.TU Clausthal. Clausthal-Zellerfeld,
2013.
[17] H. J. ZIMMERMANN: Neuro + Fuzzy: Technologien – Anwendungen ; VDI-Verlag,
Düsseldorf, 2013.