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Article

Sustainable Assessment of Aerosol Pollution Decrease Applying Multiple Attribute Decision-Making Methods

1
Department of Mechanical Engineering, Vilnius Gediminas Technical University, Basanavičiaus str. 28, Vilnius LT-03324, Lithuania
2
Research Institute of Smart Building Technologies, Vilnius Gediminas Technical University, Saulėtekio al. 11, Vilnius LT-10223, Lithuania
3
Department of Economics, Management, Society and Institutions (EGSI), University of Molise, Via De Sanctis, Campobasso 86100, Italy
4
Department of Mathematical Statistics, Vilnius Gediminas Technical University, Saulėtekio al. 11, Vilnius LT-10223, Lithuania
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sustainability 2016, 8(7), 586; https://doi.org/10.3390/su8070586
Submission received: 18 March 2016 / Revised: 15 June 2016 / Accepted: 17 June 2016 / Published: 23 June 2016

Abstract

:
Air pollution with various materials, particularly with aerosols, increases with the advances in technological development. This is a complicated global problem. One of the priorities in achieving sustainable development is the reduction of harmful technological effects on the environment and human health. It is a responsibility of researchers to search for effective methods of reducing pollution. The reliable results can be obtained by combining the approaches used in various fields of science and technology. This paper aims to demonstrate the effectiveness of the multiple attribute decision-making (MADM) methods in investigating and solving the environmental pollution problems. The paper presents the study of the process of the evaporation of a toxic liquid based on using the MADM methods. A schematic view of the test setup is presented. The density, viscosity, and rate of the released vapor flow are measured and the dependence of the variation of the solution concentration on its temperature is determined in the experimental study. The concentration of hydrochloric acid solution (HAS) varies in the range from 28% to 34%, while the liquid is heated from 50 to 80 °C. The variations in the parameters are analyzed using the well-known VIKOR and COPRAS MADM methods. For determining the criteria weights, a new CILOS (Criterion Impact LOSs) method is used. The experimental results are arranged in the priority order, using the MADM methods. Based on the obtained data, the technological parameters of production, ensuring minimum environmental pollution, can be chosen.

1. Introduction

The social, economic, technological, and biological processes taking place in the environment are so closely related today that production can be considered a complex sustainable ecological and economic system, linking the social production with the environment [1].
The intense technological development is accompanied by the increase in air pollution with various materials and, particularly, with aerosols [2,3]. Over the past decades, drastic measures have been taken to reduce air pollution. However, the level of air pollution is still too high, and the air quality problem is still acute. A large part of the world’s population lives in urban areas, where pollution, not complying with the air quality standards, poses a threat to human health and the environment [4].
One of the most acute global sustainable development problems is associated with decreasing harmful technological effects on the environment and the human health. Various methods of reducing pollution should be investigated and applied [5].
The best production technologies, ensuring the lowest possible environmental pollution, should be implemented [6,7]. Special attention must be paid to the production processes, where harmful substances, such as electrochemical etching with a hydrochloric acid solution (HAS), are used. These processes occur in baths, where hydrochloric acid vapors are formed.
Usually, vapors of water or many other substances are invisible. Vapor is a mixture of fume and spray, consisting of liquid particles suspended in a gaseous medium [8,9]. The aerosol absorbs gases due to the large total surface area of these particles. It quickly reacts with the fluid, diffuses light effectively, and is a good catalyst.
A rise in the air temperature at the workplace is associated with a higher risk of poisoning because when the air temperature rises, the volatility and evaporation of toxic materials increase, thereby increasing the air humidity, which, in turn, increases the toxicity of hydrochloric acid (hydrogen fluoride).
The work of Murugappa, Huang, Cabaleiro, et al. [10,11,12] is aimed at improving the equipment for gas storage and cleaning, as well as developing the methods for describing the processes related to the movement of gas, and determining the nature of pollution caused by manufacturing processes.
The action of transferring mass from the vaporized fluid into the environment can be associated with the process of diffusion and the forced or natural convection. The dynamics of streams created over the bath with the fluid is very complicated [13,14,15]. Many mechanical devices, removing the volatile aerosols from the surface of the vaporized fluid are not sufficiently effective. Therefore, absorbers of aerosol pollutants are used now. However, their work efficiency is often not sufficiently high. It depends on the transfer processes taking place on the surface of the vaporized liquid. The complicated process taking place on the surface of the hot liquid in an absorber fixed to the bath wall [16]. The fumes start to rise, while the absorber on the edge of the bath should force the air to move at the speed, at which vapors would change the direction of movement and flow towards the absorber’s slot [17].
Rota, Marzal, et al. [13,18] have examined the exhaust, suction and the influence of rising vapor flow velocity on the pollutant concentration at the workplace, but have not proposed any methods for reducing the concentrations of pollutants at the place of their occurrence. Article [16] presents the views of the vapours’ spread at the rate of 0.3 m/s and vapours’ take-off velocity of 0.05 m/s, when the velocity of the pumped air flow is within the range of 1.55 m/s to 4.17 m/s. This proves that the pumping rate determines the elevation angle of pollutants above the emission surface of the liquid, but its value depends on the speed of blowing and the vapors’ take-off.
When an absorber does not work or its efficiency is insufficient, the vapors found on the opposite side of the slit absorber are not evacuated. With the increase in the temperature of the hydrochloric acid solution, the take-off velocity increases, which results in the increase in the concentration of vapors and pollutants. By varying the hydrochloric acid solution concentration and heating temperature, the intensity of evaporation can be reduced. The optimal solution concentration and temperature can be determined using the multiple attribute decision-making (MADM) techniques [19,20,21,22]. A great number of MADM methods have been developed and applied [23,24,25,26,27,28,29]. Currently, hybrid MADM methods are used for solving the evaluation problems [30,31,32]. A very feasible way to apply hybrid DEMATEL + ANP (DANP) methods [33,34,35,36]. MADM methods are based on the decision matrix, R, criterion statistics (experimental criterion values), and the criteria weights’ vector, Ω [20,37,38].
For comparison, 16 groups of criteria were used in the analysis based on the application of such MCDM methods as VIKOR (from Serbian Vise Kriterijumska Optimizacija I Kompromisno Resenje) [39,40,41,42] and COPRAS (Complex Proportional Assessment) [43,44,45,46].
In practice, subjective weights determined by experts are commonly used [47,48,49]. However, experts usually cannot assess the impact of experimental results on the criteria.
The data structure and the degree of dominance (or the objective weight) of each criterion can be determined. In practice, the objective weights are applied less frequently than the subjective weights [19]. The combined weighting is based on the integration of the subjective and objective weights [50].
In performing the experimental study, an objective method for evaluating the criteria weights, a new CILOS (Criterion Impact LOSs) method [51,52,53], was applied.

2. The Test Setup

Hydrochloric acid heating processes were investigated, using a test setup, schematically shown in Figure 1.
The vapor flow is formed above the container with the hydrochloric acid solution (1), which is heated by a heater (2) and whose temperature and humidity changes are detected by a multifunctional humidity and temperature sensor (3). The aerosols formed on the surface of the hydrochloric acid solution are drawn in by the side absorber (4), which is fixed to the container (1) and is connected to the edge of the fan (5). The exhaust airflow can be adjusted with a frequency drive (6). The dynamic steam viscosity is determined by a viscometer (9), while its density is measured by a hygrometer (8), while the velocity is determined by the flow meter (7).
The hydrochloric acid solution was heated in the bath (Figure 2) with the width B of 0.3 m, length L = 0.415 m, and the height from the hydrochloric acid solution surface to the top of the bath, H = 0.08 m, depending on the solution temperature at the ambient temperature of 20 °C. It is recommended that the blowing slot would be at least 5 mm wide (0.011B, where B is the bath width) and the space from which the air has been removed, would be at least 50 mm (0.21B) wide.
Two conditions should be satisfied in designing the absorber: the flow generated by the pollution source should be drawn in, while the flow towards the absorber in the critical cross-section should be steady. The flow is considered steady if its speed at the critical cross-section is as follows:
u = 7 u k r ,   when   H 0.15
or:
u k r = 1.83 g B Δ t t a ,   when   H 0.15 B
where ut is the speed of the disruptive torrent (permissible air speed inside a building is 0.2–0.5 m/s), g is gravity, m/s2, ∆t is the temperature difference between the ambient air and hydrochloric acid solution, °C, and ta is the ambient temperature (20 °C).

3. Vapor Viscosity, Density, and the Flow Rate

A complicated process takes place during the vapor suction above the bath. The stream rises over the bath and this process is followed by the suction itself and blowing. The hot surface of the hydrochloric acid solution (HAS) is vaporized, and, with the increase in the temperature, the concentration of vapors also increases, which, in turn, activates the movement of the flow and increases environmental pollution.
A rising vapor flow is convective and passes two stages: the formation, when the axial velocity on the surface of the source increases from zero to a maximum value, and the main stage, when the axial velocity decreases or remains constant, when the flow is receding from the source.
The variation of the rate of vapor spreading (m/s) in the main convection stream was calculated for the bath length of one meter by the formula:
v g = 0.053 q k 0 3
where qk0 is the amount of the convection heat, W.
The speed of the flow along the bath length should not be lower than the critical value (Ukr = 1.4 to 3.5 m/s). In this case, about 90% of the pollutants would be removed [54].
The determination of the vapor components’ concentration depends on the partial pressure of the component above the hydrochloric acid solution surface, as well as on its molecular weight and temperature. A thin saturated vapor layer is above the surface of the hydrochloric acid solution. Therefore, the partial pressure of the component vapor above the HAS surface is equal to the partial pressure of the saturated vapor at the actual temperature on the solution surface. When the hydrochloric acid solution is stirred, its surface temperature is equal to the temperature at its depth, and the evaporation surface can be several times larger than the surface area of still fluid.
The weighted vapor concentration (mg/m3) above the surface of the hydrochloric acid solution was calculated by the formula:
C i = 16 p i M i × 1000 / [ ( 273 + t s k ) × 133.3 ]
where Mi is the molecular weight of the component; tSK is the hydrochloric acid solution temperature, °C.
The vapor density was calculated according to the formula [55], mg/m3:
p g = i = 1 n C i
When a gaseous matrix above the liquid surface is covered with a number of substances (components), i.e., air, fluid (solution) vapor, then, theoretically, its dynamic viscosity can be calculated by the formula [55]:
μ g = M g i = 1 n i i M i / μ i
where Mg is the relative molecular mass above the hydrochloric acid solution surface: M g = i = 1 n i i C i , ii = pi/p is the volume part of the medium component above the hydrochloric acid solution, i is the component; pi is the component’s partial pressure above the hydrochloric acid solution surface at the temperature tk, Pa; p is the ambient pressure, Pa; µi is the dynamic viscosity component over the hydrochloric acid solution surface at the hydrochloric acid solution temperature tk: μ i = μ 0 273 + S a t T s k + S a t ( T s k 273 ) 1.5 , µ0 is dynamic viscosity of the component above the hydrochloric acid solution surface, when tk = 0 °C, Pa·s; Sat is Sutherland’s constant, Tk is the temperature of the hydrochloric acid solution, K.
The experimental studies of the flow spread were performed to determine the temperature of the liquid (hydrochloric acid solution) and the concentration of the substance at ambient temperature of 20 °C.
During the investigation, the hydrochloric acid solution was poured into the bath, which was heated to the temperature from 50 to 80 °C. Dry ice pellets were used for visualization of evaporation, which is shown in Figure 3.
With the increase in the hydrochloric acid solution surface temperature, the upward movement of vapors also increases. Under the action of the rising forces, the aerosol moves upwards, and the concentration of aerosols and pollution increase.
The take-off velocity vg largely depends on the amount of heat. When the temperature of the hydrochloric acid solution rises from 50 to 80 °C, the vapor’s take-off velocity increases from 0.21 to 0.32 m/s.
When the temperature of the hydrochloric acid solution rises from 50 to 80 °C, while the concentration is in the range of 28%–34%, its vapor density varies between 0.9883 and 1.24448 kg/m3. The largest value of the dynamic vapor viscosity, making 1.873 × 10−5 Pa·s, was obtained at 50 °C and 28% hydrochloric acid solution concentration, while the smallest 1.680 × 10−5 Pa·s was obtained at 70 °C and 34% hydrochloric acid solution concentration. In order to get optimal combinations of the obtained results, the MADM methods should be applied to the analysis of the data.

4. Experimental Results

Spreading of aerosol vapors depends on the concentration and temperature of the hydrochloric acid solution. The concentration of the hydrochloric acid solution varied during the experiment from 28% to 34%, and the temperature changed from 50 to 80 °C. The measured parameters included vapor density, dynamic viscosity, and the velocity of the vapor flow.
Smaller pollution values were obtained when the density of the vapor mixture and its dynamic viscosity were higher, but the velocity of vapor flow was lower.
Sixteen groups of the experimental data are presented in Table 1. However, the data do not show what experimental results are optimal; therefore, the effective MADM methods were applied to the obtained data analysis.

5. MADM Methods

In the present work, the MADM methods, including VIKOR and COPRAS, were applied. The new CILOS method was also used.

5.1. The VIKOR Method

The VIKOR method (abbreviated from Serbian [36] Vise Kriterijumska Optimizacija I Kompromisno Resenje) uses the following normalization:
r ¯ k j = ( r j * r k j ) / ( r j * r j )
where r j * = max k   r k j , r j = min k   r k j , if the jth criterion describes the benefit and r j * = min k   r k j , r j = max k   r k j , if the jth criterion describes cost 0 r ¯ k j 1 .
The method uses three evaluation criteria, Sk, Rk, Qk (k = 1,…,m).
The criteria Sk and Rk were calculated according to the formulas:
S k = j = 1 n ω j r ¯ k j
R k = max j ( ω j r ¯ k j )
where ωj is the weight of the j-th criterion and r ¯ k j is the normalized value (ratios of common units) of the j-th criterion for the k-th alternative.
The aggregate criterion Qk of VIKOR is based on the criteria Sk and maximal gap/regret Rk in alternative k and may be calculated by the formula:
Q k = v ( S k min k S k ) / ( max k S k min k S k ) + ( 1 v ) ( R k min R k k ) / ( max k R k min k R k )
where ν is the strategic weight, the majority criterion (in further calculations ν = 0.5).

5.2. The COPRAS Method

The criterion of the COPRAS (Complex Proportional Assessment) method [43] Zk was calculated as follows:
Z k = S + k + k = 1 m S k S k k = 1 m 1 S k
  • S + k = j = 1 n ω j r ¯ + k j is the sum of the weighted values of the maximized criteria r ˜ + i j ,
  • S k = j = 1 n ω j r ¯ k j is same for the minimized criteria,
where ωj is the weight of the j-th criterion and r ¯ k j is the normalized value of the j-th criterion for the k-th alternative:
r ¯ k j = r k j k = 1 m r k j

5.3. The CILOS (Criterion Impact LOSs) Method of Criteria Weight Determination

This is another promising method based on the criteria impact loss and determination of the objective weights [51,52,53]. The method evaluates the impact (significance) loss of each criterion until one of the remaining criteria reaches the optimum, the maximum or the minimum, value. The method’s algorithm, formalization, description, and application are presented in [53]. The logic of the method of the criteria impact (significance) loss, as well as the basic ideas, stages, and the calculation algorithm are described below.
The criteria that are minimized were converted into the maximized criteria, according to the following equation:
r ¯ k j = min k r k j r k j
A new matrix was denoted as X= x k j . The maximum values of each column (i.e., every criterion) were calculated as follows: x j = m a x k x k j = x t j j where tjj denotes the lines of the column with the largest number of the element.
The square matrix A = a i j was formed from the values of tj = s rows of the matrix X; xij correspond to the j-th maximum criterion: ajj = xj (i, j = 1,2, ..., n; n is the number of criteria), which means that the maximum values of all the criteria will appear in the main diagonal of the matrix.
The matrix P = p i j of the relative losses is given below:
p i j = x j + a i j x j , p i i = 0 ;   i , j     { 1 , 2 , , n }
The elements pij of the matrix P show, what the relative loss of the j-th criterion will be, if the i-th criterion is selected to be the best.
The weights q = (q1, q2,..., qn) can be found from the system:
Fq = 0
where the matrix F is as follows:
F = ( i = 1 n p i 1 p 12 ... p 1 n p 21 i = 1 n p i 2   p 2 n ... p n 1 p n 2 ... i = 1 n p i n )
The method based on the criterion impact (significance) loss offsets the drawbacks of the entropy method. Thus, when the values of a criterion do not considerably differ, the elements p i j of the matrix P of the relative loss of the criterion impact (14) approach zero, while the respective criterion weight increases and has a strong impact on the evaluation. In the case of homogeneity, when the values of one of the criteria are the same in all the alternatives, all relative losses of the criterion, as well as its total loss, are equal to zero. Therefore, the linear system of Equation (15) makes no sense because one column of the elements in the matrix P is equal to zero.

6. The Evaluation Results

The calculated CILOS weights of criteria are presented in Table 1. The values obtained in weighting are given in Table 2.
The priorities determined by using a general theory of weights and various MADM methods are presented in Table 3.
MADM analysis, based on using the VIKOR and COPRAS methods, shows that the best results were obtained in the second experiment, when the HAS concentration reached 30% and the temperature was 50 °C. The worst results were obtained in test 13, when the hydrochloric acid solution density was 28% and the temperature reached 80 °C. This means that the dispersion of pollutants depends not only on the density of the hydrochloric acid solution but also on its temperature.
The evaluation of the weights of criteria allows us to state that the dynamic viscosity criterion (X2) has the largest weight according to the entropy method. It was confirmed by the criterion weight calculation results obtained by summarizing the weights yielded by the CILOS method.
Over the past decades, drastic measures have been taken to reduce the air pollution. However, the level of air pollution is still too high, and the air quality problem is still acute. A large part of the world's population lives in urban areas, where pollution, not complying with the air quality standards, poses a threat to human health and the environment.
Based on the obtained data, the technological parameters of production, ensuring minimum environmental pollution, can be chosen. The best manufacturing technology to ensure the lowest possible environmental pollution should be implemented using these research results.

7. Conclusions

Summarizing the results of the analysis, the authors came to the conclusion that the dispersion of pollutants depends not only on the concentration of the hydrochloric acid solution (HAS), but also on its temperature. The optimal variant was found, when the concentration reached 30% and the temperature was about 50 °C. The evaluation of the results yielded by two MADM methods allowed the authors to conclude that the second variant (alternative) was optimal (the best). It was followed by alternative 1 and alternative 3.
The obtained results confirmed the possibility of optimizing pollution characteristics using MADM methods. In the experimental study, the method of the criteria impact loss (CILOS), suggested by the authors of the present paper, was used for determining the objective weights of the considered criteria. This method compensates for the drawbacks of the entropy method used for the same purpose.
The correlation coefficient for the values of the evaluation criterion Qk of the VIKOR method and the criterion Zk of the COPRAS method is r = 0.75. This shows a relatively high consistency of the obtained results.
One of the global sustainable development priorities is the reduction of harmful technological effects on the environment and the human health. The MADM methods can be effectively used for solving various environmental problems. In the future it is planned to carry out environmental pollution researches, depending on the size of aerosol particles.

Acknowledgments

We appreciate anonymous referees and the editor for their remarkable comments and manuscript processing.

Author Contributions

The individual contribution and responsibilities of the authors were as follows: Audrius Čereška provided the research concept and the purpose; Edmundas Kazimieras Zavadskas and Fausto Cavallaro provided extensive advice throughout the study, regarding the research design, methodology, findings and revised the manuscript; Valentinas Podvezko collected and analyzed the data; Ina Tetsman designed the research; Irina Grinbergienė dealt with the main research, analyzed the obtained results and performed the development of the paper. All the authors have read and approved the final manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Matthew, A.C. Air Pollution and ‘Dirty’ Industries: How and Why Does the Composition of Manufacturing Output Change with Economic Development? Environ. Resour. Econ. 2000, 17, 109–123. [Google Scholar] [CrossRef]
  2. Färe, R.; Grosskopf, S.; Pasurka, C.A., Jr. Accounting for Air Pollution Emissions in Measures of State Manufacturing Productivity Growth. J. Reg. Sci. 2001, 41, 381–409. [Google Scholar] [CrossRef]
  3. Bilde, M.; Pandis, S.N. Evaporation Rates and Vapor Pressures of Individual Aerosol Species Formed in the Atmospheric Oxidation of α- and β-Pinene. Environ. Sci. Technol. 2001, 35, 3344–3349. [Google Scholar] [CrossRef] [PubMed]
  4. Dockery, D.W.; Pope, C.A. Acute Respiratory Effects of Particulate Air Pollution. Annu. Rev. Public Health 1994, 15, 107–132. [Google Scholar] [CrossRef] [PubMed]
  5. King, M.D.; Menzel, W.P.; Kaufman, Y.J.; Tanre, D.; Gao, B.C.; Platnick, S.; Ackerman, S.A.; Remer, L.A.; Pincus, R.; Hubanks, P.A. Cloud and aerosol properties, precipitable water, and profiles of temperature and water vapor from MODIS. IEEE Trans. Geosci. Remote Sens. 2003, 41, 442–458. [Google Scholar] [CrossRef]
  6. Lohmann, U.; Feichter, J. Global indirect aerosol effects: A review. Atmos. Chem. Phys. 2005, 5, 715–737. [Google Scholar] [CrossRef]
  7. Chan, C.K.; Yao, X. Air pollution in mega cities in China. Atmos. Environ. 2008, 42, 1–42. [Google Scholar] [CrossRef]
  8. O’Donovan, T.S.; Murray, D.B. Jet impinge mentheat transfer—Part I: Meanandroot-mean-square heat transfer and velocity distributions. Int. J. Heat Mass Transf. 2007, 50, 3291–3301. [Google Scholar] [CrossRef]
  9. O’Donovan, T.S.; Murray, D.B. Jet impingement heat transfer—Part II: A temporal investigation of heat transfer and local fluid velocities. Int. J. Heat Mass Transf. 2007, 50, 3302–3314. [Google Scholar] [CrossRef]
  10. Murugappan, S.; Gutmark, E. Parametric study of the Hartmann–Sprenger tube. Exp. Fluids 2005, 38, 813–823. [Google Scholar] [CrossRef]
  11. Huang, R.F.; Lin, S.Y.; Jan, S.-Y.; Hsieh, R.H.; Chen, Y.-K.; Chen, C.-W.; Yeh, W.-Y.; Chang, C.-P.; Shih, T.-S.; Chen, C.-C. Aerodynamic Characteristics and Design Guidelines of Push–Pull Ventilation Systems. Ann. Occup. Hyg. 2005, 49, 1–15. [Google Scholar] [CrossRef] [PubMed]
  12. Cabaleiro, D.; Segovia, J.J.; Martín, M.C.; Lugo, L. Isobaric heat capacity at high pressure, density, and viscosity of (diphenyl ether + biphenyl) mixtures. J. Chem. Thermodyn. 2016, 93, 86–94. [Google Scholar] [CrossRef]
  13. Rota, R.; Nano, G.; Canossa, L. Design guidelines for push–pull ventilation systems through computational fluid dynamics modelling. Am. Ind. Hyg. Assoc. 2001, 62, 141–148. [Google Scholar] [CrossRef]
  14. Salin, A.A.; Galeev, A.D.; Ponikarov, S.I. Study of the evaporation of hydrochloric acid: Modeling and experiment. J. Eng. Phys. Thermophys. 2014, 87, 753–762. [Google Scholar] [CrossRef]
  15. Toro, J.C.O.; Dobrosz-Gómez, I.; García, M.A.G. Sodium sulfate solubility in (water + ethanol) mixed solvents in the presence of hydrochloric acid: Experimental measurements and modeling. Fluid Phase Equilib. 2014, 384, 106–113. [Google Scholar] [CrossRef]
  16. Marzal, F.; Gonzalez, E.; Minana, A. Determination and interpretation of total and transversal linear efficiencies in push–pull ventilation systems for open surface tanks. Ann. Occup. Hyg. 2002, 46, 629–635. [Google Scholar] [CrossRef] [PubMed]
  17. Liu, Z.; Thorpe, S.A.; Smyth, W.D. Instability and hydraulics of turbulent stratified shear flows. J. Fluid Mech. 2012, 695, 235–256. [Google Scholar] [CrossRef]
  18. Marzal, F.; Gonzalez, E.; Minana, A. Visualization of airflows in push–pull ventilation systems applied to surface treatment tanks. Am. Ind. Hyg. Assoc. 2003, 64, 455–460. [Google Scholar] [CrossRef]
  19. Hwang, C.L.; Yoon, K. Multiple Attribute Decision Making—Methods and Applications; Springer: New York, NY, USA, 1981. [Google Scholar]
  20. Tzeng, G.H.; Huang, J.J. Multiple Attribute Decision Making: Methods and Applications; CRC Press, Taylor & Francis Group: Boca Raton, FL, USA, 2011. [Google Scholar]
  21. Liou, J.J.H.; Tzeng, G.H. Comments on “Multiple criteria decision making (MCDM) methods in economics: An overview”. Technol. Econ. Dev. Econ. 2012, 18, 672–695. [Google Scholar] [CrossRef]
  22. Mardani, A.; Jusoh, A.; Nor, K.M.D.; Khalifah, Z.; Zakwan, N.; Valipour, A. Multiple criteria decision-making techniques and their applications—A review of the literature from 2000 to 2014. Econ. Res.-Ekon. Istraz. 2015, 28, 516–571. [Google Scholar] [CrossRef]
  23. Liou, J.J.H.; Chuang, Y.C.; Tzeng, G.H. A fuzzy integral-based model for supplier evaluation and improvement. Inform. Sci. 2014, 266, 199–217. [Google Scholar] [CrossRef]
  24. Tamosaitiene, J.; Zavadskas, E.K.; Liou, J.J.H.; Tzeng, G.H. Selecting Suppliers in Green Supply Chain Management. In Proceedings of the 8th International Scientific Conference on Business and Management Vilnius, Lithuania, 15–16 May 2014; pp. 770–776.
  25. Mardani, A.; Zavadskas, E.K.; Govindan, K.; Senin, A.A.; Jusoh, A. VIKOR Technique: A Systematic Review of the State of the Art Literature on Methodologies and Applications. Sustainability 2016, 8, 37. [Google Scholar] [CrossRef] [Green Version]
  26. Mardani, A.; Jusoh, A.; Zavadskas, E.K.; Cavallaro, F.; Khalifah, Z. Sustainable and Renewable Energy: An Overview of the Application of Multiple Criteria Decision Making Techniques and Approaches. Sustainability 2015, 7, 13947–13984. [Google Scholar] [CrossRef]
  27. Zavadskas, E.K.; Turskis, Z.; Kildiene, S. State of Art Surveys of Overviews on MCDM/MADM Methods. Technol. Econ. Dev. Econ. 2014, 20, 165–179. [Google Scholar] [CrossRef]
  28. Filip, F.G.; Suduc, A.M.; Bizoi, M. DSS in number. Technol. Econ. Dev. Econ. 2014, 20, 154–164. [Google Scholar] [CrossRef]
  29. Liou, J.J.H.; Tamosaitiene, J.; Zavadskas, E.K.; Tzeng, G.H. New hybrid COPRAS-G MADM Model for improving and selecting suppliers in green supply chain management. Int. J. Prod. Res. 2016, 54, 114–134. [Google Scholar] [CrossRef]
  30. Zavadskas, E.K.; Antucheviciene, J.; Turskis, Z.; Adeli, H. Hybrid multiple-criteria decision-making methods: A review of applications in engineering. Sci. Iran. A 2016, 23, 1–20. [Google Scholar]
  31. Zavadskas, E.K.; Bausys, R.; Lazauskas, M. Sustainable Assessment of Alternative Sites for the Construction of a Waste Incineration Plant by Applying WASPAS Method with Single-Valued Neutrosophic Set. Sustainability 2015, 7, 15923–15936. [Google Scholar] [CrossRef]
  32. Liou, J.J.H. New concepts and trends of MCDM for tomorrow—In honor of Professor Gwo-Hshiung Yzeng on the occasion of his 70th birthday. Technol. Econ. Dev. Econ. 2013, 19, 267–375. [Google Scholar] [CrossRef]
  33. Pourahmad, A.; Hosseini, A.; Banaitis, A.; Nasiri, H.; Banaitiene, N.; Tzeng, G.-H. Combination of Fuzzy-AHP and DEMATEL-ANP with GIS in a New Hybrid MCDM Model Used for the Selection of the Best Space for Leisure in a Blighted Urban Site. Technol. Econ. Dev. Econ. 2015, 21, 773–796. [Google Scholar] [CrossRef]
  34. Huang, K.-W.; Huang, J.-H.; Tzeng, G.-H. New Hybrid MADM Model for Improving Competence Sets: Enhancing a Company’s Core Competitiveness. Sustainability 2016, 8, 175. [Google Scholar] [CrossRef]
  35. Chou, S.-Y.; Yu, C.-C.; Tzeng, G.-H. A Novel Hybrid MCDM Procedure for Achieving Aspired Earned Value Management Applications. J. Math. Probl. Eng. 2016. [Google Scholar] [CrossRef]
  36. Gölcük, I.; Baykasoglu, A. An analysis DEMATEL approaches for criteria interaction handling within ANP. Expert Syst. Appl. 2016, 46, 346–366. [Google Scholar] [CrossRef]
  37. Gudienė, N.; Banaitis, A.; Podvezko, V.; Banaitienė, N. Identification and evaluation of the critical success factors for construction projects in Lithuania: AHP approach. J. Civ. Eng. Manag. 2014, 20, 350–359. [Google Scholar] [CrossRef]
  38. Lazauskaitė, D.; Burinskienė, M.; Podvezko, V. Subjectively and objectively integrated assessment of the quality indices of the suburban residential environment. Int. J. Strateg. Prop. Manag. 2015, 19, 297–308. [Google Scholar] [CrossRef]
  39. Opricovic, S.; Tzeng, G.H. The Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. Eur. J. Oper. Res. 2004, 156, 445–455. [Google Scholar] [CrossRef]
  40. Opricovic, S.; Tzeng, G.H. Extended VIKOR method in comparison with outranking methods. Eur. J. Oper. Res. 2007, 178, 514–529. [Google Scholar] [CrossRef]
  41. Keshavarz Ghorabaee, M.; Amiri, M.; Sadaghiani, J.S.; Zavadskas, E.K. Multi-criteria project selection using an extended VIKOR method with interval type-2 fuzzy sets. Int. J. Inform. Technol. Decis. Mak. 2015, 14, 993–1016. [Google Scholar]
  42. Bausys, R.; Zavadskas, E.K. Multicriteria Decision Making Approach by VIKOR under Interval Neutrosophic Set Environment. Econ. Comput. Econ. Cybern. Stud. Res. 2015, 49, 33–48. [Google Scholar]
  43. Zavadskas, E.K.; Kaklauskas, A.; Šarka, V. The new method of multi-criteria complex proportional assessment of projects. Technol. Econ. Dev. Econ. 1994, 1, 131–139. [Google Scholar]
  44. Podvezko, V. The Comparative Analysis of MCDM Methods SAW and COPRAS. Inž. Ekon.–Eng. Econ. 2011, 22, 134–146. [Google Scholar]
  45. Mulliner, E.; Malys, N.; Maliene, V. Comparative analysis of MCDM methods for the assessment of sustainable housing affordability. Omega 2016, 59, 146–156. [Google Scholar] [CrossRef]
  46. Nguyen, H.T.; Md Dawal, S.Z.; Nukman, Y.; Aoyama, H.; Case, K. An Integrated Approach of Fuzzy Linguistic Preference Based AHP and Fuzzy COPRAS for Machine Tool Evaluation. PLoS ONE 2015, 10, e0133599. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  47. Saaty, T.L. The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation; McGrawHill: New York, NY, USA, 1980. [Google Scholar]
  48. Podvezko, V.; Sivilevicius, H. The use of AHP and rank correlation methods for determining the significance of the interaction between the elements of a transport system having a strong influence on traffic safety. Transport 2013, 28, 389–403. [Google Scholar] [CrossRef]
  49. Podviezko, A.; Podvezko, V. Absolute and Relative Evaluation of Socio-Economic Objects Based on Multiple Criteria Decision Making Methods. Inz. Ekon.-Eng. Econ. 2014, 25, 522–529. [Google Scholar] [CrossRef]
  50. Ma, J.Z.; Fan, P.; Huang, L.H. A subjective and objective integrated approach to determine attribute weights. Eur. J. Oper. Res. 1999, 112, 397–404. [Google Scholar] [CrossRef]
  51. Mirkin, B.G. Problema Grupovogo Vibora; Nauka: Moskva, Russia, 1974. (In Russian) [Google Scholar]
  52. Mirkin, B.G. Group Choice; Winston & Sons: Wachington, DC, USA, 1979. [Google Scholar]
  53. Zavadskas, E.K.; Podvezko, V. Integrated determination of objective criteria weights in MCDM. Int. J. Inform. Technol. Decis. Mak. 2016. [Google Scholar] [CrossRef]
  54. Tishchenko, N.F. Atmospheric Air Protection. Calculation of the Content of Harmful Substances and their Distribution in Air; Khimiya: Moscow, Russia, 1991. (In Russian) [Google Scholar]
  55. Posokhin, V.N. Design of Local Ventilation Systems for the Process Equipment with Heat and Gas Release (in Russian); Mashinostroyeniye: Moscow, Russia, 1984. (in Russian) [Google Scholar]
Figure 1. The schematic view of the test setup of the side absorber: 1 an open-surface tank; 2 a heater; 3 a thermometer; 4 a side absorber; 5 a fan; 6 a frequency drive; 7 a device for measuring pressure and flow velocity; 8 a gas density meter; and 9 a viscometer.
Figure 1. The schematic view of the test setup of the side absorber: 1 an open-surface tank; 2 a heater; 3 a thermometer; 4 a side absorber; 5 a fan; 6 a frequency drive; 7 a device for measuring pressure and flow velocity; 8 a gas density meter; and 9 a viscometer.
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Figure 2. The push system and its geometric parameters.
Figure 2. The push system and its geometric parameters.
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Figure 3. Visualization of evaporation.
Figure 3. Visualization of evaporation.
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Table 1. Experimental data on the evaporation of hydrochloric acid.
Table 1. Experimental data on the evaporation of hydrochloric acid.
Experiment(HAS) Concentration, %(HAS) Temperature, T, °CVapor Density, ρg kg/m3 X1Dynamic Viscosity of Vapor, μg, 105 Pa·s X2Velocity of Vapor Flow, vg, m/s X3
128501.08391.8680.224
230501.09951.8550.223
332501.12781.8250.242
434501.17891.7750.250
528601.04881.8730.259
630601.07421.8450.265
732601.1191.8100.272
834601.19861.7400.278
928701.01551.8350.284
1030701.0551.8150.291
1132701.12431.7650.297
1234701.24481.6800.304
1328800.98831.8200.302
1430801.01181.9750.309
1532801.11941.7350.314
1634801.23701.6550.323
Table 2. Weights obtained by the CILOS method.
Table 2. Weights obtained by the CILOS method.
CriterionX1X2X3
Weight0.36410.50190.1340
Rank213
Table 3. The ranking of the alternatives based on using the theory of aggregating weights and various MADM methods.
Table 3. The ranking of the alternatives based on using the theory of aggregating weights and various MADM methods.
Weights Yielded by CILOS Method
Method Alternatives (Variants)VIKORCOPRASThe Total Rank *
Sj RankRj RankQj RankZi Rank
10.397620.228420.042320.065022
20.394510.20631010.065111
30.426830.235330.099830.064433
40.443440.313780.258550.064244
50.486460.278270.266170.063066
60.502380.242240.230040.062885
70.503090.258850.259260.062977
80.5079100.3686130.4525100.063159
90.6268140.325590.5663120.06081412–13
100.6115130.269460.447490.06111310
110.5996120.3294100.5301110.06151111
120.5712110.4627150.7111130.06231012–13
130.7131160.3641120.7670150.05491616
140.446050.3307110.291480.062598
150.6764150.3764140.7402140.06051515
160.501970.5019160.8963160.06131214
* The total rank presents the result obtained based on the sum of the ranks of all considered criteria and is given in the last column in Table 3.

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Čereška, A.; Zavadskas, E.K.; Cavallaro, F.; Podvezko, V.; Tetsman, I.; Grinbergienė, I. Sustainable Assessment of Aerosol Pollution Decrease Applying Multiple Attribute Decision-Making Methods. Sustainability 2016, 8, 586. https://doi.org/10.3390/su8070586

AMA Style

Čereška A, Zavadskas EK, Cavallaro F, Podvezko V, Tetsman I, Grinbergienė I. Sustainable Assessment of Aerosol Pollution Decrease Applying Multiple Attribute Decision-Making Methods. Sustainability. 2016; 8(7):586. https://doi.org/10.3390/su8070586

Chicago/Turabian Style

Čereška, Audrius, Edmundas Kazimieras Zavadskas, Fausto Cavallaro, Valentinas Podvezko, Ina Tetsman, and Irina Grinbergienė. 2016. "Sustainable Assessment of Aerosol Pollution Decrease Applying Multiple Attribute Decision-Making Methods" Sustainability 8, no. 7: 586. https://doi.org/10.3390/su8070586

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