Analysis of PM10 Substances via Intuitionistic Fuzzy Decision-Making and Statistical Evaluation
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area and Data
2.2. Intuitionistic Fuzzy AHP (IF-AHP)
2.3. One-Way ANOVA
2.4. Expert Modeler
3. Results
3.1. Findings of IF-AHP
3.2. Findings of One-Way ANOVA
3.3. Findings of the Expert Modeler
4. Discussion
5. Conclusions
- As stated in the flowchart given in Figure 1, the study aimed to propose a decision model using deterministic and statistical analyses. For deterministic analysis, the criteria affecting PM10 concentration values in Kocaeli city center are determined from a literature review and decision-makers’ opinions. The IF-AHP method is used for criterion weighting. The purpose of determining criterion weights with IF-AHP logic in the evaluation of criteria affecting PM10 concentration values on a regional basis stems from the nature of the decision problem. The degree of hesitation is important for such intuitive decisions. According to the results obtained with IF-AHP, the most important criterion is determined to be C2: density of industrial facilities. The criterion that has the least impact on PM10 concentration values is C1: adverse meteorological conditions.
- In the statistical analysis part of the study, PM10 concentration values between 2017 and 2023, covering the pandemic period, are examined. Although significant decreases in air pollutant levels are observed in Europe and other regions during the pandemic period, these changes are not permanent as a result of the decrease in outdoor activities, transportation, and even industrial activities due to quarantine and restriction periods. Looking at the center of Kocaeli, the highest annual average PM10 concentration value was recorded in 2018, when pandemic-related restrictions had not yet occurred. PM10 concentration values started to increase significantly in 2020, 2021, and 2022. This increase may be an indication that heavy restrictions and measures are starting to lose their effectiveness. When this situation is associated with the results obtained using IF-AHP, effects such as remote working decisions or production restrictions in industrial facilities may have caused changes. Surprisingly, the monthly average concentration value reached its lowest level in 2023. The impact of this situation can be shown as the impact of economic pressure on industrial centers, but it may also be related to the adoption of some measures developed in 2023. The COVID-19 period quickly became permanent. Looking at the one-way ANOVA test results, there is a significant difference between the monthly average PM10 concentration values for the years. Accordingly, it can be said that the harmful effects of air pollution and PM10 in 2022 will be similar to the monthly average levels in 2019. The results obtained with the Games–Howell test valid for groups without variance homogeneity are given in Table A1 in Appendix A. According to Table A1 in Appendix A, considering that 2020 is the most critical year for COVID-19, there are significant differences between the years before 2020 and those after 2020. In this context, it is important to base the forecast on the period after 2020 for the sake of accuracy.
- In this study, monthly average PM10 values in 2020–2023 are estimated with the Expert Modeler tool in the software IBM® SPSS® Statistics version 22. The Expert Modeler tool determined the most appropriate forecasting model to be the simple seasonal model. Using the model data, 36-month estimated PM10 concentration values for 2024–2026 were obtained (Table A2 in Appendix A). In checking the estimated values obtained, the criterion weights obtained with IF-AHP can be examined by policymakers and decision-makers, and this examination may pave the way for more detailed analyses in future studies.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
(I) Time | (J) Time | Mean Difference (I − J) | Std. Error | Sig. | 95% Confidence Interval | |
---|---|---|---|---|---|---|
Lower Bound | Upper Bound | |||||
2017 | 2018 | −3.438 | 5.906 | 0.996 | −23.293 | 16.418 |
2019 | 3.812 | 6.243 | 0.996 | −16.807 | 24.432 | |
2020 | 18.397 * | 5.532 | 0.044 | −0.815 | 37.609 | |
2021 | 15.990 | 5.751 | 0.147 | −3.568 | 35.548 | |
2022 | 13.295 | 6.110 | 0.356 | −7.007 | 33.597 | |
2023 | 18.672 | 6.232 | 0.090 | −1.920 | 39.263 | |
2018 | 2017 | 3.437 | 5.906 | 0.996 | −16.418 | 23.293 |
2019 | 7.250 | 4.090 | 0.579 | −6.062 | 20.562 | |
2020 | 21.834 * | 2.892 | 0.000 | 12.244 | 31.424 | |
2021 | 19.427 * | 3.290 | 0.000 | 8.752 | 30.103 | |
2022 | 16.732 * | 3.885 | 0.005 | 4.130 | 29.335 | |
2023 | 22.109 * | 4.073 | 0.000 | 8.856 | 35.362 | |
2019 | 2017 | −3.812 | 6.243 | 0.996 | −24.432 | 16.807 |
2018 | −7.250 | 4.090 | 0.579 | −20.562 | 6.062 | |
2020 | 14.584 * | 3.530 | 0.012 | 2.666 | 26.502 | |
2021 | 12.178 | 3.863 | 0.065 | −0.509 | 24.865 | |
2022 | 9.482 | 4.380 | 0.353 | −4.704 | 23.669 | |
2023 | 14.859 * | 4.549 | 0.047 | 0.138 | 29.580 | |
2020 | 2017 | −18.397 | 5.532 | 0.064 | −37.609 | 0.815 |
2018 | −21.834 * | 2.892 | 0.000 | −31.424 | −12.244 | |
2019 | −14.584 * | 3.530 | 0.012 | −26.502 | −2.666 | |
2021 | −2.407 | 2.560 | 0.961 | −10.803 | 5.990 | |
2022 | −5.102 | 3.289 | 0.713 | −16.140 | 5.937 | |
2023 | 0.275 | 3.510 | 1.000 | −11.570 | 12.120 | |
2021 | 2017 | −15.990 | 5.751 | 0.147 | −35.548 | 3.568 |
2018 | −19.427 * | 3.290 | 0.000 | −30.103 | −8.752 | |
2019 | −12.177 | 3.863 | 0.065 | −24.865 | 0.510 | |
2020 | 2.407 | 2.560 | 0.961 | −5.990 | 10.803 | |
2022 | −2.695 | 3.644 | 0.988 | −14.604 | 9.214 | |
2023 | 2.68167 | 3.845 | 0.991 | −9.941 | 15.304 | |
2022 | 2017 | −13.295 | 6.110 | 0.356 | −33.597 | 7.007 |
2018 | −16.732 * | 3.884 | 0.005 | −29.335 | −4.130 | |
2019 | −9.482 | 4.380 | 0.353 | −23.669 | 4.704 | |
2020 | 5.102 | 3.290 | 0.713 | −5.937 | 16.140 | |
2021 | 2.695 | 3.644 | 0.988 | −9.214 | 14.604 | |
2023 | 5.377 | 4.364 | 0.874 | −8.756 | 19.510 | |
2023 | 2017 | −18.672 | 6.232 | 0.090 | −39.263 | 1.920 |
2018 | −22.110 * | 4.073 | 0.000 | −35.362 | −8.856 | |
2019 | −14.859 * | 4.549 | 0.047 | −29.580 | −0.138 | |
2020 | −0.275 | 3.510 | 1.000 | −12.120 | 11.571 | |
2021 | −2.682 | 3.845 | 0.991 | −15.304 | 9.941 | |
2022 | −5.377 | 4.364 | 0.874 | −19.510 | 8.756 |
Months/Years | Forecast | UCL | LCL |
---|---|---|---|
Jan 2024 | 44.06 | 57.06 | 31.05 |
Feb 2024 | 47.53 | 60.60 | 34.46 |
Mar 2024 | 41.99 | 55.13 | 28.86 |
Apr 2024 | 42.77 | 55.97 | 29.57 |
May 2024 | 36.62 | 49.89 | 23.36 |
Jun 2024 | 33.53 | 46.86 | 20.21 |
Jul 2024 | 33.56 | 46.95 | 20.17 |
Aug 2024 | 36.95 | 50.40 | 23.50 |
Sep 2024 | 36.04 | 49.55 | 22.52 |
Oct 2024 | 44.33 | 57.91 | 30.76 |
Nov 2024 | 50.04 | 63.68 | 36.41 |
Dec 2024 | 52.64 | 66.34 | 38.94 |
Jan 2025 | 44.06 | 57.82 | 30.30 |
Feb 2025 | 47.53 | 61.35 | 33.71 |
Mar 2025 | 41.99 | 55.88 | 28.11 |
Apr 2025 | 42.77 | 56.71 | 28.83 |
May 2025 | 36.62 | 50.63 | 22.62 |
Jun 2025 | 33.53 | 47.60 | 19.47 |
Jul 2025 | 33.56 | 47.68 | 19.44 |
Aug 2025 | 36.95 | 51.14 | 22.77 |
Sep 2025 | 36.04 | 50.28 | 21.80 |
Oct 2025 | 44.33 | 58.63 | 30.03 |
Nov 2025 | 50.04 | 64.40 | 35.68 |
Dec 2025 | 52.64 | 67.06 | 38.22 |
Jan 2026 | 44.06 | 58.53 | 29.58 |
Feb 2026 | 47.53 | 62.06 | 33.00 |
Mar 2026 | 41.99 | 56.59 | 27.40 |
Apr 2026 | 42.77 | 57.42 | 28.12 |
May 2026 | 36.62 | 51.33 | 21.92 |
Jun 2026 | 33.53 | 48.30 | 18.77 |
Jul 2026 | 33.56 | 48.38 | 18.74 |
Aug 2026 | 36.95 | 51.83 | 22.08 |
Sep 2026 | 36.04 | 50.97 | 21.10 |
Oct 2026 | 44.33 | 59.32 | 29.34 |
Nov 2026 | 50.04 | 65.09 | 35.00 |
Dec 2026 | 52.64 | 67.74 | 37.54 |
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Pollutant | Period | WHO | Türkiye National Values |
---|---|---|---|
PM10 | Daily | 15 μg/m3 | 50 μg/m3 |
Annually | 5 μg/m3 | 40 μg/m3 | |
Winter Period | - | 40 μg/m3 |
Years | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | |
---|---|---|---|---|---|---|---|---|
Months | ||||||||
January | 37.89 | 69.94 | 59.45 | 43.18 | 44.67 | 39.85 | 49.55 | |
February | 74.28 | 59.72 | 58.4 | 44.53 | 47.74 | 52.45 | 46.42 | |
March | 69.05 | 77.92 | 51.69 | 45.11 | 38.3 | 51.08 | 34.51 | |
April | 47.29 | 71.63 | 45.47 | 39.39 | 42.58 | 55.93 | 34.2 | |
May | 39.08 | 49.6 | 58.05 | 39.24 | 37.77 | 40.1 | 30.41 | |
June | 47.22 | 55.1 | 51.42 | 39.51 | 37.68 | 34.81 | 23.16 | |
July | 42.61 | 58.52 | 40.78 | 31.59 | 40.25 | 29.9 | 33.52 | |
August | 42.53 | 54.11 | 41.98 | 30.73 | 38.91 | 39.89 | 39.31 | |
September | 53.8 | 54.39 | 45.53 | 38.4 | 37.24 | 39.69 | 29.84 | |
October | 76.98 | 67.92 | 55.55 | 45.54 | 39.74 | 39.22 | 53.85 | |
November | 86.18 | 57.28 | 78.06 | 39.5 | 63.62 | 55.72 | 42.35 | |
December | 85.29 | 67.32 | 70.07 | 44.72 | 41.82 | 64.02 | 61.02 |
Year | Mean | Std. Deviation (Standard Deviation) | Std. Error (Standard Error) | Max | Min | |
---|---|---|---|---|---|---|
2017 | 12 | 58.52 | 18.51 | 5.34 | 86.18 | 37.89 |
2018 | 12 | 61.95 | 8.70 | 2.51 | 77.92 | 49.60 |
2019 | 12 | 54.70 | 11.18 | 3.23 | 78.06 | 40.78 |
2020 | 12 | 40.12 | 4.96 | 1.43 | 45.54 | 30.73 |
2021 | 12 | 42.53 | 7.36 | 2.12 | 63.62 | 37.24 |
2022 | 12 | 45.22 | 10.26 | 2.96 | 64.02 | 29.90 |
2023 | 12 | 39.84 | 11.10 | 3.20 | 61.02 | 23.16 |
Total | 84 | 48.98 | 13.65 | 1.49 | 86.18 | 23.16 |
Comparison Preference | AHP Preference Correspondence | Triangular Intuitive Fuzzy Numbers | Opposite Triangular Intuitive Fuzzy Numbers |
---|---|---|---|
Equal Important | 1 | (0.02 0.18 0.80) | (0.02 0.18 0.80) |
Middle | 2 | (0.06 0.23 0.70) | (0.23 0.06 0.70) |
Somewhat Important | 3 | (0.13 0.27 0.60) | (0.27 0.13 0.60) |
Middle | 4 | (0.22 0.28 0.50) | (0.28 0.22 0.50) |
Strongly Important | 5 | (0.33 0.27 0.40) | (0.27 0.33 0.40) |
Middle | 6 | (0.47 0.23 0.30) | (0.23 0.47 0.30) |
Very Strongly Important | 7 | (0.62 0.18 0.20) | (0.18 0.62 0.20) |
Middle | 8 | (0.80 0.10 0.10) | (0.10 0.80 0.10) |
Absolutely Important | 9 | (1 0 0) | (0 1 0) |
Linguistic Variables | Triangular Intuitive Fuzzy Number Correspondence |
---|---|
Very important | (0.90 0.05 0.05) |
Important | (0.75 0.20 0.05) |
Somewhat Important | (0.50 0.40 0.10) |
Insignificant | (0.25 0.60 0.15) |
Very unimportant | (0.10 0.80 0.10) |
1–2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |
---|---|---|---|---|---|---|---|---|
RI | 0.0 | 0.58 | 0.90 | 1.12 | 1.24 | 1.32 | 1.41 | 1.45 |
Tests Using the Equal Variance Theory | Tests Using Different Variance Theory |
---|---|
LSD (Fisher’s significant difference test) | Tamhane Test |
Bonferroni Test | Dunnet T3 Test |
Tukey HSD Test | Games–Howell Test |
Scheffe Test | Dunnet–C Test |
Duncan Test | |
Dunnet Test | |
Waller–Duncan Test |
Criteria Affecting PM10 Concentration Values | Criteria Number | Reference |
---|---|---|
Adverse Meteorological Conditions | C1 | [54,55] |
Density of Industrial Facilities | C2 | [11] |
Population Growth | C3 | [56] |
Unplanned Urbanization | C4 | [57] |
Lack of Green Areas | C5 | [58] |
Topographic Structure | C6 | [59] |
Motor Vehicles Emissions | C7 | [11] |
DM Group | Triangular Intuitive Fuzzy Number | λk (Weights) |
---|---|---|
DM1 | (0.90 0.05 0.05) | 0.2351 |
DM2 | (0.90 0.05 0.05) | 0.2351 |
DM3 | (0.75 0.20 0.05) | 0.1959 |
DM4 | (0.75 0.20 0.05) | 0.1959 |
DM5 | (0.50 0.40 0.10) | 0.1378 |
Criteria Number | DM1 | DM2 | ||||
C1 | 0.1744 | 0.3299 | 0.4957 | 0.2200 | 0.1741 | 0.6059 |
C2 | 0.2256 | 0.2725 | 0.5019 | 0.2839 | 0.2295 | 0.4866 |
C3 | 0.2544 | 0.2346 | 0.5110 | 0.3228 | 0.2405 | 0.4367 |
C4 | 0.1818 | 0.3171 | 0.5011 | 0.2106 | 0.1928 | 0.5966 |
C5 | 0.2463 | 0.2179 | 0.5358 | 0.1792 | 0.2431 | 0.5777 |
C6 | 0.3262 | 0.2153 | 0.4585 | 0.1792 | 0.2431 | 0.5777 |
C7 | 0.2087 | 0.2566 | 0.5347 | 0.1792 | 0.2431 | 0.5777 |
Criteria Number | DM3 | DM4 | ||||
C1 | 0.1771 | 0.4247 | 0.3983 | 0.1432 | 0.4535 | 0.4033 |
C2 | 0.3807 | 0.2789 | 0.3404 | 0.3863 | 0.1727 | 0.4410 |
C3 | 0.3355 | 0.2834 | 0.3812 | 0.2152 | 0.3252 | 0.4596 |
C4 | 0.2106 | 0.3896 | 0.3999 | 0.2236 | 0.2728 | 0.5036 |
C5 | 0.2660 | 0.3051 | 0.4289 | 0.5095 | 0.1912 | 0.2993 |
C6 | 0.4374 | 0.2168 | 0.3458 | 0.4754 | 0.2003 | 0.3242 |
C7 | 0.4991 | 0.2033 | 0.2976 | 0.2949 | 0.2746 | 0.4305 |
Criteria Number | DM5 | |||||
C1 | 0.1735 | 0.4233 | 0.4031 | |||
C2 | 0.3807 | 0.2789 | 0.3404 | |||
C3 | 0.2642 | 0.2769 | 0.4589 | |||
C4 | 0.1766 | 0.3573 | 0.4661 | |||
C5 | 0.2660 | 0.3051 | 0.4289 | |||
C6 | 0.4374 | 0.2168 | 0.3458 | |||
C7 | 0.5430 | 0.1869 | 0.2701 |
Criteria Number | |||
---|---|---|---|
C1 | 0.1798 | 0.3286 | 0.4916 |
C2 | 0.3258 | 0.2412 | 0.4329 |
C3 | 0.2815 | 0.2671 | 0.4514 |
C4 | 0.2019 | 0.2899 | 0.5082 |
C5 | 0.2993 | 0.2439 | 0.4568 |
C6 | 0.3673 | 0.2189 | 0.4138 |
C7 | 0.3387 | 0.2348 | 0.4265 |
Criteria Number | Final Weights |
---|---|
C1 | 0.1387 |
C2 | 0.1478 |
C3 | 0.1428 |
C4 | 0.1420 |
C5 | 0.1424 |
C6 | 0.1437 |
C7 | 0.1426 |
Levene Statistic | df1 | df2 | Sig. |
---|---|---|---|
7.255 | 6 | 77 | 0.001 |
Sum of Squares | df | Mean Square | F | Sig. | |
---|---|---|---|---|---|
Between Groups | 6117.137 | 6 | 1019.523 | 8.389 | 0.002 |
Within Groups | 9358.425 | 77 | 121.538 | ||
Total | 15,475.562 | 83 |
Years | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 |
---|---|---|---|---|---|---|---|
2017 | - | X | X | ✓ | X | X | X |
2018 | X | - | X | ✓ | ✓ | ✓ | ✓ |
2019 | X | X | - | ✓ | X | X | ✓ |
2020 | ✓ | ✓ | ✓ | - | X | X | X |
2021 | X | ✓ | X | X | - | X | X |
2022 | X | ✓ | X | X | X | - | X |
2023 | X | ✓ | ✓ | X | X | X | - |
Model Description | ||||||||
Model Type | ||||||||
Model ID | PM1010_values_after_COVID-19 | Model_1 | Simple Seasonal | |||||
Model Statistics | ||||||||
Model | Model Fit Statistics | Ljung-Box Q(18) | Number of Outliers | |||||
Stationary R-squared | R-squared | RMSE | MAPE | Statistics | DF | Sig. | ||
PM10_values_after_COVID-19-Model_1 | 0.732 | 0.467 | 6.461 | 12.787 | 14.925 | 16 | 0.530 | 0 |
Exponential Smoothing Model Parameters | ||||||||
Model | Estimate | SE | Sig. | |||||
PM10_values_after_ COVID-19-Model_1 | No Transformation | Alpha (Level) | 0.100 | 0.077 | 1.291 | 0.203 | ||
Delta (Season) | 4.673 × 10−5 | 0.104 | 0.000 | 1.000 |
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Güler, E.; Yerel Kandemir, S. Analysis of PM10 Substances via Intuitionistic Fuzzy Decision-Making and Statistical Evaluation. Sustainability 2024, 16, 7851. https://doi.org/10.3390/su16177851
Güler E, Yerel Kandemir S. Analysis of PM10 Substances via Intuitionistic Fuzzy Decision-Making and Statistical Evaluation. Sustainability. 2024; 16(17):7851. https://doi.org/10.3390/su16177851
Chicago/Turabian StyleGüler, Ezgi, and Süheyla Yerel Kandemir. 2024. "Analysis of PM10 Substances via Intuitionistic Fuzzy Decision-Making and Statistical Evaluation" Sustainability 16, no. 17: 7851. https://doi.org/10.3390/su16177851