Developing GA-ANFIS model to predict long-term 〖PM〗_10 concentration The Case study of Tehran city

Document Type : Full length article


Department of Remote Sensing and Geographic Information System, Faculty of Geography, University of Tehran, Tehran, Iran



Among the types of airborne particles, particles with a diameter of less than 10 microns have many adverse effects on human health. Meteorological parameters and the movement of a large volume of vehicles are considered the most important modulating factors in the distribution and concentration of atmospheric pollutants. In this study, in order to predict the concentration of PM-10 pollutant during a long-term interval in Tehran city, GA-ANFIS hybrid model was used. Wind speed, wind direction, temperature, relative humidity and traffic volume were considered as inputs and pollutant concentration PM_10 as the output of the model. The results of the calculation of the performance indicators showed that the combined GA-ANFIS model provides a better framework than the ANFIS model in predicting the pollutant concentration PM_10. In order to evaluate the spatio-temporal patterns of PM_10 pollutant concentration and to identify hot and cold spots in Tehran city, local Moran's statistic and Ard-J Gettys statistic were calculated. The results showed that there is a high level of clustering of PM_10 pollutant in Tehran (with 95% confidence level). The clusters of PM_10 have divided the city into two northern and southern parts so that most of the cold spots in the northern half and the hot spots in the south have spread to the center of the city
Extend Abstract
Among the types of air particles, particles with a diameter of less than 10 microns have many adverse effects on human health. The main sources of emissions of this pollutant are fuel combustion, industrial processes, agriculture, cars and diesel machines do not have the necessary standards. Meteorological parameters and high volume displacement of vehicles are the most important modulating factors in the distribution and accumulation of air pollutants. Tehran city is the capital of Iran, it is the most populous cities in Iran over 8.69 million of population. Due to the special geographical location of the city, in the northern part it has a temperate and mountainous climate and in low-lying areas it is semi-arid. The metropolis of Tehran, as the second most populous capital in the Middle East and the twenty-fourth most populous city in the world, is one of the most polluted capitals in the world. Tehran is ranked 12th among 26 metropolises in terms of ambient   levels. The two main causes of the spread of this pollutant in Tehran are mobile sources (vehicles) and resident sources of pollution such as construction workshops and refineries around Tehran.
Climatic parameters such as temperature, wind speed, wind direction and relative humidity have an important effect on the formation, transportation, accumulation and deposition of atmospheric pollutants. The role of wind speed as an important factor in the distribution of pollutants in the city of Tehran on a regional and local scale is undeniable. Due to the significant effect that high-speed winds have on the dispersion of pollutants and, consequently, on the concentration of pollutants in the atmosphere, as a result, chemical reactions are reduced. Regarding the role of temperature parameter in the process of changes in the concentration of air pollutants, it can be noted that in general, high temperature is associated with unfavorable air quality. The trend of relative humidity changes is in line with the pattern of temperature changes so that the maximum and minimum relative humidity are in accordance with the minimum and maximum temperatures. The next very influential factor in the process of changes in the concentration of pollutants is urban road traffic. The high number of vehicles in the central streets of the city and the subsequent increase in the volume and duration of traffic has led to an increase and accumulation of concentrations of pollutants in the streets and central areas of Tehran.  Recently, intelligent and data-driven methods have become increasingly well known for forecasting of air pollution. In the current investigation, an approach was presented for the training ANFIS by using GA based on a population algorithm. At first, Genetic algorithm was implemented for optimal consequent parameters of ANFS. Then, these optimal values was applied for training ANFIS model. At the next step, for spatial modeling of  concentration level during in the studied period, hotspot analysis in GIS was used to investigate the spatial changes of  concentration and to identify hot and cold spots. In this study, modeling of   by GA-ANFIS was done on three dataset such as air pollution data, meteorological, traffic volume. Meteorological parameter include the hourly wind speed (m/s), wind direction (), temperature () and relative humidity (%) were taken the weather measurement stations in Tehran. The air quality data, which comprises the hourly concentration of  (ppb). Concentration of pollutant () were obtained from the Air Quality Control Company of Tehran. Traffic data was obtained from Tehran Traffic Control Company. The time interval of traffic data and meteorological data were considered the same. These data, covering the period from 2010 to 2020, were acquired from 21 air monitoring stations. In this study, MATLAB 2020a programming language was used to implement ANFIS and hybrid of GA-ANFIS were proposed for  concentration prediction and four indices were used for evaluate the results of the predication by ANFIS and GA-ANFIS models. ANFIS is a combination of neuronal networks and fuzzy system that proposed by Jang and Sun. To reach the optimized output, the ANFIS can coupled with hybrid learning methods like Imperialist Competitive algorithm (ICA), PSO or GA. Genetic Algorithms (GA) is a type of evolutionary heuristic search algorithm based on genetic science. GA provides a random search that was used to solve optimization problems. It can be noted that GA-ANFIS algorithm can successfully improve the performance of the ANFIS model. In order to assessment of the patterns of spatial changes of   concentration in the study period at the seasons of the year, a GIS modeling was applied to produce the simulation maps at different seasons during a study period. Spatial correlation of case study overall pollution concentrations was measured using global Moran’s I and Getis-Ord general G indices.In order to identify hot and cold spots on maps, G statistics was used by Getis.
Result and discussion
The results of the global spatial autocorrelation analyses found high clustering levels for  in the case study, which are significantly different from random at the 95% confidence level. In the monthly results, the critical values for   pollutant occurred in the June to August (due to dust storms originating outside of Tehran). In general, there was no sensible similarity in the seasonal results. As the results of spatial autocorrelation analysis showed,  Clusters separated the city into southwest parts, as most of cold spots were situated in the north and west. With the increase in traffic volume at the beginning of the fall season due to the reopening of universities and schools, we are witnessing polluted critical days in Tehran. Starting of rainfall in late of winter, air pollution decreases and wind speed up to 4 m / s are ineffective in reducing pollution concentrations. The highest emission of particulate matter less than 10 microns is due to natural resources and due to the prevailing wind direction in Tehran from southwest to northeast, the high concentration of this pollutant in the districts of 18, 9, 10 and 15 at the entrances of Tehran, it seems logical. The lowest  concentration was observed in the northeastern areas of the city.
There is no funding support.
Authors Contribution
All of the authors approved thecontent of the manuscript and agreed on all aspects of the work.
Conflict of Interest
Authors declared no conflict of interest.
We are grateful to all the scientific consultants of this paper.


Main Subjects

  1. AL-Qaness, M. A., Fan, H., Ewees, A. A., Yousri, D. & Abd Elaziz, M. (2021). Improved ANFIS model for forecasting Wuhan City air quality and analysis COVID-19 lockdown impacts on air quality, Environmental Research, 194,
  2. Akbary, M., Kermani, A. & Alijani, B. (2018). Simulation and analysis of polluted days in Tehran. International Journal of Environmental Research, 12, 67-75.
  3. Arhami, M., Hosseini, V., Shahne, M. Z., Bigdeli, M., LAI, A. & Schauer, J. J. (2017). Seasonal trends, chemical speciation and source apportionment of fine PM in Tehran. Atmospheric Environment, 153, 70-82.
  4. Akinyemi, M., Emetere, M., & Akinwumi, S. (2016). Dynamics of wind strength and wind direction on air pollution dispersion. Asian Journal of Applied Sciences, 4 (2), 1-12.
  5. Azarmi, F., Kumar, P., Marsh, D. & Fuller, G. (2016). Assessment of the long-term impacts of PM 10 and PM 2.5 particles from construction works on surrounding areas. Environmental Science: Processes & Impacts, 18, 208-221.
  6. Alizadeh-Choobari, O., Bidokhti A A., Ghafarian, P., & Najafi, MS. (2016). Temporal and spatial variations of particulate matter and gaseous pollutants in the urban area of Tehran. Atmos Environ, 141, 443-53.
  7. Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical analysis, 27, 93-115.
  8. Anselin, L. & Getis, A. (1992). Spatial statistical analysis and geographic information systems. The Annals of Regional Science, 26, 19-33.
  9. Arjun, K., & Aneesh, K. (2015). Modelling studies by application of artificial neural network using matlab.
  1.  Journal of Engineering Science and Technology, 10, 1477-1486.
  1. Budnik, T., & Casteleyn, L. (2019). Mercury pollution in modern times and its socio-medical consequences. Science of The Total Environment, 654, 720-734.
  2. BaldaufL, R. W., Isakov, V., Deshmukh, P., Venkatram, A., Yang, B. & Zhang, K. M. (2016). Influence of solid noise barriers on near-road and on-road air quality. Atmospheric Environment, 129, 265-276.
  3. Baghban, , Ahmadi, M. A., & Shahraki, B. H. (2015). Prediction carbon dioxide solubility in presence of various ionic liquids using computational intelligence approaches. The Journal of supercritical fluids, 98, 50-64.
  4. Basser, H., Karami, H., Shamshirband, S., Akib, S., Amirmojahedi, M., Ahmad, R., Jahangirzadeh, A. & Javidnia, H. (2015). Hybrid ANFIS–PSO approach for predicting optimum parameters of a protective spur dike. Applied Soft Computing, 30 ,649-642.
  5. Banu, G., & Suja, S. (2014). Fault location technique using GA-ANFIS for UHV line. Archives of Electrical Engineering,12, 247-262.
  6. Berkowicz, R., Palmgren, F., Hertel, O. & Vignati, E. (1996). Using measurements of air pollution in streets for evaluation of urban air quality—meterological analysis and model calculations. Science of the total environment, 189, 259-265.
  7. Cichowicz, R., Wielgosinski, G., & Fetter, W. (2020). Effect of wind speed on the level of particulate matter PM10 concentration in atmospheric air during winter season in vicinity of large combustion plant. Journal of Atmospheric Chemistry, 77, 35-48.
  8. Cujia, A., Agudelo-Castaneda, D., Pacheco-Bustos, C. & Teixeira, E. C. (2019). Forecast of PM10 time-series data: A study case in Caribbean cities. Atmospheric Pollution Research, 10, 2053-2062.
  9. Ceylan, Z., Pekel, E., Ceylan, S. & Bulkan, S. (2018). Biomass higher heating value prediction analysis by ANFIS, PSO-ANFIS and GA-ANFIS. Global Nest Journal, 20, 589-597.
  10. Chen, Y. (2018). Prediction algorithm of PM2. 5 mass concentration based on adaptive BP neural network. Computing, 100, 825-838.
  11. Chinneck, J. W. (2006). Practical optimization: a gentle introduction. Systems and Computer Engineering), Carleton University, Ottawa. http://www. sce. carleton. ca/faculty/chinneck/po. html, 11.
  12. De Rooij, M. M., Heederik, D. J., Borlee, F., Hoek, G. & Wouters, I. M. (2017). Spatial and temporal variation in endotoxin and PM10 concentrations in ambient air in a livestock dense area. Environmental research, 153, 161-170.
  13. Fang, C., Wang, Z., & Xu, G. (2016). Spatial-temporal characteristics of PM 2.5 in China: A city-level perspective analysis. Journal of Geographical Sciences, 26, 1519-1532.
  14. Feng, Q., Wu, S., Du, Y., Xue, H., Xiao, F., Ban, X. & Li, X. (2013). Improving neural network prediction accuracy for PM10 individual air quality index pollution levels. Environmental engineering science, 30, 725-732.
  15. Ghasemi, A., & Amanollahi, J. (2019). Integration of ANFIS model and forward selection method for air quality forecasting. Air Quality, Atmosphere & Health, 12, 59-72.
  16. Ghotbi, S., Sotoudeheian, S. & Arhami, M. (2016). Estimating urban ground-level PM10 using MODIS 3km AOD product and meteorological parameters from WRF model. Atmospheric Environment, 141, 333-346.
  17. Goldberg, D. E., & Holland, J. H. (1988). Genetic algorithms and machine learning.
  18. Goodchild, M. F. (1986). Spatial autocorrelation. Geo Books.
  19. Humpe, A., Brehm, L., & Gunzel, H. (2021). Forecasting Air Pollution in Munich: A Comparison of MLR, ANFIS, and SVM. ICAART, (2), 500-506.
  20. Han, L., Zhao, J., Gao, Y., Gu, Z., Xin, K., & Zhang, J. (2020). Spatial distribution characteristics of PM2. 5 and PM10 in Xi’an City predicted by land use regression models. Sustainable Cities and Society, 61, 102329.
  21. Heger, M., & SARRAF, M. (2018). Air pollution in Tehran: Health costs, sources, and policies, World Bank.
  22. Hossaini, M., Mekhilef, S., Afifi, F., Halabi, L. M., Olatomiwa, L., Seyedmahmoudian, M., Horan, B. & Stojcevski, A. (2018). Application of the hybrid ANFIS models for long term wind power density prediction with extrapolation capability. PloS one, 13, e0193772.
  23. Habibi, R., Alesheikh, A. A., Mohammadinia, A. & Sharif, M. (2017). An assessment of spatial pattern characterization of air pollution: A case study of CO and PM2. 5 in Tehran, Iran. ISPRS international journal of Geo-information, 6, 270.
  24. Hosseini, V., & Shahbazi, H. (2016). Urban air pollution in Iran. Iranian Studies, 49, 1029-1046.
  25. Haupt, R. L., & HAUPT, S. E. (2004). Practical genetic algorithms. John Wiley & Sons.
  26. Johansson, C., Norman, M., & Gidhagen, L. (2007). Spatial & temporal variations of PM10 and particle number concentrations in urban air. Environmental monitoring and assessment, 127, 477-487.
  27. Jang, J. S. (1996). Input selection for ANFIS learning. Proceedings of IEEE 5th International Fuzzy Systems, IEEE, 1493-1499.
  28. Jang, J. S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23, 665-685.
  29. Keykhosrowi, G., & Lashkari, H. (2014). Analysis of the relationship between the thickness and height of the inversion and the severity of air pollution in Tehran.
  30. Khan, F., Latif, M. T., Juneng, L., Amil, N., Mohd Nadzir, M. S. & Syedul Hoque, H. M. (2015). Physicochemical factors and sources of particulate matter at residential urban environment in Kuala Lumpur. Journal of the Air & Waste Management Association, 65, 958-969.
  31. Koza, J. (2007). Introduction to genetic programming. Proceedings of the 9th annual conference companion on Genetic and evolutionary computation, 3323-3365.
  32. Katsouyanni, K., Pantazopoulou, A., Touloumi, G., Tselepidaki, I., Moustris, K., Asimakopoulos, D., Poulopoulou, G. & Trichopoulos, D. (1993). Evidence for interaction between air pollution and high temperature in the causation of excess mortality. Archives of Environmental Health: An International Journal, 48, 235-242.
  33. Li, H., Guo, B., Han, M., Tian, M. & Zhang, J. (2015). Particulate matters pollution characteristic and the correlation between PM (PM 2.5, PM 10) and meteorological factors during the summer in Shijiazhuang. Journal of Environmental Protection, 6, 457.
  34. Lei, K. S., & Wan, F. (2012). Applying ensemble learning techniques to ANFIS for air pollution index prediction in Macau. International Symposium on Neural Networks, Springer, 509-516.
  35. Masoudi, M., Sakhaei, M., Behzadi, F. & Jokar, P. (2016). Status of PM10 as an air pollutant and its prediction using meteorological parameters in Tehran, Iran. Environ. Bull, 25, 2008-201.
  36. Mihalache, S. F., Popescu, M. & Oprea, M. (2015). Particulate matter prediction using ANFIS modelling techniques. 19th International Conference on System Theory, Control and Computing (ICSTCC), 2015. IEEE, 895-900.
  37. Ord, J. K., & Getis, A. (1995). Local spatial autocorrelation statistics: distributional issues and an application. Geographical analysis, 27, 286-306.
  38. Prasad, K., Gorai, A. K. & GOYAL, P. (2016). Development of ANFIS models for air quality forecasting and input optimization for reducing the computational cost and time. Atmospheric environment, 128, 246-262.
  39. Paschalidou, A. K., Karakitsios, S., Kleanthous, S. & Kassomenos, P. A. (2011). Forecasting hourly PM 10 concentration in Cyprus through artificial neural networks and multiple regression models: Implications to local environmental management. Environmental Science and Pollution Research, 18, 316-327.
  40. Rezakazemi, M., Dashti, A., Asghari, M. & Shirazian, S. (2017). H2-selective mixed matrix membranes modeling using ANFIS, PSO-ANFIS, GA-ANFIS. International Journal of Hydrogen Energy, 42, 15211-15225.
  41. Rosenlund, M., Picciotto, S., Forastiere, F., Stafoggia, M., & Perucci, C. A. (2008). Traffic-related air pollution in relation to incidence and prognosis of coronary heart disease. Epidemiology,15, 121-128.
  42. Roberts, S. (2004). Interactions between particulate air pollution and temperature in air pollution mortality time series studies. Environmental research, 96, 328-337.
  43. Sulaiman, G., & Younes, M. K. (2018). Modelling of traffic emissions using modified synchro-anfis integrated model on traffic signals. Feb-Fresenius Environmental Bulletin, 8308.
  44. Shahbazi, H., Ganjiazad, R., Hosseini, V. & Hamedi, M. (2017). Investigating the influence of traffic emission reduction plans on Tehran air quality using WRF/CAMx modeling tools. Transportation Research Part D: Transport and Environment, 57, 484-495.
  45. Stoimenova, M., Voynikova, D., Ivanov, A., Gocheva-Ilieva, S. & Iliev, I. (2017). Regression trees modeling and forecasting of PM10 air pollution in urban areas. AIP Conference Proceedings, AIP Publishing LLC, 030005.
  46. Sbihi, H., Tamburic, L., Koehoorn, & Brauer, M. (2016). Perinatal air pollution exposure and development of asthma from birth to age 10 years. European Respiratory Journal, 47, 1062-1071.
  47. Shahbazi, H., Reyhanian, M., Hosseini, V. & Afshin, H. (2016). The relative contributions of mobile sources to air pollutant emissions in Tehran, Iran: an emission inventory approach. Emission control science and technology, 2, 44-56.
  48. Shahraiyni, H. T., Sodoudi, S., Kerschbaumer, A. & Cubasch, U. (2015). A new structure identification scheme for ANFIS and its application for the simulation of virtual air pollution monitoring stations in urban areas. Engineering Applications of Artificial Intelligence, 41, 175-182.
  49. Sharipour, Z., & Akbaribidokhti, A. (2014). Investigation of spatial and temporal distributions of air pollutants over Tehran in cold months of 2011-2013. Journal of Environmental Science and Technology, 16, 149-166.
  50. Tella, A., & Balogun, A.-L. (2021). Prediction of ambient PM10 concentration in Malaysian cities using geostatistical analyses. Journal of Advanced Geospatial Science & Technology, 1, 115-127.
  51. Taheri Shahraiyni, H., & Sodoudi, S. (2016). Statistical modeling approaches for PM10 prediction in urban areas; A review of 21st-century studies. Atmosphere, 7, 1-15.
  52. Venegas, L., & MAazzeo, N. (2006). Modelling of urban background pollution in Buenos Aires City (Argentina). Environmental Modelling & Software, 21, 577-586.
  53. Wu, J., Winer, A. M., & Delfino, R. J. (2006). Exposure assessment of particulate matter air pollution before, during, and after the 2003 Southern California wildfires. Atmospheric Environment, 40, 3333-3348.
  54. Yanosky, J. D., Fisher, J., Liao, D., Rim, D., Vander Wal, R., Groves, W. & Puett, C. (2018). Application and validation of a line-source dispersion model to estimate small scale traffic-related particulate matter concentrations across the conterminous US. Air Quality, Atmosphere & Health, 11, 741-754.
  55. Yavari, H., & Saligheh, M. (2011). Air pollution inversion levels in Tehran city.
  56. Zhang, J. & Ding, W. (2017). Prediction of air pollutants concentration based on an extreme learning machine the case of Hong Kong. International journal of environmental research and public health, 14, 114.
  57. Zhu, Y., Hinds, W. C., Kim, S., Shen, S. & Sioutas, C. (2002). Study of ultrafine particles near a major highway with heavy-duty diesel traffic. Atmospheric environment, 36, 4323-4335.