Application of Computational Intelligence on Analysis of Air Quality Monitoring Big Data

  • Tzu-Yi PaiEmail author
  • Moo-Been Chang
  • Shyh-Wei Chen
Part of the Studies in Big Data book series (SBD, volume 8)


For controlling air pollution, the Taiwan Environmental Protection Administration (TEPA) installed automatic air quality monitoring stations (AQMSs) and TEPA prescribed the industries to install continuous emission monitoring systems (CEMS). By 2014, there were a total of 76 AQMS and 351 CEMS in the entire nation. Therefore, the huge amount of air quality monitoring data forms big data. The processing, interpretation, collection and organization of air quality monitoring big data (AQMBD) have emerged in air quality control including industry management, traffic reduction, and residential health. In this chapter, the application of computational intelligence on analysis of air quality monitoring big data was reviewed worldwide. Additionally, the application of computational intelligence (CI) including artificial neural network, fuzzy theory, and adaptive network-based fuzzy inference system (ANFIS) was discussed. Finally, the implementation of CI on AQMBD granular computing was proposed.


Computational intelligence Air quality monitoring big data Artificial neural network Swarm intelligence 



The authors are grateful to the National Science Council of Taiwan, R.O.C. for financial support under the Grant Number NSC101-2621-M-142-001-MY2.


  1. 1.
    Atimtay, A.T., Emri, S., Bagci, T., Demir, A.U.: Urban CO exposure and its health effects on traffic policemen in Ankara. Environ. Res. 82(3), 222–230 (2000)CrossRefGoogle Scholar
  2. 2.
    Bargiela, A., Pedrycz, W.: Granular Computing: an Introduction. Kluwer Academic Publishers, Boston (2002)Google Scholar
  3. 3.
    Boznar, M., Lesjak, M., Mlakar, P.: A neural network-based method for short-term predictions of ambient SO2 concentrations in highly polluted industrial areas of complex terrain. Atmos. Environ. Part B 27(2), 221–230 (1993)CrossRefGoogle Scholar
  4. 4.
    Cunningham, W.P., Cunningham, M.A.: Principles of Environmental Science Inquiry & Applications. McGraw-Hill Company, New York (2008)Google Scholar
  5. 5.
    Cai, M., Yin, Y., Xie, M.: Prediction of hourly air pollutant concentrations near urban arterials using artificial neural network approach. Transp. Res. D-Tr E 14(1), 32–41 (2009)CrossRefGoogle Scholar
  6. 6.
    Chen, S.M., Yang, M.W., Lee, L.W., Yang, S.W.: Fuzzy multiple attributes group decision-making based on ranking interval type-2 fuzzy sets. Expert Syst. Appl. 39(5), 5295–5308 (2012)CrossRefGoogle Scholar
  7. 7.
    Comrie, A.C.: Comparing neural networks and regression models for ozone forecasting. J. Air Waste Manage. Assoc. 47, 653–663 (1997)CrossRefGoogle Scholar
  8. 8.
    De Kok, T.M.C.M., Driece, H.A.L., Hogervorst, J.G.F., Briedé, J.J.: Toxicological assessment of ambient and traffic-related particulate matter: a review of recent studies. Mutat. Res. 613(2–3), 103–122 (2006)CrossRefGoogle Scholar
  9. 9.
    Delfino, R.J., Murphy-Moulton, A.M., Becklake, M.R.: Emergency room visits for respiratory illnesses among the elderly in Montreal: association with low level ozone exposure. Environ. Res. 76, 67–77 (1998)CrossRefGoogle Scholar
  10. 10.
    Finkelstein, M.M., Jerrett, M.: A study of the relationships between Parkinson’s disease and markers of traffic-derived and environmental manganese air pollution in two Canadian cities. Environ. Res. 104(3), 420–432 (2007)CrossRefGoogle Scholar
  11. 11.
    Gardner, M.W., Dorling, S.R.: Artificial neural networks (the multilayer feed-forward neural networks)—a review of applications in the atmospheric science. Atmos. Environ. 30(14/15), 2627–2636 (1998)CrossRefGoogle Scholar
  12. 12.
    Gautam, A.K., Chelani, A.B., Jain, V.K., Devotta, S.: A new scheme to predict chaotic time series of air pollutant concentrations using artificial neural network and nearest neighbor searching. Atmos. Environ. 42(18), 4409–4417 (2008)CrossRefGoogle Scholar
  13. 13.
    Hyder, A.A., Ghaffar, A.A., Sugerman, D.E., Masood, T.I., Ali, L.: Health and road transport in Pakistan. Publ. Health 120(2), 132–141 (2006)CrossRefGoogle Scholar
  14. 14.
    Lee, E., Chan, C.K., Paatero, P.: Application of positive matrix factorization in source apportionment of particulate pollutants. Atmos. Environ. 33, 3201–3212 (1999)CrossRefGoogle Scholar
  15. 15.
    Lu, W.Z., Wang, W.J.: Potential assessment of the “support vector machine” method in forecasting ambient air pollutant trends. Chemosphere 59, 693–701 (2005)CrossRefGoogle Scholar
  16. 16.
    Lu, W.Z., Fan, H.Y., Leung, A.Y.T., Wong, J.C.K.: Analysis of pollutant levels in central Hong Kong applying neural network method with particle swarm optimization. Environ. Monit. Assess. 79(3), 217–230 (2002)CrossRefGoogle Scholar
  17. 17.
    Lu, W.Z., Fan, H.Y., Lo, S.M.: Application of evolutionary neural network method in predicting pollutant levels in downtown area of Hong Kong. Neurocomputing 51, 387–400 (2003)CrossRefGoogle Scholar
  18. 18.
    Lu, W.Z., Wang, W.J., Wang, X.K., Xu, Z.B., Leung, A.Y.T.: Using improved neural network model to analyze RSP, NOx and NO2 levels in urban air in Mong Kok. Hong Kong. Environ. Monit. Assess. 87(3), 235–254 (2003)Google Scholar
  19. 19.
    Pai, T.Y., Hanaki, K., Ho, H.H., Hsieh, C.M.: Using grey system theory to evaluate transportation on air quality trends in Japan. Transp. Res. D-Tr E 12(3), 158–166 (2007)CrossRefGoogle Scholar
  20. 20.
    Pai, T.Y., Tsai, Y.P., Lo, H.M., Tsai, C.H., Lin, C.Y.: Grey and neural network prediction of suspended solids and chemical oxygen demand in hospital wastewater treatment plant effluent. Comput. Chem. Eng. 31(10), 1272–1281 (2007)CrossRefGoogle Scholar
  21. 21.
    Pai, T.Y., Chuang, S.H., Ho, H.H., Yu, L.F., Su, H.C., Hu, H.C.: Predicting performance of grey and neural network in industrial effluent using online monitoring parameters. Process Biochem. 43(2), 199–205 (2008)CrossRefGoogle Scholar
  22. 22.
    Pai, T.Y., Chuang, S.H., Wan, T.J., Lo, H.M., Tsai, Y.P., Su, H.C., Yu, L.F., Hu, H.C., Sung, P.J.: Comparisons of grey and neural network prediction of industrial park wastewater effluent using influent quality and online monitoring parameters. Environ. Monit. Assess. 146(1–3), 51–66 (2008)CrossRefGoogle Scholar
  23. 23.
    Pai, T.Y., Lin, K.L., Shie, J.L., Chang, T.C., Chen, B.Y.: Predicting the co-melting temperatures of municipal solid waste incinerator fly ash and sewage sludge ash using grey model and neural network. Waste Manage. Res. 29(3), 284–293 (2011)CrossRefGoogle Scholar
  24. 24.
    Pai, T.Y., Yang, P.Y., Wang, S.C., Lo, H.M., Chiang, C.F., Kuo, J.L., Chu, H.H., Su, H.C., Yu, L.F., Hu, H.C., Chang, Y.H.: Predicting effluent from the wastewater treatment plant of industrial park based on fuzzy network and influent quality. Appl. Math. Model. 35(8), 3674–3684 (2011)CrossRefGoogle Scholar
  25. 25.
    Pai, T.Y., Ho, C.L., Chen, S.W., Lo, H.M., Sung, P.J., Lin, S.W., Lai, W.J., Tseng, S.C., Ciou, S.P., Kuo, J.L., Kao, J.T.: Using seven types of GM (1, 1) model to forecast hourly particulate matter concentration in Banciao City of Taiwan. Water Air Soil Pollut. 217(1–4), 25–33 (2011)CrossRefGoogle Scholar
  26. 26.
    Pai, T.Y., Hanaki, K., Su, H.C., Yu, L.F.: A 24-h forecast of oxidant concentration in Tokyo using neural network and fuzzy learning approach. Clean-Soil, Air, Water 41(8), 729–736 (2013)CrossRefGoogle Scholar
  27. 27.
    Pedrycz, A., Hirota, K., Pedrycz, W.: Fangyan Dong: Granular representation and granular computing with fuzzy sets. Fuzzy Sets Syst. 203, 17–32 (2012)CrossRefMathSciNetGoogle Scholar
  28. 28.
    Pedrycz, W., Bargiela, A.: An optimization of allocation of in-formation granularity in the interpretation of data structures: toward granular fuzzy clustering. IEEE Trans Syst. Man Cybern. B Cybern. 42(3), pp. 582–590 (2012)Google Scholar
  29. 29.
    Pedrycz, W., Song, M.: Granular fuzzy models: a study in knowledge management in fuzzy modeling. Int. J. Approx. Reason. 53(7), 1061–1079 (2012)CrossRefMathSciNetGoogle Scholar
  30. 30.
    Pedrycz, W., Homenda, W.: Building the fundamentals of granular computing: a principle of justifiable granularity. Appl. Soft Comput. 13(10), 4209–4218 (2013)CrossRefGoogle Scholar
  31. 31.
    Perez, P., Trier, A., Reyes, J.: Prediction of PM2.5 concentrations several hours in advance using neural networks in Santiago Chile. Atmos. Environ. 34, 1189–1196 (2000)CrossRefGoogle Scholar
  32. 32.
    Reich, S.L., Gomez, D.R., Dawidowski, L.E.: Artificial neural network for the identification of unknown air pollution sources. Atmos. Environ. 33, 3045–3052 (1999)CrossRefGoogle Scholar
  33. 33.
    Roadknight, M., Balls, G.R., Mills, G.E., Palmer-Brown, B.D.: Modelling complex environmental data. IEEE Trans Neural Netw. 8(4), pp. 852–861 (1997)Google Scholar
  34. 34.
    Song, X.H., Hopke, P.K.: Solving the chemical mass balance problem using an artificial neural network. Environ. Sci. Technol. 30(2), 531–535 (1996)CrossRefGoogle Scholar
  35. 35.
    Wang, D., Lu, W.Z.: Forecasting of ozone level in time series using MLP model with a novel hybrid training algorithm. Atmos. Environ. 40(5), 913–924 (2006)CrossRefGoogle Scholar
  36. 36.
    Wang, W., Lu, W., Wang, X., Leung, A.Y.T.: Prediction of maximum daily ozone level using combined neural network and statistical characteristics. Environ. Int. 29(5), 555–562 (2003)CrossRefGoogle Scholar
  37. 37.
    Wang, W., Xu, Z., Lu, W.: Three improved neural network models for air quality forecasting. Eng. Computation 20(2), 192–210 (2003)CrossRefzbMATHGoogle Scholar
  38. 38.
    Yi, J., Prybutok, R.: A neural network model forecasting for prediction of daily maximum ozone concentration in an industrialized urban area. Environ. Pollut. 92(3), 349–357 (1996)CrossRefGoogle Scholar
  39. 39.
    Zadeh, L.A.: Towards a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets Syst. 90(2), 111–127 (1997)CrossRefzbMATHMathSciNetGoogle Scholar
  40. 40.
    Zadeh, L.A., Gupta, M., Ragade, R.K., Yager, R.R. (eds.): Fuzzy Sets and Information Granulation, Advances in Fuzzy Set Theory and Applications. North-Holland Publishing Company, Amsterdam (1979)Google Scholar
  41. 41.
    Zadeh, L.A.: Some reflections on soft computing, granular computing and their roles in the conception, design and utilization of information/intelligent systems. Soft. Comput. 2(1), 23–25 (1998)CrossRefGoogle Scholar
  42. 42.
    Zhang, B., Zhang, L.: Theory and Application of Problem Solving. North Holland (1992)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Master Program of Environmental Education and Management, Department of Science Application and DisseminationNational Taichung University of EducationTaichungRepublic of China
  2. 2.Institute of Environmental EngineeringNational Central UniversityChungliRepublic of China
  3. 3.Environmental Protection BureauTaoyuan County GovernmentTaoyuanRepublic of China

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