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Statistical Approaches for Forecasting Air pollution: A Review

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Accelerating Discoveries in Data Science and Artificial Intelligence II (ICDSAI 2023)

Part of the book series: Springer Proceedings in Mathematics & Statistics ((PROMS,volume 438))

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Abstract

With the rapid growth of energy consumption, acceleration of industrialization and urbanization, and the emission of automobile and industrial exhausts, polluting gases are causing incredible harm to nature and also impacting the health of people. The control and prevention of air pollution become required to protect the environment and human lives. Additionally, the prediction of air pollution may offer reliable data on air pollution by predicting the future concentration of pollutants in the air. These days, concentrating on tackling exceptional ecological issues and

undertaking activities to forestall and lessen air contamination has become a fundamental and challenging task. Machine learning is an efficient approach in the field of environmental modelling, which can reliably forecast air pollution in advance. Thise chapter focuses on the proposed study, analyzes and reviews forecasting air pollution using different learning techniques and then suggests a possible solution for future work.

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References

  1. L. Wang et al., Segmentation of yeast cell’s bright-field image with an edge-tracing algorithm. J. Biomed. Optics 23(11), 116503 (2018)

    Article  Google Scholar 

  2. T. Duan, A. Wang, Fuzzy neural network learning based on hierarchical agglomerative TS fuzzy inference. Int. J. Reasoning-Based Intell. Syst. 10(2), 83–89 (2018)

    Google Scholar 

  3. S. Masmoudi, H. Elghazel, D. Taieb, O. Yazar, A. Kallel, A machine-learning framework for predicting multiple air pollutants’ concentrations via multi-target regression and feature selection. Sci. Total Environ. 715, 136991 (2020)

    Article  Google Scholar 

  4. S. Zhu, J. Sun, Y. Liu, M. Lu, X. Liu, The air quality index trend forecasting based on improved error correction model and data preprocessing for 17 port cities in China. Chemosphere 252, 126474 (2020)

    Article  Google Scholar 

  5. P. Jiang, Q. Dong, P. Li, A novel hybrid strategy for PM 2.5 concentration analysis and prediction. J. Environ. Manage. 196, 443–457 (2017)

    Article  Google Scholar 

  6. P. Jiang, C. Li, R. Li, H. Yang, An innovative hybrid air pollution early-warning system based on pollutants forecasting and extenics evaluation. Knowledge-Based Syst. 164, 174–192 (2019)

    Article  Google Scholar 

  7. I. Martínez-Silva, J. Roca-Pardiñas, C. Ordóñez, Forecasting SO2 pollution incidents by means of quantile curves based on additive models. Environmetrics 27(3), 147–157 (2016)

    Article  MathSciNet  Google Scholar 

  8. S.A. Alvarado, C.S. Silva, D.D. Cáceres, Modelación de episodios críticos de contaminación por material particulado (PM10) en Santiago de Chile. Comparación de la eficiencia predictiva de los modelos paramétricos y no paramétricos. Gaceta Sanitaria 24(6), 466–472 (2010)

    Article  Google Scholar 

  9. I.G. McKendry, Evaluation of artificial neural networks for fine particulate pollution (PM 10 and PM 2.5) forecasting. J. Air Waste Manage. Assoc. 52(9), 1096–1101 (2002)

    Article  Google Scholar 

  10. C. Li, Z. Zhu, Research and application of a novel hybrid air quality early-warning system: a case study in China. Sci. Total Environ. 626, 1421–1438 (2018)

    Article  Google Scholar 

  11. K. Hu, A. Rahman, H. Bhrugubanda, V. Sivaraman, HazeEst: machine learning based metropolitan air pollution estimation from fixed and mobile sensors. IEEE Sens. J. 17(11), 3517–3525 (2017)

    Article  Google Scholar 

  12. G. Miskell, W. Pattinson, L. Weissert, D. Williams, Forecasting short-term peak concentrations from a network of air quality instruments measuring PM2.5 using boosted gradient machine models. J. Environ. Manage. 242, 56–64 (2019)

    Article  Google Scholar 

  13. L.K. Kwok, Y.F. Lam, C.-Y. Tam, Develo** a statistical based approach for predicting local air quality in complex terrain area. Atmos. Pollut. Res. 8(1), 114–126 (2017)

    Article  Google Scholar 

  14. A. Kumar, P. Goyal, Forecasting of air quality index in Delhi using neural network based on principal component analysis. Pure Appl. Geophys. 170(4), 711–722 (2013)

    Article  MathSciNet  Google Scholar 

  15. J. Wang, L. Bai, S. Wang, C. Wang, Research and application of the hybrid forecasting model based on secondary denoising and multi-objective optimization for air pollution early warning system. J. Clean. Prod. 234, 54–70 (2019)

    Article  Google Scholar 

  16. S. Zhu, X. Lian, H. Liu, J. Hu, Y. Wang, J. Che, Daily air quality index forecasting with hybrid models: a case in China. Environ. Pollut. 231, 1232–1244 (2017)

    Article  Google Scholar 

  17. R. Li, Y. Dong, Z. Zhu, C. Li, H. Yang, A dynamic evaluation framework for ambient air pollution monitoring. Appl. Math. Modell. 65, 52–71 (2019)

    Article  Google Scholar 

  18. Y. Hao, C. Tian, A novel two-stage forecasting model based on error factor and ensemble method for multi-step wind power forecasting. Appl. Energy 238, 368–383 (2019)

    Article  Google Scholar 

  19. L. Wu, H. Zhao, Using FGM(1,1) model to predict the number of the lightly polluted day in **g-**-Ji region of China. Atmos. Pollut. Res. 10(2), 552–555 (2019)

    Article  Google Scholar 

  20. C. Zafra, Y. Angel, E. Torres, ARIMA analysis of the effect of land surface coverage on PM10 concentrations in a high-altitude megacity. Atmos. Pollut. Res. 8(4), 660–668 (2017)

    Article  Google Scholar 

  21. D. Slottje, M. Nieswiadomy, M. Redfearn, Economic inequality and the environment. Environ. Modell. Softw. 16(2), 183–194 (2001)

    Article  Google Scholar 

  22. L. Wu, N. Li, Y. Yang, Prediction of air quality indicators for the Bei**g-Tian**-Hebei region. J. Clean. Prod. 196, 682–687 (2018)

    Article  Google Scholar 

  23. P.J. García Nieto, F. Sánchez Lasheras, E. García-Gonzalo, F.J. de Cos Juez, PM10 concentration forecasting in the metropolitan area of Oviedo (Northern Spain) using models based on SVM, MLP, VARMA and ARIMA: a case study. Sci. Total Environ. 621, 753–761 (2018)

    Article  Google Scholar 

  24. C. Song, X. Fu, Research on different weight combination in air quality forecasting models. J. Clean. Prod. 261, 121169 (2020)

    Article  Google Scholar 

  25. W. Qiao, W. Tian, Y. Tian, Q. Yang, Y. Wang, J. Zhang, The forecasting of PM2.5 using a hybrid model based on wavelet transform and an improved deep learning algorithm. IEEE Access 7, 142814–142825 (2019)

    Article  Google Scholar 

  26. C. Olah, Understanding lstm networks (2015)

    Google Scholar 

  27. S. Alhirmizy, B. Qader, Multivariate time series forecasting with LSTM for Madrid, Spain pollution, in 2019 International Conference on Computing and Information Science and Technology and Their Applications (ICCISTA), (2019), pp. 1–5

    Google Scholar 

  28. T. Lin, B.G. Horne, P. Tino, C.L. Giles, Learning long-term dependencies in NARX recurrent neural networks. IEEE Trans. Neural Netw. 7(6), 1329–1338 (1996)

    Article  Google Scholar 

  29. Y.-T. Tsai, Y.-R. Zeng, Y.-S. Chang, Air pollution forecasting using RNN with LSTM, in 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech), vol. 2018, pp. 1074–1079

    Google Scholar 

  30. Q. Shen et al., Visual interpretation of recurrent neural network on multi-dimensional time-series forecast, in 2020 IEEE Pacific Visualization Symposium (PacificVis), (2020), pp. 61–70

    Chapter  Google Scholar 

  31. K. Cho et al., Learning phrase representations using RNN encoder–decoder for statistical machine translation, in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), (2014), pp. 1724–1734

    Chapter  Google Scholar 

  32. M. Schuster, K.K. Paliwal, Bidirectional recurrent neural networks. IEEE Trans. Signal Process. 45(11), 2673–2681 (1997)

    Article  Google Scholar 

  33. Y. Chen, Q. Cheng, Y. Cheng, H. Yang, H. Yu, Applications of recurrent neural networks in environmental factor forecasting: a review. Neural Comput. 30(11), 2855–2881 (2018)

    Article  MathSciNet  Google Scholar 

  34. S.H.I. **ngjian, Z. Chen, H. Wang, D.-Y. Yeung, W.-K. Wong, W. Woo, Convolutional LSTM network: a machine learning approach for precipitation nowcasting, in Advances in Neural Information Processing Systems, (2015), pp. 802–810

    Google Scholar 

  35. M. Oprea, M. Popescu, S.F. Mihalache, A neural network based model for pm 2.5 air pollutant forecasting, in 2016 20th International Conference on System Theory, Control and Computing (ICSTCC), (2016), pp. 776–781

    Chapter  Google Scholar 

  36. K. Gan, S. Sun, S. Wang, Y. Wei, A secondary-decomposition-ensemble learning paradigm for forecasting PM2.5 concentration. Atmos. Pollut. Res. 9(6), 989–999 (2018)

    Article  Google Scholar 

  37. Q. Fan et al., Process analysis of regional aerosol pollution during spring in the Pearl River Delta region, China. Atmos. Environ. 122, 829–838 (2015)

    Article  Google Scholar 

  38. Q. Zhang, D. Xue, X. Liu, X. Gong, H. Gao, Process analysis of PM2.5 pollution events in a coastal city of China using CMAQ. J. Environ. Sci. 79, 225–238 (2019)

    Article  Google Scholar 

  39. R. Timmermans et al., Source apportionment of PM2.5 across China using LOTOS- EUROS. Atmos. Environ. 164, 370–386 (2017)

    Article  Google Scholar 

  40. L. Wu, X. Gao, Y. **ao, S. Liu, Y. Yang, Using grey Holt–Winters model to predict the air quality index for cities in China. Na. Hazards 88(2), 1003–1012 (2017)

    Article  Google Scholar 

  41. M. Niu, Y. Wang, S. Sun, Y. Li, A novel hybrid decomposition-and-ensemble model based on CEEMD and GWO for short-term PM2.5 concentration forecasting. Atmos. Environ. 134, 168–180 (2016)

    Article  Google Scholar 

  42. Y. Bai, Y. Li, X. Wang, J. **e, C. Li, Air pollutants concentrations forecasting using back propagation neural network based on wavelet decomposition with meteorological conditions. Atmos. Pollut. Res. 7(3), 557–566 (2016)

    Google Scholar 

  43. P. Lee, R.S. And, J. McQueen, Air Quality Monitoring and Forecasting (2017)

    Google Scholar 

  44. D. Wang, S. Wei, H. Luo, C. Yue, O. Grunder, A novel hybrid model for air quality index forecasting based on two phase decomposition technique and modified extreme learning machine. Sci. Total Environ. 580, 719–733 (2017)

    Google Scholar 

  45. P. Wang, H. Zhang, Z. Qin, G. Zhang, A novel hybrid-Garch model based on ARIMA and SVM for PM 2.5 concentrations forecasting. Atmos. Pollut. Res. 8(5), 850–860 (2017)

    Google Scholar 

  46. D. Voukantsis, K. Karatzas, J. Kukkonen, T. Räsänen, A. Karppinen, M. Kolehmainen, Intercomparison of air quality data using principal component analysis, and forecasting of PM10 and PM2.5 concentrations using artificial neural networks, in Thessaloniki and Helsinki. Sci. Total Environ. 409(7), 1266–1276 (2011)

    Google Scholar 

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Conflicts of Interest

The authors declare that there is no conflict of interest.

Author information

Authors and Affiliations

  1. Department of Information Technology, MVGR College of Engineering (A), Vizianagaram, Andhra Pradesh, India

    Marada Srinivasa Rao

  2. Department of Computer Science and Engineering, Anil Neerukonda Institute of Technology & Sciences, Visakhapatnam, Andhra Pradesh, India

    Bangaru Sailaja

  3. Department of Computer Science and Engineering, Vignan’s Institute of Engineering for Women, Visakhapatnam, Andhra Pradesh, India

    Mugada Swetha

  4. Department of Computer Science and Engineering, Vignan’s Institute of Information Technology, Visakhapatnam, Andhra Pradesh, India

    Gorle Kumari

  5. Department of Computer Science and Engineering, Gayatri Vidya Parishad College of Engineering (A), Visakhapatnam, India

    Bodduru Keerthana

  6. Department of Computer Science and Engineering, Lendi Institute of Engineering and Technology (A), Vizianagaram, India

    Bosubabu Sambana

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  1. Marada Srinivasa Rao
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  2. Bangaru Sailaja
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  3. Mugada Swetha
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Editor information

Editors and Affiliations

  1. Information & Decision Sciences, College of Business and Public Administration, California State University, San Bernard, San Bernardino, CA, USA

    Frank M. Lin

  2. Computer & Information Science, University of Massachusetts Dartmouth, Dartmouth, MA, USA

    Ashokkumar Patel

  3. Department of Computer Science, Central University of Rajasthan, Ajmer, Rajasthan, India

    Nishtha Kesswani

  4. Lendi Institute of Engineering and Technology, Vizianagaram, Andhra Pradesh, India

    Bosubabu Sambana

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Rao, M.S., Sailaja, B., Swetha, M., Kumari, G., Keerthana, B., Sambana, B. (2024). Statistical Approaches for Forecasting Air pollution: A Review. In: Lin, F.M., Patel, A., Kesswani, N., Sambana, B. (eds) Accelerating Discoveries in Data Science and Artificial Intelligence II. ICDSAI 2023. Springer Proceedings in Mathematics & Statistics, vol 438. Springer, Cham. https://doi.org/10.1007/978-3-031-51163-9_5

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