Abstract
Air quality improvement is directly associated with the Sustainable Development Goals (SDGs), established by the United Nations in 2015. To reduce potential impacts from air pollution, computational intelligence by supervised machine learning, using different artificial neural networks (ANNs) techniques, shows to be a promising tool. To enhance their abilities to predict air quality, ANNs have been combined with data preprocessing. The present work performs short-term forecasting of hourly ground-level ozone using long short-term memory (LSTM), a type of recurrent neural network, with the discrete wavelet transform. The study was performed using data from a tropical coastal-urban site in Southeast Brazil, highly influenced by intense convective weather with complex terrain. The models’ performance was carried out by comparing statistical indices of errors and agreement, namely: mean squared error (MSE), normalized mean squared error (NMSE), mean absolute error (MAE), Pearson’s r, R2 and mean absolute percentage error (MAPE). When comparing the statistical metrics values, it is shown that the combination of artificial neural networks with wavelet transform enhanced the model’s ability to forecast ozone levels compared to the baseline model, which did not use wavelets.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Al-Dabbous, A.N., Kumar, P., Khan, A.R.: Prediction of airborne nanoparticles at roadside location using a feed–forward artificial neural network. Atmos. Pollut. Res. 8(3), 446–454 (2017). https://doi.org/10.1016/j.apr.2016.11.004
AlOmar, M.K., Hameed, M.M., AlSaadi, M.A.: Multi hours ahead prediction of surface ozone gas concentration: robust artificial intelligence approach. Atmos. Pollut. Res. 11(9), 1572–1587 (2020)
Alves, A., Nascimento, E.G.S., Moreira, D.M.: Hourly tropospheric ozone concentration forecasting using deep learning. WIT Trans. Ecol. Environ. 236, 129–138 (2019). https://doi.org/10.2495/AIR190131
Arjomandi, M., Wong, H., Donde, A., Frelinger, J., Dalton, S., Ching, W., Power, K., Balmes, J.R.: Exposure to medium and high ambient levels of ozone causes adverse systemic inflammatory and cardiac autonomic effects. Am. J. Physiol. Heart Circ. 308, H1499–H1509 (2015). https://doi.org/10.1152/ajpheart.00849.2014
Bai, Y., Li, Y., Wang, X., Xie, J., Li, C.: Air pollutants concentrations forecasting using back propagation neural network based on wavelet decomposition with meteorological conditions. Atmos. Polut. Res. 7(3), 557–566 (2016). https://doi.org/10.1016/j.apr.2016.01.004
Baklanov, A., Zhang, Y.: Advances in air quality modeling and forecasting. Glob. Transit. 2, 261–270 (2020). https://doi.org/10.1016/j.glt.2020.11.001
Bruyn, S., Vries, J.: Health costs of air pollution in European cities and the linkage with transport. Available at https://www.cedelft.eu/en/publications/2534/health-costs-of-air-pollution-in-european-cities-and-the-linkage-with-transport (2020).Accessed on 18 Apr 2021
Cheng, Y., Zhang, H., Liu, Z., Chen, L., Wang, P.: Hybrid algorithm for short-term forecasting of PM2.5 in China. Atmos. Environ. 200, 264–279 (2019). https://doi.org/10.1016/j.atmosenv.2018.12.025
Chollet, F.: Deep Learning with Python. Manning, New York (2018)
Dunea, D., Pohoata, A., Iordache, S.: Using wavelet–feedforward neural networks to improve air pollution forecasting in urban environments. Environ. Monit. Assess. 187(7), 1–16 (2015)
Feng, Z., Marco, A., Anav, A., Gualtieri, M., Sicard, P., Tian, H., Fornasier, F., Tao, F., Guo, A., Paoletti, A.: Economic losses due to ozone impacts on human health, forest productivity and crop yield across China. Environ. Int. 131, 104966 (2019). https://doi.org/10.1016/j.envint.2019.104966
Graps, A.: An introduction to wavelets. IEEE Comput. Sci. Eng. 2(2), 50–61 (1995)
Guo, Q., He, Z., Li, S., Li, X., Meng, J., Hou, Z., Liu, J., Chen, Y.: Air pollution forecasting using artificial and wavelet neural networks with meteorological conditions. Aerosol Air Qual. Res. 20, 1429–1439 (2020). https://doi.org/10.4209/aaqr.2020.03.0097
Hu, X., He, L., Zhang, J., Qiu, X., Zhang, Y., Mo, J., Day, D.B., Xiang, J., Gong, J.: Inflammatory and oxidative stress responses of healthy adults to changes in personal air pollutant exposure. Environ. Pollut. 263(A), 114503 (2020). https://doi.org/10.1016/j.envpol.2020.114503
Huang, J., Song, Y., Chu, M., Dong, W., Miller, M.R., Loh, M., Xu, J., Yang, D., Chi, R., Yang, X., Wu, S., Guo, X., Deng, F.: Cardiorespiratory responses to low-level ozone exposure: the inDoor ozone study in childrEn (DOSE). Environ. Int. 131, 105021 (2019). https://doi.org/10.1016/j.envint.2019.105021
IEMA (Instituto Estadual de Meio Ambiente e Recursos Hídricos): Ambient Air Quality Monitoring System data. Available at https://iema.es.gov.br/qualidadedoar/dadosdemonitoramento/automatica (2021)
Junior, A.S.R., Nascimento, E.G.S., Moreira, D.M.: Assessing recurrent and convolutional neural networks for tropospheric ozone forecasting in the region of Vitória, Brazil. WIT Trans. Ecol. Environ. 244, 101–112 (2020). https://doi.org/10.2495/AIR200091
Kitagawa, Y.K.L., Nascimento, E.G.S., Souza, N.B.P., Zucatelli, P.J., Kumar, P., Albuquerque, T.T.A., Moraes, R.M., Moreira, D.M.: Evaluation of the WRF-ARW model during an extreme rainfall event: subtropical storm Guará. Atmósfera (2021). (in press). https://doi.org/10.20937/ATM.52977
Langford, A.O., Senff, C.J., Alvarez, R.J., Banta, R.M., Hardesty, R.M.: Long-range transport of ozone from the Los Angeles Basin: a case study. Geophys. Res. Lett 37, L06807 (2010). https://doi.org/10.1029/2010GL042507
Lee, G., Gommers, R., Waselewski, F., Wohlfahrt, K., O’Leary, A.: PyWavelets: a Python package for wavelet analysis. J. Open Source Softw. 4(36), 1237 (2019)
Liu, H., Yin, S., Chen, C., Duan, Z.: Data multi-scale decomposition strategies for air pollution forecasting: a comprehensive review. J. Clean. Prod. 277, 124023 (2020). https://doi.org/10.1016/j.jclepro.2020.124023
Lu, Y., Salem, F.M.: Simplified gating in long short-term memory (LSTM) recurrent neural networks. In: 2017 IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS) (2017). https://doi.org/10.1109/mwscas.2017.8053244
Maji, K.J., Ye, W.-F., Arora, M., Shiva, S.M.N.: Ozone pollution in Chinese cities: assessment of seasonal variation, health effects and economic burden. Environ. Pollut. 247, 792–801 (2019). https://doi.org/10.1016/j.envpol.2019.01.049
Mallet, V., Sportisse, B.: Air quality modeling: from deterministic to stochastic approaches. Comput. Math. Appl. 55(10), 2329–2337 (2008). https://doi.org/10.1016/j.camwa.2007.11.004
Mbatha, N., Bencherif, H.: Time series analysis and forecasting using a novel hybrid LSTM data-driven model based on empirical wavelet transform applied to total column of ozone at Buenos Aires, Argentina (1966–2017). Atmosphere 11(5), 457 (2020)
Nielsen, M.A.: Neural Networks and Deep Learning, vol. 25. Determination Press, San Francisco (2015)
Prüss-Ustün, A., Wolf, J., Corvalán, C., Bos, R., Neira, M.: Preventing disease through healthy environments: a global assessment of the environmental burden of disease. Toxicol. Lett. 259, S1 (2016). https://doi.org/10.1016/j.toxlet.2016.07.028
Sayeed, A., Lops, Y., Choi, Y., Jung, J., Salman, A.K.: Bias correcting and extending the PM forecast by CMAQ up to 7 days using deep convolutional neural networks. Atmos. Environ. 253, 118376 (2021). https://doi.org/10.1016/j.atmosenv.2021.118376
WHO (World Health Organization): Ambient air pollution: a global assessment of exposure and burden of disease (2016)
Yafouz, A., Ahmed, A.N., Zaini, N., El-Shafie, A.: Ozone concentration forecasting based on artificial intelligence techniques: a systematic review. Water Air Soil Pollut. 232, 79 (2021). https://doi.org/10.1007/s11270-021-04989-5
Young, P.J., Archibald, A.T., Bowman, K.W., Lamarque, J.-F., Naik, V., Stevenson, D.S., Tilmes, S., Voulgarakis, A., Wild, O., Bergmann, D., Cameron-Smith, P., Cionni, I., Collins, W.J., Dalsøren, S.B., Doherty, R.M., Eyring, V., Faluvegi, G., Horowitz, L.W., Josse, B., Lee, Y.H., MacKenzie, I.A., Nagashima, T., Plummer, D.A., Righi, M., Rumbold, S.T., Skeie, R.B., Shindell, D.T., Strode, S.A., Sudo, K., Szopa, S., Zeng, G.: Pre-industrial to end 21st century projections of tropospheric ozone from the atmospheric chemistry and climate model intercomparison project (ACCMIP). Atmos. Chem. Phys. 13, 2063–2090 (2013). https://doi.org/10.5194/acp-13-2063-2013
Zhen, Z., Wan, X., Wang, Z., Wang, F., Ren, H., Mi, Z.: Multi-level wavelet decomposition based day-ahead solar irradiance forecasting. In: 2018 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT) (pp. 1–5). IEEE, Feb 2018
Zucatelli, P.J., Nascimento, E.G.N., Santos, A.A.B., Moreira, D.M.: Nowcasting prediction of wind speed using computational intelligence and wavelet in Brazil. Int. J. Comput. Methods Eng. Sci. Mech. 21(6), 343–369 (2020). https://doi.org/10.1080/15502287.2020.1841335
Acknowledgements
We thank the Reference Center on Artificial Intelligence (CRIA), the Supercomputing Center for Industrial Innovation (CS2i), both at the Manufacturing and Technology Integrated Campus—SENAI CIMATEC for their support, and the Fundação de Amparo à Pesquisa do Estado da Bahia (FAPESB) for its financial aid.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Araujo, M.L.S., Kitagawa, Y.K.L., Moreira, D.M., Nascimento, E.G.S. (2022). Forecasting Tropospheric Ozone Using Neural Networks and Wavelets: Case Study of a Tropical Coastal-Urban Area. In: Verdegay, J.L., Brito, J., Cruz, C. (eds) Computational Intelligence Methodologies Applied to Sustainable Development Goals. Studies in Computational Intelligence, vol 1036. Springer, Cham. https://doi.org/10.1007/978-3-030-97344-5_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-97344-5_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-97343-8
Online ISBN: 978-3-030-97344-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)