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Air quality index assessment prelude to mitigate environmental hazards

Author

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  • Sutapa Chaudhuri

    (University of Calcutta)

  • Arumita Roy Chowdhury

    (University of Calcutta)

Abstract

Air pollution has been a major transboundary problem and a matter of global concern for decades. Climate change and air pollution are closely coupled. Just as air pollution can have adverse effects on human health and ecosystems, it can also impact the earth’s climate. As we enter an era of rapid climate change, the implications for air quality need to be better understood, both for the purpose of air quality management and as one of the societal consequences of climate change. In this study, an attempt has been made to estimate the current air quality to forecast the air quality index of an urban station Kolkata (22.65°N, 88.45°E), India for the next 5 years with neural network models. The annual and seasonal variability in the air quality indicates that the winter season is mostly affected by the pollutants. Air quality index (AQI) is estimated as a geometric mean of the pollutants considered. Different neural network models are attempted to select the best model to forecast the AQI of Kolkata. The meteorological parameters and AQI of the previous day are utilized to train the models to forecast the AQI of the next day during the period from 2003 to 2012. The selection of the best model is made after validation with observation from 2013 to 2015. The radial basis functional (RBF) model is found to be the best network model for the purpose. The RBF model with various architectures is tried to obtain precise forecast with minimum error. RBF of 5:5-91-1:1 structure is found to be the best fit for forecasting the AQI of Kolkata.

Suggested Citation

  • Sutapa Chaudhuri & Arumita Roy Chowdhury, 2018. "Air quality index assessment prelude to mitigate environmental hazards," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 91(1), pages 1-17, March.
    • Handle: RePEc:spr:nathaz:v:91:y:2018:i:1:d:10.1007_s11069-017-3080-3
    DOI: 10.1007/s11069-017-3080-3
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    References listed on IDEAS

    as
    1. Sutapa Chaudhuri & Debashree Dutta & Sayantika Goswami & Anirban Middey, 2013. "Intensity forecast of tropical cyclones over North Indian Ocean using multilayer perceptron model: skill and performance verification," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 65(1), pages 97-113, January.
    2. Debashree Dutta & Sutapa Chaudhuri, 2015. "Nowcasting visibility during wintertime fog over the airport of a metropolis of India: decision tree algorithm and artificial neural network approach," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 75(2), pages 1349-1368, January.
    3. Ghiassi, M. & Saidane, H. & Zimbra, D.K., 2005. "A dynamic artificial neural network model for forecasting time series events," International Journal of Forecasting, Elsevier, vol. 21(2), pages 341-362.
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    Cited by:

    1. Salem Badjoudj & Aissa Benselhoub & Souad Narsis & Nadiia Dovbash & Abdelaziz Idres & Khadouja Marame Benghadab & Fares Boutarfa & Mohamed Bounouala & Stefano Bellucci, 2023. "Assessment of air quality index in Annaba," Technology audit and production reserves, PC TECHNOLOGY CENTER, vol. 4(3(72)), pages 24-32, July.
    2. Ying Wang & Jianzhou Wang & Hongmin Li & Hufang Yang & Zhiwu Li, 2022. "Multi‐step air quality index forecasting via data preprocessing, sequence reconstruction, and improved multi‐objective optimization algorithm," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(7), pages 1483-1511, November.
    3. Jianzhou Wang & Pei Du, 2021. "Quarterly PM2.5 prediction using a novel seasonal grey model and its further application in health effects and economic loss assessment: evidences from Shanghai and Tianjin, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(1), pages 889-909, May.

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    More about this item

    Keywords

    ANN; Pollution; MLP; T; Rh; Wind speed; Visibility; NO2; SO2; PM10;
    All these keywords.

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