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Air pollution prediction by using an artificial neural network model

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Abstract

Air pollutants impact public health, socioeconomics, politics, agriculture, and the environment. The objective of this study was to evaluate the ability of an artificial neural network (ANN) algorithm to predict hourly criteria air pollutant concentrations and two air quality indices, air quality index (AQI) and air quality health index (AQHI), for Ahvaz, Iran, over one full year (August 2009–August 2010). Ahvaz is known to be one of the most polluted cities in the world, mainly owing to dust storms. The applied algorithm involved nine factors in the input stage (five meteorological parameters, pollutant concentrations 3 and 6 h in advance, time, and date), 30 neurons in the hidden phase, and finally one output in last level. When comparing performance between using 5% and 10% of data for validation and testing, the more reliable results were from using 5% of data for these two stages. For all six criteria pollutants examined (O3, NO2, PM10, PM2.5, SO2, and CO) across four sites, the correlation coefficient (R) and root-mean square error (RMSE) values when comparing predictions and measurements were 0.87 and 59.9, respectively. When comparing modeled and measured AQI and AQHI, R2 was significant for three sites through AQHI, while AQI was significant only at one site. This study demonstrates that ANN has applicability to cities such as Ahvaz to forecast air quality with the purpose of preventing health effects. We conclude that authorities of urban air quality, practitioners, and decision makers can apply ANN to estimate spatial–temporal profile of pollutants and air quality indices. Further research is recommended to compare the efficiency and potency of ANN with numerical, computational, and statistical models to enable managers to select an appropriate toolkit for better decision making in field of urban air quality.

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Acknowledgements

The authors would like to thank Ahvaz Jundishapur University of Medical Sciences for providing financial support (APRD-9802) of this research. AS acknowledges support from Grant 2 P42 ES04940–11 from the National Institute of Environmental Health Sciences (NIEHS) Superfund Research Program.

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Authors and Affiliations

  1. Air Pollution and Respiratory Diseases Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

    Heidar Maleki & Gholamreza Goudarzi

  2. Environmental Engineering, School of Water Sciences Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran

    Heidar Maleki & Mojtaba Rahmati

  3. Department of Chemical and Environmental Engineering, University of Arizona, Tucson, AZ, USA

    Armin Sorooshian

  4. Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, USA

    Armin Sorooshian

  5. Department of Environmental Health Engineering, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

    Gholamreza Goudarzi & Yaser Tahmasebi Birgani

  6. Environmental Technologies Research Center (ETRC), Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran

    Gholamreza Goudarzi & Yaser Tahmasebi Birgani

  7. Department of Environmental Health Engineering, Behbahan Faculty of Medical Sciences, Behbahan, Iran

    Zeynab Baboli

Authors
  1. Heidar Maleki
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  2. Armin Sorooshian
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  3. Gholamreza Goudarzi
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  4. Zeynab Baboli
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  5. Yaser Tahmasebi Birgani
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  6. Mojtaba Rahmati
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Correspondence to Gholamreza Goudarzi.

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Maleki, H., Sorooshian, A., Goudarzi, G. et al. Air pollution prediction by using an artificial neural network model. Clean Techn Environ Policy 21, 1341–1352 (2019). https://doi.org/10.1007/s10098-019-01709-w

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  • DOI: https://doi.org/10.1007/s10098-019-01709-w

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