Skip to main content

Advertisement

SpringerLink
Log in

Air pollution prediction by using an artificial neural network model

  • Original Paper
  • Published:
Clean Technologies and Environmental Policy Aims and scope Submit manuscript

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.

Graphical abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Alimissis A, Philippopoulos K, Tzanis CG, Deligiorgi D (2018) Spatial estimation of urban air pollution with the use of artificial neural network models. Atmos Environ 191:205–213

    Article  CAS  Google Scholar 

  • Alizadeh-Choobari O, Bidokhti AA, Ghafarian P, Najafi MS (2016) Temporal and spatial variations of particulate matter and gaseous pollutants in the urban area of Tehran. Atmos Environ 141:443–453

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  • Boznar M, Lesjak M, Mlakar P (1993) 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 Urban Atmos 27(2):221–230

    Article  Google Scholar 

  • Cai M, Yin Y, Xie M (2009) Prediction of hourly air pollutant concentrations near urban arterials using artificial neural network approach. Transp Res Part D Transp Environ 14(1):32–41

    Article  Google Scholar 

  • Chang SC, Lee CT (2007) Assessment of PM10 enhancement by yellow sand on the air quality of Taipei, Taiwan in 2001. Environ Monit Assess 132:297–309

    Article  CAS  Google Scholar 

  • Chen L, Pai T-Y (2015) Comparisons of GM (1, 1), and BPNN for predicting hourly particulate matter in Dali area of Taichung City, Taiwan. Atmos Pollut Res 6(4):572–580

    Article  CAS  Google Scholar 

  • de Gennaro G, Trizio L, Di Gilio A, Pey J, Perez N, Cusack M, Alastuey A, Querol X (2013) Neural network model for the prediction of PM10 daily concentrations in two sites in the Western Mediterranean. Sci Total Environ 463:875–883

    Article  CAS  Google Scholar 

  • Durão RM, Mendes MT, Pereira MJ (2016) Forecasting O3 levels in industrial area surroundings up to 24 h in advance, combining classification trees and MLP models. Atmos Pollut Res 7(6):961–970

    Article  Google Scholar 

  • Elangasinghe MA, Singhal N, Dirks KN, Salmond JA, Samarasinghe S (2014) Complex time series analysis of PM10 and PM2.5 for a coastal site using artificial neural network modelling and k-means clustering. Atmos Environ 94:106–116

    Article  CAS  Google Scholar 

  • Elfwing S, Uchibe E, Doya K (2018) Sigmoid-weighted linear units for neural network function approximation in reinforcement learning. Neural Netw 107:3–11

    Article  Google Scholar 

  • El-Latef EMA, Zaki GR, Issa AI (2018) Traffic air quality health index in a selected street, Alexandria. J High Inst Public Health 48(2):67–76

    Article  Google Scholar 

  • Feng X, Li Q, Zhu Y, Hou J, Jin L, Wang J (2015) Artificial neural networks forecasting of PM 2.5 pollution using air mass trajectory based geographic model and wavelet transformation. Atmos Environ 107:118–128

    Article  CAS  Google Scholar 

  • Goudie AS (2014) Desert dust and human health disorders. Environ Int 63:101–113

    Article  CAS  Google Scholar 

  • Gupta P, Christopher SA (2009) Particulate matter air quality assessment using integrated surface, satellite, and meteorological products: 2. A neural network approach. J Geophys Res Atmos 114(D14)

  • Ho S, Xie M, Goh T (2002) A comparative study of neural network and Box-Jenkins ARIMA modeling in time series prediction. Comput Ind Eng 42(2):371–375

    Article  Google Scholar 

  • Hou Q, An X, Tao Y, Sun Z (2016) Assessment of resident’s exposure level and health economic costs of PM 10 in Beijing from 2008 to 2012. Sci Total Environ 563:557–565

    Article  CAS  Google Scholar 

  • Iliyas SA, Elshafei M, Habib MA, Adeniran AA (2013) RBF neural network inferential sensor for process emission monitoring. Control Eng Pract 21(7):962–970

    Article  Google Scholar 

  • Lu W-Z, Wang W-J, Wang X-K, Yan S-H, Lam JC (2004) Potential assessment of a neural network model with PCA/RBF approach for forecasting pollutant trends in Mong Kok urban air, Hong Kong. Environ Res 96(1):79–87

    Article  CAS  Google Scholar 

  • Maghrabi A, Alharbi B, Tapper N (2011) Impact of the March 2009 dust event in Saudi Arabia on aerosol optical properties, meteorological parameters, sky temperature and emissivity. Atmos Environ 45(13):2164–2173

    Article  CAS  Google Scholar 

  • Maleki H, Sorooshian A, Goudarzi G, Nikfal A, Baneshi MM (2016) Temporal profile of PM10 and associated health effects in one of the most polluted cities of the world (Ahvaz, Iran) between 2009 and 2014. Aeolian Res 22:135–140

    Article  Google Scholar 

  • Mazaheri Tehrani A, Karamali F, Chimehi E (2015) Evaluation of 5 air criteria pollutants Tehran, Iran. Int Arch Health Sci 2(3):95–100

    Google Scholar 

  • Naddafi K, Hassanvand MS, Yunesian M, Momeniha F, Nabizadeh R, Faridi S, Gholampour A (2012) Health impact assessment of air pollution in megacity of Tehran, Iran. Iran J Environ Health Sci Eng 9(1):1

    Article  CAS  Google Scholar 

  • Nagendra SS, Khare M (2005) Modelling urban air quality using artificial neural network. Clean Technol Environ Policy 7(2):116–126

    Article  CAS  Google Scholar 

  • Naimabadi A, Ghadiri A, Idani E, Babaei AA, Alavi N, Shirmardi M, Khodadadi A, Marzouni MB, Ankali KA, Rouhizadeh A (2016) Chemical composition of PM 10 and its in vitro toxicological impacts on lung cells during the Middle Eastern Dust (MED) storms in Ahvaz, Iran. Environ Pollut 211:316–324

    Article  CAS  Google Scholar 

  • Nourmoradi H, Khaniabadi YO, Goudarzi G, Daryanoosh SM, Khoshgoftar M, Omidi F, Armin H (2016) Air quality and health risks associated with exposure to particulate matter: a cross-sectional study in Khorramabad, Iran. Health Scope 5(2):e31766

    Article  Google Scholar 

  • Patra AK, Gautam S, Majumdar S, Kumar P (2016) Prediction of particulate matter concentration profile in an opencast copper mine in India using an artificial neural network model. Air Qual Atmos Health 9:697–711

    Article  CAS  Google Scholar 

  • Pokrovsky OM, Kwok RH, Ng C (2002) Fuzzy logic approach for description of meteorological impacts on urban air pollution species: a Hong Kong case study. Comput Geosci 28(1):119–127

    Article  CAS  Google Scholar 

  • Prasad K, Gorai AK, Goyal P (2016) Development of ANFIS models for air quality forecasting and input optimization for reducing the computational cost and time. Atmos Environ 128:246–262

    Article  CAS  Google Scholar 

  • Qin SS, Liu F, Wang JZ, Sun BB (2014) Analysis and forecasting of the particulate matter (PM) concentration levels over four major cities of China using hybrid models. Atmos Environ 98:665–675

    Article  CAS  Google Scholar 

  • Ragosta M, Gioscio G (2009) Neural network model for forecasting atmospheric particulate levels. Chem Environ Impact Health Eff, Aerosols, pp 149–160

    Google Scholar 

  • Russo A, Lind PG, Raischel F, Trigo R, Mendes M (2015) Neural network forecast of daily pollution concentration using optimal meteorological data at synoptic and local scales. Atmos Pollut Res 6:540–549

    Article  CAS  Google Scholar 

  • Sadorsky P (2006) Modeling and forecasting petroleum futures volatility. Energy Econ 28(4):467–488

    Article  Google Scholar 

  • Shahraiyni HT, Sodoudi S, Kerschbaumer A, Cubasch U (2015) A new structure identification scheme for ANFIS and its application for the simulation of virtual air pollution monitoring stations in urban areas. Eng Appl Artif Intell 41:175–182

    Article  Google Scholar 

  • Vlachokostas C, Nastis S, Achillas C, Kalogeropoulos K, Karmiris I, Moussiopoulos N, Chourdakis E, Banias G, Limperi N (2010) Economic damages of ozone air pollution to crops using combined air quality and GIS modelling. Atmos Environ 44(28):3352–3361

    Article  CAS  Google Scholar 

  • Wang D, Lu W-Z (2006) Forecasting of ozone level in time series using MLP model with a novel hybrid training algorithm. Atmos Environ 40(5):913–924

    Article  CAS  Google Scholar 

  • Wang F, Chen D, Cheng S, Li J, Li M, Ren Z (2010) Identification of regional atmospheric PM 10 transport pathways using HYSPLIT, MM5-CMAQ and synoptic pressure pattern analysis. Environ Model Softw 25(8):927–934

    Article  Google Scholar 

  • Yao L, Lu N (2014) Spatiotemporal distribution and short-term trends of particulate matter concentration over China, 2006–2010. Environ Sci Pollut R 21:9665–9675

    Article  CAS  Google Scholar 

  • Zhang M, Song Y, Cai X, Zhou J (2008) Economic assessment of the health effects related to particulate matter pollution in 111 Chinese cities by using economic burden of disease analysis. J Environ Manag 88(4):947–954

    Article  Google Scholar 

  • Zhang L, Wang T, Lv M, Zhang Q (2015) On the severe haze in Beijing during January 2013: unraveling the effects of meteorological anomalies with WRF-Chem. Atmos Environ 104:11–21

    Article  CAS  Google Scholar 

  • Zhang Y, Zhang X, Wang L, Zhang Q, Duan F, He K (2016) Application of WRF/Chem over East Asia: part I. Model evaluation and intercomparison with MM5/CMAQ. Atmos Environ 124:285–300

    Article  CAS  Google Scholar 

Download references

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.

Author information

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
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Armin Sorooshian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Gholamreza Goudarzi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zeynab Baboli
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yaser Tahmasebi Birgani
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mojtaba Rahmati
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gholamreza Goudarzi.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

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

Keywords

Search

Navigation