Skip to main content

Advertisement

SpringerLink
Log in

Air quality index assessment prelude to mitigate environmental hazards

  • Original Paper
  • Published:
Natural Hazards Aims and scope Submit manuscript

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.

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
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Benjamin NL, Sharma S, Pendharker U, Shrivastava K (2014) Air quality prediction using artificial neural network. Int J Chem Stud 2(4):07–09

    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 27:221–230

    Article  Google Scholar 

  • Chaudhuri S (2006) Artificial neural network model to forecast maximum wind speed associated with severe thunderstorms. Vatabaran 30(1):14–19

    Google Scholar 

  • Chaudhuri S (2010) Convective energies in forecasting severe thunderstorms with one hidden layer neural net and variable learning rate back propagation algorithm. Asia-Pacific J Atmos Sci 46(2):173–183

    Article  Google Scholar 

  • Chaudhuri S, Middey A (2014) Comparison of tropical and midlatitude thunderstorm characteristics anchored in thermodynamic and dynamic aspects. Asia-Pacific J Atmos Sci 50(2):179–189

    Article  Google Scholar 

  • Chaudhuri S, Dutta D, Goswami S, Middey A (2013) Intensity forecast of tropical cyclones over North Indian Ocean using multilayer perceptron model: skill and performance verification. Nat Hazards 65:97–113

    Article  Google Scholar 

  • Chaudhuri S, Dutta D, Goswami S, Middey A (2014a) Track and intensity forecast of tropical cyclones over the North Indian Ocean with multilayer feed forward neural nets. Meteorol Appl 22:563–575

    Article  Google Scholar 

  • Chaudhuri S, Goswami S, Middey A (2014b) Medium-range forecast of cyclogenesis over North Indian Ocean with multilayer perceptron model using satellite data. Nat Hazards 70:173–193

    Article  Google Scholar 

  • Chaudhuri S, Goswami S, Middey A, Das D, Chowdhury S (2015a) Predictability of landfall location and surge height of tropical cyclones over North Indian Ocean (NIO). Nat Hazards 75:1369–1388

    Article  Google Scholar 

  • Chaudhuri S, Das D, Sarkar I, Goswami S (2015b) Multilayer perceptron model for nowcasting visibility from surface observations: results and sensitivity to dissimilar station altitudes. Pure Appl Geophys. https://doi.org/10.1007/s00024-015-1065-2

    Google Scholar 

  • Cogliani E (2001) Air pollution forecast in cities by an air pollution index highly correlated with meteorological variables. Atmos Environ 35:2871–2877

    Article  Google Scholar 

  • Dutta D, Chaudhuri S (2015) Nowcasting visibility during wintertime fog over the airport of a metropolis of India: decision tree algorithm and neural artificial network approach. Nat Hazards 75:1349–1368

    Article  Google Scholar 

  • Elangasinghe MA, Singhal N, Dirks KM, Salmond JA (2014) Development of an ANN–based air pollution forecasting system with explicit knowledge through sensitivity analysis. Atmos Pollut Res 5:696–708

    Article  Google Scholar 

  • Feng X, Li Q, Zhu Y, Hou J, Jin L, Wang J (2015) Artificial neural networks forecasting of PM25 pollution using air mass trajectory based geographic model and wavelet transformation. Atmos Environ 107:118–128

    Article  Google Scholar 

  • Gardner MW, Dorling SR (1999) Neural network modeling and prediction of hourly NO x and NO2 concentrations in urban air in London. Atmos Environ 33:709–719

    Article  Google Scholar 

  • Ghiassi M, Saidaneb H, Zimbrac DK (2005) A dynamic artificial neural network model for forecasting time series events. Int J Forecast 21:341–362

    Article  Google Scholar 

  • Goulermas JY, Liatsis P, Zeng XJ, Cook P (2007) Density-driven generalized regression neural networks (DD-GRNN) for function approximation. IEEE Trans Neural Netw 18(6):1683–1696

    Article  Google Scholar 

  • Gunaseelan I, Bhaskar BV, Muthuchelian K (2014) The effect of aerosol optical depth on rainfall with reference to meteorology over metro cities in India. Environ Sci Pollut Res 21:8188–8197

    Article  Google Scholar 

  • Hannan SA, Manza RR, Ramteke RJ (2010) Generalized regression neural network and radial basis function for heart disease diagnosis. Int J Comput Appl 7:7–13

    Google Scholar 

  • Hsieh WW, Tang B (1998) Applying neural network models to prediction and data analysis in meteorology and oceanography. Bull Am Meteorol Soc 79:1855–1870

    Article  Google Scholar 

  • Jacob DJ, Winner DA (2009) Effect of climate change on air quality. Atmos Environ 43:51–63

    Article  Google Scholar 

  • Jiang D, Zhang Y, Hu X, Zeng Y, Tan J, Shao D (2004) Progress in developing an ANN model for air pollution index forecast. Atmos Environ 38:7055–7064

    Article  Google Scholar 

  • Kaushik CP, Ravindra K, Yadav K, Mehta S, Haritash AK (2005) Assessment of ambient air quality in urban centres of Haryana (India) in relation to different anthropogenic activities and health risks. Environ Monit Assess 122:27–40

    Article  Google Scholar 

  • Ma L, Shen Y, Ma J (2007) Neural network based correction scheme for image interpolation: advances in neural networks ISNN. Part III, Lecture Notes in Computer Science, vol 4493, pp 840–845

  • Middey A, Chaudhuri S (2013) The reciprocal relation between lightning and pollution and their impact over Kolkata, India. Environ Sci Pollut Res 20:3133–3139

    Article  Google Scholar 

  • National Ambient Air Quality Standards CPCB (2009) New Delhi resource document. http://www.cpcbnicin/National_Ambient_Air_Quality_Standardsphp

  • Rao VL (2014) An estimation of air quality index of a coastal station—a case study. Int J Curr Microbiol Appl Sci 3(6):759–763

    Google Scholar 

  • Ravindra K, Mittal AK, Grieken RV (2001) Health risk assessment of urban suspended particulate matter with special reference to polycyclic aromatic hydrocarbons: a review. Rev Environ Health 16:169–189

    Article  Google Scholar 

  • Reicha SL, Gomezb DR, Dawidowski LE (1999) Artificial neural network for the identification of unknown air pollution sources. Atmos Environ 33:3045–3052

    Article  Google Scholar 

  • Russo A, Raischel F, Lind PG (2013) Air quality prediction using optimal neural networks with stochastic variables. Atmos Environ 79:822–830

    Article  Google Scholar 

  • Sarkar S, Chokngamwong R, Cervone G, Singh RP, Kafatos M (2006) Variability of aerosol optical depth and aerosol forcing over India. Adv Space Res 37:2153–2159

    Article  Google Scholar 

  • Sicard P, Lesne O, Alexandre N, Mangin A, Collomp R (2011) Air quality trends and potential health effects—development of an aggregate risk index. Atmos Environ 45(5):1145–1153

    Article  Google Scholar 

  • Tiwari TN, Ali M (1987) Air quality index for Calcutta and its monthly variation for various localities Indian. J Environ Prot 7:172–176

    Google Scholar 

  • WHO/UNEP Report (1992) Urban air pollution in mega-cities of the world World Health Organisation and United Nations Environment Programme Blackwell Publishers, 108 Cowley Road, Oxford OX4 1JF Cambridge, UK Resource document

  • World Meteorological Organization (1992) GCOS: responding to the need for climate observations. WMO, Geneva

    Google Scholar 

  • Yi J, Prybutok VR (1996) A neural network model forecasting for prediction of daily maximum ozone concentration in an industrialized urban area. Environ Pollut 92:349–357

    Article  Google Scholar 

  • Yin C, Shen Y, Liu S, Yin Q, Guo W, Pan Z (2000) Simultaneous quantitative UV spectrophotometric determination of multicomponents of amino acids using linear neural network. Comput Chem 25:239–243

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Atmospheric Science, University of Calcutta, 51/2 Hazra Road, Kolkata, 700019, India

    Sutapa Chaudhuri & Arumita Roy Chowdhury

Authors
  1. Sutapa Chaudhuri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Arumita Roy Chowdhury
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sutapa Chaudhuri.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chaudhuri, S., Chowdhury, A.R. Air quality index assessment prelude to mitigate environmental hazards. Nat Hazards 91, 1–17 (2018). https://doi.org/10.1007/s11069-017-3080-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11069-017-3080-3

Keywords

Search

Navigation