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Development of model for sustainable nitrogen dioxide prediction using neuronal networks

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Abstract

Air pollution nowadays is a serious threat to life. In terms of the global air quality, nitrogen dioxide is one of the prominent pollutants as per the reports of the World Health Organization. Nitrogen dioxide is the cause of about 92% of the asthma cases. Epidemiological studies have unfolded nitrogen dioxide contribution to mortality. Apart from the significant health effects, it also plays a role in the formation of other major pollutants ozone and particulate matter. The monitoring and assessment of pollutants is a complex and expensive procedure, simultaneously very important for the country’s wealth and health. The problem is dealt with before using various statistic and deterministic models considering the dependence of nitrogen dioxide on different pollutants and meteorological parameters. The present study contributes to the prediction of nitrogen dioxide for good policy making. The proposed model is less resource-intensive and more effective compared to the existing models.

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Abbreviations

\(\phi (t)\) :

Father wavelet

\(\psi (t)\) :

Mother wavelet

x(t):

Signal

M(A):

A node in layer A

E q :

The error term in layer A to input vector q

d k :

kth term of the desired output for premise parameter

\(X_{k,q}^{M}\) :

kth term of output generated for premise parameter

B :

Matrix of training data corresponding to the consequent parameter

N :

Number of consequent parameters

R :

Count of training data

Z :

Output vector

\(\nu\) :

Solution to consequent parameter elements

µ(x):

Membership function

w i :

Weight

O i :

Observed data

P i :

Predicted data

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Acknowledgements

The authors are thankful to Guru Gobind Singh Indraprastha University, Delhi, India, for providing research facilities and financial support.

Author information

Correspondence to R. Bhardwaj.

Additional information

Editorial responsibility: S.R. Sabbagh-Yazdi.

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Bhardwaj, R., Pruthi, D. Development of model for sustainable nitrogen dioxide prediction using neuronal networks. Int. J. Environ. Sci. Technol. (2020). https://doi.org/10.1007/s13762-019-02620-z

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Keywords

  • Air pollution
  • Time series
  • Wavelet
  • Adaptive neuro-fuzzy inference system

Mathematics Subject Classification

  • 62P12