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.
Similar content being viewed by others
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
References
Aghel B, Rezaei A, Mohadesi M (2019) Modeling and prediction of water quality parameters using a hybrid particle swarm optimization–neural fuzzy approach. Int J Environ Sci Technol 16:4823–4832
Asif Z, Chen Z, Zhu Z (2018) An integrated life cycle inventory and artificial neural network model for mining air pollution management. Int J Environ Sci Technol 16:1847–1856
Bandyopadhyay G, Chattopadhyay S (2007) Single hidden layer artificial neural network models versus multiple linear regression model in forecasting the time series of total ozone. Int J Environ Sci Tech 4(1):141–149
Bhardwaj R, Pruthi D (2018) Statistical time series and predictability analysis of nitrogen dioxide. Jnanabha Spec Issue 48:5–12
Bojja P, Nerella D, Chakka H (2017) Prediction of nitrogen dioxide & ozone concentrations in the ambient air using artificial neural networks for visakhapatnam model. Int J Pure Appl Math 117(19):83–88
Burnett RT, Stieb D, Brook JR, Cakmak S, Dales R, Raizenne M, Vincent R, Dann T (2004) Associations between short-term changes in nitrogen dioxide and mortality in Canadian cities. Arch Environ Health 59(5):228–236
Cabalar A, Cevik A, Gokceoglu C (2012) Some applications of adaptive neuro-fuzzy inference system (ANFIS) in geotechnical engineering. Comput Geotech 40:14–33
Capilla C (2015) Artificial neural network approach for forecasting nitrogen oxides concentrations. Environ Eng Sci 32(9):150630062814006
Choi G, Bell ML, Lee JT (2017) A study on modeling nitrogen dioxide concentrations using land-use regression and conventionally used exposure assessment methods. Environ Res Lett 12(4):044003
Debry E, Mallet V (2014) Ensemble forecasting with machine learning algorithms for ozone, nitrogen dioxide and PM10 on the Prev’Air. Atmos Environ 91:71–84
Dghais A, Ismail Mohd T (2014) A study of stationarity in time series by using wavelet transform. AIP Conf Proc 1605:798–804
Dragomir CM, Voiculescu M, Constantin DE, Georgescu LP (2015) Prediction of the NO2 concentration data in an urban area using multiple regression and neuronal networks. AIP Conf Proc 1694:040003–1–040003-6
Elangasinghe A, Singhal N, Dirks K, Salmond J (2014) Development of an ANN–based air pollution forecasting system with explicit knowledge through sensitivity analysis. Atmos Pollut Res 5(4):696–708
Ghadi M, Qaderi F, Babanezhad E (2018) Prediction of mortality resulted from NO2 concentration in Tehran by Air Q + software and artificial neural network. Int J Environ Sci Technol 16:1351–1368
He H, Li M, Wang W, Yu X (2018) Prediction of PM2.5 concentration based on the similarity in air quality monitoring network. Build Environ 137:11–17
Jalalifar H, Mojeddifar S, Sahebi AA, Nezamabadi-pour H (2011) Application of the adaptive neuro-fuzzy inference system for prediction of a rock engineering classification system. Comput Geotech 38:783–790
Jang JS (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23:665–685
Karthikeyan L, Kumar DN (2013) Predictability of nonstationary time series using wavelet and EMD based ARMA models. J Hydrol 502:103–119
Khoshnevisan B, Rafiee S, Omid M, Mousazadeh H (2014) Development of an intelligent system based on ANFIS for predicting wheat grain yield on the basis of energy inputs. Inf Process Agric 1:14–22
Latza U, Gerdes S, Baur X (2009) Effects of nitrogen dioxide on human health: systematic review of experimental and epidemiological studies conducted between 2002 and 2006. Int J Hyg Environ Health 212:271–287
Lawson AR, Ghosh B (2011) Brian Broderick prediction of traffic-related nitrogen oxides concentrations using structural time-series models. Atmos Environ 45:4719–4727
Masoudi M, Asadifard E (2015) Status and prediction of nitrogen dioxide as an air pollutant in Ahvaz City, Iran. Pollut Atmos 225:1–10
Mishra D, Goyal P (2015) Development of artificial intelligence based NO2 forecasting models at Taj Mahal, Agra. Atmos Pollut Res 6(1):99–106
Nagendra SMS, Khare M (2006) Artificial neural network approach for modelling nitrogen dioxide dispersion from vehicular exhaust emissions. Ecol Model 190:99–115
O’Shea PM, Roy SS, Singh RB (2015) Diurnal variations in the spatial patterns of air pollution across Delhi. Theor Appl Climatol 124:609–620
Rahimi A (2017) Short-term prediction of NO2 and NOx concentrations using multilayer perceptron neural network: a case study of Tabriz, Iran. Ecol Process 6:4
Ross Z, English PB, Scalf R, Gunier R, Smorodinsky S, Wall S, Jerrett M (2006) Nitrogen dioxide prediction in Southern California using land use regression modeling: potential for environmental health analyses. J Expo Sci Environ Epidemiol 16:106–114
Singh R, Kainthola A, Singh TN (2012) Estimation of elastic constant of rocks using an ANFIS approach. Appl Soft Comput 12:40–45
Umaro R, Sharma LK, Singh R, Singh TN (2018) Determination of strength and modulus of elasticity of heterogenous sedimentary rocks: an ANFIS predictive technique. Measurement 126:194–201
Zhang L, Guan Y, Leaderer BP, Holford TR (2013) Estimating daily nitrogen dioxide level: exploring traffic effects. Ann Appl Stat 7(3):1763–1777
Acknowledgements
The authors are thankful to Guru Gobind Singh Indraprastha University, Delhi, India, for providing research facilities and financial support.
Author information
Authors and Affiliations
Corresponding author
Additional information
Editorial responsibility: S.R. Sabbagh-Yazdi.
Rights and permissions
About this article
Cite this article
Bhardwaj, R., Pruthi, D. Development of model for sustainable nitrogen dioxide prediction using neuronal networks. Int. J. Environ. Sci. Technol. 17, 2783–2792 (2020). https://doi.org/10.1007/s13762-019-02620-z
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13762-019-02620-z