Abstract
Ozone (O3) in the troposphere is considered as a secondary air pollutant and has an adverse impact on human health and climatic condition. In many countries including India, O3 is listed as one of the criteria pollutants. Thus, a proper forecasting technique of ozone concentration is necessary for protecting the human health. The concentration of ozone in the troposphere depends on the meteorological condition and precursor’s levels. Hence, it is essential to consider these dependent factors in the development of prediction model. The study aims to develop an ozone forecasting model using artificial neural network (ANN). Three-year air pollution and meteorological data (1 January 2009 to 31 December 2011) of Kolkata City was used for model development. Two types of learning algorithms [feed forward back propagation (FFBP) and layer recurrent (LR)] were used for training the ANN model. Four meteorological factors (relative humidity, temperature, wind speed, and wind direction) along with the NO2 concentration and previous day’s ozone concentration were used as input parameters in the model for predicting the ozone concentration. The number of neurons in the hidden layers of a neural network model was optimized for both the algorithms. The number of input combinations was also optimized using forward search algorithm. The model performances were tested using four statistical indices [percentage of root mean square error (RMSE), coefficient of determination (R 2), fractional bias (FB), index of agreement (IOA)] for evaluating the ANN models.
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References
Abdul-Wahab SA, Al-Alawi SM (2008) Prediction of sulfur dioxide (SO2) concentration levels from the Mina Al-Fahal Refinery in Oman using artificial neural networks. Am J Environ Sci 4:473–481
Anderson HR (2009) Air pollution and mortality: a history. Atmos Environ 43:142–152
Awang NR, Elbayoumi M, Ramli NA, Yahaya AS (2016) Diurnal variations of ground-level ozone in three port cities in Malaysia. Air Qual Atmos Health 9(1):25–39
Bascom R, Bromberg PA, Costa DA, Devlin R, Dockery DW, Frampton MW, Lambert W, Samet JM, Speizer FE, Utell M (1996) Health effects of outdoor air pollution. Am J Resp Crit Care Med 153:3–50
Bnanankhah A, Nejadkoorki F (2012) Artificial neural network: a nonlinear tool for air quality modelling and monitoring. International Conference on Applied Life Sciences Turkey September 10–12.
Brimblecombe P (1987) The Big Smoke: a history of air pollution in London since medieval times. Routledge, Methuen, London
Camalier L, Cox W, Dolwick P (2007) The effects of meteorology on ozone in urban areas and their use in assessing ozone trends. Atmos Environ 41:7127–7137
Chaloulakou A, Assimacopoulas D, Lekkas T (1999) Forecasting daily maximum ozone concentrations in the Athens Basin. Environ Monit Assess 56:97–112
Chen S, Billings SA, Luo W (1989) Orthogonal least squares methods and their application to nonlinear system identification. Int J Control 50:1873–1896
Chen S, Hong X, Harris CJ, Sharkey PM (2004) Sparse modeling using orthogonal forward regression with PRESS statistic and regularization. IEEE Trans Syst Man Cybern Part B 34:898–911
Comrie AC (1997) Comparing neural networks and regression models for ozone forecasting. J Air Waste Manag Assoc 47:653–663
Cox WM, Chu S-H (1996) Assessment of inter annual ozone variation in urban areas from a climatological perspective. Atmos Environ 30:2615–2625
Dapeng XU, Yap D, Taylor PA (1996) Meteorologically adjusted ground level ozone trends in Ontario. Atmos Environ 30(7):1117–1124
Dawson JP, Adams PJ, Pandis SN (2007) Sensitivity of ozone to summertime climate in the Eastern USA: a modeling case study. Atmos Environ 41:1494–1511
Gardner MW, Dorling SR (1999) Neural network modelling and prediction of hourly NOx and NO2 concentrations in urban air in London. Atmos Environ 33:709–719
Gardner MW, Dorling SR (2000) Statistical surface ozone models: an improved methodology to account for non-linear behaviour. Atmos Environ 34:21–34
Gorai AK, Tuluri F, Tchounwou PB, Ambinakudige S (2015) Influence of local meteorology and NO2 conditions on ground level ozone concentration in eastern part of Texas, USA. Air Qual Atmos Health 8:81–96. doi:10.1007/s11869-014-0276-5
Hadjiiski L, Geladi P, Hopke P (1999) A comparison of modelling nonlinear systems with artificial neural networks and partial least squares. Chemometr Intell Lab 49(1):91–103
Karaca F, Alagha O, Erturk F (2005) Statistical characterization of atmospheric PM10 and PM2.5 concentrations at a non-impacted suburban site of Istanbul, Turkey. Chemosphere 59(8):1183–1190
Khan JA, Van Aelst S, Zamar RH (2007) Building a robust linear model with forward selection and stepwise procedures. Comput Stat Data An 52(1):239–248
Kolehmainen M, Martikainen H, Hiltunen T, Ruusaknen J (2000) Forecasting air quality parameters using hybrid neural network modelling. Environ Monit Assess 65:277–286
Korsog PE, Wolff GT (1991) An examination of ozone urban trends in the northeastern US (1973–1983) using a robust statistical method. Atmos Environ B 25:47–57
Kumar A, Goyal P (2011) Forecasting of air quality in Delhi using principal component regression technique. Atmos Pollut Res 2:436–444
Kumar A, Goyal P (2013) Forecasting of air quality index in Delhi using neural network based on principal component analysis. Pure Appl Geophys 170(4):711–722
Lippmann M (1991) Health effects of tropospheric ozone. Environ Sci Techno 25:1954–1962
Mathew RH, Kumar P, Harrison RM (2012) Particles air quality policy and health. Chem Soc Rev 41:6606–6630
Nagendra SMS, Khare M (2006) Artificial neural network approach for modelling nitrogen dioxide dispersion from vehicular exhaust emissions. Eco Model 190(1–2):99–115
National Ambient Air Quality Standard (NAAQS), New Delhi, India (2009) Available online http://cpcb.nic.in/National_Ambient_Air_Quality_Standards.php (Last accessed on 12th December 2014)
Nunnari G, Dorling S, Schlink U, Cawley G, Foxall R, Chatterton T (2004) Modelling SO2 concentration at a point with statistical approaches. Environ Model Softw 19(10):887–905
Panchal G, Ganatra A, Kosta YP, Pancha D (2011) Behaviour analysis of multilayer perceptrons with multiple hidden neurons and hidden layers. IJCTE 3(2):332–337
Rao ST, Sistla G, Pagnotti V, Peterson WB, Irwin JS, Turner DB (1985) Evaluation of the performance of RAM with the regional air pollution study data base. Atmos Environ 19:229–245
Robeson SM, Steyn DG (1990) Evaluation and comparison of statistical forecast models for daily maximum ozone concentrations. Atmos Environ B 24:303–312
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by error propagation. In: Rumelhart DE, McClelland JL (eds) Parallel distributed processing, 8th edn. MIT Press, Cambridge, England, pp 45–76
Srinivasan D, Liew AC, Chang CS (1994) A neural network short-term load forecaster. Elect Power Syst Res 28:227–234
Swingler K (1996) Applying neural networks: a practical guide. Academic Press, London
Wang XX, Chen S, Lowe D, Harris CJ (2006) Sparse support vector regression based on orthogonal forward selection for the generalised kernel model. Neurocomputing 70:462–474
Willmott CJ (1982) Some comments on the evaluation of model performance. Bull Am Meteorol Soc 63:1309–1313
Willmott CJ, Ackleson SG, Davis RE, Feddema JJ, Klink KM, Legates DR, O’donnell J, Rowe CM (1985) Statistics for the evaluation and comparison of models. J Geophys Res 90(C5):8995–9005
Yi J, Prybutok VR (1996) A neural network model forecasting for prediction of daily maximum region concentration in an industrialized urban area. Environ Pollut 92:349–357
Ziomas IC, Melas D, Zerefos CS, Bais AF, Paliatsos AG (1995) Forecasting peak pollutant levels from meteorological variables. Atmos Environ 29:3703–3711
Acknowledgments
The authors would like to acknowledge the financial support from the Department of Science and Technology, New Delhi Grant No. SR/FTP/ES-17/2012. The authors are thankful to the West Bengal Pollution Control Board for providing the air quality data. The authors are thankful to the anonymous reviewer for putting their valuable comments on the manuscript for improving the quality.
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Gorai, A.K., Mitra, G. A comparative study of the feed forward back propagation (FFBP) and layer recurrent (LR) neural network model for forecasting ground level ozone concentration. Air Qual Atmos Health 10, 213–223 (2017). https://doi.org/10.1007/s11869-016-0417-0
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DOI: https://doi.org/10.1007/s11869-016-0417-0