Environmental Science and Pollution Research

, Volume 22, Issue 23, pp 18849–18858 | Cite as

Modeling of ammonia emission in the USA and EU countries using an artificial neural network approach

  • Lidija J. Stamenković
  • Davor Z. Antanasijević
  • Mirjana Đ. Ristić
  • Aleksandra A. Perić-Grujić
  • Viktor V. Pocajt
Research Article
First Online:
Received:
Accepted:

Abstract

Ammonia emissions at the national level are frequently estimated by applying the emission inventory approach, which includes the use of emission factors, which are difficult and expensive to determine. Emission factors are therefore the subject of estimation, and as such they contribute to inherent uncertainties in the estimation of ammonia emissions. This paper presents an alternative approach for the prediction of ammonia emissions at the national level based on artificial neural networks and broadly available sustainability and economical/agricultural indicators as model inputs. The Multilayer Perceptron (MLP) architecture was optimized using a trial-and-error procedure, including the number of hidden neurons, activation function, and a back-propagation algorithm. Principal component analysis (PCA) was applied to reduce mutual correlation between the inputs. The obtained results demonstrate that the MLP model created using the PCA transformed inputs (PCA-MLP) provides a more accurate prediction than the MLP model based on the original inputs. In the validation stage, the MLP and PCA-MLP models were tested for ammonia emission predictions for up to 2 years and compared with a principal component regression model. Among the three models, the PCA-MLP demonstrated the best performance, providing predictions for the USA and the majority of EU countries with a relative error of less than 20 %.

Keywords

ANN MLP PCA National emissions Ammonia emissions 
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Notes

Acknowledgment

The authors are grateful to the Ministry of Education, Science and Technological Development of the Republic of Serbia, Project No. 172007 for financial support.

Compliance with Ethical Standards

ᅟ

Conflict of interest

The authors declare that they have no conflict of interest.

Statement of Human Rights and Statement on the Welfare of Animals

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. Abdul-Wahab SA, Bakheit CS, Al-Alawi SM (2005) Principal component and multiple regression analysis in modelling of ground-level ozone and factors affecting its concentrations. Environ Model Softw 20:1263–1271CrossRefGoogle Scholar
  2. Agirre-Basurko E, Ibarra-Berastegi G, Madariaga I (2006) Regression and multilayer perceptron-based models to forecast hourly O3 and NO2 levels in the Bilbao area. Environ Model Softw 21:430–446CrossRefGoogle Scholar
  3. Alalawi S, Abdulwahab S, Bakheit C (2008) Combining principal component regression and artificial neural networks for more accurate predictions of ground-level ozone. Environ Model Softw 23:396–403CrossRefGoogle Scholar
  4. Amann M, Bertok I, Borken-Kleefeld J, Cofala J, Heyes C, Höglund-Isaksson L, Klimont Z, Nguyen B, Posch M, Rafaj P, Sandler R, Schöpp W, Wagner F, Winiwarter W (2011) Cost-effective control of air quality and greenhouse gases in Europe: modeling and policy applications. Environ Model Softw 26:1489–1501CrossRefGoogle Scholar
  5. Aneja VP, Blunden J, James K, Schlesinger WH, Knighton R, Gilliam W, Jennings G, Niyogi D, Cole S (2008) Ammonia assessment from agriculture: U.S. status and needs. J Environ Qual 37:515–520CrossRefGoogle Scholar
  6. Antanasijević DZ, Pocajt VV, Povrenović DS, Ristić MĐ, Perić-Grujić AA (2013) PM 10 emission forecasting using artificial neural networks and genetic algorithm input variable optimization. Sci Total Environ 443:511–519CrossRefGoogle Scholar
  7. Antanasijević DZ, Ristić MĐ, Perić-Grujić AA, Pocajt VV (2014) Forecasting GHG emissions using an optimized artificial neural network model based on correlation and principal component analysis. Int J Greenh Gas Control 20:244–253CrossRefGoogle Scholar
  8. Barrancos J, Briz S, Nolasco D, Melián G, Padilla G, Padrón E, Fernández I, Pérez N, Hernández PA (2013) A new method for estimating greenhouse gases and ammonia emissions from livestock buildings. Atmos Environ 74:10–17CrossRefGoogle Scholar
  9. Behera SN, Sharma M, Aneja VP, Balasubramanian R (2013) Ammonia in the atmosphere: a review on emission sources, atmospheric chemistry and deposition on terrestrial bodies. Environ Sci Pollut Res 20:8092–8131CrossRefGoogle Scholar
  10. Beusen AHW, Bouwman AF, Heuberger PSC, Van Drecht G, Van Der Hoek KW (2008) Bottom-up uncertainty estimates of global ammonia emissions from global agricultural production systems. Atmos Environ 42:6067–6077CrossRefGoogle Scholar
  11. Bishop CM (2006) Pattern recognition and machine learning, information. Springer, SingaporeGoogle Scholar
  12. Bolanča T, Cerjan-Stefanović Š, Ukić Š, Rogošić M, Luša M (2008) Application of different training methodologies for the development of a back propagation artificial neural network retention model in ion chromatography. J Chemom 22:106–113CrossRefGoogle Scholar
  13. 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 14:32–41CrossRefGoogle Scholar
  14. Cherkassky V, Lari-Najafi H (1992) Data representation for diagnostic neural networks. IEEE Expert 7:43–53CrossRefGoogle Scholar
  15. Cooter EJ, Bash JO, Benson V, Ran L (2012) Linking agricultural crop management and air quality models for regional to national-scale nitrogen assessments. Biogeosciences 9:4023–4035CrossRefGoogle Scholar
  16. Despagne F, Massart DL (1998) Neural networks in multivariate calibration. Analyst 123:157R–178RCrossRefGoogle Scholar
  17. Erisman JW, Sutton MA, Galloway J, Klimont Z, Winiwarter W (2008) How a century of ammonia synthesis changed the world. Nat Geosci 1:636–639CrossRefGoogle Scholar
  18. European Environment Agency (EEA) (2014) Ammonia (NH3) emissions. http://www.eea.europa.eu/data-and-maps/indicators/eea-32-ammonia-nh3-emissions-1. Accessed 20 May 2014
  19. Eurostat (2014a) Final energy consumption by sector. http://epp.eurostat.ec.europa.eu/tgm/refreshTableAction.do?tab=table&plugin=1&pcode=tsdpc320&language=en. Accessed 18 May 2014
  20. Eurostat (2014b) Statistics database. http://epp.eurostat.ec.europa.eu/portal/page/portal/statistics/search_database. Accessed 18 May 2014
  21. Gardner M, Dorling S (1998) Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmos Environ 32:2627–2636CrossRefGoogle Scholar
  22. Gilliland AB, Wyat Appel K, Pinder RW, Dennis RL (2006) Seasonal NH3 emissions for the continental united states: inverse model estimation and evaluation. Atmos Environ 40:4986–4998CrossRefGoogle Scholar
  23. Gruber N, Galloway JN (2008) An Earth-system perspective of the global nitrogen cycle. Nature 451:293–296CrossRefGoogle Scholar
  24. Hájek P, Olej V (2012) Ozone prediction on the basis of neural networks, support vector regression and methods with uncertainty. Ecol Inform 12:31–42CrossRefGoogle Scholar
  25. Houlton BZ, Boyer E, Finzi AJ, Galloway J, Leach A, Liptzin D, Melillo J, Rosenstock TS, Sobota D, Townsend AR (2013) Intentional versus unintentional nitrogen use in the United States: trends, efficiency and implications. Biogeochemistry 114:11–23CrossRefGoogle Scholar
  26. Kaiser HF (1974) Index of factorial simplicity. Psychometrika 39:31–36CrossRefGoogle Scholar
  27. Karacan CÖ (2008) Modeling and prediction of ventilation methane emissions of U.S. longwall mines using supervised artificial neural networks. Int J Coal Geol 73:371–387CrossRefGoogle Scholar
  28. Li C, Salas W, Zhang R, Krauter C, Rotz A, Mitloehner F (2012) Manure-DNDC: a biogeochemical process model for quantifying greenhouse gas and ammonia emissions from livestock manure systems. Nutr Cycl Agroecosysts 93:163–200CrossRefGoogle Scholar
  29. Lim Y, Moon Y-S, Kim T-W (2007) Artificial neural network approach for prediction of ammonia emission from field-applied manure and relative significance assessment of ammonia emission factors. Eur J Agron 26:425–434CrossRefGoogle Scholar
  30. Moriasi DN, Arnold JG, Van Liew MW, Bingner RL, Harmel RD, Veith TL (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE 50:885–900CrossRefGoogle Scholar
  31. Noori R, Khakpour A, Omidvar B, Farokhnia A (2010) Comparison of ANN and principal component analysis-multivariate linear regression models for predicting the river flow based on developed discrepancy ratio statistic. Expert Syst Appl 37:5856–5862CrossRefGoogle Scholar
  32. Paulot F, Jacob DJ, Pinder RW, Bash JO, Travis K, Henze DK (2014) Ammonia emissions in the United States, Europe, and China derived by high-resolution inversion of ammonium wet deposition data: Interpretation with a new agricultural emissions inventory (MASAGE_ NH3). J Geophys Res Atmos 119:4343–4364CrossRefGoogle Scholar
  33. Pinder RW, Pekney NJ, Davidson CI, Adams PJ (2004) A process-based model of ammonia emissions from dairy cows: improved temporal and spatial resolution. Atmos Environ 38:1357–1365CrossRefGoogle Scholar
  34. Pires JCM, Martins FG, Sousa SIV, Alvim-Ferraz MCM, Pereira MC (2008) Selection and validation of parameters in multiple linear and principal component regressions. Environ Model Softw 23:50–55CrossRefGoogle Scholar
  35. Radojević D, Pocajt V, Popović I, Perić-Grujić A, Ristić M (2013) Forecasting of greenhouse gas emissions in Serbia using artificial neural networks. Energy Sources, Part A 35:733–740CrossRefGoogle Scholar
  36. Raimundo IM Jr, Narayanaswamy R (2001) Simultaneous determination of relative humidity and ammonia in air employing an optical fibre sensor and artificial neural network. Sensors Actuators B 74:60–68CrossRefGoogle Scholar
  37. Ramesh J, Vanathi PT, Gunavathi K (2008) Fault classification in phase-locked loops using back propagation neural networks. ETRI J 30:546–554CrossRefGoogle Scholar
  38. Rotz CA, Montes F, Hafner SD, Heber AJ, Grant RH (2014) Ammonia emission model for whole farm evaluation of dairy production systems. J Environ Qual 43:1143–1158CrossRefGoogle Scholar
  39. Rumelhart DE, Hinton G, Williams R (1986) Learning representations by back-propagating errors. Nature 323:534–536CrossRefGoogle Scholar
  40. Russo A, Raischel F, Lind PG (2013) Air quality prediction using optimal neural networks with stochastic variables. Atmos Environ 79:822–830CrossRefGoogle Scholar
  41. Sousa S, Martins F, Alvimferraz M, Pereira M (2007) Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrations. Environ Model Softw 22:97–103CrossRefGoogle Scholar
  42. Sundvor I, Balaguer NC, Guerreiro C, Viana M, Querol X, Reche C, Amato F, Mellios G (2013) Road traffic’s contribution to air quality in European cities ETC./ACM Technical Paper 2012/14.Google Scholar
  43. Tripathy M (2010) Power transformer differential protection using neural network Principal Component Analysis and Radial Basis Function Neural Network. Simul Model Pract Theory 18:600–611CrossRefGoogle Scholar
  44. US Department of Agriculture (USDA) (2014) National Agricultural Statistics Service. http://usda.mannlib.cornell.edu/MannUsda/viewDocumentInfo.do?documentID=1017. Accessed 18 May 2014
  45. US Energy Information Administration (USEIA) (2014) Total energy. http://www.eia.gov/totalenergy/data/annual/. Accessed 18 May 2014
  46. US Environmental Protection Agency (EPA) (2014) National Emissions Inventory (NEI) air pollutant emissions trends data. http://www.epa.gov/ttnchie1/trends/. Accessed 18 May 2014
  47. Vogl TP, Mangis JK, Zigler AK, Zink WT, Alkon DL DL (1988) Accelerating the convergence of the backpropagation method. Biol Cybern 59:256Google Scholar
  48. Ward Systems Group Inc. (2007) Neuroshell 2 version 4.2. MD, USAGoogle Scholar
  49. Ward Systems Group Inc. (2008) Backpropagation—weight updates. http://www.wardsystems.com/manuals/neuroshell2/index.html?backpropagation_weightupdates.htm
  50. Ward Systems Group Inc. (2008) Contribution factors detail. http://www.wardsystems.com/manuals/neuroshell2/index.html?contributionfactorsdetail.htm
  51. Webb J, Misselbrook TH (2004) A mass-flow model of ammonia emissions from UK livestock production. Atmos Environ 38:2163–2176CrossRefGoogle Scholar
  52. Willmott CJ, Robeson SM, Matsuura K (2012) A refined index of model performance. Int J Climatol 32:2088–2094CrossRefGoogle Scholar
  53. Wu W, Dandy GC, Maier HR (2014) Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modelling. Environ Model Softw 54:108–127CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Lidija J. Stamenković
    • 1
  • Davor Z. Antanasijević
    • 2
  • Mirjana Đ. Ristić
    • 1
  • Aleksandra A. Perić-Grujić
    • 1
  • Viktor V. Pocajt
    • 1
  1. 1.Faculty of Technology and MetallurgyUniversity of BelgradeBelgradeSerbia
  2. 2.Innovation Center of the Faculty of Technology and MetallurgyBelgradeSerbia

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