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Environmental Data Retrieval and Prediction Using the Auto-regression Moving Average and Polynomial Experimentation

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Abstract

Data recovery is important in environmental modeling because of its multifaceted challenges that includes technical know-how, climate change, poor instrumentation analysis, and the integration of non-uniform systems. Aerosol optical depth data set from 2000 to 2013 was obtained from the Multi-angled Imaging Spectro-Reflectometry (MISR). The Auto-regression Moving Average (ARMA) was used to recover and predict the aerosol optical properties of 2014–2016 and 2017–2020, respectively. The third-order polymeric curve-fitting were used to synchronize the yearly average and the ARMA results. The output of the statistical experimentation showed high level of accuracy. Hence, this technique can be applied to environmental studies of air, noise, and water pollution.

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References

  • Brockwell P, Davis R (2009) Time series: theory and methods. Springer, Heidelberg

    Google Scholar 

  • Chen B, Choi J, Escanciano JC (2017) Testing for fundamental vector moving average representations. Quant Econ 8:149–180. https://doi.org/10.3982/QE393

    Article  Google Scholar 

  • Dudek AE, Hurd H, Wójtowicz W (2016) Periodic autoregressive moving average methods based on Fourier representation of periodic coefficients. WIREs Comput Stat 8:130–149. https://doi.org/10.1002/wics.1380

    Article  Google Scholar 

  • Emetere ME, Akinyemi ML (2017) Documentation of atmospheric constants over Niamey, Niger: a theoretical aid for measuring instruments. Meteorol Appl 24(2):260–267

    Article  Google Scholar 

  • Emetere ME, Akinyemi ML, Akinojo O (2015a) Parametric retrieval model for estimating aerosol size distribution via the AERONET, LAGOS station. Environ Pollut 207:381–390

    Article  Google Scholar 

  • Emetere ME, Akinyemi ML, Uno UE (2015b) Computational analysis of aerosol dispersion trends from cement factory. In: IEEE proceedings 2015 international conference on space science and communication, pp 288–291. https://doi.org/10.1109/iconspace.2015.7283768

  • Emetere Moses E, Akinyemi ML, Oladimeji TE (2016) Statistical examination of the aerosols loading over Kano-Nigeria: the satellite observation analysis. Sci Rev Eng Environ Sci 72:167–176

    Google Scholar 

  • Escanciano JC, Lobato IN, Zhu L (2013) Automatic specification testing for vector autoregressions and multivariate nonlinear time series models. J Bus Econ Stat 31:426–437

    Article  Google Scholar 

  • Ge M, Kerrigan EC (2016) Short-term ocean wave forecasting using an autoregressive moving average model. In: UKACC 11th International Conference on Control (CONTROL), Belfast, 2016, pp 1–6. https://doi.org/10.1109/control.2016.7737594

  • Hansen J, Sato M, Ruedy R (1997) Radiative forcing and climate response. J Geophys Res 102:6831–6864

    Article  Google Scholar 

  • Ives AR, Abbott KC, Ziebarth NL (2010) Analysis of ecological time series with ARMA(p, q) models. Ecology 91:858–871

    Article  Google Scholar 

  • Jacobs PA, Lewis PAW (1977) A mixed autoregressive moving average exponential sequence and point process EARMA(1,1). Adv Appl Probab 9:87–104

    Article  Google Scholar 

  • NASA (2018). Aerosols and incoming sunlight (direct effects). https://earthobservatory.nasa.gov/Features/Aerosols/page3.php

  • Ohata Sho, Moteki Nobuhiro, Mori Tatsuhiro, Koike Makoto, Kondo Yutaka (2016) A key process controlling the wet removal of aerosols: new observational evidence. Sci Rep 6:34113

    Article  Google Scholar 

  • Ramachandran S, Kedia S, Sheel V (2015) Spatiotemporal characteristics of aerosols in India: observations and model simulations. Atmos Environ 116:225–244

    Article  Google Scholar 

  • Shi S, Wu Z, Liu F, Fan W (2016) Retention of atmospheric particles by local plant leaves in the Mount Wutai scenic area, China. Atmosphere 7:104

    Article  Google Scholar 

  • Shunsuke N (2004) http://chem.atmos.colostate.edu/shun/research.htm. Accessed 11 Sept 2015

  • Sun WY, Yang KJS, Lin NH (2013) Numerical simulations of asian dust-aerosols and regional impacts on weather and climate- Part II: PRCM-dust model simulation. Aerosol Air Qual Res 13:1641–1654

    Article  Google Scholar 

  • Zhao DF, Buchhol A, Kortner B, Schlag P, Rubach F, Fuchs H, Kiendler-Scharr A, Tillmann R, Wahner A, Watne ÅK, Hallquist M, Flores JM, Rudich Y, Kristensen K, Hansen AMK, Glasius M, Kourtchev I, Kalberer M, Mentel TF (2015) Cloud condensation nuclei activity, droplet growth kinetics and hygroscopicity of biogenic and anthropogenic secondary organic aerosol (SOA). Atmospheric Chem Phys Discuss 15:19903–19945

    Article  Google Scholar 

  • Zhou Y, Xing X, Lang J, Chen D, Cheng S, Wei L, Wei X, Liu C (2017) A comprehensive biomass burning emission inventory with high spatial and temporal resolution in China. Atmos Chem Phys 17:2839–2864. https://doi.org/10.5194/acp-17-2839-2017

    Article  Google Scholar 

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Acknowledgements

The author acknowledges the partial sponsorship of Covenant University and University of Johannesburg. The author acknowledges the assistance from NASA to use their satellite data set. The author declares no conflict of interest.

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Authors and Affiliations

  1. Department of Physics, Covenant University Canaan Land, P.M.B 1023, Ota, Nigeria

    M. E. Emetere

  2. Department of Mechanical Engineering Science, University of Johannesburg, Auckland Park Kingsway, Johannesburg, South Africa

    M. E. Emetere

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  1. M. E. Emetere
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Correspondence to M. E. Emetere.

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Emetere, M.E. Environmental Data Retrieval and Prediction Using the Auto-regression Moving Average and Polynomial Experimentation. Aerosol Sci Eng 2, 99–108 (2018). https://doi.org/10.1007/s41810-018-0027-3

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  • DOI: https://doi.org/10.1007/s41810-018-0027-3

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