Advances in environmental research

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Spatio-temporal estimation of air quality parameters using linear genetic programming

  • Tikhe, Shruti S. (Department of Civil Engineering, Sinhgad College of Engineering) ;
  • Khare, K.C. (Department of Civil Engineering Symbiosis Institute of Technology) ;
  • Londhe, S.N. (Department of Civil Engineering, Vishwakarma Institute of Information Technology)
  • Received : 2016.12.06
  • Accepted : 2017.05.03
  • Published : 2017.06.25
⟨ Previous Next ⟩

Abstract

Air quality planning and management requires accurate and consistent records of the air quality parameters. Limited number of monitoring stations and inconsistent measurements of the air quality parameters is a very serious problem in many parts of India. It becomes difficult for the authorities to plan proactive measures with such a limited data. Estimation models can be developed using soft computing techniques considering the physics behind pollution dispersion as they can work very well with limited data. They are more realistic and can present the complete picture about the air quality. In the present case study spatio-temporal models using Linear Genetic Programming (LGP) have been developed for estimation of air quality parameters. The air quality data from four monitoring stations of an Indian city has been used and LGP models have been developed to estimate pollutant concentration of the fifth station. Three types of models are developed. In the first type, models are developed considering only the pollutant concentrations at the neighboring stations without considering the effect of distance between the stations as well the significance of the prevailing wind direction. Second type of models are distance based models based on the hypothesis that there will be atmospheric interactions between the two stations under consideration and the effect increases with decrease in the distance between the two. In third type the effect of the prevailing wind direction is also considered in choosing the input stations in wind and distance based models. Models are evaluated using Band Error and it was observed that majority of the errors are in +/-1 band.

Keywords

References

  1. Asadollahfardi, G., Zamanian, M., Mirmohammadi, M., Asadi, M. and Tameh, F.I. (2015), "Air pollution study using factor analysis and univariate box-jenkins modelling for the northwest of Tehran", Adv. Environ. Res., 4(4), 233-246. https://doi.org/10.12989/aer.2015.4.4.233
  2. Central Pollution Control Board (CPCB), New Delhi, India (2006), Report on Air Quality Trends and Action Plan for Control of Air Pollution from Seventeen Cities.
  3. Chaudhary, N., Londhe, S. and Khare, K. (2014), "Spatial mapping of pan evaporation using linear genetic programming", J. Hydrol. Sci. Technol., 4(3), 234-244. https://doi.org/10.1504/IJHST.2014.067731
  4. Chen, J., Chen, J., Liao, A., Cao, X., Chen, L., Chen, X., He, C., Han, G., Peng, S., Lu, M., Zhang, W., Tong, X. and Mills, J. (2015), "Global land cover mapping at 30 m resolution: A POK based operational approach", ISPRS J. Photogram. Rem. Sens., 103, 7-27. https://doi.org/10.1016/j.isprsjprs.2014.09.002
  5. Daily News and Analysis (2016), http://www.dnaindia.com/, India.
  6. Denby, B., Sundvor, I., Cassiani, M., De Smet, P., De Leeuw, F. and Horalek, J. (2010), "Spatial mapping of ozone and $SO_2$ trends in Europe", Sci. Tot. Environ., 408(20), 4795-4806. https://doi.org/10.1016/j.scitotenv.2010.06.021
  7. Diem, J. and Comrie, A. (2002), "Predictive mapping of air pollution involving sparse spatial observations", Environ. Pollut., 119, 99-117. https://doi.org/10.1016/S0269-7491(01)00308-6
  8. Environmental Protection Authority (2016), http://www.epa.wa.gov.au/ the clean air act 1970/, U.S.A.
  9. Feng, X., Zhu, Q.L.Y., Hou, J., Jin, L. and Wang, J. (2015), "Artificial neural networks forecasting of $PM_{2.5}$ pollution using air ass trajectory based geographic model and wavelet transformation", Atmosph. Environ., 107, 118-128. https://doi.org/10.1016/j.atmosenv.2015.02.030
  10. Guttikunda, S. and Gurjar, B. (2010), "Role of meteorology in seasonality of air pollution in megacity Delhi, India", Environ. Monitor. Assess., 185(5), 3199-3211.
  11. Kalra, R., Deo, M.C., Kumar, R. and Agarwal, V.K. (2005), "RBF network for spatial mapping of wave heights", Mar. Struct., 18(3), 289-300. https://doi.org/10.1016/j.marstruc.2005.09.003
  12. Koza, J.R. (1992), Genetic Programming, MIT Press.
  13. Kurt, A. and Oktay, A.B. (2010), "Forecasting air pollutant indicator levels with geographic models 3 days in advance using neural networks", Exp. Syst. Appl., 37(12), 7986-7992. https://doi.org/10.1016/j.eswa.2010.05.093
  14. Londhe, S.N. (2008), "Soft computing approach for real-time estimation of missing wave heights", Ocean Eng., 35(11-12), 1080-1089. https://doi.org/10.1016/j.oceaneng.2008.05.003
  15. Londhe, S.N. and Dixit, P.R. (2012), Genetic Programming-New Approaches and Successful Applications, Tech Publications, 199-224.
  16. Maharashtra Pollution Control Board (2016), http://mpcb.gov.in/, Maharashtra State, India.
  17. MPCB (2013-2014), Air Quality Report.
  18. Noack, S., Knoblosh, A., Etzold, S.H., Barth, A. and Kallmeier, E. (2014), Spatial Predictive Mapping Using Artificial Neural Networks, The International Achieves of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-2, 79-86.
  19. Pires, J.C.M. and Martins, F.G. (2011), "Prediction of tropospheric ozone concentrations: Application of a methodology based on the Darwin‟s theory of evolution", Exp. Syst. Appl., 38(3), 1903-1908. https://doi.org/10.1016/j.eswa.2010.07.122
  20. Tikhe, S.S., Khare, K.C. and Londhe, S.N. (2015), "Multicity seasonal air quality index forecasting using soft computing techniques", Adv. Environ. Res., 4(2), 83-104. https://doi.org/10.12989/aer.2015.4.2.083
  21. Yadav, S., Pravin, O. and Satsangi, G. (2015), "The effect of climate and meteorological changes on particulate matter in pune", Environ. Monitor. Assess., 187, 402. https://doi.org/10.1007/s10661-015-4634-z
  22. Yuegang, Z. and Jian, Z. (2005), "Effects of oxalate on Fe-catalyzed photooxidation of dissolved sulfur dioxide in atmospheric water", Atmosph. Environ., 39, 27-37. https://doi.org/10.1016/j.atmosenv.2004.09.058
  23. Yuegang, Z. and Jurg, H. (1993), "Evidence for photochemical formation of $H_2O_2$ and oxidation of $SO_2$ in authentic fog water", Sci., 260, 71-73. https://doi.org/10.1126/science.260.5104.71
  24. Yuegang, Z., Chengjun, W. and Thuan, V. (2006), "Simultaneous determination of nitrite and nitrate in dew, rain snow and lake water samples by ion-pair high performance liquid chromatography", Talanta, 70, 281-285. https://doi.org/10.1016/j.talanta.2006.02.034
  25. Yuegang, Z., Jian, Z. and Taixing, W. (2005), "Effects of monochromatic UV-visible light and sunlight on Fe(III)-catalyzed oxidation of dissolved sulphur dioxide", J. Atmosph. Chem., 50, 195-210. https://doi.org/10.1007/s10874-005-2813-y