Abstract
Air pollution is a major issue in all around world, it directly impact on human health, which affecting the lungs and respiratory system. This deposited on soil, plants and in the water, further contributing to human exposure and It mixes in the blood and pumped all-around the body. The most important air pollutants found over Delhi were sulfur dioxide (SO2), nitrogen dioxides (NO2) and suspended particulate matter (SPM). Statistical and wavelet analysis of these air pollutants at three different sample sites two residential namely Janakpuri, Nizamuddin, and one industrial namely Shahazada Bagh over Delhi for the more than 20 year period from 1987 to 2010 in India have been studied. The results shown that the mean concentration of SO2 decreased for both residential (Janakpuri, Nizamuddin) as well as industrial (Shahzada Bagh) area, whereas NO2 increased but it is under the prescribed limits of National Ambient Air Quality Standards (NAAQS). Janakpuri and Nizamuddin represent almost equal but lower mean values of SO2 concentration than Shahzada Bagh. SO2, NO2 and SPM at all sites depicts symmetrical and platykurtic behaviour except Shahzada bagh, for that it follows leptokurtic. Discrete wavelet analysis of air pollutants using Daubechies wavelet (level 5) have been calculated for the study. It is also observed that the values at five different levels of signal data for all air pollutants varies between −225 and +225.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Adarsh S, Shyma M (2017) Analyzing the non-linear trend and multiscale teleconnections of regional monsoon indices using empirical mode decomposition. Model Earth Syst Environ 3(2):669–682
Balyani S, Khosravi Y, Ghadami F, Naghavi M, Bayat A (2017) Modeling the spatial structure of annual temperature in Iran. Model Earth Syst Environ 3(2):581–593. 10.1007/s40808-017-0319-7
Biswas BN, Islam MN, Islam MN (2017) Modeling on management strategies of slope stability and susceptibility to landslides catastrophe at hilly region in Bangladesh. Model Earth Syst Environ. doi:10.1007/s40808-017-0346-4
Can Z, Aslan Z, Oguz O, Siddiqi AH (2005) Wavelet transform of metrological parameter and gravity waves. Ann Geophys 23:659–663
Daneshvar MRM, Abadi NH (2017) Spatial and temporal variation of nitrogen dioxide measurement in the Middle East within 2005–2014. Model Earth Syst Environ. doi.10.1007/s40808-017-0293-0
Daubechies I (1992) Ten lectures on wavelets. SIAM, Philadelphia, PA
Diodato N, Guerriero L, Fiorillo F, Esposito L, Revellino P, Grelle G, Guadagno FM (2014) Predicting monthly spring discharges using a simple statistical model. Water Resour Manag 28:969–978
Dökmen F, Aslan Z (2013) Evaluation of the parameters of water quality with wavelet techniques. Water Resour Manag 27:4977–4988
Goyal P (2003) Present scenario of air quality in Delhi: a case study of CNG implementation. Atmos Environ 37(38):5423–5431
Grossmann A, Morlet J (1984) Decomposition of hardy functions into square integrable wavelets of constant shape. SIAM J Math Anal 15(4):723–736
Hong G, Zhang Y (2008) Wavelet-based image registration technique for high-resolution remote sensing images. Comput Geosci 34:1708–1720
Jaiswal RK, Lohani AK, Tiwari HL (2015) Statistical analysis for change detection and trend assessment in climatological parameters. Environ Process 2:729. doi:10.1007/s40710-015-0105-3
Karimi H, Soffianian A, Mirghaffari N, Soltani S (2016) Determining air pollution potential using geographic information systems and multi-criteria evaluation: a case study in Isfahan Province in Iran. Environ Process 3:229. doi:10.1007/s40710-016-0136-4
Kisi O, Parmar KS, Soni K, Vahdettin D (2017) Modeling of air pollutants using least square support vector regression, multivariate adaptive regression spline, and M5 model tree models. Air Quality Atmos Health. doi:10.1007/s11869-017-0477-9 (press)
Osowski S, Garanty K (2007) Forecasting of the daily meteorological pollution using wavelets and support vector machine. Eng Appl Artif Intell 20(6):745–755
Parmar KS, Bhardwaj R (2012) Analysis of Water parameters using Haar wavelet (level 3). Int J Curr Eng Technol 2(1):166–171
Parmar KS, Bhardwaj R (2013a) Wavelet and statistical analysis of river water quality parameters. Appl Math Comput 219:10172–10182
Parmar KS, Bhardwaj R (2013b) Analysis of water parameters using Daubechies wavelet (level 5) (Db5). Am J Math Stat 2(3):57–63
Parmar KS, Bhardwaj R (2015) River water prediction modeling using neural networks, fuzzy and wavelet coupled model. Water Resour Manag 29(1):17–33
Pellegrini M, Sini F, Taramasso AC (2012) Wavelet-based automated localization and classification of peaks in streamflow data series. Comput Geosci 40:200–204
Quiroz R, Yarlequé C, Posadas A, Mares V, Immerzeel WW (2011) Improving daily rainfall estimation from NDVI using a wavelet transform. Environ Model Softw 26:201–209
Renu V, Kumar GS (2016) Numerical modeling on benzene dissolution into groundwater and transport of dissolved benzene in a saturated fracture-matrix system. Environ Process 3:781. doi:10.1007/s40710-016-0166-y
Soni K, Kapoor S, Parmar KS, Kaskaoutis DG (2014) Statistical analysis of aerosols over the Gangetic–Himalayan region using ARIMA model based on long-term MODIS observations. Atmos Res 149:174–119. doi:10.1016/j.atmosres.2014.05.025
Soni K, Parmar KS, Kapoor S, Kumar N (2016) Statistical variability comparison in MODIS and AERONET derived aerosol optical depth over Indo-Gangetic plains using time series. Sci Total Environ 553:258–265
Varotsos C, Ondov J, Efstathiou M (2005) Scaling properties of air pollution in Athens, Greece and Baltimore. Maryland Atmos Environ 39:4041–4047
Yousefi S, Weinrich I, Reinarz D (2005) Wavelet based prediction of oil prices. Chaos Solitons Fractals 25(2):265–275
Acknowledgements
The authors are thankful to the Central Pollution Control Board (CPCB) (http://164.100.43.188/cpcbnew/movie.html), Government of India for providing the research data; First author (KS) gratefully acknowledge the encouragement by Director, NPL. Second author (KSP) thankful to IKG Punjab Technical University (Government of Punjab) for providing research facilities.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Soni, K., Parmar, K.S. & Agrawal, S. Modeling of air pollution in residential and industrial sites by integrating statistical and Daubechies wavelet (level 5) analysis. Model. Earth Syst. Environ. 3, 1187–1198 (2017). https://doi.org/10.1007/s40808-017-0366-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40808-017-0366-0