A statistical model to predict total column ozone in Peninsular Malaysia

Abstract

This study aims to predict monthly columnar ozone in Peninsular Malaysia based on concentrations of several atmospheric gases. Data pertaining to five atmospheric gases (CO₂, O₃, CH₄, NO₂, and H₂O vapor) were retrieved by satellite scanning imaging absorption spectrometry for atmospheric chartography from 2003 to 2008 and used to develop a model to predict columnar ozone in Peninsular Malaysia. Analyses of the northeast monsoon (NEM) and the southwest monsoon (SWM) seasons were conducted separately. Based on the Pearson correlation matrices, columnar ozone was negatively correlated with H₂O vapor but positively correlated with CO₂ and NO₂ during both the NEM and SWM seasons from 2003 to 2008. This result was expected because NO₂ is a precursor of ozone. Therefore, an increase in columnar ozone concentration is associated with an increase in NO₂ but a decrease in H₂O vapor. In the NEM season, columnar ozone was negatively correlated with H₂O (−0.847), NO₂ (0.754), and CO₂ (0.477); columnar ozone was also negatively but weakly correlated with CH₄ (−0.035). In the SWM season, columnar ozone was highly positively correlated with NO₂ (0.855), CO₂ (0.572), and CH₄ (0.321) and also highly negatively correlated with H₂O (−0.832). Both multiple regression and principal component analyses were used to predict the columnar ozone value in Peninsular Malaysia. We obtained the best-fitting regression equations for the columnar ozone data using four independent variables. Our results show approximately the same R value (≈ 0.83) for both the NEM and SWM seasons.

Graphical abstract

Keywords

ozone / SCIAMACHY / principal component analysis / Peninsular Malaysia

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Tan Kok Chooi is a Senior Lecturer at the School of Physics, Universiti Sains Malaysia. He obtained his Ph.D in image processing from Universiti Sains Malaysia. His research interests cover the field of remote sensing applications and environmental monitoring, such as land use/land cover changes, land surface properties, and image classification. Currently, his research focuses on the environmental remote sensing, atmospheric chemistry and physics. E-mail: [e-mail].

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Lim Hwee San is an Associate Professor of Geophysics and Remote Sensing at the School of Physics, Universiti Sains Malaysia. He obtained his Ph.D from Universiti Sains Malaysia in environmental remote sensing. His research interests lie generally in the field of optical remote sensing- both passive and active-and digital image processing, particularly as it applies to spectral image data. In both cases, the primary applications are water quality monitoring, air quality monitoring, land cover/change detection, land surface properties and digital images classification. He also interested in modelling of the optical properties of atmospheric aerosols. His current effort focus on the applications of ground based LIDAR and satellite based LIDAR (e.g., CALIPSO, AIRS) data for air pollution and green house effects study. E-mail: [e-mail].

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Mohd. Zubir Mat Jafri is a Professor of Optical Sensing and Remote Sensing at the School of Physics, Universiti Sains Malaysia. He obtained his Ph.D from Universiti College of Swansea, Wales, UK, in digital image processing. He has more than twenty years teaching and research experience in the area of physics, optical communication, digital image processing, electronics and microprocessor technology. E-mail: [e-mail].

References

[1] Abdul-Wahab S A, Bakheit C S, Al-Alawi S M (2005). Principle component and multiple regression analysis in modelling of ground-level ozone and factors affecting its concentrations. Environ Model Softw, 20(10): 1263–1271

[2] Ahammed Y N, Reddy R R, Gopal K R, Narasimhulu K, Basha D B, Reddy L S S, Rao T V R (2006). Seasonal variation of the surface ozone and its precursor gases during 2001−2003, measured at Anantapur (14.628N), a semi-arid site in India. Atmos Res, 80(2−3): 151–164

[3] Al-Alawi S M, Abdul-Wahab S A, Bakheit C S (2008). Combining principal component regression and artificial neural networks for more accurate predictions of ground-level ozone. Environ Model Softw, 20: 1263–1271

[4] Azid A, Juahir H, Toriman M E, Kamarudin M K A, Saudi A S M, Hasnam C N C, Aziz N A A, Azaman F, Latif M T, Zainuddin S F M, Osman M R, Yamin M (2014). Prediction of the level of air pollution using principal component analysis and artificial neural network techniques: a case study in Malaysia. Water Air Soil Pollut, 225(8): 2063

[5] Baker D J, Richards G, Grainger A, Gonzalez P, Brown S, Defries R, Held A, Kellndorfer J, Ndunda P, Ojima D, Skrovseth P E, Souza C Jr, Stolle F (2010). Achieving forest carbon information with higher certainty: a five-part plan. Environ Sci Policy, 13(3): 249–260

[6] Bian J, Gettelman A, Chen H, Pan L (2007). Validation of satellite ozone profile retrievals using Beijing ozonesonde data. J Geophys Res, 112(D6): D06305

[7] Bracher A, Lamsal L N, Weber M, Bramstedt K, Coldewey-Egbers M, Burrows J P (2005). Global satellite validation of SCIAMACHY O₃ columns with GOME WFMDOAS. Atmos Chem Phys, 5(9): 2357–2368

[8] Buchwitz M, de Beek R, Burrows J P, Bovensmann H, Warneke T, Notholt J, Meirink J F, Goede A P H, Bergamaschi P, Körner S, Heimann M, Schulz A (2005). Atmospheric methane and carbon dioxide from SCIAMACHY satellite data: initial comparison with chemistry and transport models. Atmos Chem Phys, 5(4): 941–962

[9] Coldewey-Egbers M, Weber M, Lamsal L N, de Beek R, Buchwitz M, Burrows J P (2005). Total ozone retrieval from GOME UV spectral data using the weighting function DOAS approach. Atmos Chem Phys, 5(4): 1015–1025

[10] Dueñas C, Fernández M C, Cañete S, Carretero J, Liger E (2004). Analyses of ozone in urban and rural sites in Málaga (Spain). Chemosphere, 56(6): 631–639

[11] Johnson C E, Stevenson D S, Collins W J, Derwent R G (2002). Interannual variability in methane growth rate simulated with a coupled Ocean-Atmosphere-Chemistry model. Geophys Res Lett, 29(19): doi: 10.1029/2002GL015269

[12] Lengyel A, Héberger K, Paksy L, Bánhidi O, Rajkó R (2004). Prediction of ozone concentration in ambient air using multivariate methods. Chemosphere, 57(8): 889–896

[13] Lin W, Xu X, Zhang X, Tang J (2008). Contributions of pollutants from North China Plain to surface ozone at the Shangdianzi GAW Station. Atmos Chem Phys, 8(19): 5889–5898

[14] Mieruch S, Noël S, Bovensmann H, Burrows J P (2008). Analysis of global water vapour trends from satellite measurements in the visible spectral range. Atmos Chem Phys, 8(3): 491–504

[15] Omar D (2009). Urban form and sustainability of a hot humid city of Kuala Lumpur. J Soc Sci, 8: 353–359

[16] Pochanart P, Kreasuwun J, Sukasem P, Geeratithadaniyom W, Tabucanon M S, Hirokawa J, Kajii Y, Akimoto H (2001). Tropical tropospheric ozone observed in Thailand. Atmos Environ, 35(15): 2657–2668

[17] Rajab J M, MatJafri M Z, Lim H S (2013). Combining multiple regression and principal component analysis for accurate predictions for column ozone in Peninsular Malaysia. Atmos Environ, 71: 36–43

[18] Reddy B S K, Kumar K R, Balakrishnaiah G, Gopal K R, Reddy R R, Sivakumar V, Lingaswamy A P, Arafath S Md, Umadevi K, Kumari S P, Ahammed Y N, Lal S (2012). Analysis of diurnal and seasonal behaviour of surface ozone and its precursor (NO_x) at a semi-arid rural site in Southern India. Aerosol Air Qual Res, 12: 1081–1094

[19] Richter A, Burrows J P, Nüß H, Granier C, Niemeier U (2005). Increase in tropospheric nitrogen dioxide over China observed from space. Nature, 437(7055): 129–132

[20] Richter A, Eyring V, Burrows J P, Bovensmann H, Lauer A, Sierk B, Crutzen P J (2004). Satellite measurements of NO₂ from international shipping emissions. Geophys Res Lett, 31(23): L23110

[21] Schneising O, Buchwitz M, Burrows J P, Bovensmann H, Bergamaschi P, Peters W (2008a). Three years of greenhouse gas column averaged dry air mole fractions retrieved from satellite-part 2: methane. Atmos Chem Phys, 8(3): 8273–8326

[22] Schneising O, Buchwitz M, Burrows J P, Bovensmann H, Reuter M, Notholt J, Macatangay R, Warneke T (2008b). Three years of greenhouse gas column-averaged dry air mole fractions retrieved from satellite − Part 1: carbon dioxide. Atmos Chem Phys, 8(14): 3827–3853

[23] Statheropoulos M, Vassiliadis N, Pappa A (1998). Principle component and canocical correlation analysis for examining air pollution and meteorological data. Atmos Environ, 32(6): 1087–1095

[24] Stephens B B, Gurney K R, Tans P P, Sweeney C, Peters W, Bruhwiler L, Ciais P, Ramonet M, Bousquet P, Nakazawa T, Aoki S, Machida T, Inoue G, Vinnichenko N, Lloyd J, Jordan A, Heimann M, Shibistova O, Langenfelds R L, Steele L P, Francey R J, Denning A S (2007). Weak northern and strong tropical land carbon uptake from vertical profiles of atmospheric CO₂. Science, 316(5832): 1732–1735

[25] Tan K C, Lim H S, Mat Jafri M Z (2012a). Total ozone column distribution over Peninsular Malaysia from Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY). Proceeding SPIE 8538, Earth Resources and Environmental Remote Sensing/GIS Applications III, 85380Y

[26] Tan K C, Lim H S, Mat Jafri M Z (2012b). Satellite observation distribution of atmospheric ozone over Peninsular Malaysia from SCIAMACHY. International Coference on Control System, Computing and Engineering (ICCSCE 2012), 238–243

[27] Tan K C, Lim H S, Mat Jafri M Z (2013). Relationship between ozone and their air pollutants in Peninsular Malaysia for 2003 retrieved from SCIAMACHY. AIP Conf Proc, 1528: 151–156

[28] Tan K C, Lim H S, Mat Jafri M Z (2014a). Analysis of total column ozone in Peninsular Malaysia retrieved from SCIAMACHY. Atmos Pollut Res, 5(1): 42–51

[29] Tan K C, Lim H S, Mat Jafri M Z (2014b). Multiple regression analysis in modeling of columnar ozone in Peninsular Malaysia. Environ Sci Pollut Res Int, 21(12): 7567–7577

[30] Tan K C, Lim H S, Mat Jafri M Z. (2012c). Total ozone column distribution over Peninsular Malaysia from scanning imaging absorption spectrometer for atmospheric cartography (SCIAMACHY). Proc. SPIE 8538, Earth Resources and Environmental Remote Sensing/GIS Applications III, 85380Y (October 25, 2012),

[31] Tian W, Chipperfield M P, Lü D (2009). Impact of increasing stratospheric water vapor on ozone depletion and temperature change. Adv Atmos Sci, 26(3): 423–437

[32] Toh Y Y, Lim S F, Von Glasow R(2013). The influence of meteorological factors and biomass burning on surface ozone concentrations at Tanah Rata, Malaysia. Atmos Environ, 70: 435–446

[33] Vaidya O C, Howell G D, Leger D A (2000). Evaluation of the distribution of mercury in lakes in Nova Scotia and Newfoundland. Water Air Soil Pollut, 117(1/4): 353–369

[34] Vingarzan R (2004). A review of surface ozone background levels and trends. Atmos Environ, 38(21): 3431–3442

[35] Wang Y S, Zhou L, Wang M X, Zheng X H (2001). Trends of atmospheric methane in Beijing. Chemosphere, Glob Chang Sci, 3(1): 65–71

[36] Wu H W Y, Chan L Y (2001). Surface ozone trends in Hong Kong in 1985–1995. Environ Int, 26(4): 213–222

[37] Yonemura S, Tsuruta H, Kawashima S, Sudo S, Leong C P, Lim S F, Zubaidi J, Masayasu H (2002). Tropospheric ozone climatology over Peninsular Malaysia from 1992 to 1999. J Geophys Res, 107(D15): 4229

Acknowledgments

The authors would like to thank the Institute of Environmental Physics at the University of Bremen for providing the SCIAMACHY data. This project was carried out with financial support from the Investigation of the Impacts of Summertime Monsoon Circulation to the Aerosols Transportation and Distribution in Southeast Asia which can Lead to Global Climate Change RUI, 1001/PFIZIK/811228, and the Environmental Effects and its Influence on Increased Greenhouse Gases in Peninsular Malaysia Science Fund, 305/PFIZIK/613615. The authors would also like to extend their gratitude to USM for providing support and encouragement and to the USM technical staff for their unwavering support and cooperation.

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