Fitting Statistical Distribution on Air Pollution: an Overview
-
2018-08-09 https://doi.org/10.14419/ijet.v7i3.23.17256
-
Air pollution, statistical distribution, pollutant concentration prediction. -
Abstract
High event of air pollution would give adverse effect to human health and cause of instability towards environment. In order to overcome these issues, the statistical air pollution modelling is an important tool to predict the return period of high event on air pollution in future. This tool also will be useful to help the related government agencies for providing a better air quality management and it can provide significantly when air quality data been analyze appropriately. In fitting air pollutant data, statistical distribution of gamma, lognormal and Weibull distribution is widely used compared to others distributions model. In addition, the aims of this overview study are to identify which distributions is the most used for predicting the air pollution concentration thus, the accuracy for prediction future air quality is the important aspect to give the best prediction. The comprehensive study need to be conducted in statistical distribution of air pollution for fitting pollutant data. By using others statistical distributions model as main suggested in this paper.
Â
-
References
[1] Zinordin.N.S.,Ramli.N.A.,Sulaiman.M.,Awang.N.R.(2014). A review of the effect of traffic, road characteristic, and meteorological comditions on ozone precursors from vehicle emission. International journal of engineering research&technology. Vol,3 issue 11.
[2] Demuzere, M., van Lipziq, N. P. M. (2010) A new method to estimate air-quality level using a synoptic- regression approach. Part 1: Present-day O3 and PM10 analysis. Atmospheric Research 44, 1341-1355.
[3] Khaniabadi.Y.O., Goudarzi.G.,Daryanoosh.S.M.,Borgini.A., Tittarelli.A.,Marco.D.A.(2016). Exposure to PM10, NO2, and NO3 and impact on human health. Environ Sci Pollut Res.DOI 10.1007/s11356-016-8083-6.
[4] Kim, K.H., Kabir, E., & Kabir, S. (2015). A Review on the Human Health Effect of Airbone Particulate Matter. Environmental International 136-142.
[5] Yahaya, A.S., Ramli, N.A. & Hamid, H.A.(2007). Review of Fitting Distribution on Air Pollution Modelling. Prosiding Simposium Kebangsaan Sains Matematik ke-XV, Malaysia.
[6] Chen, R., Kan, H., Chen, B., Huang, W., Bai, Z., Song, G., Pan, G. (2012). Association of particulate air pollution with daily mortality the China air pollution and health effects study. Am. J. Epidemiol. 175 (11), 1173-1181.
[7] Mabahwi, N.A., Leh, O, L, H and Omar, D. (2014). Human Health and Wellbeing: Human health effect of air pollution. Procedia – Social and Behavioral Science 153, 221-229.
[8] Standers, L.H. (2000). Regulatory Aspects of Air Pollution Control in the United States. Air & Waste Management Association, 8-21.
[9] Fang, X., Bi, X., Wu, J., Zhang, Y., and Feng, Y. (2017) Source apportionment of ambient PM10 and PM 2.5 in Haikou, China. Atmospheric Research 190, 1-9.
[10] Department of Environment Malaysia (2015) Malaysia Environmental Quality Report 2015. Kuala Lumpur: Department of Environment, Ministry of Natural Resources and Environment Malaysia.
[11] Othman,J.,Sahani,M.,Mahmud,M.,Ahmad,Md,K,S. (2014). Transboundary smoke haze pollution in Malaysia: Inpatient health impacts and economic valuation. Environmental pollution 189,194-201.
[12] Li, M., and Zhang, L. (2014). Haze in China: Current and future challenges. Environmental pollution, Volume 189, 85-86.
[13] Rumberg,B.,Richard,A. and Claiborn, C.(2001) Statistical Distribution of particulate matter and the error associated with sampling frequency. Atmospheric Enviroment, 35, p2907-2920.
[14] Georgopoulus,P.G, and Seinfeld,J.H (1982) Statistical Distribution of air quality concentrations. Enviroment Science and Technology,16,401A-416A.
[15] Lu,H.C. (2002) The statistical characters of PM10 concentration in Taiwan area. Atmospheric Environemnt, 36,p.491-502.
[16] Lu,H.C. (2004) Estimating the emission Source reduction of PM10 in central Taiwan. Journal of chemosphere, 54(7),p.805-814.
[17] Sedek.J.N.M.,Ramli.N.A.,Yahaya.A.S.(2006). Air quality predictions using log normal distribution functions of particulate matter in Kuala Lumpur. Malaysian journal of Environmental Management 7:33-41.
[18] [18]Hamid.H.A.,Yahaya.A.S.,Ramli.N.A.,Ul-saufie.A.Z.(2013). Finding the best statistical distribution model in PM10 concentration modeling by using log normal distribution. Journal of Applied Sciences, 12(2): 294-300.
[19] Jiang.X.,Deng.S.,Liu.N.(2011). The statistical distribution of SO2, NO2 and PM10 concentration in Xi’an, China. International Symposium on Water resources and Environmental Protection. Institute of Electrical and Electronics Engineers, 2206-2212.
[20] Yusuf.N.F.F.Md..,Ramli.N.A.,Yahaya.A.S.(2011). Extreme value distribution for prediction of future PM10 exceedances. International Journal of Environmental Protection. IJEP Vol 1,No. 4 PP.28-36.
[21] Noor.N.M.,Abdullah.M.M.A.,Tan.C.Y.,Ramli.N.A.,Yahaya.A.S.,Fitri N.F.M.Y. (2011). Modeling of PM10 concentration for industrialized area in Malaysia : A case study in Shah Alam. Physics Procedia 22 :318-324.
[22] Ghazali.N.A.,Yahaya.A.S.,Nasir.M.Y.,Mokhtar.M.I.Z.(2014). Predicting ozone concentration levels using probability distributions. ARPN Journal of Engineering and Applied Sciences. Vol 9,NO.11.
[23] Oguntunde.P.E.,Odetunmibi.O.A.,Adejumo.A.O.(2014). A study of probability models in monitoring environmental pollution in Nigeria. Journal Probability and Statistics, Article ID 864965,6 pages.
[24] Maciejewska.K.,Rezlar.K.J.,Reizer.M.,Klejnowski.K.(2015). Modelling of black carbon statistical distribution and return periods of extreme concentrations. Environmental Modelling & software 74: 212-226.
[25] Nasir.M.Y.,Ghazali.N.A.,Moktar.M.I.Z.,Suhaimi.N.(2016). Fitting statistical distribution function on ozone concentration data at coastal areas. Malaysian journal of analytical sciences, Vol 20 No3:551-559.
[26] AlDhurafi.N.A.,Razali.A.M.,Masseran.N.,Zamzuri.Z.H.(2016). The probability distribution model of air pollution index and its dominants in Kuala Lumpur. American Institute of Physics, doi, 10.1063/1.4966829. Engineering Science and Technology. Vol,10,No12, 1560-1574.
[27] Sansudin.N.,Ramli.N.A.,Yahaya.A.S.,Yusof.N.F.F MD.,Ghazali.N.A.,Madhoun.W.A.A.(2011). Statistical analysis of PM10 concentration at different locations in Malaysia. Environ Monit Assess 180: 573-588.
[28] Noor.N.M.,Tan.C.Y.,Abdullah.M.M.A.,Ramli.N.A., Yahaya.A.S.(2011). Modeling of PM10 concentration for industrialized area in Malaysia : A case study in Nilai. IPCBEE vol.12 IACSIT press,Singapore.
[29] Xi.W.,Jie.C.R.,Heng.C.B.,Dong.K.H.(2013). Application of statistical distribution of PM10 concentration in air quality management in 5 representative cities of China. Biomed Environ Sci, 26(8): 638-646.
[30] Vivekanandan.N.(2013). Comparison of parameter estimation procedures of gumbel and frechet distribution for modelling annual maximum rainfall. Wyno Journal of Engineering & Technology Reasearch Vol.1(1),PP.1-9.
[31] Ahmat.H.,Yahaya.A.S.,Ramli.N.A.(2015).The Malaysia PM10 analysis using extreme value. Journal of Engineering Science and Technology. Vol, 10,No 12, 1560-1574.
[32] Yahaya.A.S.,Yee.C.S.,Ramli.N.A.,Ahmad.F.(2012). Determination of the best probability plotting position for predicting parameter of the Weibull distribution. International Journal of Applied Science and Technology. Vol.2 No.3.
[33] Pekasiewics.D.(2014). Applicaltion of quantile methods to estimation of Cauchy distribution parameters. STATISTICS IN TRANSITION new series, Vol. 15, No.1,PP.133-144.
[34] Ozay.C.,Celiktas.M.S.(2016). Statistical analysis of wind speed using two-parameter Weibull distribution in Alacati region. Energy Conversion and Management 121: 49-54.
[35] Ouarda.T.B.M.J.,Charron.C.,Chebana.F. (2016). Review of criteria for the selection of probability distributions for wind speed data and introduce the moment and L-moment ration diagram methods with a case study. Energy Conversion and Management 124:247-265.
[36] Papanastasiou.D.K,Melas.Dimitris.(2010). Application of PM10’s statistical distribution to air quality management-A case study in Central Greece. Water and soil pollut (2010) 207:115-122.
[37] Zaharim.A.,Razali.A.M.,Abidin.R.Z.,Sopian.K(2009) Fitting of statistical distribution to wind speed data in Malaysia. European Journal Of Scientific Research. ISSN 1450-216X. Vol.26 No.1(2009),pp.6-12.
[38] Berthe.K.A.,Abdramane.B.,Reichenbach.S (2015) Gumbel Weibull distribution function for Sahel precipitation modeling and predicting: Case of Mali. ISSN 1996-0786. Vol. (5),pp. 405-412.
[39] [Yunus.R.M.,Hasan.M.M (2017). Predicting hourly PM10 concentration in Seberang Perai and Petaling Jaya using log normal linear model. Proceeding of IASTEM Internation conference. ISBN: 978-93-86083-34-0.
-
Downloads
-
How to Cite
Ismail Jaffar, M., Abdul Hamid, H., Yunus, R., & Fauzi Raffee, A. (2018). Fitting Statistical Distribution on Air Pollution: an Overview. International Journal of Engineering & Technology, 7(3.23), 40-44. https://doi.org/10.14419/ijet.v7i3.23.17256Received date: 2018-08-09
Accepted date: 2018-08-09
Published date: 2018-08-09