Evaluating the Influence of Meteorological Parameters on Ozone Concentration Levels


  • Amina Nazif
  • Nurul Izma Mohammed
  • Amirhossein Malakahmad
  • Motasem S. Abualqumboz






Air pollution, Ozone, Particulate matter, Multiple linear regression, Stepwise regression.


Over the years, anthropogenic activities have led to the increase in air pollution concentration levels in the atmosphere, this persistent increase in pollution levels can be influenced by meteorological parameters. These parameters assist in the formation and transportation of air pollutants in the atmosphere. Hence, this study aims at evaluating the association between meteorological parameters and air pollutants. The analysis was carried out using Ozone (O3), Particulate matter (PM10), Nitrogen dioxide (NO2), temperature, humidity, wind speed, and wind direction data from 2006 to 2010, from two industrial air quality monitoring stations. Stepwise regression (SR) analysis was used to assess the influence of meteorological parameters in accounting for the variability of O3 concentration levels. The SR analysis showed that meteorological parameters accounted for more than 50 % of O3 variability. It can be concluded that different relationship between meteorological parameters and O3 can exist in different locations in the same region.




[1] Latif, M.T., L.S. Huey, and L. Juneng, Variations of surface ozone concentration across the Klang Valley, Malaysia. Atmospheric Environment, 2012. 61: p. 434-445.

[2] Dominick, D., et al., Spatial assessment of air quality patterns in Malaysia using multivariate analysis. Atmospheric Environment, 2012. 60: p. 172-181.

[3] Tarasova, O. and A.Y. Karpetchko, Accounting for local meteorological effects in the ozone time-series of Lovozero (Kola Peninsula). Atmospheric Chemistry and Physics, 2003. 3(4): p. 941-949.

[4] Ebi, K.L. and G. McGregor, Climate change, tropospheric ozone and particulate matter, and health impacts. Environ Health Perspect, 2008. 116(11): p. 1449-1455.

[5] Conti, S., et al., Cardiorespiratory treatments as modifiers of the relationship between particulate matter and health: A case-only analysis on hospitalized patients in Italy. Environmental research, 2015. 136: p. 491-499.

[6] Peng, R.D., et al., Seasonal analyses of air pollution and mortality in 100 US cities. American journal of epidemiology, 2005. 161(6): p. 585-594.

[7] Slini, T., K. Karatzas, and A. Papadopoulos, Regression analysis and urban air quality forecasting: An application for the city of Athens. Global Nest, 2002. 4(2-3): p. 153-162.

[8] Pires, J.C., et al., Comparison of several linear statistical models to predict tropospheric ozone concentrations. Journal of Statistical Computation and Simulation, 2012. 82(2): p. 183-192.

[9] Afroz, R., et al., Benefits of air quality improvement in Klang Valley Malaysia. International journal of environment and pollution, 2007. 30(1): p. 119-136.

[10] D.o.S, Population and housing census of Malaysia 2010. 2010, Department of Statistics, MAlaysia: Malaysia.

[11] Azid, A., et al., Source Apportionment of Air Pollution: A Case Study In Malaysia. Jurnal Teknologi, 2014. 72(1).

[12] Thomas, S. and R.B. Jacko, Model for forecasting expressway fine particulate matter and carbon monoxide concentration: application of regression and neural network models. Journal of the Air & Waste Management Association, 2007. 57(4): p. 480-488.

[13] Huberty, C.J., Problems with stepwise methods—better alternatives. Advances in social science methodology, 1989. 1: p. 43-70.

[14] Thompson, B., Stepwise Regression and Stepwise Discriminant Analysis Need Not Apply. 1995.

[15] KovaÄ-Andrić, E., J. Brana, and V. Gvozdić, Impact of meteorological factors on ozone concentrations modelled by time series analysis and multivariate statistical methods. Ecological Informatics, 2009. 4(2): p. 117-122.

View Full Article: