Tzu-Yi Pai a,1, Pao-Jui Sung a, b, Chung-Yi Lin b, Horng-Guang Leu c, Yein-Rui Shieh c, Shuenn-Chin Chang c, Shyh-Wei Chen c, Jin-Juh Jou c

a Department of Environmental Engineering and Management, Chaoyang University of Technology, Wufeng, Taichung, 41349, Taiwan
b Dali City Administration, Taichung County Government, Dali, Taichung, 41261, Taiwan
c Environmental Protection Administration, Taipei, 10042, Taiwan

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ABSTRACT

In this study, multiple linear regression (MLR) method was used to establish the relationship between the O3 at time t + 1 and other indices including hourly air pollutant concentrations and meteorological conditions at time t. Then O3 was predicted using the obtained best-fitting MLR. The results indicated that the relationship between the O3 at time t + 1 and other indices including hourly air pollutant concentrations and meteorological conditions at time t agreed with MLR well, The values of mean absolute percentage error (MAPE), correlation coefficient (R), coefficient of determination (R2), mean square error (MSE), and root mean square error (RMSE) were 29.09%, 0.95, 0.90, 45.33, and 6.73, respectively when determining the best-fitting equation. In addition, MLR could predict hourly ozone concentrations successfully. The values of MAPE, R, R2, MSE, and RMSE were 10.37%, 0.93, 0.86, 0.33, and 0.57, respectively when predicting. It also indicated that the hourly air pollutant concentrations and meteorological conditions at time t could be applied on the prediction of ozone of time t +1.


Keywords: multiple linear regression; ozone; air quality; meteorological conditions; photochemical reaction


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REFERENCES

  1. [1] Cunningham, W. P., and Cunningham, M. A. 2006. Principles of Environmental Science. Inquiry & Applications. Mc Graw- Hill Company, New York.

  2. [2] Faiz, A., Gautam, S., and Burki, E. 1995. Air pollution from motor vehicles: issues and options for Latin American countries. The Science of the Total Environment, 169, 1-3: 303-310.

  3. [3] Fischer, P. H., Hoek, G., van Reeuwijk, H., Briggs, D. J., Lebret, E., van Wijnen, J.H., and Kingham, S. 2000. Traffic-related differences in outdoor and indoor concentrations of particles and volatile organic compounds in Amsterdam. Atmospheric Environment, 34, 22: 3713-3722.

  4. [4] Kingham, S., Briggs, D., Elliott, P., Fischer, P., and Erik, L. 2000. Spatial variations in the concentrations of traffic-related pollutants in indoor and outdoor air in Huddersfield, England. Atmospheric Environment, 34, 6: 905-916.

  5. [5] Lipfert, F. W., Wyzga, R. E., Baty, J. D., and Miller, J. P. 2006. Traffic density as a surrogate measure of environmental exposures in studies of air pollution health effects: long-term mortality in a cohort of US veterans. Atmospheric Environment, 40, 1: 154-169.

  6. [6] Pai, T. Y., Hanaki, K., Ho H. H., and Hsieh, C. M. 2007. Using grey system theory to evaluate transportation on air quality trends in Japan, Transportation Research Part D: Transport and Environment, 12, 3: 158-166.

  7. [7] Gao, H. O., 2007. Day of week effects on diurnal ozone/NOx cycles and transportation emissions in Southern California. Transportation Research Part D: Transport and Environment, 12, 4: 292- 305.

  8. [8] Gao, H. O., and Niemeier, D. A. 2007. The impact of rush hour traffic and mix on the ozone weekend effect in southern California. Transportation Research Part D: Transport and Environment, 12, 2: 83-98.

  9. [9] Wang, G., Bai, S., and Ogden, J. M., 2009. Identifying contributions of on-road motor vehicles to urban air pollution using travel demand model data. Transportation Research Part D: Transport and Environment, 14, 3: 168–179.

  10. [10] Delucchi, M. A., Greene, D.L., and Wang, M. Q. 1994. Motor-vehicle fuel economy: The forgotten hydrocarbon control strategy. Transportation Research Part A: Policy and Practice, 28, 3: 223-244.

  11. [11] Abdul-Wahab, S. A., Bakheit, C. S., and Al-Alawi, S. M. 2005. Principal component and multiple regression analysis in modelling of ground-level ozone and factors affecting its concentrations. Environmental Modelling and Software, 20, 10: 1263-1271.


ARTICLE INFORMATION



Accepted: 2010-01-25
Available Online: 2010-07-01


Cite this article:

Pai, T.-Y., Sung, P.-J., Lin, C.-Y., Leu, H.-G., Shieh, Y.-R., Chang, S.-C., Chen, S.-W., Jou, J.-J. 2010. Predicting hourly ozone concentration in Dali area of Taichung County based on multiple linear regression method. International Journal of Applied Science and Engineering, 7, 127–132. https://doi.org/10.6703/IJASE.2010.7(2).127

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