Journal of School of Public Health and

  Anderson, H.R., 2009. Air pollution and mortality: A history. Atmospheric Environment, 43, pp. 142-152 .

  Box, GEP. and Jenkins, G.M., 1976. Time series analysis: forecasting and control, San Francisco, Holden Day Pulications .

  Duenas, C., Fernandez, M.C., Canete, S., Carretero,Liger E, 2005. Stocastic model to forecast ground level ozone concentration at urban and rural areas . Chemosphere, 61(10), pp. 1379-1389 .

  Ghorbani, M. and Younesian, M., 1389. Research Projects in Air pollution Epidemiology. Iranian Epidemiology Journal . 5, pp. 44-52 [In Persian].

  Goyal, P., Chan, A.T. and Jaiswal, N., 2006. Statistical models for the prediction of respirable suspended particulate matter in urban cities. Atmospheric Environment, 40, pp. 2068-2077 .

  Hamilton, JD., 1994. Time series analysis, Princeton Publications, USA .

  Hosseinpour, A.R., Forouzanfar, M.H., Yunesian, M., Asghari, F., Holakouie Naieni, K. and Farhood, D., 2005. Air pollution and hospitalization due to angina pectoris in Tehran, Iran: A time-series study. Environmental Research, 99, pp. 126-131 [In Persian].

  Ingrisch, M., Sourbron, S., Reiser, M.F. and Peller, M., 2009. Model selection in dynamic contrast enhanced MRI: The Akaike Information Criterion. In Dössel, O. and Schlegel, WC. (Eds.) IFMBE Proceedings 25/IV

  Khosravi Dehkordi, A. and Modarres, R., 1386. Time Series analysis of the daily air pollution in Isfahan from the Petrolium Industry. Mohit shenasi. 33, pp. 33-42 [In Persian].

  Kumar, U. and De Ridder, K., 2010. GARCH modelling in association with FFT-ARIMA to forecast ozone episodes. Atmospheric Environment, 44, pp. 4252-4265.

  Lau, J.C., Hung, W.T., Yuen, D.D. and Cheung, C.S., 2009. Long memory characteristics of urban roadside air quality. Transportation Research Part D, 14, pp. 353-359 .

  Liang, W., Wei, H. and Kuo, H., 2009. Association between daily mortality from respiratory and cardiovascular diseases and air pollution in Taiwan. Environmental Research, 109, pp. 51-58 .

  Liu, P.G., 2009. Simulation of the daily average PM10 concentrations at Ta-Liao with Box–Jenkins time series models and multivariate analysis. Atmospheric Environment, 43, pp. 2104 - 2113 .

  López-Villarrubia, E., Ballester, F., Iñiguez, C. and Peral, N., 2010. Air pollution and mortality in the Canary Islands:a time-series analysis. Environmental Health, 9 .

  Lumbreras, J., Garcia-Martos, C., Mira, J. and Borge, R., 2009. Computation of uncertainty for atmospheric emission projections from key pollutant sources in Spain. Atmospheric Environment, 43, pp. 1557-1564 .

  Masjedi, M.R., Jamaati, H.R., Dokoohki, P., Ahmadzadeh, Z., Alinejad Taheri, S., Bigdeli, M., Agin, K., Ghavam, S.M., Rostiman, A. and Izadi S., 2001. The correlation between air pollution and acute respiratory and cardiac attacks. Pazhoohesh dar pezeshki, 25, pp. 25-33 [In Persian].

  Nasrollhi, Z. and Ghaffari Goolak, M., 2010. Air pollution and its effective factors. Faslnameh Pazhoohesh Eghtesadi, 3, pp. 375-395 [In Persian].

  Quintela-del-Rio, A. and Francisco-Fernandez, M., 2011. Nonparametric functional data estimation applied to ozone data: Prediction and extreme value analysis. Chemosphere, 82, pp. 800-808 .

  Rajarathnam, U., Sehgal M., Nairy S., Patnayak R.C., Chhabra S.K., Kilnani, K.V., R and Committee., HHR, 2011. Time Series study on air pollution and mortality in Dehli. Res Rep Health Eff Inst, Mar, pp. 47-74 .

  Samet, J.M., Dominici, F., Zeger, S.L., Schwartz, J. and Dockery, D.W., 2000. The national morbidity, mortality and air pollution study. Part 1: Methods and Methodologic Issues. Research Report 94 Cambridge, MA, Health Effects institute .

  Sharma, P., Chandra, A. and Kaushik, S.C., 2009. Forecasts using Box–Jenkins models for the ambient air quality data of Delhi City. Environ Monit Assess, 157, pp. 105–112 .

  Wagemakers, E. and Farrell, S., 2004. AIC model selection using Akaike weights. Psychonomic Bulletin and Review, 11, pp. 192-196 .

  Zhang, F., Wang, W., Lv, J., Krafft, T. and Xu, J., 2011. Time-series studies on air pollution and daily outpatient visits for allergic rhinitis in Beijing, China. Science of the Total Environment, 409, pp. 2486–2492 .

  Scientific Journal of School of Public Health and Institute of Public Health Research /85

  Vol. 11, No. 2, Summer 2013

  Forecasting ambient air pollutants by time series models in Kerman, Iran

  Mansouri, F., MS.c. Student, Dept of Environmental Health Engineering, Faculty of Public Health, Kerman Medical University, Kerman, Iran

  Khanjani, N., Ph.D. Assistant Professor, Department of Epidemiology and Department of Environmental Health, Faculty of Public Health, Kerman Medical University, Kerman, Iran - Corresponding author: n_khanjani@kmu.ac.ir

  Rananadeh Kalankesh, L., MS.c. Student, Department of Environmental Health Engineering, Faculty of Public Health, Kerman Medical University, Kerman, Iran

  Pourmousa, R., MS.c. Lecturer, Department of Statistics, School of Mathematics and Statistics, Shahid Bahonar University, Kerman, Iran

  Received: Apr 3, 2012 Accepted: Feb 14, 2013

  ABSTRACT

  Background and Aim: Air pollution is one of the most important problems of big cities in developing countries and can have several negative health effects on humans. Therefore studying these pollutants can help in developing programs for air pollution control. The aim of this study was to estimate and predict the changes of air pollutants in Kerman, Iran.

  Materials and Methods: In this ecological study, data about seven important air pollutants in Kerman including NO, CO, NO₂, NO_x, PM₁₀, SO₂ and O₃ from March 2006 until September 2010 was inquired from the Kerman Province Environmental Protection Agency. Then the data was calculated as averages per month and by incorporating time series models, predictions were done for each pollutant.

  Results: All of the pollutants were steady in Kerman, except CO which is significantly decreasing and PM₁₀ which is increasing. All of the pollutants had a seasonal pattern. Time series models with a 12, 3, 8, 12, 12, 12 and 6 month seasonal pattern were fit for O₃ , SO₂ , PM₁₀ , NO_x , NO₂ , CO and NO consecutively.

  Conclusion: The production of ambient CO is decreasing in Kerman and one reason is probably replacing and retiring old automobiles. However PM₁₀ is increasing in Kerman and in most seasons it is above standard and therefore control initiatives should be implemented.