Document Type : Research Paper


1 Associate professor, Dept. of RS & GIS, University of Tabriz

2 M.A Student in RS & GIS, Faculty of Geography, University of Tehran, Tehran, Iran


Extended Abstract
Air pollution is a major problemin large industrial cities and affects the life of urban citizens.Due to population growth,significant increase in the number of motor vehicles as well as the concentration and accumulation of industries, Tehran is in the grip of an air pollution crisis. Previous studies have indicated that once every three days, Tehran faces increased levels of pollutants and air pollution.Ozone is produced through photochemical reactions between hydrocarbons in carexhaust and nitrogen oxides in the atmosphere. Producedthrough reactions between atmospheric pollutants,this pollutant is not primarily released into the environment by a specific sourceand thus, it is called a secondary pollutant.Concentration of ground-level ozone has doubled over the last century.Exposure to this pollutant is very harmful for human health, especially those who exercise outdoors because it severely damages their lungs.Therefore, increased concentration of pollutants has become a major challenge for the management of metropolises such as Tehran. Having information about the spatial distribution of pollutants allows urban managers to take appropriate measures and reduce pollution related risksfor areas and people in danger.Due to excessive concentration of industries and factories inside the geographical boundaries of Tehran, along with its specific geographical condition, topography and climate, Tehran has become one of the seven most polluted cities of the world.The present study seeks to model the spatial and temporal changes of ozone and nitrogen oxidesin Tehran metropolis.
Methods and Materials
In this cross-sectional descriptive study, spatial analysis of pollutants (ozone(O3) and nitrogen oxides)is performed based on data measured by Tehran air quality monitoring stations for the 2008, 2009, and 2018reference periods. For 2008 reference period, data were collected on a monthly basisfrom the website ofTehranair quality control company,while for 2008 and 2018, data were collected annually. Arc GIS 10.5 released by ESRI was usedfor spatial analysis, and Microsoft Excel 2013 was usedto drawdiagrams and perform other analysis.Inverse distance weighting (IDW) model was used for spatial analysis of ozone and nitrogen oxidesin Tehran metropolitan area inthe three reference periods. Finally, the reference periods were compared and the most polluted one was zoned using the IDW model. In the second method, Google Earth Engine was used to model the spatial distribution of ozone and nitrogen oxides. In this method, Sentinel-5p NRTI O3: Near Real Time Ozone product was used to model ozone and nitrogen oxideson an annual basis (11/01/2018 and 28/03/2020).This is the date in which sentinel has started monitoring ozone and nitrogen pollutants. As the most important product available for measuring the average rate of change,column of ozone and nitrogen oxides’ changes in the atmosphere (O3_Column_number_density) was used in this study. Annual average concentration of ozone and nitrogen pollutants in Tehran was compared with the Sentinel-5 product in Google Earth Engine.
Results & Discussion
In 2018, average annual concentration of ozone and nitrogen oxides in studied stations equaled 12.7 ppb. The accuracy of modeling was also calculated using the coefficient of determination(R2) or coefficient of detection (CD). The average annual concentration of ozone and nitrogen oxides in 2008 was also measured for all air quality control stations to determine their correlation.All independent variables used in this model had an acceptable level of significance (P.> 0.001).In other words, all parameters improved the performance of the model in estimating the concentration of ozone and nitrogen oxidespollutants. The model was developed and R2 rate for 2008 monthly average equaled 0.9188%.The coefficient of determination (R2)for ozone and nitrogen oxides’ concentration in 2009 equaled 0.9134%, but the annual average of 2018showed a much lower R2which equaled 0.476%.It should be noted that not all stations have been evaluated in this study, because the concentrations of ozone and nitrogen oxidesin some air quality monitoring stations equaled zero. Thus, only stations showing a greater than zero value have been used in this study.
As previously mentioned, various models have been proposed for modeling the concentration of ozone and nitrogen oxides, each showing a different result. In the present study, the inverse distance weighting (IDW) model was used for three reference periods (2008, 2009 and 2018), and the concentrations of ozone and nitrogen oxides in the atmosphere were also modeled using the variables related to air quality monitoring stations.Ozone concentration modeled by inverse distance weighting method was compared with the average annual change of ozone concentration derived from Sentinel-5 product in Google Earth Engine. Results obtained from the concentration of ozone and nitrogen oxides in the three reference periods were investigated using thecoefficient of detection.The resulting coefficient of determination for ozone concentration in 2008 and 2009 equaled 0.9188% and 0.9134%, respectively. The lowest coefficient ofdetermination for ozone and nitrogen oxidesconcentration was obtained for 2018 which equaled 0.476%. Regarding the spatial distribution of ozone and nitrogen oxides in 2008, the highest concentrations were observed inMasoudiyeh, Punak, Rose Park and Aqdasiyeh stations, and the highest concentration of nitrogen oxides was observed in District4, Crisis Management Headquarterand Sadr Expressway(District 3). In 2009,the station in Rose Park (District 22) showed the highest concentration of ozone and nitrogen oxides.In 2018, IDW modelling and spatial distribution of ozone and nitrogen oxidesshowed a different result. In this reference period, the station in district 4 received the highest annual concentration of ozone and nitrogen oxides, and north eastern areas ofTehran was regarded as the most polluted areas based on the concentration of these pollutants. But stations in16th, 19th and 20th districts and Masoudieh station (15th district) had the lowest annual concentration of ozone and nitrogen oxides. In general, it can be said that spatial modeling with Sentinel-5 product has been able to model the concentration of ozone and nitrogen oxides inall stationsof Tehran on a pixel by pixel basis.


Main Subjects

1- اردکانی، چراغی، قاسم‌پوری؛ سهیل، علی، محمد (1385). “تعیین کیفیت بهداشتی هوای تهران در سال 1383 با استفاده از شاخص کیفیت هوا.” علم و تکنولوژی محیط زیست (84): 33 - 38.
2- افندی‌زاده، رحیمی، علی (1387). ارزیابی اثرات آلودگی هوا ناشی از سیستم حمل و نقل در تهران. چهارمین کنگره ملی مهندسی عمران، دانشگاه تهران 351. ص  56- 47.
3- اکبری، فرح‌بخشی؛ محمد. (1393). تحلیل و ردیابی خطر گرد و غبار در سال‌های اخیر در استان کرمانشاه. مجله بین‌المللی تحقیقات محیطی، 9 (2): 673-682.
4- رحیمی، رحیمی، جلیل، سعید. (2010). بررسی تأثیرات توسعه فضایی شهری بر آلودگی هوای کلان‌شهر تهران. چهارمین کنفرانس زمین شناسی فنی زمین شناسی تهران، ص 124- 115.
5- رفیعی‌پور، محمد (2013). "مقایسه کارایی شبکه‌های مختلف عصبی در پیش‌بینی مکانی - زمانی آلودگی هوای تهران". پایان‌نامه کارشناسی ارشد.
6- شاکر خطیبی، محمدی، ظروفچی بنیس، شاکری،  فاتحی‌فر، محمودیان؛ محمد، ناهیده، خالد، مسعود، اسماعیل، امیر (1394). تحلیل ارتباط بین ازن سطحی و اکسید نیتروژن جوی در هوای شهر تبریز. نشریه مهندسی عمران و محیط زیست، 9 (1): 107 - 114.
7- شرعی‌پور، زهرا. (1396). بررسی غلظت آلاینده ازن سطحی و ارتباط آن با اکسید نیتروژن جوی و دمای هوا. ششمین همایش ملی مدیریت آلودگی هوا و صدا تهران، ص 12- 35.
8- شفیع‌پور، مهدی (1380). الگوی تهیه اقدام اطلاعاتی پایه برای مطالعه آلودگی هوا شهرها، پژوهشگاه هواشناسی و علوم جوی.
9- صفوی، علیجانی؛ یحیی، بهلول (1385). بررسی عوامل جغرافیایی در آلودگی هوای تهران. تحقیقات جغرافیایی، 58: 99 - 112.
10- قنبری، حیدر  (2003). نقش عوامل طبیعی در آلودگی هوای تهران، پایان‌نامه کارشناسی ارشد، دانشکده علوم زمین، دانشگاه شهید بهشتی.
11- کاووسی، سفیدکار، علوی مجد، رشیدی؛ امیر، ریحانه، حمید، یوسف (2013). "تجزیه و تحلیل فضایی آلاینده‌های CO و PM10 در شهر تهران" مجله علوم پیراپزشکی.4. صص 41-50.
12- کاووسی، سفیدکار، علوی مجد ، ایمان‌زاد، نورمرادی؛ امیر، ریحانه، حمید، معصومه، حشمت‌اله (2013). "تجزیه و تحلیل مکانی آلودگی هوا در تهران با استفاده از رگرسیون اتولوژیستی، رگرسیون خودمتمرکز و کریجینگ اشاره‌گر، مجله پژوهشی دانشگاه پزشکی ایلام ، 21  (7): صص 206-214.
13- هاشمی، فرهاد. (1390). بررسی و ارزیابی الگوریتم‌های مونت کارلو و شبکه عصبی برای پیش‌بینی آلودگی هوا در محیط یک سیستم اطلاعات مکانی زمانمند. کارشناسی ارشد، دانشکده مهندسی نقشه‌برداری، دانشگاه صنعتی خواجه نصرالدین طوسی رشته نقشه‌برداری سیستم اطلاعات مکانی.
14- Collins WJ, Stevenson DS, Johnson CE, Derwent RG. (2015). The European regional ozone distribution and its links with the global scale for the years 1992 and Atmospheric Environment. 2000; 34(2):255-67.
15- Dabiri M. (2008). Pollution of the air. Tehran: Union p. 399. (In Persian).
16- Dai F, Zhou Q, Lv Z, Wang X, Liu G. (2014). Spatial prediction of soil organic matter content integrating artificial neural network and ordinary kriging in Tibetan Plateau. Ecol Indicators; 45(0):184-94.
17- Devlin RB, Duncan KE, Jardim M, Schmitt MT, Rappold AG, Diaz-Sanchez D. (2012). Controlled exposure of healthy young volunteers to ozone causes cardiovascular effects. Circulation.; 126(1):104-11.
18- Fernando, H.J., Mammarella, M., Grandoni, G. Fedele, P., Di Marco, R., Dimitrova, R. & Hyde, P., (2012). Forecasting PM 10 in Metropolitan Areas: Efficacy of Neural Networks, Environmental Pollution 163, and PP 67-62.
19- Fuhrer J, Val Martin M, Mills G, Heald CL, Harmens H, Hayes F, et al. (2016). Current and future ozone risks to global terrestrial biodiversity and ecosystem processes. Ecology and Evolution; 6(24):87-99.
20- Gong G, Mattevada S, O’Bryant SE. (2014). Comparison of the accuracy of kriging and IDW interpolations in estimating groundwater arsenic concentrations in Texas. Environ Res; 130(0):59-69.
21- Hidy G. (2000). Ozone process insights from field experiments–part I: overview. Atmospheric Environment; 34(12):200-22.
22- Hollaway MJ, Arnold SR, Challinor AJ, Emberson LD. (2012). Intercontinental trans-boundary contributions to ozone-induced crop yield losses in the Northern Hemisphere. Biogeosciences; 9(1):271-92.
23- Hosoi, S., Yoshikado, H., Gaidajis, G., Sakamoto, K. (2011). Study of the relationship between elevated concentration of photochemical oxidants and prevailing meteorological conditions in the North Kanto area, Japan, Water, Air, & Soil Pollution, 215, 105-116.
24- Ibarra – Berastegi, G., A. Elias, A. Barona, J. Saenz, A. Ezcurra and j. Diaz de Argandona (2008). “From diagnosis to prognosis for forecasting air Pollution using neural networks: Air pollution monitoring in Bilbao.” Environmental Modelling & Software 23 (5): 622 – 637.
25- Kelly. 14 nitrogen to humans of exposure following fluid lavage lung in kinetics: DOI. 5-1700):1 Pt 6(154; 1996. Med Care Crit Respir J Am. dioxide 8970358.
26- Klein PM, Hu X-M, Xue M. (2013). Impacts of Mixing Processes in Nocturnal Atmospheric Boundary Layer on Urban Ozone Concentrations. Boundary-Layer Meteorology; 150(1):107-30.
27- Liu, P.G., Simulation of the daily average PM10 concentrations at Ta-Liao with Box– Jenkins time series models and 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.
28- Liu S, An N, Yang J, Dong S, Wang C, Yin Y. (2015). Prediction of soil organic matter variability associated with different land use types in mountainous landscape in southwestern Yunnan province, China. CATENA; 133(0):137-44.
29- Li X, Rappenglück B. (2014). A WRF–CMAQ study on spring time vertical ozone structure in Southeast Texas. Atmospheric Environment; 97:363-85.
30- Mishra, D. and Goyal, P. (2016). Neuro-Fuzzy Approach to Forecast NO2 Pollutants Addressed to Air Quality Dispersion Model over Delhi, India. Aerosol and Air Quality Research, 16: 166–174.
31- Robinson, D.P., Lloyd, C.D., McKinley, J.M. (2013). Increasing the accuracy of nitrogen dioxide (NO2) pollution mapping using geographically weighted regression (GWR) and geostatistics. International Journal of A applied Earth Observation and Geoinformation. VOL. 21. pp. 374–383.
32- Schurmann. G.J, Algieri. A, Hedgecock. I.M., Manna. Pirrone. G, N, Sprovieri. F. (2009). Modelling local and synoptic scale influences on ozone concentrations in a topographically complex region of Southern Italy, Atmospheric Environment 35, pp 4424–4434.
33- Stamenković, L.J., Perić -Grujić, A.A., Pocajt, V.V., Ristić, M.D. and Antanasijević, D.Z. (2016). Prediction of nitrogen oxides emissions at the national level based on optimized artificial neural network model. Air Quality Atmosphere and Health, 10 (1): 15-23. DOI: 10.1007/s11869-016-0403-6.
34- Sun, W., Zhang, H., Palazoglu, A., Singh, A., Zhang, W., Liu, S. (2013). Prediction of 24-hour-average PM2.5concentrations using a hidden Markov model with different emission distributions in Northern California, Science of the Total Environment, 443, (pp. 93–103).
35- Tobler, W.R. (1970). A computer movie simulating urban growth in the Detroit region, Economic Geography VOL. 46. Pp.234-240.
36- World Health Organization. (2006). Air Quality Guidelines: Global Update 2005: Particulate Matter, Ozone, Nitrogen Dioxide, and Sulfur Dioxide. World Health Organization.
37- Xiao. H, Lei. T, Hulihing. Z, Jie Yu, Mengment. H, Nengbin. X, Jingjing. Z, Feizhong. Q, Jiayong. F (2016). Characteristics of surface ozone and nitrogen oxides at urban, suburban and rural sites in Ningbo, China, Journal Atmospheric Research, 34, PP 236-269.
38- Zamir, R., Iikani, M. (2001). Pollution of Tehran Third Iranian National Energy Conference. Tehran’s. 26-18. (In Persian).