A case study on air pollution diffusion using data from the Bushehr meteorology tower

Document Type : Research Article


1 Associate Professor, Atmospheric Science and Meteorological Research Center (ASMERC), Tehran, Iran

2 Forecasting adviser, Iran Meteorological Organization, Tehran, Iran


A serious problem which threatens life in metropolises is air pollution released in boundary layers in local and regional scales due to human activities. Pollutants accumulate in specific meteorology conditions in cities. Air stagnation, temperature inversion, cold air damming, topography, mountain and valley winds, urban buildings wakes and atmospheric stability are metrological factors. These conditions are recorded in most air pollution episodes in the world. Many researchers have used Gaussian distribution model for analyzing the manner of pollutants distribution in long term. In these works not only Gaussian model for distribution and deposition has been analyzed but the meteorological conditions for running the model and estimation of coefficients of model have also been analyzed.
In this article the potential air pollution in Bushehr city is studied by the Gaussian diffusion model to calculate the horizontal and vertical standard deviations of the model outputs, using the Hosker-Smith formula. The plume rise height is calculated by Briggs method and the height of the mixed layer by the Heffter algorithm. For the model run we used 2016 archived data from the 100m height Bushehr meteorology tower. In this research for winter the months of December of 2015, January and February of 2016 are considered, for spring, March, April and May, for summer June, July and August and for fall, September, October and November are considered. The annual covers from January to December of 2016.
Regarding the presented conditions, Gaussian distribution model run for a hypothetical point source in open rural area. The process is as following:
1- Data of direction and speed of wind in height of 10 to 100 meters are analyzed in different days of 2016 and December of 2015 and after omitting doubtful data, the average values of daily, monthly, seasonal and annual wind field are extracted.
2- These data were used in calculation of average value of multiplying classified wind speed by normalized coefficient instability classes.
3- Data of radiation and temperature in different levels of meteorology tower are analyzed and after omitting the noises and attaining adequate accuracy, vertical gradient of temperature was calculated and regarding the wind speed, the stability classes for day and night were calculated.
4- Vertical and horizontal standard deviations were calculated based on Hosker-Smith equations.
5- Height of plume rise was calculated for estimating effective height by using chimney inner diameter, gas discharging velocity and its temperature in different stability classes according to Briggs method.
6- Mean length of mixed layer was calculated by using long term data of nearest upper air station to Bushehr meteorology tower.
Analysis of wind field at the height of 100 meters of Bushehr meteorology tower showed that in spring the abundance of northwesterly wind is 19.5%, north wind 11.4% and northeasterly wind is 10.4%. In summer abundance of north- northwesterly wind is 15.6% and northwesterly wind is 15.3%. In fall the abundance of northerly wind is 17.4%, northwesterly 14.5%, north- northwesterly 14.5% and north-northeasterly wind is 12.3%. In winter abundance of northerly wind is 31.6%, north- northwesterly 17.2% and north-northeasterly is 15.6%. The annual abundance in 2016 of northerly wind is 18.3% and north-northeasterly wind is 11%.
The vertical and horizontal standard deviations are estimated in different stability and instability classes. Calculating the horizontal standard deviation by different methods in all classes, does not make significant difference is. Calculation of vertical standard deviation by Hosker-Smith method has a significant difference in all classes. It is very similar to Briggs method in very severe instability classes and by increasing the distant from pollutant source, its quantity slightly increases. In stability classes, this method gives higher values in comparison with the others.    
The model results show that hypothetical pollutants distribute in winter toward south, in spring southeast and Southwest, in summer to southeast and north and in fall to southeast, south and southwest. The annual distribution is toward south and southeast. The maximum values of this quantity in spring, summer and fall spread up to 2km along the mentioned directions but the maxima in summer spreads up to 3km from the source. Annual maxima do not extend more than 2 km from the source.
Analyzing the results and adjusting them with the results of 100-meter meteorological tower seasonal wind rose, results show that, how perfectly simplified Gaussian model depicts the manner of pollutants distribution. The model results indicate that the hypothetical pollution dispersion in winter time around the Bushehr meteorology tower is toward south, while in summer the dominant dispersion is toward southeast and north. The difference in dispersion direction between summer and winter is due to stronger sea breeze in summer. Some northward emission may exist due to southerly winds in the annual wind regime.


Main Subjects

فیضی‌نژاد، م.، و خاموشی، س.، 1383، مدلسازی پخش جوی در نیروگاه هسته‌ای بوشهر، م. علوم و فنون، 31، 42-35.
فیضی‌نژاد،م.، ملکوتی، ح.، صدری نسب، م.، قادر،س.، و یازجی، د.، 1397، شبیه‌سازی پاشش جوّی و ارزیابی دُز با کاربست مدل جفت‌شده WRF-HYSPLIT برای نیروگاه بوشهر، م. ژئوفیزیک ایران، 12(1)، 50-19.
کاویانی، ف.، معماریان، م.،ح.، و اسلامی کلانتری، م.، 1396، شبیه‌سازی انتقال، پخش جوی و نهشت آلاینده‌های هسته‌ای رها‌شده از یک حادثۀ فرضی در نیروگاه بوشهر، م. فیزیک زمین و فضا، 43(3)، 650-635.
مرادی، م.، رضازاده، پ. و رنجبر سعادت آبادی، ع، 1391، بررسی الگوی انتشار آلاینده‌ها در نقاط فرضی تهران به‌کمک مدل گوسی، نشریه پژوهش‌های اقلیم شناسی، 3(10)، 89-103.
ملکوتی، ح.، محمدیها، ا.، و فیضی‌نژاد، م.، 1395، شبیه‌سازی عددی سالانه پخش جوی رادیو نکلوئیدها و طراحی شبکه پایش بهینه در واحد یکم نیروگاه بوشهر، م. محیط شناسی، 42(3)، 487-475.
Briggs, G. A., 1972, Discussion on chimney plumes in neutral and stable surroundings. Atmospheric Environment, 6, 507-510.
Briggs, G. A., 1984, Plume Rise and Buoyancy Effects. Chapter 8 in Atmospheric Science and Power Production. Publication no. DOE/TIC-27601, U.S. Dept. of Energy, Washington, D.C.
Carbon,B.,2004,Good practice guide for atmospheric dispersion modeling. Ministry for the Environment press, New Zealand, pp 152.
USEPA, 1977, Improvements to single-source model. Volume II: Testing and evaluation of model improvement, EPA-450/3-77-003b. US Environmental Protection Agency: North Carolina.
USEPA, 1985, User's Guide for the Industrial Source Complex Dispersion Models, Volume II Description of Model Algorithms. EPA-454/B-95-003b. U.S. Environmental Protection Agency, Research Triangle Park, NC.
USEPA, 1995, Quality assurance handbook for air pollution measurement systems. Vol. IV, Meteorological Measurements. EPA/600/R-94/038d, U.S. Environmental Protection Agency, Research Triangle Park, NC.
USEPA, 2000, Meteorological Monitoring Guidance for Regulatory Modeling Applications. United States Office of Air Quality EPA-454/R-99-005 Environmental Protection Planning and Standards Agency Research Triangle Park, NC 27711 February 2000.
Heffter, J. L., 1980, Transport layer depth calculations. Second Joint Conference on Applications of Air Pollution Meteorology, New Orleans, LA (1980).
Hosker,R.P.,1973, Estimates of dry deposition and plume depletion over forests any grasslands,in Physical Behavior or Radioactive Contaminants in the Atmosphere Symposium Proceedings,Vienna,International Atomic Energy Agency, Vienna, 291-308.
Pasquilll, F., 1961, The Estimation of the Dispersion of Windborne Material. Meteorol. Mag. 90, 34-49.
Pasquilll, F, 1974, Atmospheric Diffusion. Ellis Horwood Limited, Chichester. ISBN 0 85312 015 3.
Sugiyama, G., Gowardhan, A., Simpson, M. and Nasstrom, J., 2014, Deposition velocity methods for DOE site safety analyses. U.S. Department of Energy by Lawrence Livermore National Laboratory.
Turner, D. B., 1969, Workbook of Atmospheric Dispersion Estimates. Publication no. 999-AP-26, U.S. Department of Health, Education and Welfare.
Turner, D. B., 1979, Atmospheric Dispersion Modeling: A Critical Review. J. Air Poll. Control Assoc. 29, 502-519.
Turner, D. B, 1994, Workbook of Atmospheric Dispersion Estimates: An Introduction to Dispersion Modeling. 2nd Ed. CRC Press, London. ISBN 1 56670 023 X.
Martin, D. O., 1976, Comment on “The Change of Concentration Standard Deviation with Distance”, J. Air Pollut. Control Assoc ., 26:145-147.
Smith, F. B., 1972, A scheme for estimating the vertical dispersion of a plume from a source near ground level, in procs. Third Meeting of the Expert Panel on Air Pollution Modelling, NATO-CCMS-14,NATO, Brusselse.
Zannetti, P., 1990, Air pollution modeling, theories, computational methods and available software,New York,NY:Van Nostrand Reinhold,1990.