Simulation of Tehran Air Pollution Dispersion Model in Windy Air Conditions

Document Type : Research Article

Authors

• Aliakbar Shamsipour ¹
• Fahimeh Najibzadeh ²
• Zeynab Hosseinpur ¹

¹ University Of Tehran

² Tehran University

10.22067/geo.v1i4.16451

Abstract

Introduction
Population growth, along with the rapid development in industrial and many urban sectors, and lack of sustainable development approach in urban planning are all caused great changes in environment in form of pollution and ravage. In urban environments, complexity of spaces and urban manmade phenomena as well as the lack of regular and continuous measurement of atmospheric elements and components such as surface fluxes, turbulence intensity, overnight stable layer depth and inversion layer, daily mixed and boundary layer and energy balance components which are generally the input of dispersion models have made uncertain mechanisms in dispersion of pollutants over Tehran. One of the important aspects in the study of air pollution is how the pollutants disperse from sources of emissions. Wind direction, turbulence conditions and fluxes in near surface atmosphere are the most important climatic factors that affect dispersion pattern and distribution of air pollutants leaving from emission sources.
Study Area
Tehran having a population of about 8,300,000 and a 15 million-plus metropolitan area is Iran's largest city and urban area, and one of the largest cities in southwest Asia. Tehran (35° 42′ N, 51° 25′ E) covers an area of 750 km2 and is situated in a semi-enclosed basin south of the Alborz Mountains. Its location for a big city is unusual, since it is not near a river or even close to the sea. The average annual rainfall is approximately 230 mm, with most precipitation falling in autumn and winter months. Due to high elevation (approximately 1140 m), aridity and latitude, the city experiences four seasons.
Climate can be extremely hot in summer (with mid-day temperatures ranging between 30 oC to 40 oC), and cold in winter when nighttime temperatures can fall below freezing point. Local precipitation is absent for six months of the year on the low-lying areas.
Tehran suffers from severe air pollution and the city often covers by smog making breathing difficult and causing widespread pulmonary illnesses. It is estimated that about 27 people die each day from pollution-related diseases. According to local officials, 3,600 people died in a single month due to the hazardous air quality. 80% of the city's air pollution is due to cars. The remaining 20% is due to industrial pollution. Other estimates suggest that motorcycles alone account for 30% of air and 50% of sound pollution in Tehran. Tehran is bound in the north by the massive Alborz mountain range that is stopping the flow of the humid Caspian wind. As a result, thermal inversion that traps Tehran's polluted air is frequently observed.
Material and Methods
The methodology is based on undertaking literature review to develop theoretical foundations and explanation of the research method. In this research, three-hourly wind velocity and wind direction data and air temperature of 2006 were obtained from Geophysics, Mehrabad and the Shemal-e-Tehran weather stations (Table 1). Also atmospheric gridded data obtained from NCEP/NCAR reanalysis dataset for surface and 700 hPa levels for prepared synoptic maps using GrADS software (Table 2). Mehrabad upper level atmospheric data has been taken from the University of Wyoming are used to identify effective thermodynamic indices on intensified or mitigation air pollution in Tehran. Moreover, two site emission data available in SO2 of chimney exits south of Tehran including Tehran oil refinery source point and thermal power plant source point were used (Table 3).
In this research, using EDMF thermodynamic index available in The Air Pollution Model (TAPM), the dispersion pattern of pollutants in the air near the surface were studied under unstable weather and windy conditions over Tehran. Three-hourly meteorological data from three stations in Tehran were used to select two case studied. So two days with significant wind in all three weather stations were selected that they also include days of warm and cold periods. Then prevailing weather condition in synoptic-scale weather systems were evaluated by providing surface and upper level weather maps. Emission data from two point sources of Power plant and Tehran’s oil refinery in south of Tehran used as the input to model. Atmospheric model with three nested cells with 4, 3.9, and 3 Km dimensions and pollution input with nets respectively 1000, 975, and 750 m were imported to model software. Pollution input is defined based on Eulerian and Lagrangian dispersion models, and their outputs were calculated for the innermost grid.
Results and Discussion
In September 27 and March 21, a low pressure is seen in the surface in southeast of Caspian Sea and a high pressure is located over northwestern Iran. Barometric trough with contours of 1008 to 1011 hPa of it is drawn to Tehran area. Wind vectors represent weak surface western wind over the area. At 4 AM local time, the values of the vertical velocity (Omega) are positive which represent the stable conditions; however, at 4 PM local time omega becomes negative up to the level of 425 hPa and represents the unstable conditions (fig 3 and 4). Based on surface synoptic map in March 21, wind vectors represent high velocity and westerly direction. At 4 AM local time, vertical velocity is positive that indicates descending conditions, but in 4 PM it becomes negative and represents ascending condition (Fig 5 and 6).
The result of model output for both dates show different atmospheric conditions in different time scales. Analysis of the Probability Density Function (PDF) for the summer wind condition (September 27) show that in 66% of winds in this day show 2 and 3 m/s velocity, and more than 53% of winds blow from northwest. Figures 7 to 11 show that the instability and turbulence conditions intensify in daytime and reduce at night. Figures 12 and 13 show the differences of spatial dispersion of pollutants in the daily average with (a) Eulerian and Lagrangian dispersion models for summer under windy conditions.
Analysis of windy conditions in winter (March 21) by PDF is shown in figure (15). It shows westerly winds dominant and in this time more than 85% of winds velocity is more than 3 m/s. During winter condition, dispersion of pollutants in both Eulerian and Lagrangian models are the same in terms of density and wind direction of dispersions (fig 19).
Conclusion
Simulation results indicate that in windy conditions of warm period, in south of Tehran with dry and hot air and soil, turbulent kinetic energy increases due to wind velocity and unstable atmospheric conditions and horizontal transfer of pollutants is associated with upward movement. Conversely, in the cold period, wind causes to increase the cooling air, so it result to decrease of turbulent kinetic energy in the atmosphere, hence pollutants are mostly transport horizontally.

Keywords

• Air Pollution
• thermodynamic indeces
• TAPM
• EDMF
• Tehran

---

References

Aliakbari Bidokhti, A.A., Shareipoor, Z. (2009). “Meteorological conditions top atmospheric and acute situation air pollution (case study: Tehran)”, Journal of Environmental studies, 35 (52),1-14.

Dosio, A. Arellano, J.V-G.D. Holtslag, A.A.M. (2005). “Relating Eulerian and Lagrangian Statistics for the Turbulent Dispersion in the Atmospheric Convective Boundary Layer”, Meteorology and Air Quality Section, Wageningen University, Wageningen, Netherlands.1175-1191.

Etabi. F., Abaspour, M., karbsi, A.R. (2007). “Modeling of emissions suspended particles using ADMS-urban model”, Environmental Science and Technology. The ninth course, The first issue, 1-15.

Gaednia, B., Mirbakhsh, M., Haghshenas, A. (2009). Methods of laboratory analysis of water, soil, air, Translation, Author: P.K. Kutpa, The Green Wave publisher.

Gasami T., Aliakbari Bidokhti, A.A., Sedaghatkerdar A, Sahraiean F. (2007). “Study of Synoptic conditions at several critical period the air pollution in Tehran”, Environmental Science and Technology, 9 (3) , (Serial34), 229-238.

Gasami T., Aliakbari Bidokhti, A.A. Sedaghatkerrdar, A., Sahraeian, F. (2010). “Assessing vertical changes of potential temperature at several critical period of air pollution in Tehran”. Environmental Science and Technology, 11(3), 13-23.

Ghanbari, H.A, Azizi, Gh. (2009). “Numerical simulation of air pollution in Tehran based on wind patterns”. Journal of Physical Geography Research, 68, 15-32.

Giasadin, M. (2006). Air pollution sources, effects and control, Tehran University Pub.

Givi, A., Sabetghadam, S. (2006). “Study of sensitivity of the meso-scale model MM5 schemas using MRF and Eta-Mellor-yamada for estimate the depth of the urban mixed layer”. Conference on Numerical Weather Prediction.

Hoseinpoor, z. (2011). Synoptic analysis of air pollution in Tehran (emphasis on suspended particles PM-10). MS Thesis, Department of Geography, Tehran University.

Hurley, P. (2005a). “TAPM V3—model description and verification”. Clean Air 39:32–36.

Hurley, P. (2005b). “The air pollution model (TAPM) version 3. Part 1: Technical description”. CSIRO Atmospheric Research Technical Paper No. 71.

Hurley, P. (2007). “Modelling mean and turbulence fields in the dry convective boundary layer with the eddy-diffusivity/mass-flux approach”. Boundary-Layer Meteorol 125:525–536.

Hurley, P. (2008). “TAPM V4. Part 1: Technical Description”, CSIRO Marine and Atmospheric Research Paper No. 25.

Hurley, P. (2008). “TAPM V4. User Manual”, CSIRO Marine and Atmospheric Research Internal Report No. 25.

Jakob, C. Siebesma, A. (2003). “A new subcloud model for mass-flux convection schemes: Influence on triggering, updraft properties and model climate”. Mon Wea Rev 131:2765–2778.

Lashkari, H. (2002). “Routing Sudan low pressure systems to Iran”. Journal of Humanities 6 (2 serial 25), 133-156.

Luhar, A.K. Hurley, P.J. (2003). “Evaluation of TAPM, a prognostic meteorological and air pollution model, using urban and rural point-source data”. Atmospheric Environment 37. 2795–2810.

Madadi, H., Ashrafzadeh, M.R., Najmi, N., Sheikhzadeh, B. (2009). “Modeling of NOx and CO emissions resulting from the combustion of natural gas combined cycle power plant in Semnan”. Second International Symposium on Environmental Engineering.

Nielinger, J. Rainer, R. Höfl, H.C. Kost,W. (2005). “Lagrange VERSUS Eulerian Dispertion Modeling Comparsion For Investigation Concerning Air Pollution Caused By Traffic”. 9th Int. Conf. on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes.

Peng Wang, H.M. (2011). “Random-walk model simulation of air pollutant dispersion in atmospheric boundary layer in China”. Environ Monit Assess. 172: 507–515.

Rnjbar Saadatabadi, A., Aliakbari Bidokhti, A.A., Sadeghi Hoseini, S.A. (2005). “Case study of the heat island of Tehran and it's numerical simulation”. Journal of Physics Earth and Space, 31(1) 63-78.

Seifolahi, M. (2002). Effects of meteorological parameters on air pollution in Tehran, BS Thesis, Natural Resources Engineering-Environmental Engineering, University of Allama Muhaddith Nouri (ra).

Shareipoor, Z. (2009). “Assessing changes Seasonal and daily air pollutants and its relationship with weather parameters”, Journal of Physics Earth and Space 2,119

Shafipour, M. (2000). Compilation of information items based model to study air pollution in cities, Institute of Meteorology and Atmospheric Sciences.

Siebesma, A.P. Teixeira, J. (2000). “An advection-diffusion scheme for the convective boundary layer: description 1D results”. 14th symposium on boundary layer turbulence, Aspen, Colorado, USA, pp 133–136.

Soares, P.M.M. Miranda , P.M.A. Teixeira, J. (2007). “An Eddy diffusivity/Mass-flux Boundary Layer Parameterization Based on the TKE Equation: a Dry Convection Case Study”. Fisica de la Tierra, 19 :147-161.

Soares, P.M.M. Miranda, PM.A. Siebesma , A.P. Teixeira ,J. (2004). “An eddy-diffusivity/mass-flux parameterisation for dry and shallow cumulus convection”. Quart J Ray Meteorol Soc 130:3365–3383.

Soares, P.M.M. Miranda, PM.A. Siebesma, A.P. Teixeira, J. (2004). “An eddy-diffusivity/mass-flux parameterisation for dry and shallow cumulus convection”. Quart J Ray Meteorol Soc 130:3365–3383.

Teixeira, J. Siebesma, A.P. (2000). “A mass-flux/K-diffusion approach to the parameterisation of the convective boundary layer, Global model results”. 14th Symposium on Boundary Layer Turbulence, 7–11 August 2000, Aspen, Colorado, USA, pp 231–234.

Vakili, Gh.A., Aliakbari Bidokhti, A.A. (1999). Observations of the structure of atmosphere turbulence and its application to the problem of air pollution distribution, MS Thesis, Geophysics Institute of University of Tehran.

Zawar-reza, p. Appelhans, T. Gharaylou, M. Shamsipur, A. (2010). “meso scale control on particulate matter pollution for mega city in a semi-arid mountainous environment”, environment and pollution, 41.

Zawar-Reza, P. Simon, K. Jamie, P. (2005). “Evaluation of a year-long dispersion modelling of PM10 using the mesoscale model TAPM for Christchurch”, New Zealand. Science of the Total Environment 349. 249– 259.