|Year : 2016 | Volume
| Issue : 2 | Page : 77-82
Dispersion Modeling of Total Suspended Particles (TSP) Emitted from a Steel Plant at Different Time Scales Using AERMOD View
Mehrshad Bajoghli, Maryam F Abari, Hadi Radnezhad
Department of Environmental Sciences, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran
|Date of Web Publication||29-Sep-2016|
Maryam F Abari
Department of Environmental Sciences, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan
Source of Support: None, Conflict of Interest: None
Introduction: One of the main challenges of this modern life is air pollution and industry is the major producer of pollutions in the environment. In this regard, air quality monitoring and assessing exceedance of air quality standards around an area or industrial plant can be a useful method in order to control and establish limits for pollutant sources. Air dispersion models could be the simplest and the most effective way for monitoring and evaluating the pollutant concentrations as well as the impact of each source on the air quality of a given area, and also can be applied for adopting management approaches and appropriate strategies to prevent and reduce air pollution. Aim: In this study, by applying AERMOD developed by U.S. Environmental Protection Agency (EPA) and recommended as one of the preferred and advanced models, dispersion of total suspended particles (TSP) emitted from electric arc furnace chimney of a steel plant in Isfahan was simulated. Materials and Methods: In this study, AERMOD View is run within an area with 30 km × 30 km extent (regional scale) and 2000 m network distance (961 grid points) for 1, 3, 8, 12, 24-h time averages and monthly and annual periods, and then maximum ground level concentrations (GLC) compared with EPA and Iran clean air standards to assess the exceedance of this pollutant. Results: Results revealed that simulated concentrations for 24-h average and annual period are far below the threshold limits of both standards. Moreover, the highest concentrations of TSP took place in a different direction with prevailing winds where there are no inhabitants. However, the cumulative impact of such activities must be considered. This study also highlights the effectiveness of bag filter systems in reducing particle matter emissions from industrial units.
Keywords: AERMOD, air quality, dispersion, emission, simulated, total suspended particles (TSP)
|How to cite this article:|
Bajoghli M, Abari MF, Radnezhad H. Dispersion Modeling of Total Suspended Particles (TSP) Emitted from a Steel Plant at Different Time Scales Using AERMOD View. J Earth Environ Health Sci 2016;2:77-82
|How to cite this URL:|
Bajoghli M, Abari MF, Radnezhad H. Dispersion Modeling of Total Suspended Particles (TSP) Emitted from a Steel Plant at Different Time Scales Using AERMOD View. J Earth Environ Health Sci [serial online] 2016 [cited 2020 Feb 20];2:77-82. Available from: http://www.ijeehs.org/text.asp?2016/2/2/77/191399
| Introduction|| |
The present-day human and the environment, which he lives in, is becoming polluted more and more. The anthropogenic contributors to air pollution are: emission from industry, farming, transportation, and house heating while natural contributors are mainly: forest fires, volcanic ash, and dust. Air pollution is the cause of health problems affecting millions of lives worldwide. The statistics released by World Health Organization (WHO) are fighting when millions of deaths can be attributed to air pollution. Over the past decades, many serious studies have been conducted about long-time exposure to outdoor air pollutant consisting of all chemical components captured in the atmosphere. Among all negative aspects of air pollution on human health, adverse effects on local ecology, atmospheric chemistry, and global climate are evident as well., The health problems associated with exposure to high concentrations of pollutants beyond national and international air quality standards [U.S. Environmental Protection Agency (EPA) 2006] is a major global concern. Researchers involved in environmental assessment constantly attempt to major and formulate the concentrations of air pollutants at different time and space scales in order to find better solutions. Air dispersion modeling is a useful and scientifically approved method for predicting and simulating ambient concentration and distribution of pollutants through mathematical algorithms and physiochemical processes. AERMOD belongs to steady-state Gaussian plume models; this model is applied in computation of pollutant dispersion in rural and urban, flat and complex terrain, leveled and elevated areas, and multiple sources (point, area, and volume) of emissions. This model is based on the planetary boundary layer (PBL) theory; the thickness of PBL may vary between 100 m at night to 3 km in daytime., Based on the assumptions of the model, the probability distribution function for the concentration of the pollutant in vertical and horizontal directions is Gaussian in stable boundary layer (SBL), whereas the convective boundary layer (CBL) in horizontal direction is Gaussian as well. The vertical distribution is recognized by a bi-Gaussian distribution function in this layer. AERMOD is able to simulate pollutant dispersion in the range of 50 km. This model is also applied in assessing and simulating the dispersion of pollutants like PM10, SO2, CO2, VOC, hydrogen cyanide (HCN), and sulfur hexafluoride (SF6) in addition to dispersion of heavy metals like hexavalent chromium and total gaseous mercury (TGM) and odorous compounds.
In reference to the capabilities of AERMOD model, the objectives here is to simulate the total suspended particulates (TSP) emitted from a steel plant using AERMOD View, with respect to (i) maximum simulated ground level concentrations (GLC) at different time averages including hourly, daily, monthly, and annually and (ii) comparing maximum simulated concentration with EPA and the clean air standard of Iran.
| Materials and Methods|| |
Isfahan is one of the major cities in Iran and is mostly known for its historical sites. The geographical coordinates of this city are 32°38′N latitude and 51°39′E longitude located in the center of Iran. This city has become one of the greatest industrial zones in Iran. The mean sea level (MSL) is 1650 m, annual precipitation rate is around 122.7 mm, and average wind speed s 2–3 m/s. The steel plant considered in this study has an annual production of 30,000 ton; this industrial complex with geographical coordinates 32.361009°N latitude and 51.616826°E longitude is located in the southwest of Isfahan in the 45 km road to Mobarakeh county [Figure 1]. The main products of this plant are heat-treated steel, cold and hot work tool steel, spring steel, plastic mold steel, and stainless steel. The plant has several gaseous stacks and only one stack for particle matters (electric arc furnace chimney), the air pollution control device of which is of bag filter. The characteristics of the emission source for this stack are tabulated in [Table 1].
Air dispersion modeling
In this study, a commercial interface, AERMOD View (Version 9) (Lakes Environmental Software, Waterloo, Ontario, Canada) is used as the modeling system., [Table 2] shows the technical features of AERMOD. In the first step, meteorological data is obtained from Mobarakeh station in the geographical location of 32°12’N latitude and 51°27’E longitude (21 March 2014 to 20 March 2015). These data include wind speed (m/s), wind direction (degrees from true north), dry bulb temperature (ambient air) (°C), total and opaque cloud cover (tenths) considered as minimum required data as well as measured pressure, station pressure (mbar), hourly precipitation amount (mm), dew point, and relative humidity (%) as additional parameters. These data are received by AERMET (Meteorological pre-processor of AERMOD) and then converted to SAMSON format file, which is a recognizable format for this pre-processor. AERMET also requires land surface parameters (surface roughness, albedo, and Bowen ratio) based on land use type. In this regard, the study area is divided into four sectors and site-specific coefficients for these sectors were determined according to the annual period [Table 3]. In the following, a 30 km × 30 km uniform Cartesian grid extending from the center of the stack with 2000 m spacing in both X and Y directions (961 receptors) is defined to calculate maximum GLC. In the last step, a Shuttle Radio Topography Mission (SRTM) with 90 m available from WebGis is imported to AERMAP (Terrain pre-processor of AERMOD) as a digital elevation model (DEM). AERMAP analyzes the terrain height for each receptor as well as the effects of topography on dispersion [Figure 2].
|Table 3: Land surface characteristics of the study area based on annual period|
Click here to view
Comparison of outputs with standards
The output concentration curves of all time averages are compared with maximum concentrations of both Iran and EPA clean air standards [Table 4] and [Table 5].
| Result|| |
Comparison of maximum simulated concentration with standards
Based on the EPA and Iran emission standards for suspended particles, two allowable limits in accordance with the diameter of the particles are determined (PM2.5 and PM10). In fact, the allowable limit for TSP is not provided separately. Therefore, in this study, assuming that suspended particles emitted from the chimney are in PM2.5 or PM10 range, the simulated concentrations by the model for the TSP is compared with both the PM2.5 and PM10.
Maximum simulated concentration of TSP for 24-h average (0.073 µg/m3) compared with EPA and Iran clean air standards (2011) for PM2.5, which are determined as 25 and 35 µg/m3, respectively reveals that concentration of this pollutant is lower than both the standards [Table 4] and [Table 5]. Comparison of maximum simulated concentration of TSP for annual average (0.016 µg/m3) with EPA and Iran standards which are determined as 10 and 12 µg/m3, respectively shows that concentration of this pollutant is well below the two standards.
At this stage the maximum simulated concentration of TSP for 24-h average (0.073 µg/m3) compared with EPA and Iran clean air standards for PM10 reveals that, this pollutant is far below the two standards, and the maximum simulated concentration of TSP for annual average (0.016 µg/m3) is considerably lower than Iran clean air threshold value. Because there is no threshold value for annual PM10 in EPA standard, no comparison is made in this respect.
Evaluation of spatial distribution of simulated concentrations
Modeling the concentration of TSP illustrated on different time scales [Figure 4], [Figure 5], [Figure 6], [Figure 7], [Figure 8], [Figure 9], [Figure 10] indicate that maximum GLC for the all-time averages occurred at a 2 km distance from north of the stack [Table 6]. Moreover, the sectors chosen as urban and cultivated lands are exposed to more high concentrations of TSP by the model.
|Table 6: Maximum simulated concentrations of TSP for electric arc furnace stack using AERMOD model|
Click here to view
| Discussion|| |
According to the above results, in general, simulated concentrations for short (24-h) and long (annual) time averages do not exceed the EPA and Iran standards for both the PM2.5 and PM10. Furthermore, the highest concentration values for all time scales are observed at almost 2 km away from the stack center in the north. By looking at satellite images, it is revealed that this location is empty of inhabitant so that there is no direct threat to public health from this unit. Regarding this issue, despite this location being not in the direction of predominant wind (West–East) in the study area [Figure 3], it should be noted that wind rose plots actually show frequency of wind speed and direction in a region while in air dispersion modeling. It is the maximum concentration that is important; in addition, there are other factors that affect wind direction, such as building downwash and topography. However, it is more common to have the highest concentrations in the direction of the predominant wind.
In an environmental impact assessment project, Seangkiatiyuth et al. applied AERMOD for analyzing the nitrogen dioxide (NO2) produced by a cement complex in Thailand wherein results indicated that in both measurement and the simulation, the NO2 concentration does not exceed the limit set by the National Ambient Air Quality Standards (NAAQS) of Thailand for NO2 concentration.
Vannucci et al. in a collaborative project with European Union called ULCOS (ultra-low carbon dioxide (CO2) steelmaking) assessed the local impacts of main pollutants concentration (NOx, SO2, TSP, PM10, PM2.5, CO, Pb, PCDD) emitted by a steel plant. The results suggested that average concentration of pollutants at ground level is below the limits established by current legislation.
| Conclusions|| |
In this study, by applying the AERMOD developed by the EPA and recommended as one of the preferred and advanced models, dispersion of TSP emitted from a steel plant chimney (electric arc furnace chimney) in Isfahan is simulated in different time scales and then maximum GLC of TSP are compared with both EPA and Iran standards. Results suggested that maximum simulated concentrations in all time scales are lower than both the EPA and Iran standards. These results can be attributed to the installation of a bag filter on the stack, which has reduced the emissions in a significant manner. Moreover, the highest concentration of TSP occurs in different directions with prevailing winds in which there is no direct adverse effect on human health by this steel plant. In spite of these results, cumulative impacts of such industrial activities must not be ignored in a holistic approach. With regard to the results of this study, the systematic application of bag filters in all industrial units across the country is recommended.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Borrego C, Coutinho M, Costa AM, Ginja J, Ribeiro C, Monteiro A et al.
Challenges for a New Air Quality Directive: The role of monitoring and modelling techniques. Urban Climate 2015;14:328-41.
Aggarwal P, Jain S. Impact of air pollutants from surface transport sources on human health: A modeling and epidemiological approach. Environ Int 2015;83:146-57.
Seangkiatiyuth K, Surapipith V, Tantrakarnapa K, Lothongkum AW. Application of the AERMOD modeling system for environmental impact assessment of NO2
emissions from a cement complex. J Environ Sci 2011;23:931-40.
Vlachokostas C, Nastis SA, Achillas C, Kalogeropoulos K, Karmiris I, Moussiopoulos N et al.
Economic damages of ozone air pollution to crops using combined air quality and GIS modelling. Atmos Environ 2010;44:3352-61.
Pope CA 3rd, Dockery DW. Health effects of fine particulate air pollution: Lines that connect. J Air Waste Manag Assoc 2006;56:709-42.
Calderón-Garcidueñas L, Kulesza RJ, Doty RL, D’Angiulli A, Torres-Jardón R. Megacities air pollution problems: Mexico City Metropolitan Area critical issues on the central nervous system pediatric impact. Environ Res 2015;137:157-69.
Zou B, Zhan FB, Wilson JG, Zeng Y. Performance of AERMOD at different time scales. Simul Model Pract Theory 2010;18:612-23.
Kesarkar AP, Dalvi M, Kaginalkar A, Ojha A. Coupling of the Weather Research and Forecasting Model with AERMOD for pollutant dispersion modeling. A case study for PM10 dispersion over Pune, India. Atmos Environ 2007;41:1976-88.
Afzali A, Rashid M, Noorhafizah K, Ammar MR. Evaluating human exposure to emission from incineration plant using AERMOD dispersion modeling. Iran J Public Health 2014;43:25-33.
Singh SK, Sharan M. Simulation of plume dispersion from single release in Fusion Field Trial-07 experiment. Atmos Environ 2013;80:50-7.
Hosseiniebalam F, Ghaffarpasand O. The effects of emission sources and meteorological factors on sulphur dioxide concentration of Great Isfahan, Iran. Atmos Environ 2015;100:94-101.
Mokhtara MM, Hassim MH, Taib RM. Health risk assessment of emissions from a coal-fired power plant using AERMOD modelling. Process Saf Environ Protect 2014;92:476-85.
Haichao W, Wenling J, Lahdelma R, Pinghua Z, Shuhui Z. Atmospheric environmental impact assessment of a combined district heating system. Build Environ 2013;64:200-12.
Vannucci M, Colla V, Haines N. Air dispersion modeling for the assessment of ULCOS technologies. Metall Res Technol 2009;106:357-62.
[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6], [Figure 7], [Figure 8], [Figure 9], [Figure 10]
[Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6]