CALPUFF-CALMET Modeling System
Disclaimer: This work has been submitted by a student. This is not an example of the work written by our professional academic writers. You can view samples of our professional work here.
Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of UK Essays.
Published: Thu, 31 Aug 2017
Nowadays, due to the fast expansion of industrial development and population increase, air pollution has become one of the most serious problems in the world, especially in large cities and consequently, the problem of air pollution and its control has become increasingly. Fossil fuel combustion, especially which based on oil and coal is one of the major causes of the environmental problems affecting both local and global levels. However, several countries are still using fossil fuels as primary energy, especially in developing countries.
Electricity consumption in Iran has experienced a considerable growth during recent years because of economical development, industrialization and population increase. In 1973, electric energy generation per capita was 310 kWh, which increased to 2935 kWh in 2008. Electricity generation using fossil fuels has destructive effects on environment.
Due to Iran’s environmental conditions, electricity generation is mainly performed by thermal power plants, so that near 85% of the required electric energy is produced by thermal power plants. Gas-fired power plants are the dominant part which accounted up to 62% of total power generation. Oil-fired power plants generated 22.4% and 14.7% produced from hydroelectric plants. Although hydroelectric plants make 14.7% proportion of plants in Iran, these plants have generated only 5.1% of the power due to a fall in precipitation over the past few years. Natural gas (71.3%) is the largest source of fuel for electricity generation followed by heavy oil (15.8%) and gas oil (12.4%). Although, the main fuel of power plants is gas, the environmental problems are still concerned
Air quality is a major determinant of human health. Meteorology plays a great role in determining air quality changes downwind of emission sources. Both the wind and atmospheric stability greatly affect dispersion conditions. Local influences due to terrain and land-cover factors can also be important. Air dispersion and deposition models are tools for estimating concentrations of air pollutants and deposition rates due to industrial or other emission sources (Prince Rupert Airshed Study). Air quality models are instrumental in providing valuable insights into the processes involved in the transport, dispersion and chemical transformation of pollutants in the atmosphere .These models use mathematical equations and numerical methods to describe the concepts involved in the atmosphere.
In recent years, CALPUFF model has good performance in the simulation of many kinds of pollutants under complicated topography, especially in the area larger than 50 km. CALPUFF can be run in any specific location around the world, and for any modeling type period selected by the user. Due to its flexibility, CALPUFF has been used in several research studies.
Over the past years, several CALPUFF-aided case studies have been published. Shiyao Li et al. (2016) used CALPUFF model to simulate the spatial distribution of sulfur dioxide in Urumqi and analyzes the source contribution to areas where the SO2 concentration is high. Prueksakorn et al. (2014) applied WRF/CALPUFF modeling system and multimonitoring methods to investigate the effect of seasonal variations on odor dispersion in Changwon City of South Korea. Abdul-Wahab et al. (2011) used CALPUFF software to measure and simulate the dispersion of sulfur dioxide (SO2) at the Mina Al-Fahal Refinery in the Sultanate of Oman. Abdul-Wahab et al. (2013) used CALPUFF to study the effect of meteorological conditions on the dispersion of an accidental release of hydrogen sulfide (H2S). Abdul-Wahab et al. (2015) applied CALPUFF to assess the quality of the proposed Miller Braeside quarry expansion in Canada. Hyung-Don Lee et al. (2014) used WRF-CALPUFF software to simulate concentration distributions of typical air pollutants (PM10 and SO2) in the Ulsan Petrochemical Industrial Complex (UPIC), and statistics are computed to determine the models’ ability to simulate observations.
In this study, a CALMET diagnostic model nested to WRF model simulation is evaluated by comparison to surface air measurements, along specific periods. Then the CALPUFF dispersion model was used to simulate and predict the concentration of SO2, NOX, CO and PM10 that are emitted from the Shahid-Montazeri power plant (SMPP) of Esfahan, Iran. The main goal of this study is to evaluate the capability of the CALPUFF model to simulate the concentrations of SO2, NOX, CO and PM10 in the nearby of power plant for special topographical and climatological conditions of the study area. First, the amounts of pollution exhausted from the stacks and the ambient concentrations of pollution due to the emitted gases from the stacks of Shahid-Montazeri power plant have been monitored in four receptors (Figure 1). Then the ambient concentration levels of pollution have been simulated for the receptors, using CALPPUF Lagrangian Gaussian puff model. Finally, the comparison of model prediction results and the monitored concentrations have been done through statistical analysis.
2. Model description
Technical description of CALPUFF-CALMET modeling system
CALPUFF is one of the US Environmental Protection Agency’s (EPA) preferred models for assessing transport of pollutants and their effects, on a case-by-case basis, or for certain near-field applications involving complex meteorological conditions. The modeling system consists of three main components and a set of preprocessing and post processing programs. The main components of the modeling system are CALMET (a diagnostic 3-dimensional meteorological model), CALPUFF (an air quality dispersion model) and CALPOST (a post processing package).
CALMET is a diagnostic meteorological model which can make use of topography, land type, meteorological observation data and meteorological simulation data to diagnosis of wind and temperature fields based on the mass conservation equation. Besides the wind and temperature fields, CALMET determines the 2D fields of micro meteorological variables needed to carry out dispersion simulations (mixing height, Monin Obukhov length, friction velocity, convective velocity and others). The quality of a meteorological preprocessor is one of the main determinants of the overall quality of the air dispersion model, and this is particularly true for the CALPUFF/CALMET modeling system in a wide range of conditions. The main purpose of CALMET is to obtain the best possible meteorological data based on the available information. In particular, CALMET can receive measured data, modeled data (i.e., generated by a meteorological model like MM5 or WRF), or both. When a high-resolution terrain data set is available, CALMET is capable of using this information to estimate local deviations from meteorological data measured or modeled at a coarser resolution (Scire J.S).
CALPUFF is a multi-species non-steady-state puff dispersion model that simulates the effects of time and space varying meteorological conditions on pollutant transport, transformation, and removal. CALPUFF allows the use of on-site turbulence measurements of the horizontal and vertical Gaussian dispersion coefficients, but also allows for the use of similarity theory and micrometeorological variables, derived from meteorological observations and surface characteristics, to obtain these coefficients. CALPUFF utilizes a Gaussian puff formulation to calculate the concentration of a pollutant (or spores, in our application) at any given location downwind, and the deposition at user’s specified locations at ground level (Use of a complex air pollution model to estimate W. Pfender). CALPOST can extract CALPUFF simulation data according to customer’s demand (Spatial distribution and source analysis of SO2 concentration in Urumqi).
a. Description of study area and model domain
Isfahan is located in the central Iran inside the plains stretching along the Zayandeh Rood River. The city is located in a relatively mountainous area in the center of the Iranian Plateau and stretches from the snowy Zagros Mountains in the West to the East and North-central deserts of Iran. There exist a variety of climatic conditions in the city thanks to regions with different altitudes. The outstanding features of Isfahan are little rainfall, average less than 125 mm. Isfahan is located in 32.67N, 51.83E, and elevation 1550-1650 m, with more than 1.7 million population (https://amar.sci.org.ir/index_e.aspx). There are more than a million automotive and heavy duty vehicles using diesels, gasoline, and natural gas in Isfahan. This city is known as the largest industrialized region in Iran, where there are many industrial states, steel companies, and etc. There is also one of the biggest electric power plant of Iran.
Shahid-Montazeri steam power plant of Esfahan is located 15 km to the northwest of Isfahan along the Isfahan-Tehran highway next to Isfahan Refinery and Petrochemical Complex in a 2.2 million m2 land (Evaluation of synchronous execution of full repowering and solar assisting in a 200 MW steam power plant, a case study) (Figure 1). This power plant has 8 similar steam units each with a capacity of 200 MW. Montazeri plant is a steam power plant which is recently use natural gas. However, Montazeri uses heavy oil during the cold days due to increasing the domestic heating.
The study area is located around as Montazeri power plant, with a total capacity of 1600 MW and two large smoke stacks (205 agl-m height, above ground level meters height and 1725 base elevetion) with four independent liners (one per boiler) in the same concrete shaft that are selected point sources (Figure 2). Therefore, it should be considered as eight different point sources practically located at the same point; alternatively, it can be considered as a two point sources, with an emission and stack section as the sum of the four liners (Validation of CALMET/CALPUFF model simulations around a large power plant stack).
In this study, dispersion of SO2, Nox, and particulate matter (pm10) emitted from the Montazeri power plant over the Esfahan basin was evaluated for two periods of — days (from 10 to 31 January 2000). A simulation domain of 100×100 km2 was selected by the power plant positioned at the center, in order to cover any pollutant source local impact. This area is divided into 10000 grids, the size of which is 1 km – 1 km. The southwest corner of the domain is located at longitude 50.96E, latitude 32.35N. The northeast corner is located at longitude 52.03E, latitude 33.24N and the elevation of the study area varies from 1500 to 2800 m. Table 1 represents the information model input which is used for defining the case study meteorological domain.
b. Emission data
The main sources of pollutants in Montazeri power plant are resulted from exhaust gases of the stacks which cause air pollution in the power plant area and it’s surrounding. The values of SO2, NO, NO2 and PM10 emissions from the stacks of Montazeri power plant have been measured by Testo 350-XL device for gases and ISOSTACK BASIC device for particulate maater, during the period of simulation. The data of stack characteristics and the emission rate of the pollutant have been presented in Tables 2.
This release huge quantities of sulfur dioxide due to Steam power plants of Iran are not equipped with FGD systems to reduce SO2 emissions, and thereby, the emission factor of this pollutant is only influenced by electricity generation efficiency and sulfur percentage of the consumed heavy oil.
In this study, we used data observed from four monitoring station to measure so2, nox and pm 10 (figure 1). Location of the monitoring stations (receptors) has been presented in Cartesian coordinate system in Table 2. Measurements at the monitoring station were done based on the average hour concentrations.
c. Meteorological data
Surface hourly observations in TD-3505 format were obtained from the Integrated Surface Hourly Database (ISHD) supported by the US National Climatic Data Center (NCDC) . Data was extracted hourly for the entire modeling period from March 10, 2012 at 00h00 UTC to March 12, 2012 at 23h00 LST. Due to the large number of missing data of the other surface meteorological parameters (such as: pressure, ceiling height and cloud cover) only temperature and wind speed were validated. The purpose of extracting this data was only to evaluate the accuracy of the calmet model to simulate the vertical profiles of wind and temperature. Figure 3 shows the location of the meteorological station used in this study and a description of the surface stations is provided in table 3
d. Modeling approach
The initial phase of CALPUFF modeling system involves the derivation of three dimensional meteorological wind fields for the study area using CALMET a diagnostic meteorological model (Estimated Public Health Exposure to H2S Emissions from a Sour Gas Well Blowout in Kaixian County, China). The input of CALMET model includes geophysical data (land use categories and terrain elevations), meteorological data (surface and upper air meteorological observations or meteorological fields generated by prognostic models) (A study of the effects of vehicle emissions on the atmosphere of Sultan Qaboos University in Oman). Due to lack of the surface and upper air meteorological data in the study area we used the Weather Research and Forecasting (WRF: version 3.5.1) model to simulate of meteorological conditions. The Weather Research and Forecasting (WRF), a prognostic meteorological model, was used to calculate the hourly three-dimensional meteorological fields For CALMET model (Applications of WRF/CALPUFF modeling system and multi-monitoring). The WRF model description presented in Table 2. Initial conditions and boundary conditions are provided by the 1.0 degree National Centers for Environmental Prediction (NCEP) Final Analysis (FNL) at 6-h intervals (Use of high-resolution MM5/CALMET/CALPUFF system: SO2 apportionment to air quality in Hong Kong). Data in WRF output files can be interpreted and converted to a format compatible with CALMET by CALWRF program (Scire et. al.2000b).
CALMET requires geophysical data to characterize the terrain and land use parameters that potentially affect dispersion. Terrain features affect flows, create turbulence in the atmosphere, and are potentially subjected to higher concentrations of elevated puffs. Different land use types exhibit variable characteristics such as surface roughness, albedo, Bowen ratio, and leafarea index that also affect turbulence and dispersion (The use of an atmospheric dispersion model to determine influence regions in the Prince George, B.C.).
Terrain elevation for the CALMET was obtained using the TERREL processor. The model was executed with terrain maps provided by Consultative Group on International Agricultural Research (CGIAR) and the Consortium for Spatial Information (CSI) website (http://www.cgiar-csi.org/data/srtm-90m-digital-elevation-database-v4-1), Data were collected as part of the Shuttle Radar Topographic Mission (SRTM) and processed by CSI into 5 x 5 degree tiles at 90-metre resolution. Land characteristics in the domain were extracted using the CALMET pre-processor CTGPROC. The input land use maps were obtained from the United States Geological Survey (USGS) websites in GeoTIFF format. Terrain characteristics map in the study area has been displayed in Fig. 3.
To provide meteorological input to the CALPUFF model, the CALMET diagnostic model and WRF mesoscale prognostic model were coupled. The CALPUFF model uses the output file from CALMET together with source, receptor, and chemical reaction information to predict hourly concentrations.
e. Statistical Data Analysis
To determine the reliability of the simulation data, verification of simulated values using the WRF and CALMET models was conducted for surface temperature and wind speed at surface monitoring station using several statistical indicators. The statistical verification of model performance in this study was performed using four statistical indicators namely the Bias Error (B), Gross Error (E), Fractional Bias (FB), Normalized Mean Square Error (NMSE), Root Mean Square Error (RMSE) and Index of Agreement (IOA). The formulas used to derive these four indicators are given in Equations
Cite This Work
To export a reference to this article please select a referencing stye below: