3. RESEARCH METHODOLOGY
3.5 Atmospheric dispersion modelling
3.5.1 AERMOD system
Additional non-statutory components of this system include AERSURFACE, a surface appearances processor, and BPIP–PRIME for handling data from structures and obstacles near emission sources to define their interference with plume rise and to approximate the variables required by AERMOD to evaluate building downwash effect (US EPA, 2005b).
3.5.1.1 AERMET
AERMET defines the ONSITE trail for recording hourly surface observations acquired from a site- specific meteorological station. AERMET can process three forms of data inputs; (i) hourly surface data, (ii) upper air meteorological normally collected by the National Weather Service (NWS), and (iii) data can also be collected from a site-specific prognostic meteorological data processed through a processor such as the Mesoscale Model Interface (MMIF) (US EPA, 2017). It also estimates the mixing height in the CBL, considering its reliance on both mechanical and convective processes. AERMET estimates the mixing height based on the following standards:
i. Throughout the day, when the Monin-Obukhov Length is negative, it is projected as higher than the convective or the mechanical mixing height.
ii. During the night, when the Monin-Obukhov Length is positive, it is estimated as equal to the mechanical mixing height.
AERMET data processing occurs in three discrete stages, each required to be run separately as shown in Figure 3.5-2 (US EPA, 2017). The first stage extracts the surface and upper-air data from files into specific archive formats that are appropriate for AERMOD. It is also at this stage that the quality assurance (QA) of the surface, upper air, and site-specific data is considered. The second stage combines the processed surface and upper-air data with the site-specific data into discrete 24-hour periods and writes the merged data to an intermediate file. The final stage reads the merged data file, calculates the boundary layer parameters (e.g., surface friction velocity, mixing height, and Monin-Obukhov length) required by AERMOD, and generates two AERMOD ready meteorological data files. The first file contains the observed surface parameters (e.g., temperature, wind speed, and wind direction) while the second file contains a profile of winds, temperature and the standard deviation of the fluctuating components of the wind if provided (US EPA, 2017).
Figure 3.5-2. AERMET processing framework (US EPA, 20016a).
3.5.1.2 AERMAP
AERMAP was established to process terrain data in combination with a layer depicting receptors and sources. It can process several standardised data formats, which makes it possible to produce terrain base elevations for specific receptors and sources as well as a hill height scale value for each respective receptor (US EPA, 2005). With the notion that terrain influences air quality concentrations at distinct receptors, AERMAP helps to define the base elevation at each receptor and source. Thus, AERMAP assumes the terrain height and location that has the greatest influence on dispersion for each receptor. This height is referred to as the hill height scale (US EPA, 2005).
One of the main limitations for the use of AERMAP in developing countries is the availability of a digital elevation model (DEM) containing topographical data of the modelling domain with an adequate resolution (US EPA, 2005). Therefore, online free DEM sources are mostly preferred when running AERMAP in cases where a local one is not readily available. For this study, the topographic data required by AERMAP was obtained from Shuttle Radar Topography Mission (SRTM1), whereas the land cover data was attained from WebGIS, with a resolution of ~30 m.
The World Geodetic System 1984 (WGS84) projection datum was used, with the terrain data calculated based on the Universal Transverse Mercator (UTM) coordinate system (35 South). The receptors’ grid was set at 50m by 50m over the two townships.
3.5.1.3 AERMOD
The execution of the AERMOD dispersion model, in addition to the previous steps, included:
i. Application of the control options:
• Regulatory options were set on default with concentration selected as output type.
• Selection of the averaging periods, thus hourly, daily and annual were opted for given the pollutants established NAAQS and analysing needs.
• Some default output options used include the concentrations and depositions at each receptor due to emissions from each group of sources, the average during long periods (annual or whole period), and the maximum in each short averaging period (PLOT-FILE option).
Secondly, concentrations higher than the threshold values due to emissions from each group of sources in each short averaging period (MAXIFILE option). Lastly, all concentrations at each receptor in each short averaging period (POST-FILE option); for substantiation against monitored data.
ii. Application of methodologies to produce the input data required by the model for the description of emission sources, which involved:
• Defining the pollutants emitted and the size of the particulate, and in this study PM10 and PM2.5
were considered.
• Determining how to best input the sources based on the data available, as well as the corresponding release height, velocity, and diameter where applicable.
iii. Deciding on locale, which included urban and rural conditions, as given by the Stats SA definition of townships.
Figure 3.5-3 summarises the processes and data flow stages required to successfully perform air dispersion modelling analysis using AERMOD.
Figure 3.5-3. AERMOD modelling system's data flow (Villalvazo, Davila & Reed G. 2007)