Presentation on Airpollution Modeling
- 1. AIR POLLUTION MODELING Team Member: Muntasir Muhit ID: 191-35-399 Sajid Anowar ID: 191-35-390 Rafidul Hasib ID:191-35-401 Ashiqur Rahman ID:191-35-410 1
- 2. INTRODUCTION TO AIR POLLUTION MODELING Air pollution modeling is a numerical tool used to describe the causal relationship between emissions, meteorology, atmospheric concentrations, deposition, and other factors. 2 Fig 1 : air pollution
- 3. Air Pollution Modeling at Urban and Larger Scales Two different modeling approaches were followed, lagrangian modeling and Eulerian modeling. In lagrangian modeling, an air parcel (or “puff”) is followed along a trajectory, and is assumed to keep its identity during its path. In Eulerian modeling, the area under investigation is divided into grid cells, both in vertical and horizontal directions. 3
- 4. Dispersion Models AERMOD: AERMOD is a steady-state Gaussian plume model. It uses a single wind field to transport emitted species. The wind field is derived from surface, upper-air, and onsite meteorological observations. 11 4 Fig 2: AERMOD Model Flowchart
- 5. Dispersion Models CALPUFF: CALPUFF is a non-steady state Lagrangian puff dispersion model. The advantage of this model over a Gaussian-based model is that is can realistically simulate the transport of substances in calm, stagnant conditions, complex terrain, and coastal regions with sea/land breezes. With the development of the VISTAS Version 6 model2, CALPUFF can use sub-hourly meteorological data and run with sub-hourly time steps. This version of CALPUFF is appropriate for both long-range and short-range simulations. 5 Fig 3: CALPUFF Model Flowchart
- 6. Photochemical Modeling Photochemical air quality models have become widely recognized and routinely utilized tools for regulatory analysis and attainment demonstrations by assessing the effectiveness of control strategies. 6 Fig 4 : Photochemical Model
- 7. Photochemical Modeling CMAQ: The primary goals for the Models-3/Community Multiscale Air Quality (CMAQ) modeling system are to improve 1) the environmental management community's ability to evaluate the impact of air quality management practices for multiple pollutants at multiple scales and 2) the scientist’s ability to better probe, understand, and simulate chemical and physical interactions in the atmosphere. 7 Fig 5: CMAQ Model
- 8. Photochemical Modeling CAMX: understanding that air quality issues are complex, interrelated, and reach beyond the urban scale, CAMX is designed to: Simulate air quality over many geographic scales Treat a wide variety of inert and chemically active pollutants Ozone Mercury and toxic 8Fig 6: CAMX Model Flowchart
- 9. Photochemical Modeling UAM: The Urban Airshed Model® (UAM®) modeling system, developed and maintained by Systems Applications International (SAI), is the most widely used photochemical air quality model in the world today. 9 Fig 7: UAM Model
- 10. Meteorological Models CALMET: CALMET is a meteorological diagnostic model that combines data fromsurface stations, upper-air stations, over-water stations, precipitation stations, with geophysical data like land use, terrain elevations, albedo, etc., to produce a fully 3-dimensional diagnostic gridded wind field for the duration of the CALPUFF simulation. This wind field is then passed into CALPUFF and is used to transport the emitted substances. 10Fig 8: CALMET Model Flowchart
- 11. Meteorological Models MM5: The PSU/NCAR mesoscale model (known as MM5) is a limited-area, nonhydrostatic, terrain-following sigma-coordinate model designed to simulate or predict mesoscale atmospheric circulation. 11 Fig 9: MM5 Model Flowchart
- 12. Meteorological Models RAMS: Its major components are: An atmospheric model which performs the actual simulations A data analysis package which prepares initial data for the atmospheric model from observed meteorological data A post-processing model visualization and analysis package, which interfaces atmospheric, model output with a variety of visualization software utilities. 12 Fig 10: RAMS Model Flowchart
- 13. Plume Rise Modules Most air pollution models include a computational module for computing plume rise, i.e., the initial behavior of a hot plume injected vertically into a horizontal wind flow. In particular, AERMOD includes PRIME (Plume Rise Model Enhancements) 13 Fig 11: Plume Rise Modules
- 14. Particle Models Particle models are based on Lagrangian methods for simulating atmospheric diffusion. In these models, plumes are represented by thousands (even hundreds of thousands) of “fictitious” particles, which often move with semi-random 14 Fig 12: Particle Models
- 15. Odor Modeling The mechanisms of dispersion of odorous chemicals (e.g., mercaptans) in the atmosphere are the same as the dispersion of other pollutants. However, when multiple pollutants are emitted, masking and enhancing effects may occur. In this case, the relationship between concentrations of individual chemicals and odor is not well defined and odor must be characterized in terms of an odor detection threshold value for the entire mixture of odorous chemicals in the air. 15 Fig 13: Odor Model
- 16. Statistical Models Statistical models are techniques based essentially on statistical data analysis of measured ambient concentrations. Two main types of statistical models exists: Air Quality Forecast and Alarm Systems: Statistical techniques (e.g., time series analysis, spectrazl analysis, Kalman filters) have been used to forecast air pollution trends a few hours in advance for the purpose of alerting the population or, for example, blocking automobile traffic. 16 Fig 14: Statistical Model
- 17. Receptor Modeling Receptor models are mathematical or statistical procedures for identifying and quantifying the sources of air pollutants at a receptor location. Unlike photochemical and dispersion air quality models, receptor models do not use pollutant emissions, meteorological data and chemical transformation mechanisms to estimate the contribution of sources to receptor concentrations. 17 Fig 15: Receptor Modeling
- 18. Modeling of Adverse Effects Special models or mathematical techniques are available to calculate the adverse effects of air pollution. These models include: Health effects (e.g., cancer risk) Visibility impairment Global effects, such as climate change Damage to materials Ecological damages 18
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