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POWER PLANT EMISSIONS IN COLOMBO AS A TOOL FOR URBAN PLANNING. K. G. T. DHARSHANA. Introduction. Current state of air pollution in Colombo. Trends of air pollution in Colombo. Link : Variation patterns of average concentrations at Fort. Monitoring of air pollution.
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POWER PLANT EMISSIONS IN COLOMBO AS A TOOL FOR URBAN PLANNING K. G. T. DHARSHANA
Introduction • Current state of air pollution in Colombo. • Trends of air pollution in Colombo.Link : Variation patterns of average concentrations at Fort. • Monitoring of air pollution. Link : 16 monitoring locations of NBRO.
Objectives • Application of modeling as a tool for urban planning. • As a case study, to study the contribution from power plants to Colombo air pollution. • Compare the outcomes of dispersion model “AUSPLUME” over the “US EPA SCREEN 3”.
Achievements • Establish a complex air dispersion model AUSPLUME for the prediction of point source emissions to general air pollution. • Evaluation of the power sector contribution to the Colombo air pollution.
Methodology • Input data – Meteorology. • Input data – Source Characteristics. • Application of “AUSPLUME” model. • Application of “US EPA SCREEN 3” model. • Analyzing of automated monitoring dataat NBRO.
Methodology Contd… Input data - Meteorology • Selecting the Northeast monsoon periods of 97/98 and 98/99. Link : Contribution from thermal power plants. • Interested parameters. • Development of “Colombo Met”.
Methodology Contd… Input data – Source Characteristics Source: NBRO prepared power sector emission inventory and EIA reports of Thermal Power Plants.
Application of AUSPLUME model Methodology Contd… • Combined stack emissions. • Default options. • Using “SURFER” as a supporting software.
Methodology Contd… Application of SCREEN 3 model • Different stability categories. • All meteorology option for the worst case scenario.
Methodology contd… General limitations of Gaussian plume models • Not considering wind stratification. • Assuming normal distribution patterns of pollution concentrations. • Assuming no chemical transformation of pollutants. • Assume constant and continuous stack emissions. • Assumption of relatively flat terrain.
Analyzing of automated monitoring data at NBRO Methodology Contd… • Selection of Northeast and Southwestmonsoon periods in 1998 and 2000. • Consideration of 9.00 pm to 12.00 midnight to identify the power sectorcontribution.
Results Variation of 1-hour SO2 concentration with the wind speed of 1 m/s
Results contd… Contributions from major thermal power plants to three city centers in Colombo All values are in µg/m3 and obtained using the AUSPLUME air dispersion model.
Results Contd… DISTRIBUTION PATTERNS OF NOX ARROUND AES 1 0 0 0 m 0 m 250 200 - 1 0 0 0 m 150 100 - 2 0 0 0 m 50 FORT 37.5 MARADANA - 3 0 0 0 m 31.25 28.125 - 4 0 0 0 m 25 0 - 5 0 0 0 m MET DEPT - 6 0 0 0 m -5000m -4000m -3000m -2000m -1000m 0m 1000m 2000m Conc. (microgram/m3); 1 hour avg. Scale X :- 1 inch = 1233.33 map units Y :- 1 inch = 1233.33 map units
Results Contd… Variation patterns of 1- hour SO2 concentration in Puttalum.
Results Contd… Comparison of AUSPLUME and SCREEN 3 model outputs
Results Contd… Diurnal variation of NO and NO2 at Fort during December 2000. Link :Diurnal variation of NO and NO2 at Fort during December 1998.
Conclusions • Power plants contribution to Fort, Maradana and Met. Dept. sites during the NE of year 2000 were 34%, 52% and 54% w.r.t. NOX as NO2 and 3%, 4% and 6% w.r.t. SO2. • Recently started AES power plant in Kelanitissa would contribute to Fort, Maradana and Met. Dept. sites during the NE period nearly by 30, 37 and 26 µg m-3 w.r.t NOX as NO2 and nearly by 3, 4 and 3 µg m-3 w.r.t. SO2.
Conclusions Contd… • “AUSPLUME” model can be recommended for air pollution regulatory purposes in Sri Lanka over the currently use “US EPA SCREEN 3” model. • It is possible to accommodate two existing 20 MW each furnace oil fired steam turbines in Kelanitissa in standby condition during SW periods in order to meet peak electricity demand.
Future developments • Institutionalize the activity • Need a multidisciplinary approach. • Develop a good data base.
Special Thanks • Mr. R. P. Samarakkody • Dr. K. P. S. C. Jayaratne • Dr. Priyaratne Dewundege • Dr. Senaka Basnayake