Dispersion Model and Ground and Satellite-Based Observation Assisted Location Optimization for Thermal Power Plants

Abstract

Coal is used as a major energy source in Türkiye, and the installed capacity of coal-fired power plants is still increasing. However, their impacts on air quality and human health depend not only on plant characteristics, but also on meteorological conditions, topography, and existing pollution levels. This study proposes a geographic information system (GIS)-based method to identify suitable locations for coal-fired power plants by combining economic, environmental, and geographical factors, in contrast to the current practices that prioritize economic factors. In addition to the one already proposed location (Çerkezköy), three alternatives (Çorlu, Marmara Ereğlisi, Havsa) were selected using GIS-based assessment criteria, ground and satellite-based observations, and a dispersion model. Annual and seasonal sulfur dioxide (SO2) distributions were obtained from the Ozone Monitoring Instrument (OMI), and ground observations indicated that air pollution in Thrace is highest in southern areas like Tekirdağ, Keşan, and Çorlu. SO2 and particulate matter (PM10) contributions and impacts of each alternative over the region were estimated via the California Puff Model (CALPUFF). SO2 and PM10 contributions at the most impacted stations were calculated for all four cases and compared to current pollution levels. Annual and daily concentrations showed that all alternative locations had less impact than the proposed site, Çerkezköy. While Çerkezköy affected around 260,500 people with daily SO2 contributions exceeding 15 μg/m3, the other alternatives impacted at most 29,000 people. Varying SO2 and PM10 contributions and the associated population exposures across the alternative locations highlighted the need for evaluation of local conditions before site location, as even nearby locations can cause noticeably different effects.

Keywords

• coal-fired power plants
• location optimization
• CALPUFF
• GIS
• SO2
• PM10

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