Monitoring For Elemental Composition Of Particulate Matter Deposited In Snow Cover Around Coal-Fired Thermal Power Plant (Karaganda, Central Kazakhstan).

Abstract

Studies on thermal power plant areas with respect to chemical composition of particulate matter deposited in snow cover are limited. This study aims to monitor (2014–2022) particulate load and trace elements associated with the particulate matter distributed around (0.5-4.5 km) the coal-fired thermal power plant in Karaganda. In this study, snow cover was used as an effective scavenger of atmospheric pollutants. Using instrumental neutron activation analysis and atomic absorption spectrometry, the content of 26 elements and Hg, respectively, was determined in the particulate phase of snow. The results showed that particulate load varied from 26 to 1751, with mean of 427 and a background of 47 mg m-2 d-1. Anthropogenic impact caused a significant increase in content of U, Hg, Ta, Zn, Na, Cr, Co, Sr, Rb, Cs, Sc, Ca, Fe, Nd, Ba (2–30 times) in the samples compared to the background. Metal-bearing phases of Zn, Ba, As, U-Ta-Nb were detected through scanning electron microscope. The highest levels of particulate load (169–1032 mg m-2 d-1) and element contents in the samples were localized up to 0.7 km from the thermal power plant. The changes of particulate load and element composition of snow deposits during the monitoring period were connected with temperature, modernization of dust-collecting equipment, composition of coal and fly ash, long-range transport of emissions from other industries. The element content and metal-bearing phases in the particulate phase of snow can be used as markers for identifying emission sources from coal combustion.

Comprehensive physicochemical characterization of Algerian coal powders for the engineering of advanced sustainable materials

“…Comparative analysis with coal from other geographical locations suggests that the unique particle size distribution and morphology of Algerian coal could confer distinct advantages or present specific challenges, depending on the intended use [20]. The combination of SEM morphological analysis with particle size data provides a robust foundation for predicting performance and guiding the optimization of coal utilization strategies.…”