CORRELATION-MATRIX–DRIVEN DIAGNOSTICS OF INDUSTRIAL EMISSIONS: A PEARSON BASELINE WITH SCATTER-PLOT EVIDENCE
DOI:
https://doi.org/10.37943/24QUFK4295Keywords:
emission diagnostics, Pearson correlation, automated monitoring system, air pollution, combustion efficiency, coal power plantAbstract
Currently environmental state became very actual in the world, especially in Kazakhstan. Air pollution of industries is a major threat to the environment and health of the people, especially in areas with high reliance on coal-powered power stations in electricity production. Fossil fuels in Kazakhstan are the largest electrical source, and they contribute to the emission of sulfur dioxide (S), nitrogen oxides (N), carbon monoxide (CO), and the particle matter (PM). Although, to formulate diagnostic and monitoring procedures at industry level it is crucial to determine relationships among emissions. The study approaches the Pearson correlation method on data taken from an automated emission monitoring system at the Coal Power Plant in Kazakhstan. The aim of the study is to discover linearity between emission indicators and industrial combustion. The observed correlation heat map and scatter-plots indicate positive trends among the CO and S, inverse correlation between CO and , and insufficient relation of CO and NO. These results show the key combustion processes, which involve reduced oxygen supply leading to the incomplete oxidation and simultaneous increased sulfur emissions. The three-dimensional description of CO dependence on S and further explains the coupled emission response and supports the explanation of underlying regularities in the operation. The correlation-based framework has diagnostic capabilities of the early identification of inefficient combustion regimes and enables scalable and data-driven methods of emission control. The research finds that Pearson-based analytics can be used to offer a strong and interpretable predictive modeling and regulatory monitoring foundation of future air-quality management in industries.
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