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Proper estimation of air pollutants, especially Particulate Matter (PM), is essential for convincing and successful air quality control. A wide range of factors impact PM prediction, and in addition to guarantee the most reliable forecasts, it is imperative to integrate the most pertinent input variables. In order to choose the most relevant input and output variables for an air pollution model, this study uses the multiple linear regression approach. In this work, the concentrations of sulfur dioxide (SO2), nitrogen dioxide (NO2), and the air quality index (AQI) are used to determine the concentration of PM10.
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Kumar et al. (2024) studied this question.
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