Air quality monitoring in Mendoza, Argentina: machine learning approaches for PM prediction.
Journal:
Environmental science and pollution research international
Published Date:
Jul 2, 2025
Abstract
In this study, different statistical methodologies were combined to assess the relationship between PM concentrations and meteorological variables (temperature, relative humidity, wind direction and speed, and atmospheric pressure) and their associations with other pollutants (CO, NO, NO, and O) recorded during the period 2021-2024 at Mendoza City, Argentina. The results indicate that increased humidity and temperature might reduce PM levels by enhancing particle dispersion and deposition. Positive correlations between PM, NO, and NO suggest a shared origin, likely from vehicle emissions. To further analyze PM behavior, prediction models were developed to categorize PM levels as "good" (≤ 45 μg/m) or "bad" (>45 μg/m) based on a air quality guidelines from WHO. The performance of the random forest (RF) and logistic regression (LR) algorithms were evaluated and compared. Additionally, the influence of atmospheric variables and pollutant concentrations was also assessed to determine their impact on PM predictions. RF model demonstrated the highest predictive performance for PM level. Results indicate that NOx (NO and NO) significantly contribute to PM formation, likely due to shared anthropogenic sources. Temperature, humidity, and wind speed also impact PM predictions, though to a lesser extent than pollutant concentrations. The inclusion of these variables highlights the role in the dispersion and transformation of air pollutants. Implementing such models could provide policymakers with real-time data to enhance pollution control and public health protection.
Authors
Keywords
No keywords available for this article.
