Abstract: Air pollution is a critical environmental issue with diverse sources contributing to its escalation. One significant contributor is mining, an activity known for emitting high concentrations of atmospheric pollutants, often surpassing established regulatory standards. Such pollution events pose serious threats to public health, necessitating a comprehensive understanding of the interplay between atmospheric pollutants and meteorological variables. This research focuses on the city of Congonhas, a locale marked by the presence of the two largest iron mining producers in the country. The study aims to analyze the behavior of atmospheric pollutants in this region and their correlation with meteorological variables, employing a three-stage approach. The first stage involves a meticulous process of data collection, organization, and preliminary analysis from four air quality stations in Congonhas, covering the period from 2017 to 2022. This phase includes calculating descriptive statistics, verifying statistical representativeness based on the Technical Guide for Monitoring and Evaluation of Air Quality, and applying the Shapiro-Wilk Test to assess distribution normality. Moving to the second stage, the focus shifts to the current air quality situation in the city. This entails a comprehensive examination of both temporal and spatial variations of pollutant concentrations and adherence to air quality standards. The analysis includes comparing stations with hourly data, scrutinizing pollutant concentration behaviors over time, and calculating percentiles for daily and annual data to evaluate compliance with air quality standards. The final stage focuses on identifying relationships with meteorological variables. Spearman’s correlations are computed to discern connections between environmental variables and contaminant concentrations. The study employs the Python programming language and Google Colab for robust and comprehensive data analysis. Anticipated outcomes include observations that data adheres to a non-parametric distribution, concentrations exhibit a declining trend over the years but persist above reference values, and pollutant concentrations are notably elevated in proximity to mining sites. Furthermore, correlations with meteorological variables such as wind speed and humidity are expected to be notably significant. Looking ahead, the study aims to leverage meteorological variables exhibiting the strongest correlations, alongside pollutant concentrations, as input variables for developing advanced machine learning models. These models are envisioned to play a pivotal role in predicting concentrations of atmospheric pollutants in the Congonhas region, offering valuable insights for proactive decision-making to mitigate the impact of mining-related air pollution. In conclusion, this research addresses the pressing issue of air pollution in Congonhas resulting from iron mining activities. Through a meticulous three-stage methodology, encompassing data collection, air quality analysis, and correlation studies with meteorological variables, the study aims to contribute profound insights into the dynamics of atmospheric pollutants in this region. The expected outcomes will serve as a robust foundation for future endeavors, particularly in the development of predictive models, fostering effective pollution management, and safeguarding public health. As society grapples with the environmental consequences of industrial activities, such research becomes paramount for sustainable development and the well-being of communities.

Keywords: Minning, air pollution, meteorological variables, correlation.