This study assessed the influence of meteorological conditions on particulate matter (PM) concentrations in Campo Grande, Brazil, from May to December 2021. Using statistical analyses, including Pearson’s correlation coefficient and multivariate regression, we analyzed secondary data on PM2.5 and PM10 concentrations and meteorological variables from the Federal University of Mato Grosso do Sul’s Physics Department. Daily PM concentrations complied with Brazil’s National Ambient Air Quality Standards (PQAr). The PM2.5/PM10 ratios averaged 0.436 (hourly) and 0.442 (daily), indicating a mix of fine and coarse particles. Significant positive correlations were found with temperature, while relative humidity showed a negative correlation, reducing PM levels through deposition. Wind speed had no significant impact. Meteorological influences suggest that air quality management should be tailored to regional conditions, particularly addressing local emission sources like vehicular traffic and biomass burning.

Meteorological elements can affect the environment and cultures differently and may alter the natural development process contributing significantly to climate change. Meteorological variables of the Brazilian Pantanal were studied and used to determine evapotranspiration with fewer variables. It was found that artificial intelligence can substantially improve environmental modeling when alternative prediction techniques are used, resulting in lower project costs and more reliable results. This work tried to find the best combination by comparing machine learning techniques such as artificial neural networks, random forests, and support vector machines. A new model was created that depends on fewer climatic variables compared to the Penman–Monteith method (the standard method for estimating reference evapotranspiration) and can efficiently describe the reference evapotranspiration. Machine learning techniques are highly efficient for modeling environmental systems since they can process large amounts of data and find the best interactions between the parameters involved. In addition, more than 98% accuracy was obtained using fewer variables compared to the standard method when artificial neural networks are utilized.

Objective: to analyze epidemic curves based on mathematical models for the state of Mato Grosso do Sul and the impacts of population density on COVID-19 transmission. Method: the linear, polynomial and exponential regression model was used to make the numerical adjustment of the respective curves empirical. Result: it was found that the models used describe very well the empirical curves in which they were tested. In particular, the polynomial model is able to identify with reasonable reliability the appearance of the inflection point in the accumulated curves, which corresponds to the maximum point of the respective daily curves. The analysis indicates a weak positive correlation between infection, mortality, lethality and deaths from COVID-19 with population density, as revealed by the correlation and analysis of R2. Conclusion: the models are very effective in describing the COVID-19 and epidemic curves in the estimation of important epidemiological parameters, such as peak case curves and daily deaths, allowing practical and efficient monitoring of the evolution of the epidemic.