This study investigated the relationship between surface meteorological variables and the levels of surface air pollutants (O₃, PM₁₀, and PM_2.5) in scenarios of simulated temperature increases in Rio Grande, a medium-sized Brazilian city with strong industrial influence. This study utilized five years of daily meteorological data (from 1 January 2019 to 31 December 2023) to model atmospheric conditions and two years of daily air pollutant data (from 21 December 2021 to 20 December 2023) to simulate how pollutant levels would respond to annual temperature increases of 1 °C and 2 °C, employing a Support Vector Machine, a supervised machine learning algorithm. Predictive models were developed for both annual averages and seasonal variations. The predictive analysis results indicated that, when considering annual averages, pollutant concentrations showed a decreasing trend as temperatures increased. This same pattern was observed in seasonal scenarios, except during summer, when O₃ levels increased with the simulated temperature rise. The greatest seasonal reduction in O₃ occurred in winter (decreasing by 10.33% and 12.32% under 1 °C and 2 °C warming scenarios, respectively), while for PM₁₀ and PM_2.5, the most significant reductions were observed in spring. The lack of a correlation between temperature and pollutant levels, along with their relationship with other meteorological variables, explains the observed pattern in Rio Grande. This research provides important contributions to the understanding of the interactions between climate change, air pollution, and meteorological factors in similar contexts.

Air pollution, particularly particulate matter (PM₁, PM_2.5, and PM₁₀), poses a significant environmental health risk globally. This study evaluates the predictive performance of three machine learning algorithms, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF), for forecasting particulate matter concentrations in four Brazilian cities (Porto Alegre, Recife, Goiânia, and Belém), which share similar demographic and urbanization characteristics but differ in geographic and climatic conditions. Using data from the Copernicus Atmosphere Monitoring Service, daily concentrations of PM₁, PM_2.5, and PM₁₀ were modeled based on meteorological variables, including air temperature, relative humidity, wind speed, atmospheric pressure, and accumulated precipitation. The models were tested under two climate change scenarios (+2 °C and +4 °C temperature increases). The results indicate that RF consistently outperformed the other models, achieving low RMSE values, around 0.3 µg/m³, across all cities, regardless of their geographic and climatic differences. KNN showed stable performance under moderate temperature increases (+2 °C) but exhibited higher errors under more extreme warming, while SVM demonstrated higher sensitivity to temperature changes, leading to greater variability in bivariate contexts. However, in multivariate contexts, SVM adjusted better, improving its predictive performance by accounting for the combined influence of multiple meteorological variables. These findings underscore the importance of selecting suitable machine learning models, with RF proving to be the most robust approach for particulate matter prediction across diverse environmental contexts. This study contributes valuable insights for the development of region-specific air quality management strategies in the face of climate change.

Air pollution, particularly from coarse particulate matter (PM₁₀), is a major public health concern, significantly contributing to respiratory and cardiovascular diseases, especially among vulnerable populations. In 2024, Brazil introduced a new air quality resolution (CONAMA Resolution No. 506/2024), aligning its ultimate goal with the World Health Organization’s 2021 guidelines while establishing specific timelines for the interim targets. However, these interim targets, set for 2025, 2033, and 2044, along with the absence of a deadline for the final target of 15 µg/m³, raise concerns about their adequacy in addressing the urgent health impacts of air pollution. This study evaluates the economic and public health benefits of accelerating these targets in the city of Rio Grande, a region characterized by an industrial and port-driven economy and an aging population particularly vulnerable to air pollution. Using health impact assessments, economic cost analyses, and mortality estimates, we modeled three scenarios with PM₁₀ concentration limits of 30 µg/m³, 20 µg/m³, and 15 µg/m³, corresponding to the resolution’s 2033 and 2044 goals and the undated final target. Our findings indicate that achieving the 15 µg/m³ target by 2025 could prevent 2568 respiratory hospitalizations, 1551 cardiac hospitalizations, and 1128 air pollution-related deaths in Rio Grande, resulting in approximately BRL 7.3 million in healthcare savings. When extrapolated to cities with similar pollution profiles across Brazil, these results suggest substantial potential for reducing the health burdens and economic costs nationwide. This study underscores the urgent need to establish more ambitious timelines in Brazil’s air quality policies to maximize public health benefits and mitigate the economic impacts of air pollution.

(1) Background: This study investigated seasonal variations in particulate matter (PM) ratios (PM₁/PM_2.5, PM_2.5/PM₁₀, and PM₁/PM₁₀) and their relationship with the meteorological conditions in Rio Grande, Brazil. (2) Methods: PM₁, PM_2.5, and PM₁₀ levels were collected using low-cost Gaia Air Quality Monitors, which measured PM concentrations at high temporal resolution. Meteorological variables, including atmospheric pressure, temperature, relative humidity, wind speed, and precipitation, were obtained from the National Institute of Meteorology (INMET). The data were analyzed through multiple linear regression to assess the influence of meteorological factors on PM ratios. (3) Results: The results show that the highest PM ratios occurred in winter, indicating a predominance of fine and ultrafine particles, while the lowest ratios were observed in spring and summer. Multiple linear regression analysis identified atmospheric pressure, wind speed, and maximum temperature as the key drivers of PM distribution. (4) Conclusions: This study highlights the importance of continuous monitoring of PM ratios, particularly PM₁, which remains underexplored in Brazil. The findings underscore the need for targeted air quality policies emphasizing seasonal mitigation strategies and improved pollution control to minimize the health risks associated with fine and ultrafine PM exposure.