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.

(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.

Regional inequalities in basic sanitation remain a major public health challenge in Brazil. This study assessed the relationship between sanitation indicators and health outcomes related to Diseases Related to Inadequate Environmental Sanitation (DRSAI) in Brazilian capitals from 2010 to 2019. Mortality and hospitalization data were obtained from DATASUS, and water and sewerage coverage indicators from the National Sanitation Information System (SNIS). Pearson correlations and hierarchical cluster analyses were used to explore spatial patterns and associations. Pronounced regional disparities were observed. Northern and Northeastern capitals, including Macapá, Recife, and Salvador, had the highest DRSAI mortality and hospitalization rates, coinciding with the lowest sewerage coverage (<30%) and limited potable water access (<60%). In contrast, Southern and Southeastern capitals, including Curitiba, Porto Alegre, and São Paulo, showed better sanitation and health indicators. Sewage coverage was significantly and negatively correlated with hospitalization and mortality rates (p<0.05), indicating an inverse association between sanitation coverage and DRSAI outcomes. The findings highlight the central role of sanitation in reducing inequalities in DRSAI-related hospitalizations and mortality. Despite progress, universal access remains distant and requires targeted investments and integrated public policies to improve sanitation and public health outcomes in Brazil, particularly in the country’s most socially vulnerable urban areas.

Exposure to contaminated soils can adversely affect health and the well-being of both humans and animals. Environmental stressors can influence the mobility and toxicity of contaminants, altering their potential impacts. This study aimed to assess the impact on the behavior of offspring from rats exposed during the gestation and lactation period to contaminated and acidified soils. Female Wistar rats were gavaged daily for 42 days with soil leachate from an industrial region known to be contaminated with metals and metalloids, using solvents with different pH values (6.0, 5.2, and 3.6). The offspring were evaluated in behavioral tests including Open Field, Elevated Plus Maze, and Inhibitory Avoidance. Our findings revealed significant statistical differences in all three tests conducted, indicating that the exposed groups exhibited lower exploratory behavior, higher anxiety behavior, and lower memory retention than the control groups. The difference was more pronounced in the soil leachate with acidified solvent, at both pH 5.2 and 3.6, suggesting that the combined effect of both stressors led to synergistic interactions, potentiating their impacts. Elemental analysis revealed elevated levels of neurotoxic metals, including Cr, Cu, and Ni, as well as the metalloid As, with acidification significantly enhancing their bioavailability. Moreover, our results demonstrate that acidification facilitated the mobilization of metals and the metalloid As, increasing their bioavailability and acting synergistically to exacerbate the behavioral impacts of contaminated soils. Special attention should be given to populations living in industrial areas that may be exposed to contaminated soils.