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The aim of this study was to verify the prevalence of use of psychoactive substances (PS) and its associated factors in undergraduate students of a university in southern Brazil. The study was carried out with 830 undergraduate students in the year 2016. The individuals answered a self-administered questionnaire about the PS and its prevalence of daily use, in the last 30 days or at any time of their lives, as well as socioeconomic conditions and academic variables. Caffeine-based energy drinks was the most consumed psychoactive substance (96.3%) among undergraduates in the last 30 days, followed by alcohol (64.0%). Among the illicit drugs most consumed in the last 30 days was marijuana (17.3%), while anxiolytics and amphetamines were the most prevalent psychoactive medicaments in the last 30 days. The prevalence of lifetime illicit drugs used by these students was 41.5%, where we highlight besides marijuana (38.6%) the high consumption of cocaine (7.8%), ecstasy (9.3%) and solvents. Socioeconomic and demographic factors such as gender, have children, religion, and financial background as well as academic variables were associated to recent consumption of these substances. This study concluded there is a high prevalence of use of PS among the undergraduate students, including illicit drugs.

Introduction: International studies have demonstrated a positive impact on air quality associated with the presence of green areas in urban conglomerates. However, in Brazil, studies addressing the impacts of urban green areas on air quality are still incipient and are predominantly focused on large urban centers. The objective of this study was to investigate the relationship between urban green areas, surface temperature (LST), and air quality across 15 medium-sized Brazilian cities. Methods: Concentrations of particulate matter fractions (PM[sub.1], PM[sub.2.5], and PM[sub.10]) were monitored from January 2023 to May 2024 using second data from low-cost sensors. The NDVI and both daytime and nighttime LST profiles were extracted via Google Earth Engine within a 1 km buffer zone surrounding each station via the Sentinel-2 and MODIS 11A1 satellite data, respectively. Spatial–temporal co-variation patterns were explored using principal component analysis (PCA). To model these dynamics while controlling for spatial dependencies, a multi-criteria framework compared linear models (simple linear regression (LM) and linear mixed (LMM)) and generalized models (generalized additive (GAM) and generalized additive mixed (GAMM)). Results: The results revealed a positive relationship between NDVI and PM[sub.2.5] and PM[sub.10] fractions in specific regions, while surface temperatures showed a direct association with finer particles (PM[sub.1] and PM[sub.2.5]). The regression coefficient showed the significant association of PM2.5 with NDVI and nighttime LST (β = 1.330; IC 95%: [0.397; 2.270]; p = 0.005). The GAMM was the best-fitting model for all particle fractions, demonstrating that incorporating monitoring stations as random intercepts successfully controls for unmeasured local heterogeneity, while penalized splines accurately capture non-linear environmental factors. Conclusions: Although many studies have shown that green areas in temperate regions typically act as consistent sinks for particulate matter, our study revealed localized and seasonal responses in tropical urban landscapes. It should be noted that our study is conducted on a national scale and that the use of low-cost sensors and remote sensing does not allow us to distinguish between the localized microclimatic benefits of vegetation and the long-range transport of regional pollutants.

Air pollution remains a major global public health concern, with fine particulate matter (PM2.5) recognized as an important environmental risk factor for lung cancer. This ecological study assessed lung cancer mortality attributable to long-term PM2.5 exposure in the 26 Brazilian state capitals and the Federal District (Brasília) from 2014 to 2023. Annual mean PM2.5 concentrations were estimated using reanalysis-based PM2.5 concentration estimates and atmospheric reanalysis data, ensuring consistent spatial and temporal coverage. Mortality data were obtained from the Brazilian Mortality Information System (SIM/DATASUS). Health impacts attributable to PM2.5 exposure were estimated using the World Health Organization’s AirQ+ model, based on exposure–response functions from the Global Burden of Disease framework. During the study period, 97.41% of annual PM2.5 means exceeded the WHO Air Quality Guideline of 5 µg/m3, and 28.52% surpassed the current Brazilian regulatory limit. Higher concentrations were observed mainly in capitals from the North and Southeast regions, reflecting the influence of biomass burning, urbanization, and regional atmospheric processes. Approximately 13.56% of lung cancer deaths in Brazilian capitals were attributable to PM2.5 exposure, with the highest absolute numbers concentrated in the Southeast region. These findings demonstrate a substantial and spatially heterogeneous lung cancer burden associated with urban air pollution in Brazil and highlight the need for strengthened air quality management and targeted urban public health policies.

Air pollution, particularly particulate matter (PM1, PM2.5, 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, PM2.5, 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.

Flax (Linum usitatissimum L.) is valued for its fibers and nutrient-rich seeds, which are increasingly consumed for their health benefits. However, flaxseeds can also accumulate potentially toxic elements (PTEs), raising concerns about safety. This study quantified 11 essential elements (e.g., Ca, Fe, Mg, and Zn) and 9 PTEs (e.g., Al, Cd, Pb, and Ni) in commercial flaxseed samples using inductively coupled plasma–optical emission spectrometry. Two intake scenarios (15 g/day and 30 g/day) were analyzed to estimate dietary exposure, with health risks assessed through the target hazard quotient (THQ) and hazard index (HI). The results showed that organic flaxseeds had higher levels of certain elements (e.g., Cu, K, and Pb), while Al and Ni were more abundant in conventional samples. Cadmium levels in both remained below the EU regulatory limit. The highest estimated daily intakes were for K, Mg, and Ca, highlighting the seeds’ nutritional value. However, HI values suggested that Al and Pb could pose health risks. These findings emphasize flaxseeds’ dual nature as both beneficial and potentially harmful, particularly given the lack of specific regulatory limits and limited data on elemental composition. Continued monitoring and risk assessment are recommended to safeguard public health.

Fish are an important food and economic source in the Amazon region. However, several researches report high levels of mercury, mainly methylmercury (MeHg), in these animals. MeHg is capable of biomagnifying in the ecosystem and negatively affect human health. Amazon region is recognized for its great diversity of fish species and high daily consumption of fish, extremely vulnerable to the toxic effects of MeHg. Therefore, this study aimed to carried out a systematic review on MeHg in Amazon fishes by their eating habits and estimate the human exposure of MeHg by fish consumption. The search was carried out according to registered search protocol in the electronic databases PubMed/Medline and Web of Science. After screening selection, only five studies were included in this review. Carnivore/piscivore fishes showed the highest concentrations of MeHg (0.51±0.37 mg kg−1), followed by planktivores/iliophages (0.45±0.32 mg kg−1). Herbivore fish have the lowest MeHg concentration (0.11±0.09 mg kg−1). Both indexes (estimated weekly intake-EWI and provisional tolerable weekly intake-PTWI) were higher among women. EWI/PTWI ratio was higher than 1 for all eating habits, suggesting a risk to MeHg associated with the consumption of fish in the region, with higher rates in carnivorous/piscivorous fish, which may cause serious health problems to Amazon population. Thus, it is necessary to establish public politics to minimize exposure to MeHg in this region and to guarantee food and nutritional security.

In this study, hematological and behavioral changes in Wistar rats exposed to soil collected from urban areas next to an industrial complex were investigated. Animals were exposed to soil samples placed at the bottom of cages for 4 days. After this period, behavioral parameters were measured by the open field test and the elevated plus-maze. Blood was collected to measure hematological parameters. The soil from the vicinity of the oil refining industry caused changes in hematological parameters and altered behavioral parameters in both tests. The soil from the vicinity of the petroleum refining industry and fertilizer industries increased the density of white blood cells and decreased exploratory activity in the exposed animals. The results demonstrate that contact with contaminated soils, even for short periods, can cause physiological damage in organisms and that special attention should be given to people who live under constant exposure to these soils.

Petroleum refining and petrochemical complexes are significant sources of air pollution, emitting a variety of harmful pollutants with substantial health risks for nearby populations. While much of the information regarding this issue and the potential health impacts of this pollution has been documented, it remains fragmented across studies focusing on specific regions or health outcomes. These studies are often clustered into meta-analyses or reviews or exist as undeclared knowledge held by experts in the field, making it difficult to fully grasp the scope of the issue. To address this gap, our review consolidates the existing knowledge on the sources of air pollution from petroleum refining and petrochemical industries, the main pollutants involved, and their associated health outcomes. Additionally, we conducted an umbrella review of systematic reviews and meta-analysis and also included critical reviews. With this approach, we identified 12 reviews that comprehensively evaluate the health impacts in populations living near petroleum refining and/or petrochemical complexes. These reviews included studies spanning several decades (from 1980 to 2020) and encompassing regions across North America, Europe, Asia, South America, and Africa, reflecting diverse industrial practices and regulatory frameworks. From these studies, our umbrella review demonstrates that residents living near these facilities face elevated risks related to leukemia, lung and pancreatic cancer, nonmalignant respiratory conditions (such as asthma, cough, wheezing, bronchitis, and rhinitis), chronic kidney disease, and adverse reproductive outcomes. Furthermore, we discuss the key challenges in mitigating these health impacts and outline future directions, including the integration of cleaner technologies, which can significantly reduce harmful emissions; strengthening policy frameworks, emphasizing stringent emission limits, continuous monitoring, and regulatory enforcement; and advancing research on underexplored health outcomes. This review emphasizes the need for coordinated global efforts to align the industry’s evolution with sustainable development goals and climate action strategies to protect the health of vulnerable communities.

Studies around the world have revealed reduced levels of atmospheric particulate matter in periods of greatest human mobility restriction to contain the spread of SARS-CoV-2 during the COVID-19 pandemic. The present study aimed to carry out a health impact assessment in Recife, Brazil, hypothesizing a scenario in which the levels of PM 10 and PM 2.5 remained, throughout the year, as in the most restrictive period of human mobility. Particular material data (PM 10 and PM 2.5 ) were measured during the pandemic and population and health (mortality, hospital admissions for heart and respiratory problems) data from 2018 were used. We observed a reduction in the concentration of PM 2.5 in up to 43.7% and PM 10 up to 29.5% during the period of social isolation in the city of Recife. The reduction in PM 2.5 would avoid 106 annual deaths from non-external causes and 58 annual deaths from cardiovascular diseases. In this scenario, $ 294.88 million would be saved ($ 114.88 million from heart problems and $ 180 million from non-external causes). When considering hospitalizations avoided by the decrease in PM 10 , we observed 57 fewer hospitalizations for respiratory diseases, 42 for heart diseases and a reduction of 37 deaths due to non-external causes. The reduction in spending on respiratory and cardiovascular hospitalizations would exceed $ 330,000. Therefore, the reduction of particulate matter could prevent hospital admissions, deaths and consequently there would be a reduction in disease burden in developing countries where economic resources are scarce. In this sense, governments should seek to reduce levels of pollution in order to improve the life quality and health of the population.

This study investigated the relationship between surface meteorological variables and the levels of surface air pollutants (O3, PM10, and PM2.5) 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 PM2.5, 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.