The wildfires in Brazil in 2024 reached unprecedented levels, serving as yet another indicator of the effects of climate change. LIDAR measurements conducted in São Paulo detected intense aerosol plumes generated during the wildfire period in August and September in the Mato Grosso do Sul and Pará regions. This study investigates the potential transport of aerosol plumes generated by these wildfires to São Paulo. To analyze the relationship between these plumes and wildfires in other regions of Brazil, data from the recently launched EarthCARE satellite, backward trajectory analysis using the HYSPLIT model, and AERONET sun photometers and Raman–LIDAR measurements were utilized. As part of the EarthCARE satellite calibration and validation process, this study compared three days of LIDAR data collected for São Paulo. After validating the backscatter and extinction coefficients and the LIDAR ratio, the HYSPLIT model was used to determine the potential trajectories of the aerosol plumes on the days with the highest heat source index. Once the source locations were identified, their correlation with wildfire-affected areas was examined. Sun photometer data were analyzed to infer the properties of the aerosol plumes. The results indicated three trajectories that coincided with fire hotspots, enabling the identification of wildfires in the city of Corumbá (Mato Grosso do Sul) and São Félix do Xingu (Pará) as the likely primary sources of the aerosol plumes observed in São Paulo.

This study examines the climatology and synoptic patterns during the warmest and driest months in Northeast Brazil (NEB) using 120 years of monthly reanalysis data. The warmest months were detected based on the 90th (10% warmest) percentile of the maximum, minimum, and mean near-surface temperature from the Climate Research Unit (CRU), while the driest months were identified based on the SPEI-1 index from the Consejo Superior de Investigaciones Científicas (CISC). Both datasets have a spatial resolution of 0.5º x 0.5º and cover the period from 1901 to 2021. A total of 141 simultaneous drought and high-temperature events were detected, spanning 314 months (about 22% of the period), with 89% occurring between Austral spring and summer (August–March). The spatial extent of the events showed a seasonal shift, starting in the western sector of NEB between August and October and progressing eastward between November and April. The average event duration was 2.2 months, with the longest and strongest event lasting 9 months (June 2016–February 2017). The mean (maximum) spatial extent covered about 6% (11%) of NEB's territory, peaking at 47% (68%) during the second strongest event (August–December 2015). The mean (maximum) temperature and SPEI values during the events were 34.66ºC (38.10ºC) and -1.98 (-2.85), respectively. A Mann–Kendall test (95% confidence) revealed increasing (or decreasing) trends in all temperature and spatial variables (SPEI), suggesting an intensification of events over time. A synoptic analysis of the 10 strongest events based on the fifth-generation ECMWF atmospheric reanalysis (ERA5) suggested atmospheric blocking over the Atlântic and anomalous positioning of Upper Tropospheric Cyclonic Vortices (UTCVs) over NEB’s central and western sectors to be the main contributors to these events. It is believed that long-lasting UTCVs contribute to surface heating and precipitation reduction due to the subsidence of cold air in their center.

The current energy crisis, together with the depletion of fossil fuels and climate change, has made it essential to find alternative energy sources that are both economically sustainable and environmentally friendly. Renewable energy resources represent sustainable strategies for reducing the use of fossil fuels. Biomass is used to produce heat, electricity or biofuels through combustion or conversion. Most biomass comes from wood. The introduction of pellets in the bioenergy market aims to reduce environmental emissions, offering an alternative solution to polluting energy sources. In recent years, the trade and use of wood pellets as fuel has increased steadily, making the quality of the pellets essential to limit air pollution. Recent studies show that the use of pellets produces pollutants such as benzene and its derivatives, as well as heavy metals. Inhaling these substances causes an increase in the amount of reactive oxygen species within the body, with potentially harmful effects on human health. The impact of pellet use on air pollution and health was evaluated through the analysis of raw materials, sampled according to the UNI EN ISO 18135:2018 standard. In this study, the analysis of inorganic fractions, carried out with ICP-AES, showed that the most abundant elements are calcium, potassium and magnesium. The analysis of organic fractions, on the other hand, carried out with GC-MS, raised concerns about the presence of formaldehyde, an indication of the use of unsuitable raw materials. Statistical analysis confirmed that the inorganic fraction is fundamental for the quality of the pellets, but also highlighted that the organic fraction should not be neglected. The adoption of UNI EN ISO 17225 has led to an improvement in pellet quality, in particular by increasing its calorific value and reducing emissions into the environment. However, current regulatory gaps require further action to protect the environment and human health.

Introduction

The insect's body may potentially absorb pollutants from its surroundings while it is in flight. Furthermore, the sources of bee products, such as pollen, nectar and water, can be exposed to environmental contaminants, which can be transferred to bee products. Because of these characteristics, bees and honey are low-cost tools that are increasingly being used for environmental biomonitoring, which seeks to detect environmental contaminants within a reasonable radius (1.5–3 km) surrounding the hive. Heavy metals, defined as inorganic chemical pollutants, are a class of contaminants that can be found either in nature or as a result of anthropogenic activity. Anthropogenic sources of heavy metals encompass a wide range of activities, including mining, smelting, vehicle emissions, and the production of cosmetics. These pollutants are of particular concern due to their ability to generate reactive oxygen species (ROS) and disrupt vital bodily functions. This study investigates heavy metals in honey to verify the safety of the honey and evaluate environmental pollution in specific areas, highlighting the interconnectedness of honey and the environment.

Methods

Honey samples, collected from six hives located in the Molise Region of Italy, were mineralised and then analysed with ICP-AES, in accordance with the EPA method 6010C.

Results

The most common metals are aluminium, selenium and antimony. Cobalt, nickel, and cadmium exhibit the highest levels of variability, with standard deviations of 253.3%, 207.2% and 82.4%, respectively. Principal component analysis (PCA) and Pearson correlation were usedto ascertain which metals most accurately represent the data sample and how they are associated with each other in order to hypothesise the anthropic pollution sources.

Conclusion

In the atmospheric domain, forest fires and vehicular traffic were identified asthe two main sources of anthropogenic pollution .

Volcano eruptions significantly impact air quality, due to their large emissions of particulate matter, SO₂ and trace metals [1,2]. Exposure to these emissions can exert adverse effects on cardiorespiratory health as well as causing dermal and ocular irritation [2]. As such, it is important to understand which characteristics of volcanic air pollution are responsible for these reported negative health outcomes. To this end, two sets of PM_2.5 samples were collected at a suburban location during a period impacted by volcanic activity and a reference period. The structural features of these aerosol WSOM samples were assessed through liquid-state NMR spectroscopy, whereas their cytotoxic effects were evaluated on the THP-1 human monocytic cell line.

Overall, a total of 25 structures were identified in the WSOM samples, of which 12 were common to both samples. The WSOM samples effectively activated THP-1 monocytes and enhanced their expression of adhesion molecules. This effect over cells suggests that some WSOM components may accelerate the formation of atherosclerotic plaques, potentially elevating the risk of cardiovascular diseases. Nonetheless, and even though both samples presented toxicological properties, overall, the volcanic activity did not appear to cause a significant alteration in the chemical properties and cytotoxic effects over monocytes of the fine aerosol WSOM.

Thanks are due to FCT/MCTES for the financial support to CESAM (UID Centro de Estudos do Ambiente e Mar (CESAM) + LA/P/0094/2020) to iBiMED (UIDB/04501/2020, DOI: https://doi.org/10.54499/UIDB/04501/2020 and project reference UIDP/04501/2020, DOI: https://doi.org/10.54499/UIDP/04501/2020) and for a PhD grant (2020.05804.BD; DOI: https://doi.org/10.54499/2020.05804.BD) through national funds and the European Social Fund.

[1] Ilyinskaya, E., Schmidt, A., Mather, T.A., et al., Earth Planet. Sci. Lett. 472, 309–322.

[2] Stewart, C., Damby, D.E., Horwell, C.J., et al., Bull. Volcanol., 2022, 84.

Air pollutant gases emitted by anthropogenic activities significantly contribute to climate change and pose serious threats to human health. Among these pollutants, nitrogen dioxide (NO₂) and carbon monoxide (CO) are particularly significant contributors to urban air pollution. Satellite-based remote sensing has long been employed to monitor global air quality. This study aimed at investigating the temporal and spatial changes in average atmospheric NO₂ and CO concentrations in the Marmara Region of Türkiye during the summer (July–August) and winter (January–February) seasons between 2019 and 2024. For this purpose, we utilized high-resolution measurements from the recently launched Sentinel-5P TROPOMI sensor, which provides detailed insights into local air quality and pollution levels. Data processing was conducted using Google Earth Engine (GEE), and spatial distribution patterns were mapped in ArcGIS Pro. Within this framework, the study was structured around the following key research question: “How have NO₂ and CO concentrations changed seasonally and annually before and after the COVID-19 outbreak?” The findings revealed that NO₂ and CO concentrations exhibited seasonal fluctuations throughout the study period, with significant increases and decreases at specific intervals. Notably, a sharp decline in these pollutant levels was observed during the COVID-19 pandemic in 2020, whereas a considerable rebound has been detected in certain regions since 2021. Moreover, NO₂ and CO concentrations were found to be significantly higher in cities characterized by high population density and intensive industrial activities, such as Istanbul, Bursa, and Kocaeli. These findings offer critical insights into the spatiotemporal changes in NO₂ and CO emissions, underscoring the effectiveness of Sentinel-5P satellite data as a powerful tool for air quality monitoring. This study serves as a roadmap for policymakers by supporting policy development processes in cities striving to enhance air quality and ensure sustainable air pollution management through comprehensive spatial analyses of NO₂ and CO concentrations.

Introduction

University laboratories are complex environments characterized by confined spaces, high occupant density, and the frequent use of potentially hazardous chemicals. These factors contribute to elevated concentrations of airborne pollutants, which may pose health risks to students and laboratory personnel. Ensuring adequate indoor air quality (IAQ) is therefore crucial for safeguarding well-being and maintaining operational efficiency. This study aims to quantitatively assess IAQ in an analytical chemistry laboratory by comparing it with a controlled room under similar baseline conditions.

Methodology

Air quality was assessed using a DustTrak aerosol monitor for real-time measurements of particulate matter (PM) concentrations, specifically PM₁₀, PM_2.5, PM₁, and total PM. Measurements were conducted in both the laboratory and the controlled room to facilitate a direct comparison of environmental conditions.

Results

The findings indicate that PM_2.5 concentrations within the laboratory consistently exceeded the recommended exposure threshold of 15 µg/m³. This increase was particularly evident during student activities, suggesting that occupant presence and experimental procedures contributed to the emission and resuspension of particulate matter.

Conclusion

The elevated levels of particulate matter observed in the laboratory highlight the need for improved ventilation strategies and pollution mitigation measures. Maintaining optimal IAQ in laboratory environments is essential to protect the health and productivity of students and academic staff.

This study investigates future projections of the DTR over the South American area using CMIP6 modeled data on the maximum (Tmax) and minimum (Tmin) temperatures under two socioeconomic scenarios, SSP2-4.5 (low greenhouse gas emissions) and SSP5-8.5 (high emissions). The continental region was divided into 10 subregions, with the boundaries drawn based on their climatic characteristics, and for each one, ensembles of the five best-performing models were generated. This selection of models is based on a comparison of their historical series with an observed reference series obtained from the Climatic Research Unit (CRU)'s time series (TS). The statistical analysis, which used the modified Mann–Kendall test and the Theil–Sen slope estimator, reveals that Tmin and Tmax show an increasing trend over time. However, the increase in Tmin is more pronounced, resulting in a reduction in the DTR in most subregions. For example, it is estimated that in SSP2-4.5, the DTR will decrease by approximately –0.71 °C between the periods 2015–2025 and 2090–2100, while in SSP5-8.5, this reduction could reach approximately –1.04 °C. Some areas, such as the south of northeast Brazil, southeast Brazil, south Brazil, and Uruguay, show opposite patterns, with an increase in the DTR. These results reinforce the influence of anthropogenic factors in modulating the DTR, and the findings provide support for the development of policies to adapt to and mitigate the impacts of climate change on the South America continent.

Biosphere–atmosphere interaction, transport energy and matter underlays various biological, physical and chemical processes. Biogenic Volatile Organic Compounds (BVOCs) are highly involved in these processes and have significant effects not only within vegetation and organisms but also in the atmosphere at different spatiotemporal scales, mainly during heat waves and in densely populated areas. Currently, there is an ongoing global health threat due to the air we inhale both in indoor and outdoor environments, as well as a health crisis following the COVID-19 pandemic. The current European Union (EU) policy and World Health Organization (WHO) significantly contributed to our understanding of urban air quality and human health from air pollutants. Monoterpenes, the second major family of BVOCs, are considered important sources of ozone formation and Secondary Organic Aerosols. Their removal in the atmosphere through chemical processes leads to the formation of oxygenated compounds as first-generation Terpene Oxidation Products (TOPs). These compounds emitted by natural sources like plants and conifers have been detected in the atmosphere during observation campaigns. Hence, this project concerns the study of pollution by terpenes emitted from plants and trees and its consequences on the quality of the air we breathe, and its contribution to the global climate. We have chosen α and β-pinene-emitted first-generation products such as Nopinone (C₉H₁₄O), Limona ketone (C₉H₁₄O) and Myrtenal (C₁₀H₁₄O). The absolute rate coefficient was measured for the first time using a cryogenically cooled cell along with the pulsed laser photolysis–laser-induced fluorescence technique for OH radical. The hydrogen abstraction and OH addition pathways were found by using density functional theory method to determine the most favourable position. Our obtained results confirmed that these processes are of high interest for the Earth system scientific community and should be taken into account in Climate Models and Earth System Models.

Global Navigation Satellite System (GNSS) Meteorology has emerged as a valuable tool for atmospheric monitoring, providing high-resolution, near-real-time data that can significantly enhance nowcasting applications. By analyzing GNSS signal delays caused by atmospheric water vapor, it is possible to retrieve accurate estimates of Precipitable Water Vapor (PWV), a crucial parameter in short-term weather forecasting. This study presents a novel two-step machine learning framework for precipitation nowcasting, integrating GNSS-derived PWV with meteorological observations.

In the first step, a Random Forest (RF) model estimates precipitation based on GNSS-derived PWV, surface weather parameters, and auxiliary atmospheric variables. In the second step, a Long Short-Term Memory (LSTM) network predicts precipitation for the next hour, leveraging temporal dependencies within the data to improve forecasting accuracy. This hybrid approach combines the ability of RF to capture nonlinear relationships with the strength of LSTM in modeling sequential patterns.

The proposed methodology demonstrates interesting performance as compared to traditional forecasting models, particularly for extreme weather events such as intense rainfall and thunderstorms. The integration of GNSS meteorology with advanced machine learning techniques enhances short-term precipitation forecasting, offering a reliable tool for meteorological services, disaster prevention agencies, and early warning systems. This study highlights the potential of GNSS-based nowcasting for real-time decision-making in weather-related risk management.