This study analyzes spatial-temporal deforestation patterns in Amazonas using 36 years of land use and land cover changes . We identified contiguous deforestation patches for each year and characterized their evolution using two geometric metrics: compactness, related to the shape of the patch and equivalent radius, proportional to the deforested area. These metrics enabled the aggregation of deforestation patches into four distinct regions within the Amazon, each exhibiting unique yet consistent characteristics with different temporal evolution. Typical distributions were found for these two metrics that allow to characterize space and time evolution of the deforestation for different land-use. Pasture patches showed a gamma distribution, while agricultural lands followed a lognormal distribution. Over time, pastures exhibited a trend towards lower compactness values, whereas agriculture and silviculture demonstrated shifts towards higher compactness. The equivalent radius distribution showed increased frequency of larger deforested areas over time. These findings underscore the utility of simple geometric metrics in understanding deforestation's spatial and temporal evolution, offering valuable insights into land-use dynamics estimation in the Amazon and providing a foundation for more effective monitoring and conservation strategies.

The Amazon rainforest is one of Earth's most diverse ecosystems, playing a key role in maintaining regional and global climate stability. However, recent changes in land use, vegetation, and the climate have disrupted biosphere-atmosphere interactions, leading to significant alterations in the water, energy, and carbon cycles. These disturbances have far-reaching consequences for the entire Earth system. Here, we quantify the relative contributions of deforestation and global climate change to observed shifts in key Amazonian climate parameters. We analyzed long-term atmospheric and land cover change data across 29 areas in the Brazilian Legal Amazon from 1985 to 2020, using parametric statistical models to disentangle the effects of forest loss and alterations of temperature, precipitation, and greenhouse gas mixing ratios. While the rise in atmospheric methane (CH4) and carbon dioxide (CO2) mixing ratios is primarily driven by global emissions (>99%), deforestation has significantly increased surface air temperatures and reduced precipitation during the Amazonian dry season. Over the past 35 years, deforestation has accounted for approximately 74% of the  ~ 21 mm dry season-1 decline and 16.5% of the 2°C rise in maximum surface air temperature. Understanding the interplay between global climate change and deforestation is essential for developing effective mitigation and adaptation strategies to preserve this vital ecosystem.

The Amazon biome is one of the largest carbon reservoirs, a relevant carbon sink in the world. The large extension and diversity of the Amazon biome hampers the assessment of regional-scale carbon budget based solely on local observations. Land surface models can provide carbon flux estimates, but they require proper calibration to represent the dynamics of the different ecosystems, abiotic conditions and vegetation characteristics in the Amazon Basin. One of the most important land surface model is JULES being increasingly used in tropical forests to estimate carbon fluxes. However, there is a lack of parameterization information that can be applied to the Amazon biome. Thus, this study presents an optimization of JULES main sensitivities parameters for different sites of the Amazon biome. For this attempt, we selected four Eddy-covariance flux towers as a reference based on different regions of the Amazon biome: K34 (Manaus, 2.614S/60.12W); K67 (Santarem, 2.85S/54.97W); RJA (Reserva Jaru, 10.08S/61.93W and ATTO (São Sebastião do Uatumã, 2.15S/59.03W). The variables analyzed to reproduce the carbon dynamics were the Net Ecosystem Exchange (NEE), Gross Primary Production (GPP) and eutrophic respiration (RESP) during one year of analysis. JULES most sensitivities parameters adjusted were related to the Upper-temperature threshold for photosynthesis (tupp_io); Scale factor for dark respiration (fd_io); The maximum ratio of internal to external CO2 (f0_io) and Quantum efficiency (alpha_io). The optimization was made using the Nelder-Mead method and after a leave-one-out cross-validation method was implemented to evaluate the simulation efficiency in each site. Also, the new parametrization in each site was compared with the default version of JULES and with another model Vegetation Photosynthesis and Respiration Model (VPRM). We selected the Wilmott index of agreement (d) and the Root Mean Square Error (RMSE) to analyze simulation efficiency. The Nelder-Mead optimization method reduced the error in GPP simulations in each Tower in comparison to the two models evaluated however the new parametrization of JULES was not able to improve RESP in these sites. However, the optimization procedure presented better results in NEE in each tower evaluated in the Amazon biome being the ATTO tower that demonstrated the most efficient simulations (d =0.60; RMSE = 2.03 g C m-2 day-1) in comparison to the default version (d= 0.52; 3.09 g C m-2 day-1) and VPRM (d = 0.58; 2.29 g C m-2 day-1). In general, results demonstrated that the new parametrization of JULES reduced the error of simulation compared to the last version of JULES for tropical forests and better represented the seasonality compared to the VPRM model.

As the impacts of climate change become increasingly evident, understanding the role of atmospheric aerosols in regulating Earth’s climate is crucial. Despite their significance, the optical properties of aerosols remain unclear to the general public and, particularly, to students in Physical and Earth sciences. This paper presents a didactic approach to exploring variations in aerosol optical properties, focusing on refractive indices and utilizing data from the National Aeronautics and Space Administration’s (NASA) AErosol RObotic NETwork (AERONET) under typical atmospheric conditions in central Amazon. The discussion presented here can be integrated into undergraduate and graduate university courses, linking Physics and environmental sciences and enhancing theoretical discussions in electromagnetism courses with observational data. The Amazon rainforest, with its dynamic atmospheric conditions influenced by seasonal changes, biomass-burning events, and long-range aerosol transport, provides a rich context for this analysis. Our study identifies seasonal patterns in aerosol optical depth, with biomass burning affecting light scattering at shorter wavelengths. African dust and smoke show strong absorption across a broader spectrum. We characterize the refractive index’s wavelength-dependent behavior for the different aerosol conditions, highlighting their unique optical properties. This interdisciplinary analysis contributes to a deeper understanding of atmospheric sciences and its implications for climate modeling and environmental assessment.

The Amazon rainforest is a critical component of the global carbon cycle, contributing approximately 16% of the terrestrial ecosystem's gross primary productivity and serving as a significant carbon sink through photosynthesis. The rainforest's ability to store carbon makes it an important sink, helping to mitigate climate change by absorbing carbon dioxide (CO₂) from the atmosphere. However, threats such as deforestation and land-use change can reduce this capacity, highlighting the importance of conserving and restoring the region. According to the Intergovernmental Panel on Climate Change (IPCC), if drastic measures are not taken to reduce greenhouse gas emissions, CO2 levels will continue to rise until 2100. This could have serious consequences for the global climate, including increased temperature, changes in precipitation patterns, and a rising sea level. One of the most concerning potential outcomes is the transition of the Amazon from a carbon sink to a carbon source, further amplifying climate change. Evaluating how the predicted climate change in Amazonas will impact the forest carbon uptake is important to quantify the effect, support adaptation, and reduce vulnerabilities.The main objective is to predict biogenic CO2 transport in the Amazon region in future land-use and climate scenarios. We will use the Weather Research and Forecasting model with Greenhouse Gases (WRF-GHG) to simulates CO2 transport in the Brazilian Amazon under two contrasting future IPCC scenarios: SSP2-4.5 ("Middle of the Road") and SSP5-8.5 ("Fossil-fueled Development"). These scenarios represent moderate and high emissions pathways, respectively. We will use climate projections from the Coupled Model Intercomparison Project Phase 6 (CMIP-6) and land-use projections from the Land-Use Harmonization 2 (LUH2) dataset for these simulations. These input data will be important to evaluate their effects on CO2 fluxes, concentrations, and transport dynamics. Through simulations under varying deforestation scenarios, we expect to observe substantial changes in CO2 distribution and atmospheric transport patterns across the Amazon.

During the dry season, the Amazonian atmosphere is strongly impacted by fires, even in remote areas. However, there are still knowledge gaps regarding how each aerosol type affects the aerosol radiative forcing. This work characterizes the chemical composition of submicrometer aerosols and source apportionment of organic aerosols (OAs) and equivalent black carbon (eBC) to study their influence on light scattering and absorption at a remote site in central Amazonia during the dry season (August-December 2013). We applied positive matrix factorization (PMF) and multilinear regression (MLR) models to estimate chemical-dependent mass scattering efficiency (MSE) and extinction efficiency (MEE). Mean PM1 aerosol mass loading was 6.3 +/- 3.3 mu g m-3, with 77 % of organics, grouped into 3 factors: biomass burning OA (BBOA), isoprene-epoxydiol-derived secondary OA (IEPOX-SOA) and oxygenated OA (OOA). The bulk scattering and absorption coefficients at 637 nm were 17 +/- 10 and 3 +/- 2 Mm-1, yielding a single scattering albedo of 0.87 +/- 0.03. Although eBC represented only 6 % of the PM1 mass loading, MSE was highest for the eBC (13.58-7.62 m2 g-1 at 450-700 nm), followed by BBOA (7.96-3.10 m2 g-1) and ammonium sulfate (AS, 4.79-4.58 m2 g-1). The MEE was dominated by eBC (30.8 %), followed by OOA (19.9 %) and AS (17.6 %). The dominance of eBC over light scattering, in addition to absorption, plays a remarkably important role for this important climate agent, with potentially broad implications for more precise radiative forcing quantification, increasing climate modeling precision and representing deep contributions to Earth's climate system comprehension.

Air quality conditions in many urban areas have improved in the last decades as a consequence of air pollution control policies and regulations. Mitigation strategies have been successful in reducing the concentration of primary pollutants like inhalable particulate matter (PM10), but the control of secondary pollutants like tropospheric ozone (O3) is still challenging in megacities like the Metropolitan Area of São Paulo (MASP) in Brazil. To support the development of effective mitigation strategies, it is crucial to characterize the statistical behavior of air pollutant concentrations and its long-term evolution. Probability Density Functions (PDF) can be useful to model site-specific air quality conditions, providing estimates for the frequency of extreme pollution events and exceedance of air quality standards. The current study aims to characterize which PDF model better fits and characterizes the variability of PM10 and O3 concentrations in the MASP. For that, daily maximum moving average concentrations were analyzed between 2000 and 2023, characterizing the long-term trends and the frequency of exceedance of air quality standards. PM10 concentrations followed a lognormal PDF, with an expected value of 31 ± 15 µg.m-3. O3 followed a Gamma PDF, with an expected value of 68 ± 25 µg.m-3. A consistent long-term decrease was observed for PM10 (-1.04 ± 0.09 µg.m-3.yr-1), while O3 showed an increasing trend of 0.51 ± 0.04 µg.m-3.yr-1 in the summer. In recent years (2021-2023), the probability of exceedance of the World Health Organization guideline was 17.4 and 11.0%, respectively, for PM10 and O3. In 2020, a statistically significant increase in O3 expected values was observed, possibly associated with changes in the emission patterns of precursors due to the mobility restrictions imposed by the COVID-19 pandemic.

Numerous studies globally have centered on atmospheric air pollution due to its profound health and climate effects. NASA’s AERONET (National Aeronautics and Space Administration - AErosol RObotic NETwork) network has been one of the world’s leading tools for accessing the physical properties of atmospheric aerosols from various sources, mainly anthropogenic ones. This study proposes a new approach to evaluate the Aerosol Optical Depth (AOD) and precipitable water vapor (PWV) seasonality and the influence of short-term perturbations, such as the presence of local and regional aerosol sources or meteorological events, based on the temporal autocorrelation function (ACF). We introduce the adimensional seasonal assessment autocorrelation function, Δ _ACF,k , as a parameter to quantify the influence of the short-term perturbation, and we use its average, ⟨Δ _ACF,k⟩ , as a proxy for seasonality loss. The smaller ⟨Δ _ACF,k⟩ , the lower the influence of high-frequency perturbations on seasonality. Nine AERONET network sites in South America with different environmental characteristics were evaluated. The selected sites were São Paulo, Rio Branco, Manaus, ATTO (Amazon Tall Tower Observatory), Alta Floresta, Ji-Paraná, Cuiabá, Arica, and La Paz. The results showed that sites with less local anthropogenic aerosol sources acting as short-term perturbations had pronounced AOD seasonality and a linear relationship between the ACF functions of AOD, PWV, and the simulated direct solar radiation. As local anthropogenic sources become more prominent, the AOD ACF is attenuated and has less amplitude in seasonal oscillations. In addition, the relationship between AOD and PWV ACF becomes more attenuated. Buenos Aires has shown to be the most affected site, with ⟨Δ _ACF,AOD⟩ of 0.47, followed by São Paulo and La Paz. The areas in the Amazonian deforestation arc had relatively close average Δ _ACF,AOD , with Alta Floresta representing the most influenced by short-term perturbations. Central Amazonian sites had the lowest Δ _ACF,AOD averages, of about 0.25, which means that constant local anthropogenic sources do not dominate the AOD seasonality and that the wet deposition still plays an essential role in regulating the aerosol sources in the atmosphere. In contrast, the behavior of ⟨Δ _ACF,PWV⟩ in the Amazon region varies mainly due to meteorological influences, with the highest values observed in the central region, likely related to the high amount of water vapor in the atmosphere, and more pronounced seasonality near deforestation arcs and major cities. The proposed method eliminates the need for a reference site when comparing seasonalities of different time series, enabling valid comparisons across different areas without a comparative reference point. The method can be further applied to other atmospheric time series, including greenhouse gases.

This study investigates the rain-initiated mixing and variability in the mixing ratio of selected trace gases in the atmosphere over the central Amazon rain forest. It builds on comprehensive data from the Amazon Tall Tower Observatory (ATTO), spanning from 2013 to 2020 and comprising the greenhouse gases (GHGs) carbon dioxide (CO2) and methane (CH4); the reactive trace gases carbon monoxide (CO), ozone (O3), nitric oxide (NO), and nitrogen dioxide (NO2); and selected volatile organic compounds (VOCs). Based on more than 1000 analyzed rainfall events, the study resolves the trace gas mixing ratio patterns before, during, and after the rain events, along with vertical mixing ratio gradients across the forest canopy. The assessment of the rainfall events was conducted independently for daytime and nighttime periods, which allows us to elucidate the influence of solar radiation. The mixing ratios of CO2, CO, and CH4 clearly declined during rainfall, which can be attributed to the downdraft-related entrainment of pristine air from higher altitudes into the boundary layer, a reduction of the photosynthetic activity under increased cloud cover, and changes in the surface fluxes. Notably, CO showed a faster reduction than CO2, and the vertical gradient of CO2 and CO is steeper than for CH4. Conversely, the O3 mixing ratio increased across all measurement heights in the course of the rain-related downdrafts. Following the O3 enhancement by up to a factor of 2, NO, NO2, and isoprene mixing ratios decreased. The temporal and vertical variability of the trace gases is intricately linked to the diverse sink and source processes, surface fluxes, and free-troposphere transport. Within the canopy, several interactions unfold among soil, atmosphere, and plants, shaping the overall dynamics. Also, the mixing ratio of biogenic VOCs (BVOCs) clearly varied with rainfall, driven by factors such as light, temperature, physical transport, and soil processes. Our results disentangle the patterns in the trace gas mixing ratio in the course of sudden and vigorous atmospheric mixing during rainfall events. By selectively uncovering processes that are not clearly detectable under undisturbed conditions, our results contribute to a better understanding of the trace gas life cycle and its interplay with meteorology, cloud dynamics, and rainfall in the Amazon.

The wet-season atmosphere in the central Amazon resembles natural conditions with minimal anthropogenic influence, making it one of the rare preindustrial-like continental areas worldwide. Previous long-term studies have analyzed the properties and sources of the natural Amazonian background aerosol. However, the vertical profile of the planetary boundary layer (PBL) has not been assessed systematically. Since 2017, such a profile assessment has been possible with the 325 m high tower at the Amazon Tall Tower Observatory (ATTO), located in a largely untouched primary forest in the central Amazon. This study investigates the variability of submicrometer aerosol concentration, size distribution, and optical properties at 60 and 325 m in the Amazonian PBL. The results show significant differences in aerosol volumes and scattering coefficients in the vertical gradient. The aerosol population was well-mixed throughout the boundary layer during the daytime but became separated upon stratification during the nighttime. We also found a significant difference in the spectral dependence of the scattering coefficients between the two heights. The analysis of downdrafts and the related rainfall revealed changes in the aerosol populations before and after rain events, with absorption and scattering coefficients decreasing as optically active particles are removed by wet deposition. The recovery of absorption and scattering coefficients is faster at 325 m than at 60 m. Convective events were concomitant with rapid increases in the concentrations of sub-50 nm particles, which were likely associated with downdrafts. We found that the aerosol population near the canopy had a significantly higher mass scattering efficiency than at 325 m. There was also a clear spectral dependence, with values for λ=450, 525, and 635 nm of 7.74±0.12, 5.49±0.11, and 4.15±0.11 m2 g−1, respectively, at 60 m, while at 325 m the values were 5.26±0.06, 3.76±0.05, and 2.46±0.04 m2 g−1, respectively. The equivalent aerosol refractive index results, which were obtained for the first time for the wet season in the central Amazon, show slightly higher scattering (real) components at 60 m compared to 325 m of 1.33 and 1.27, respectively. In contrast, the refractive index's absorptive (imaginary) component was identical for both heights, at 0.006. This study shows that the aerosol physical properties at 60 and 325 m are different, likely due to aging processes, and strongly depend on the photochemistry, PBL dynamics, and aerosol sources. These findings provide valuable insights into the impact of aerosols on climate and radiative balance and can be used to improve the representation of aerosols in global climate models.

Air pollution is one of the main environmental problems in metropolitan areas. Negative impacts to human health are intensified when poor air quality conditions persist for many consecutive days, with gradual accumulation of pollutants in the atmosphere. Persistent air quality deterioration events are typically associated with occurrence of stagnant atmospheric conditions and reach the regional scale. In this study, data-driven models were developed to forecast the occurrence of persistent air pollution events of inhalable particulate matter (PM10) and ozone (O3) in the metropolis of Sao Paulo, Brazil. On average, 8 events per year were observed between 2005 and 2022, comprising 73 event days per year. The logistic regression method was used in a supervised learning framework. Daily timeseries of surface weather variables were used as predictors. In the case of PM10, a consistent long-term decrease in the number events impacted the model performance. The PM10 model benefited from the restriction of the training set to recent years, with a significant increase in the model accuracy despite the reduction in the volume of data. The final models correctly reproduced the seasonal distribution of events, with overall accuracies of 0.92 and 0.87 for O3 and PM10, respectively, in 2022. Despite the fact that persistent exceedance events are relatively rare, the models were able to detect 81% and 97% of the event days in 2022, respectively for O3 and PM10. Daily maximum temperature was an important predictor, increasing the event odds by 483% (O3) and 84% (PM10). The classification models developed in this study can successfully forecast the occurrence of regional air pollution events concerning both primary and secondary air pollutants, which have different drivers for accumulation in the atmosphere. The models require simple input data and low computational resources, aiming to stimulate future usage by the general public and decision-makers, in order to mitigate exposure to harmful air pollutant concentrations.

Objective Air pollution emission associated with wildfires is a global concern, contributing to air quality deterioration and severely impacting public health. This narrative review aims to provide an overview of wildfire smoke (WFS) characteristics and associated impacts on adults’ and children's health. Data source Literature review based on a bibliographic survey in PubMed (National Library of Medicine, United States), SciELO (Scientific Electronic Library Online), and Google Scholar databases. Observational, cross-sectional, longitudinal, and review studies were considered, prioritizing peer-reviewed articles published in the last 10 years (2014–2024). Data synthesis Wildfire smoke (WFS) contributes to the deterioration of air quality, resulting in increased exposure to air pollution especially in wildland-urban interfaces. WFS contains particulate matter (PM) in a range of sizes and chemical compositions, as well as multiple toxic gasses. The health impacts of WFS are systemic, affecting the respiratory, cardiovascular, and neurological systems. Exposure to WFS is associated with inflammatory and oxidative stress, DNA damage, epigenetic modulations, and stress-disorders in adults and children. Children may be at an increased risk of WFS respiratory impacts, due to their smaller airways and developing lungs. Conclusion Wildfires are increasing in frequency and intensity, resulting in thousands of premature deaths and hospitalizations worldwide, each year. Preventive measures against wildfire spread must be reinforced, considering the increasing trends of global warming and extreme weather events. Adaptation strategies should be undertaken especially in wildland-urban interface regions, including the improvement of early warning systems, improvement of health care facilities and household preparedness and promotion of risk communication campaigns.

Abstract Soils are a major source of nitrogen oxides, which in the atmosphere help govern its oxidative capacity. Thus the response of soil nitric oxide (NO) emissions to forcings such as warming or forest loss has a meaningful impact on global atmospheric chemistry. We find that the soil emission rate of NO in Amazonia from a common inventory is biased low by at least an order of magnitude in comparison to tower‐based observations. Accounting for this regional bias decreases the modeled global methane lifetime by 1.4%–2.6%. In comparison, a fully deforested Amazonia, representing a 37% decrease in global emissions of isoprene, decreases methane lifetime by at most 4.6%, highlighting the sensitive response of oxidation rates to changes in emissions of NO compared to those of terpenes. Our results demonstrate that improving our understanding of soil NO emissions will yield a more accurate representation of atmospheric oxidative capacity.

Interactions between atmospheric aerosols, clouds, and precipitation impact Earth's radiative balance and air quality, yet remain poorly constrained. Precipitating clouds serve as major sinks for particulate matter, but recent studies suggest that precipitation may also act as a particle source. The magnitude of the sources versus sinks, particularly for cloud condensation nuclei (CCN) numbers, remain unquantified. This study analyzes multi-year in situ observations from tropical and boreal forests, as well as Arctic marine environment, showing links between recent precipitation and enhanced particle concentrations, including CCN-sized particles. In some cases, the magnitude of precipitation-related source equals or surpasses corresponding removal effect. Our findings highlight the importance of cloud-processed material in determining near-surface particle concentrations and the value of long-term in situ observations for understanding aerosol particle life cycle. Robust patterns emerge from sufficiently long data series, allowing for quantitative assessment of the large-scale significance of new phenomena observed in case studies. Atmospheric aerosols, clouds, and precipitation play a significant role in Earth's temperature regulation and air quality. However, understanding their interactions is still a challenge. While clouds and precipitation help remove particles from the atmosphere, recent research suggests rain could also introduce new particles. The extent of this particle source and its impact on climate are still unknown. In this study, we analyzed years of observational data from clean environments, including tropical and boreal forests and the Arctic marine boundary layer. We discovered that after precipitation, new particles were sometimes added to the surface atmosphere. In some cases, rain introduced as many or even more particles than it removed. Our findings highlight the importance of considering how clouds and rain recycle particles when studying air quality and climate. Long-term, real-world observations help us understand atmospheric particle life cycles and identify consistent patterns, ultimately improving our knowledge of the complex interactions between aerosols, clouds, and precipitation. Precipitation can act as a source for particles of varying sizes depending on the environment, reflecting diverse underlying mechanismsRecycling cloud-processed material influences near-surface particle concentrations, emphasizing its relevance for climate model implementationStudying the time-dependent instead of total accumulated precipitation elucidates direct versus indirect effects on aerosol populations