Modeling traffic by vehicle type and speed at street level across all hours remains a global challenge. We predicted hourly traffic flow and speed over southeastern and southern Brazil using XGBoost regression on 200,420 OpenStreetMap road segments. Training data combined toll plaza counts from the São Paulo State Regulatory Agency for Delegated Public Transport Services (ARTESP; 464 stations, March 4–10, 2024), EMME/2 travel demand model outputs from the São Paulo Company of Traffic Engineering (CET) and São Paulo Transporte (SPTRANS) (47,894 urban segments), Traffic Engineering Company of Rio de Janeiro (CET-Rio) loop detectors (967 sensors, used for speed training and cross-city validation), and TomTom probe speed data (14,703 segments). Spatial covariates included DMSP-OLS nighttime lights, IBGE population density, ESA WorldCover land cover, and road density at three buffer scales. The passenger car model achieved R² = 0.728 on held-out temporal data (Thursday-Sunday), with R² = 0.79 on weekdays degrading to 0.56 on Sundays. Motorcycles (R² = 0.611), light commercial vehicles (R² = 0.549), and trucks (R² = 0.559) followed similar spatial patterns. Buses follow fixed transit routes and were reconstructed from GTFS schedule interpolation instead of spatial modeling. The speed model achieved R² = 0.92 (MAE = 5.8 km/h) on held-out data. Cross-city validation against CET-Rio data in Rio de Janeiro, about 400 km from the training data coverage, showed that spatial relationships transfer between cities but absolute magnitudes do not: the uncalibrated model overpredicts Rio traffic by 1.6–1.8×, while blending a minimal local temporal sample (three days) recovers R² = 0.54-0.62 with bias within ±5%. We provide the complete VEIN-ready dataset (five classes × 24 hours × weekday/weekend) for all 200,420 road segments.

Global emission inventories often fail to capture the complexities of vehicular pollution in regions with unique fuel mixes, such as Brazil's extensive biofuel use, leading to significant uncertainties in atmospheric modeling. This study presents a century-long (1960-2100) bottom-up vehicular emission inventory for Brazil, leveraging locally derived emission factors. Our estimates reveal substantial discrepancies in magnitude, timing, and speciation of non-CO2 pollutants (CO, NMHC, PM2.5) compared to leading global inventories (EDGAR, CEDS, CAMS), highlighting critical inaccuracies in widely used data sets. More critically, future projections under Shared Socioeconomic Pathways (SSPs) uncover a novel positive feedback mechanism: rising temperatures significantly enhance vehicular evaporative nonmethane hydrocarbon (NMHC) emissions. This temperature-dependent increase and subsequent NMHC oxidation to CO2 suggest an overlooked pathway that could amplify climate warming and air pollution globally, particularly after a breakpoint around 2050 (p < 0.05). While historical emissions peaked in the 1990s-2000s, nonexhaust PM becomes increasingly important. Air quality simulations using our inventory in the MUSICA model show good regional PM2.5 agreement but highlight challenges in resolving local primary pollutant peaks. This comprehensive inventory provides crucial data for Brazil and uncovers globally relevant climate-chemistry interactions, urging a re-evaluation of regional specificities in global emission assessments.

Brazil presents a distinctive convergence of continental-scale climatic diversity, extensive urbanization, large-scale biomass burning, rapid land-use change, persistent air-quality monitoring gaps, and deep social inequalities, producing highly heterogeneous and compound environmental health risks. In this context, treating air pollution and climate change as parallel environmental crises obscures their structural interconnections through shared emission sources, mutually reinforcing exposure pathways, and overlapping health and social consequences. In this narrative review, we critically synthesize scientific and institutional lines of evidence and argue that air pollution and climate risks can be more effectively addressed in Brazil through a single strategic agenda for science, public health, and governance. We first discuss why these challenges cannot be managed in isolation, emphasizing the effects of heat, drought, stagnation events, biomass burning, and extreme weather on pollutant formation, dispersion, and health burden. We then examine Brazil as a critical case where recent regulatory advances coexist with structural limitations in monitoring, data integration, and territorial coverage. Based on this diagnosis, we propose an integrated national agenda organized around five mutually reinforcing priorities: monitoring through hybrid networks; predictive science through climate-informed modeling and early warning; public health through the convergence of epidemiology, toxicology, and mechanistic research; equity-oriented research and action through the explicit incorporation of vulnerability, inequality, and climate justice; and policy appraisal through the assessment of disease burden, economic costs, mitigation co-benefits, and trade-offs. We further discuss the governance mechanisms needed to connect these priorities and translate evidence into coordinated action and adaptive public policies. We also argue that the Amazon should be approached not as an isolated ecological exception but as a central component of a broader Brazilian and Global South discussion on environmental health, land-use change, and climate justice. In this scenario, Brazil has the scientific capacity and regulatory momentum to become a reference in the integrated management of air pollution and climate risks, but this will depend on replacing fragmented approaches with a coordinated framework capable of linking exposure, mechanism, burden, inequality, and action.

This study provides an integrated assessment of the climatology, variability, and long-term trends of precipitation and air temperature in a key agricultural region of northwestern São Paulo, Brazil, and evaluates their relationship with five large-scale climate oscillations. Using 40 years of high-resolution CHIRPS precipitation and ERA5-Land temperature data, the analysis identifies a significant long-term drying trend (–3.66 mm year⁻1) alongside a concurrent warming trend (+ 0.03 °C year⁻1). Breakpoint analysis reveals a statistically significant intensification of warming after 2000, indicating a shift in the thermal regime in recent decades. Composite analyses demonstrate that regional hydroclimatic variability reflects the combined influence of tropical and extratropical forcings, with seasonally varying influence. The El Niño–Southern Oscillation (ENSO) acts as the primary driver of spring precipitation, whereas the Antarctic Oscillation (AAO) predominantly controls autumn and winter temperatures and contributes to winter precipitation variability, with Atlantic variability acting as a secondary modulator. The Pacific Decadal Oscillation (PDO) further exhibits a seasonally contrasting thermal influence, with warming in autumn and cooling in winter. These results indicate increasing climate risk for the region and its agricultural systems, while also providing a basis for seasonal predictability and climate-informed resource management in transitional regions.

Air quality is a critical issue in metropolitan areas such as Sao Paulo, where high concentrations of secondary pollutants such as ozone (O3) are frequently observed. This study aims to analyze the contribution of several emission sources, especially vehicle and industrial emissions, to the variability of O3 concentrations in Sao Paulo State. Using results modeled by WRF-Chem and regression models, we evaluate the role of nitrogen oxides (NOx), volatile organic compounds (VOCs), and other important pollutants in the formation and removal of O3. The results indicate that NOx has a reducing effect on O3 concentrations in NOx-limited regions, while VOCs such as formaldehyde and ethanol have variable impacts depending on the emission source. Industrial areas, such as Paul & iacute;nia, show significant positive contributions of NO2 to O3 formation, while areas dominated by vehicle emissions, such as Interlagos, present more complex interactions. These findings provide insights into the effectiveness of pollution control strategies and highlight the importance of considering local and regional emission sources in O3 management efforts in Sao Paulo.

Air pollution is a growing public health concern, with diverse impacts on human health. This study aimed to conduct an exploratory analysis of the associations between air pollutants (O3, PM2.5, and PM10) and health outcomes, using ICD-10 Chapters, across 24 cities with different dimensions in the state of Rio Grande do Sul, Brazil. Three models were developed for both annual and monthly data: one encompassing all 24 cities (Model 1), another with medium and small-sized cities (Model 2), and the last exclusively focusing on small cities (Model 3). Multiple linear regression analyses were conducted with air pollutants and meteorological variables as independent variables, and hospitalization rates within each ICD-10 Chapters and specific respiratory diseases as dependent variables. Our analysis revealed significant positive associations among diverse chapters of the ICD-10 and air pollutants, with Model 3 exhibiting the most robust and significant positive associations with 12 chapters of the ICD-10 (Chapters: II, V, VI, VII, IX, X, XI, XII, XIV, XVI, XVII, XVIII, XIX, and XXI), highlighting the broad impact of pollution on human health beyond traditional respiratory and cardiovascular concerns. Moreover, positive associations were identified with specific respiratory diseases, including asthma, pneumonia, bronchiolitis, and acute bronchitis. Temperature, precipitation, and wind speed emerged as the meteorological factors most frequently associated with multiple health outcomes and ICD chapters. Notably, our findings reveal distinct patterns in associations across cities with different population sizes, highlighting the importance of considering contextual factors, such as city size, when assessing the health impacts of air pollution.

The Metropolitan Area of São Paulo (MASP) in Brazil has reduced its vehicular emissions in the last decades. However, it is still affected by air pollution events, mainly in the winter, characterized as a dry season. The chemical composition of fine particulate matter (PM2.5) was studied in the MASP during a 100 d dry period in 2019. PM2.5 samples underwent an extensive chemical characterization (including inorganic and organic species), ecotoxicity was assessed using a bioluminescence-based assay, and submicrometer particle number size distributions were simultaneously monitored. PM2.5 concentrations exceeded the new World Health Organization's daily guidelines on 75 %​​​​​​​ of sampling days, emphasizing the need for strengthening local regulations. Source apportionment (positive matrix factorization, PMF5.0) was performed, and the sources related to vehicular emissions remain relevant (over 40 % of PM2.5). A high contribution of biomass burning was observed, reaching 25 % of PM2.5 mass and correlated with sample ecotoxicity. This input was associated with north and northwest winds, suggesting other emerging sources besides sugarcane burning (forest fires and sugarcane bagasse power plants). A mixed factor of vehicular emissions and road dust resuspension increased throughout the campaign was related to stronger winds, suggesting a significant resuspension. The sulfate secondary formation was related to humid conditions. Additionally, monitoring size particle distribution allowed the observation of particle growth on days impacted by secondary formation. The results pointed out that control measures of high-PM2.5 events should include the control of emerging biomass-burning sources in addition to stricter rules concerning vehicular emissions.

Study region: The Paranapanema River Basin, located in southeastern Brazil, is characterized by a cascade of large hydropower plants regulated by a nationally coordinated dispatch system. This basin is a representative case of reservoir-regulated rivers in the country, where multiple dams interact to supply electricity while reshaping natural flow regimes. Study focus: This study examines hydropower-induced variability in river discharge and reservoir volumes using multiresolution wavelet decomposition and signal reconstruction. By analyzing continuous records under operational conditions, the method isolates fluctuations from sub-daily to multi-annual scales. This approach moves beyond average-based analyses, providing a scale-specific view of hydropower modulation. It shows how discharge dynamics arise not only from cascade configuration but also from dispatch coordination, plant design, and hydrological conditions. New hydrological insights for the region: Results show that flow variability patterns align with electricity demand profiles, drought episodes, and institutional milestones in the Brazilian power sector. Hydropower operations display distinct signatures at different time scales, highlighting the responsiveness and complexity of reservoir management. Reconstructing signals in original units improves interpretability and supports regulatory evaluation and energy planning. The proposed framework provides a standardized and reproducible way to assess variability in reservoir-regulated systems, enhancing comparability of hydropower assessments and identifying operational dynamics that shape river flow regimes. It also supports more adaptive and ecologically grounded approaches to hydropower governance in the Paranapanema Basin and beyond.

Atmospheric emissions inventories are a key tool for environmental managers, providing insights into emission sources and their effects on air quality, health, and climate. This study develops the atmospheric emissions inventory for four major industrial sectors in Brazil with a bottom-up approach, available at https://github.com/Martins-UTFPR/Brazil-s-industrial-emissions-inventory. We quantified atmospheric emissions from refining crude oil, producing cement, pulp and paper, and generating electricity by thermoelectric plants using biomass, gas, oil, and mineral coal. The quantification of atmospheric emissions and the standardization of the calculation matrices, processed using R code, follow the guidelines defined by the United States Environmental Protection Agency (EPA) and the European Environment Agency (EEA). The highest emission rates are in the country's southeastern region, specifically in outlying areas of the São Paulo state. In 2019, more than 50% of the estimated emissions are associated with electricity generation with contributions of 4.85 GgBC yr-1, 22.40 GgCO yr-1, 159.11 GgPM10 yr-1, 132.42 GgPM2.5 yr-1, 87.17 GgNMVOC yr-1, 360.90 GgNOx yr-1, 182.94 GgTSP yr-1, and 596.33 GgSOx yr-1. The production of fuels and oil derivatives is the second source of emissions with the highest contribution, despite operating 17 refineries in the country. Further, the comparison with the EDGAR inventory indicates consistency, although it shows higher values for some pollutants and sectors, as well as certain spatial differences. Finally, at National level, the magnitude of emissions is comparable to those from road transport. Thus, this study provides a region-specific and detailed emission inventory, offering crucial insights for both the scientific community and environmental agencies in improving air quality management.

Several studies have shown that the urbanization process has an impact on precipitation pattern, especially leading to an enhancement. Urban centers are expected to expand following the prospects of urban population increase. Understanding the possible impacts of future urbanization on precipitation becomes essential to aid in the development of mitigation plans and to reduce population vulnerability. The Metropolitan Area of Sao Paulo (MASP) is, mainly during summer, affected by heavy rain episodes with serious economic and social impacts. Here, the effects of three future scenarios on a storm event over the MASP are analyzed using numerical simulation. The scenarios are: LULC change, anthropogenic heat increase and a combination of both for 2050. Our results showed that rainfall rate is more intense in all future scenarios resulting in an increase on total precipitation. Still, urban expansion increased precipitation coverage area, while the increase in anthropogenic heat fluxes had a higher contribution on the intensity of precipitation. Low-level convergence and consequent upward motion were enhanced on future scenarios intensifying convection and consequent precipitation. Increasing anthropogenic heat flux also led to an increase in instability further contributing to convective enhancement. According to our conclusions, the scenario with both LULC change and anthropogenic heat increase is the most problematic one for the MASP with the most intense rainfall rates and an increase in 16 % in the area affected by precipitation.

Air pollution caused by Total Suspended Particles (TSP) poses significant environmental and health risks, particularly in regions with intensive soil-disturbing activities, such as mining areas. TSP concentrations are influenced by complex and nonlinear interactions between meteorological conditions and surface-related factors, presenting substantial challenges for controlling TSP emissions and their associated impacts. Few studies have developed data-driven approaches to identify and anticipate high-risk periods based on the dynamics of these drivers. This study aims to characterise the temporal dynamics and key drivers of TSP, and to delineate thresholds of these drivers associated with critical TSP episodes in a mining-intensive region of southeastern Brazil. Using observational data from 2023 and applying Kernel Ridge Regression (KRR), we explored hourly, daily, and monthly TSP patterns, assessed meteorological and surface-related drivers, and delineated thresholds of key drivers associated with extreme pollution episodes. Results show that TSP levels peak during dry months (July-September), on weekdays, and during morning and evening rush hours. Strong associations were observed between TSP and PM₁₀, with dry and low-humidity conditions favouring the increase of these pollutants. The KRR model explained 71% of the TSP variability, highlighting soil moisture, wind gusts, and consecutive dry periods as key predictors. Thresholds such as soil moisture <0.15 m3/m3, wind gusts >16 m/s, and consecutive dry periods >470 hours were critical for extreme events. This study provides a framework for anticipating critical TSP episodes based on key meteorological and surface-related drivers, enabling informed mitigation strategies in mining-impacted regions.

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.

Air pollution is a widespread problem that affects all areas of society because poor air quality results in an increase in the incidence of various diseases that are influenced by the environment, notable among which are kidney diseases. Studies indicate that prolonged exposure to moderate-tohigh concentrations of particulate matter (PM) can decrease renal function, promoting the progression of chronic kidney diseases and related problems. Despite emission controls on the road transport sector, people of the Metropolitan Area of São Paulo (MASP) have been exposed to air pollutants concentrations above the WMO guidelines for many years, especially surface ozone and fine particles.

The combination of urbanization and global warming leads to urban overheating and compounds the frequency and intensity of extreme heat events due to climate change. Yet, the risk of urban overheating can be mitigated by urban green-blue-grey infrastructure (GBGI), such as parks, wetlands, and engineered greening, which have the potential to effectively reduce summer air temperatures. Despite many reviews, the evidence bases on quantified GBGI cooling benefits remains partial and the practical recommendations for implementation are unclear. This systematic literature review synthesizes the evidence base for heat mitigation and related co-benefits, identifies knowledge gaps, and proposes recommendations for their implementation to maximize their benefits. After screening 27,486 papers, 202 were reviewed, based on 51 GBGI types categorized under 10 main divisions. Certain GBGI (green walls, parks, street trees) have been well researched for their urban cooling capabilities. However, several other GBGI have received negligible (zoological garden, golf course, estuary) or minimal (private garden, allotment) attention. The most efficient air cooling was observed in botanical gardens (5.0 ± 3.5°C), wetlands (4.9 ± 3.2°C), green walls (4.1 ± 4.2°C), street trees (3.8 ± 3.1°C), and vegetated balconies (3.8 ± 2.7°C). Under changing climate conditions (2070–2100) with consideration of RCP8.5, there is a shift in climate subtypes, either within the same climate zone (e.g., Dfa to Dfb and Cfb to Cfa) or across other climate zones (e.g., Dfb [continental warm-summer humid] to BSk [dry, cold semi-arid] and Cwa [temperate] to Am [tropical]). These shifts may result in lower efficiency for the current GBGI in the future. Given the importance of multiple services, it is crucial to balance their functionality, cooling performance, and other related co-benefits when planning for the future GBGI. This global GBGI heat mitigation inventory can assist policymakers and urban planners in prioritizing effective interventions to reduce the risk of urban overheating, filling research gaps, and promoting community resilience.