Publicações

Low-density peats from many countries have long been studied by many authors. They are mostly forest and moss peats. Different peat types, from different locations in the world, may present degradation behavior that differs significantly. Bulk density, which is one the most important physical parameters governing the smoldering dynamics of solid fuels, varies among countries’ peatlands. This work compares experimental data for low (200 kg m(-3)) and high (450 kg m(-3)) bulk density peat smoldering in a test bench. Peat sam-ples with different densities, organic matter contents, and particle sizes were considered in this work. The physicochemical characterization included ultimate analysis, ash composition, the density of the solid, and thermogravimetric and differential thermogravimetric analysis. The influences of the organic matter content, as well as the bulk surface area to volume ratio on peat burning, are also discussed. It is showed that, for the same organic matter and bulk surface area to volume ratio, the high bulk density peat burning rate is as low as one-third of the low bulk density peat burning rate. Once the High Heating Value of both peats is found similar, this lower burning rate is related to chemical kinetics, which shows higher oxidation temperature, around 500 degrees C for the high bulk density peat, and 300 degrees C for the low bulk density peat. For the high bulk density peat, the higher the bulk surface area to volume ratio the higher both horizontal and vertical smoldering spread rates.

O aquecimento global torna necessário o benchmarking do tecido urbano para desenvolver políticas que possam promover a resiliência urbana ao calor. O objetivo desta pesquisa é identificar padrões do tecido urbano que produzem a retroalimentação negativa do sistema climático urbano naturalmente. O método contempla o uso de imagens Landsat-8, para estimar e mapear a temperatura das superfícies e suas características espectrais (albedo, emissividade, fração de cobertura de vegetação e índice de umidade por diferença normalizada), a integração dos dados em um espaço celular georreferenciado, análises de correlação, autocorrelação e de clusters, e identificação de zonas climáticas locais. A área de estudos é São José dos Campos, SP, Brasil, onde a zona climática local 3, com construções compactas de pequeno porte, é predominante. A maior diferença de temperatura de superfície entre clusters foi 12 °C e ocorreu entre áreas verdes e permeáveis e áreas áreas plenamente impermeabilizadas com extensas construções. As análises permitiram inferir a dependência espacial da temperatura da superfície, apesar da variabilidade sazonal, indicando que ela pode ser utilizada para identificar fatores que influenciam processos de retroalimentação do sistema climático urbano, zonas climáticas locais, e auxiliar no desenvolvimento de políticas de adaptação e resiliência urbana ao calor.

The United Nations Sustainable Development Goals (SDGs) are described as integrated and indivisible, where sustainability challenges must be addressed across sectors and scales to achieve global-level sustainability. However, SDG monitoring mostly focuses on tracking progress at national-levels, for each goal individually. This approach ignores local and cross-border impacts of national policies and assumes that global-level progress is the sum of national, sector-specific gains. In this study, we investigate effects of reforestation programs in China on countries supplying forest and agricultural commodities to China. Using case studies of rubber and palm oil production in Southeast Asian countries, soy production in Brazil and logging in South Pacific Island states, we investigate cross-sector effects of production for and trade to China in these exporting countries. We use a three-step multi-method approach. 1) We identify distal trade flows and the narratives used to justify them, using a telecoupling framework; 2) we design causal loop diagrams to analyse social-ecological processes of change in our case studies driven by trade to China and 3) we link these processes of change to the SDG framework. We find that sustainability progress in China from reforestation is cancelled out by the deforestation and cross-sectoral impacts supporting this reforestation abroad. Narratives of economic development support commodity production abroad through unrealised aims of benefit distribution and assumptions of substitutability of socio-ecological forest systems. Across cases, we find the analysed trade supports unambiguous progress on few SDGs only, and we find many mixed effects where processes that support the achievement of SDGs exist, but are overshadowed by counterproductive processes. Our study represents a useful approach for tracking global-level impacts of national sustainability initiatives and provides cross-scale and cross-sectoral lenses through which to identify drivers of unsustainability that can be addressed in the design of effective sustainability policies.

Reservoirs of hydropower plants (HPP) can amend water, energy, and food security in semi-arid regions. However, during severe droughts, the priority of energy demand leads to critical conditions of water availability. To reduce water use for energy, one possible measure is the adoption of solar power, an abundant energy source in semi-arid regions. This study assessed the influence of adding floating photovoltaic power (PV) in the largescale reservoir of Sobradinho HPP, located in the S ao Francisco River (SFR), in Brazil, from 2009 to 2018. The simulated scenarios varied the installed PV power capacity from 50 to 1000 MW. For each scenario, water allocation was modified based on the solar-hydro equivalence that restrained the historical outflow of Sobradinho to maintain water in the reservoir. Besides, a diverse operation rule for the reservoirs in cascade of SFR was adopted to avoid ecological impacts of low streamflow. The scenarios were assessed in water security, solarhydro electricity output, capacity factor of the powerplant, water and energy losses by evaporation and spilled water. Results show that a PV system starting from 250 MW was necessary to improve water security during the severe drought, reserving 0.72.3 of the annual water demand. In addition, the capacity factor was optimized from 29% to 3447%. However, as the HPPs installed at SFR work as one system, the constrain of the river flow reduced the hydroelectricity by 4.4% for 750 MW. We concluded that PV significantly influenced water security and ecological conditions of SFR, with benefits in the range of 250750 MW. The research provides assessment on substituting hydro for solar power on the operation of reservoirs in cascade and identifies the correlated benefits in social and ecological aspects. This information can support decisions of water and energy supply system operators and public policies focused on integrated resources management in semi-arid regions.

Large volumes of water are released to the atmosphere through evaporation from soil and transpiration from vegetation, constituting evapotranspiration (ET). Estimating the water consumption in vegetated areas is important for the management and rational use of this resource. For this study were processed orbital images which correspond to Quixeré-CE, with interest at the Frutacor Farm, where there is predominance the banana crop. The main objective of this study was to assess the accuracy and applicability of S-SEBI and SSEB algorithms with regard to SEBAL to estimate the actual daily evapotranspiration ETa) of a semi-arid region of Northeast Brazil, containing areas of banana orchard, native vegetation (caatinga) and bare soil. SSEBI. The SSEB and SSEB algorithms showed strong correlation (r > 0.93) with statistical significance of 5%. The S-SEBI exhibited errors less than 12% in the orchard and caatinga and SSEB exhibited greater errors at 22%, though for the bare soil, both models showed large discrepancies when compared with SEBAL, with errors greater than 36%. Therefore, among the two algorithms compared with SEBAL, S-SEBI had a better performance in ETa estimation with lower deviations.

We evaluate the performance of a large ensemble of Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) over South America for a recent past reference period and examine their projections of twenty-first century precipitation and temperature changes. The future changes are computed for two time slices (20402059 and 20802099) relative to the reference period (19952014) under four Shared Socioeconomic Pathways (SSPs, SSP12.6, SSP24.5, SSP37.0 and SSP58.5). The CMIP6 GCMs successfully capture the main climate characteristics across South America. However, they exhibit varying skill in the spatiotemporal distribution of precipitation and temperature at the sub-regional scale, particularly over high latitudes and altitudes. Future precipitation exhibits a decrease over the east of the northern Andes in tropical South America and the southern Andes in Chile and Amazonia, and an increase over southeastern South America and the northern Andesa result generally consistent with earlier CMIP (3 and 5) projections. However, most of these changes remain within the range of variability of the reference period. In contrast, temperature increases are robust in terms of magnitude even under the SSP12.6. Future changes mostly progress monotonically from the weakest to the strongest forcing scenario, and from the mid-century to late-century projection period. There is an increase in the seasonality of the intra-annual precipitation distribution, as the wetter part of the year contributes relatively more to the annual total. Furthermore, an increasingly heavy-tailed precipitation distribution and a rightward shifted temperature distribution provide strong indications of a more intense hydrological cycle as greenhouse gas emissions increase. The relative distance of an individual GCM from the ensemble mean does not substantially vary across different scenarios. We found no clear systematic linkage between model spread about the mean in the reference period and the magnitude of simulated sub-regional climate change in the future period. Overall, these results could be useful for regional climate change impact assessments across South America.

The objective of this study was to evaluate variations in the composition and abundance of the phytoplankton community after an exceptional harmful bloom of Akashiwo sanguinea that occurred in Todos os Santos Bay (BTS) in early March, 2007. Samples were collected every ten days, between April, 2007 and March, 2008, from the estuarine gradient of the Paraguacu River to BTS. The physical and chemical variables were measured in situ using a multiparameter sensor. Water samples were collected for analysis of the dissolved inorganic nutrient concentrations and for the study of composition and abundance of the microphytoplankton. Overall, 135 taxa were identified, with a higher richness of diatoms. The total cell density ranged from 2.92 x 10(3) to 1.16 x 10(7) (5.47 x 10(5) +/- 1.69 x 10(6)) cells L-1, with higher values in the freshwater zone than in the marine area. Five species showed peaks of abundance throughout the study, forming small blooms. Four of these blooms occurred in the rainy season, formed by the species Guinardia striata (April, 2007), Scrippsiella cf. acuminata (August, 2007), Euglena gracilis (August, 2007), and Skeletonema cf. costatum (September, 2007), while a new bloom of the species Akashiwo sanguinea occurred during the dry season (December, 2007). The environment was typically oligotrophic, with low spatiotemporal variation in the concentrations of dissolved nutrients. Even so, we observed short-term variations in the structure and composition of the phytoplankton community, demonstrated by rapid bloom events, followed by an increase in the total abundance of microphytoplankton, especially during the rainy season. The blooms did not cause any notable changes in the water column and did not present any harmful effects on the system.

The development of analytical software for big Earth observation data faces several challenges. Designers need to balance between conflicting factors. Solutions that are efficient for specific hardware architectures can not be used in other environments. Packages that work on generic hardware and open standards will not have the same performance as dedicated solutions. Software that assumes that its users are computer programmers are flexible but may be difficult to learn for a wide audience. This paper describes sits, an open-source R package for satellite image time series analysis using machine learning. To allow experts to use satellite imagery to the fullest extent, sits adopts a time-first, space-later approach. It supports the complete cycle of data analysis for land classification. Its API provides a simple but powerful set of functions. The software works in different cloud computing environments. Satellite image time series are input to machine learning classifiers, and the results are post-processed using spatial smoothing. Since machine learning methods need accurate training data, sits includes methods for quality assessment of training samples. The software also provides methods for validation and accuracy measurement. The package thus comprises a production environment for big EO data analysis. We show that this approach produces high accuracy for land use and land cover maps through a case study in the Cerrado biome, one of the worlds fast moving agricultural frontiers for the year 2018.

Objectives: Crime counts are sensitive to granularity choice. There is an increasing interest in analyzing crime at very fine granularities, such as street segments, with one of the reasons being that coarse granularities mask hot spots of crime. However, if granularities are too fine, counts may become unstable and unrepresentative. In this paper, we develop a method for determining a granularity that provides a compromise between these two criteria. Methods: Our method starts by estimating internal uniformity and robustness to error for different granularities, then deciding on the granularity offering the best balance between the two. Internal uniformity is measured as the proportion of areal units that pass a test of complete spatial randomness for their internal crime distribution. Robustness to error is measured based on the average of the estimated coefficient of variation for each crime count. Results: Our method was tested for burglaries, robberies and homicides in the city of Belo Horizonte, Brazil. Estimated optimal granularities were coarser than street segments but finer than neighborhoods. The proportion of units concentrating 50% of all crime was between 11% and 23%. Conclusions: By balancing internal uniformity and robustness to error, our method is capable of producing more reliable crime maps. Our methodology shows that finer is not necessarily better in the micro-analysis of crime, and that units coarser than street segments might be better for this type of study. Finally, the observed crime clustering in our study was less intense than the expected from the law of crime concentration.

Standardized crime rates (e.g., homicides per 100,000 people) are commonly used in crime analysis as indicators of victimization risk but are prone to several issues that can lead to bias and error. In this study, a more robust approach (GWRisk) is proposed for tackling the problem of estimating victimization risk. After formally defining victimization risk and modeling its sources of uncertainty, a new method is presented: GWRisk uses geographically weighted regression to model the relation between crime counts and population size, and the geographically varying coefficient generated can be interpreted as the victimization risk. A simulation study shows how GWRisk outperforms naïve standardization and Empirical Bayesian Estimators in estimating risk. In addition, to illustrate its use, GWRisk is applied to the case of residential burglaries in Belo Horizonte, Brazil. This new approach allows more robust estimates of victimization risk than other traditional methods. Spurious spikes of victimization risk, commonly found in areas with small populations when other methods are used, are filtered out by GWRisk. Finally, GWRisk allows separating a reference population into segments (e.g., houses, apartments), estimating the risk for each segment even if crime counts were not provided per segment.

By analyzing 6,480 tree leaf samples from 57 sites within Brazilian biomes, we considered whether vegetation types in terrestrial ecosystems reflect biogeochemical diversity and whether they fit into a leaf economics spectrum (LES). To achieve this, we investigated the relations among leaf carbon (C) and nitrogen (N) concentrations, their isotope natural abundance and C:N ratio. In addition, we tested their correlations with mean annual temperature (MAT) and precipitation (MAP), as climatic factors. We found consistent differences in the C and N concentrations and their isotopic composition among the vegetation types. MAP is the main climatic driver of changes in N, C:N ratio, δ15N, and δ13C, correlating negatively with N and positively with C:N ratio. These relations show that these biomes follow an LES. The Caatinga had the highest δ15N values, suggesting that N residence time in soil is longer due to low leaching and plant uptake. We observed that MAP is not the only factor influencing δ13C values in different biomes; instead canopy effect probably explains the highest values observed in the Cerrado. Our results reinforce earlier findings that life diversity in the tropics reflects biogeochemistry diversity and leaf δ15N opens the possibility for investigating plant trade-offs dictated by the LES. Finally, we expect our findings to contribute to a better understanding of the tropics in global climate models.