Search

The energy transition towards a low-emission matrix has motivated efforts to reduce the use of fossil fuels in the transportation sector. The growth of the electric mobility market has been consistent in recent years. In Brazil, there has been an accelerated growth in the sales rate of new electric (and hybrid) vehicles (EVs). Fiscal incentives provided by governments, along with the reduction in vehicle costs, are factors contributing to the exponential growth of the EV fleet—creating a favorable environment for the dissemination of new technologies and enabling the participation of players from sectors such as battery manufacturing and charging stations. Considering the international context, the E-Lounge R&D joint initiative aims to evaluate different strategies to economically enable the electric mobility market, exploring EV charging service sales by energy distribution utility companies in Brazil. This work describes the step-by-step development of an ideal model of a charging hub and discusses its operation based on a real deployment, as well as its associated technical and economic feasibility. Using EV charging data based on the E-Lounge's operational behavior, an artificial neural network (ANN) is applied to forecast future energy consumption to each EV charging station. This paper also presents an economic analysis of the E-Lounge case study, which can contribute to proposals for electric vehicle charging ecosystems in the context of smart energy systems. Based on the operational results collected, as well as considering equipment usage projections, it is possible to make EV charging enterprises feasible, even when high investments in infrastructure and equipment (charging stations and battery storage systems) are necessary, since the net present value is positive and the payback period is 4 years. This work contributes by presenting real operational data from a charging hub, a projection model aimed at evaluating future operations, and a realistic economic evaluation model based on a case study implemented in São Paulo, Brazil. [ABSTRACT FROM AUTHOR]

Featured Application: This paper presents a multi-method to evaluate urban sustainability of social housing using two kinds of environmental certifications: an international and Brazilian. The sustainability potential of Brazilian social housing (SH) needs to be assessed, considering the impacts on the population's quality of life and urban systems. One of the main weaknesses of Casa Azul Label refers to the absence of a procedure that quantifies the measures for assessing urban sustainability. Based on the post-occupation analysis of an SH project implemented in Araraquara, São Paulo state, Brazil, evaluation of urban sustainability was developed, using a multi-method approach combining the Casa Azul Label and Sustainable Building Tool (SBTool) Urban certification tools. It was proposed to apply the calculations established in SBTool Urban according to the Casa Azul Label certification requirements, as well as insert a national benchmark as a reference for the analysis and normalizing results. These procedures made it possible to base an improvement framework on a Brazilian environmental certification system, including a quantification system to evaluate the efficiency of the applied measures regarding urban sustainability. The multi-method approach, associated with the SBTool Urban procedures, could significantly help to improve the application of a sustainable assessment system in social housing in developing countries. The high number of residential units produced in recent years could be qualitatively evaluated, improving both the construction procedures and the criteria for the urban insertion of human settlements. [ABSTRACT FROM AUTHOR]

The cownose ray (Rhinoptera bonasus) faces vulnerability primarily due to unregulated fishing, resource overexploitation, and habitat degradation. Consequently, individuals maintained under human care play a pivotal role in species conservation, particularly when their welfare is prioritized. Achieving optimal welfare in aquarium settings relies heavily on effective management practices, notably environmental enrichment. However, research on the efficacy of such techniques for cownose rays remains limited. Thus, this study sought to evaluate the impact of various food enrichment items on the behavior of four individuals at the São Paulo Aquarium in Brazil. The project encompassed three phases: baseline, enrichment, and post-enrichment. Enrichment items, designed to mimic the species' natural foraging behavior, included an ice block containing food, food hidden in vegetables fixed to structures at the bottom of the tank, a tray with substrate and food, and a perforated plastic container with food inside. Behavioral observations utilized focal sampling with instantaneous recording every minute. Results showed increased foraging activity in the post-enrichment phase, whereas swimming increased and following behaviors decreased during the enrichment phase. Additionally, foraging behaviors predominantly occurred near the aquarium bottom. Overall, findings suggest that enrichment items effectively stimulated natural behaviors in cownose rays and were very attractive to the fish, advocating for their integration into species management protocols to enhance welfare. [ABSTRACT FROM AUTHOR]

This pioneering study aimed to evaluate the cryopreservation of semen from P. falkneri (n = 4) and P. motoro (n = 4), maintained ex situ at the Sao Paulo Aquarium, Brazil. For this purpose, the animals were physically restrained, biometric data of the disc and clasper were obtained, and semen was collected through manual massage. Total motility and progressive motility parameters were evaluated using Computer-Assisted Sperm Analysis (CASA) with IVOS II equipment and Animal Breeders II software. The semen extenders INRA 96 and OptiXcell were used to assess their efficacy in sperm cryopreservation. INRA required the addition of 5% dimethyl sulfoxide (DMSO) as a cryoprotectant. The results indicated that there was no difference in semen motility values before and after freezing with INRA + DMSO (p = 0.6226). On the other hand, samples cryopreserved with OptiXcell showed a difference in semen motility post-thaw (p = 0.0156). These findings contribute to a broader study on optimizing cryopreservation protocols to ensure long-term viability and fertility of semen, enhancing genetic diversity and supporting wild population restoration. A multidisciplinary approach integrating reproductive biology, ecology, physiology, and assisted reproduction technologies, aligned with the One Conservation concept, is essential for advancing conservation and management strategies for these threatened species. [ABSTRACT FROM AUTHOR]

In this paper, I seek to extend our understanding of global art markets by focusing on the relationships between different art world agents and their perceived responsibilities and roles in a market considered locally 'incipient' and emergent on the global scene. For this purpose, I draw on over 50 interviews with art gallerists, independent art spaces and visual artists represented by them, living in São Paulo and Rio de Janeiro, the two largest clusters of the contemporary art market in Brazil, at a time of market expansion and internationalisation. In an incipient market, two main functions are considered important: Developing the commercial circuit and opening up the market, and; enhancing the value of art in society. Such functions occur against the backdrop of a large and complex country, where the 'eixo' (axis) of the main cities offers greater opportunities for visibility and valorization. The findings help to elucidate the perceptions of responsibility and roles in a context of market development, as well as the emerging boundaries between culture and the market. Moreover, the paper explores the emerging dynamics and strategies of art world development as they are enacted, offering insights into how art market actors perceive their roles and responsibilities, as well as the strategies available to them to support market consolidation. [ABSTRACT FROM AUTHOR]

Using wood for power generation necessitates a more efficient production chain in the various steps: harvesting, forwarding, storage, chipping, transport, and conversion systems. In this context, the moisture content (MC) of wood can impact the harvesting operation, the volume to be chipped, the transportation of raw materials, the storage time, and other factors, thereby influencing the economic aspects of the chain. The primary objective of this study is to investigate the influence of wood moisture content on the yield and costs of different forest operation chains for power generation in São Paulo State, Brazil. Our findings reveal that harvesting and forest transport are the primary cost components (over 80%) in the supply chains under study. We observed a difference of up to 17.6% in the unit cost of the energy generated among the studied supply chains. In economic and sustainable terms, our results suggest that logs should be stored in the field for three to four months and the transport distance to the power plant should not exceed 100 km. [ABSTRACT FROM AUTHOR]

The inaugural AIDS Program in Brazil was established in São Paulo in 1983, with the Instituto Adolfo Lutz appointed for laboratory assistance. Subsequently, research on HIV infections and HIV/HTLV (HIV/HTLV-1 and HIV/HTLV-2) co-infections was conducted. This narrative review focuses on studies from the Immunology Department (1983–2017) that significantly influenced AIDS diagnosis or provided epidemiological data such as prevalence rates, sex, age, and risk factors. Twelve studies, encompassing over 8000 individuals, are discussed. During 1983–1985, nearly all AIDS cases were attributed to homosexual/bisexual men aged 31 years old. Subsequently, heterosexual men and women emerged as risk groups owing to intravenous drug use (IDU) and/or unprotected sexual intercourse with AIDS patients or multiple partners per year. From 1985 onwards, vertical transmission led to child infections. HIV/HTLV co-infection rates decreased over time, initially associated with male IDU, and in the 2010s with female IDU, and individuals aged >40 years. Trends in HIV and HIV/HTLV co-infections among younger men and women (<30 years of age) were observed from 2015 to 2017. The changing characteristics and risk groups for HIV and HIV/HTLV co-infections over the years underscore the necessity for ongoing public policies to prevent retrovirus transmission, particularly among adolescents and young adults. [ABSTRACT FROM AUTHOR]

The global surge in urbanization and population growth has led to a significant increase in municipal solid waste generation, posing a considerable challenge in identifying suitable landfill sites. This study proposes a novel framework that enhances landfill site monitoring and assessment by combining GIS-based hierarchical analytical processes with a fuzzy inference system (FIS). The study employs a systematic approach involving phases such as feature selection, spatial analysis, criteria weighting, FIS building, and a case study conducted in São Paulo State, Brazil. The proposed framework effectively assesses landfill suitability and offers practical recommendations for landfill management and future site selection. This framework provides actionable recommendations for landfill monitoring and assessment, supporting landfill management while minimizing environmental and social impacts. It offers a comprehensive approach to landfill assessment, enhancing the sustainability of waste management practices. Further research can improve the proposed framework by refining feature selection and incorporating real-time data for continuous monitoring. Additionally, exploring the integration of emerging technologies, such as remote sensing and artificial intelligence, can further enhance landfill site monitoring and assessment. [ABSTRACT FROM AUTHOR]

Sugarcane dry rot emerged as an important stalk disorder in newly expanded plantations in northwestern São Paulo, Brazil, under the current no-burning fully mechanical harvest policy gradually implemented in the past 20 years. This emergence was probably due to a considerable increase in both pathogen inocula and insect pest populations in sugarcane crop residues kept in the field. In this study, we surveyed the incidence of three stalk-related disorders in commercial sugarcane fields in six municipalities in northwestern São Paulo and the corresponding yield losses. The three stalk-related disorders surveyed were as follows: the red rot disease caused by the fungal pathogen Colletotricum falcatum, the spittlebug-induced shoot stunting, and the stem dry rot, which is associated with the simultaneous infection of C. falcatum and Thielaviopsis paradoxa, the pineapple set rot pathogen. Red rot disease was detected in 88.2% of the fields surveyed, while the spittlebug-induced shoot stunting disorder and the internal stem dry rot were found in 97.1% of the fields. Stem dry rot had the highest incidence and resulted in the highest yield losses. Total sugarcane yield losses were estimated at 20.1%, with an average of 14.2 (±3.8) t·ha−1 per field. The multiple regression model constructed to determine which of the three stem-related disorders contributed the most to total yield losses was not significant. Subsequently, the performance analyses of single-variable polynomial regression models indicated that the simple linear model was the best fit in terms of independently predicting sugarcane yield losses based on each stem-related disorder. Positive and significant correlations were only detected between sugarcane yield losses in t·ha−1 and the incidence of red rot disease or leafhopper-induced shoot stunting. We concluded that the stalk's internal dry rot, as a disease complex associated with both C. falcatum and T. paradoxa, was the most important disorder in sugarcane fields in the northwest region of São Paulo state. A sustainable pest management program is needed to reduce the impact of all three stalk-associated disorders on regional sugarcane production. [ABSTRACT FROM AUTHOR]

Despite the concern about climate change and the associated negative impacts, fossil fuels continue to prevail in the global energy consumption. This paper aimed to propose the first model that relates CO2 emissions of Sao Paulo, the main urban center emitter in Brazil, with gross national product and energy consumption. Thus, we investigated the accuracy of three different methods: multivariate linear regression, elastic-net regression, and multilayer perceptron artificial neural networks. Comparing the results, we clearly demonstrated the superiority of artificial neural networks when compared with the other models. They presented better results of mean absolute percentage error (MAPE = 0.76%) and the highest possible coefficient of determination (R2 = 1.00). This investigation provides an innovative integrated climate-economic approach for the accurate prediction of carbon emissions. Therefore, it can be considered as a potential valuable decision-support tool for policymakers to design and implement effective environmental policies. [ABSTRACT FROM AUTHOR]

This work aims to develop a new method to map Land Use and Land Cover (LULC) classes in the São Paulo State, Brazil, using Landsat-8 Operational Land Imager (OLI) data. The novelty of the proposed method consists of selecting the images based on the spectral and temporal characteristics of the LULC classes. First, we defined the six classes to be mapped in the year 2020 as forest, forest plantation, water bodies, urban areas, agriculture, and pasture. Second, we visually analyzed their variability spectral characteristics over the year. Then, we pre-processed these images to highlight each LULC class. For the classification, the Random Forest algorithm available on the Google Earth Engine (GEE) platform was utilized individually for each LULC class. Afterward, we integrated the classified maps to create the final LULC map. The results revealed that forest areas are primarily concentrated in the eastern region of São Paulo, predominantly on steeper slopes, accounting for 19% of the study area. On the other hand, pasture and agriculture dominated 73% of all São Paulo's landscape, reaching 39% and 34%, respectively. The overall accuracy of the classification achieved 89.10%, while producer and user accuracies were greater than 84.20% and 76.62%, respectively. To validate the results, we compared our findings with the MapBiomas Project classification, obtaining an overall accuracy of 85.47%. Therefore, our method demonstrates its potential to minimize classification errors and offers the advantage of facilitating post-classification editing for individual mapped classes. [ABSTRACT FROM AUTHOR]

Brazil is one of the world's leading producers of Nile tilapia, Oreochromis niloticus. However, the industry faces a major challenge in terms of infectious diseases, as at least five new pathogens have been formally described in the last five years. Aeromonas species are Gram-negative anaerobic bacteria that are often described as fish pathogens causing Motile Aeromonas Septicemia (MAS). In late December 2022, an epidemic outbreak was reported in farmed Nile tilapia in the state of São Paulo, Brazil, characterized by clinical signs and gross pathology suggestive of MAS. The objective of this study was to isolate, identify, and characterize in vitro and in vivo the causative agent of this epidemic outbreak. The bacterial isolates were identified as Aeromonas veronii based on the homology of 16S rRNA (99.9%), gyrB (98.9%), and the rpoB gene (99.1%). A. veronii showed susceptibility only to florfenicol, while it was resistant to the other three antimicrobials tested, oxytetracycline, enrofloxacin, and amoxicillin. The lowest florfenicol concentration capable of inhibiting bacterial growth was ≤0.5 µg/mL. The phenotypic resistance of the A. veronii isolate observed for quinolones and tetracycline was genetically confirmed by the presence of the qnrS2 (colE plasmid) and tetA antibiotic-resistant genes, respectively. A. veronii isolate was highly pathogenic in juvenile Nile tilapia tested in vivo, showing a mortality rate ranging from 3 to 100% in the lowest (1.2 × 104) and highest (1.2 × 108) bacterial dose groups, respectively. To our knowledge, this study would constitute the first report of highly pathogenic and multidrug-resistant A. veronii associated with outbreaks and high mortality rates in tilapia farmed in commercial net cages in Brazil. [ABSTRACT FROM AUTHOR]

Forecasting photovoltaic electricity generation is one of the key components to reducing the impacts of solar power natural variability, nurturing the penetration of renewable energy sources. Machine learning is a well-known method that relies on the principle that systems can learn from previously measured data, detecting patterns which are then used to predict future values of a target variable. These algorithms have been used successfully to predict incident solar irradiation, but the results depend on the specificities of the studied location due to the natural variability of the meteorological parameters. This paper presents an extensive comparison of the three ML algorithms most used worldwide for forecasting solar radiation, Support Vector Machine (SVM), Artificial Neural Network (ANN), and Extreme Learning Machine (ELM), aiming at the best prediction of daily solar irradiance in a São Paulo context. The largest dataset in Brazil for meteorological parameters, containing measurements from 1933 to 2014, was used to train and compare the results of the algorithms. The results showed good approximation between measured and predicted global solar radiation for the three algorithms; however, for São Paulo, the SVM produced a lower Root-Mean-Square Error (RMSE), and ELM, a faster training rate. Using all 10 meteorological parameters available for the site was the best approach for the three algorithms at this location. [ABSTRACT FROM AUTHOR]

Urban landslides are increasing globally, mainly caused by human-induced changes in hillsides. Most of these events have caused low-intensity damages to housing and infrastructure. A total of 2038 locations of landslides in the hydrological year of 2010 were used to model landslides' occurrence in the metropolitan region of São Paulo—Brazil—using a social–ecological–technological system's approach, which enables the analysis of urban landslides as the outcome of dynamic socioeconomic and infrastructural conditions alongside climatic and geophysical conditions. A multi-step model approach was used to select the best set of variables related to landslides' occurrence and assess their importance. The value of AUC of the model was 0.9087, denoting the high level of discrimination achieved. Antecedent rainfall played the most important role, followed by terrain slope. Informal settlements, associated with poor constructive practices and a lack of municipal inspection on civil works and buildings, as well as the number of households, which stands for built density and greater alteration in hillsides, yielded a slightly lower contribution. Other variables showed a marginal contribution. These results reinforce the role of local ordinances aimed at restricting occupation in steeper slopes and public policies to promote adequate housing and constructive practices. Future climate projections to MRSP point to the increase in intense rainfall days, making disasters caused by landslides a major source of risk. [ABSTRACT FROM AUTHOR]

São Paulo is the most populous state in Brazil, home to around 22% of the country's population. The total number of Covid-19-infected people in São Paulo has reached more than 1 million, while its total death toll stands at 25% of all the country's fatalities. Joining the Brazilian academia efforts in the fight against Covid-19, in this paper we describe a unified framework for monitoring and forecasting the Covid-19 progress in the state of São Paulo. More specifically, a freely available, online platform to collect and exploit Covid-19 time-series data is presented, supporting decision-makers while still allowing the general public to interact with data from different regions of the state. Moreover, a novel forecasting data-driven method has also been proposed, by combining the so-called Susceptible-Infectious-Recovered-Deceased model with machine learning strategies to better fit the mathematical model's coefficients for predicting Infections, Recoveries, Deaths, and Viral Reproduction Numbers. We show that the obtained predictor is capable of dealing with badly conditioned data samples while still delivering accurate 10-day predictions. Our integrated computational system can be used for guiding government actions mainly in two basic aspects: real-time data assessment and dynamic predictions of Covid-19 curves for different regions of the state. We extend our analysis and investigation to inspect the virus spreading in Brazil in its regions. Finally, experiments involving the Covid-19 advance in other countries are also given. [ABSTRACT FROM AUTHOR]

Riparian zones consist of important environmental regions, specifically to maintain the quality of water resources. Accurately mapping forest vegetation in riparian zones is an important issue, since it may provide information about numerous surface processes that occur in these areas. Recently, machine learning algorithms have gained attention as an innovative approach to extract information from remote sensing imagery, including to support the mapping task of vegetation areas. Nonetheless, studies related to machine learning application for forest vegetation mapping in the riparian zones exclusively is still limited. Therefore, this paper presents a framework for forest vegetation mapping in riparian zones based on machine learning models using orbital multispectral images. A total of 14 Sentinel-2 images registered throughout the year, covering a large riparian zone of a portion of a wide river in the Pontal do Paranapanema region, São Paulo state, Brazil, was adopted as the dataset. This area is mainly composed of the Atlantic Biome vegetation, and it is near to the last primary fragment of its biome, being an important region from the environmental planning point of view. We compared the performance of multiple machine learning algorithms like decision tree (DT), random forest (RF), support vector machine (SVM), and normal Bayes (NB). We evaluated different dates and locations with all models. Our results demonstrated that the DT learner has, overall, the highest accuracy in this task. The DT algorithm also showed high accuracy when applied on different dates and in the riparian zone of another river. We conclude that the proposed approach is appropriated to accurately map forest vegetation in riparian zones, including temporal context. [ABSTRACT FROM AUTHOR]

The Cantareira System is one of the largest water supply systems in the world, supplying about half of the water consumed by 22 million inhabitants in the Metropolitan Region of São Paulo, in southeastern Brazil. In this scenario, in view of climate change, silting is a serious environmental threat and a major challenge to the sustainability of water reservoirs. Therefore, identifying the provenance of sediments is an essential tool to support soil conservation policies, slowing erosion processes and mitigating the deposition of sediments in water reservoirs. Thus, this study aimed to model soil losses—sediment production, by water erosion in the Cantareira System, based on the RUSLE model—Revised Universal Soil Loss Equation, GIS—Geographic Information System and SR—Remote Sensing. The work was conducted on data obtained from online platforms of Brazilian public institutions. The results indicate an average rate of soil loss of 13 Mg ha−1 yr−1, which corresponds to an annual loss of 3 million tons, of which 22% reaches water bodies. The data also show that: (1) in 66 % of the Cantareira System, soil losses are below the soil loss tolerance limits, and, in 34% of the region, water erosion is compromising the sustainability of water and soil resources; (2) the areas with the greatest soil losses are predominantly located in planted forests, agricultural crops and non-vegetated areas; and (3) sectors with high rates of soil loss require the adoption of conservationist practices aimed at reducing sediment production rates and thereby increasing supply and improving water quality. [ABSTRACT FROM AUTHOR]

In two small and isolated wetlands located at the interface of the Atlantic Forest and Brazilian savanna (Cerrado) in São Paulo State, Brazil, we employed a pixel-based supervised classification approach using a combination of panchromatic and multispectral bands obtained from Landsat 2, 5, 7, and CBERS-04A satellites (ranging from 80 to 2 m/pixel). In addition, we acquired DJI Phantom 4 Pro UAV-RGB images in twelve different periods with a resolution of +5 cm/pixel. Furthermore, we utilized 2D and 3D Electrical Resistivity Tomography (ERT) to obtain data on the surroundings and center of the wetlands. Finally, we conducted a climatological data analysis. The results from the multisource data allowed us to classify the ecosystems as geographically isolated wetlands (GIWs), for which we documented a seasonal month-to-month (12 months) spatial variation of inundated area, vegetation pattern, soil water interaction, and a point of surface and deep-subsurface water interaction. These results are essential for high-accuracy characterization of small wetlands' hydrodynamics and hydroperiods at the local scale. Our study contributes to optimizing GIWs understanding, monitoring, and reapplication of the methodology in other wetlands or small ecosystems. [ABSTRACT FROM AUTHOR]

In recent years, the water–energy–food (WEF) nexus approach has been widely used as a framework in the context of urban Sustainability. However, some elements of the approach are normative, leading to a technical view of resources and technocratic policy implementation. To avoid such tendencies, this study uses the framework of 'nexus of humility'. We used insights from the Science and Technology Studies to better assess the interactions between water, energy, and food, and consider the social construction aspects of the nexus itself. The approach of Pathways to Sustainability is combined with this framework to analyze two government programs in the cities of São Paulo and Guarulhos, Brazil; namely, the Green and Healthy Environments Program and the Environmental Health Program, respectively. We interviewed 20 individuals linked to these policies and analyzed narratives inductively and deductively. The results showed six groups of narratives, namely: environmental and social determinants of health, health prevention and promotion, intersectorality, politics and economy, territory, learning, and participation. Moreover, we concluded that narratives related to the WEF nexus, even if not explicitly part of the government guidelines, are present within the existing axes of action. Public health was understood as an important support pillar for the development of synergies related to Sustainability in urban areas. Finally, we sought to contribute to the literature by showing how this new framework can 'open up' avenues for sustainability within the contexts of high urban vulnerability and social inequality. [ABSTRACT FROM AUTHOR]

Land-use conversion changes soil properties and their microbial communities, which, combined with the overuse of antibiotics in human and animal health, promotes the expansion of the soil resistome. In this context, we aimed to profile the resistome and the microbiota of soils under different land practices. We collected eight soil samples from different locations in the countryside of São Paulo (Brazil), assessed the community profiles based on 16S rRNA sequencing, and analyzed the soil metagenomes based on shotgun sequencing. We found differences in the communities' structures and their dynamics that were correlated with land practices, such as the dominance of Staphylococcus and Bacillus genera in agriculture fields. Additionally, we surveyed the abundance and diversity of antibiotic resistance genes (ARGs) and virulence factors (VFs) across studied soils, observing a higher presence and homogeneity of the vanRO gene in livestock soils. Moreover, three β-lactamases were identified in orchard and urban square soils. Together, our findings reinforce the importance and urgency of AMR surveillance in the environment, especially in soils undergoing deep land-use transformations, providing an initial exploration under the One Health approach of environmental levels of resistance and profiling soil communities. [ABSTRACT FROM AUTHOR]

Obesity is increasing in incidence worldwide, especially in women, which can affect the outcome of pregnancy. During this period, viral infections represent a risk to the mother, the placental unit, and the fetus. The Zika virus (ZIKV) outbreak in Brazil has been the cause of congenital Zika syndrome (CZS), with devastating consequences such as microcephaly in newborns. Herein, we analyzed the impact of maternal overweight/obesity on the antiviral factors' expression in the placental tissue of Zika-infected mothers. We accessed placentas from women with and without obesity from 34 public health units (São Paulo) and from Zika-infected mothers with and without obesity from the Clinical Cohort Study of ZIKV pregnant women (Rio de Janeiro, Brazil). We first verified that obesity, without infection, did not alter the constitutive transcriptional expression of antiviral factors or IFN type I/III expression. Interestingly, obesity, when associated with ZIKV infection, showed a decreased transcriptional expression of RIG-I and IFIH1 (MDA-5 protein precursor gene). At the protein level, we also verified a decreased RIG-I and IRF-3 expression in the decidual placenta from the Zika-infected obese group, regardless of microcephaly. This finding shows, for the first time, that obesity associated with ZIKV infection leads to an impaired type I IFN downstream signaling pathway in the maternal–fetal interface. [ABSTRACT FROM AUTHOR]

Despite its rich water resources, Brazil is increasingly facing extreme hydrologic events such as droughts and floods. The Sao Paulo Cantareira water supply system (CWSS) offers an opportunity to examine the potential economic benefits of nature-based solutions (NbS) to improve water security and reduce the economic cost of drought. This study explores the potential benefits under a counterfactual NbS land-use scenario compared to actual land use and assesses the economic viability of NbS investments in the CWSS. Specifically, we estimate the economic cost of the 2014–2015 drought in Sao Paulo state for the industrial and water sectors served by the CWSS. We estimate the potential avoided costs under the NbS scenario and conduct a cost–benefit analysis of the NbS scenario investments, including both water supply and carbon sequestration benefits. We estimate that the economic losses of this single drought event totaled BRL 1.6 billion. If NbS had been implemented, this cost could have been reduced by 28%. A cost–benefit analysis that includes only the water supply or both the water supply and carbon sequestration benefits indicates that the NbS scenario has a positive net present value of BRL 144 million and BRL 632 million, respectively. Thus, our results highlight the economic viability of the hypothetical NbS investment in mitigating extreme climatic events. [ABSTRACT FROM AUTHOR]

This study describes a collaborative modeling process deployed at the Cantareira Water Supply System (CWSS) in São Paulo City Metropolitan Area, Brazil. The CWSS faces challenges for meeting the increasing water demand, while land-use and climate change and their combined effect on its water cycle and balance have created a complex water resources management problem. Through a stakeholder engagement process—involving scientists and policymakers, the water utility company, and state administration—environmental simulation models were developed to elicit and represent multiple environmental, economic, and policy perspectives, developing a mutual language to communicate and establish common goals of water resources management. Study outputs include estimation of biophysical and economic benefits associated with prioritized native vegetation restoration activities in the source watersheds. These outputs are deployed in support of landscape planning and the decision process integrating multiple stakeholder perspectives in São Paulo state administration, the water utility company, and municipalities. [ABSTRACT FROM AUTHOR]

In Brazil, irrigated agriculture is responsible for 46% of withdrawals of water bodies and 67% of use concerning the total water abstracted volume, representing the most significant consumptive use in the country. Understanding how different crops use water over time is essential for planning and managing water allocation, water rights, and farming production. In this work, we propose a methodology to estimate water used in agriculture irrigated by center pivots in the municipality of Itobi, São Paulo, in the Brazilian Savanna (known as Cerrado), which has strong potential for agricultural and livestock production. The methodology proposed for the water use estimate is based on mapping crops irrigated by center pivots for the 2015/2016 crop year and actual evapotranspiration (ETa). ETa is derived from the Operational Simplified Surface Energy Balance model (SSEBop) and parameterized for edaphoclimatic conditions in Brazil (SSEBop-Br). Three meteorological data sources (INMET, GLDAS, CFSv2) were tested for estimating ETa. The water use was estimated for each meteorological data source, relating the average irrigation balance and the total area for each crop identified in the map. We evaluated the models for each crop present in the center pivots through global accuracy and f1-score metrics, and f1-score was more significant than 0.9 for all crops. The potato was the crop that consumed the most water in irrigation, followed by soy crops, beans, carrots, and onions, considering the three meteorological data sources. The total water volume consumed by center pivots in the municipality of Itobi in the 2015/2016 agricultural year for each meteorological data source was 3.2 million m3 (INMET), 2.5 million m3; (GLDAS), and 1.8 million m3 (CFSv2). [ABSTRACT FROM AUTHOR]

Nutrient diagnosis of orange (Citrus sinensis) groves in Brazil relies on regional information from a limited number of studies transferred to other environments under the ceteris paribus assumption. Interpretation methods are based on crude nutrient compositions that are intrinsically biased by genetics X environment interactions. Our objective was to develop accurate and unbiased nutrient diagnosis of orange groves combining machine learning (ML) and compositional methods. Fruit yield and foliar nutrients were quantified in 551 rainfed 7–15-year-old orange groves of 'Hamlin', 'Valência', and 'Pêra' in the state of São Paulo, Brazil. The data set was further documented using soil classification, soil tests, and meteorological indices. Tissue compositions were log-ratio transformed to account for nutrient interactions. Ionomes differed among scions. Regression ML models showed evidence of overfitting. Binary ML classification models showed acceptable values of areas under the curve (>0.7). Regional standards delineating the multivariate elliptical hyperspace depended on the yield cutoff. A shapeless blob hyperspace was delineated using the k-nearest successful neighbors that showed comparable features and reported realistic yield goals. Regionally derived and site-specific reference compositions may lead to differential interpretation. Large-size and diversified data sets must be collected to inform ML models along the learning curve, tackle model overfitting, and evaluate the merit of blob-scale diagnosis. [ABSTRACT FROM AUTHOR]

The aim of this study was to conduct an up-to-date investigation on the occurrence levels of aflatoxin M1 (AFM1) in samples of raw milk (n = 40), pasteurized milk (n = 44), ultra-high temperature (UHT) milk (n = 27), Minas cheese (n = 57), and yogurt (n = 44) traded in São Paulo state, Brazil. AFM1 was extracted from fluid milks and dairy products using immunoaffinity columns and determined by high performance liquid chromatography. AFM1 was detected at the mean level of 0.080 ± 0.071 µg/L or kg in 72 samples (34.0%) evaluated in the study (n = 212). Detectable levels of AFM1 were observed in five samples of raw milk (12.5%), 16 samples of pasteurized milk (36.4%), 13 samples of UHT milk (48.1%), 27 samples of cheese (47.4%), and 11 samples of yogurt (25.0%), although none of them had concentrations above the maximum permitted levels (MPL) for AFM1 adopted in Brazil. However, 11.7% (n = 13) of samples of raw, pasteurized, and UHT milks would have AFM1 concentrations above the MPL of 0.05 μg/L adopted in the EU. The maximum level was detected in one cheese sample containing 0.695 µg/kg. Although none of the samples exceeded the Brazilian MPL, the high frequencies of AFM1 in Brazilian milk products warrant concern about their contribution to the human exposure to aflatoxins. Because aflatoxins are among the most potent carcinogens known, the results of this trial stress the need for stringent measures in the milk production system to avoid AFM1 in milk and derived products. [ABSTRACT FROM AUTHOR]

Drought is a natural hazard with critical societal and economic consequences to millions of people around the world. In this paper, we present the climatology of severe drought events that occurred during the 20th century in the region of Sao Paulo, Brazil. To account for the effects of rainfall deficit and changes in temperature at a climatic timescale, we chose the Standardized Precipitation Evapotranspiration Index (SPEI) to identify severe droughts over the city of Sao Paulo, and the eastern and central-western regions of the state. Events were identified using weather station data and European Centre for Medium-Range Weather Forecast (ECMWF) reanalysis data, in order to assess the representation of drought periods in both datasets. Results show that the reanalysis seems suitable to represent the number of events and their mean duration, severity and intensity, but the timing and characteristics of individual events are not well reproduced. The correlation between observation and reanalysis SPEI time series is low to moderate in all cases. A linear trend analysis between 1901 and 2010 shows a tendency of increasing (decreasing) severe drought events in the central and western (eastern) Sao Paulo state, according to observational data. This is in agreement with previous findings, and the reanalysis presents this same signal. The weakened trend values in the reanalysis may be associated with issues in representing precipitation in this dataset. [ABSTRACT FROM AUTHOR]

This article presents a method, based on orbital remote sensing, to map the extent of forest plantations in São Paulo State (Southeast Brazil). The proposed method uses the random forest machine learning algorithm available on the Google Earth Engine (GEE) cloud computing platform. We used 30 m annual mosaics derived from Landsat-5 Thematic Mapper (TM) images and from Landsat-8 Operational Land Imager (OLI) images for the 1985 to 1995 and 2013 to 2021 time periods, respectively. These time periods were selected based on the planted areas' rotation, especially the eucalypt's short rotation. To classify the forest plantations, green, red, NIR, and MIR spectral bands, NDVI, GNDVI, NDWI, and NBR spectral indices, and vegetation, shade, and soil fractions were used for both sensors. These indices and the fraction images have the advantage of reducing the volume of data to be analyzed and highlighting the forest plantations' characteristics. In addition, we also generated one mosaic for each fraction image for the TM and OLI datasets by computing the maximum value through the period analyzed, facilitating the classification of areas occupied by forest plantations in the study area. The proposed method allowed us to classify the areas occupied by two forest plantation classes: eucalypt and pine. The results of the proposed method compared with the forest plantation areas extracted from the land use and land cover maps, provided by the MapBiomas product, presented the Kappa values of 0.54 and 0.69 for 1995 and 2020, respectively. In addition, two pilot areas were used to evaluate the classification maps and to monitor the phenological stages of eucalypt and pine plantations, showing the rotation cycle of these plantations. The results are very useful for planning and managing planted forests by commercial companies and can contribute to developing an automatic method to map forest plantations on regional and global scales. [ABSTRACT FROM AUTHOR]

Nitrogen (N) is one of the promising nutrients for lawn growth and is required for the lawn's proper growth and development, but it also increases mowing frequency. Glyphosate herbicide application in sub-doses, as a growth regulator, can reduce the maintenance costs without any adverse reduction in the density and nutritional status of grasses. The objective of this study was to evaluate the influences of nitrogen and glyphosate doses on the growth, aesthetic quality and nutritional status of emerald grass (Zoysia japonica Steud.). The experiment was conducted at the Research and Extension Education Farm of São Paulo State University (UNESP), Ilha Solteira, SP, Brazil, in an Ultisol. The experiment was designed as a randomized block with 12 treatments arranged in a 3 × 4 factorial scheme with 4 replications, comprised of a control (without N), 15 and 30 g N m−2 of urea, applied in five splits annually, and glyphosate doses (0, 200, 400 and 600 g ha−1 of the active ingredient, a.i.). The split N fertilization at the rate of 15 g m−2 and glyphosate at the dose of 400 g ha−1 maintained nutritional status of emerald grass. Nitrogen at the rate 15 g N m−2 (in five splits per year) was observed to produce lower growth traits, an adequate aesthetic quality and longer stability of the nutrients in emerald grasses through lower exportation, with removal of "clipping" after mowing. In addition, glyphosate, at the dose of 400 g a.i. ha−1, was efficient in reducing the leaf area, plant height, shoot dry matter and total dry matter by 18.3, 14.7, 6.8 and 8.1%, respectively, as compared to the control. However, this dose did not impair the coloration and resulted in a lower exportation of nutrients by reducing the need to replenish by fertilization. Therefore, fertilization with 15 g N m−2, associated with application of 400 g a.i. ha−1 of glyphosate, is recommended for emerald grass in the tropical savannah of Brazil. [ABSTRACT FROM AUTHOR]

With more than 80% of Brazilians living in cities, urbanization has had an important impact on climatic variations. São José dos Campos is located in a region experiencing rapid urbanization, which has produced a remarkable Urban Heat Island (UHI) effect. This effect influences the climate, environment and socio-economic development on a regional scale. In this study, the brightness temperatures and land cover types from Landsat TM images of São José dos Campos from 1986, 2001 and 2010 were analyzed for the spatial distribution of changes in temperature and land cover. A quantitative approach was used to explore the relationships among temperature, land cover areas and several indices, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Normalized Difference Built-up Index (NDBI). The results showed that urban and bare areas correlated positively with high temperatures. Conversely, areas covered in vegetation and water correlated positively with low temperatures. The indices showed that correlations between the NDVI and NDWI and temperature were low (<0.5); however, a moderate correlation was found between the NDBI and temperature. [ABSTRACT FROM AUTHOR]

The algorithms for determining sugarcane harvest dates are proposed; the algorithms allow the ability to monitor large areas and are based on the publicly available Synthetic Aperture Radar (SAR) and optical satellite data. Algorithm 1 uses the NDVI (Normalized Difference Vegetation Index) time series derived from Sentinel-2 data. Sharp and continuous decrease in the NDVI values is the main sign of sugarcane harvest. The NDVI time series allows the ability to determine most harvest dates. The best estimates of the sugarcane areas harvested per month have been obtained from March to August 2018 when cloudy pixel percentage is less than 45% of the image area. Algorithm 2 of the harvest monitoring uses the coherence time series derived from Sentinel-1 Single Look Complex (SLC) images and optical satellite data. Low coherence, demonstrating sharp growth upon the harvest completion, corresponds to the harvest period. The NDVI time series trends were used to refine the algorithm. It is supposed that the descending NDVI trend corresponds to harvest. The algorithms were used to identify the harvest dates and calculate the harvested areas of the reference sample of 574 sugarcane parcels with a total area of 3745 ha in the state of São Paulo, Brazil. The harvested areas identified by visual interpretation coincide with the optical-data algorithm (algorithm 1) by 97%; the coincidence with the algorithm based on SAR and optical data (algorithm 2) is 90%. The main practical applications of the algorithms are harvest monitoring and identification of the harvested fields to estimate the harvested area. [ABSTRACT FROM AUTHOR]

The sustainability of water utility companies is threatened by non-stationary drivers, such as climate and anthropogenic changes. To cope with potential economic losses, instruments such as insurance are useful for planning scenarios and mitigating impacts, but data limitations and risk uncertainties affect premium estimation and, consequently, business sustainability. This research estimated the possible economic impacts of business interruption to the Sao Paulo Water Utility Company derived from hydrological drought and how this could be mitigated with an insurance scheme. Multi-year insurance (MYI) was proposed through a set of "change" drivers: the climate driver, through forcing the water evaluation and planning system (WEAP) hydrological tool; the anthropogenic driver, through water demand projections; and the economic driver, associated with recent water price policies adopted by the utility company during water scarcity periods. In our study case, the evaluated indices showed that MYI contracts that cover only longer droughts, regardless of the magnitude, offer better financial performance than contracts that cover all events (in terms of drought duration). Moreover, through MYI contracts, we demonstrate solvency for the insurance fund in the long term and an annual average actuarially fair premium close to the total expected revenue reduction. [ABSTRACT FROM AUTHOR]

The effects of an explicit three dimensional (3D) urban canopy representation on the development of convective thunderstorms were analyzed with the tropical town energy budget (tTEB) scheme integrated into the advanced regional prediction system (ARPS). The study provides a detailed description of the procedure to couple the system ARPS-tTEB and analyzed the simulation results of the 12 January 2015 sea-breeze event that developed a severe thunderstorm above the metropolitan area of São Paulo (MASP), Brazil. The simulation used realistic boundary and initial conditions from the Global Forecast System (GFS) and sea surface temperature (SST) from the Tropical Rainfall Measurement Mission (TRMM). The system ARPS-tTEB runs of up to 3 km horizontal resolution were carried out with high resolution topography features and land-use types currently available for Southeastern Brazil. The simulated spatial distribution of precipitation was verified against the Climate Prediction Center Morphing Technique (CMORPH), the Global Precipitation Measurement (GPM) and the São Paulo weather radar (SPWR) precipitation estimates by indexes scores. Time series of grid precipitation estimates (ARPS-tTEB and SPWR) and point measurements (rain gauges) were evaluated with a Bayesian statistical method. Results indicate that the urban area of the MASP modulates the precipitation spatial distribution over it. Furthermore, phase and amplitude precipitation accuracy increased with the 3D urban canyon and the urban energy budget scheme in relationship to control runs without urban environment effects. [ABSTRACT FROM AUTHOR]