Green missing spots: Information entropy on greenhouse gas emission disclosure by Brazilian companies.

This study aims to address a critical gap in the literature by examining the incorporation of uncertainty in measuring carbon emissions using the greenhouse gas (GHG) Protocol methodology across all three scopes. By comprehensively considering the various dimensions of CO2 emissions within the context of organizational activities, our research contributes significantly to the existing body of knowledge. We address challenges such as data quality issues and a high prevalence of missing values by using information entropy, techniques for order preference by similarity to ideal solution (TOPSIS), and an artificial neural network (ANN) to analyze the contextual variables. Our findings, derived from the data sample of 56 companies across 18 sectors and 13 Brazilian states between 2017 and 2019, reveal that Scope 3 emissions exhibit the highest levels of information entropy. Additionally, we highlight the pivotal role of public policies in enhancing the availability of GHG emissions data, which, in turn, positively impacts policy-making practices. By demonstrating the potential for a virtuous cycle between improved information availability and enhanced policy outcomes, our research underscores the importance of addressing uncertainty in carbon emissions measurement for advancing effective climate change mitigation strategies.

Observed kinetics for the production of diethyl carbonate from CO2 and ethanol catalyzed by CuNi nanoparticles supported on activated carbon.

Monometallic and bimetallic Cu:Ni catalysts with different Cu:Ni molar ratios (3:1, 2:1, 1:1, 1:2, 1:3) were synthesized by wetness impregnation on activated carbon and characterized by TPR (temperature programmed reduction), XRD (X-ray diffraction) and XPS (X-ray photoelectron spectroscopy). The synthesized catalysts were evaluated in the gas phase production of diethyl carbonate from ethanol and carbon dioxide. The largest catalytic activity was obtained over the bimetallic catalyst with a Cu:Ni molar ratio of 3:1. Its improved activity was attributed to the formation of a Cu-Ni alloy on the surface of the catalyst, evidenced by XPS and in agreement with a previous assignment based on Vegard law and TPR analysis. During the reaction rate experiments, it observed the presence of a maximum of the reaction rate as a function of temperature, a tendency also reported for other carbon dioxide-alcohol reactions. It showed that the reaction rate-temperature data can be adjusted with a reversible rate equation. The initial rate as a function of reactant partial pressure data was satisfactorily adjusted using the forward power law rate equation and it was found that the reaction rate is first order in CO2 and second order in ethanol.

The role of microbial communities in biogeochemical cycles and greenhouse gas emissions within tropical soda lakes.

Although anthropogenic activities are the primary drivers of increased greenhouse gas (GHG) emissions, it is crucial to acknowledge that wetlands are a significant source of these gases. Brazil's Pantanal, the largest tropical inland wetland, includes numerous lacustrine systems with freshwater and soda lakes. This study focuses on soda lakes to explore potential biogeochemical cycling and the contribution of biogenic GHG emissions from the water column, particularly methane. Both seasonal variations and the eutrophic status of each examined lake significantly influenced GHG emissions. Eutrophic turbid lakes (ET) showed remarkable methane emissions, likely due to cyanobacterial blooms. The decomposition of cyanobacterial cells, along with the influx of organic carbon through photosynthesis, accelerated the degradation of high organic matter content in the water column by the heterotrophic community. This process released byproducts that were subsequently metabolized in the sediment leading to methane production, more pronounced during periods of increased drought. In contrast, oligotrophic turbid lakes (OT) avoided methane emissions due to high sulfate levels in the water, though they did emit CO2 and N2O. Clear vegetated oligotrophic turbid lakes (CVO) also emitted methane, possibly from organic matter input during plant detritus decomposition, albeit at lower levels than ET. Over the years, a concerning trend has emerged in the Nhecolândia subregion of Brazil's Pantanal, where the prevalence of lakes with cyanobacterial blooms is increasing. This indicates the potential for these areas to become significant GHG emitters in the future. The study highlights the critical role of microbial communities in regulating GHG emissions in soda lakes, emphasizing their broader implications for global GHG inventories. Thus, it advocates for sustained research efforts and conservation initiatives in this environmentally critical habitat.

Real driving cycles and emissions for urban freight transport.

This paper aims to evaluate the driving style effects, through the construction of driving cycles, on the polluting gases, in the context of urban freight transportation. For this, the method used was the construction of cycles through the Vehicle Specific Power (VSP) parameter, which considers instantaneous vehicle and road parameters better to represent driving patterns and freight transportation's environmental impacts. The study was conducted in Fortaleza city, Ceará, Brazil, with a professional driver's group. The road types, land use and traffic light location were considered to analyze and discuss the results. The results show collector roads presented higher speeds than arterial roads, and the use of the land around the road also directly impacted vehicle driving patterns. Regarding CO2 emissions, higher concentrations measured were observed on the arterial roads.

Design of an innovative system for hydrogen production by electrolysis using waste heat recovery technology in natural gas engines.

This research proposes designing and implementing a system to produce hydrogen, utilizing the thermal energy from the exhaust gases in a natural gas engine. For the construction of the system, a thermoelectric generator was used to convert the thermal energy from the exhaust gases into electrical power and an electrolyzer bank to produce hydrogen. The system was evaluated using a natural gas engine, which operated at a constant speed (2400 rpm) and six load conditions (20 %, 40 %, 60 %, 80 %, and 100 %). The effect of hydrogen on the engine was evaluated with fuel mixtures (NG + 10 % HEF and NG + 15 % HEF). The results demonstrate that the NG + 10 % HEF and NG + 15 % HEF mixtures allow for a decrease of 1.84 % and 2.33 % in BSFC and an increase of 1.88 % and 2.38 % in BTE. Through the NG + 15 % HEF mixture, the engine achieved an energy efficiency of 34.15 % and an exergetic efficiency of 32.84 %. Additionally, the NG + 15 % HEF mixture reduces annual CO, CO2, and HC emissions by 9.52 %, 15.48 %, and 13.39 %, respectively. The addition of hydrogen positively impacts the engine's economic cost, allowing for a decrease of 1.56 % in the cost of useful work and a reduction of 3.32 % in the cost of exergy loss. In general, the proposed system for hydrogen production represents an alternative for utilizing the residual energy from exhaust gases, resulting in better performance parameters, reduced annual pollutant emissions, and lower economic costs.

A Comprehensive Approach for Designing Low Carbon Wood Bio-Concretes.

This paper presents a method for designing low carbon bio-based building materials, also named bio-concretes, produced with wood wastes in shavings form (WS) and cementitious pastes. As the aggregates phase of bio-concretes is composed of plant-based particles, known as porous and high water-absorbing materials, the bio-concretes cannot be designed by using the traditional design rules used for conventional mortar or concrete. Then, the method used in the current paper is an adaptation of a previous one that has been developed in a recent paper where bio-concretes were produced with a cement matrix, three types of bio-aggregates, and a proposal of a design abacus. However, when that abacus is used for designing WBC with low cement content in the matrix, the target compressive strength is not reached. In the present paper, the method is extended to low cement content matrix (up to 70% of cement substitution) and also considering the greenhouse gas (GHG) emission of the WBC. To obtain data for proposing a new design abacus, an experimental program was carried out by producing nine workable WBCs, varying wood volumetric fractions (40-45-50%), and water-to-binder ratios. The bio-concretes produced presented adequate consistency, lightness (density between 715 and 1207 kg/m3), and compressive strength ranging from 0.64 to 12.27 MPa. In addition, the GHG emissions of the WBC were analysed through the Life Cycle Assessment methodology. From the relationships obtained between density, compressive strength, water-to-binder ratio, cement consumption, and GHG emissions of the WBC, calibration constants were proposed for developing the updated and more complete abacus regarding an integrated mix design methodology.

Comparative assessment of emissions, performance, and economics parameters for a dual-fuel diesel generator operating with rice bran biodiesel and hydrogen.

The first step to achieving an energy transition is partially substituting fossil fuels with other more environmentally friendly alternatives, such as hydrogen gas. The current research aims to evaluate the influence of hydrogen in a diesel generator fueled with rice bran biodiesel. The above encourages the use of hydrogen and biodiesel production from residual raw material. For the development of the research, a diesel engine bench was used, which operated in five load conditions: 20 %, 40 %, 60 %, 80 %, and 100 %, and was fed with three fuels: -100 %, RB-10 %, and RB-10 % + H2(30 %). The results show that the mixture RB-10 % + H2(30 %) causes a 3.14 % reduction in BSFC and a 3.26 % increase in energy conversion efficiency. In addition, it is observed that a 9.90 %, 12.57 %, and 10.99 % decrease in HC, CO, and smoke opacity emissions compared to pure diesel. On the other hand, the mixture RB-10 % + H2(30 %) reduces by 4.44 %, 5.07 %, and 7.06 % the environmental, social, and ecological impact due to CO2, HC, and CO emissions, as well as a 3.93 % reduction in engine operating cost compared to RB-10 % biodiesel. In general, hydrogen injection is a promising alternative to promote the use of rice bran biodiesel due to its increased performance characteristics and reduced pollutant emissions without the need to modify the engine.

Particulate matter forecast and prediction in Curitiba using machine learning.

Introduction: Air quality is directly affected by pollutant emission from vehicles, especially in large cities and metropolitan areas or when there is no compliance check for vehicle emission standards. Particulate Matter (PM) is one of the pollutants emitted from fuel burning in internal combustion engines and remains suspended in the atmosphere, causing respiratory and cardiovascular health problems to the population. In this study, we analyzed the interaction between vehicular emissions, meteorological variables, and particulate matter concentrations in the lower atmosphere, presenting methods for predicting and forecasting PM2.5. Methods: Meteorological and vehicle flow data from the city of Curitiba, Brazil, and particulate matter concentration data from optical sensors installed in the city between 2020 and 2022 were organized in hourly and daily averages. Prediction and forecasting were based on two machine learning models: Random Forest (RF) and Long Short-Term Memory (LSTM) neural network. The baseline model for prediction was chosen as the Multiple Linear Regression (MLR) model, and for forecast, we used the naive estimation as baseline. Results: RF showed that on hourly and daily prediction scales, the planetary boundary layer height was the most important variable, followed by wind gust and wind velocity in hourly or daily cases, respectively. The highest PM prediction accuracy (99.37%) was found using the RF model on a daily scale. For forecasting, the highest accuracy was 99.71% using the LSTM model for 1-h forecast horizon with 5 h of previous data used as input variables. Discussion: The RF and LSTM models were able to improve prediction and forecasting compared with MLR and Naive, respectively. The LSTM was trained with data corresponding to the period of the COVID-19 pandemic (2020 and 2021) and was able to forecast the concentration of PM2.5 in 2022, in which the data show that there was greater circulation of vehicles and higher peaks in the concentration of PM2.5. Our results can help the physical understanding of factors influencing pollutant dispersion from vehicle emissions at the lower atmosphere in urban environment. This study supports the formulation of new government policies to mitigate the impact of vehicle emissions in large cities.

Assessing methane emissions from paddy fields through environmental and UAV remote sensing variables.

Concerns about methane (CH4) emissions from rice, a staple sustaining over 3.5 billion people globally, are heightened due to its status as the second-largest contributor to greenhouse gases, driving climate change. Accurate quantification of CH4 emissions from rice fields is crucial for understanding gas concentrations. Leveraging technological advancements, we present a groundbreaking solution that integrates machine learning and remote sensing data, challenging traditional closed chamber methods. To achieve this, our methodology involves extensive data collection using drones equipped with a Micasense Altum camera and ground sensors, effectively reducing reliance on labor-intensive and costly field sampling. In this experimental project, our research delves into the intricate relationship between environmental variables, such as soil conditions and weather patterns, and CH4 emissions. We achieved remarkable results by utilizing unmanned aerial vehicles (UAV) and evaluating over 20 regression models, emphasizing an R2 value of 0.98 and 0.95 for the training and testing data, respectively. This outcome designates the random forest regressor as the most suitable model with superior predictive capabilities. Notably, phosphorus, GRVI median, and cumulative soil and water temperature emerged as the model's fittest variables for predicting these values. Our findings underscore an innovative, cost-effective, and efficient alternative for quantifying CH4 emissions, marking a significant advancement in the technology-driven approach to evaluating rice growth parameters and vegetation indices, providing valuable insights for advancing gas emissions studies in rice paddies.

Multifunctional Eco-Friendly Adsorbent Cryogels Based on Xylan Derived from Coffee Residues.

Agricultural and animal farming practices contribute significantly to greenhouse gas (GHG) emissions such as NH3, CH4, CO2, and NOx, causing local environmental concerns involving health risks and water/air pollution. A growing need to capture these pollutants is leading to the development of new strategies, including the use of solid adsorbents. However, commonly used adsorbent materials often pose toxicity and negative long-term environmental effects. This study aimed to develop responsive eco-friendly cryogels using xylan extracted from coffee parchment, a typical residue from coffee production. The crosslinking in cryogels was accomplished by "freeze-thawing" and subsequent freeze-drying. Cryogels were characterized in terms of morphology by using scanning electron microscopy, porosity, and density by the liquid saturation method and also moisture adsorption and ammonia adsorption capacity. The analysis showed that the porosity in the cryogels remained around 0.62-0.42, while the apparent densities varied from 0.14 g/cm3 to 0.25 g/cm3. The moisture adsorption capacity was the highest at the highest relative humidity level (80%), reaching 0.25-0.43 g of water per gram of sample; the amount of water adsorbed increased when the xylan content in the cryogel increased up to 10% w/v, which was consistent with the hygroscopic nature of xylan. The ammonia adsorption process was modeled accurately by a pseudo-second-order equation, where the maximum adsorption capacity in equilibrium reached 0.047 mg NH3/g when xylan reached 10% w/v in cryogels, indicating a chemisorption process. The cryogels under investigation hold promise for ammonia adsorption applications and GHG separation, offering a sustainable alternative for gas-capturing processes.

Trends in the contribution of greenhouse gas emissions from food and beverage purchases in Mexico: 1989-2020.

BACKGROUND: Assessing the trends in dietary GHGE considering the social patterning is critical for understanding the role that food systems have played and will play in global emissions in countries of the global south. Our aim is to describe dietary greenhouse gas emissions (GHGE) trends (overall and by food group) using data from household food purchase surveys from 1989 to 2020 in Mexico, overall and by education levels and urbanicity. METHODS: We used cross-sectional data from 16 rounds of Mexico's National Income and Expenditure Survey, a nationally representative survey. The sample size ranged from 11,051 in 1989 to 88,398 in 2020. We estimated the mean total GHGE per adult-equivalent per day (kg CO2-eq/ad-eq/d) for every survey year. Then, we estimated the relative GHGE contribution by food group for each household. These same analyses were conducted stratifying by education and urbanicity. RESULTS: The mean total GHGE increased from 3.70 (95%CI: 3.57, 3.82) to 4.90 (95% CI 4.62, 5.18) kg CO2-eq/ad-eq/d between 1989 and 2014 and stayed stable between 4.63 (95% CI: 4.53, 4.72) and 4.89 (95% CI: 4.81, 4.96) kg CO2-eq/ad-eq/d from 2016 onwards. In 1989, beef (19.89%, 95% CI: 19.18, 20.59), dairy (16.87%, 95% CI: 16.30, 17.42)), corn (9.61%, 95% CI: 9.00, 10.22), legumes (7.03%, 95% CI: 6.59, 7.46), and beverages (6.99%, 95% CI: 6.66, 7.32) had the highest relative contribution to food GHGE; by 2020, beef was the top contributor (17.68%, 95%CI: 17.46, 17.89) followed by fast food (14.17%, 95% CI: 13.90, 14.43), dairy (11.21%, 95%CI: 11.06, 11.36), beverages (10.09%, 95%CI: 9.94, 10.23), and chicken (10.04%, 95%CI: 9.90, 10.17). Households with higher education levels and those in more urbanized areas contributed more to dietary GHGE across the full period. However, households with lower education levels and those in rural areas had the highest increase in these emissions from 1989 to 2020. CONCLUSIONS: Our results provide insights into the food groups in which the 2023 Mexican Dietary Guidelines may require to focus on improving human and planetary health.

Performance and emissions data of an internal combustion engine operating with different ethanol/water mixtures and compression ratios.

This study presents performance and emissions data of an Otto cycle mono-cylinder combustion engine operating with two different compression rates and several mixtures of anhydrous ethanol fuel and water. The instrumented engine was mounted on a dynamometer with the ignition point and injection fuel advance calibrated to obtain the maximum torque and mixture in stoichiometric conditions. Characteristic engine performance parameters and emission fractions from its exhaust system were acquired from 2,000 rpm to 4,000 rpm with fuel mixtures of up to 50% water content. To our knowledge, data on this extreme operating condition are not available in the literature.

Ethanol, acetaldehyde, and methanol in the gas phase and rainwater in different biomes and urban regions of Brazil.

In the context of the increasing global use of ethanol biofuel, this work investigates the concentrations of ethanol, methanol, and acetaldehyde, in both the gaseous phase and rainwater, across six diverse urban regions and biomes in Brazil, a country where ethanol accounts for nearly half the light-duty vehicular fuel consumption. Atmospheric ethanol median concentrations in São Paulo (SP) (12.3 ± 12.1 ppbv) and Ribeirão Preto (RP) (12.1 ± 10.9 ppbv) were remarkably close, despite the SP vehicular fleet being ∼13 times larger. Likewise, the rainwater VWM ethanol concentration in SP (4.64 ± 0.38 µmol L-1) was only 26 % higher than in RP (3.42 ± 0.13 µmol L-1). This work demonstrated the importance of evaporative emissions, together with biomass burning, as sources of the compounds studied. The importance of biogenic emissions of methanol during forest flooding was identified in campaigns in the Amazon and Atlantic forests. Marine air masses arriving at a coastal site led to the lowest concentrations of ethanol measured in this work. Besides vehicular and biomass burning emissions, secondary formation of acetaldehyde by photochemical reactions may be relevant in urban and non-urban regions. The combined deposition flux of ethanol and methanol was 6.2 kg ha-1 year-1, avoiding oxidation to the corresponding and more toxic aldehydes. Considering the species determined here, the ozone formation potential (OFP) in RP was around two-fold higher than in SP, further evidencing the importance of emissions from regional distilleries and biomass burning, in addition to vehicles. At the forest and coastal sites, the OFP was approximately 5 times lower than at the urban sites. Our work evidenced that transition from gasoline to ethanol or ethanol blends brings the associated risk of increasing the concentrations of highly toxic aldehydes and ozone, potentially impacting the atmosphere and threatening air quality and human health in urban areas.

Estimating the carbon footprint of Mexican food consumption based on a high-resolution environmentally extended input-output model.

Increased global attention is being paid to the food-health-climate trilemma. In this study, we evaluate the climate impacts of Mexico's food consumption patterns by creating a high-resolution (262 sectors) Environmentally Extended Input-Output (EEIO) model called MXEEIO. We focus on the differences between food away from home (FAFH) and food at home (FAH) and compare Mexico's results with those of the USA. The results show that the main components of food spending in Mexico were meat, baked products, and beverages, raising concerns about their potential negative health effects if consumed excessively. Mexico's total greenhouse gas (GHG) emissions from food consumption were estimated at 149 million metric tons (MMT) in 2013, as opposed to 797 MMT for the USA. Meat and dairy products were the main contributors to Mexico's food-related GHG emissions, accounting for 57% of total emissions. Mexico spent a much smaller proportion of food-related income on FAFH than the USA (13% vs. 52%), suggesting great potential for growth as Mexico's per capita GDP continues to rise. Detailed contribution analysis shows that reducing Mexico's food-related GHG emissions would benefit most from a transition to low-carbon cattle farming, but mitigation efforts in other sectors such as crop cultivation and electricity generation are also important. Overall, our study underscores the significance of food-related GHG emissions in Mexico, especially those from meat and dairy products, and the mitigation challenges these sectors face.

Mercury in Fish and Human Hair and Estimated Dietary Intake in a Riverside Community of the Madeira River Basin in the Brazilian Amazon.

Mercury (Hg) is a chemical element that poses risks to human health due to its high toxicity and environmental persistence. We determined the total Hg (THg) and methyl Hg (MeHg) concentrations in hair samples from residents of the Demarcação District (Porto Velho, Rondônia) in the Brazilian Amazon, as well as in water and fish samples, to evaluate factors influencing human exposure. The average THg concentration in human hair was 7.86 ± 6.78 mg kg-1 and it was significantly higher in men, with an increasing trend related to age. There was no significant difference between female age groups. Human exposure to Hg through water was negligible compared to fish consumption. The average weekly intake estimates in the community varied between 1.54 and 4.62 µg kg-1, substantially higher than the recommended limit. The fish species with the highest amounts safe for daily consumption were herbivores and detritivores. Our results contribute to an understanding of how exposure to Hg affects the health of riverside populations and provide insights for new research to develop methods to mitigate such exposure and thus improve the quality of life of Amazonian people.

Microbial communities present in Sargassum spp. leachates from the Mexican Caribbean which are involved in their degradation in the environment, a tool to tackle the problem.

The Sargassum phenomenon is currently affecting the Caribbean in several ways; one of them is the increase of greenhouse gases due to the decomposition process of this macroalgae; these processes also produce large amounts of pollutant leachates, in which several microbial communities are involved. To understand these processes, we conducted a 150-day study on the Sargassum spp environmental degradation under outdoor conditions, during which leachates were collected at 0, 30, 90, and 150 days. Subsequently, a metagenomic study of the microorganisms found in the leachates was carried out, in which changes in the microbial community were observed over time. The results showed that anaerobic bacterial genera such as Thermofilum and Methanopyrus were predominant at the beginning of this study (0 and 30 days), degrading sugars of sulfur polymers such as fucoidan, but throughout the experiment, the microbial communities were changed also, with the genera Fischerella and Dolichospermum being the most predominant at days 90 and 150, respectively. A principal component analysis (PCA) indicated, with 94% variance, that genera were positively correlated at 30 and 90 days, but not with initial populations, indicating changes in community structure due to sargassum degradation were present. Finally, at 150 days, the leachate volume decreased by almost 50% and there was a higher abundance of the genera Desulfobacter and Dolichospemum. This is the first work carried out to understand the degradation of Sargassum spp, which will serve, together with other works, to understand and provide a solution to this serious environmental problem in the Caribbean.

Procedures to combine estimators of greenhouse gases emission factors.

BACKGROUND: This article describes a new procedure to estimate the mean and variance of greenhouse gases (GHG) emission factors based on different, possibly conflicting, estimates for these emission factors. The procedure uses common information such as mean and standard deviation usually reported in IPCC (Intergovernmental Panel on Climate Change) database and other references in the literature that estimate emission factors. Essentially, it is a procedure in the class of meta-analysis, based on the computation of [Formula: see text], a new estimator for the variance of the emission factor. RESULTS: We discuss the quality of this estimator in terms of its probability distribution and show that it is unbiased. The resulting confidence interval for the mean emission factor is tighter than those that would have resulted from using other estimators such as pooled variance and thus, the new procedure improves the accuracy in estimating GHG emissions. The application of the procedure is illustrated in a case study involving the estimation of methane emissions from rice cultivation. CONCLUSIONS: The estimation of emission factors using [Formula: see text] was demonstrated to be more accurate because it is not biased and more precise than alternative methods.

Estimation of the main air pollutants from different biomasses under combustion atmospheres by artificial neural networks.

The production of biofuels to be used as bioenergy under combustion processes generates some gaseous emissions (CO, CO2, NOx, SOx, and other pollutants), affecting living organisms and requiring careful assessments. However, obtaining such information experimentally for data evaluation is costly and time-consuming and its in situ obtaining for regional biomasses (e.g., those from Northeast Brazil (NEB) is still a major challenge. This paper reports on the application of artificial neural networks (ANNs) for the prediction of the main air pollutants (CO, CO2, NO, and SO2) produced during the direct biomass combustion (N2/O2:80/20%) with the use of ultimate analysis (carbon, hydrogen, nitrogen, sulfur, and oxygen). 116 worldwide biomasses were used as input data, which is a relevant alternative to overcome the lack of experimental resources in NEB and obtain such information. Cross-validation was conducted with k-fold to optimize the ANNs and performance was analyzed with the use of statistical errors for accuracy assessments. The results showed an acceptable statistical performance for all architectures of ANNs, with 0.001-12.41% MAPE, 0.001-5.82 mg Nm-3 MAE, and 0.03-52.30 mg Nm-3 RMSE, highlighting the high precision of the emissions studied. On average, the differences between predicted and real values for CO, CO2, NO, and SO2 emissions from NEB biomasses were approximately 0.01%, 10-6%, 0.14%, and 0.05%, respectively. Pearson coefficient provided consistent results of concentration of the ultimate analysis in relation to the emissions studied and effectiveness of the test set in the developed models.

Understanding the Relations between Soil Biochemical Properties and N2O Emissions in a Long-Term Integrated Crop-Livestock System.

Edaphoclimatic conditions influence nitrous oxide (N2O) emissions from agricultural systems where soil biochemical properties play a key role. This study addressed cumulative N2O emissions and their relations with soil biochemical properties in a long-term experiment (26 years) with integrated crop-livestock farming systems fertilized with two P and K rates. The farming systems consisted of continuous crops fertilized with half of the recommended P and K rates (CCF1), continuous crops at the recommended P and K rates (CCF2), an integrated crop-livestock system with half of the recommended P and K rates (ICLF1), and an integrated crop-livestock at the recommended P and K rates (ICLF2). The ICLF2 may have promoted the greatest entry of carbon into the soil and positively influenced the soil's biochemical properties. Total carbon (TC) was highest in ICLF2 in both growing seasons. The particulate and mineral-associated fractions in 2016 and 2017, respectively, and the microbial biomass fraction in the two growing seasons were also very high. Acid phosphatase and arylsulfatase in ICLF1 and ICLF2 were highest in 2016. The soil properties correlated with cumulative N2O emissions were TC, total nitrogen (TN), particulate nitrogen (PN), available nitrogen (AN), mineral-associated organic carbon (MAC), and microbial biomass carbon (MBC). The results indicated that ICLF2 induces an accumulation of more stable organic matter (OM) fractions that are unavailable to the microbiota in the short term and result in lower N2O emissions.

A 35-Year Record (1987-2022) of Hg Concentrations in Two of the Fish Species Most Consumed by People Living in the Upper Madeira River Basin, Brazilian Amazon Region.

This study presents a 35-year record of total mercury (Hg) concentrations in the detritivore fish Prochilodus nigricans (Curimatã) and the carnivore Cichla pleiozona (Tucunaré), two of the most widely distributed, ecologically important and consumed fish species in the upper Madeira River Basin in the Western Brazilian Amazon. Fish samples from the major Madeira River and marginal lakes and tributaries were compared. Irrespective of site, Hg concentrations were higher in the carnivore fish compared to the detritivore. Hg concentrations increased 5-fold in C. pleiozona in the past three decades, whereas they remained relatively constant in P. nigricans when analyzing the entire 35-year period. When analyzed separately, fish in the main river and marginal lake and tributaries presented the same pattern of Hg variation, with a significant increase in Hg concentrations in the carnivore and in the detritivore in marginal lakes and tributaries but not in the main river. This was in line with the increase in methyl-Hg production in tributaries, mostly associated with deforestation in the past decade in the basin. Although an increase in direct emissions from artisanal gold mining also occurred in the past decade, this caused virtually no impact on fish Hg concentrations, suggesting atmospheric emission and deposition in forests and further export to water systems as an intermediate link with fish Hg concentrations.