Search Results (156)

Wastewater treatment plants (WWTPs) function as engineered ecosystems in which microbial consortia mediate nutrient cycling, xenobiotic degradation, and heavy metal detoxification. This review discusses a forward-looking roadmap that integrates microbial ecology, multi-omics diagnostics, and artificial intelligence (AI) for next-generation treatments. Meta-analyses suggest that a globally conserved core microbiome indicates sludge functions, with high predictive value for treatment stability. Multi-omics approaches, including metagenomics, metatranscriptomics, and environmental DNA (eDNA) profiling, have integrated microbial composition with greenhouse gas (GHG) emissions, showing that WWTPs contribute 2–5% of anthropogenic nitrous oxide (N₂O) emissions. Emerging AI-enhanced eDNA models have achieved >90% predictive accuracy for effluent quality and antibiotic resistance gene (ARG) prevalence, facilitating near-real-time monitoring and adaptive control of effluent quality. Key advances include microbial strategies for degrading organic pollutants, pesticides, and heavy metals and monitoring industrial effluents. This review highlights both translational opportunities, including engineered microbial consortia, AI-driven digital twins and molecular indices, and persistent barriers, including ARG dissemination, resilience under environmental stress and regulatory integration. Future WWTPs are envisioned as adaptive, climate-conscious biorefineries that recover resources, mitigate ecological risks, and reduce their carbon footprint. Full article

Manure is a valuable organic resource for sustainable agriculture, enhancing soil fertility and promoting nutrient cycling; however, it also contributes significantly to methane and nitrous oxide emissions. The European Green Deal and Latvia’s National Energy and Climate Plan have set targets for reducing agricultural greenhouse gas (GHG) emissions, including those related to improved manure management. Therefore, this research aims to estimate the future manure production in Latvia to determine the potential for reducing GHG emissions by 2050. Using the LASAM model developed in Latvia, the number of farm animals, the amount of manure, and the associated GHG emissions were projected for the period up to 2050. The calculations followed the Intergovernmental Panel on Climate Change (IPCC) methodology and were based on national indicators and current national GHG inventory data covering the period of 2021–2050. Significant changes in the structure of manure in Latvia are predicted by 2050, with the proportion of liquid manure expected to increase while the amounts of solid manure and manure deposited by grazing animals are expected to decrease. The GHG emission projection results indicate that by 2050, total emissions from manure management will decrease by approximately 5%, primarily due to a decline in the number of farm animals and, consequently, a reduction in the amount of manure. In contrast, methane emissions are expected to increase by approximately 5% due to production intensification. The research results emphasise the need to introduce more effective methane emission reduction technologies and improved projection approaches. Full article

This study evaluated the seasonal variability of soil–atmosphere greenhouse gas (GHG) fluxes—carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O)—across a land-use gradient in the Andean–Amazon transition zone of Colombia. The gradient included five land-use types incorporating at least one innovative climate-smart practice—improved pasture (IP), cacao agroforestry system (CaAS), copoazu agroforestry system (CoAS), secondary forest with agroforestry enrichment (SFAE), and moriche palm swamp ecosystem (MPSE)—alongside the dominant regional land uses, old-growth forest (OF) and degraded pasture (DP). Soil GHG fluxes varied markedly among land-use types and between seasons. CO₂ fluxes were consistently higher during the dry season, whereas CH₄ and N₂O fluxes peaked in the rainy season. Agroecological and restoration systems exhibited substantially lower CO₂ emissions (7.34–9.74 Mg CO₂-C ha⁻¹ yr⁻¹) compared with DP (18.85 Mg CO₂-C ha⁻¹ yr⁻¹) during the rainy season, and lower N₂O fluxes (0.21–1.04 Mg CO₂-C ha⁻¹ yr⁻¹) during the dry season. In contrast, the MPSE presented high CH₄ emissions in the rainy season (300.45 kg CH₄-C ha⁻¹ yr⁻¹). Across all land uses, CO₂ was the dominant contributor to the total GWP (>95% of emissions). The highest global warming potential (GWP) occurred in DP, whereas CaAS, CoAS and MPSE exhibited the lowest values. Soil temperature, pH, exchangeable acidity, texture, and bulk density play a decisive role in regulating GHG fluxes, whereas climatic factors, such as air temperature and relative humidity, influence fluxes indirectly by modulating soil conditions. These findings underscore the role of diversified agroforestry and restoration systems in mitigating GHG emissions and the need to integrate soil and climate drivers into regional climate models. Full article

In the context of global carbon neutrality, municipal wastewater treatment plants (WWTPs), as key sources of greenhouse gas emissions, urgently require quantification of carbon emissions and implementation of mitigation strategies. This study establishes a life-cycle carbon footprint model encompassing the stages of pretreatment, biological treatment (AAO process), and sludge treatment, with integrated consideration of municipal sewer networks. Key findings reveal the following: The biological treatment stage contributes 68.14% of total carbon emissions. N₂O (nitrous oxide), due to its high global warming potential (GWP), is the primary source of direct emissions (0.333 kg CO₂eq/m³). In the pretreatment stage, 80.4% of carbon emissions originate from the electricity consumption of sewage lifting pump stations (0.030 kg CO₂eq/m³). During the sludge treatment stage, carbon emissions are concentrated in residual sludge lifting (0.0086 kg CO₂eq/m³) and sludge dewatering/pressing (0.0088 kg CO₂eq/m³). Accordingly, this study proposes the following mitigation strategies: novel nitrogen removal processes should be implemented to optimize aeration control and enhance methane (CH₄) recovery during the biological period, and variable frequency drive (VFD) pumps and IoT (Internet of Things) technologies should be employed to reduce energy consumption during the pretreatment period, and during the sludge treatment period, low-carbon dewatering technologies should be adopted. This work provides a theoretical foundation for process-specific carbon management in WWTPs and facilitates the synergistic advancement of environmental stewardship and dual-carbon objectives through technology–system integration. Full article

As part of the GreenRAIM activity of the European Space Agency (ESA), an extensive test campaign involving various monopropellants was undertaken. In this work, design and test results of an additively manufactured 1-Newton monopropellant thruster are shown. The detailed design of the thruster and the experimental setup are presented. The first part of the test campaign was conducted with 98 wt.% hydrogen peroxide as the propellant and a commercially available Pt/Al₂O₃ catalyst. The second part was carried out with the same thruster but using nitrous oxide as the propellant and an iridium-based catalyst. The test data acquired was used to validate a comprehensive, generic model for monopropellant thrusters within the simulation software EcosimPro/ESPSS v3.7, which was developed within the activity. Full article

Quantifying air emissions from livestock pastures remains challenging due to spatial variability and temporal fluctuations in emissions due to weather conditions. In this study we used a small unmanned aerial vehicle (sUAV) equipped with real-time sensors and an air sample collection system to directly measure carbon dioxide (CO₂), methane (CH₄), ammonia (NH₃), nitrous oxide (N₂O), nitrogen dioxide (NO₂), hydrogen sulfide (H₂S), total volatile organic compound (VOC), and particulate matter (PM₁, PM_2.5, PM₁₀) emissions across two dairy pastures, two beef pastures, and one sheep pasture in Wisconsin. Emission rates were calculated using the Lagrangian mass balance model and validated against ground-level dynamic flux chamber (DFC) measurements. UAV-based CO₂ concentrations showed a strong correlation with DFC measurements (R² = 0.86, RMSE = 21.5 ppm, MBE = +9.7 ppm). Dairy 1 yielded the highest emissions for most compounds, with average emission rates of 0.50 ± 0.28 g m⁻² day⁻¹ head⁻¹ for CO₂, 8.48 ± 2.75 mg m⁻² day⁻¹ head⁻¹ for CH₄, and 0.20 ± 0.60 mg m⁻² day⁻¹ head⁻¹ for NH₃. The sheep pasture, on the other hand, had the lowest CH₄ and NH₃ emission rates, averaging 0.35 ± 0.22 mg m⁻² day⁻¹ head⁻¹ and 0.02 ± 0.05 mg m⁻² day⁻¹ head⁻¹, respectively. Rainfall events (≥ 5 mm within five days of sampling) significantly elevated N₂O emissions (0.56 ± 0.40 vs. 0.13 ± 0.17 mg m⁻² day⁻¹ head⁻¹). Particulate matter emissions were significantly affected by forage density. PM_2.5 emission rates reached 1.25 × 10⁻⁴ g m⁻² day⁻¹ head⁻¹ under low vegetative cover. It was concluded that emissions were affected by both animal species and the environmental conditions. The findings of this study provide a foundation for further development of emission inventories for pasture-based livestock production systems. Full article

The oxidization of sulfur dioxide (SO₂) occurs in the gas and liquid phase and this oxidation contributes to particulate matter and acid precipitation. The production of sulfate particles is significant because of their impact on climate, precipitation acidification, and human health. In this paper, the focus is on the oxidation of SO₂ and on the possibility of unknown heterogeneous reactions that may occur on sulfate aerosol surfaces. These results are based on the reanalysis of a foundational set of SO₂ laboratory oxidation measurements. The experiments involved two sets of photochemical studies of nitrous acid (HONO), nitrogen oxides (NO_x = NO + NO₂), SO₂, carbon monoxide (CO), and water vapor (H₂O) mixtures made in molecular nitrogen (N₂) with traces of molecular oxygen or in synthetic air. The reanalysis strongly suggests that there are uncharacterized processes for the oxidation of SO₂ that are nearly three times faster than the known gas-phase reactions. The uncharacterized processes may involve sulfate aerosol surface reactions in the presence of nitrogen oxides. If these processes can be included in current atmospheric chemistry models, greater conversion rates of SO₂ to sulfate aerosol will be calculated and this may reduce modeling bias. Full article

Nitrous oxide (N₂O) is a potent greenhouse gas, with emissions occurring mostly from agricultural soils, especially acidic soils. This research aimed to elucidate the response of soils dominated by nitrification-driven N₂O production to alkaline amendments, given that nitrification is a key process in N₂O emission. This study investigated the impact of an alkaline mineral amendment (CSMP) on N₂O emission, nitrification rate, and functional gene abundance. Using a robotic automated incubation system, CSMP both alone and in combination with urea was applied to two acidic soils (CL: pH 5.81; WS: pH 4.91). The results demonstrated that, relative to the CK, the CSMP-only treatment significantly increased N₂O emissions by 18.4-fold in these acidic soils, with a 61.6-fold increase in the U + CSMP treatment. This very large increase was driven by a rise in AOB-amoA abundance and a concurrent decline in AOA-amoA, which was confirmed by structural equation modeling, which showed that the increase in pH strongly influenced N₂O emission primarily through AOB-amoA. Although CSMP is effective for reversing soil acidification, its use must be carefully managed to prevent stimulation of N₂O emissions. Future strategies should explore combining CSMP with approaches that can mitigate nitrification while maintaining its soil improvement benefits. This study provides critical insights for developing balanced management practices that address both soil health and climate change mitigation in acidic agricultural systems. Full article

The dataset presented in this manuscript consists of three distinct sets of data collected during a laboratory experiment aimed at quantifying the emissions of greenhouse gases (GHGs), specifically methane (CH₄), carbon dioxide (CO₂), and nitrous oxide (N₂O). The experiment was conducted in three phases, each initiated at different times. The first phase began on 6 June 2022, using a biocover composed of 60% fine-fraction waste, 20% clay soil, and 20% stabilized compost. The second phase commenced on 26 August 2022, with two biocover variants: one composed of 50% fine-fraction waste and 50% clay soil, and the other consisting of 40% fine-fraction waste, 40% clay soil, and 20% shredded paper. The final phase started on 27 October 2022, introducing two biocovers: one containing 25% dried algae, 25% fine-fraction waste, 25% gravel (0–20 mm), and 25% ash, and the other composed of 40% fine-fraction waste, 40% dried algae, and 20% chernozem. Emission assessments were conducted three weeks after the biocover installation to allow for settling and stabilization, followed by weekly measurements two to three days before irrigation with 250 mL of water to simulate field conditions. GHG emission quantification was carried out using the Cavity Ring-Down Spectroscopy gas measurement device, Picarro G2508. This dataset offers substantial scientific value for advancing biocover technologies aimed at reducing GHG emissions in landfill environments, particularly for mitigating methane emissions. In addition to initial experimental use, the dataset offers a wide range of possibilities for reuse, including modeling landfill gas emissions, validating gas flow measurement methods, developing machine learning models, and performing meta-analyses. Its detailed structure facilitates multi-faceted environmental research and supports optimization of landfill management. Full article

This study investigates the impact of environmental changes induced by systematic manipulation of flooding depth and breeding density on greenhouse gas emissions in the field-based giant rice–fish hybrid farming model. Compared with traditional agricultural practices, increasing cultured density in giant rice–fish co-cultivation significantly alleviated the adverse consequences of flooding on soil nutrient dynamics, microbial activity community structure, and greenhouse gas emissions. Relative to the traditional alternating wet and dry irrigation, the soil concentrations of ammonium, total nitrogen, and phosphate significantly increased. Cultured fish had significantly increased soil microbial biomass carbon, nitrogen, and phosphorus contents and improved soil β-glucosidase and aryl-sulfatase activates relative to flooding alone. Cultured fish increased the relative abundances of Actinobacteria, Nitrospirae, Planctomycetes, Verrucomicrobia, and Aminicenantes. An increasing cultured fish density reduced cumulative methane and nitrous oxide emissions and GWP (global warming potential). Relative to the continuous flooding throughout the growing period, cumulative methane emissions and GWP in the flooding with high-density cultured fish were reduced by 5.32% and 1.48%, respectively. Notably, this co-cultivation strategy has the potential to transform traditional practices for sustainable agriculture. Nevertheless, it is imperative to remain vigilant about the potential consequences of greenhouse gas emissions associated with these innovative practices. Continuous monitoring and refinement are essential to ensure the long-term sustainability and viability of this agricultural approach. Full article

Nitrous oxide (${\mathrm{N}}_2\mathrm{O}$) emissions from agriculture are rising due to increased fertilizer use and intensive farming, posing a major challenge for climate mitigation. This study introduces a novel reinforcement learning (RL) framework to optimize farm management strategies that balance crop productivity with environmental impact, particularly ${\mathrm{N}}_2\mathrm{O}$ emissions. By modeling agricultural decision-making as a partially observable Markov decision process (POMDP), the framework accounts for uncertainties in environmental conditions and observational data. The approach integrates deep Q-learning with recurrent neural networks (RNNs) to train adaptive agents within a simulated farming environment. A Probabilistic Deep Learning (PDL) model was developed to estimate ${\mathrm{N}}_2\mathrm{O}$ emissions, achieving a high Prediction Interval Coverage Probability (PICP) of 0.937 within a 95% confidence interval on the available dataset. While the PDL model’s generalizability is currently constrained by the limited observational data, the RL framework itself is designed for broad applicability, capable of extending to diverse agricultural practices and environmental conditions. Results demonstrate that RL agents reduce ${\mathrm{N}}_2\mathrm{O}$ emissions without compromising yields, even under climatic variability. The framework’s flexibility allows for future integration of expanded datasets or alternative emission models, ensuring scalability as more field data becomes available. This work highlights the potential of artificial intelligence to advance climate-smart agriculture by simultaneously addressing productivity and sustainability goals in dynamic real-world settings. Full article

Conservation tillage can improve soil health and carbon sequestration and is helpful for sustainable agricultural development. However, its effect on crop yields and nitrous oxide (N₂O) emissions is still controversial. In this study, a two-year field experiment of spring maize was conducted from 2019 to 2020 in the Phaeozems region of Northeast China, involving two tillage practices (strip tillage and conventional tillage) and two nitrogen inhibitors (N-butylthiophosphorotriamine, NBPT and 3,4-Dimethylpyrazole phosphate, DMPP). The WHCNS (Soil Water Heat Carbon Nitrogen Simulator) model was calibrated and validated with field observations, and the effects of different tillage practices and nitrification inhibitors on spring maize yield, N₂O emissions, water use efficiency (WUE), and nitrogen use efficiency (NUE) were simulated using the WHCNS model. Precipitation scenarios were set up to simulate and analyze the changes in patterns of crop yield and N₂O emissions under long-term conservation tillage for 30 years (1991–2020). The results showed that concerning maize yield, under conservation tillage, the type of straw and nitrogen fertilizer inhibitor could explain 72.1% and 7.1%, respectively, of the total variance in maize yield, while precipitation explained only 14.1% of the total variance, with a 28.5% increase in crop yield in a humid year compared to a dry year. N₂O emissions were principally influenced by precipitation, which could explain 46.4% of the total variance in N₂O emissions. Furthermore, N₂O emissions were 385% higher in humid years than in dry years. Straw under conservation tillage and inhibitor type explained 8.1% and 19.4% of the total variance in N₂O emissions, respectively. Conservation tillage with nitrification inhibitors is recommended to increase crop yields, improve soil quality and reduce greenhouse gas emissions in the Phaeozems region of Northeast China, thus ensuring sustainable agricultural development in the region. Full article

The transportation sector significantly contributes to greenhouse gas (GHG) emissions and remains a key research focus on emission quantification and mitigation. Although numerous models exist for estimating vehicle-based emissions, most lack accuracy at regional scales, particularly in remote or underdeveloped areas, including backcountry national parks and mountainous regions lacking basic infrastructure. This study addresses that gap by developing and applying a terrain-adjusted, segment-based methodology to estimate GHG emissions from tourist vehicles in Altai Tavan Bogd National Park, one of Mongolia’s most remote protected areas. The proposed method uses Tier 1 IPCC emission factors but incorporates field-segmented route analysis, vehicle categorization, and terrain-based fuel adjustments to achieve a spatially disaggregated Tier 1 approach. Results show that carbon dioxide (CO₂) emissions increased from 118.7 tons in 2018 to 2239 tons in 2024. Tourist vehicle entries increased from 712 in 2018 to 13,192 in 2024, with 99.1% of entries occurring between May and October. Over the same period, cumulative methane (CH₄) and nitrous oxide (N₂O) emissions were estimated at 300.9 kg and 45.75 kg, respectively. This modular approach is especially suitable for high-altitude, infrastructure-limited regions where real-time emissions monitoring is not feasible. By integrating localized travel patterns with global frameworks such as the IPCC 2006 Guidelines, this model enables more precise and context-sensitive GHG estimates from vehicles in national parks and similar environments. Full article

Reclaimed fields have a low soil fertility and low productivity compared to conventional arable land, necessitating research on productivity enhancement. The rice–frog co-culture model is an ecologically intensive practice that combines biodiversity objectives with agricultural production needs, offering high ecological and economic value. However, there is a lack of research on this model that has focused on factors other than soil nutrient levels. The present study evaluated the rice–frog co-culture model in a reclaimed paddy field across three experimental plots with varying frog stocking densities: a rice monoculture (CG), low-density co-culture (LRF), and high-density co-culture (HRF). We investigated the effects of the frog density on greenhouse gas emissions throughout the rice growth. The rice–frog co-culture model significantly reduced methane (CH₄) emissions, with fluxes highest in the CG plot, followed by the LRF and then HRF plots. This reduction was achieved by altering the soil pH, the cation exchange capacity, the mcrA gene abundance, and the mcrA/pmoA gene abundance ratio. However, there was a contrasting nitrous oxide (N₂O) emission pattern. The co-culture model actually increased N₂O emissions, with fluxes being highest in the HRF plots, followed by the LRF and then CG plots. The correlation analysis identified the soil nosZ gene abundance, redox potential, urease activity, nirS gene abundance, and ratio of the combined nirK and nirS abundance to the nosZ abundance as key factors associated with N₂O emissions. While the co-cultivation model increased N₂O emissions, it also significantly reduced CH₄ emissions. Overall, the rice–frog co-culture model, especially at a high density, offers a favorable sustainable agricultural production model. Full article

The DNDC-Rice model effectively simulates yield and greenhouse gas emissions within a paddy system, while its performance under upland conditions remains unclear. Using data from a long-term cover crop experiment (fallow [FA] vs. rye [RY]) in a soybean field, this study validated the DNDC-Rice model’s performance in simulating soil dynamics, crop growth, and C-N cycling processes in upland systems through various indicators, including soil temperature, water-filled pore space (WFPS), soybean biomass and yield, CO₂ and N₂O fluxes, and soil organic carbon (SOC). Based on simulated results, the underestimation of cumulative N₂O flux (25.6% in FA and 5.1% in RY) was attributed to both underestimated WFPS and the algorithm’s limitations in simulating N₂O emission pulses. Overestimated soybean growth increased respiration, leading to the overestimation of CO₂ flux. Although the model captured trends in SOC stock, the simulated annual values differed from observations (−9.9% to +10.1%), potentially due to sampling errors. These findings indicate that the DNDC-Rice model requires improvements in its N cycling algorithm and crop growth sub-models to improve predictions for upland systems. This study provides validation evidence for applying DNDC-Rice to upland systems and offers direction for improving model simulation in paddy-upland rotation systems, thereby enhancing its applicability in such contexts. Full article