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The proliferation of plastic pollution has led to the widespread accumulation of microplastics (MPs) in marine ecosystems. While surface sediment contamination is relatively well studied, knowledge of vertical MP distri- bution within sediment columns remains limited. This study examines the abundance, vertical distribution, and characteristics of MPs in subtidal and intertidal sediments of Panjang Island, Java Sea. Fifteen shallow (10 cm) and three deep (~100 cm) sediment cores were analyzed for MP abundance, morphology, size, color, and polymer using microscopy and ATR-FTIR. MPs were detected in all cores, with an average concentration of 0.49 ± 0.28 MPs g⁻¹ in surface sediments. The highest surface concentration (2.08 ± 0.22 MPs g⁻¹) occurred in the southwest, a sheltered site with greater anthropogenic influence, while the lowest (0.05 ± 0.07 MPs g⁻¹) was recorded in the northwest, a remote and less disturbed area. Fibers dominated particle types. White, black, and blue were the most common colors, and size distributions were skewed toward particles <1 mm. Polypropylene and polyethylene were the most frequent polymers, reflecting their widespread use and persistence. Vertical profiles revealed higher MP concentrations near the surface, indicating intensified inputs in recent decades. No MPs were detected below 70 cm, suggesting limited downward migration and marking the onset of contami- nation during the plastic era. This study also found MPs in deeper sediment layer, likely due to post-depositional processes such as bioturbation. These findings demonstrate that sediment cores serve as valuable archives of historical MP deposition, capturing both global production trends and local environmental influences, and provide a basis for targeted management strategies. 

Amazon forests are undergoing rapid transformations driven by deforestation, climate change, fire, and other anthropogenic pressures, leading to the hypothesis that they may be nearing a catastrophic tipping point—beyond which ecosystems could shift to a permanently altered state. This review revisits the concept of an Amazon tipping point and assesses the risk of forest collapse from an ecological perspective. We synthesize evidence showing that environmental stressors can drive critical ecosystem transitions, either gradually through incremental loss of resilience or abruptly via synergistic feedbacks. The interplay between climate and land-use change amplifies risks to biodiversity, ecosystem services, and livelihoods. Yet, there is limited evidence for a single, system-wide tipping point. Instead, the Amazon's resilience—although not unlimited—offers meaningful pathways for recovery. The most immediate and effective strategies to support this resilience include slowing forest loss, mitigating climate change, reducing fire activity, curbing defaunation, and restoring degraded ecosystems. Without decisive action to address direct threats, the Amazon system may be pushed beyond safe ecological-climatological operating limits—even in the absence of sharply defined thresholds—due to the scale and persistence of anthropogenic pressures. Preserving the Amazon's ecological integrity and its vital role in regulating the global climate requires urgent, sustained conservation efforts in collaboration with local and Indigenous communities. 

Over the past three decades, assessments of the contemporary global carbon budget consistently report a strong net land carbon sink. Here, we review evidence supporting this paradigm and quantify the differences in global and Northern Hemisphere estimates of the net land sink derived from atmospheric inversion and satellite-derived vegetation biomass time series. Our analysis, combined with additional synthesis, supports a hypothesis that the net land sink is substantially weaker than commonly reported. At a global scale, our estimate of the net land carbon sink is 0.8 ± 0.7 petagrams of carbon per year from 2000 through 2019, nearly a factor of two lower than the Global Carbon Project estimate. With concurrent adjustments to ocean (+8%) and fossil fuel (−6%) fluxes, we develop a budget that partially reconciles key constraints provided by vegetation carbon, the north-south CO₂gradient, and O₂trends. We further outline potential modifications to models to improve agreement with a weaker land sink and describe several approaches for testing the hypothesis. 

Satellite-based evapotranspiration (ET) products inform decision-making regarding water availability and plant water use from sub-field to watershed scales. These products are validated using eddy covariance flux towers, where observations are subject to the influence of landscape heterogeneity due to a constantly shifting contributing area, or flux footprint, governed by surface and atmospheric drivers. In agricultural regions with multiple crop types, local heterogeneity may amplify the importance of appropriate grid cell selection for satellite ET comparisons. We evaluate the extent to which different satellite ET products capture both ET magnitudes and spatial variability due to landscape heterogeneity. We compare ECOSTRESS 70m instantaneous and daily ET products to flux tower observations in central Illinois with measurements at two heights, representing different but overlapping flux footprint areas. We estimate satellite ET based on a flux footprint weighting, gridded averages around the tower, and for crop-specific corn or soybean grid cells. In this region, the disALEXI daily ET product has better overall performance relative to the PT-JPL daily product, which is more sensitive to overpass times and tends to overestimate instantaneous ET. However, the PT-JPL model reflects more variability at high spatial resolution. Specifically, a footprint-derived ET improves the data-model comparison relative to disALEXI, and PT-JPL more closely replicates crop-specific and field-specific differences as inferred from the 2-height experimental setup. This study highlights differences in how models integrate spatial inputs, which lead to different representations of spatial variability for the same nominal resolution. This can also have important implications for understanding and predicting field-level differences in land-atmosphere fluxes. 

Environmental observation networks, such as AmeriFlux, are foundational for monitoring ecosystem response to climate change, management practices, and natural disturbances; however, their effectiveness depends on their representativeness for the regions or continents. We proposed an empirical, time series approach to quantify the similarity of ecosystem fluxes across AmeriFlux sites. We extracted the diel and seasonal characteristics (i.e., amplitudes, phases) from carbon dioxide, water vapor, energy, and momentum fluxes, which reflect the effects of climate, plant phenology, and ecophysiology on the observations, and explored the potential aggregations of AmeriFlux sites through hierarchical clustering. While net radiation and temperature showed latitudinal clustering as expected, flux variables revealed a more uneven clustering with many small (number of sites < 5), unique groups and a few large (> 100) to intermediate (15–70) groups, highlighting the significant ecological regulations of ecosystem fluxes. Many identified unique groups were from under-sampled ecoregions and biome types of the International Geosphere-Biosphere Programme (IGBP), with distinct flux dynamics compared to the rest of the network. At the finer spatial scale, local topography, disturbance, management, edaphic, and hydrological regimes further enlarge the difference in flux dynamics within the groups. Nonetheless, our clustering approach is a data-driven method to interpret the AmeriFlux network, informing future cross-site syntheses, upscaling, and model-data benchmarking research. Finally, we highlighted the unique and underrepresented sites in the AmeriFlux network, which were found mainly in Hawaii and Latin America, mountains, and at under- sampled IGBP types (e.g., urban, open water), motivating the incorporation of new/unregistered sites from these groups. 

Given the complexity of global poverty and climate change, it reasons that engineering education has focused on “Engineers as Changemakers,” seeking to inspire engineers to tackle the world’s wicked problems. However, in practice, the desire for engineers to see themselves as changemakers eclipses the autonomy of local communities, especially in international interventions. By focusing on empowerment, engineers unintentionally reinforce themselves as the power and knowledge holders. Inspired from works by Robert Chambers and Paulo Freire, we propose a new mindset for engineering education that shifts the focus from engineers as changemakers to engineers as facilitators and consultants. In this framework, the local community is viewed as the changemaker, affirming them as the primary acting agent of their lives. We illustrate the impact of this mindset shift in practice through the analysis of two technological case studies, both of which follow non-governmental organizations (NGOs) seeking to make long-lasting change with community members. Although the explicitly stated intentions of both groups are very similar, the NGO in one of the cases seeks to affirm the agency of community partners, referring to them as experts and drivers of the project. Meanwhile, the NGO in the other case utilizes language of empowerment, referring to themselves as the educators and providers of sustainable practices and technology. The impact of these mindsets is illustrated through qualitative data regarding stakeholder relationships and the community’s response to each project. Through these case studies, we see that liberative and collaborative technical interventions require a reimagining of the relationship between the engineer and community. As engineering educators, we are responsible for challenging the 

The energy extraction from urban wind at small-to-medium scale is limited due to lower performance and high capital cost of wind energy systems. This study aims to optimize the passive yaw control mechanism of a motionless wind energy system utilizing opposing S1210 airfoils, focusing on enhancing alignment with variable wind directions. A computational fluid dynamics (CFD) framework evaluates five tail vane designs NACA0012, triangular, trapezoidal, arrow, and rounded to assess their aerodynamic performance in generating lift, minimizing drag, and producing turning moments for self-alignment. Results demonstrate that the NACA0012 airfoilshaped vane achieves superior efficiency, balancing high lift (15 % greater than alternatives) and low drag, enabling robust turbine orientation even at wind speeds of 8 m/s. However, off-axis wind angles exceeding 15◦ degrade performance drastically, reducing the coefficient of performance (COP) by 26 % and 81 % at 20◦ and 25◦, respectively, highlighting the importance of passive yaw control. The findings contribute to optimizing the paired airfoil wind energy system for improved performance in urban wind conditions. The study concludes that integrating streamlined tail vanes, such as the NACA0012, significantly enhances the viability of motionless wind turbines for urban deployment, offering a cost-effective solution to harness low-to-medium wind speeds with minimal maintenance. 

Nitrous oxide (N2O) 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 N2O 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 N2O 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 N2O 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.