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Climate change can alter wetland extent and function, but such impacts are perplexing. Here, changes in wetland characteristics over North America from 25° to 53° North are projected under two climate scenarios using a state-of-the-science Earth system model. At the continental scale, annual wetland area decreases by ~10% (6%-14%) under the high emission scenario, but spatiotemporal changes vary, reaching up to ±50%. As the dominant driver of these changes shifts from precipitation to temperature in the higher emission scenario, wetlands undergo substantial drying during summer season when biotic processes peak. The projected disruptions to wetland seasonality cycles imply further impacts on biodiversity in major wetland habitats of upper Mississippi, Southeast Canada, and the Everglades. Furthermore, wetlands are projected to significantly shrink in cold regions due to the increased infiltration as warmer temperature reduces soil ice. The large dependence of the projections on climate change scenarios underscores the importance of emission mitigation to sustaining wetland ecosystems in the future.

Organophosphate pesticides (OPs) are often used as insecticides and acaricides in agriculture, thus improving yields. OP residues may pose a serious threat, duetoinhibitionof the enzymeacetylcholinesterase(AChE). Therefore, a competitive bio-barcode immunoassay was designed for simultaneous quantification of organophosphate pesticide residues using AuNP signal amplification technology and Au@Pt catalysis. The AuNP probes were labelled with antibodies and corresponding bio-barcodes (ssDNAs), MNP probes coated with ovalbumin pesticide haptens and Au@Pt probes functionalized with the complementary ssDNAs were then prepared. Subsequently, pesticides competed with MNP probes to bind the AuNP probes. The recoveries of the developed assay were ranged from 71.26 to 117.47% with RSDs from 2.52 to 14.52%. The LODs were 9.88, 3.91, and 1.47 ng·kg-1, for parathion, triazophos, and chlorpyrifos, respectively. The assay was closely correlated with the data obtained from LC-MS/MS. Therefore, the developed method has the potential to be used as an alternative approach for detection of multiple pesticides.

Wildfires have shown increasing trends in both frequency and severity across the Contiguous United States (CONUS). However, process-based fire models have difficulties in accurately simulating the burned area over the CONUS due to a simplification of the physical process and cannot capture the interplay among fire, ignition, climate, and human activities. The deficiency of burned area simulation deteriorates the description of fire impact on energy balance, water budget, and carbon fluxes in the Earth System Models (ESMs). Alternatively, machine learning (ML) based fire models, which capture statistical relationships between the burned area and environmental factors, have shown promising burned area predictions and corresponding fire impact simulation. We develop a hybrid framework (ML4Fire-XGB) that integrates a pretrained eXtreme Gradient Boosting (XGBoost) wildfire model with the Energy Exascale Earth System Model (E3SM) land model (ELM) version 2.1. A Fortran-C-Python deep learning bridge is adapted to support online communication between ELM and the ML fire model. Specifically, the burned area predicted by the ML-based wildfire model is directly passed to ELM to adjust the carbon pool and vegetation dynamics after disturbance, which are then used as predictors in the ML-based fire model in the next time step. Evaluated against the historical burned area from Global Fire Emissions Database 5 from 2001–2020, the ML4Fire-XGB model outperforms process-based fire models in terms of spatial distribution and seasonal variations. Sensitivity analysis confirms that the ML4Fire-XGB well captures the responses of the burned area to rising temperatures. The ML4Fire-XGB model has proved to be a new tool for studying vegetation-fire interactions, and more importantly, enables seamless exploration of climate-fire feedback, working as an active component in E3SM. [ABSTRACT FROM AUTHOR]

In order to weaken the torque ripple caused by cogs torque and increase the output torque of permanent magnet synchronous motor, this paper proposes a multi-objective optimization method of permanent magnet synchronous motor based on Improved Multi-Objective Salp Swarm Optimization Algorithm (IMOSSA) and Spearman correlation analysis. Firstly, the improved strategy of Multi-Objective Salp Swarm Algorithm (MOSSA) was proposed, and the effectiveness of the improved algorithm was further verified by the performance test of the algorithm. Then, the motor simulation model was established, and the high correlation parameters were filtered out by Spearman correlation analysis. The response surface model was established by Response Surface Method (RSM), and the regression equation was given. The IMOSSA was used for iterative optimization. Through optimization, the experimental results show that under the premise of meeting the efficiency design requirements, the average output torque of the motor is increased by 21.35%, the torque ripple and cogging torque are reduced by 45.04% and 39.17% respectively, which verifies the effectiveness and feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

Sea‐level rise (SLR) poses a severe threat to the coastal environment through seawater intrusion into freshwater aquifers. The rising groundwater table also exacerbates the risk of pluvial, fluvial, and groundwater flooding in coastal regions. However, current Earth system models (ESMs) commonly ignore the exchanges of water at the land‐ocean interface. To address this gap, we developed a novel land‐ocean hydrologic coupling scheme in a state‐of‐the‐science ESM, the Energy Exascale Earth System Model version 2 (E3SMv2). The new scheme includes the lateral exchange between seawater and groundwater and the vertical infiltration of seawater driven by the SLR‐induced inundation. Simulations were performed with the updated E3SMv2 for the global land‐ocean interface to assess the impacts of SLR on coastal groundwater under a high CO2 emission scenario. By the middle of this century, seawater infiltration on the inundated areas will be the dominant component in the land‐ocean coupling process, while the lateral subsurface flow exchange will be much smaller. The SLR‐induced seawater infiltration will raise the groundwater levels, enhance evapotranspiration, and increase runoff with distinct spatial patterns globally in the future. Although the coupling process is induced by SLR, we found topography and warming temperature have more control on the coupling impacts, probably due to the relatively modest magnitude of SLR during the selected future period. Overall, our study suggests significant groundwater and seawater exchange at the land‐ocean interface, which needs to be considered in ESMs. Plain Language Summary: As ocean levels rise, seawater threatens to intrude into coastal freshwater aquifers that millions of people depend on for drinking water and irrigation. While regional studies have examined the impacts of sea‐level rise (SLR) on coastal groundwater systems, current Earth system models (ESMs) overlook the exchange of water between ocean and groundwater. Our work addresses this gap by developing a water exchange process between ocean and land components in a state‐of‐the‐science ESM. This coupling scheme includes lateral exchanges between seawater and groundwater, as well as vertical seawater infiltration resulting from oceanic inundation. We used this new coupled model to assess SLR impacts on the global coastal groundwater table under a higher CO2 emission scenario. We found that SLR will raise groundwater levels and intensify the hydrological cycle by midcentury mainly due to increased seawater infiltration. Further, while SLR triggers this increased seawater infiltration, topography and warming temperature play more significant roles in determining its magnitude. Key Points: A novel land‐ocean coupling scheme is developed and implemented in Energy Exascale Earth System Model version 2 to evaluate the hydrologic exchange at the land‐ocean interfaceThe impact of land‐ocean coupling on the groundwater table is dominated by seawater infiltration, while lateral exchange is negligibleFuture sea‐level rise will induce significant seawater intrusion into the coastal groundwater system [ABSTRACT FROM AUTHOR]

Reducing fruit and vegetable waste and maintaining quality has become challenging for everyone. Nanotechnology is a new and intriguing technology that is currently being implemented in fruit and vegetable preservation. Silver nanomaterials provide superior antibacterial qualities, biodegradability, and biocompatibility, which expands their potential applications in fruit and vegetable preservation. Silver nanomaterials include silver nanocomposites and Ag-MOF, of which silver nanocomposites are mainly composed of silver nanoparticles. Notably, not all kinds of silver nanoparticles utilized in the preservation of fruits and vegetables are thoroughly described. Therefore, the synthesis, mechanism of action, and advancements in research on silver nanocomposites for fruit and vegetable preservation were discussed in this study. [ABSTRACT FROM AUTHOR]