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Despite the increasing impact of atmospheric nitrogen (N) deposition on terrestrial greenhouse gas (GHG) budget, through driving both the net atmospheric CO2 exchange and the emission or uptake of non‐CO2 GHGs (CH4 and N2O), few studies have assessed the climatic impact of forests and grasslands under N deposition globally based on different bottom‐up approaches. Here, we quantify the effects of N deposition on biomass C increment, soil organic C (SOC), CH4 and N2O fluxes and, ultimately, the net ecosystem GHG balance of forests and grasslands using a global comprehensive dataset. We showed that N addition significantly increased plant C uptake (net primary production) in forests and grasslands, to a larger extent for the aboveground C (aboveground net primary production), whereas it only caused a small or insignificant enhancement of SOC pool in both upland systems. Nitrogen addition had no significant effect on soil heterotrophic respiration (RH) in both forests and grasslands, while a significant N‐induced increase in soil CO2 fluxes (RS, soil respiration) was observed in grasslands. Nitrogen addition significantly stimulated soil N2O fluxes in forests (76%), to a larger extent in grasslands (87%), but showed a consistent trend to decrease soil uptake of CH4, suggesting a declined sink capacity of forests and grasslands for atmospheric CH4 under N enrichment. Overall, the net GHG balance estimated by the net ecosystem production‐based method (forest, 1.28 Pg CO2‐eq year−1 vs. grassland, 0.58 Pg CO2‐eq year−1) was greater than those estimated using the SOC‐based method (forest, 0.32 Pg CO2‐eq year−1 vs. grassland, 0.18 Pg CO2‐eq year−1) caused by N addition. Our findings revealed that the enhanced soil C sequestration by N addition in global forests and grasslands could be only marginally offset (1.5%–4.8%) by the combined effects of its stimulation of N2O emissions together with the reduced soil uptake of CH4. [ABSTRACT FROM AUTHOR]

Inland waters (rivers, reservoirs, lakes, ponds, streams) and estuaries are significant emitters of methane (CH4) and nitrous oxide (N2O) to the atmosphere, while global estimates of these emissions have been hampered due to the lack of a worldwide comprehensive data set of CH4 and N2O flux components. Here, we synthesize 2997 in‐situ flux or concentration measurements of CH4 and N2O from 277 peer‐reviewed publications to estimate global CH4 and N2O emissions from inland waters and estuaries. Inland waters including rivers, reservoirs, lakes, and streams together release 95.18 Tg CH4 year−1 (ebullition plus diffusion) and 1.48 Tg N2O year−1 (diffusion) to the atmosphere, yielding an overall CO2‐equivalent emission total of 3.06 Pg CO2 year−1. The estimate of CH4 and N2O emissions represents roughly 60% of CO2 emissions (5.13 Pg CO2 year−1) from these four inland aquatic systems, among which lakes act as the largest emitter for both CH4 and N2O. Ebullition showed as a dominant flux component of CH4, contributing up to 62%–84% of total CH4 fluxes across all inland waters. Chamber‐derived CH4 emission rates are significantly greater than those determined by diffusion model‐based methods for commonly capturing of both diffusive and ebullitive fluxes. Water dissolved oxygen (DO) showed as a dominant factor among all variables to influence both CH4 (diffusive and ebullitive) and N2O fluxes from inland waters. Our study reveals a major oversight in regional and global CH4 budgets from inland waters, caused by neglecting the dominant role of ebullition pathways in those emissions. The estimated indirect N2O EF5 values suggest that a downward refinement is required in current IPCC default EF5 values for inland waters and estuaries. Our findings further indicate that a comprehensive understanding of the magnitude and patterns of CH4 and N2O emissions from inland waters and estuaries is essential in defining the way of how these aquatic systems will shape our climate. [ABSTRACT FROM AUTHOR]

Gaseous reactive nitrogen (Nr) emissions from agricultural soils to the atmosphere constitute an integral part of global N cycle, directly or indirectly causing climate change impacts. The extensive use of N fertilizer in crop production will compromise our efforts to reduce agricultural Nr emissions in China. A national inventory of fertilizer N‐induced gaseous Nr emissions from croplands in China remains to be developed to reveal its role in shaping climate change. Here we present a data‐driven estimate of fertilizer N‐induced soil Nr emissions based on regional and crop‐specific emission factors (EFs) compiled from 379 manipulative studies. In China, agricultural soil Nr emissions from the use of synthetic N fertilizer and manure in 2018 are estimated to be 3.81 and 0.73 Tg N yr−1, with a combined contribution of 23%, 20% and 15% to the global agricultural emission total of ammonia (NH3), nitrous oxide (N2O) and nitric oxide (NO), respectively. Over the past three decades, NH3 volatilization from croplands has experienced a shift from a rapid increase to a decline trend, whereas N2O and NO emissions always maintain a strong growth momentum due to a robust and continuous rise of EFs. Regionally, croplands in Central south (1.51 Tg N yr−1) and East (0.99 Tg N yr−1) of China exhibit as hotspots of soil Nr emissions. In terms of crop‐specific emissions, rice, maize and vegetable show as three leading Nr emitters, together accounting for 61% of synthetic N fertilizer‐induced Nr emissions from croplands. The global warming effect derived from cropland N2O emissions in China was found to dominate over the local cooling effects of NH3 and NO emissions. Our established regional and crop‐specific EFs for gaseous Nr forms provide a new benchmark for constraining the IPCC Tier 1 default EF values. The spatio‐temporal insight into soil Nr emission data from N fertilizer application in our estimate is expected to advance our efforts towards more accurate global or regional cropland Nr emission inventories and effective mitigation strategies. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNNs) have shown great performance in computer vision tasks, from image classification to pattern recognition. However, CNNs′${\rm CNNs}^{\prime }$ superior performance arises at the expense of high computational costs, which restricts their employment in real‐time decision‐making applications. Computationally intensive convolutions can be offloaded to optical metasurfaces, enabling sub‐picosecond latency and nearly zero energy consumption, but the currently reported approaches require additional bulk optics and can only process polarized light, which limits their practical usages in integrated lightweight systems. To solve these challenges, a novel design of the metasurface‐based optical convolutional accelerator is experimentally demonstrated, offering an ultra‐compact volume of 0.016 mm3${\rm mm}^{3}$, a low cross‐talk of ‐20 dB, polarization insensitivity, and is capable of implementing multiple convolution operations and extracting simultaneously various features from light‐encoded images. The ultra‐compact metasurface‐based optical accelerator can be compactly integrated with a digital imaging system to constitute an optical‐electronic hybrid CNN, which experimentally achieves a consistent accuracy of 96 % in arbitrarily polarized MNIST digits classification. The proposed ultra‐compact metasurface‐based optical convolutional accelerator paves the way for power‐efficient edge‐computing platforms for a range of machine vision applications. [ABSTRACT FROM AUTHOR]