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Recent studies suggest that hypolimnetic respiration may be responsible for greenhouse gas (GHG) emissions from deep reservoirs. Currently, quantitative evaluation of aerobic vs. anaerobic processes and priming (enhanced processing of organic matter due to the addition of labile carbon) in regulating GHG production and emissions across the reservoir-downstream continuum remains largely unknown. High-resolution, annual time-series observations in a large, subtropical reservoir (Shuikou) experiencing seasonal hypoxia in southeast China indicate that aerobic hypolimnetic CO2 production dominated in most periods of the stratified spring/summer with higher rates at higher temperatures. In addition, anaerobic production of hypolimnetic CO2 occurred in the late stratified spring/summer period, which stimulated hypolimnetic production of CH4 and N2O. Incubation experiments showed that priming in spring enhanced both aerobic and anaerobic production of excess GHGs. A late spring flood event generated the highest daily efflux of CO2 through the flushing of GHG-enriched hypolimnion waters. Turbine degassing contributed 59%, 93%, and 63% of annual CO2, CH4, and N2O effluxes, respectively. Moreover, annual downstream GHG emissions were similar to those in the transition/lacustrine zone of the Shuikou reservoir. Diurnal variation observations revealed net CO2 emissions even during algal bloom seasons. The reservoir-downstream river continuum was a year-round source of GHGs (218.5 ± 18.9 Gg CO2-equivalent yr−1; CO2 contributed 91%). However, the loss of oxygen also leads to increased production and storage of recalcitrant dissolved organic carbon (RDOC). Thus, identifying mechanisms controlling both GHG emissions and RDOC production is crucial to constrain the carbon neutrality issue of hydroelectric reservoirs in the context of climate change mitigation strategies.

Recent studies suggest that hypolimnetic respiration may be responsible for greenhouse gas (GHG) emissions from deep reservoirs. Currently, quantitative evaluation of aerobic vs. anaerobic processes and priming (enhanced processing of organic matter due to the addition of labile carbon) in regulating GHG production and emissions across the reservoir‐downstream continuum remains largely unknown. High‐resolution, annual time‐series observations in a large, subtropical reservoir (Shuikou) experiencing seasonal hypoxia in southeast China indicate that aerobic hypolimnetic CO2 production dominated in most periods of the stratified spring/summer with higher rates at higher temperatures. In addition, anaerobic production of hypolimnetic CO2 occurred in the late stratified spring/summer period, which stimulated hypolimnetic production of CH4 and N2O. Incubation experiments showed that priming in spring enhanced both aerobic and anaerobic production of excess GHGs. A late spring flood event generated the highest daily efflux of CO2 through the flushing of GHG‐enriched hypolimnion waters. Turbine degassing contributed 59%, 93%, and 63% of annual CO2, CH4, and N2O effluxes, respectively. Moreover, annual downstream GHG emissions were similar to those in the transition/lacustrine zone of the Shuikou reservoir. Diurnal variation observations revealed net CO2 emissions even during algal bloom seasons. The reservoir‐downstream river continuum was a year‐round source of GHGs (218.5 ± 18.9 Gg CO2‐equivalent yr−1; CO2 contributed 91%). However, the loss of oxygen also leads to increased production and storage of recalcitrant dissolved organic carbon (RDOC). Thus, identifying mechanisms controlling both GHG emissions and RDOC production is crucial to constrain the carbon neutrality issue of hydroelectric reservoirs in the context of climate change mitigation strategies. [ABSTRACT FROM AUTHOR]

With the development of Industry 4.0, although some smart meters have appeared on the market, traditional mechanical meters are still widely used due to their long-standing presence and the difficulty of modifying or replacing them in large quantities. Most meter readings are still manually taken on-site, and some are even taken in high-risk locations such as hazardous chemical storage. However, existing methods often fail to provide real-time detections or result in misreadings due to the complex nature of natural environments. Thus, we propose a lightweight network called DAMP-YOLO. It combines the deformable CSP bottleneck (DCB) module, aggregated triplet attention (ATA) mechanism, meter data augmentation (MDA), and network pruning (NP) with the YOLOv8 model. In the meter reading recognition dataset, the model parameters decreased by 30.64% while mAP50:95 rose from 87.92% to 88.82%, with a short inference time of 129.6 ms for the Jetson TX1 intelligent car. In the VOC dataset, our model demonstrated improved performance, with mAP50:95 increasing from 41.03% to 45.64%. The experimental results show that the proposed model is competitive for general object detection tasks and possesses exceptional feature extraction capabilities. Additionally, we have devised and implemented a pipeline on the Jetson TX1 intelligent vehicle, facilitating real-time meter reading recognition in situations where manual interventions are inconvenient and hazardous, thereby confirming its feasibility for practical applications. [ABSTRACT FROM AUTHOR]