The geography patterns and generation mechanisms of greenhouse gases (GHGs) in groundwater, especially in saline groundwater, are critical but rarely studied. Herein, we investigated the GHGs distribution in an aquifer, located upstream of Baiyangdian Lake, China, with a distinctive salinity gradient. A total of 132 groundwater samples were collected from 44 new-constructed wells along the lateral dimensions, and analyzed for dissolved GHGs concentrations, physiochemical parameters, and isotopes. The results showed that the dissolved CO2, CH4 and N2O concentrations ranged from 9.47 to 79.3 mg/L, 1.05-56.9 mu g/L, and 0.84-7.03 mu g/L, respectively. The groundwater was supersaturated with GHGs with respect to atmospheric equilibrium, suggesting groundwater discharge as a potential source of GHGs emission. CO2 significantly decreased while CH4 and N2O distinctively increased with the decline of total dissolved solids (TDS) concentration, illustrating an obvious spatial pattern in the GHGs distribution. The CO2 distributions mainly depended on the bicarbonate radical and TDS, indicating carbonate equilibrium as the main process involving in the CO2 generation. CH4 and N2O was primarily generated through the methanogenesis and denitrification processes, respectively. Nutrients including SO42- and total organic carbon predominately shaped the CH4 distributions, while nitrate mainly governed the N2O dis-tributions. Our study highlights the important roles of hydrochemistry and nutrients in the GHGs generation and distributions, which provides a significant insight on managing the GHGs emissions from the saline groundwater.
Underwater wireless optical communication has attracted widespread attention due to its advantages of high bandwidth and low delay. Seawater environment contains different substances, which will affect the received intensity and time delay of communication. This paper proposes an adaptive Monte-Carlo method to analyze the impact of zooplankton on the received intensity, receiving time, spatial distribution of energy, transmission distance and misalignment. According to the simulation results, when seawater contains more zooplankton, the received intensity is weaker, the receiving time is longer, and the energy is more dispersed.
Fenton reaction is an effective method to remove refractory organics such as carbamazepine (CBZ) from water streams. Nevertheless, its application is greatly compromised by extra hydrogen peroxide (H2O2) addition and iron mud accumulation. Herein, Fenton-like process with in situ produced H2O2 by biosynthesized palladium nanoparticles (bioPd-NPs) and natural iron-bearing clay minerals is proposed for CBZ degradation. The bioPd-NPs prepared by Shewanella loihica PV-4 were in the size range of 5–20 nm, which catalyzed the in situ production of H2O2 from formic acid (FA) and oxygen. Then the in situ generated H2O2 underwent Fenton-like reactions with nontronite for CBZ degradation. With bioPd-NPs and nontronite dosage of 1 g/L and FA concentration of 20 mM, the complete CBZ (10 mg/L) degradation was achieved within 60 min. Oxidative radicals such as HO· and H2O2 generated in our constructed system played key roles in CBZ degradation. Intermediates/products identification and theoretical calculation revealed that hydroxylation was the main CBZ degradation pathway. This work provides a promising Fenton-like technology for elimination of CBZ from environment with prevention of additional H2O2 supplementation and excessive iron mud production.
Spiking Neural Networks (SNNs) have attracted enormous research interest due to temporal information processing capability, low power consumption, and high biological plausibility. However, the formulation of efficient and high-performance learning algorithms for SNNs is still challenging. Most existing learning methods learn weights only, and require manual tuning of the membrane-related parameters that determine the dynamics of a single spiking neuron. These parameters are typically chosen to be the same for all neurons, which limits the diversity of neurons and thus the expressiveness of the resulting SNNs. In this paper, we take inspiration from the observation that membrane-related parameters are different across brain regions, and propose a training algorithm that is capable of learning not only the synaptic weights but also the membrane time constants of SNNs. We show that incorporating learnable membrane time constants can make the network less sensitive to initial values and can speed up learning. In addition, we reevaluate the pooling methods in SNNs and find that max-pooling will not lead to significant information loss and have the advantage of low computation cost and binary compatibility. We evaluate the proposed method for image classification tasks on both traditional static MNIST, Fashion-MNIST, CIFAR-10 datasets, and neuromorphic N-MNIST, CIFAR10-DVS, DVS128 Gesture datasets. The experiment results show that the proposed method outperforms the state-of-the-art accuracy on nearly all datasets, using fewer time-steps. Our codes are available at https://github.com/fangwei123456/Parametric-Leaky-Integrate-and-Fire-Spiking-Neuron.