Nanobubble aeration enhanced wastewater treatment and bioenergy generation in constructed wetlands coupled with microbial fuel cells

Artificial aeration is a widely used approach in wastewater treatment to enhance the removal of pollutants, however, traditional aeration techniques have been challenging due to the low oxygen transfer rate (OTR). Nanobubble aeration has emerged as a promising technology that utilise nano-scale bubbles to achieve higher OTRs owing to their large surface area and unique properties such as longevity and reactive oxygen species generation. This study, for the first time, investigated the feasibility of coupling nanobubble technology with constructed wetlands (CWs) for treating livestock wastewater. The results demonstrated that nanobubble-aerated CWs achieved significantly higher removal efficiencies of total organic carbon (TOC) and ammonia (NH4+-N), at 49 % and 65 %, respectively, compared to traditional aeration treatment (36 % and 48 %) and the control group (27 % and 22 %). The enhanced performance of the nanobubble-aerated CWs can be attributed to the nearly three times higher amount of nanobubbles (Ø < 1 μm) generated from the nanobubble pump (3.68 × 108 particles/mL) compared to the normal aeration pump. Moreover, the microbial fuel cells (MFCs) embedded in the nanobubble-aerated CWs harvested 5.5 times higher electricity energy (29 mW/m2) compared to the other groups. The results suggested that nanobubble technology has the potential to trigger the innovation of CWs by enhancing their capacity for water treatment and energy recovery. Further research needs are proposed to optimise the generation of nanobubbles, allowing them to be effectively coupled with different technologies for engineering implementation

Mapping of Forest Biomass in Shangri-La City Based on LiDAR Technology and Other Remote Sensing Data

Forest ecosystems can be regarded as huge carbon sinks. In order to effectively assess carbon balance in such ecosystems, rapid and accurate estimation of the aboveground biomass of a forest is critically needed. However, the current methods for biomass estimation and mapping are of limited spatial resolution and mostly depend on large numbers of measurements. In order to obtain better biomass estimation outcomes with higher spatial resolution, a rapid method is introduced for region-scale biomass estimation in alpine and canyon areas using space-borne light detection and ranging (LiDAR) data and optical remote-sensing images. Specifically, we explored alpine and canyon areas in Shangri-La City in China using space-borne LiDAR data from ICESAT-2 and optical remote-sensing images from Landsat8 OLI, Sentinel-2, and Microwave remote sensing Sentinel-1. An extrapolation model of the forest canopy heights in these areas was constructed with a 30-m resolution of continuous canopy height outputs. For continuously estimating the diameter at breast height (DBH) in Shangri-La City, a tree height-DBH growth model was constructed based on the LiDAR and remote-sensing measurements. Finally, based on the average DBH of the explored forests, a model was constructed for estimating and mapping the aboveground biomass and carbon storage in Shangri-La with a spatial resolution of 30 m. The results show that the forest canopy height in Shangri-La City is mainly in the range of 2.82&ndash;30.96 m, and that the estimation accuracy is verified by the LiDAR-based canopy height model (CHM) with a coefficient of determination of R2 = 0.7143. The inversion results were still largely affected by geospatial location factors (longitude, latitude), terrain factors (slope, elevation), and vegetation indices (NBR, NDGI, NDVI). Based on the relationship between the tree height and the DBH, the DBH of trees in Shangri-La City was estimated to be mainly in the range of 20 cm to 30 cm, and this estimate was verified by actual measurements with R2 greater than 0.7 all. Finally, the established model estimated the aboveground forest biomass and carbon storage of the study area of Shangri-La City in 2020 to be 1.28 &times; 108 t and 6.41 &times; 107 t, respectively. These estimates correspond to total accuracies of 92.28%, respectively

An improved machine learning-based model to predict estuarine water levels

The areas around estuaries are typically densely populated and economically developed. Therefore, robust flood risk assessment in these areas is critical. One of the key elements of flood risk assessment is the accurate prediction of estuarine water levels. However, the nonlinear interactions between riverine (i.e., upstream river discharge) and marine (i.e., tides) forces complicate the prediction of estuarine water levels. Traditional physics-based and data-driven models have made significant progress in predicting estuarine water levels, but they require upstream river discharge data as inputs. Considering the lack of such data, the development of new approaches is crucial. This study investigated a machine-learning-based light gradient boosting machine (LightGBM) framework for predicting estuarine water levels using historical water levels as the only inputs. Two prediction models based on the LightGBM framework, denoted as LightGBM1 and LightGBM2, are developed. The LightGBM1 model constructs only a single regression model and uses a recursive approach to generate multidimensional outputs. The LightGBM2 model constructs multiple regression models between the same inputs and outputs in each dimension. The LightGBM1 and LightGBM2 models were applied to the Yangtze estuary as a test case. The results demonstrate that both models are effective at predicting short-term (within 48 hours) estuarine water levels, but the statistical performance of LightGBM2 is better overall. For 24-hour prediction, the root-mean-squared errors of the LightGBM1 and LightGBM2 models are in the ranges of 0.14–0.17 m and 0.12–0.15 m, respectively

Field measurement and wind tunnel experimental investigation of a supertall building with closely spaced modes under typhoon Mangkhut

This paper presents the analysis of full-scale measurement of the Leatop Plaza (303m) under a super typhoon (i.e., Mangkhut), during which field data such as wind speed, wind direction, and wind-excited structural response were recorded. The modal properties, e.g., natural frequency, damping ratios, of the building at consecutive short time windows are identified using a Bayesian frequency domain approach, which provides information on the most probable value and identification (estimation) uncertainty. Time-varying and amplitude-dependent features of natural frequencies and damping ratios are investigated, taking into account identification uncertainty. The power spectral density of modal wind load identified from field data also provides an opportunity for benchmarking with the prediction from wind tunnel tests developed at the design stage. As Leatop Plaza has similar stiffness and mass properties along the two horizontal directions, the frequencies of corresponding vibration modes are very similar, which inevitably poses a challenge to modal identification. Issues associated with closely spaced modes are highlighted and further studied via a recently developed theory of achievable precision of ambient modal identification.Submitted/Accepted versionThis work was supported by grants from the National Natural Science Foundation of China (Grant No. 51925802), the 111 Project (Grant No. D21021), the Overseas Master Program of Guangdong Province, China (2020A1414010079), and the Guangzhou Municipal Science and Technology Project (Grant No. 20212200004). These supports are gratefully acknowledged

Study on the spatiotemporal variation of the Yangtze estuarine tidal species

Owing to the nonlinear influence of river discharge, estuarine tides are nonstationary and more complicated than oceanic tides. This study investigated the spatiotemporal variation pattern of the Yangtze estuarine tides by considering tidal species separated using the Variational Mode Decomposition (VMD) model. The separated tidal species were compared with tidal species reconstructed using the NS_TIDE model. The results showed that the subtidal (D0) amplitude increased landward because of the enhanced influence of river discharge and river-tide interactions; diurnal (D1) and semi-diurnal (D2) tidal amplitudes consistently decayed landward because of frictional dissipation and nonlinear energy transfer to other tidal constituents; while quarter-diurnal (D4) and six-diurnal (D6) tidal amplitudes initially increased and then decreased in the landward direction because of the transformation of the dominant role of river discharge from nonlinear energy transfer to frictional dissipation. The spatiotemporal variations in different tidal species can illustrate other tidal processes, such as seasonal differences in tidal duration asymmetry. Although the VMD model neglects the separation of each tidal constituent, it can provide reasonable information, even when NS_TIDE may provide implausible results during the flood season because of the highly nonstationary conditions

Rare variants in GPR3 in POI patients: a case series with review of literature

Abstract Background Premature ovarian insufficiency (POI) is a highly heterogeneous disease, and up to 25% of the cases can be explained by genetic causes. G protein-coupled receptor 3 (GPR3) plays an important role in oocyte arrest, and Gpr3-deficient mice exhibited POI-like phenotypes. Case presentation We identified two heterozygous missense variants of GPR3: NM_005281: c.C973T (p.R325C) and c.G772A (p.A258T) in two sporadic Han Chinese POI cases through whole exome sequencing and genetic analysis. The two patients were diagnosed as POI in their late 20s, presenting elevated serum levels of follicle stimulating hormone and secondary amenorrhea. Both variants are very rare in the population databases of ExAC, gnomAD and PGG.Han. The affected amino acids are conserved across species and the mutated amino acids are predicted deleterious with bioinformatics prediction tools and the protein three-dimensional structure analysis. Conclusions It is the first report of rare GPR3 variants associated with POI women, providing an important piece of evidence for GPR3 as a candidate gene which should be screened in POI. This finding suggested the necessity of including GPR3 in etiology study and genetic counseling of POI patients

Development and Characterization of 25 EST-SSR markers in <i>Pinus sylvestris</i> var. <i>mongolica</i> (Pinaceae)

Premise of the study: A set of novel expressed sequence tag (EST) microsatellite markers was developed in Pinus sylvestris var. mongolica to promote further genetic studies in this species. Methods and Results: One hundred seventy-five EST&#8211;simple sequence repeat (SSR) primers were designed and synthesized for 31,653 isotigs based on P. tabuliformis EST sequences. The primer pairs were used to identify 25 polymorphic loci in 48 individuals. The number of alleles ranged from two to eight with observed and expected heterozygosity values of 0.0435 to 0.8125 and 0.0430 to 0.7820, respectively. Conclusions: These new polymorphic EST-SSR markers will be useful for assessing genetic diversity, molecular breeding and genetic improvement, and conservation of P. sylvestris var. mongolica

Effects of a water hammer and cavitation on vibration transients in a reservoir-pipe-valve system

An extraordinary phenomenon with violent oscillation, accompanied by an abnormal “click” sound is observed during the valve closing in a fuel feeding pipe system. A fluctuation model with flow cavitation, in which time-varying stiffness, time-varying damping coefficients and flow cavitation are comprehensively considered, is proposed. On this basis, a dynamic vibration equation is established and an expression of flow pressure at the valve port is derived. The critical displacement of the flow cavitation system is defined. When the vibration amplitude reaches the critical displacement, cavitation occurs. Based on this model, simulation of vibration displacement and flow pressure is given. The simulated transient process shows the phenomena of vibration fluctuation with cavitation. The results are compared with the experimental data measured by a pressure sensor. Under our experimental conditions, the critical displacement of vibration is 1.41mm, and the average relative pressure peak error is 0.022. The comparison between the simulation and experimental results shows that they are in an acceptable agreement