Deciphering environmental factors influencing phytoplankton community structure in a polluted urban river.

Tuojiang River Basin is a first-class tributary of the upper reaches of the Yangtze River-which is the longest river in China. As phytoplankton are sensitive indicators of trophic changes in water bodies, characterizing phytoplankton communities and their growth influencing factors in polluted urban rivers can provide new ideas for pollution control. Here, we used direct microscopic count and environmental DNA (eDNA) metabarcoding methods to investigate phytoplankton community structure in Tuojiang River Basin (Chengdu, Sichuan Province, China). The association between phytoplankton community structure and water environmental factors was evaluated by Mantel analysis. Additional environmental monitoring data were used to pinpoint major factors that influenced phytoplankton growth based on structural equation modeling. At the phylum level, the dominant phytoplankton taxa identified by the conventional microscopic method mainly belonged to Bacillariophyta, Chlorophyta, and Cyanophyta, in contrast with Chlorophyta, Dinophyceae, and Bacillariophyta identified by eDNA metabarcoding. In α-diversity analysis, eDNA metabarcoding detected greater species diversity and achieved higher precision than the microscopic method. Phytoplankton growth was largely limited by phosphorus based on the nitrogen-to-phosphorus ratios > 16:1 in all water samples. Redundancy analysis and structural equation modeling also confirmed that the nitrogen-to-phosphorus ratio was the principal factor influencing phytoplankton growth. The results could be useful for implementing comprehensive management of the river basin environment. It is recommended to control the discharge of point- and surface-source pollutants and the concentration of dissolved oxygen in areas with excessive nutrients (e.g., Jianyang-Ziyang). Algae monitoring techniques and removal strategies should be improved in 201 Hospital, Hongrihe Bridge and Colmar Town areas.

Early detection and lesion visualization of pear leaf anthracnose based on multi-source feature fusion of hyperspectral imaging.

Pear anthracnose, caused by Colletotrichum bacteria, is a severe infectious disease that significantly impacts the growth, development, and fruit yield of pear trees. Early detection of pear anthracnose before symptoms manifest is of great importance in preventing its spread and minimizing economic losses. This study utilized hyperspectral imaging (HSI) technology to investigate early detection of pear anthracnose through spectral features, vegetation indices (VIs), and texture features (TFs). Healthy and diseased pear leaves aged 1 to 5 days were selected as subjects for capturing hyperspectral images at various stages of health and disease. Characteristic wavelengths (OWs1 and OWs2) were extracted using the Successive Projection Algorithm (SPA) and Competitive Adaptive Reweighted Sampling (CARS) algorithm. Significant VIs were identified using the Random Forest (RF) algorithm, while effective TFs were derived from the Gray Level Co-occurrence Matrix (GLCM). A classification model for pear leaf early anthracnose disease was constructed by integrating different features using three machine learning algorithms: Support Vector Machine (SVM), Extreme Learning Machine (ELM), and Back Propagation Neural Network (BPNN). The results showed that: the classification identification model constructed based on the feature fusion performed better than that of single feature, with the OWs2-VIs-TFs-BPNN model achieving a highest accuracy of 98.61% in detection and identification of pear leaf early anthracnose disease. Additionally, to intuitively and effectively monitor the progression and severity of anthracnose in pear leaves, the visualization of anthracnose lesions was achieved using Successive Maximum Angle Convex Cone (SMACC) and Spectral Information Divergence (SID) techniques. According to our research results, the fusion of multi-source features based on hyperspectral imaging can be a reliable method to detect early asymptomatic infection of pear leaf anthracnose, and provide scientific theoretical support for early warning and prevention of pear leaf diseases.

Serum isthmin-1 is a potential biomarker for metabolic dysfunction associated fatty liver disease in patients with metabolic syndrome and type 2 diabetes mellitus.

INTRODUCTION: Metabolic dysfunction associated fatty liver disease (MAFLD) is a prevalent condition in patients with type 2 diabetes mellitus (T2DM). Isthmin-1 (ISM1) is an adipokine that promotes glucose uptake and improves glucose tolerance and hepatic steatosis. Although ISM1 has been shown to be associated with T2DM, its role in patients with MAFLD and metabolic syndrome (MetS) remains insufficiently examined. This study aimed to investigate the relationship between serum ISM1 and MAFLD in patients with T2DM and the potential involvement of MetS in this association. RESEARCH DESIGN AND METHODS: A total of 250 participants were divided into four groups: 60 patients with T2DM and MAFLD, 60 with newly diagnosed T2DM, 60 with MAFLD, and 70 healthy controls. Serum ISM1 levels were measured using ELISA. The distribution of ISM1 concentration in the combined data was divided into quartiles, and the Cochran-Armitage trend test was performed to estimate the significant trends across increasing quartiles. RESULTS: Compared with the controls, patients with coexisting MAFLD, MetS, and T2DM exhibited significantly elevated serum ISM1 concentrations. Serum ISM1 levels in the overweight/obese group were also higher than those in the lean group. Serum ISM1 levels were positively correlated with body mass index (BMI), uric acid, alanine aminotransferase, aspartate aminotransferase, total cholesterol (TC), low-density lipoprotein cholesterol, fasting insulin, and homeostasis model assessment of insulin resistance and negatively associated with age and high-density lipoprotein cholesterol (HDL-C). BMI, TC, and HDL-C were independently associated with serum ISM1 concentration. The relative risks for MAFLD, T2DM, and T2DM with MAFLD increased significantly with higher ISM1 quartiles. Furthermore, a positive correlation was observed between serum ISM1 levels and the number of MetS components, with the elevated plasma levels of ISM1 escalating the risk of developing MetS to some extent. CONCLUSIONS: The combination of ISM1 with TG and UA was identified as the best predictive factor for diagnosing MAFLD and MetS, potentially due to their contribution to aggravating the metabolic state.

Microplastics increase the microbial functional potential of greenhouse gas emissions and water pollution in a freshwater lake: A metagenomic study.

Aquatic ecosystems are being increasingly polluted by microplastics (MPs), which calls for an understanding of how MPs affect microbially driven biogenic element cycling in water environments. A 28-day incubation experiment was conducted using freshwater lake water added with three polymer types of MPs (i.e., polyethylene, polypropylene, polystyrene) separately or in combination at a concentration of 1 items/L. The effects of various MPs on microbial communities and functional genes related to carbon, nitrogen, phosphorus, and sulfur cycling were analyzed using metagenomics. Results showed that Sphingomonas and Novosphingobium, which were indicator taxa (genus level) in the polyethylene treatment group, made the largest functional contribution to biogenic element cycling. Following the addition of MPs, the relative abundances of genes related to methane oxidation (e.g., hdrD, frhB, accAB) and denitrification (napABC, nirK, norB) increased. These changes were accompanied by increased relative abundances of genes involved in organic phosphorus mineralization (e.g., phoAD) and sulfate reduction (cysHIJ), as well as decreased relative abundances of genes involved in phosphate transport (phnCDE) and the SOX system. Findings of this study underscore that MPs, especially polyethylene, increase the potential of greenhouse gas emissions (CO2, N2O) and water pollution (PO43-, H2S) in freshwater lakes at the functional gene level.

Urban waterlogging structure risk assessment and enhancement.

Urban waterlogging patches reflect spatial patterns indicative of drainage system limitations and management challenges, and help pinpoint potential waterlogging impacts and spread risks. Therefore, by constructing an urban waterlogging model to simulate the extent and depth of waterlogging, the Number of Patches index (NP) is used to reflect the number of waterlogging patches, the Related Circumscribing Circle index (Circle) is used to evaluate the potential impact range of waterlogging, the Euclidean Nearest-Neighbor Distance index (ENN) is used to assess the potential connectivity of waterlogging, and the Interspersion and Juxtaposition Index (IJI) is used to assess the difficulty of retrofitting vulnerable points. Finally, the improvement of waterlogging structure is achieved by utilizing Vehicle-mounted Drainage Pump (VDPs). The research results demonstrate that as the return period increases, the waterlogging area (TA) and NP index show an upward trend, while the ENN index shows a downward trend. The Circle index initially decreases and then increases, reaching its lowest point at a one-year return period (1yr). The IJI index is related to the growth of TA, and in the two-year return period (2yr) and fifty-year return period (50yr) design scenarios, both TA and IJI indexshow significant growth. After the deployment of VDPs, the maximum area of waterlogging elimination reaches 0.46 km2 at a five-year return period (5yr). The drainage system reaches its drainage limit at 2yr, and the VDPs achieves its drainage limit at 5yr. The NP index does not decrease significantly, but in the case of a 5yr, the high-density area decreases by 1.66 km2. The Circle index values decrease across the board, and in the case of a 5yr, the potential impact range decreases by 1.92 km2, with 134 roads restored for traffic. The change in the ENN index decreased from 23.35 to 0.82, indicating that the spread of waterlogging can be more effectively controlled at lower return periods. The changes in the IJI index are more complex, with negative adjustments between 5 and 20yr, reducing the degree of mixing of different levels of waterlogging in the remaining return periods. Overall, with the increase of rainfall return period, the waterlogging area increases, the number of patches increases, the shape becomes irregular, the distance between patches decreases, and the potential connectivity increases. After the deployment of VDPs, the system integrity is improved, the waterlogging impact range is reduced, the impact on pedestrians and facilities is mitigated, and the risk of pollutant propagation and expansion of waterlogging area is reduced. This study contributes to reducing the potential risk of waterlogging, improving urban drainage effectiveness, and enhancing the resilience and emergency response capability of cities.

Spatial variation of dissolved organic matter in the Tuojiang River Basin in Chengdu, China: insights based on EEMs-PARAFAC analysis.

Three-dimensional excitation-emission matrix fluorescence spectroscopy coupled with parallel factor analysis was adopted to investigate the characteristics of dissolved organic matter (DOM) components in water samples collected from the Tuojiang River Basin in Chengdu, including its main stream and tributaries. Four DOM components that matched with three fluorescence peaks were identified in the whole river basin and tributaries; while three components corresponding to four fluorescence peaks were identified in the main stream. In all cases, humic-like components accounted for high proportions of the DOM. Correlation analysis revealed the same sources for four components in the whole river basin and its tributaries, whereas two components had different sources in the main stream. Ultraviolet absorbance parameters (SUVA254, SR) and fluorescence parameters (BIX, HIX, FI, ß:α) indicated the dominant autochthonous sources of DOM in the whole river basin. Higher terrestrial inputs of DOM were observed in the tributaries than in the main stream. In the areas influenced by human activities (6#, 17#, 18#), the sources of DOM showed strong terrestrial characteristics and high degrees of humification and aromatization, as well as serious pollution. The results of this study have potentially far-reaching implications for environmental water management in the area.

Micro-pressure promotes endogenous phosphorus release in a deep reservoir by favouring microbial phosphate mineralisation and solubilisation coupled with sulphate reduction.

Deep reservoirs vary in their hydrostatic pressure owing to artificial water level control. The potential migration of phosphorus (P) in reservoir sediments raises the risk of harmful algal blooms. To ascertain the mechanisms of endogenous P release in reservoirs, we characterised aquatic microbial communities associated with coupled iron (Fe), P and sulphur (S) cycling at the sediment-water interface. The responses of microbial communities to hydrostatic pressures of 0.2-0.7 mega pascals (MPa; that is, micro-pressures) were investigated through a 30-day simulation experiment. Our findings unravelled a potential mechanism that micro-pressure enhanced the solubilisation of Fe/aluminium (Al)-bound P caused by microbially-driven sulphate reduction, leading to endogenous P release in the deep reservoir. Although the vertical distribution of labile Fe was not affected by pressure changes, we did observe Fe resupply at sediment depths of 2-5 cm. Metagenomic analysis revealed increased abundances of functional genes for P mineralisation (phoD, phoA), P solubilisation (pqqC, ppx-gppA) and sulphate reduction (cysD, cysC) in sediments subjected to micro-pressure, which contrasted with the pattern of S oxidation gene (soxB). There was a tight connection between P and S cycling-related microbial communities, based on significant positive correlations between labile element (P and S) concentrations and functional gene (phoD, cysD) abundances. This provided strong support that Fe-P-S coupling processes were governed by micro-pressure through modulation of P and S cycling-related microbial functions. Key taxa involved in P and S cycling (for example, Bradyrhizobium, Methyloceanibacter) positively responded to micro-pressure and as such, indirectly drove P release from sediments by facilitating P mineralisation and solubilisation coupled with sulphate reduction.

Rapid quantification of the surface overflow and underground infiltration in sewer pipes based on computer vision and continuous optimization.

The overloading of the sewer network caused by unwarranted infiltration of stormwater may lead to waterlogging and environmental pollution. The accurate identification of infiltration and surface overflow is essential to predict and reduce these risks. To retrieve the limitations of infiltration estimation and the failure of surface overflow perception using the common stormwater management model (SWMM), a surface overflow and underground infiltration (SOUI) model is proposed to estimate the infiltration and overflow. First, the precipitation, water level of the manhole, surface water depth and images of the overflowing point, and volume at the outfall are collected. Then, the surface waterlogging area is identified based on computer vision to reconstruct the local digital elevation model (DEM) by spatial interpolation, and the relationship between the waterlogging depth, area and volume is established to identify the real-time overflow. Next, a continuous genetic algorithm optimization (CT-GA) model is proposed for the underground sewer system to determine the inflow rapidly. Finally, surface and underground flow estimations are combined to perceive the state of the urban sewer network accurately. The results show that, compared with the common SWMM simulation, the accuracy of the water level simulation is improved by 43.5% during the rainfall period, and the time cost of the computational optimization is reduced by 67.5%. The proposed method can effectively diagnose the operation state and overflow risk of the sewer networks in real time during rainfall seasons.

A novel multi-strategy hydrological feature extraction (MHFE) method to improve urban waterlogging risk prediction, a case study of Fuzhou City in China.

Reliable hydrological data ensure the precision of the urban waterlogging simulation. To reduce the simulation error caused by insufficient basic data, a multi-strategy method (MHFE) for extracting hydrological features is proposed, which includes land use/land cover (LULC) extraction (LE) and digital elevation model (DEM) reconstruction (DR). First, the high-resolution remote image, satellite DEM, precipitation, flood points and depth, and planned LULC were collected. Second, the buildings, roads, and other areas of the satellite image were segmented using the U-Net model, and the LULC data with drainage features were extracted by combining the segmentation result with the planned LULC and drainage data. Then, the terrain features of the road were enhanced to construct high-precision DEM based on the fusion of multi-source data, such as elevation points, LULC, and satellite DEM. Finally, the waterlogging model was implemented under different return periods of rainfalls and typhoon rainfall to obtain the waterlogging distribution and water depth. The simulation results were compared with historical waterlogging event data and water depth observations. The results indicated that the proposed method significantly improved the accuracy of the simulation. In terms of identifying the waterlogging points, the average F1 score increased by 0.36, 0.20, and 0.07 compared to the raw model and the single LE and DR methods, respectively. In terms of water depth simulation, the average Nash-Sutcliffe efficiency (NSE) was increased from -0.24 to 0.86, with DR and LE contributing to 79.1 % and 20.9 %, respectively. The principal contribution and novelty of this paper is to explore the generic method that enhance the hydrological data, and the findings of this study improved the performance of urban waterlogging simulation.

Research on orchard navigation method based on fusion of 3D SLAM and point cloud positioning.

Accurate navigation is crucial in the construction of intelligent orchards, and the need for vehicle navigation accuracy becomes even more important as production is refined. However, traditional navigation methods based on global navigation satellite system (GNSS) and 2D light detection and ranging (LiDAR) can be unreliable in complex scenarios with little sensory information due to tree canopy occlusion. To solve these issues, this paper proposes a 3D LiDAR-based navigation method for trellis orchards. With the use of 3D LiDAR with a 3D simultaneous localization and mapping (SLAM) algorithm, orchard point cloud information is collected and filtered using the Point Cloud Library (PCL) to extract trellis point clouds as matching targets. In terms of positioning, the real-time position is determined through a reliable method of fusing multiple sensors for positioning, which involves transforming the real-time kinematics (RTK) information into the initial position and doing a normal distribution transformation between the current frame point cloud and the scaffold reference point cloud to match the point cloud position. For path planning, the required vector map is manually planned in the orchard point cloud to specify the path of the roadway, and finally, navigation is achieved through pure path tracking. Field tests have shown that the accuracy of the normal distributions transform (NDT) SLAM method can reach 5 cm in each rank with a coefficient of variation that is less than 2%. Additionally, the navigation system has a high positioning heading accuracy with a deviation within 1° and a standard deviation of less than 0.6° when moving along the path point cloud at a speed of 1.0 m/s in a Y-trellis pear orchard. The lateral positioning deviation was also controlled within 5 cm with a standard deviation of less than 2 cm. This navigation system has a high level of accuracy and can be customized to specific tasks, making it widely applicable in trellis orchards with autonomous navigation pesticide sprayers.

Hydrostatic pressure drives microbe-mediated biodegradation of microplastics in surface sediments of deep reservoirs: Novel findings from hydrostatic pressure simulation experiments.

Microplastics originate from the physical, chemical, or biological degradation of plastics in the environment. Once ingested by organisms at the bottom of the food chain, microplastics are passed on to organisms at higher trophic levels, posing a threat to human health. The distribution of microplastics and the metabolic pathways involved in their microbial degradation in surface sediments of drinking water reservoirs are still poorly understood. This study analyzed the occurrence patterns of microplastics and microbial community structure associated with microplastic biodegradation in surface sediments from a deep reservoir at various hydrostatic pressures. Based on the results of Fourier-transform and laser direct infrared spectroscopy, elevating the pressure resulted in altered sizes and shapes of microplastics in sediment samples with the presence of microorganisms. The influence of hydrostatic pressure on small-sized microplastics (20-500 µm) was pronounced. For instance, high pressure accelerated the breakdown of fibers, pellets, and fragments into smaller-sized microplastics. In particular, the mean size of polyethylene terephthalate microplastics decreased from 425.78 µm at atmospheric pressure to 366.62 µm at 0.7 Mpa. Metagenomic analysis revealed an increase in the relative abundances of plastic-degrading genera, such as Rhodococcus, Flavobacterium, and Aspergillus, in response to elevated pressures. Eight functional genes for biodegradation of polystyrene, polyethylene, and polyethylene terephthalate microplastics were annotated, including paaK, ladA, tphA3. Of these, tphA3 gene abundance was negatively influenced by hydrostatic pressure, providing direct evidence for the pathway by which microbial metabolism of polyethylene terephthalate led to decreased microplastic size under high pressure conditions. This study presents novel insights into hydrostatic pressure-driven microbial community structure, functional gene abundance, and key metabolic pathways associated with biodegradation of microplastics in reservoir sediments.

Actinobacteria produce taste and odor in drinking water reservoir: Community composition dynamics, co-occurrence and inactivation models.

Taste and odor (T&O) has become a significant concern for drinking water safety. Actinobacteria are believed to produce T&O during the non-algal bloom period; however, this has not been widely investigated. In this study, the seasonal dynamics of the actinobacterial community structure and inactivation of odor-producing actinobacteria were explored. The results indicated that the diversity and community composition of actinobacteria exhibited significant spatiotemporal distribution. Network analysis and structural equation modeling showed that the actinobacterial community occupied a similar environmental niche, and the major environmental attributes exhibited spatiotemporal dynamics, which affected the actinobacterial community. Furthermore, the two genera of odorous actinobacteria were inactivated in drinking water sources using chlorine. Amycolatopsis spp. have a stronger chlorine resistance ability than Streptomyces spp., indicating that chlorine inactivates actinobacteria by first destroying cell membranes and causing the release of intracellular compounds. Finally, we integrated the observed variability in the inactivation rate of actinobacteria into an expanded Chick-Watson model to estimate its effect on inactivation. These findings will deepen our understanding of the seasonal dynamics of actinobacterial community structure in drinking water reservoirs and provide a foundation for reservoir water quality management strategies.

Characterizing the effects of stormwater runoff on dissolved organic matter in an urban river (Jiujiang, Jiangxi province, China) using spectral analysis.

The effect of stormwater runoff on dissolved organic matter (DOM) in rivers is one of the central topics in water environment research. Jiujiang is one of the first cities established in the green development demonstration zone of the Yangtze River Economic Belt (Jiangxi Province, China). Three-dimensional excitation-emission matrix fluorescence with parallel factor analysis (3DEEM-PARAFAC) and ultraviolet-visible (UV-Vis) spectroscopy were used to explore the effects of runoff on organic matter in Shili River (Jiujiang, Jiangxi Province, China). The results show that the runoff led to an increase of some critical pollutants and DOM concentrations, especially in the middle reaches of the river. The concentration and relative molecular weight of DOM in water increased as a result of runoff. Three humic-like (C1-C3) and two protein-like (C4 and C5) components of DOM were identified using the PARAFAC model. The sources of the three humic-like components (C1, C2, C3) were consistent, unlike those of the protein-like component C4. Compared with the pre-rainfall period, the content of humus compounds flowing into the river through the early rainwater runoff was lower, which caused the relative content and proportion of humic substances little change and protein-like species increasing. The DOM mainly derived from autochthonous sources, and runoff had limited effect on its characteristics. Jiujiang is a key demonstration city for Yangtze River conservation. Rainwater runoff is one of the pollution sources of urban rivers, which leads to the deterioration of water quality and influences the distribution characteristics of DOM in water bodies. The PARAFAC components could adequately represent different indicators and sources of DOM in urban rivers, providing an important reference for urban river management.

Assessment of the spatiotemporal water quality variations in the Middle Route of China's South-to-North Water Diversion Project by multivariate analysis.

As an important drinking water source for North China, the Middle Route of China's South-to-North Water Diversion Project (MRP) must provide high-quality water to maintain the health and safety of more than 60 million people. However, different water transfer operation modes may affect the water quality status, and the spatiotemporal characteristics of water quality in the MRP, with high water transfer volumes, remain poorly understood. In this study, the differences in water quality in the MRP between the initial stage (Nov. 2015 to Oct. 2017, low transfer volumes) and the current stage (Nov. 2017 to Oct. 2020, high transfer volumes) were compared, and the spatiotemporal water quality variations in the current stage were evaluated using multivariate statistical methods. For this purpose, approximately 12,528 observations, including the datasets of 12 water quality parameters collected from 29 monitoring sites, were used. The results showed that the water quality status improved significantly during the current stage. Based on principal component analysis (PCA), physical parameters (natural), nutrients, organic matter and microbes (anthropogenic), and heavy metals (natural and anthropogenic) were the key factors influencing water quality variations. Based on hierarchical cluster analysis, 12 months were classified into two groups: the high-flow period (HFP, Jun.-Oct.) and the low-flow period (LFP, Nov.-May). Additionally, 29 sampling sites were grouped into three sections: the Henan section (HN; S1-S16), Hebei section (HB; S17-S24), and Tianjin-Beijing section (TB; S25-S29). From the perspective of water quality regulation, the total nitrogen concentration and permanganate index in the HB and TB sections of the MRP should be considered throughout the year, and the faecal coliform concentrations in these two sections should also be considered during the HFP. The results of this study could be helpful for local administrations to understand and control pollution and better protect the quality of water in the MRP.

Microbial pathways in the coupling of iron, sulfur, and phosphorus cycles at the sediment-water interface of a river system: An in situ study involving the DGT technique.

It is imperative to solve the problem of endogenous phosphorus (P) release from sediments in the governance of natural water bodies. Deciphering P migration and transformation patterns that are coupled to iron (Fe) and sulfur (S) cycling at the sediment-water interface (SWI) is the key to understanding the mechanisms underlying endogenous P release. In the present study, we deployed diffusive gradients in thin films (DGT) probes in situ at the SWI in Fuyang River, Hebei Province, China. When the probes were retrieved, the surrounding sediments were synchronously sampled. We analyzed the longitudinal spatiotemporal distribution of Fe, S, and P at the SWI. We also explored how functional bacterial community diversity was associated with the coupling reactions of Fe, S, and P as well as endogenous P release from sediments at the functional gene level. The results showed that labile Fe, S, and P occurred at low concentrations in sediments 0-2 cm below the SWI, while they were enriched in sediments at depths of 4-8 cm. The longitudinal distribution of different labile elements exhibited greater differences between October and February than regional differences, with higher concentrations at downstream locations than upstream locations. In February, Fe/Al-bound P and sulfide (S2-) concentrations increased in sediments compared with those in October owing to an increase in the relative abundances of dominant genera among P-mineralizing bacteria and sulfate-reducing bacteria. As a result, Fe in Fe-bound P precipitated as FeS2, which induced P remobilization and release into the overlying water. The spatiotemporal distribution patterns of functional genes related to P (phoD and ppk) and S (aprA) transformation were consistent with those of labile P and S, which strongly suggests that microorganisms played a role in driving and regulating the coupled cycling of P and S at the SWI.

Multi-scenario pear tree inflorescence detection based on improved YOLOv7 object detection algorithm.

Efficient and precise thinning during the orchard blossom period is a crucial factor in enhancing both fruit yield and quality. The accurate recognition of inflorescence is the cornerstone of intelligent blossom equipment. To advance the process of intelligent blossom thinning, this paper addresses the issue of suboptimal performance of current inflorescence recognition algorithms in detecting dense inflorescence at a long distance. It introduces an inflorescence recognition algorithm, YOLOv7-E, based on the YOLOv7 neural network model. YOLOv7 incorporates an efficient multi-scale attention mechanism (EMA) to enable cross-channel feature interaction through parallel processing strategies, thereby maximizing the retention of pixel-level features and positional information on the feature maps. Additionally, the SPPCSPC module is optimized to preserve target area features as much as possible under different receptive fields, and the Soft-NMS algorithm is employed to reduce the likelihood of missing detections in overlapping regions. The model is trained on a diverse dataset collected from real-world field settings. Upon validation, the improved YOLOv7-E object detection algorithm achieves an average precision and recall of 91.4% and 89.8%, respectively, in inflorescence detection under various time periods, distances, and weather conditions. The detection time for a single image is 80.9 ms, and the model size is 37.6 Mb. In comparison to the original YOLOv7 algorithm, it boasts a 4.9% increase in detection accuracy and a 5.3% improvement in recall rate, with a mere 1.8% increase in model parameters. The YOLOv7-E object detection algorithm presented in this study enables precise inflorescence detection and localization across an entire tree at varying distances, offering robust technical support for differentiated and precise blossom thinning operations by thinning machinery in the future.

Assessment of the impact of urban water system scheduling on urban flooding by using coupled hydrological and hydrodynamic model in Fuzhou City, China.

With climate change and urbanization development, urban areas are facing more serious floods. As a result, hydrological and hydrodynamic models have recently shown a broad application prospect in urban flood simulating and forecasting. For the area with rich inland rivers, urban water resources can be effectively regulated and redistributed through river networks and hydraulic structures scheduling. However, the lack of research on the effect of scheduling becomes a major limitation in model applications. Based on a coupled hydrodynamics model, the current study simulates the flooding response to the combined rainstorm and scheduling scenarios and analyzes the river overflow at the community scale. The result indicated that three local regions in the Jin'an study area are inundated easily. The locations near Qinting Lake were more sensitive to the water regulation rules than others. In the model of control on Qinting Lake, section A is more sensitive to the schedule control than section B, while for section A, the water level increased by 1.44% under the return period (RP) (10 a), and the rate changed to 2.64% under the RP (100 a). The differences in inundation from various scenarios are relatively small. In the mode of joint discharge rules under RP (50 a), the water level changed by 4.77% in section A and 1.24% in section B. The simulation at the community scale considers the overflow process, and the results indicated that the total inundation area decreased by 12.8 ha under joint schedules. The significant effects to alleviate urban inundation mainly come from the decreased flood overflow from the channel, but not from the flooding nodes. This study provides promising references for urban flood management.

Release process identification of non-instantaneous point source pollution in rivers via reverse flow and pollution routing.

Source identification is fundamental for managing sudden river water pollution; however, it is a challenging task. Although numerous studies have investigated this issue, most involve optimization or statistical models for instantaneous pollution and do not consider the reverse propagation and release processes. Herein, we propose an approach for identifying the release process of non-instantaneous point source pollution in rivers, based on reverse flow and pollution routing. The identification approach can trace the historical trajectory of pollutants and their release processes, providing the necessary information for treating accidental pollution. The effectiveness and efficiency of the proposed approach were tested and demonstrated using hypothetical and real-world river cases. The results indicated that the approach identified the release process with high accuracy, and second-round identification using the ensemble Kalman filter could generally improve the identification results from the reverse routing model. This approach was feasible in different cases of observation error, although the error considerably reduced its accuracy. The identification results were also found to be substantially influenced by release duration, with a shorter release time corresponding to an inferior identification result. Nevertheless, the approach worked well in real-world river cases and was generally not affected by the release location, pollutant diffusion, or river geomorphology. In addition, the new approach has advantages in computational efficiency and applicability over traditional methods.

Succession and Driving Factors of Periphytic Community in the Middle Route Project of South-to-North Water Division (Henan, China).

The Middle Route Project of the South-to-North Water Diversion is an artificially independent system that does not connect to other surface waters. Excessive periphyton proliferation causes a series of environmental problems in the canal. In this study, the periphyton community and environmental factors on the left and right banks of the canal in the algal growing area were investigated and sampled six times (June, September, and November of 2019 and 2020). The succession pattern of the attached organism community in the artificial canal was analyzed, and the key factors affecting the algal community were analyzed using RDA and GAM. The results showed that the seasonal variability of the environmental factors was more significant than the spatial variability. A total of 114 taxa of periphytic algae were found, belonging to seven phyla and 69 genera, and mainly composed of Bacillariophyta. Species richness was ranked as Bacillariophyta (60 taxa), Chlorophyta (31 taxa) and Cyanobacteria (15 taxa), and higher in autumn than in summer. The dominant taxa were Cymbella sp., Fragilaria sp., Navicula sp. and Diatoma sp. The abundance of periphytic algal varied from 0.07 × 105 to 8.99 × 105 ind./cm2, with trends similar to that of species richness. The redundancy analysis and generalized additive model showed that water temperature and nutrient concentration were the key factors influencing the structure of the algal community, followed by discharge rate and velocity, which were the determinants of the spatial and temporal patterns of the algal community. In view of the influence of discharge and velocity on the structure of algal communities, it is suggested that ecological scheduling could be used to regulate the structure of the algal community on the canal wall in the operation of later water division projects to ensure the safety of water division.

An Assessment of Temporal and Spatial Dynamics of Regional Water Resources Security in the DPSIR Framework in Jiangxi Province, China.

Water resources are critical for the survival and prosperity of both natural and socioeconomic systems. A good and informational water resources evaluation system is substantial in monitoring and maintaining sustainable use of water. The Driver-Pressure-State-Impact-Response (DPSIR) framework is a widely used general framework that enabled the measurement of water resources security in five different environmental and socioeconomic subsystems: driver, pressure, state, impact, and response. Methodologically, outcomes of water resources evaluation based on such framework and using fuzzy set pair analysis method and confidence interval rating method depend critically on a confidence threshold parameter which was often subjectively chosen in previous studies. In this work, we demonstrated that the subjectivity in the choice of this critical parameter can lead to contradicting conclusions about water resources security, and we addressed this caveat of subjectivity by proposing a simple modification in which we sample a range of thresholds and pool them to make more objective evaluations. We applied our modified method and used DPSIR framework to evaluate the regional water resource security in Jiangxi Province, China. The spatial-temporal analysis of water resources security level was carried out in the study area, despite the improvement in Pressure, Impact, and Response factors, the Driver factor is found to become less safe over the years. Significant variation of water security across cities are found notably in Pressure and Response factors. Furthermore, we assessed both cross-sectionally and longitudinally the inter-correlations among the DPSIR nodes in the DPSIR framework. The region-specific associations among the DPSIR nodes showed important deviances from the general DPSIR framework, and our analysis showed that in our study region, although Responses of regional government work effectively in improving Pressure and State security, more attention should be paid to improving Driver security in future regional water resources planning and management in Jiangxi Province, China.