Dependence of evolution of Cyanobacteria superiority on temperature and nutrient use efficiency in a meso-eutrophic plateau lake.

Algal blooms in lakes have been a challenging environmental issue globally under the dual influence of human activity and climate change. Considerable progress has been made in the study of phytoplankton dynamics in lakes; The long-term in situ evolution of dominant bloom-forming cyanobacteria in meso-eutrophic plateau lakes, however, lacks systematic research. Here, the monthly parameters from 12 sampling sites during the period of 1997-2022 were utilized to investigate the underlying mechanisms driving the superiority of bloom-forming cyanobacteria in Erhai, a representative meso-eutrophic plateau lake. The findings indicate that global warming will intensify the risk of cynaobacteria blooms, prolong Microcystis blooms in autumn to winter or even into the following year, and increase the superiority of filamentous Planktothrix and Cylindrospermum in summer and autumn. High RUETN (1.52 Biomass/TN, 0.95-3.04 times higher than other species) under N limitation (TN < 0.5 mg/L, TN/TP < 22.6) in the meso-eutrophic Lake Erhai facilitates the superiority of Dolichospermum. High RUETP (43.8 Biomass/TP, 2.1-10.2 times higher than others) in TP of 0.03-0.05 mg/L promotes the superiority of Planktothrix and Cylindrospermum. We provided a novel insight into the formation of Planktothrix and Cylindrospermum superiority in meso-eutrophic plateau lake with low TP (0.005-0.07 mg/L), which is mainly influenced by warming, high RUETP and their vertical migration characteristics. Therefore, we posit that although the obvious improvement of lake water quality is not directly proportional to the control efficacy of cyanobacterial blooms, the evolutionary shift in cyanobacteria population structure from Microcystis, which thrives under high nitrogen and phosphorus conditions, to filamentous cyanobacteria adapted to low nitrogen and phosphorus levels may serve as a significant indicator of water quality amelioration. Therefore, we suggest that the risk of filamentous cyanobacteria blooms in the meso-eutrophic plateau lake should be given attention, particularly in light of improving water quality and global warming, to ensure drinking water safety.

Small object detection algorithm incorporating swin transformer for tea buds.

Accurate identification of small tea buds is a key technology for tea harvesting robots, which directly affects tea quality and yield. However, due to the complexity of the tea plantation environment and the diversity of tea buds, accurate identification remains an enormous challenge. Current methods based on traditional image processing and machine learning fail to effectively extract subtle features and morphology of small tea buds, resulting in low accuracy and robustness. To achieve accurate identification, this paper proposes a small object detection algorithm called STF-YOLO (Small Target Detection with Swin Transformer and Focused YOLO), which integrates the Swin Transformer module and the YOLOv8 network to improve the detection ability of small objects. The Swin Transformer module extracts visual features based on a self-attention mechanism, which captures global and local context information of small objects to enhance feature representation. The YOLOv8 network is an object detector based on deep convolutional neural networks, offering high speed and precision. Based on the YOLOv8 network, modules including Focus and Depthwise Convolution are introduced to reduce computation and parameters, increase receptive field and feature channels, and improve feature fusion and transmission. Additionally, the Wise Intersection over Union loss is utilized to optimize the network. Experiments conducted on a self-created dataset of tea buds demonstrate that the STF-YOLO model achieves outstanding results, with an accuracy of 91.5% and a mean Average Precision of 89.4%. These results are significantly better than other detectors. Results show that, compared to mainstream algorithms (YOLOv8, YOLOv7, YOLOv5, and YOLOx), the model improves accuracy and F1 score by 5-20.22 percentage points and 0.03-0.13, respectively, proving its effectiveness in enhancing small object detection performance. This research provides technical means for the accurate identification of small tea buds in complex environments and offers insights into small object detection. Future research can further optimize model structures and parameters for more scenarios and tasks, as well as explore data augmentation and model fusion methods to improve generalization ability and robustness.

Elucidating phytoplankton limiting factors in lakes and reservoirs of the Chinese Eastern Plains ecoregion.

Knowledge of phytoplankton limiting factors is essential for cost-efficient lake eutrophication management. Herein, we propose a statistical framework to explore site-specific phytoplankton limiting factors and their dependence on water depth (WD) in 54 lakes in the Chinese Eastern Plains ecoregion. First, the maximal chlorophyll a (Chla) response to total N (TN) or P (TP), representing a region-specific "standard" model where phytoplankton were primarily N- or P-limited, was quantified using a 95% quantile regression. Second, site-specific limiting factors were identified using analogical residual analysis. N- or P-limitation was inferred if FractionTN (i.e. fraction of Chla observed and predicted by the "standard" model for a given TN) > 0.95 or FractionTP >0.95; if both FractionTN and FractionTP <0.95 in a specific environmental condition (e.g. high non-algal turbidity), light limitation was suggested. As a result, 5%, 7%, 4%, 36%, 16%, 2%, and 30% of the sampling sites were limited by N, P, N+P, light availability, rapid flushing, abundant macrophytes, and unmeasured factors, respectively. Bloom control suggestions in the short run are proposed considering these actual limiting factors. Furthermore, the maximal FractionTN or FractionTP response to WD was explored, reflecting the effect of WD on FractionTN (or FractionTP) without significant confounders. The results indicated that phytoplankton in the studied freshwaters would be potentially light-limited, N-limited, N+P-co-limited, or P-limited depending on WD (<1.8, 1.8-2.1, 2.1-5.2, or >5.2 m, respectively), because N will gradually become surplus with increasing WD, while at very shallow depths, strong sediment re-suspension induces light limitation. This finding implies that long-term nutrient management strategies in the studied freshwaters that have WDs of 0-2.1, 2.1-5.2, and >5.2 m can entail control of N, N+P, and P, respectively. This study provides essential information for formulating context-dependent bloom control for lakes in our study area and serves as a valuable reference for developing a cost-efficient eutrophication management framework for other regions.

[Long-term Changes and Drivers of Ecological Security in Shahe Reservoir, China].

Monthly datasets of ecological indicators from 2010 to 2020 in Shahe Reservoir, Tianmuhu, China, were examined to reveal the long-term variations in water ecological security and its driving factors. The results of Secchi disk depth(SD) measurements revealed significantly spatial variation(P<0.05) within the reservoir. The highest SD was recorded in the downstream-linked reservoir, and the lowest SD was recorded in the upstream tributaries. In contrast, the values of other water ecological indicators were higher in the upstream tributaries than in the transition region and the downstream-linked reservoir area. In summer and autumn, the SD was low, while the concentrations of total phosphorous(TP), chlorophyll a(Chl-a), the permanganate index, and cyanobacterial biomass(BMc) were high. During the thermal stratification period from May to September, the concentrations of 2-methylisoborneol(MIB) and Chl-a were highest at a depth of 4 m, while diatom biomass(BMb) and BMc reached their maximum at depths of 2 m and 0.5 m, respectively. Therefore, spatial and temporal variations should be fully considered when evaluating aquatic ecological security. Focusing on spring and summer, when the risk of water ecological security was high, Chl-a combined with SD and MIB along with their correlation with other water quality indexes, was used to evaluate and optimize the ecological security of Shahe Reservoir. The evaluation results showed that the aquatic ecological security of the reservoir was excellent over the last 10 years; however, annual fluctuations have been large and the evaluation scores were spatially variable. While seasonal sampling strategies focusing on three layers depths are economical and reliable for lake regions with thermal stratification, our results indicate that tailored monitoring may be required to determine the aquatic ecological security of lakes and reservoirs. In Shahe Reservoir, the decrease in the SD and the increase in MIB caused by high TP and algal blooms were the most important drivers of ecological service function in the reservoir. Furthermore, hydrometeorological factors may also play important roles in the aquatic ecological security of reservoirs.

Use of conductivity to indicate long-term changes in pollution processes in Lake Taihu, a large shallow lake.

Conductivity is a very reliable, sensitive, and easily obtained indicator of surface water conditions; however, whether it could be used to evaluate lake pollution is less understood. To verify the effectiveness of using conductivity to evaluate the pollution status in lakes, Lake Taihu was analyzed, the third largest freshwater lake in China, which provides drinking water to about 10 million of residents. We analyzed 25-year conductivity data in 32 sampling sites in Lake Taihu, in relation to human population, industrial development, and GDP. The results showed that the conductivity first increased and then decreased following rapid economic growth and subsequent strict pollution control activities. The conductivity is related to industrial sewage (r = 0.90); SO42- and Cl- concentrations in the water were closely related to the industrial production value (r = 0.98, 0.99) before 2007. The conductivity increased rapidly from 1992 to 2007 due to eutrophication processes. After 2007, with the implementation of various pollution control measures, the pollution situation gradually abated. The conductivity was closely related to the concentrations of nitrogen and phosphorus (r = 0.53, 0.14) in Lake Taihu, which suggests that conductivity could be indicator of eutrophication in lakes to some extent. Based on the research results, conductivity can largely be used to characterize the impact of human activities on lakes. Our study suggests that conductivity is a very reliable parameter for evaluating lake pollution. In addition to providing ion information, this measurement could successfully determine pollution sources and restoration effectiveness in lakes, such as ecologically complex Lake Taihu.

A dynamic modelling of nutrient metabolism in a cyclic activated sludge technology (CAST) for treating low carbon source wastewater.

A new mathematical model incorporating biopolymer kinetics and the process of the simultaneous storage and growth are established for the treatment of low carbon source wastewater with a high effluent quality and energy efficiency. A set of initial parameter values was assigned as a combination of estimated values, literature-based values, and fitted values to simulate a cyclic activated sludge technology (CAST) system effectively. Compared with experimental data from the CAST system, the calibrated model demonstrated a good performance. Model simulations indicated that the recommended condition for a CAST fed with low carbon source wastewater was a volume ratio of the anoxic zone to the aerobic zone of 7/28. Moreover, using high-throughput 16S rRNA gene sequencing not only characterised the microbial communities in the CAST reactors operated under two feeding ratios but also indirectly validated the model predictions.