Jianjian Wang

In view of the limitations of existing studies, in which remote sensing extraction of algal blooms is easily affected by cloud interference, and mechanistic models are constrained by excessive parameters and inadequate representation of nonlinear relationships, resulting in limited timeliness and accuracy, this study took Taihu Lake as the study area and constructed a research framework of bloom extraction-scale matching-spatial prediction-scenario response based on Landsat imagery and gridded meteorological data, constructing the relationship between meteorological factors and algal blooms using machine learning methods. First, the Tasseled Cap transformation (TCap) and Floating Algae Index (FAI) were combined to extract the spatial distribution and area of algal blooms, while cloud interference was addressed to improve recognition stability under complex background conditions. Next, the spatial scales of bloom rasters and meteorological factors were unified to build a matched bloom-meteorological dataset. On this basis, a U-Net model driven by multiple meteorological factors was developed to predict remote-sensing-based bloom distribution/extent patterns under three warming scenarios. The results showed that: (1) the combination of TCap and FAI improved the accuracy and efficiency of bloom extraction; FAI was more sensitive but tended to overestimate bloom area, whereas TCap was more stable under cloud interference; (2) the U-Net model achieved an overall accuracy of 95% and a prediction accuracy of 88%; and (3) bloom area increased under all three warming scenarios, and the extent of expansion generally became more pronounced with increasing warming magnitude, although the response was not strictly monotonic across all cases. Based on the seasonal mean bloom-area increase relative to the baseline condition (S0), the warming response was strongest in spring, followed by summer and autumn, and weakest in winter. This study can provide a reference for cyanobacterial bloom early warning and water environment management in Lake Taihu.

We selected Tai Lake in China as the research area, and based on the Eco-lab model, we parameterized seven main external input conditions: discharge, carbon, nitrogen, phosphorus, wind speed, elevation, and temperature. We combined the LHS uncertainty analysis method and the Morris sensitivity analysis method to study the relationship between water quality and input conditions. The results showed that (1) the external input conditions had an uncertain impact on water quality. Among them, the uncertainties in total nitrogen concentration (TN) and total phosphorus concentration (TP) were mainly reflected in the lake entrance area, and the uncertainties of chlorophyll-a (Chl-a) and dissolved oxygen (DO) were mainly reflected in the lake center area. (2) The external input conditions had different sensitivities to different water layers. The bottom layer was most clearly and stably affected by input conditions. The TN and TP of the three different water layers were closely related to the flux into the lake, with average sensitivities of 83% and 78%, respectively. DO was mainly related to temperature and water elevation, with the bottom layer affected by temperatures as high as 98%. Chl-a was affected by all input factors except nitrogen and was most affected by wind speed, with an average of about 34%. Therefore, the accuracy of external input conditions can be effectively improved according to specific goals, reducing the uncertainty impact of the external input conditions of the model, and the model can provide a scientific reference for the determination of the mid- to long-term governance plan for Tai Lake in the future.

To complement the current studies of risk assessment on water sources which seldom consider the distribution effects of multiple risk sources concentration, and improve the efficiency of water source supervision, this study establishes a method system for risk assessment of water sources and regulatory rating evaluation under the distribution effects of multiple risk sources concentration. The method system includes: (1) utilizing the single-risk source impact index to characterize the risk impact degree of the single-risk source on the water source, the index calculation method of single-risk source impact considers the risk degree and the distribution location of risk source; (2) using the multiple risk sources impact index to characterize the risk impact degree of the multiple risk sources on the water source, the index calculation method of multiple risk sources considers the impact index of single-risk source, the number of risk sources, and the distribution concentration of multiple risk sources; (3) the environmental risk assessment and regulatory rating evaluation method consider multiple risk sources impact index, anti-risk ability of water source, and the importance of water source. This method system has been applied to the environmental risk assessment and regulatory rating of eight water sources along the Nanjing Yangtze River, with excellent achievements. As the results reveal, eight water sources suffer 437 risk impacts from 175 risk sources, 69.04% of which are low-risk impacts and 13 are high-risk impacts. Longtan water source suffers the most risk impacts of 86, among which eight are high-risk impacts. The impacts of multiply-risk sources on the eight water sources are high-risk impacts. The impact index of multiple risk sources increases from the upstream water sources to the downstream water sources, reaching the maximum value of 5.267 at the most downstream Longtan water source. Though the environmental risk and supervision rating of Longtan water source is high, those of other water sources are rated as medium.

Water pollution in urban rivers is serious in China. Eutrophication and other issues are prominent. Taking the artificial Playground Lake in Zhenjiang as an example, a numerical model combining particle tracing, hydrodynamics, water quality and eutrophication was constructed to simulate the water quality improvement in Playground Lake with or without water diversion by pump and sluice. Simulation results using particle tracking showed that the water residence time depended on wind direction: east wind, 125 h; southeast wind, 115 h; south wind, 95 h. With no water diversion, the lower the flow velocity of Playground Lake under three wind fields, the more serious the eutrophication. Under pump diversion, the water body in Playground Lake can be entirely replaced by water diversion for 30 h. When the temperature is lower than 15 °C, from 15 °C to 25 °C and higher than 25 °C, the water quality can be maintained for 15 d, 10 d and 7 d, respectively. During high tide periods of spring tides in the Yangtze River from June to August, the water can be diverted into the lake through sluices. The greater the Δh (the water head between the Yangtze River and Playground Lake), the more the water quality will improve. Overall, the good-to-bad order of water quality improvements for Playground Lake is as follows: pumping 30 h > sluice diversion > no water diversion. This article is relevant for the environmental management of the artificial Playground Lake, and similar lakes elsewhere.

The eutrophication of Lake Taihu is the joint result of internal and external pollution. Research on the sources and flux loads of total nitrogen (TN) and total phosphorus (TP) has great significance for the control and management of the eutrophication issues facing Lake Taihu. We used hydrologic and water quality surveys of the surrounding rivers, precipitation investigations, and an experimental simulation of suspended sediment to analyze the sources of TN and TP to Lake Taihu and to estimate the flux loads of TN and TP. Our results indicate that (1) surrounding rivers, atmospheric deposition, and sediment suspension were the three main sources of TN and TP, with annual flux loads of 29.6 thousand t and 2.8 thousand t, respectively; (2) for the flux load of TN, influxes from surrounding rivers accounted for 61.1%, while atmospheric deposition contributed 23.5%, with both of these sources varying seasonally. Total nitrogen released from sediments contributed 15.4% of the TN load of Lake Taihu; (3) for TP flux load, surrounding rivers accounted for 42.8% and also showed seasonal variation. Atmospheric deposition and sediment release accounted for 17.9 and 39.3% with no significant seasonal variation. This article quantitatively analyzed the sources and loads of TN and TP and provided a basis for pollution control in Lake Taihu.

Quantitative studies on sediment release fluxes and their impact on water quality are important for water pollution control, ecological restoration, water safety, and human health. In this study, we conducted high-frequency, synchronous field observations of meteorology, hydrology, and water quality to determine the relationship between sediment release rate and wind speed in the central region of Lake Taihu. We combined these results with our previous findings from other regions to establish the temporal–spatial variation in sediment release patterns for this lake. We then calculated the annual total nitrogen (TN) and total phosphorus (TP) release fluxes. We constructed an environmental fluid dynamics code (EFDC) model; we then loaded the temporal–spatial variation parameters and simulated the effects on different TN and TP concentrations. Overall, the following results were observed: (1) the critical wind speed at which sediment was first suspended in the central region of the lake was 4 m/s, and the fitted curve of the sediment release rate and wind speed was r = 144.7x−100 (R2 = 0.851); and (2) the annual TN and TP release fluxes of Lake Taihu were approximately 3,086 and 740 tons, respectively. This research would provide a basis for decision-making regarding pollution control in this region.

To provide a reference for risk management of water sources, this study screens the key risk supervision areas around river-type water sources (hereinafter referred to as the water sources) threatened by multiple fixed risk sources (the risk sources), and establishes a comprehensive methodological system. Specifically, it comprises: (1) method of partitioning risk source concentrated sub-regions for screening water source perimeter key risk supervision areas; (2) approach of determining sub-regional risk indexes (SrRI, which characterizes the scale of sub-regional risks) considering factors like risk distribution intensity within sub-regions, risk indexes of risk sources (RIRS, characterizing the risk scale of risk sources) and the number of risk sources; and (3) method of calculating sub-region’s risk threats to the water sources (SrTWS) which considers the positional relationship between water sources and sub-regions as well as SrRI, and the criteria for determining key supervision sub-regions. Favorable effects are achieved by applying this methodological system in determining water source perimeter sub-regions distributed along the Yangtze’s Nanjing section. Results revealed that for water sources, the key sub-regions needing supervision were SD16, SD06, SD21, SD26, SD15, SD03, SD02, SD32, SD10, SD11, SD14, SD05, SD27, etc., in the order of criticality. The sub-region with the greatest risk threats on the water sources was SD16, which was located in the middle reaches of Yangtze River. In general, sub-regions along the upper Yangtze reaches had greater threats to water sources than the lower reach sub-regions other than SD26 and SD21. Upstream water sources were less subject to the threats of sub-regions than the downstream sources other than NJ09B and NJ03.

Dissolved oxygen (DO) is not only a fundamental parameter of coastal water quality, but also an indication of organics decomposed in water and their degree of eutrophication. There has been a concern about the deterioration of dissolved oxygen conditions in the Minjiang River Estuary, the longest river in Fujian Province, Southeast China. In this study, the syntheses effects on DO was analyzed by using a four year time series of DO concentration and ancillary parameters (river discharge, water level, and temperature) from the Fuzhou Research Academy of Environmental Sciences, at three automated stations along the Minjiang River Estuary. Hypoxia occurred exclusively in the fluvial sections of the estuary during the high temperature and low river discharge period and was remarkably more serious in the river reach near the large urban area of Fuzhou. Enhancement of respiration by temperature and discharge of domestic sewage and industrial wastewater, versus regeneration of waters and dilution of pollutant concentration with increased river discharge, which regarded as the dominant antagonist processes that controlled the appearance of seasonal hypoxia. During the high temperature and the drought period, minimal mainstream flow above 700 m³Ÿs⁻¹, reduction of pollutants and forbidding sediment dredging in the South Channel should be guaranteed for strong supports on water quality management and drinking water source protection.

Contaminants released by wind-induced sediment resuspension could influence the water quality in shallow lakes. This study aims to reveal the quantitative relationship between wind speed (v) and sediment resuspension rate (r) in Meiliang Bay of Lake Taihu. The study was conducted in three steps. First, the in situ wind speed and current velocity were measured over a period of 2 days in Meiliang Bay to establish the relationship between wind and hydrodynamic conditions; second, an indoor experiment was conducted in a cylindrical simulator with sediment from the study area to determine sediment resuspension rates under different hydrodynamic conditions; and third, linkages between sediment resuspension and wind were determined. The average sediment resuspension rate was highly correlated with the wind speed (R² = 0.99), and was expressed by r = 20.72v^2.034 at wind speeds in the range of 0–14 m/s. The critical wind speed for sediment resuspension is about 7 m/s. Under these conditions, the average resuspension rate could reach 1,000 g/(m²d), with a total phosphorus release rate of 1.1 g/(m²d) and a total nitrogen release rate of 18.1 g/(m²d).