Long-Term Satellite Observations of Microcystin Concentrations in Lake Taihu during Cyanobacterial Bloom Periods

Photolysis triggers multiple microbial responses: New insights of phosphorus compensation for algal blooms

Photolysis and microbial degradation enabling the rapid mineralization of organic phosphorus constitute the crucial mechanism for phosphorus compensation during algal bloom outbreaks in shallow lakes. This study explored the key pathways of microbial degradation of algae-derived organic phosphorus (ADOP) exacerbated by photolysis through molecular biology techniques. The results showed that photolysis could exacerbate microbial degradation, and the effects on microbial degradation were multifaceted. The photolysis process changes the composition of dissolved organic matter (DOM) in the environment and generates DOM components required for microbial activity, among which the saturated compounds significantly promote the increase of microbial biomass. Differential analysis showed that the photolysis process mainly affected the distribution of bacteria and fungi. The saturated compounds and highly aromatic compounds accompanying the photolysis process stimulated the increase of the abundance of phosphorus-cycling functional bacteria and related functional genes. Simultaneously, photolysis also promoted the growth of extracellular reactive oxygen species (ROS)-producing bacteria, and enhanced biological metabolism by stimulating the significant upregulation and differentiation of multiple enzyme protein subunits in cells. In summary, various changes in microorganisms caused by photolysis enhanced their mineralization of ADOP. These results bring new insights into the mechanism of the persistence of algal blooms.

Satellite mapping reveals phytoplankton biomass's spatio-temporal dynamics and responses to environmental factors in a eutrophic inland lake

Chlorophyll a (Chla) concentration can be used as an indicator of algal biomass, and the accumulation of algal biomass in water column is essential for the emergence of surface blooms. By using Moderate Resolution Imaging Spectrometer (MODIS) data, a machine learning algorithm was previously developed to assess algal biomass within the euphotic depth (Beu). Here, a long-term Beu dataset of Lake Taihu from 2003 to 2020 was generated to examine its spatio-temporal dynamics, sensitivity to environmental factors, and variations in comparison to the surface algal bloom area. During this period, the daily Beu (total Beu within the whole lake) exhibited temporal fluctuations between 40 and 90 t Chla, with an annual average of 63.32 ± 5.23 t Chla. Notably, it reached its highest levels in 2007 (72.34 t Chla) and 2017 (73.57 t Chla). Moreover, it demonstrated a clear increasing trend of 0.197 t Chla/y from 2003 to 2007, followed by a slight decrease of 0.247 t Chla/y after 2017. Seasonal variation showed a bimodal annual cycle, characterized by a minor peak in March ∼ April and a major peak in July ∼ September. Spatially, the average pixel-based Beu (total Beu of a unit water column) ranged from 21.17 to 49.85 mg Chla, with high values predominantly distributed in the northwest region and low values in the central region. The sensitivity of Beu to environmental factors varies depending on regions and time scales. Temperature has a significant impact on monthly variation (65.73%), while the level of nutrient concentrations influences annual variation (55.06%). Wind speed, temperature, and hydrodynamic conditions collectively influence the spatial distribution of Beu throughout the entire lake. Algal bloom biomass can capture trend changes in two mutant years as well as bimodal phenological changes compared to surface algal bloom area. This study can provide a basis for scientific evaluation of water environment and a reference for monitoring algal biomass in other similar eutrophic lakes.

Characteristics of microbial community structure and influencing factors of Yangcheng Lake and rivers entering Yangcheng Lake during the wet season

Yangcheng Lake, a typical fishery lake in the middle and lower reaches of the Yangtze River, is threatened by eutrophication. As the main performers of biogeochemical cycles, microorganisms affect the ecological stability of the lake. To study the structural characteristics of the microbial community in Yangcheng Lake and rivers entering Yangcheng Lake and the response relationship with environmental factors, the microbial community was categorized based on the contour of Yangcheng Lake, the major rivers entering Yangcheng Lake, and the pollution sources. The distribution characteristics of seven physicochemical indices were analyzed, including total organic carbon (TOC), total nitrogen (TN), total phosphorus (TP), water temperature (WT), pH, dissolved oxygen (DO), and ratio of total nitrogen to total phosphorus (TN/TP). Characterization of microbial community structure based on 16S rRNA high-flux sequencing technology and ANOSIM analysis were used to explore the differences in the relative abundance of microorganisms at each sampling point in the lake and rivers, and redundancy analysis (RDA) was used to analyze the relationship between the microbial community and physicochemical factors. The results showed that the dominant phyla, genera of microorganisms, and the total number of OTUs in the lake and rivers were similar. The dominant phyla included Proteobacteria, Actinobacteria, Bacteroidetes, Cyanobacteria, and Verrucomicrobia; the dominant genera included the hgcI clade, CL500-29 marine group, Microcystis PCC-7914, Chloroplast_norank, Clade III_norank, and Flavobacterium. ANOSIM analyses revealed that the microbial community of Yangcheng Lake exhibited an association with geographical space, while the microbial community in the rivers that was linked to the type of pollution source. Redundancy analysis (RDA) indicated that dissolved oxygen (DO), total nitrogen (TN), and pH were significantly correlated with the dominant phyla in Yangcheng Lake (p < 0.05), while total nitrogen (TN), water temperature(WT), and the ratio of total nitrogen to total phosphorus (TN/TP) were significantly related with the dominant genera in Yangcheng Lake (p < 0.05). Total nitrogen (TN) was also significantly linked to the dominant phyla and genera of the tributaries (p < 0.05). Despite the structural similarities in microbial communities between Yangcheng Lake and its inflowing rivers, environmental factors demonstrated significant associations with these communities, providing crucial data support for pollution prevention and the ecological restoration of Yangcheng Lake.

Cyanobacteria and cyanotoxins in the environment and water intakes: Reports, diversity of congeners, detection by mass spectrometry and their impact on health

Cyanobacteria are aquatic microorganisms of high interest for research due to the production of secondary metabolites, among which the most popular are cyanotoxins, responsible for causing severe poisoning in humans and animals through ingestion or contact with contaminated water bodies. Monitoring the number of cyanobacteria in water and concentrations of secreted cyanotoxins with the aid of sensitive and reliable methods is considered the primary action for evaluating potentially toxic blooms. There is a great diversity of methods to detect and identify these types of micro contaminants in water, differing by the degree of sophistication and information provided. Mass Spectrometry stands out for its accuracy and sensitivity in identifying toxins, making it possible to identify and characterize toxins produced by individual species of cyanobacteria, in low quantities. In this review, we seek to update some information about cyanobacterial peptides, their effects on biological systems, and the importance of the main Mass Spectrometry methods used for detection, extraction, identification and monitoring of cyanotoxins.

Changes in microcystin concentration in Lake Taihu, 13 years (2007-2020) after the 2007 drinking water crisis

Since the 2007 water crisis occurred in Lake Taihu, substantial measures have been taken to restore the lake. This study evaluates the effectiveness of these restoration activities. We examined the physicochemical parameters and the distribution of microcystin and Microcystis in both the water column and sediment during the bloom period of May 2020 to October 2020. The mean value of extracellular and intracellular microcystin content was 0.12 μg L-1 and 16.26 μg L-1, respectively. The mean value of microcystin in sediment was 172.02 ng g-1 and peaked in August. The concentration in the water and sediment was significantly lower than the historical average concentration. The abundance of toxigenic Microcystis and total Microcystis in the water column ranged from 2.61 × 102 to 2.25 × 109 copies·L-1 and 8.28 × 105 to 2.76 × 109 copies·L-1, respectively. The proportion of toxic Microcystis in the sediment ranging from 31.2% to 19.12%. The highest and lowest region was Meiliang Bay and Grass-algae type zone, respectively. The copy number of the 16S rRNA gene was 1-4 orders of magnitude higher than that of mcyA gene in populations of Microcystis, indicating that non-toxic Microcystis was the dominant form in the majority of the lake. The abundance of toxic Microcystis in the water column was positively correlated with total phosphorus, PO43--P and pH, while the water temperature played distinct role to the distribution of toxic Microcystis in sediment. Our research indicated phosphorus remains a key factor influencing the toxic Microcystis and microcystins in the water column. pH played distinct roles in the distribution of microcystins in sediment and water column. The increasing water temperature is a threat. Explicit management actions and policies, which take into account nutrient concentrations, pH, and increasing temperatures, are necessary to understand and control the distribution of microcystin and Microcystis in Lake Taihu.

Microcystins risk assessment in lakes from space: Implications for SDG 6.1 evaluation

Cyanobacterial blooms release a large number of algal toxins (e.g., Microcystins, MCs) and seriously threaten the safety of drinking water sources what the SDG 6.1 pursues (to provide universal access to safe drinking water by 2030, United Nations Sustainable Development Goal). Nevertheless, algal toxins in lake water have not been routinely monitored and evaluated well and frequently so far. In this study, a total of 100 large lakes (>25 km2) in densely populated eastern China were studied, and a remote sensing scheme of human health risks from MCs based on Sentinel-3 OLCI data was developed. The spatial and temporal dynamics of MCs risk in eastern China lakes since OLCI satellite observation data (2016-2021) were first mapped. The results showed that most of the large lakes in eastern China (80 out of 100) were detected with the occurrence of a high risk of more than 1 pixel (300×300 m) at least once. Fortunately, in terms of lake areas, the frequency of high human health risks in most waters (70.93% of total lake areas) was as less as 1%. This indicates that drinking water intakes can be set in most waters from the perspective of MCs, yet the management departments are required to reduce cyanobacterial blooms. This study highlights the potential of satellite in monitoring and assessing the risk of algal toxins and ensuring drinking water safety. It is also an important reference for SDG 6.1 reporting for lakes that lack routine monitoring.

Investigation of phytoplankton community structure and formation mechanism: a case study of Lake Longhu in Jinjiang

In order to explore the species composition, spatial distribution and relationship between the phytoplankton community and environmental factors in Lake Longhu, the phytoplankton community structures and environmental factors were investigated in July 2020. Clustering analysis (CA) and analysis of similarities (ANOSIM) were used to identify differences in phytoplankton community composition. Generalized additive model (GAM) and variance partitioning analysis (VPA) were further analyzed the contribution of spatial distribution and environmental factors in phytoplankton community composition. The critical environmental factors influencing phytoplankton community were identified using redundancy analysis (RDA). The results showed that a total of 68 species of phytoplankton were found in 7 phyla in Lake Longhu. Phytoplankton density ranged from 4.43 × 105 to 2.89 × 106 ind./L, with the average density of 2.56 × 106 ind./L; the biomass ranged from 0.58-71.28 mg/L, with the average biomass of 29.38 mg/L. Chlorophyta, Bacillariophyta and Cyanophyta contributed more to the total density, while Chlorophyta and Cryptophyta contributed more to the total biomass. The CA and ANOSIM analysis indicated that there were obvious differences in the spatial distribution of phytoplankton communities. The GAM and VPA analysis demonstrated that the phytoplankton community had obvious distance attenuation effect, and environmental factors had spatial autocorrelation phenomenon, which significantly affected the phytoplankton community construction. There were significant distance attenuation effects and spatial autocorrelation of environmental factors that together drove the composition and distribution of phytoplankton community structure. In addition, pH, water temperature, nitrate nitrogen, nitrite nitrogen and chemical oxygen demand were the main environmental factors affecting the composition of phytoplankton species in Lake Longhu.

Quantification of Diffusive Methane Emissions from a Large Eutrophic Lake with Satellite Imagery

Lakes are major emitters of methane (CH4); however, a longstanding challenge with quantifying the magnitude of emissions remains as a result of large spatial and temporal variability. This study was designed to address the issue using satellite remote sensing with the advantages of spatial coverage and temporal resolution. Using Aqua/MODIS imagery (2003-2020) and in situ measured data (2011-2017) in eutrophic Lake Taihu, we compared the performance of eight machine learning models to predict diffusive CH4 emissions and found that the random forest (RF) model achieved the best fitting accuracy (R2 = 0.65 and mean relative error = 21%). On the basis of input satellite variables (chlorophyll a, water surface temperature, diffuse attenuation coefficient, and photosynthetically active radiation), we assessed how and why they help predict the CH4 emissions with the RF model. Overall, these variables mechanistically controlled the emissions, leading to the model capturing well the variability of diffusive CH4 emissions from the lake. Additionally, we found climate warming and associated algal blooms boosted the long-term increase in the emissions via reconstructing historical (2003-2020) daily time series of CH4 emissions. This study demonstrates the great potential of satellites to map lake CH4 emissions by providing spatiotemporal continuous data, with new and timely insights into accurately understanding the magnitude of aquatic greenhouse gas emissions.

Environmental DNA metabarcoding serves as a promising method for aquatic species monitoring and management: A review focused on its workflow, applications, challenges and prospects

Marine and freshwater biodiversity is under threat from both natural and manmade causes. Biological monitoring is currently a top priority for biodiversity protection. Given present limitations, traditional biological monitoring methods may not achieve the proposed monitoring aims. Environmental DNA metabarcoding technology reflects species information by capturing and extracting DNA from environmental samples, using molecular biology techniques to sequence and analyze the DNA, and comparing the obtained information with existing reference libraries to obtain species identification. However, its practical application has highlighted several limitations. This paper summarizes the main steps in the environmental application of eDNA metabarcoding technology in aquatic ecosystems, including the discovery of unknown species, the detection of invasive species, and evaluations of biodiversity. At present, with the rapid development of big data and artificial intelligence, certain advanced technologies and devices can be combined with environmental DNA metabarcoding technology to promote further development of aquatic species monitoring and management.

Effect of Metal Cations on Colloids-Microcystin-LR Interaction

Colloidal particles, mixture with continuous molecular weight distribution and multiple organic components, is widespread in lake and have significant impact on the retention, migration, transportation, and fate of contaminants in lake ecosystems. Here we extract sedimentary colloids from algal growth dominant area (AD) in Taihu Lake and further separated into four different particle size ranges by cross-flow ultra-filtration (CFUF). The interaction mechanism between colloids and Microcystin-LR (MC-LR) was investigated under different cation conditions by dialysis equilibrium experiment method. Adsorption kinetics research shows the adsorption of MC-LR by colloids follows second-order kinetics and can be simulated by Freundlich isotherms. The effects of different cations on colloids-MC-LR interaction shows the addition of Mg(II) decreased colloids-MC-LR interaction, while Cu(II) increased colloids-MC-LR binding. MC-LR also increased Cu(II) binding to colloids, while MC-LR decreased Mg(II) binding. Therefore, different effect of cations to colloids-MC-LR interaction was proposed.

Eutrophication control of large shallow lakes in China

Large shallow lake refers to a polymictic system that is often well mixed without stratification during summer. Similar to a small and deep lake, a large and shallow lake has a high nutrient retention rate. Differing from a small and deep lake, it has an extensive sediment-water interface and internal loading from sediment, which has led to high susceptibility to eutrophication. There are many large and shallow freshwater lakes in the middle and lower Yangtze River (MLYR), China, experienced eutrophication and cyanobacteria blooms. To address this issue, a variety of methods focused on in-lake physical and biogeochemical processes was explored. The main gains of these studies included: (1) shallow lakes in the floodplain of the Yangtze River are prone to eutrophication because of their high trophic conditions; (2) wind-induced waves determine sediment resuspension, downward dissolved oxygen penetration, and upward soluble reactive nutrient mobilization, while wind-driven currents regulate the spatial distribution of water quality metrics and algal blooms; (3) the low P loss of shallow lakes via sedimentation and high N loss via denitrification lead to a low N:P ratio and N and P colimitation, which demonstrated the significance of dual N and P reduction for eutrophication control in shallow lakes; (4) extensive submerged macrophyte could suppress internal loading in large, shallow waters, but nutrient loading must be reduced and water clarity must be increased; and (5) climate warming promotes cyanobacterial blooms through positive feedback to exacerbate eutrophication in shallow lakes. The lack of action to address the challenges of non-point source pollution and internal loading from the sediment has led to limited effectiveness of eutrophication control in large shallow lakes under climate warming. In the future, the management of large shallow eutrophic lakes in China must combine social sciences (economic development) with natural technology (pollution reduction) to achieve sustainability.

Predicting water quality variability in a Mediterranean hypereutrophic monomictic reservoir using Sentinel 2 MSI: the importance of considering model functional form

Anthropogenic eutrophication is a global environmental problem threatening the ecological functions of many inland freshwaters and diminishing their abilities to meet their designated uses. Water authorities worldwide are being pressed to improve their abilities to monitor, predict, and manage the incidence of harmful algal blooms (HABs). While most water quality management decisions are still based on conventional monitoring programs that lack the needed spatio-temporal resolution for effective lake/reservoir management, recent advances in remote sensing are providing new opportunities towards better understanding water quality variability in these important freshwater systems. This study assessed the potential of using the Sentinel 2 Multispectral Instrument to predict and assess the spatio-temporal variability in the water quality of the Qaraoun Reservoir, a poorly monitored Mediterranean hypereutrophic monomictic reservoir that is subject to extensive periods of HABs. The work first evaluated the ability to transfer and recalibrate previously developed reservoir-specific Landsat 7 and 8 water quality models when used with Sentinel 2 data. The results showed poor transferability between Landsat and Sentinel 2, with most models experiencing a significant drop in their predictive skill even after recalibration. Sentinel 2 models were then developed for the reservoir based on 153 water quality samples collected over 2 years. The models explored different functional forms, including multiple linear regressions (MLR), multivariate adaptive regression splines (MARS), random forests (RF), and support vector regressions (SVR). The results showed that the RF models outperformed their MLR, MARS, and SVR counterparts with regard to predicting chlorophyll-a, total suspended solids, Secchi disk depth, and phycocyanin. The coefficient of determination (R2) for the RF models varied between 85% for TSS up to 95% for SDD. Moreover, the study explored the potential of quantifying cyanotoxin concentrations indirectly from the Sentinel 2 MSI imagery by benefiting from the strong relationship between cyanotoxin levels and chlorophyll-a concentrations.

Spatiotemporal optical properties of dissolved organic matter in a sluice-controlled coastal plain river with both salinity and trophic gradients

Due to the combined effect of sluices and sea tide, the sluice-controlled coastal plain river would be characterized by both trophic state and salinity gradients, affecting the spatiotemporal optical properties of dissolved organic matter (DOM). In this study, we investigated the spatiotemporal variation of water quality parameters and optical properties of DOM in the Haihe River, a representative sluice-controlled coastal plain river in Tianjin, China. A significant salinity gradient and four trophic states were observed in the water body of the Haihe River. Two humic- and one protein-like substances were identified from the DOM by the three-dimensional fluorescence spectra combined with the parallel factor (PARAFAC) analysis. Pearson's correlation analysis and redundancy analysis (RDA) showed that the salinity significantly affected the abundance of chromophoric DOM (CDOM) but did not cause significant changes in the fluorescence optical characteristics. In addition, the effect of Trophic state index (TSI) on the CDOM abundance was greater than that on the fluorescence intensity of fluorescent dissolved organic matter (FDOM). In the water body with both salinity and trophic state gradients, TSI posed a greater influence than salinity on the CDOM abundance. Our results fill the research gap in spatiotemporal DOM characteristics and water quality variation in water bodies with both salinity and trophic state gradients. These results are beneficial for clarifying the joint influence of saline intrusion and sluices on the DOM characteristics and water quality in sluice-controlled coastal plain rivers.

Algal biomass mapping of eutrophic lakes using a machine learning approach with MODIS images

Algal blooms are a widespread issue in eutrophic lakes. Compared with the satellite-derived surface algal bloom area and chlorophyll-a (Chla) concentration, algae biomass is a more stable way to reflect water quality. Although satellite data have been adopted to observe the water column integrated algal biomass, the previous methods mostly are empirical algorithms, which are not stable enough for widespread use. This paper proposed a machine learning algorithm based on Moderate Resolution Imaging Spectrometer (MODIS) data to estimate the algal biomass, which was successfully applied to a eutrophic lake in China, Lake Taihu. This algorithm was developed by linking Rayleigh-corrected reflectance to in situ algae biomass data in Lake Taihu (n = 140), and the different mainstream machine learning (ML) methods were compared and validated. The partial least squares regression (PLSR) (R² = 0.67, mean absolute percentage error (MAPE) = 38.88 %) and support vector machines (SVM) (R² = 0.46, MAPE = 52.02 %) performed poor satisfactory. In contrast, random forest (RF) and extremely gradient boosting tree (XGBoost) algorithms had higher accuracy (RF: R² = 0.85, MAPE = 22.68 %; XGBoost: R² = 0.83, MAPE = 24.06 %), demonstrating greater application potential in algal biomass estimation. Field biomass data were further used to estimate the RF algorithm, which showed acceptable precision (R² = 0.86, MAPE < 7 mg Chla). Subsequently, sensitivity analysis showed that the RF algorithm was not sensitive to high suspension and thickness of aerosols (rate of change <2 %), and inter-day and consecutive days verification showed stability (rate of change <5 %). The algorithm was also extended to Lake Chaohu (R² = 0.93, MAPE = 18.42 %), demonstrating its potential in other eutrophic lakes. This study for algae biomass estimation provides technical means with higher accuracy and greater universality for the management of eutrophic lakes.

Increase in chlorophyll-a concentration in Lake Taihu from 1984 to 2021 based on Landsat observations

Lake Taihu, located in a densely populated and highly industrialized area in eastern China, has experienced dramatic changes in water quality since the reform and opening-up in the 1980s. Landsat data can be used to trace water quality changes over approximately 40 years. However, chlorophyll-a (Chla) estimation, which characterizes the trophic status, has not been thoroughly explored (especially in turbid water using wide bandwidth Landsat) due to the interference of suspended particulate matter (SPM) to Chla. In this study, we used Landsat TM/OLI for turbid water Chla inversion and to analyze the spatiotemporal variation of Chla in Lake Taihu for 38 years and its influencing factors. An optical classification algorithm based on R_rs(green)/R_rs(red) was used to exclude highly turbid waters dominated by SPM; Chla was estimated only in waters with low SPM. We constructed an exponential estimation model based on R_rs(NIR)/R_rs(red), and verified the accuracy of the model using the measured Chla synchronized with satellite data. The model was applied to Landsat images to calculate the Chla concentration in Lake Taihu during 1984-2021, and its spatiotemporal distribution patterns were further analyzed. Spatially, the Chla concentrations in the western and northern regions of Lake Taihu were higher than those in other regions, probably because these areas are estuaries with large exogenous pollutant discharge and more nutrients are imported from exogenous sources. Chla showed an overall significant upward trend from 1984 to 2021 probably because of temperature rise, wind speed reduction, and nutrient increase. The results of the spatial and temporal variation of Chla and the influencing factors in this study provide supporting data for eutrophication monitoring and management in Lake Taihu. The proposed Chla estimation method can be extended to assess the spatial and temporal distribution of eutrophication in other inland waters with similar optical properties.

Long-term remote observations of particulate organic phosphorus concentration in eutrophic Lake Taihu based on a novel algorithm

Monitoring the long-term spatiotemporal variations in particulate organic phosphorus concentration (C_POP) is imperative for clarifying the phosphorus cycle and its biogeochemical behavior in waters. However, little attention has been devoted to this owing to a lack of suitable bio-optical algorithms that allow the application of remote sensing data. In this study, based on Moderate Resolution Imaging Spectroradiometer (MODIS) data, a novel absorption-based algorithm of C_POP was developed for eutrophic Lake Taihu, China. The algorithm yielded a promising performance with a mean absolute percentage error of 27.75% and root mean square error of 21.09 μg/L. The long-term MODIS-derived C_POP demonstrated an overall increasing pattern over the past 19 years (2003-2021) and a significant temporal heterogeneity in Lake Taihu, with higher value in summer (82.06 ± 3.81 μg/L) and autumn (78.74 ± 3.8 μg/L), and lower C_POP in spring (79.52 ± 3.81 μg/L) and winter (81.97 ± 3.8 μg/L). Spatially, relatively higher C_POP was observed in the Zhushan Bay (85.87 ± 7.5 μg/L), whereas the lower value was observed in the Xukou Bay (78.95 ± 3.48 μg/L). In addition, significant correlations (r > 0.6, P < 0.05) were observed between C_POP and air temperature, chlorophyll-a concentration and cyanobacterial blooms areas, demonstrating that C_POP was greatly influenced by air temperature and algal metabolism. This study provides the first record of the spatial-temporal characteristics of C_POP in Lake Taihu over the past 19 years, and the C_POP results and regulatory factors analyses could provide valuable insights for aquatic ecosystem conservation.

Long-term dynamics and drivers of particulate phosphorus concentration in eutrophic lake Chaohu, China

Particulate phosphorus (PP) plays an important biological role in the eutrophication process, and is thus an important water quality parameter for assessing climatic change and anthropogenic activity factors that affect aquatic ecosystems. Here, we used 20-year Moderate Resolution Imaging Spectroradiometer (MODIS) data to explore the patterns and trends of PP concentration (C_PP) in eutrophic Lake Chaohu based on a new empirical model. The validation results indicated that the developed model performed satisfactorily in estimating C_PP, with a mean absolute percentage error of 31.89% and root mean square error of 0.022 mg/L. Long-term MODIS observations (2000-2019) revealed that the C_PP of Lake Chaohu has experienced an overall increasing trend and distinct spatiotemporal heterogeneity. The driving factor analysis revealed that the chemical fertilizer consumption, municipal wastewater, industrial sewage, precipitation, and air temperature were the five potential driving factors and collectively explained more than 81% of the long-term variation in C_PP. This study provides the long-term datasets of C_PP in inland waters and new insights for future water eutrophication control and restoration efforts.

Temporal continuous monitoring of cyanobacterial blooms in Lake Taihu at an hourly scale using machine learning

Cyanobacterial blooms in most lakes exhibit extraordinary changes in time and space. Herein, a cyanobacterial prediction model was designed for Lake Taihu based on a machine learning method. This method can generate temporally continuous (24 moments throughout the day) cyanobacterial data at a fine spatial scale of 9 km. The hourly meteorological data for 24 moments of the day were obtained from ERA5-Land data. Areal coverage of cyanobacterial blooms was derived from the hourly Geostationary Ocean Color Imager reflectance data observed only eight times a day (from ~8:00 to ~15:00, UTC+8). The cyanobacterial and meteorological data of eight moments in Lake Taihu from 2011 to 2020 were used to design the prediction model. The results were compared and validated employing nine training strategies to determine the best cyanobacterial prediction model for Lake Taihu (R = 0.42; root mean square error = 0.10). With the best-fitted model utilizing meteorological data (2011-2020), the area coverage of cyanobacterial blooms at the other 16 moments during a day were estimated. Based on this, the regional and temporal characteristics of diurnal bloom variation were evaluated at an hourly scale. The results indicated that the hourly variations in the areal coverage of cyanobacterial blooms at 24 moments of the day had similar patterns in each subregion of Lake Taihu with minor seasonal variations. The six meteorological variables adopted to construct the model had similar diurnal changes but with diverse value ranges among the seasons. Further analysis revealed that three meteorological variables (temperature, surface pressure, and evaporation) were positively related to diurnal bloom variations at an hourly scale. Overall, these results illustrate that meteorological conditions can affect the occurrence of cyanobacterial blooms at multiple time scales (e.g., hourly, daily, or monthly). The developed cyanobacterial prediction model can provide cyanobacterial data when cyanobacterial data is unavailable for the target waterbody.

Cyanobacteria bloom hazard function and preliminary application in lake taihu, China

In recent years, due to the intensification of human activities, water ecological problems are gradually increasing. As the third largest freshwater lake in China, Lake Taihu is an important drinking water source for several densely populated cities in China. The prominent water ecological problem in this area is mainly Cyanobacteria Bloom. Cyanobacterial blooms have been erupting which have affected local residents' drinking water and caused losses to the national economy. Based on the interpretation results of MODIS data in the Lake Taihu region from 2004 to 2014, this paper analyzes the main driving factors of cyanobacterial bloom are phosphorus and potassium through the correlation analysis of nitrogen, phosphorus, potassium and cyanobacteria area, normalizes nutrient, and identifies that the water level of Lake Taihu is the influencing factor of cyanobacterial bloom. A Lake Taihu cyanobacteria bloom hazard function is constructed to quantitatively assess the losses (economic losses) caused by cyanobacterial blooms from 2001 to 2012, supporting for cyanobacteria control management in Lake Taihu.

Accelerated carbonate dissolution caused by anthropogenic acidification - contrast of watershed soils to lake sediments in Taihu Region, China

Although Taihu watershed is an "acid-insensitive" region, anthropogenic acidification has greatly changed the water chemistry in Taihu Lake. However, how soil carbonates responded to the long-term human-induced acidification received less attention. In this work, we investigated soil carbonate concentrations from different land uses in the upstream of the lake and sediment carbonate profiles in the lake, to explore the linkage of carbonates dissolution in the land and sedimentation in the lake. The result showed that the wheat-rice surface soil, the most acidification-impacted by fertilization and acid deposition, had significantly lower pH than vegetable and wetland soils (p < 0.05). Meanwhile, the carbonate concentration in wetland soils, only impacted by acid deposition, was significantly higher than that in wheat-rice and vegetable soils (p < 0.05). The pH profile of fertilized soils, with an increasing trend from the surface to bottom, further indicated the acidifying effect of fertilization. Although the average soil pH across all land uses was 6.6 in the upstream of the lake, remaining carbonate buffering system, the significant carbonate decrease especially in surface soils evidenced the definite carbonate dissolution by acidification, which is cumulative and irreversible. Contrary to the topsoils, the sediment carbonate concentration presented an increasing trend from the depth of 15 cm (denoting around the early 1980s) to the surface, indicating that lake sediment is a major sink of carbonate Ca and Mg from the watershed, particular under an alkaline lake environment caused by frequent algae blooms in the past decades. In addition, Ca/Mg ratio in the sediment, having higher values in a higher pH environment, was quite different from the watershed soil pattern, suggesting different biogeochemical processes Ca and Mg underwent during their transportation and sedimentation. The effects of acidification-altered re-distribution of carbonate Ca and Mg and Ca/Mg ratio in the terrestrial and aquatic environments deserve wider considerations of ecosystem consequence.

Deactivation of cyanobacteria blooms and simultaneous recovery phosphorus through electrolysis method

A novel method for remediating eutrophic lakes through electrolysis was made possible by one titanium (Ti) mesh, which serves as a cathode and two anodes of Ti mesh coated with ruthenium (IV) oxide and iridium (IV) oxide (RuO₂-IrO₂/Ti). Once the three-electrode components RuO₂-IrO₂/Ti and Ti are stabilized, they can carry out electrolytic reaction to control cyanobacteria blooms and assist with the remediation of eutrophic water. The order of influence on the theoretical energy consumption involved in removing algae is as follows: The electrode spacing was more effective than electrode voltage, which proved more effective than electrolysis time through the orthogonal test method. Thus, an electrode spacing of 60 mm, an electrode voltage of 30 V, and an electrolysis time of 12 h are the optimal electrolysis methods used to remove cyanobacterial blooms. The strong acidic environment produced by the anode increased the concentration of hydroxyl radical (•OH) and other strong oxidizing substances, which were the main roles that made cyanobacteria bloom inactivation. The electrolysis reaction was conducive to the transformation of organophosphorus in cyanobacterial blooms to dissolved inorganic phosphorus (DIP) in water. Some DIP was most deposited on the cathode after electro-depositing enhanced the removal of P in water with the 12-h prolonged electrolysis time. Meanwhile, it was beneficial to reduce the total nitrogen (TN) and ammonia nitrogen (NH₃-N) in the water. Thus, electrolysis proved to be an effective way to the inactivation of cyanobacteria blooms and simultaneously recover P as the concentration became higher.

A ground-based remote sensing system for high-frequency and real-time monitoring of phytoplankton blooms

The worldwide expansion of phytoplankton blooms has severely threatened water quality, food webs, habitat stability and human health. Due to the rapidity of phytoplankton migration and reproduction, high-frequency information on phytoplankton bloom dynamics is crucial for their forecasting, treatment, and management. While several approaches involving satellites, in situ observations and automated underwater monitoring stations have been widely used in the past several decades, they cannot fully provide high-frequency and continuous observations of phytoplankton blooms at low cost and with high accuracy. Thus, we propose a novel ground-based remote sensing system (GRSS) that can monitor real-time chlorophyll a concentrations (Chla) in inland waters with a high frequency. The GRSS mainly consists of three platforms: the spectral measurement platform, the data-processing platform, and the remote access control, display and storage platform. The GRSS is capable of obtaining a remote sensing irradiance ratio (R(λ)) of 400-1000 nm at a high frequency of 20 s. Eight different Chla retrieval algorithms were calibrated and validated using a dataset of 481 pairs of GRSS R(λ) and in situ Chla measurements collected from four inland waters. The results showed that random forest regression achieved the best performance in deriving Chla (R² = 0.95, root mean square error = 13.40 μg/L, and mean relative error = 25.7%). The GRSS successfully captured two typical phytoplankton bloom events in August 2021 with rapid changes in Chla from 20 μg/L to 325 μg/L at the minute level, highlighting the critical role that this GRSS can play in the high-frequency monitoring of phytoplankton blooms. Although the algorithm embedded into the GRSS may be limited by the size of the training dataset, the high-frequency, continuous and real-time data acquisition capabilities of the GRSS can effectively compensate for the limitations of traditional observations. The initial application demonstrated that the GRSS can capture rapid changes of phytoplankton blooms in a short time and thus will play a critical role in phytoplankton bloom management. From a broader perspective, this approach can be extended to other carriers, such as aircraft, ships and unmanned aerial vehicles, to achieve the networked monitoring of phytoplankton blooms.

Spatiotemporal correlations between water quality and microbial community of typical inflow river into Taihu Lake, China

Changxing River, which is a typical inflow river into Taihu Lake and occurs severe algae invasion, is selected to study the effect of different pollution sources on the water quality and ecological system. Four types of pollution sources, including the estuary of Taihu Lake, discharge outlets of urban wastewater treatment plants, stormwater outlets, and nonpoint source agricultural drainage areas, were chosen, and next-generation sequencing and multivariate statistical analyses were used to characterize the microbial communities and reveal their relationship with water physicochemical properties. The results showed that ammonia nitrogen (NH₄⁺-N), total nitrogen (TN), and total phosphorus (TP) were the main pollutants in Changxing River, especially at stormwater outlets. At the same time, the diversity of microbial communities was the highest in the summer, and dominant microbes included Proteobacteria (40.9%), Bacteroidetes (21.0%), and Euryarchaeota (6.1%). The results of BIOENV analysis showed that the major seasonal differences in the diversity of microbial community of Changxing river were explained by the combination of water temperature (T), air pressure (P), TP, and COD_Mn. From the perspective of different pollution types, relative abundances of Microcystis and Nostocaceae at the estuary of Taihu Lake were correlated positively with dissolved oxygen (DO) and pH, and relative abundances of Pseudomonas and Arcobacter were correlated positively with concentrations of TN and nitrate nitrogen (NO₃^--N) at stormwater outlets. This study provided a reference for the impact of pollution types on river microbial ecosystem under complex hydrological conditions and guidance for the selection of restoration techniques for polluted rivers entering the important lake.

Classifying diurnal changes of cyanobacterial blooms in Lake Taihu to identify hot patterns, seasons and hotspots based on hourly GOCI observations

Occurrence of cyanobacterial blooms in most lakes has dramatic changes in time and space. However, most current studies only focused on daily or seasonal scales to obtain a relatively coarse resolution result. To explore the possibility of fine changes occurring within a day in Lake Taihu (China), the area coverage of surface cyanobacterial blooms was quantified from the hourly Geostationary Ocean Color Imager (GOCI) data using a GOCI-derived cyanobacterial index. Based on that, diurnal change characteristics were explored at two scales, and the environmental impacts were investigated. For that, an classification method was first designed to identify the types of diurnal change patterns of cyanobacterial blooms automatically. This method classified the patterns into four types, including the decreasing (Type1), decreasing first and then increasing (Type2), increasing (Type3), increasing first and then decreasing (Type4). Based on that, the types of diurnal change patterns of blooms in Lake Taihu (from April 1, 2011 to October 31, 2020) were identified at pixel (500 m) and synoptic scales. Results indicated that Type1 and Type3 were two hot diurnal change patterns of blooms, and lakeshore was the hotspot occurring severe diurnal changes, and autumn was the hot season occurring frequent diurnal changes. Specifically, hotspot of Type1 was lakeshore, while hotspot of Type3 was Central Regions. Environmental impacts were analyzed at two scales. At pixel scale (500 m), diurnal variation of temperature affected the regional occurence of each type ofdiurnal changes patterns of blooms, and the afternoon temperature played the most critical role (p < 0.001, N = 8316). The occurrence frequency of Type1 was positively (R = 0.41) related with the afternoon temperature, and the occurrence frequency of Type3 was negatively (R = -0.37) related with it. Diurnal variation of wind speed was another key factor impacting the occurrence of obvious diurnal blooms changes, and the wind impacts should be distinguished when the wind speed was over or below 3.5 m/s. At synoptic scale, the interaction of multi environmental factors influenced the diurnal change degree of blooms area, and the environmental contributions were 71%.Comparing with the existing manual classifying workat synoptic scale, the designed classification method can identify the types of diurnal change patterns of blooms at a higher spatial resolution (500 m). These explorations on diurnal dynamics of cyanobacterial blooms in Lake Taihu provide a new insight for advanced cyanobacteria dynamics studies and regional water management.

Removal of microcystin (MC-LR) in constructed wetlands integrated with microbial fuel cells: Efficiency, bioelectricity generation and microbial response

Microcystins (MCs) pollution caused by cyanobacteria harmful blooms (CHBs) has posed short- and long-term risks to aquatic ecosystems and public health. Constructed wetlands (CWs) have been verified as an effective technology for eutrophication but the removal performance for MCs did not achieve an acceptable level. CWs integrated with microbial fuel cell (MFC-CWs) were developed to intensify the nutrient and Microcystin-LR (MC-LR) removal efficiencies in this study. The results indicated that closed-circuit MFC-CWs (T1) exhibited a better NO₃^--N, NH₄⁺-N, TP and MC-LR removal efficiency compared to that of open-circuit MFC-CWs (CK, i.e., traditional CWs). Therein, a MC-LR removal efficiency of greater than 95% was observed in both trials in T1. The addition of sponge iron to the anode layer of MFC-CWs (T2) improved only the NO₃^--N removal and efficiency bioelectricity generation performance compared to T1, and the average effluent MC-LR concentration of T2 (1.14 μg/L) was still higher than the provisional limit concentration (1.0 μg/L). The microbial community diversity of T1 and T2 was simplified compared to CK. The relative abundance of Sphingomonadaceae possessing the degradation capability for MCs increased in T1, which contributed to the higher MC-LR removal efficiency compared to CK and T2. While the relative abundance of electrochemically active bacteria (EAB) (i.e., Desulfuromonadaceae and Desulfomicrobiaceae) in the anode of T2 was promoted by the addition of sponge iron. Overall, this study suggests that integrating MFC into CWs provides a feasible intensification strategy for eutrophication and MCs pollution control.

Sensitivity of phytoplankton to climatic factors in a large shallow lake revealed by column-integrated algal biomass from long-term satellite observations

There are some uncertainties of using chlorophyll a (Chla) concentrations in water surface to address phytoplankton dynamics, especially in large shallow lakes, because of the dramatic vertical migration of phytoplankton. The column-integrated algal biomass (CAB) can reflect the whole water column information, so it is considered as a better indicator for phytoplankton total biomass. An algal biomass index (ABI) and an empirical algorithm were proposed previously to measure algal biomass inside and outside euphotic zone from the Moderate Resolution Imaging Spectrometer (MODIS) data. A long-term CAB time series was generated in this study to clarify the temporal and spatial changes in phytoplankton and address its sensitivity to climatic factors in Lake Chaohu, a shallow eutrophic lake in China, from 2000 to 2018. Overall, the CAB for Lake Chaohu showed significant temporal and spatial dynamics. Temporally, the annual average CAB (total CBA within the whole lake) was increased at rate of 0.569 t Chla/y, ranging from 62.06±8.89 t Chla to 76.03±10.01 t Chla during the 19-year period. Seasonal and periodic variations in total CAB presented a bimodal annual cycle every year, the total CAB was highest in summer, followed by that in autumn, and it was the lowest in winter. The pixel-based CAB (total CAB of a unit water column), ranging from 112.42 to 166.85 mg Chla, was the highest in the western segment, especially its northern part, and was the lowest in the central parts of eastern and central segments. The sensitivity of CAB dynamics to climatic conditions was found to vary by region and time scale. Specifically, the change of pixel-based algal biomass was more sensitive to the temperature change on the monthly and annual scales, while wind speed impacted directly on the short-term spatial-temporal redistribution of algal biomass. High temperature and low wind speed could prompt the growth of total CAB for the whole lake, and the hydrodynamic situations affected by wind and so on determined the spatial details. It also indicated that Lake Chaohu may face more severe challenges with the future climatic warming. This study may serve as a reference to support algal bloom forecasting and early warning management for other large eutrophic lakes with similar problems.

Research Trends in the Remote Sensing of Phytoplankton Blooms: Results from Bibliometrics

Phytoplankton blooms have caused many serious public safety incidents and eco-environmental problems worldwide and became a focus issue for research. Accurate and rapid monitoring of phytoplankton blooms is critical for forecasting, treating, and management. With the advantages of large spatial coverage and high temporal resolution, remote sensing has been widely used to monitor phytoplankton blooms. Numerous advances have been made in the remote sensing of phytoplankton blooms, biomass, and phenology over the past several decades. To fully understand the development history, research hotspots, and future trends of remote-sensing technology in the study of phytoplankton blooms, we conducted a comprehensive review to systematically analyze the research trends in the remote sensing of phytoplankton blooms through bibliometrics. Our findings showed that research on the use of remote-sensing technology in this field increased substantially in the past 30 years. “Oceanography,” “Environmental Sciences,” and “Remote Sensing” are the most popular subject categories. Remote Sensing of Environment, Journal of Geophysical Research: Oceans, and International Journal of Remote Sensing were the journals with the most published articles. The results of the analysis of international influence and cooperation showed that the United States had the greatest influence in this field and that the cooperation between China and the United States was the closest. The Chinese Academy of Sciences published the largest number of papers, reaching 542 articles. Keyword and topic analysis results showed that “phytoplankton,” “chlorophyll,” and “ocean” were the most frequently occurring keywords, while “eutrophication management and monitoring,” “climate change,” “lakes,” and “remote-sensing algorithms” were the most popular research topics in recent years. Researchers are now paying increasing attention to the phenological response of phytoplankton under the conditions of climate change and the application of new remote-sensing methods. With the development of new remote-sensing technology and the expansion of phytoplankton research, future research should focus on (1) accurate observation of phytoplankton blooms; (2) the traits of phytoplankton blooms; and (3) the drivers, early warning, and management of phytoplankton blooms. In addition, we discuss the future challenges and opportunities in the use of remote sensing in phytoplankton blooms. Our review will promote a deeper and wider understanding of the field.

Numerical simulation of microcystin distribution in Liangxi River, downstream of Taihu Lake

Microcystins (MCs), the algal toxins produced by cyanobacteria, raised a worldwide concern in recent decades. Limited monitoring stations for MCs make it hard to map the MC spatial distribution in certain areas. To tackle such problems, we selected Liangxi River as our research area and developed an integrated model to get spatial continuous MC data without too many sampling sites, which integrates a hydro-environment model and an artificial neural network algorithm (ANN). The ANN algorithm can estimate concentration MCs via environmental factors. In this paper, we selected chl-a, TN, TP, NO 2 - , NO 3 - , NH₃ -N, and PO 4 3 - as stressors. The ANN model we established showed good performances both in train (R²  = 0.8407) and test set (R²  = 0.7543). In the hydro-environment model, by inputting river geometry and model boundary data, the spatial continuous water quality data could be simulated. The water quality data returned from the hydro-environmental model were used as input variables of the well-trained ANN model; the continuous MC data were derived. To evaluate this model on geo-mapping the MC distribution in Liangxi River, we compared the performance of this model and spatial interpolation on the test set, it turns out the integrated model showed a better performance. © 2020 Water Environment Federation PRACTITIONER POINTS: The cost of microcystin (MC) detection is too high for routine monitoring. We integrated regression method and hydro-environment model to predict MCs. Results derived from spatial interpolation are not robust in unmonitored area. The new integration model can minimize the drawback of spatial interpolation.

Simultaneous Removal of the Freshwater Bloom-Forming Cyanobacterium Microcystis and Cyanotoxin Microcystins via Combined Use of Algicidal Bacterial Filtrate and the Microcystin-Degrading Enzymatic Agent, MlrA

Freshwater cyanobacterial blooms (e.g., Microcystis blooms) constitute a major global environmental problem because of their risks to public health and aquatic ecological systems. Current physicochemical treatments of toxic cyanobacteria cause the significant release of cyanotoxin microcystins from damaged cells. Biological control is a promising eco-friendly technology to manage harmful cyanobacteria and cyanotoxins. Here, we demonstrated an efficient biological control strategy at the laboratory scale to simultaneously remove Microcystis and microcystins via the combined use of the algicidal bacterial filtrate and the microcystin-degrading enzymatic agent. The algicidal indigenous bacterium Paenibacillus sp. SJ-73 was isolated from the sediment of northern Lake Taihu, China, and the microcystin-degrading enzymatic agent (MlrA) was prepared via the heterologous expression of the mlrA gene in the indigenous microcystin-degrading bacterium Sphingopyxis sp. HW isolated from Lake Taihu. The single use of a fermentation filtrate (5%, v/v) of Paenibacillus sp. SJ-73 for seven days removed the unicellular Microcystis aeruginosa PCC 7806 and the native colonial Microcystis strain TH1701 in Lake Taihu by 84% and 92%, respectively, whereas the single use of MlrA removed 85% of microcystins. Used in combination, the fermentation filtrate and MlrA removed Microcystis TH1701 and microcystins by 92% and 79%, respectively. The present biological control thus provides an important technical basis for the further development of safe, efficient, and effective measures to manage Microcystis blooms and microcystins in natural waterbodies.

Risk prediction of microcystins based on water quality surrogates: A case study in a eutrophicated urban river network

Microcystins (MCs), the toxic by-products from harmful algal bloom (HAB), have caused world-wide concern due to their acute toxicity in freshwater ecosystems. Most studies on HAB have been conducted for shallow freshwater lakes, such as Taihu Lake in China. However, algal blooms in urban rivers located downstream of eutrophicated lakes are also a serious problem for local administrators. It is important for them to know the current and potential risk level of MCs. This environmental issue is rarely reported or discussed. Within this context, we monitored MC concentrations in the Binhu River Network (BRN) in the algal bloom season (Aug, Sep, and Oct) in 2019. To note if the MC concentrations were dangerous, we used 1.0 μg/L suggested by the World Health Organization as the standard value. The proportions of MC samples violating the standard value were 31.78% (Aug), 21.14% (Sep) and 30.77% (Oct). We also designed two statistical models to predict MC concentrations and the possibility to exceed the standard level based on 10 water quality surrogates: Artificial Neural Network (ANN) and Logistic Regression (LR) models. These two models were trained and validated by the monitoring dataset (n = 224). Both models had good performances during training and testing. Although the water quality varied diversely both in spatial and temporal scale, Cluster Analysis (CA) could detect similarities among the samples and separated them into 3 classes, with each class denoting different types of rivers based on the 10 water quality surrogates. Then the ANN and LR were applied as a function of chl-a in each class; by gradually increasing chl-a concentration, we detected chl-a thresholds in class 1, 2, 3 were 25.5, 224, and 109.5 μg/L, respectively, when MCs have a 50% possibility to exceed standard level. The threshold values provided important implications for MC management in the BRN.

Improving the spatial and temporal monitoring of cyanotoxins in Iowa lakes using a multiscale and multi-modal monitoring approach

Cyanobacterial harmful algal blooms (CyanoHABs) are pervasive and negatively impact lake water quality, resulting in economic losses and public health risks through exposure to cyanotoxins. Therefore, it is critical to better monitor and understand the complexity of CyanoHABs, but current methods do not fully describe the spatial and temporal variability of bloom events. In this work, we developed a framework for a multiscale and multi-modal monitoring approach for CyanoHABs combining drone-based near-range remote sensing with analytical measurements of microcystin cyanotoxins and chlorophyll-a. We analyzed weekly beach monitoring samples from 37 lakes geographically distributed across the state of Iowa (USA) over a 15-week period in the summer of 2019 to quantify ELISA (bioassay), 12 microcystin congeners (LC-MS/MS), and chlorophyll-a. We developed a novel microcystin congener-normalized equivalent toxin metric to compare CyanoHAB impacted waters; this microcystin-LR normalized sum-of-congeners approach yields lower predicted toxicity than parallel ELISA results suggesting ELISA is conservative for assessment. A significant linear relationship existed between chlorophyll-a and microcystin for lakes throughout Iowa (R² = 0.39, p < 0.001); lakes with low watershed:lake area ratio and long residence times exhibited a stronger correlation. We then developed a novel geometry-based image processing approach to allow for stitching over-water drone images, a previous barrier in photogrammetry. We applied our mutli-modal framework to a case study on Green Valley Lake to assess initial viability and predicted microcystin concentrations within 33%. We concluded that multispectral imaging is possible but may presently be insufficient for predicting microcystin concentrations due to limitations in the spectral capabilities of the multispectral camera, but technologies are quickly advancing, and lightweight hyperspectral imaging could soon become feasible for investigating spatial bloom variability on lakes.

Climatic versus Anthropogenic Controls of Decadal Trends (1983-2017) in Algal Blooms in Lakes and Reservoirs across China

The proliferation of algal blooms (ABs) in lakes and reservoirs (L&Rs) poses a threat to water quality and the ecological health of aquatic communities. With global climate change, there is a concern that the frequency and geographical expansion of ABs in L&Rs could increase. China has experienced rapid economic growth and major land-use changes over the last several decades and therefore provides an excellent context for such an analysis. About 289,600 Landsat images were used to examine the spatiotemporal distribution of ABs in L&Rs (>1 km²) across China (1983-2017). Results showed significant changes in the temporal slope of the sum of normalized area (0.26), frequency (2.28), duration (6.14), and early outbreak (-3.48) of AB events in L&Rs across China. Specifically, AB-impacted water bodies expanded longitudinally, and the time range of AB observation has expanded starting in the 2000s. Spearman correlation and random forest regression analyses further indicated that, among climatic factors, wind speed and temperature contributed the most to AB expansion. Overall, anthropogenic forces have overridden the imprints of climatic factors on the temporal evolution of ABs in China's L&Rs and therefore could inform policy decisions for the management of these resources.

Distribution of microcystins in environmental multimedia and their bioaccumulation characteristics in marine benthic organisms in the Geum River Estuary, South Korea

Spatio-temporal distributions and bioaccumulation characteristics of freshwater cyanobacterial toxins, such as microcystins (MCs) in the Geum River Estuary, South Korea, were investigated during summer. Environmental multimedia samples (water, suspended particulate matter (SPM), and sediments) and tidal flat organisms (polychaetes, decapods, amphipods, and bivalves) were collected from regions inside and outside of the estuary dam for MCs analysis. Phytoplankton communities in the Geum River (freshwater) and estuarine area (brackish water) were also analyzed in order to understand the relationship with MCs concentrations. Seasonal variation in the structure of phytoplankton communities was detected in the Geum River, with a relatively high density of Cyanophyta in summer. MC concentrations were strongly correlated to water temperature, chlorophyll a, and cyanobacterial density. MC-LR was the most abundant MC variants in environmental samples. Dissolved MCs remained for longer periods and were more widely distributed in the coastal environments compared to particulate MCs. The distribution coefficients between water and SPM (K_d-SPM) and between water and sediments (K_d-sediment) of MCs showed that the phase shift of MCs in the environmental samples occurred in the estuary. K_d-SPM declined from the inside to outside regions of the estuary dam, and was mainly attributed to differences in the half-lives of MCs in dissolved (4.7 d for MC-LR) and particulate phases (0.44-0.52 d for MC-LR). Species-specific bioaccumulation of MCs occurred in tidal flat organisms, with relatively high bioaccumulation factors of MCs being detected in polychaetes and decapods compared to amphipods and bivalves. Overall, this study advances our understanding on the distribution, transport, fate, and bioaccumulation of MCs in estuarine and coastal environments.

Treatment of microcystin (MC-LR) and nutrients in eutrophic water by constructed wetlands: Performance and microbial community

Cyanobacterial harmful algal blooms and microcystins (MCs) pollution pose serious threat to aquatic ecosystem and public health. Planted and unplanted constructed wetlands (CWs) filled with four substrates (i.e., gravel (G-CWs), ceramsite (C-CWs), iron-carbon (I-CWs) and slag (S-CWs)) were established to evaluate nutrients and a typical MCs variant (i.e., MC-LR) removal efficiency from eutrophic water affected by the presence of plant and different substrate. The response of the microbial community to the above factors was also analyzed in this study. The results indicate that the presence of plant can generally enhance nutrients and MC-LR removal efficiency in CWs, except for I-CWs. Throughout the experiment, all CWs exhibited good nitrogen removal efficiency with removal percentages exceeding 90%; TP and MC-LR average removal efficiency of C-CWs and I-CWs were greater than G-CWs and S-CWs irrespective of the presence of plant. The best MC-LR removal efficiency under different MC-LR loads was observed in planted C-CWs (ranged from 91.56% to 95.16%). Except for I-CWs, the presence of plant can enhance relative abundances of functional microorganisms involved in nutrients removal (e.g., Comamonadaceae and Planctomycetaceae) and MCs degradation (e.g., Burkholderiaceae). The microbial community diversity of I-CWs was simplified, while the relative abundance of Proteobacteria was highest in this study. The highest relative abundances of Comamonadaceae, Planctomycetaceae and Burkholderiaceae were observed in planted C-CWs. Overall, ceramisite and iron-carbon were more suitable to be applied in CWs for nutrients and MC-LR removal. This study provides a theoretical basis for practical application of CWs in eutrophication and MCs pollution control.

Cyanobacterial bloom expansion caused by typhoon disturbance in Lake Taihu China

It remains unclear why the area of a cyanobacterial bloom increases in a shallow lake after a typhoon passes. In this study, the mechanisms of cyanobacterial bloom expansion were investigated by studying meteorological factors, water quality, algal biomass, and bloom area in Lake Taihu before and after typhoons (2007-2016). Our results showed that typhoon-induced sediment resuspension caused a short-term increase in nutrients, but nutrients returned to pre-typhoon levels after the typhoon passages. The short-term nutrient release during a typhoon did not result in an obvious increase in Microcystis cell density in two bays of Lake Taihu (Gonghu and Meiliang). Under strong winds, Microcystis aggregates were uniformly distributed in the water column downwind and then dispersed into different directions by wind-driven currents. In particular, Microcystis in the surface water were transported to the center of Lake Taihu. After a typhoon, dispersed Microcystis refloated and formed blooms. Thus, the bloom area was enlarged compared with before a typhoon. Several days after a typhoon, the bloom area gradually reduced as a result of a steady breeze on the horizontal accumulation of Microcystis.

Satellite Estimation of Dissolved Carbon Dioxide Concentrations in China's Lake Taihu

Lakes play an important role in the global carbon cycle; however, there are still large uncertainties in the estimation of global lake carbon emission due to the limitations in conducting field surveys at large geographic scales. Using long-term Moderate-Resolution Imaging Spectroradiometer (MODIS) imagery and field observation data in eutrophic Lake Taihu, we developed a novel approach to estimate the concentration of dissolved carbon dioxide (cCO₂) in lakes. Based on the MODIS-derived chlorophyll-a concentration, lake surface temperature, diffuse attenuation coefficient of photosynthetically active radiation, and photosynthetically active radiation, a spatially explicit cCO₂ model was developed using multivariate quadratic polynomial regression (coefficient of determination (R²) = 0.84, root-mean-square error (RMSE) = 11.81 μmol L^-1, unbiased percent difference (UPD) = 22.46%). Monte Carlo simulations indicated that the model is stable with relatively small deviations in cCO₂ estimates caused by input variables (UPD = 26.14%). MODIS data from 2003 to 2018 showed a significant declining trend (0.42 μmol L^-1 yr^-1, p < 0.05) in the annual mean cCO₂. This was associated with a complex balance between the increasing algae biomass and decreasing external inputs of inorganic carbon, nutrients, and organic matter. The high spatiotemporal variabilities in cCO₂ were attributed to river inputs and seasonal changes in temperature and algae biomass. The study shows that satellite remote sensing can play an important role in the field of inland water carbon cycling, providing timely much-needed insights into the drivers of the spatial and temporal changes in dissolved CO₂ concentrations in inland waters.

Properties of CaO2 for H2O2 release and phosphate removal and its feasibility in controlling Microcystis blooms

Calcium peroxide (CP) has been widely applied in environmental remediation, but few studies have reported its application in controlling Microcystis blooms. To recognize its feasibility for mitigating Microcystis blooms, the properties of CP in terms of hydrogen peroxide (HP) release and phosphate removal were investigated at different CP doses, temperatures, and initial pH values. HP release kinetics followed the Higuchi model. Batch experiments conducted in this study suggested that the HP yield and release rate were positively correlated with the CP dose. Increasing temperature decreased the HP yield but accelerated the HP release rate. The phosphate removal kinetics were well simulated by the pseudo-second-order model. The batch experiments suggested that an increased CP dose enhanced the phosphate removal capacity, but it did not affect the phosphate removal rate. Moreover, increased temperature accelerated both phosphate removal capacity and rate. However, the initial pH of low-buffer-capacity solutions did not notably affect HP release and phosphate removal. According to laboratory experiments, HP released from CP could impair photosynthetic activity, resulting in Microcystis mortality. Furthermore, the reduced phosphate concentration in the solutions suggested that CP could facilitate the control of eutrophication, which directly reduced bloom formation. Hence, our results confirmed CP as a promising algicide for Microcystis bloom control, and it is worthwhile to develop novel methods for bloom mitigation based on CP. Graphic abstract.

Quantifying the dependence of cyanobacterial growth to nutrient for the eutrophication management of temperate-subtropical shallow lakes

The increasing global occurrence of cyanobacterial blooms, attributed primarily to human-induced nutrient enrichment, significantly degrades freshwater ecosystems and poses serious risks to human health. The current study examined environmental variables and cyanobacterial biovolume (B_Cyano) of 28 shallow lakes in the eastern China plains during the spring and summer of 2018. We used a 95% quantile regression model to explore season-specific response of B_Cyano to total nitrogen (TN), or total phosphorus (TP), and robust linear relationships were observed between log(B_Cyano+0.001) and log(TN), or log(TP) in both spring and summer periods. Based on these regressions, regional-scale and season-specific TN and TP thresholds are proposed for these lakes to ensure the safety for recreational waters and drinking water source. However, actual B_Cyano for a given concentration of TN (or TP) for many observations were considerably lower than the results of the 95% regression model predict, indicating that other factors significantly modulated nutrient limitation of B_Cyano. Generalized additive model and quantile regression model were used together to explore potentially significant modulating factors, of which lake retention time, macrophytes cover and N: P ratio were identified as most important. Thus, it is necessary to develop type-specific nutrient thresholds with the consideration of these significant modulating factors. Furthermore, nutrient-B_Cyano relationships of our studied lakes with lake retention time>100 days and no macrophyte were further explored and nutrient thresholds of this lake type were proposed. Nutrient thresholds proposed in this study may play an essential role in achieving a cost-effective eutrophication management for shallow lakes both in the eastern China plains and elsewhere with similar climatic background. On a broader scale, the approaches and findings of this study may provide valuable reference to formulate reasonable nutrient reduction targets for other ecoregions with different climatic conditions.

The case for research integration, from genomics to remote sensing, to understand biodiversity change and functional dynamics in the world's lakes

Freshwater ecosystems are heavily impacted by multiple stressors, and a freshwater biodiversity crisis is underway. This realization has prompted calls to integrate global freshwater ecosystem data, including traditional taxonomic and newer types of data (e.g., eDNA, remote sensing), to more comprehensively assess change among systems, regions, and organism groups. We argue that data integration should be done, not only with the important purpose of filling gaps in spatial, temporal, and organismal representation, but also with a more ambitious goal: to study fundamental cross-scale biological phenomena. Such knowledge is critical for discerning and projecting ecosystem functional dynamics, a realm of study where generalizations may be more tractable than those relying on taxonomic specificity. Integration could take us beyond cataloging biodiversity losses, and toward predicting ecosystem change more broadly. Fundamental biology questions should be central to integrative, interdisciplinary research on causal ecological mechanisms, combining traditional measures and more novel methods at the leading edge of the biological sciences. We propose a conceptual framework supporting this vision, identifying key questions and uncertainties associated with realizing this research potential. Our framework includes five interdisciplinary "complementarities." First, research approaches may provide comparative complementarity when they offer separate realizations of the same focal phenomenon. Second, for translational complementarity, data from one research approach is used to translate that from another, facilitating new inferences. Thirdly, causal complementarity arises when combining approaches allows us to "fill in" cause-effect relationships. Fourth, contextual complementarity is realized when together research methodologies establish the wider ecological and spatiotemporal context within which focal biological responses occur. Finally, integration may allow us to cross inferential scales through scaling complementarity. Explicitly identifying the modes and purposes of integrating research approaches, and reaching across disciplines to establish appropriate collaboration will allow researchers to address major biological questions that are more than the sum of the parts.

In-situ, high-resolution evidence from water-sediment interface for significant role of iron bound phosphorus in eutrophic lake

Potential release of phosphorus (P) bound to iron (Fe) is critical because of the aggravating effects on P load in aquatic ecosystems. However, the process is largely unknown due to the absence of in-situ high-resolution evidence. Dissolved oxygen (DO), ferrous ion (Fe²⁺), and dissolved reactive phosphate (DRP) in interstitial water of sediment columns from a eutrophic shallow lake were measured using the novel colorimetric planar optode imaging method and ZrO-Chelex DGT technology during controlled experimental episodes. The solid Fe and P fractions in sediments were also simultaneously evaluated by employing sequential extraction procedure and spectra scanning analysis including SEM-EDS and ⁵⁷Fe-Mössbauer spectroscopy. The results demonstrated that the DO penetration depths were accordingly regulated with time, the depths depended on the oxygen supply patterns, and oxygen depletion occurred at anaerobic intervals. Considerable increases of concentrations and diffusion of Fe²⁺ and DRP in interstitial water upward from the deep layer into the overlying water were mirrored by decreased concentrations of solid Fe bound P and mineral phase Fe(II) during an anaerobic episode. This confirmed that the re-dissolution of solid Fe bound P pools is the most important source of labile P, and aggravates the P budget in lake water via anaerobic intervals. The reduction-precipitation mechanism of Fe bound P during different oxidation scenarios indicated that the Fe bound P in sediments can act as intermediates between Po and Ca bound P, and result in the permanent burying of authigenic Ca bound P. Significantly positive correlations (R² ≥ 0.7783, n = 74) between labile Fe²⁺ and DRP on both redox conditions also provided explicit evidence for the critical role of redox controlling Fe in labile P cycling at the lacustrine sediment-water interface. These findings provide improved insight for potential controlling effort of Fe coupled P to labile P depending on the oxygen supply in shallow-water hypereutrophic lakes.

Human activities determine quantity and composition of dissolved organic matter in lakes along the Yangtze River

Dissolved organic matter (DOM) plays important roles in the aquatic biogeochemical cycle and the global carbon cycle. However, it is highly spatially and temporally varied due to complex sources from the catchment (allochthonous) and from within the system (autochthonous). Satellite remote sensing provides the ability to monitor DOM and identify the spatio-temporal variations in lakes on a global or regional scale. In this study, field work was conducted in 55 lakes in August 2012 along the middle and lower reaches of the Yangtze River (MLR-YR), where most lakes were characterized by eutrophication due to intense human activities. The results showed that both colored DOM (CDOM) and total DOM differed significantly by and were linearly related to the human-induced trophic state index (TSI), with R² = 0.41 and 0.61, respectively. Autochthonous substances by phytoplankton contributed to 38.5% of CDOM and 35.2% of DOM, and allochthonous terrestrial substance indexed by land cover change and aquaculture contributed to almost half, with 49.7% of CDOM and 49.8% of DOM. In total, human activities explained as much as 81.7% and 87.5% of the variations in CDOM and DOM, respectively. Finally, a flowchart for estimating DOM from satellite-derived TSI was proposed. This study has great significance for synchronously monitoring and managing aquatic environment quality in regional eutrophic lakes around the world.

Formation characteristics of carbonaceous and nitrogenous disinfection by-products depending on residual organic compounds by CGS and DAF

Allogenic organic matter (AOM) composed of extracellular and intracellular organic matter (EOM and IOM) is a major precursor of halogenated carbonaceous and nitrogenous disinfection by-products (C-DBPs and N-DBPs) upon chlorination. The EOM and IOM extracted from Microcystis aeruginosa were analyzed based on bulk parameters and organic fractions with different molecular weight by liquid chromatography with organic carbon detection (LC-OCD). It investigated the efficiency of a conventional gravity system (CGS) and dissolved air flotation (DAF) in the removal of organic precursors, together with measurement of the formation of four major trihalomethanes (THMs) and haloacetonitriles (HANs) in treated water upon chlorination. The results showed that EOM accounted for 59% of building blocks and humic substances, whereas for IOM, 54% were low molecular weight (LMW) neutrals. Both CGS and DAF showed 57-59% removal of dissolved organic carbon (DOC) from EOM and IOM. Regarding DON removal, DAF was found to be more effective, i.e., 8% higher than CGS for EOM. Moreover, the removal of LMW acids and neutrals (not easy to remove and are major precursors of DBPs) from EOM and IOM by DAF was higher than from CGS. The amounts of DBPs measured in all the samples treated for interchlorination were much lower than in the samples for prechlorination. Although the precursors of EOM had a higher concentration than in IOM, THMs and HANs were detected for IOM at a higher concentration, which might be attributed to higher amounts of aromatic, aliphatic moisture and protein compounds in the IOM. Comparatively, DAF showed lower THM and HAN values than CGS water, particularly for IOM. Also, DAF showed a sharp decrease in THMs and an insignificant increase in HANs according to time.

Estimation of Kd(PAR) in inland waters across China in relation to the light absorption of optically active components

The comprehensive analysis of the relationships between the attenuation of photosynthetic active radiation (K_d(PAR)) and light absorption is an imperative requirement to retrieve K_d(PAR) from remote sensing data for aquatic environments. The spatial distributions of the K_d(PAR) and light absorption of optically active components (a_OACs) were routinely estimated in China lakes and reservoirs. Spatial K_d(PAR) was relatively dependent on the inorganic particles (average relative contribution of 57.95%). The a_OACs could explain 70-87% of K_d(PAR) variations. A linear model is used to predict K_d(PAR), as a function of light absorption coefficient of phytoplankton (a_phy), colored dissolved organic matter (a_CDOM), and inorganic particles (a_NAP): K_d(PAR) = 0.41 + 0.57 × a_CDOM + 0.96 × a_NAP + 0.57 × a_phy (R² = 0.87, n = 741, p < 0.001). In the lakes with low TSM concentration and non-eutrophic lakes with high TSM, a_CDOM was the most powerful predicting factor on K_d(PAR). In eutrophic lakes with high TSM, a_NAP had the most significant impact on K_d(PAR). This study allowed K_d(PAR) to be predicted from a_OACs values in the inland waters.

Long-Term Spatial and Temporal Monitoring of Cyanobacteria Blooms Using MODIS on Google Earth Engine: A Case Study in Taihu Lake

As cyanobacteria blooms occur in many types of inland water, routine monitoring that is fast and accurate is important for environment and drinking water protection. Compared to field investigations, satellite remote sensing is an efficient and effective method for monitoring cyanobacteria blooms. However, conventional remote sensing monitoring methods are labor intensive and time consuming, especially when processing long-term images. In this study, we embedded related processing procedures in Google Earth Engine, developed an operational cyanobacteria bloom monitoring workflow. Using this workflow, we measured the spatiotemporal patterns of cyanobacteria blooms in China’s Taihu Lake from 2000 to 2018. The results show that cyanobacteria bloom patterns in Taihu Lake have significant spatial and temporal differentiation: the interannual coverage of cyanobacteria blooms had two peaks, and the condition was moderate before 2006, peaked in 2007, declined rapidly after 2008, remained moderate and stable until 2015, and then reached another peak around 2017; bays and northwest lake areas had heavier cyanobacteria blooms than open lake areas; most cyanobacteria blooms primarily occurred in April, worsened in July and August, then improved after October. Our analysis of the relationship between cyanobacteria bloom characteristics and environmental driving factors indicates that: from both monthly and interannual perspectives, meteorological factors are positively correlated with cyanobacteria bloom characteristics, but as for nutrient loadings, they are only positively correlated with cyanobacteria bloom characteristics from an interannual perspective. We believe reducing total phosphorous, together with restoring macrophyte ecosystem, would be the necessary long-term management strategies for Taihu Lake. Our workflow provides an automatic and rapid approach for the long-term monitoring of cyanobacteria blooms, which can improve the automation and efficiency of routine environmental management of Taihu Lake and may be applied to other similar inland waters.

From Top–Down Regulation to Bottom–Up Solutions: Reconfiguring Governance of Agricultural Nutrient Loading to Waters

Animal agriculture is shifting toward larger farms and regional agglomerations in many countries. In step with this development, manure nutrients have started accumulating regionally, and are leading to increasing eutrophication problems. Nevertheless, the same trend may also prompt innovations in manure treatment. For example, Valio Ltd (the largest dairy processer in Finland) is planning a network of facilities that would remove water from manure, fraction the nutrients in it, and produce biogas from the excess methane. One of the main hurdles in developing this technology is that the current regulatory framework does not support a shift from diffuse loading, which is seen in the traditional application of manure on fields, to point-source loading; the regulations may even prevent such a change. This article analyzes a governance framework that addresses this dilemma in EU–Finland, and discusses how the governance described could curtail the nutrient loading of agriculture to waters. The approach is based on adaptive governance theory. We argue that traditional top–down regulation, which emphasizes food security, contains serious shortcomings when it comes to managing agricultural nutrient loading to waters, and that the current regulatory framework does not necessarily have the adaptive capacity to facilitate new, bottom–up solutions for manure treatment. Interestingly, the strict water quality requirements of the EU Water Framework Directive (2000/60/EC) open new windows of opportunity for such solutions, and thus for improving the overall sustainability of animal agriculture.

Development of a validated direct injection-liquid chromatographic tandem mass spectrometric method under negative electrospray ionization for quantitation of nine microcystins and nodularin-R in lake water

Microcystins (MCs) are cyclic heptapeptide toxins produced by various cyanobacterial genera that are toxic to both animals and humans. In this study, a novel strategy was proposed for the quantitation of nine MCs and Nodularin-R (NOD) in lake water using UHPLC-MS/MS under negative ionization mode, in which only centrifugation was employed during sample preparation. As a result, limits of quantification (LOQ) ranging from 0.05 to 0.1 μg/L for all studied compounds were obtained in water samples, which were lower than the results obtained using positive ionization mode. Additionally, validation was performed by spiking three different levels of MCs at 0.05 or 0.1, 0.5, 1.0 μg/L (n = 6). Recoveries ranged from 88.6% to 101.8%, and intraday and interday variability were lower than 12% and 14%, respectively, for all targeted compounds. Furthermore, the proposed method was applied to investigate microcystins contamination in fifty lake water samples collected in different regions in China. As a result, MC-LR, MC-RR, MC-YR, MC-WR, MC-LW, MC-LA, MC-LY, and MC-HilR were detected in lake water samples at trace level ranging from 0.06 to 0.37 μg/L. The obtained results indicated that it was necessary to monitor the presence of MCs in lake water, especially during regular cyanobacterial blooms during warmer months.

Decomposition of β-N-methylamino-L-alanine (BMAA) and 2,4-diaminobutyric acid (DAB) during chlorination and consequent disinfection byproducts formation

β-N-methylamino-L-alanine (BMAA) and 2,4-diaminobutyric acid (DAB) are two newly identified algal toxins, and they may react with chlorine to undergo decomposition and generate disinfection byproducts (DBPs) during pre-chlorination as well as chlorine disinfection. In this study, the decomposition of BMAA and DAB during chlorination and the consequent DBPs formation were investigated. The BMAA and DAB concentrations in source waters were determined, the decomposition kinetics of BMAA and DAB and the formation of DBPs during chlorination were studied, the formation pathways of DBPs from BMAA and DAB were explored, and the factors which may affect the decomposition and DBPs formation were examined. The results revealed that BMAA and DAB were commonly detected in source waters from Taihu Lake, and the highest level of BMAA reached 230.8 ng/L, while the concentrations of DAB were generally around 2.0 ng/L. The decomposition of BMAA and DAB during chlorination both followed pseudo-first-order decay while the decomposition rate constant of DAB was significantly higher than that of BMAA. Trihalomethanes (THMs), haloacetic acids (HAAs), and haloacetonitriles (HANs) were all generated during the chlorination of BMAA and DAB with relatively high yields. Notably, the THMs, HAAs, and HANs yields of each carbon atom from BMAA and DAB were significantly higher than that from other organic precursors, and the formation of HANs from DAB was significantly higher than that from BMAA. The formation pathways of DBPs from BMAA and DAB were tentatively proposed and verified through theoretical calculations. Of note, the proposed formation pathways of THMs and HAAs from BMAA were similar to that from DAB, while the proposed formation pathways of HANs from BMAA and DAB showed some differences. Chlorine dose, pH and temperature all affected the decomposition of BMAA and DAB and DBPs formation during chlorination.

Human-induced eutrophication dominates the bio-optical compositions of suspended particles in shallow lakes: Implications for remote sensing

Suspended particulate matter (SPM) is generally divided into inorganic (SPIM) and organic (SPOM) parts; they come from different sources, and have different impacts on the optical properties and/or water quality of lake. However, in a specific remote sensing process, they are not retrieved separately. Using in-situ data of 59 lakes along the middle and lower reaches of the Yangtze River (MLR-YR) in dry season (April) and wet season (August) in 2012, we first studied the absorption properties and sources of different SPM. On this basis, we proposed a workflow for simultaneously estimating SPIM and SPOM from satellite data. Our results are as follows: Bio-optical compositions of SPM in these eutrophic shallow lakes tempo-spatially varied greatly and were dominated by human-induced eutrophication. Phytoplankton contributed 18.42 ± 18.92% of SPIM and 26.22 ± 19.24% of SPOM in April 2012, but 30.4 ± 23.41% of SPIM and 47.03 ± 18.1% of SPOM in August 2012. The trophic state index explained 42.84% of SPOM variation in April 2012, and 54.64% in August 2012. Moreover, there were strong linear relationships between SPIM concentration and non-algal particle absorption coefficient (Pearson's r = 0.73; p < 0.01) and between SPOM concentration and phytoplankton absorption coefficient (r = 0.76; p < 0.01). Based on these results, SPIM and SPOM concentrations in the lakes along the MLR-YR could be retrieved from OLCI/Sentinel-3A satellite data, respectively. This study has a great significance for real-time monitoring and managing aquatic environment in various eutrophic and/or shallow lakes as a group.

Study on an Online Detection Method for Ground Water Quality and Instrument Design

The online measurement of ground water quality, as one important area of water resource protection, can provide real-time measured water quality parameters and send out warning information in a timely manner when the water resource is polluted. Based on ultraviolet (UV) spectrophotometry, a remote online measurement method is proposed and used to measure the ground water quality parameters chemical oxygen demand (COD), total organic carbon (TOC), nitrate nitrogen (NO₃-N), and turbidity (TURB). The principle of UV spectrophotometry and the data processing method are discussed in detail, the correlated mathematical modeling of COD and TOC is given, and a confirmatory experiment is carried out. Turbidity-compensated mathematical modeling is proposed to improve the COD measurement accuracy and a confirmatory experiment is finished with turbidity that ranges from 0 to 100 NTU (Nephelometric Turbidity Unit). The development of a measurement instrument to detect the ground water COD, TOC, NO₃-N, and TURB is accomplished; the test experiments are completed according to the standard specification of China's technical requirement for water quality online automatic monitoring of UV, and the absolute measuring errors of COD, TOC, and NO₃-N are smaller than 5.0%, while that of TURB is smaller than 5.4%, which meets the requirements for the online measurement of ground water quality.

Long-term variation of phytoplankton biomass and physiology in Taihu lake as observed via MODIS satellite

Estimation of phytoplankton biomass (noted as phytoplankton carbon, C_phyto) and evaluation of phytoplankton physiology is central to the estimation of primary productivity and the carbon cycle. This issue has been widely considered in oceans but not in inland water. Here, we develop experiential and semi-analytical models, which validated by independent in situ measurement data, respectively, to derive C_phyto and phytoplankton absorption coefficient at 675 nm (a_ph(675)) from MODIS. The effects of nutrients and temperature on the seasonal variation of phytoplankton physiology were assessed through a novel proxy of C_phyto to a_ph(675) ratio (C_phyto/a_ph(675)) over the Lake Taihu, the third largest lake in China. Significant seasonal climatological cycles of C_phyto, a_ph(675) and C_phyto/a_ph(675) were observed in Lake Taihu, especially in Meiliang Bay and Zhushan Bay, where algal blooms occur frequently. The highest C_phyto and a_ph(675) values were observed in summer due to the growth of phytoplankton biomass and chlorophyll-a concentration. Lower values were observed in winter and spring, which are characterized by relatively high total nitrogen levels and low irradiance, owing to the low temperature astricts the algae growth. However, the C_phyto/a_ph(675) shows an opposite trend compared to C_phyto and a_ph(675), which have high values in winter and low values in summer. The analysis of C_phyto, a_ph(675) and C_phyto/a_ph(675) with total phosphorus (TP) levels and temperature indicates that TP are the main positive driver of the increase in C_phyto and a_ph(675) and negatively regulate C_phyto/a_ph(675). Warming promotes an increase in C_phyto and a_ph(675) and restricts C_phyto/a_ph(675) in summer. Biomass and nutrient levels are the primary drivers of the decrease of C_phyto/a_ph(675) in such a typical eutrophic lake. The results present some new findings compared to previous oceanic studies and expand our knowledge in the study of phytoplankton biomass and physiology in eutrophic lakes.