Detection and variation of microcystin contents of Microcystis blooms in eutrophic Lalla Takerkoust Lake, Morocco

Hepatotoxic microcystins produced by cyanobacteria represent a significant health hazard. In Morocco, although blooms of cyanobacteria are known to occur frequently in some water‐bodies, studies on toxicology and toxinology of these potentially harmful algae have been scarcely developed. This paper presents results of the detection of Microcystin‐LR (Mcyst‐LR) and other microcystin variants and their content variations from Microcystis natural blooms and from the isolated strain culture. During the cyanobacteria dynamic study, 11 bloom samples were collected between 1994 and 2000 and were assessed for toxicity and quantification of microcystins, respectively, by mice bioassay and the enzyme‐linked immunosorbent assay. The bloom samples exhibit a high toxicity with mice intraperitoneal injection (i.p.) LD₅₀ < 100 mg/kg and the total microcystin contents were 496–8800 μg/g dry weight with a coefficient of variation of less than 11%. However, the Microcystis isolated strain cultured on Z8 medium under controlled laboratory conditions produced an amount of 707 μg/g dry weight, its toxicity level corresponds to a LD₅₀ i.p mice of 31 mg/kg. The detection of microcystin variants and the identification of Mcyst‐LR from some natural bloom samples collected in 1994, 1996 and 1999 have been carried out by high performance liquid chromatography with a photodiode array detector, which confirmed the identification from each bloom sample of at least four microcystin variants, among which the Mcyst‐LR one represents a relative proportion of 30% to 62% of total microcystin contents. Only one variant of microcystin that was not a Mcyst‐LR has been detected from a Microcystis isolated strain. The toxicity level of the material depended either on the amount of microcystins or the number of these variants and their proportion. According to these results, the survey of cyanobacteria and cyanotoxins monitoring in water used for human consumption is needed to avoid any sanitary risk.     

Cyanobacterial blooms in Argentinean inland waters

The occurrence of cyanobacterial blooms in Argentina is documented and note is made of particular water-bodies at risk from blooms.     

基于1D CNN-SLSTM模型的太湖蓝藻密度预测方法

太湖中的水华爆发对太湖及其沿岸居民造成巨大影响,因此保证提前预测蓝藻密度非常重要。为精确预测太湖蓝藻密度,在长短时记忆网络（LSTM）模型的基础上,加入一维卷积模型,筛选并优化激活函数,提出一种基于1D CNN-SLSTM的预测模型预测蓝藻密度。试验结果表明,1D CNN-SLSTM模型的R_RMSE、M_MAPE、M_MAE值分别比单独使用LSTM模型降低30.38%、1.85%、16.89%,R值和N_NSE值则提升了0.08、0.17,验证了使用扩展型指数线性单元激活函数（Selu）的LSTM神经网络（1D CNN-SLSTM）预测效果最好。     

The extended Kalman filter for forecast of algal bloom dynamics

A deterministic ecosystem model is combined with an extended Kalman filter (EKF) to produce short term forecasts of algal bloom and dissolved oxygen dynamics in a marine fish culture zone (FCZ). The weakly flushed FCZ is modelled as a well-mixed system; the tidal exchange with the outer bay is lumped into a flushing rate that is numerically determined from a three-dimensional hydrodynamic model. The ecosystem model incorporates phytoplankton growth kinetics, nutrient uptake, photosynthetic production, nutrient sources from organic fish farm loads, and nutrient exchange with a sediment bed layer. High frequency field observations of chlorophyll, dissolved oxygen (DO) and hydro-meteorological parameters (sampling interval Δt = 1 day, 2 h, 1 h, respectively) and bi-weekly nutrient data are assimilated into the model to produce the combined state estimate accounting for the uncertainties. In addition to the water quality state variables, the EKF incorporates dynamic estimation of algal growth rate and settling velocity. The effectiveness of the EKF data assimilation is studied for a wide range of sampling intervals and prediction lead-times. The chlorophyll and dissolved oxygen estimated by the EKF are compared with field data of seven algal bloom events observed at Lamma Island, Hong Kong. The results show that the EKF estimate well captures the nonlinear error evolution in time; the chlorophyll level can be satisfactorily predicted by the filtered model estimate with a mean absolute error of around 1-2 μg/L. Predictions with 1-2 day lead-time are highly correlated with the observations (r = 0.7-0.9); the correlation stays at a high level for a lead-time of 3 days (r = 0.6-0.7). Estimated algal growth and settling rates are in accord with field observations; the more frequent DO data can compensate for less frequent algal biomass measurements. The present study is the first time the EKF is successfully applied to forecast an entire algal bloom cycle, suggesting the possibility of using EKF for real time forecast of algal bloom dynamics.     

Sorption of malachite green (a cationic dye) and heavy metals by dead biomass of Phormidesmis molle (cyanobacteria)‐dominated mat

The test mat biomass rapidly sorbed malachite green (MG; a cationic dye) and heavy metals from mono‐component, binary and ternary systems. In mono‐component system, the sorption of Cu, Cd and MG increased when pH was increased from 3 to 4. However, in binary and ternary systems sorption of metals and MG were not substantially influenced by pH within the range 3–5. The presence of Cu or MG in binary or ternary systems reduced the sorption of each other from the solution. However, Cd enhanced the sorption of MG from binary solution and vice versa. Moreover, the ability of mat biomass to rapidly sorb metal ions and malachite green from mono‐component, binary and ternary systems, together with easy harvesting of biomass due to self‐immobilized nature, make it a suitable sorbent for the treatment of wastewaters that concomitantly bear the both heavy metals and dyes as pollutants.     

基于遥感图像3D–CNN及氮磷循环的水华预测

针对现有的藻类水华分析存在忽略水体底物中的氮磷反馈机制对藻类生长过程影响以及缺乏对整体水域的全面分析的问题,本研究以富营养化状态和叶绿素a浓度作为藻类水华的表征指标,以遥感图像及水体中的总氮和总磷为主要研究对象,提出一种基于遥感图像3D–CNN及氮磷循环的水华形成过程分析新方法.首先通过3D–CNN对遥感图像进行特征提取,并采用细菌觅食算法优化网络结构,预测水体富营养化等级,在此基础上,根据"氮–磷–藻"之间的耦合关系及底物反馈机制,建立三维生化动力学时变参数模型,确定水华暴发程度及临界条件,并融合遥感图像提取的特征信息建立ENN模型,预测水华暴发的时间及程度.本研究选用由MODIS卫星获取的太湖流域遥感图像及水域中的总氮和总磷等水质监测数据.仿真结果表明,基于遥感图像3D–CNN结合氮磷循环的分析方法在富营养化等级和水华暴发预测方面均取得良好效果.     

三峡水库夏季不同支流倒灌特性及其影响分析

为研究三峡库区夏季不同支流库湾倒灌特性及其对水温分层和营养盐分布的作用特性,以及其对水华的影响,于2017年7月对三条不同支流库湾（香溪河、大宁河、小江）的流速、水温、营养盐和叶绿素a等进行了监测。结果表明：夏季三峡库区干流水体主要以中层倒灌潜入支流库湾,均主要为温差异重流,且倒灌潜入深度与厚度主要与支流库湾河口水温分布有关。受干流倒灌的补给作用,三条支流中下游营养盐普遍呈现出“中层>底层>表层”的垂向分布规律,由于上游有磷矿产业,香溪河TP分布情况较其他支流不同,呈现上游至下游逐渐减小的趋势。干、支流间不同的温度差使支流间倒灌异重流的强度存在差异,倒灌异重流将影响支流的水温分层结构,导致支流库湾存在不同水华优势藻种及强度,进而对支流水华产生不同的影响。     

基于多源卫星数据的淀山湖富营养化时空分布研究

准确监测蓝藻水华是湖泊富营养化研究的基础,可为水环境部门的管理和决策提供依据。以淀山湖为研究对象,利用Sentinel-2/MSI、Landsat-8/OLI两种影像数据,并通过改进的浮游藻类指数(Floating Algae Index, FAI)监测了2019～2021年共21幅淀山湖区域影像的水华情况,然后在此基础上分析了淀山湖富营养化的分布规律。结果表明：(1)淀山湖连续三年都有蓝藻水华出现,存在富营养化情况;(2)每年的6～9月份是蓝藻水华的高发期,湖体富营养化发生的面积较大、频率较高,2020年9月和2021年8月爆发水华面积超过20 km²;(3)水华爆发速度较快,持续时间较短。     

Harmful algae and their potential impacts on desalination operations off southern California

Seawater desalination by reverse osmosis (RO) is a reliable method for augmenting drinking water supplies. In recent years, the number and size of these water projects have increased dramatically. As freshwater resources become limited due to global climate change, rising demand, and exhausted local water supplies, seawater desalination will play an important role in the world's future water supply, reaching far beyond its deep roots in the Middle East. Emerging contaminants have been widely discussed with respect to wastewater and freshwater sources, but also must be considered for seawater desalination facilities to ensure the long-term safety and suitability of this emerging water supply. Harmful algal blooms, frequently referred to as ‘red tides’ due to their vibrant colors, are a concern for desalination plants due to the high biomass of microalgae present in ocean waters during these events, and a variety of substances that some of these algae produce. These compounds range from noxious substances to powerful neurotoxins that constitute significant public health risks if they are not effectively and completely removed by the RO membranes. Algal blooms can cause significant operational issues that result in increased chemical consumption, increased membrane fouling rates, and in extreme cases, a plant to be taken off-line. Early algal bloom detection by desalination facilities is essential so that operational adjustments can be made to ensure that production capacity remains unaffected. This review identifies the toxic substances, their known producers, and our present state of knowledge regarding the causes of toxic episodes, with a special focus on the Southern California Bight.     

Evaluating visible derivative spectroscopy by varimax-rotated,principal component analysis of aerial hyperspectral images from the western basin of Lake Erie

The Kent State University (KSU) spectral decomposition method provides information about the spectral signals present in multispectral and hyperspectral images. Pre-processing steps that enhance signal to noise ratio (SNR) by 7.37–19.04 times, enables extraction of the environmental signals captured by the National Aeronautics and Space Administration (NASA) Glenn Research Center's, second generation, Hyperspectral imager (HSI2) into multiple, independent components. We have accomplished this by pre-processing of Level 1 HSI2 data to remove stripes from the scene, followed by a combination of spectral and spatial smoothing to further increase the SNR and remove non-Lambertian features, such as waves. On average, the residual stochastic noise removed from the HSI2 images by this method is 5.43 ± 1.42%. The method also enables removal of a spectrally coherent residual atmospheric bias of 4.28 ± 0.48%, ascribed to incomplete atmospheric correction. The total noise isolated from signal by the method is thus <±7% based on analysis of multiple swaths. The method is essentially independent of the order of operations, extracting the same spectral components within error in all cases, spatial patterns that are very similar and explaining nearly identical amounts of information from each image. Based on comparison between multiple swaths the uncertainty in the variance associated with each component averages ±1.69% and is as low as ±0.08% and in all cases <±3.15%.