Algal Blooms In Lakes Research Articles

Global divergent trends of algal blooms detected by satellite during 1982-2018.

Algal blooms (ABs) in inland lakes have caused adverse ecological effects, and health impairment of animals and humans. We used archived Landsat images to examine ABs in lakes (>1km2 ) around the globe over a 37-year time span (1982-2018). Out of the 176032lakes with area >1km2 detected globally, 863 were impacted by ABs, 708had sufficiently long records to define a trend, and 66% exhibited increasing trends in frequency ratio (FRQR, ratio of the number of ABs events observed in a year in a given lake to the number of available Landsat images for that lake) or area ratio (AR, ratio of annual maximum area covered by ABs observed in a lake to the surface area of that lake), while 34% showed a decreasing trend. Across North America, an intensification of ABs severity was observed for FRQR (p<.01) and AR (p<.01) before 1999, followed by a decrease in ABs FRQR (p<.01) and AR (p<.05) after the 2000s. The strongest intensification of ABs was observed in Asia, followed by South America, Africa, and Europe. No clear trend was detected for the Oceania. Across climatic zones, the contributions of anthropogenic factors to ABs intensification (16.5% for fertilizer, 19.4% for gross domestic product, and 18.7% for population) were slightly stronger than climatic drivers (10.1% for temperature, 11.7% for wind speed, 16.8% for pressure, and for 11.6% for rainfall). Collectively, these divergent trends indicate that consideration of anthropogenic factors as well as climate change should be at the forefront of management policies aimed at reducing the severity and frequency of ABs in inland waters.

Photocatalysis-enhanced coagulation for removal of intracellular organic matter from Microcystis aeruginosa: Efficiency and mechanism

• Optimized photocatalysis with coagulation for IOM removal from algae. • Photocatalysis is complementary with coagulation for IOM fractionations removal. • Bi 2 O 3 -TiO 2 /PAC composite adsorbing IOM as flocculation core to assist coagulation. • Control of co-existence of algal pollution by combined process is observed. Photocatalysis is an efficient advanced oxidation process that provides a promising alternative for pollution control during algal blooms in lakes or water reservoirs. Previous research has mainly focused on the destruction of algal cells by photocatalysis, whereas the release and control of concomitant secretions specially intracellular organic matter (IOM) is undervalued. Herein, we studied the removal efficiency and mechanism of algal IOM extracted from Microcystis aeruginosa during stationary phase by the combined process of pre-photocatalysis with a novel composite of Bi 2 O 3 -TiO 2 /PAC (Bi-doped TiO 2 nano-composites supported by powdered activated carbon) followed by enhanced coagulation with aluminum sulfate (AS) coagulant. Optimized photocatalysis and coagulation combined treatment (PC) was indicated to enhance IOM removal with 64%, 92% and 100% for DOC, absorbance at 254 nm (UV 254 ) and the optical density at 680 nm (OD 680 ) respectively. Pre-photocatalysis reduced the required AS coagulant dose and weakened the effect of solution pH. Photocatalysis was more inclined to remove humic substances and building blocks than high-molecular weight components, while coagulation alone displayed preference for high-molecular weight biopolymers and protein-like substance. Pre-photocatalysis was complementary with subsequent coagulation for efficient removal of protein-like substances and fractionation with medium molecular weight of 0.5–1 kDa. The produced flocs revealed that the Bi 2 O 3 -TiO 2 /PAC adsorbing IOM acted as a flocculation core to improve coagulation performance. Besides, the combined PC process was proved to perform well to co-existing algal pollution without damaging cells integrity and enhanced algal cells removal from 26% by coagulation alone to 61%. Therefore, this combined treatment was an effective method for the removal of IOM of Microcystis aeruginosa and control cyanobacteria bloom.

Hydroclimatic drivers associated with an unusual bloom of Microcystis aeruginosa and increase of CyanoHABs in a deep oligotrophic lake

Abstract Hydroclimatic and anthropogenic factors in climate change scenarios contribute to ecological changes of harmful cyanobacterial algal blooms in lakes. This study describes the variability of the bathymetric and temporal distribution of cyanobacterial communities in a deep oligotrophic lake. An increase of two to nine in the detection of CyanoHABs potential was observed, likely linked to nitrogen contributions to Lake Rupanco during the last 36 years related to changes in land use and the cultivation of salmonids. We recorded an unusual bloom of Microcystis aeruginosa in winter, 2017 at 30 m during the total mixing period of the lake, associated with the highest N/P index (16.24), the increase of total nitrogen (75 μg/L) and climatic conditions that favored the availability of nitrogen of this cyanobacterium. This study provides information on the causes of increased cyanobacteria in a deep oligotrophic lake. Long-term information on the composition of cyanobacterial communities is very helpful in determining ecological changes.

Characterization of Phosphate Solubilizing Bacterial Endophytes and Plant Growth Promotion In Vitro and in Greenhouse.

Phosphate is one of the most important nutrients for plant growth and development, and only 0.1% of the phosphate in soils is available to plants. Currently, the use of excess phosphate fertilizer has caused surface and ground water pollution and water eutrophication, resulting in algal blooms in lakes and oceans. Therefore, it is imperative to explore alternative ways to solve these problems for sustainable agricultural production and improvement of soil fertility, while protecting the environment. Microorganisms from the rhizosphere and within plants are able to solubilize insoluble soil phosphate, making it available to plants. Five high phosphate solubilizing bacteria from our bacterial endophyte library were chosen for this study and identified as Pantoea vagans IALR611, Pseudomonas psychrotolerans IALR632, Bacillus subtilis IALR1033, Bacillus safensis IALR1035 and Pantoea agglomerans IALR1325. All five bacteria significantly promoted tall fescue growth in vitro. Greenhouse experiments showed that IALR1325 significantly promoted pepper and tomato growth, and IALR632 was the best in promoting tomato growth. In addition, all these bacteria had extracellular acid phosphatase and phytase activities. One of the mechanisms for phosphate solubilization by bacteria is pH reduction caused by gluconic acid production. Our results indicate that P. agglomerans IALR1325 is a promising bacterium for future applications.

Quantifying Scales of Spatial Variability of Cyanobacteria in a Large, Eutrophic Lake Using Multiplatform Remote Sensing Tools

Harmful algal blooms of cyanobacteria are increasing in magnitude and frequency globally, degrading inland and coastal aquatic ecosystems and adversely affecting public health. Efforts to understand the structure and natural variability of these blooms range from point sampling methods to a wide array of remote sensing tools. This study aims to provide a comprehensive view of cyanobacterial blooms in Clear Lake, California — a shallow, polymictic, naturally eutrophic lake with a long record of episodic cyanobacteria blooms. To understand the spatial heterogeneity and temporal dynamics of cyanobacterial blooms, we evaluated a satellite remote sensing tool for estimating coarse cyanobacteria distribution with coincident, in situ measurements at varying scales and resolutions. The Cyanobacteria Index (CI) remote sensing algorithm was used to estimate cyanobacterial abundance in the top portion of the water column from data acquired from the Ocean and Land Color Instrument (OLCI) sensor on the Sentinel-3a satellite. We collected hyperspectral data from a handheld spectroradiometer; discrete 1 m integrated surface samples for chlorophyll-a and phycocyanin; multispectral imagery from small Unmanned Aerial System (sUAS) flights (∼12 cm resolution); Autonomous Underwater Vehicle (AUV) measurements of chlorophyll-a, turbidity, and colored dissolved organic matter (∼10 cm horizontal spacing, 1 m below the water surface); and meteorological forcing and lake temperature data to provide context to our cyanobacteria measurements. A semivariogram analysis of the high resolution AUV and sUAS data found the Critical Scale of Variability for cyanobacterial blooms to range from 70 to 175 m, which is finer than what is resolvable by the satellite data. We thus observed high spatial variability within each 300 m satellite pixel. Finally, we used the field spectroscopy data to evaluate the accuracy of both the original and revised CI algorithm. We found the revised CI algorithm was not effective in estimating cyanobacterial abundance for our study site. Satellite-based remote sensing tools are vital to researchers and water managers as they provide consistent, high-coverage data at a low cost and sampling effort. The findings of this research support continued development and refinement of remote sensing tools, which are essential for satellite monitoring of harmful algal blooms in lakes and reservoirs.

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.

Responses of alkaline phosphatase activity to wind-driven waves in a large, shallow lake: Implications for phosphorus availability and algal blooms

Phosphorus is a vital nutrient for algal growth, thus, a better understanding of phosphorus availability is essential to mitigate harmful algal blooms in lakes. Wind waves are a ubiquitous characteristic of lake ecosystems. However, its effects on the cycling of organic phosphorus and its usage by phytoplankton remain poorly elucidated in shallow eutrophic lakes. A mesocosm experiment was carried out to investigate the responses of alkaline phosphatase activity fractions to wind waves in large, shallow, eutrophic Lake Taihu. Results showed that wind-driven waves induced the release of alkaline phosphatase and phosphorus from the sediment, and dramatically enhanced phytoplanktonic alkaline phosphatase activity. However, compared to the calm conditions, bacterial and dissolved alkaline phosphatase activity decreased in wind-wave conditions. Consistently, the gene copies of Microcystis phoX increased but bacterial phoX decreased under wind-wave conditions. The ecological effects of these waves on phosphorus and phytoplankton likely accelerated the biogeochemical cycling of phosphorus and promoted phytoplankton production in Lake Taihu. This study provides an improved current understanding of phosphorus availability and the phosphorus strategies of plankton in shallow, eutrophic lakes.

Climate change impacts on human health at an actionable scale: a state-level assessment of Indiana, USA

Climate change is already being felt on local levels, with historical records from the State of Indiana (USA) revealing warmer winters and more extreme precipitation events. To refine our understanding of climate change impacts on human health, we conducted a state-level assessment of future climate change impacts on human health using outputs from advanced climate model projections for this century. Future projections show a steep increase in extreme heat events, leading to greater potential vulnerability to heat disasters for Indiana communities. Additionally, a 2- to 4-fold increase in days with “uncomfortable night” conditions by the end of the century will strongly impact the cardiopulmonary health of more vulnerable populations (i.e., elderly, those with pre-existing conditions, children, and those with inadequate access to cooling). Continued trends for warmer winters and more flooding suggest a much greater risk for the expansion and virulence of a number of vector-borne diseases, such as Lyme disease, West Nile Virus, and “tropical” diseases for which the mosquito vectors will thrive. Higher temperatures will also drive more frequent and severe harmful algal blooms in lakes and reservoirs, with implications for human and animal health. Food systems will also be impacted, particularly with increased risk of contamination by bacteria and mycotoxins due to elevated heat and humidity.

Exploring temperature and precipitation impacts on harmful algal blooms across continental U.S. lakes

AbstractClimate change is expected to impact the severity of harmful algal blooms in lakes and reservoirs through a number of mechanisms related to the influence of warming temperatures and changes to precipitation patterns. Evidence on the prevalence of individual mechanisms is lacking, however, with knowledge of many mechanisms restricted to studies of individual or small subsets of lakes. Here, we leverage over twelve hundred summertime lake observations from across the continental U.S. to explore evidence for the hypothesized risks from climate change attributable to specific mechanisms. Using a statistical model selection approach, we examine associations between temperature and precipitation variables and indicators of total phytoplankton abundance, species dominance, and toxicity. We find evidence in support of the hypotheses that summer temperatures drive total abundance, that the length of the summer drives cyanobacterial abundance, and that increased temperatures may reduce the observed toxicity of blooms in some cases. We find that nutrient concentrations are also likely to be impacted by lake warming, as increased temperatures are robustly associated with increased total phosphorus concentrations. Evidence for the impact of precipitation is mixed, however, as there is evidence to support that increased nutrient runoff from precipitation could support blooms but also that nutrient concentrations could be reduced through greater flushing due to precipitation. While statistical associations are not definitive evidence of formal mechanistic links, the geographic scale of the results is useful for identifying hypothesized mechanisms that are widespread across the continental U.S., and therefore for informing understanding of the influence of climate change.

The time delays influence on the dynamical complexity of algal blooms in the presence of bacteria

Bacteria associated with oceanic algal blooms are acknowledged to play important roles in carbon, nitrogen, and sulfur cycling. They influence the climate, mediate primary production, participate in biogeochemical cycles, and maintain ecological balance. A greater insight on the control of the interactions between microalgae and other microorganisms, particularly bacteria, would be helpful in exploring the role of bacteria on algal blooms in lakes. The present study is to investigate the effects of bacteria on the occurrence of algal blooms in lakes. We propose a nonlinear mathematical model by taking into account interactions among nutrients, algae, detritus and bacteria in a lake. We assume that bacteria enhance the growth of algal biomass through remineralization only. Equilibria are analyzed for feasibility and stability, substantiated via numerical simulations. Increase in uptake rate of nutrients by algae and bacteria death rate generates transcritical bifurcations. We perform a global sensitivity analysis to identify the important parameters of the model having a significant impact on the densities of algae and bacteria in the lake. Our findings show that massive algal production might occur in the presence of bacteria, and microalgae-bacteria interactions can be beneficial to the massive production of microalgae. Further, the effect of time delays involved in the bacterial decomposition conversion of detritus into nutrients is studied. Chaotic oscillations may arise via equilibrium destabilization on increasing the values of the time lag. To support chaos occurrence, the Poincaré map is drawn and the Lyapunov exponents are also computed. The findings, critically important for lake restoration, indicate that hypoxia in the lake can be prevented if detritus removal is performed on a regular basis, at time intervals smaller than the critical threshold in the delay with which detritus is decomposed into nutrients.

Determining remote sensing spatial resolution requirements for the monitoring of harmful algal blooms in the Great Lakes

Harmful algal blooms (HABs) have become a major health and environmental concern in the Great Lakes. In 2014, severe HABs prompted the State of Ohio to request NASA Glenn Research Center (GRC) to assist with monitoring algal blooms in Lake Erie. The most notable species of HAB is Microcystis aeruginosa, a hepatotoxin producing cyanobacteria that is responsible for liver complications for humans and other fauna that come in contact with these blooms. NASA GRC conducts semiweekly flights in order to gather up-to-date imagery regarding the blooms' spatial extents and concentrations. Airborne hyperspectral imagery is collected using two hyperspectral imagers, HSI-2 and HSI-3. Hyperspectral imagery is necessary in order to conduct experiments on differentiation of algal bloom types based on their spectral reflectance. In this analysis, imagery from September 19, 2016 was utilized to study the subpixel variability within the footprint of arbitrary sized pixels using several analysis techniques. This particular data set is utilized because it represents a worst case scenario where there is significant potential for public health concern due to high concentrations of microcystin toxin found in the water on this day and the concurrent observational challenges to accurately measure the algal bloom concentration variability with a remote sensing system due to the blooms high spatial variability. It has been determined that the optimal spatial resolution to monitor algal blooms in the Great Lakes is at most 50 m, and for much lower error 25 m, thus allowing for greater ease in identifying high concentration blooms near the surface. This resolution provides the best sensitivity to high concentration areas that are of significant importance in regard to human health and ecological damage.

Stricter nutrient criteria are required to mitigate the impact of climate change on harmful cyanobacterial blooms

Climate change has the potential to exacerbate eutrophication and the risk of harmful algal blooms in lakes. Periodic adjustment of nutrient criteria, as the cornerstone of eutrophication control and water quality management, is expected to help mitigate the impact of climate change on harmful cyanobacterial blooms. The nutrient criteria that do not threaten the function of the water body determined with generalized additive models (GAMs) were 0.88 mg/L for total nitrogen (TN) and 0.021 mg/L for total phosphorus (TP) during the period of 1996–2005 based on 26 lakes and reservoirs in China. Climate change-induced temperature increases will substantially reduce the required nutrient criteria by 46.17% for TN and 15.24% for TP in lakes and reservoirs by the end of the century under the “business-as-usual” scenario. The results indicate that some lakes that currently meet the nutrient criteria could become dominated by cyanobacteria and experience blooms in a warmer future, even without increases in nutrient inputs. Therefore, as climate change persists, nutrient loads should be further reduced to achieve the desired water quality without the threat of eutrophication. Strict control of TP would be particularly advantageous to suppress algal blooms. It is imperative that water quality management strategies account for the impact of projected future climate changes on nutrient criteria and that nutrient criteria are periodically modified. The corresponding nutrient risk thresholds should be determined to ensure that lakes and reservoirs escape the threat of harmful algal blooms under the impact of extreme weather events.

Flotation of cyanobacterial particles without chemical coagulant under auto-flocculation

Although flotation techniques are often used for the removal of algal particles, the practicality of algae-harvesting technologies is limited owing to the complex and expensive facilities and equipment required for chemical coagulation. Here, we examined the feasibility of an approach to separating algal particles from water bodies without the need for chemical coagulants, depending on the condition of the algae, and to determine the optimal conditions. Using Anabaena sp., a cyanobacterium causes algal blooms in lakes, we stimulated auto-flocculation in algal particles without coagulants and conducted solid-liquid separation experiments of algal particles under various conditions. The six cultivation columns included in our analysis comprised four factors: Water temperature, light intensity, nutrients, and carbon source; auto-flocculation was induced under all treatments, with the exception of the treatment involving no limits to all factors, and algal particles were wellsettled under all conditions for which auto-flocculation occurred. Meanwhile, flotation removal of auto-flocculated algal particles was attained only when nutrients were blocked after algae were grown in an optimal medium. However, no significant differences were detected between the functional groups of the extracellular polymeric substances (EPSs) of floated and settled algal particles in the FT-IR peak, which can cause attachment by collision with micro-bubbles.

Metabolic Capability and Phylogenetic Diversity of Mono Lake during a Bloom of the Eukaryotic Phototroph Picocystis sp. Strain ML.

Algal blooms in lakes are often associated with anthropogenic eutrophication; however, they can occur without the human introduction of nutrients to a lake. A rare bloom of the alga Picocystis sp. strain ML occurred in the spring of 2016 at Mono Lake, a hyperalkaline lake in California, which was also at the apex of a multiyear-long drought. These conditions presented a unique sampling opportunity to investigate microbiological dynamics and potential metabolic function during an intense natural algal bloom. We conducted a comprehensive molecular analysis along a depth transect near the center of the lake from the surface to a depth of 25 m in June 2016. Across sampled depths, rRNA gene sequencing revealed that Picocystis-associated chloroplasts were found at 40 to 50% relative abundance, greater than values recorded previously. Despite high relative abundances of the photosynthetic oxygenic algal genus Picocystis, oxygen declined below detectable limits below a depth of 15 m, corresponding with an increase in microorganisms known to be anaerobic. In contrast to previously sampled years, both metagenomic and metatranscriptomic data suggested a depletion of anaerobic sulfate-reducing microorganisms throughout the lake's water column. Transcripts associated with photosystem I and II were expressed at both 2 m and 25 m, suggesting that limited oxygen production could occur at extremely low light levels at depth within the lake. Blooms of Picocystis appear to correspond with a loss of microbial activity such as sulfate reduction within Mono Lake, yet microorganisms may survive within the sediment to repopulate the lake water column as the bloom subsides.IMPORTANCE Mono Lake, California, provides a habitat to a unique ecological community that is heavily stressed due to recent human water diversions and a period of extended drought. To date, no baseline information exists from Mono Lake to understand how the microbial community responds to human-influenced drought or algal bloom or what metabolisms are lost in the water column as a consequence of such environmental pressures. While previously identified anaerobic members of the microbial community disappear from the water column during drought and bloom, sediment samples suggest that these microorganisms survive at the lake bottom or in the subsurface. Thus, the sediments may represent a type of seed bank that could restore the microbial community as a bloom subsides. Our work sheds light on the potential photosynthetic activity of the halotolerant alga Picocystis sp. strain ML and how the function and activity of the remainder of the microbial community responds during a bloom at Mono Lake.

Transcriptomic responses of the freshwater snail (Parafossarulus striatulus) following dietary exposure to cyanobacteria

Freshwater snails are promising bioindicators that can be used in ecotoxicological testing and ecological risk assessments. To screen molecular responses following mollusk exposure to algal blooms, whole transcriptome sequencing was performed with the freshwater snail (Parafossarulus striatulus) fed with blue algae (Microcystis aeruginosa). A total of 86,848 unigenes were assembled, and 10,413 unigenes were annotated in the TrEMBL, Pfam, KEGG, and SwissProt databases. In snails fed with both green and blue algae, a total of 276 differentially expressed unigenes were identified, though there were limited differences in snails fed with only green algae. In addition, ten randomly selected differentially expressed unigenes were analyzed in snails collected from Taihu Lake, China. The expression of four unigenes exhibited a trend consistent with that observed in transcriptome profiling of laboratory snails. The results of this study provide an invaluable resource for enhancing our understanding of ecotoxicology following the occurrence of algal blooms in lakes.

Application of the Random Forest model for chlorophyll-a forecasts in fresh and brackish water bodies in Japan, using multivariate long-term databases

Abstract There is a growing world need for predicting algal blooms in lakes and reservoirs to better manage water quality. We applied the random forest model with a sliding window strategy, which is one of the machine learning algorithms, to forecast chlorophyll-a concentrations in the fresh water of the Urayama Reservoir and the saline water of Lake Shinji. Both water bodies are situated in Japan and have historical water records containing more than ten years of data. The Random Forest (RF) model allowed us to forecast trends in time series of chlorophyll-a in these two water bodies. In the case of the reservoir, we used the data separately from two sampling stations. We found that the best model parameters for the number of min-leaf, and with/without pre-selection of predictors, varied at different stations in the same reservoir. We also found that the best performance of lead-time and accuracy of the prediction varied between the two stations. In the case of the lake, we found the best combination of a min-leaf and pre-selection of predictors was different from that of the reservoir case. Finally, the most influential parameters for the random forest model in the two water bodies were identified as biochemical oxygen demand (BOD), chemical oxygen demand (COD), pH, and total nitrogen/total phosphorus (TN/TP).

Modeling the Control of Algal Bloom in a Lake by Applying Some External Efforts with Time Delay

One of the crucial issues in ecology is related to algal bloom in lakes. Vast amount of efforts has been channeled to combat this problem, but for successful implementation of any algae eradication effort, their impact on the dynamics of water body must be studied explicitly. In view of this, we propose and analyze a mathematical model to assess the effect of applying efforts for removal of algae and detritus from a lake on the occurrence of algal bloom. The system is governed by the interactions of four variables, namely, concentration of nutrients, density of algae, density of detritus and efforts applied to remove algae as well as detritus from the lake. The biologically feasible equilibria and their stability properties are analyzed and discussed. Considering the growth rate of applied efforts as time dependent, we also obtain the optimal control strategy to minimize both the algae and the associated costs. The optimality system is derived and solved numerically. Further, to make the system more realistic, two discrete time delays; one in conversion of detritus into nutrients and other in applying the control efforts are considered. We study various cases for the time delays and show that in general the system develops limit cycle oscillation through a Hopf-bifurcation for increasing time delays. However, we observe that in some cases, increase in time delays may diminish the oscillations. Our results indicate that high concentration of nutrient intensifies the occurrence of algal bloom and in that case applied efforts play a pivotal role in reducing the bloom.

Urea Release by Intermittently Saturated Sediments from a Coastal Agricultural Landscape.

Urea-N is linked to harmful algal blooms in lakes and estuaries, and urea-N-based fertilizers have been implicated as a source. However, the export of urea-N-based fertilizers appears unlikely, as high concentrations of urea-N are most commonly found in surface waters outside periods of fertilization. To evaluate possible autochthonous production of urea-N, we monitored urea-N released from drainage ditch sediments using mesocosms. Sediments from a cleaned (recently dredged) drainage ditch, uncleaned ditch, forested ditch, riparian wetland, and an autoclaved sand control were isolated in mesocosms and flooded for 72 h to quantify urea-N, NH-N, and NO-N in the floodwater. Sediments were flooded with different N-amended solutions (distilled HO, 1.5 mg L NH-N, 3.0 mg L NH-N, 2.6 mg L NO-N, or 5.1 mg L NO-N) and incubated at three water temperatures (16, 21, and 27°C). Urea-N concentrations in mesocosms representing uncleaned and cleaned drainage ditches were significantly greater than nonagricultural sediments and controls. While flooding sediments with N-enriched solution had no clear effect on urea-N, warmer (27°C) temperatures resulted in significantly higher urea-N. Data collected from field ditches that were flooded by a summer rainstorm showed increases in urea-N that mirrored the mesocosm experiment. We postulate that concentrations of urea-N in ditches that greatly exceed environmental thresholds are mediated by biological production in sediments and release to stagnant surface water. Storm-driven urea-N export from ditches could elevate the risk of harmful algal blooms downstream in receiving waters despite the dilution effect.

A Novel Group Decision-Making Method Based on Sensor Data and Fuzzy Information.

Algal bloom is a typical phenomenon of the eutrophication of rivers and lakes and makes the water dirty and smelly. It is a serious threat to water security and public health. Most scholars studying solutions for this pollution have studied the principles of remediation approaches, but few have studied the decision-making and selection of the approaches. Existing research uses simplex decision-making information which is highly subjective and uses little of the data from water quality sensors. To utilize these data and solve the rational decision-making problem, a novel group decision-making method is proposed using the sensor data with fuzzy evaluation information. Firstly, the optimal similarity aggregation model of group opinions is built based on the modified similarity measurement of Vague values. Secondly, the approaches’ ability to improve the water quality indexes is expressed using Vague evaluation methods. Thirdly, the water quality sensor data are analyzed to match the features of the alternative approaches with grey relational degrees. This allows the best remediation approach to be selected to meet the current water status. Finally, the selection model is applied to the remediation of algal bloom in lakes. The results show this method’s rationality and feasibility when using different data from different sources.

Integrative Indicator for Assessing the Alert Levels of Algal Bloom in Lakes: Lake Taihu as a Case Study.

Algal blooms have recently become one of the most serious environmental problems in eutrophic freshwater ecosystems worldwide. Although many observation and simulation approaches have been applied to predict algal blooms, few studies have addressed the alert levels of algal blooms using integrative indicators in a large lake with multiple service function and significant horizontal heterogeneity. This study developed an integrative indicator assessment system (IIAS) to rank the alert level of algal blooms. In the IIAS, algal biomass, area percentage, distance from drinking water intake points, distance from scenic zones and duration of algal bloom were used as indicators to calculate a comprehensive alert level, which was classified into five grades (Vigilance, Low, Moderate, High, and Severe). Lake Taihu was taken as a case study to assess the comprehensive alert level of algal blooms in 2007 and 2010. The comprehensive alert level showed obvious spatial-temporal patterns, with an acceptable accuracy in Lake Taihu. The comprehensive alert levels were relatively higher in typical phytoplankton subzones than typical hydrophytes subzones and are more sensitive to weight factor in the northern and western subzones where high biomass usually occurs. Case study showed a very good application of the proposed comprehensive alert level assessment methodology, which can be adjusted to predict the degree of hazard of algal blooms in multi-service function large lakes to help the government and decision makers to act to prevent the disaster from algal bloom spreading.