Manipulating nutrient limitation using modified local soils: A case study at Lake Taihu (China)

The effect of geo-engineering materials of chitosan modified local soil (MLS) on nutrient limitation was studied in comparable whole ponds in Lake Taihu in October 2013. After 20 kg MLS were sprayed onto the whole water pond (400 m2), the chlorophyll-a (Chl-a) concentration was decreased from 42 to 18 µg L-1 within 2 hours and remained below 20 µg L-1 in the following 15 months, while the average Chl-a was 36 µg L-1 in the control pond throughout the experiment. In situ nutrient addition bioassay experiments indicated that the nutrient limitation was shifted from nitrogen (N) and phosphorus (P) co-limitation to P limitation after MLS treatment from October 2013 to March 2014 compared to the control pond. In the cyanobacterial bloom season of June 2014, N and P co-limitation remained and N was the primary limiting nutrient and P was a secondary one in the control pond, because phytoplankton biomass (as Chl-a) showed significant increase by N addition and further increase by N+P additions, while both N and P became the limiting nutrient for phytoplankton growth on the basis that only combining N and P additions showed significant Chl-a stimulation in the treatment pond. In the next summer (June 2014), a cyanobacteria-dominated state still remained in the control pond but chlorophytes, bacillariophytes and cyanophytes distributed equally and submerged vegetation was largely restored in the treatment pond. Meanwhile, the upper limiting concentration of DIN was enhanced from 0.8 to 1.5 mg L-1 and SRP from 0.1 to 0.3 mg L-1 compared to the control pond. This study indicates that nutrient limitation can be manipulated by using MLS technology

Factors Affecting Temporal and Spatial Variations of Microcystins in Gonghu Bay of Lake Taihu, with Potential Risk of Microcystin Contamination to Human Health

A field survey of the seasonal variation of microcystin (MC) concentration was performed in Gonghu Bay (a total of 15 sampling sites) of Lake Taihu from January to December 2008. Microcystis spp. biomass and intra-/extracellular MCs were significantly correlated with water temperature, suggesting the importance of temperature in cyanobacterial blooming in the lake. Higher MC concentration was found in summer and autumn, and peaks of Microcystis biomass and intra-/extracellular MC concentrations were all present in October. Spatially, risk of MCs was higher in littoral zones than in the pelagic area. There were significant correlations between N or P concentrations, and Microcystis biomass or MC content, suggesting that N and P levels affected MC production through influencing Microcystis biomass. Intra-/extracellular MCs and Microcystis biomass had negative exponential relationships with TN:TP, and the maximum values all occurred when TN:TP was <25. Multivariate analyses by PCCA indicated that intra- and extracellular MC concentrations had better correlations with biological factors (such as Microcystis biomass and chl-a) than with physicochemical factors. The maximum MC concentration reached up to 17 mu g/L MC-LReq, considerably higher than the drinking water safety standard (1 mu g/L) recommended by the WHO. So it is necessary to take measures to reduce the exposure risk of cyanobacterial toxins to human beings

Advancing Knowledge on Cyanobacterial Blooms in Freshwaters

Cyanobacterial blooms are a water quality problem that is widely acknowledged to have detrimental ecological and economic effects in drinking and recreational water supplies and fisheries. There is increasing evidence that cyanobacterial blooms have increased globally and are likely to expand in water resources as a result of climate change. Of most concern are cyanotoxins, along with the mechanisms that induce their release and determine their fate in the aquatic environment. These secondary metabolites pose a potential hazard to human health and agricultural and aquaculture products that are intended for animal and human consumption; therefore, strict and reliable control of cyanotoxins is crucial for assessing risk. In this direction, a deeper understanding of the mechanisms that determine cyanobacterial bloom structure and toxin production has become the target of management practices. This Special Issue, entitled “Advancing Knowledge on Cyanobacterial Blooms in Freshwaters”, aims to bring together recent multi- and interdisciplinary research, from the field to the laboratory and back again, driven by working hypotheses based on any aspect of mitigating cyanobacterial blooms, from ecological theory to applied research

The potential impacts of climate change factors on freshwater eutrophication: Implications for research and countermeasures of water management in China

Water eutrophication has become one of the most serious aquatic environmental problems around the world. More and more research has indicated climate change as a major natural factor that will lead to the acceleration of eutrophication in rivers and lakes. However, understanding the mechanism of climate change's effect on water eutrophication is difficult due to the uncertainties caused by its complex, non-linear process. There is considerable uncertainty about the magnitude of future temperature changes, and how these will drive eutrophication in water bodies at regional scales under the effect of human activities. This review collects the existing international and domestic literature from the last 10 years, discussing the most sensitive factors of climate change (i.e., temperature, precipitation, wind, and solar radiation) and analyzing their interaction with water eutrophication. Case studies of serious eutrophication and algal bloom problems in China are discussed to further demonstrate the conclusion. Finally, adaptation countermeasures and related implications are proposed in order to foster the development of sustainability strategies for water management in China

Forecasting Harmful Algal Blooms for Western Lake Erie Using Data Driven Machine Learning Techniques

Harmful algal blooms (HAB) have been documented for more than a century occurring all over the world. The western Lake Erie has suffered from Cyanobacteria blooms for many decades. There are currently two widely available HAB forecasting models for Lake Erie. The first forecasting model gives yearly peak bloom forecast while the second provides weekly short-term forecasting and offers size as well as location. This study focuses on bridging the gap of these two models and improve HAB forecast accuracy in western Lake Erie by letting historical observations tell the behavior of HABs. This study tests two machine learning techniques, artificial neural network (ANN) and classification and regression tree (CART), to forecast monthly HAB indicators in western Lake Erie for July to October. ANN and CART models were created with two methods of selecting input variables and two training periods: 2002 to 2011 and 2002 to 2013. First a nutrient loading period method which considers all nutrient contributing variables averaged from March to June and second a Spearman rank correlation to choose separate input sets for each month considering 224 different combinations of averaging and lag periods. The ANN models showed a correlation coefficient increase from 0.70 to 0.77 for the loading method and 0.79 to 0.83 for the Spearman method when extending the training period. The CART models followed a similar trend increasing overall precision from 85.5% to 92.9% for the loading method and 82.1% to 91% for the Spearman method. Both selection methods had similar variable importance with river discharge and phosphorus mass showing high importance across all methods. The major limitation for ANN is the time required for each forecast to be complete while the CART forecasts earlier is only able to produce a class forecast. In future work, the ANN model accuracy can be improved and use new sets of variables to allow earlier HAB forecasts. The final form of ANN and CART models will be coded in a user interface system to forecast HABs. The monthly forecasting system developed allows watershed planners and decision-makers to timely manage HABs in western Lake Erie

Mitigating eutrophication and toxic cyanobacterial blooms in large lakes: The evolution of a dual nutrient (N and P) reduction paradigm

Cyanobacterial harmful algal blooms (CyanoHABs) are an increasingly common feature of large, eutrophic lakes. Non-N2-fixing CyanoHABs (e.g., Microcystis) appear to be proliferating relative to N2-fixing CyanoHABs in systems receiving increasing nutrient loads. This shift reflects increasing external nitrogen (N) inputs, and a > 50-year legacy of excessive phosphorus (P) and N loading. Phosphorus is effectively retained in legacy-impacted systems, while N may be retained or lost to the atmosphere in gaseous forms (e.g., N2, NH3, N2O). Biological control on N inputs versus outputs, or the balance between N2 fixation versus denitrification, favors the latter, especially in lakes undergoing accelerating eutrophication, although denitrification removal efficiency is inhibited by increasing external N loads. Phytoplankton in eutrophic lakes have become more responsive to N inputs relative to P, despite sustained increases in N loading. From a nutrient management perspective, this suggests a need to change the freshwater nutrient limitation and input reduction paradigms; a shift from an exclusive focus on P limitation to a dual N and P co-limitation and management strategy. The recent proliferation of toxic non-N2-fixing CyanoHABs, and ever-increasing N and P legacy stores, argues for such a strategy if we are to mitigate eutrophication and CyanoHAB expansion globally

Parameter uncertainty and sensitivity analysis of water quality model in Lake Taihu, China

Lake Taihu was chosen as a case for parameter uncertainty and sensitivity analysis of water quality simulation in large shallow lakes. Forty parameters in Environmental Fluid Dynamic Code model (EFDC) were filtered and analyzed. The results showed that parameters had a considerable influence on simulation and three groups of parameters related to algal kinetics (i.e. PMc, BMRc and PRRc), light (KeChl) and temperature (KTG1c) were very sensitive. For shallow lakes with frequent algal blooms, light extinction due to Chlorophyll-a is also a sensitive parameter. While the temperature effect coefficient for algal growth is sensitive for lakes with seasonal temperature variation. Sensitive parameters and their relevant uncertainty varied spatially. For high nutrients and algae concentration subareas, temperature was more likely to be a limiting factor, whereas sensitive factors could be light in lower concentration subareas. Since most sensitive parameters were related to algae, uncertainty in simulation increased with increasing algal kinetic processes over time and varied in different subareas. Lower nutrients and algae concentration subareas were more easily influenced by model parameters while nearshore areas were highly influenced by boundary conditions. For better simulation of water quality, variable stoichiometry phytoplankton models should be considered and zooplankton need to be integrated into the model explicitly rather than a fixed predation rate

Monitoring, Modelling and Management of Water Quality

Different types of pressures, such as nutrients, micropollutants, microbes, nanoparticles, microplastics, or antibiotic-resistant genes, endanger the quality of water bodies. Evidence-based pollution control needs to be built on the three basic elements of water governance: Monitoring, modeling, and management. Monitoring sets the empirical basis by providing space- and time-dependent information on substance concentrations and loads, as well as driving boundary conditions for assessing water quality trends, water quality statuses, and providing necessary information for the calibration and validation of models. Modeling needs proper system understanding and helps to derive information for times and locations where no monitoring is done or possible. Possible applications are risk assessments for exceedance of quality standards, assessment of regionalized relevance of sources and pathways of pollution, effectiveness of measures, bundles of measures or policies, and assessment of future developments as scenarios or forecasts. Management relies on this information and translates it in a socioeconomic context into specific plans for implementation. Evaluation of success of management plans again includes well-defined monitoring strategies. This book provides an important overview in this context