Analysis of algal bloom risk with uncertainties in lakes by integrating self-organizing map and fuzzy information theory

Phosphorus release from the sediment of a drinking water reservoir under the influence of seasonal hypoxia

Seasonal sediment internal phosphorus (P) release may cause water eutrophication and impair water quality in drinking water reservoir. During a year-long field investigation, the effects of the microenvironment on the release of internal phosphorus were meticulously analyzed using high-resolution peepers technique and microelectrode system. The release mechanisms of P fractions from the reservoir sediments were also explored. The results showed that seasonal fluctuations in temperature, dissolved oxygen, redox potential, and pH at the sediment-water interface impacted the release of P fractions from the studied reservoir sediment. Higher diffusive fluxes of soluble reactive PO43- and Fe2+ across the sediment-water interface (SWI) were observed in the warmer season and were approximately 14.5 times and 16.5 times than those in winter, respectively. Driven by seasonal hypoxia, the reservoir sediment functioned as a P sink in winter and became a P source in summer and autumn. The reduction of Fe-bound P and mineralization of organic P were the primary mechanisms driving sediment P release, which explains the increased P flux in the warmer season and lower P flux in winter. The findings indicated that elevated temperatures and anaerobic conditions were conducive to the activation of P in sediments, whereas lower temperatures and aerobic conditions promoted the immobilization of P. This study provided new insights into seasonal P cycling in reservoirs that can contribute to the formulation of targeted reservoir management strategies.

Long-term prediction of sea surface chlorophyll-a concentration based on the combination of spatio-temporal features

Harmful algal blooms (HABs) events have a serious impact on marine fisheries and marine management. They occur globally with high frequency and are characterized by a long duration and difficult governance. HABs incidents have occurred in the South China Sea (SCS), and the frequency of occurrence has been on the rise in recent decades. Predicting the long-term chlorophyll-a (Chl-a) concentration has the potential to facilitate long-term monitoring and early warning of HABs events. Currently, long-term predictions of ocean circulation and temperature are common, while long-term predictions of marine biochemistry are still in their infancy. Traditional Chl-a prediction methods have problems, such as low accuracy and the inability to carry out long-term predictions. This research improved the CNN-LSTM model by combining spatio-temporal features to predict Chl-a concentrations. This model can extract both the temporal and spatial features of Chl-a, expand the dataset, and improve the prediction accuracy and training speed. The predictions were made using a Chl-a dataset for the Reed Tablemount in the SCS. The time series of Chl-a used was the satellite data of NASA's official website from January 2002 to June 2020. The results indicate that the predictions of the CNN-LSTM model are better than those of the LSTM and SARIMA models. The five-year long-term rolling prediction of Chl-a was carried out, and the three-year Pearson correlation coefficient reached 0.5. The novelty of this study is the realization of a three-year long-term prediction of Chl-a concentrations. The Mann-Kendall trend test method and the least square method were used to fit the straight line to detect the trend of the five-year predicted value and the true value, respectively. The results indicated that the prediction value and true value of the sea surface Chl-a from 2015 to 2020 both exhibited an overall upward trend. In addition, the prediction performance of the model in large-scale prediction is better than that in small-scale prediction.

Improving mariculture insurance premium rate calculation using an information diffusion model

Mariculture is a well-known high-risk industry. However, mariculture insurance, which is an important risk management tool, is facing serious market failure. An important reason for this market failure lies in the unsound premium rate and pricing method. Due to a lack of long-term yield data, empirical rates are often adopted, but this adoption can lead to a high loss ratio. This paper provides an improved method for premium computation of mariculture insurance using an information diffusion model (IDM). An example of oyster insurance in China shows that, compared with the traditional pricing approach, the IDM can greatly improve the accuracy and stability of premium rate calculations, especially in cases of small samples.

A Novel Method for Non-invasive Estimation of Primary Productivity in Aquatic Ecosystems Using a Chlorophyll Fluorescence-Induced Dynamic Curve

Photosynthetic microalgae are a major contributor to primary productivity in aquatic ecosystems, but typical measurements of their biomass and productivity are costly and relatively inefficient. The chlorophyll fluorescence induced dynamic (OJIP) curve can reflect the original photochemical reaction and the changes to the function and structure of photosystems as well as the effects of environmental factors on photosynthetic systems. Here, we present a novel method for estimating the Chl a content and photosynthetic microalgal cell density in water samples using the integral area of the OJIP curve. We identify strong linear relationships between OJIP curve integrals and both Chl a contents and cell densities for a variety of microalgal cultures and natural communities. Based on these findings, we present a non-invasive method to estimate primary productivity in aquatic ecosystems and monitor microalgal populations. We believe that this technique will allow for widespread, rapid, and inexpensive estimating of water primary productivity and monitoring of microalgal populations in natural water. This method is potentially useful in health assessment of natural water and as an early warning indicator for algal blooms.

Risks for cyanobacterial harmful algal blooms due to land management and climate interactions

The frequency and severity of cyanobacteria harmful blooms (CyanoHABs) have been increasing with frequent eutrophication and shifting climate paradigms. CyanoHABs produce a spectrum of toxins and can trigger neurological disorder, organ failure, and even death. To promote proactive CyanoHAB management, geospatial risk modeling can act as a predictive mechanism to supplement current mitigation efforts. In this study, iterative AIC analysis was performed on 17 watershed-level biophysical parameters to identify the strongest predictors based on Sentinel-2-derived cyanobacteria cell densities (CCD) for 771 waterbodies in Georgia Piedmont. This study used a streamlined watershed delineation technique, a 1-meter LULC classification with ~88% accuracy, and a technique to predict CyanoHAB risk in small-to-medium sized waterbodies. Landscape characteristics were computed utilizing the Google Earth Engine platform that enabled large spatio-temporal scope and variable inclusion. Watershed maximum winter temperature, percent agriculture, percent forest, percent impervious, and waterbody area were the strongest predictors of CCD with a 0.33 R-squared. Warmer winter temperatures allow cyanobacteria to be photosynthetically active year-round, and trigger CyanoHABs when warmer temperatures and nutrients are introduced in early spring, typically referred to as Spring Bloom in southeast U.S. The risk models revealed an unexpected significant linear relationship between percent forest and CCD. It is due to the fact that land reclamation via reforestation in the piedmont have left legacy sediment and nutrients which are mobilized as surface runoff to the watershed after rain events. A Jenks Natural Break scheme assigned waterbodies to CyanoHAB risk groups, and of the 771 waterbodies, 24.38% were low, 37.35% and 38.26% were medium and high risk respectively. This research supplements existing cyanobacteria risk modeling methods by introducing a novel, scalable, and reproducible method to determine yearly regional risk. Future studies should include factors such as demographic, socioeconomic, labor, and site-specific environmental conditions to create more holistic CyanoHAB risk outputs.

Estimating spatio-temporal dynamics of stream total phosphate concentration by soft computing techniques

This study attempts to model the spatio-temporal dynamics of total phosphate (TP) concentrations along a river for effective hydro-environmental management. We propose a systematical modeling scheme (SMS), which is an ingenious modeling process equipped with a dynamic neural network and three refined statistical methods, for reliably predicting the TP concentrations along a river simultaneously. Two different types of artificial neural network (BPNN-static neural network; NARX network-dynamic neural network) are constructed in modeling the dynamic system. The Dahan River in Taiwan is used as a study case, where ten-year seasonal water quality data collected at seven monitoring stations along the river are used for model training and validation. Results demonstrate that the NARX network can suitably capture the important dynamic features and remarkably outperforms the BPNN model, and the SMS can effectively identify key input factors, suitably overcome data scarcity, significantly increase model reliability, satisfactorily estimate site-specific TP concentration at seven monitoring stations simultaneously, and adequately reconstruct seasonal TP data into a monthly scale. The proposed SMS can reliably model the dynamic spatio-temporal water pollution variation in a river system for missing, hazardous or costly data of interest.

A pollutant load hierarchical allocation method integrated in an environmental capacity management system for Zhushan Bay, Taihu Lake

An environmental capacity management (ECM) system was developed to help practically implement a Total Maximum Daily Load (TMDL) for a key bay in a highly eutrophic lake in China. The ECM system consists of a simulation platform for pollutant load calculation and a pollutant load hierarchical allocation (PLHA) system. The simulation platform was developed by linking the Environmental Fluid Dynamics Code (EFDC) and Water Quality Analysis Simulation Program (WASP). In the PLHA, pollutant loads were allocated top-down in several levels based on characteristics of the pollutant sources. Different allocation methods could be used for the different levels with the advantages of each method combined over the entire allocation. Zhushan Bay of Taihu Lake, one of the most eutrophic lakes in China, was selected as a case study. The allowable loads of total nitrogen, total phosphorus, ammonia, and chemical oxygen demand were found to be 2122.2, 94.9, 1230.4, and 5260.0 t·yr(-1), respectively. The PLHA for the case study consists of 5 levels. At level 0, loads are allocated to those from the lakeshore direct drainage, atmospheric deposition, internal release, and tributary inflows. At level 1 the loads allocated to tributary inflows are allocated to the 3 tributaries. At level 2, the loads allocated to one inflow tributary are allocated to upstream areas and local sources along the tributary. At level 3, the loads allocated to local sources are allocated to the point and non-point sources from different towns. At level 4, the loads allocated to non-point sources in each town are allocated to different villages. Compared with traditional forms of pollutant load allocation methods, PLHA can combine the advantages of different methods which put different priority weights on equity and efficiency, and the PLHA is easy to understand for stakeholders and more flexible to adjust when applied in practical cases.