Elucidation and short-term forecasting of microcystin concentrations in Lake Suwa (Japan) by means of artificial neural networks and evolutionary algorithms

Decision tree ensemble with Bayesian optimization to predict the spatial dynamics of chlorophyll-a concentration: A case study in Bay of Bengal

An accurate prediction of the spatial distribution of phytoplankton biomass, as represented by Chlorophyll-a (CHL-a) concentrations, is important for assessing ecological conditions in the marine environment. This study developed a hyperparameter-optimized decision tree-based machine learning (ML) models to predict the geographical distribution of marine phytoplankton CHL-a in the Bay of Bengal. To predict CHL-a over a large spatial extent, satellite-derived remotely sensed data of ocean color features (CHL-a, colored dissolved organic matter, photosynthetically active radiation, particulate organic carbon) and climatic factors (nighttime sea surface temperature, surface absorbed longwave radiation, sea level pressure) from 2003 to 2022 are used to train and test the models. Results obtained from this study have shown the highest concentrations of CHL-a occurred near the Bay's coastal belts and river estuaries. Analysis revealed that aside from photosynthetically active radiation, organic components exhibited a stronger positive relationship with CHL-a than climatic features, which are correlated negatively. Results showed the chosen decision tree methods to all possess higher R2 and lower root mean square error (RMSE) errors. Furthermore, XGBoost outperforms all other models in predicting the geographic distribution of CHL-a. To assess the model efficacy on seasonal basis, a best performing XGBoost model was validated in the Bay of Bengal region which has shown a good performance in predicting the spatial distribution of Chl-a as well as the pixel values during the summer, winter and monsoon seasons. This study provides the best ML model to researchers for predicting CHL-a in the Bay of Bengal. Further it helps to improve our knowledge of CHL-a spatial dynamics and assist in monitoring marine resources in the Bay of Bengal. It worth noting that the water quality in the Indian Ocean is very dynamic in nature, therefore, additional efforts are needed to test the efficacy of this study model over different seasons and spatial gradients.

Multi-class secondary metabolites in cyanobacterial blooms from a tropical water body: Distribution patterns and real-time prediction

Cyanobacterial blooms that produce toxins occur in freshwaters worldwide and yet, the occurrence and distribution patterns of many cyanobacterial secondary metabolites particularly in tropical regions are still not fully understood. Moreover, predictive models for these metabolites by using easily accessible water quality indicators are rarely discussed. In this study, we investigated the co-occurrence and spatiotemporal trends of 18 well-known and less-studied cyanobacterial metabolites (including [D-Asp3] microcystin-LR (DM-LR), [D-Asp3] microcystin-RR (DM-RR), microcystin-HilR (MC-HilR), microcystin-HtyR (MC-HtyR), microcystin-LA (MC-LA), microcystin-LF (MC-LF), microcystin-LR (MC-LR), microcystin-LW (MC-LW), microcystin-LY (MC-LY), microcystin-RR (MC-RR) and microcystin-WR (MC-WR), Anatoxin-a (ATX-a), homoanatoxin-a (HATX-a), cylindrospermospin (CYN), nodularin (NOD), anabaenopeptin A (AptA) and anabaenopeptin B (AptB)) in a tropical freshwater lake often plagued with blooms. Random forest (RF) models were developed to predict MCs and CYN and assess the relative importance of 22 potential predictors that determined their concentrations. The results showed that 11 MCs, CYN, ATX-a, HATX-a, AptA and AptB were found at least once in the studied water body, with MC-RR and CYN being the most frequently occurring, intracellularly and extracellularly. AptA and AptB were detected for the first time in tropical freshwaters at low concentrations. The metabolite profiles were highly variable at both temporal and spatial scales, in line with spatially different phytoplankton assemblages. Notably, MCs decreased with the increase of CYN, possibly revealing interspecific competition of cyanobacteria. The rapid RF prediction models for MCs and CYN were successfully developed using 4 identified drivers (i.e., chlorophyll-a, total carbon, rainfall and ammonium for MCs prediction; and chloride, total carbon, rainfall and nitrate for CYN prediction). The established models can help to better understand the potential relationships between cyanotoxins and environmental variables as well as provide useful information for making policy decisions.

Cyanobacterial blooms in China: diversity, distribution, and cyanotoxins

Cyanobacterial blooms, which refer to the massive growth of harmful cyanobacteria, have altered the global freshwater ecosystems during the past decades. China has the largest population in the world, and it is suffering from the harmful effect of water eutrophication and cyanobacterial blooms along with rapid development of the economy and society. Research on cyanobacterial blooms and cyanotoxins in China have been overwhelmingly enhanced and emphasized during the past decades. In the present review, the research on cyanobacterial blooms in China is generally introduced, including the history of cyanobacterial bloom studies, the diversity of the bloom-forming cyanobacteria species (BFCS), and cyanotoxin studies in China. Most studies have focused on Microcystis, its blooms, and microcystins. Newly emerging blooms with the dominance of non-Microcystis BFCS have been gradually expanding to wide regions in China. Understanding the basic features of these non-Microcystis BFCS and their blooms, including their diversity, occurrence, physio-ecology, and harmful metabolites, will provide direction on future studies of cyanobacterial blooms in China.

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.

Aberrant Expressional Profiling of Known MicroRNAs in the Liver of Silver Carp (Hypophthalmichthys molitrix) Following Microcystin-LR Exposure Based on samllRNA Sequencing

Microcystin-LR (MC-LR) poses a serious threat to human health due to its hepatotoxicity. However, the specific molecular mechanism of miRNAs in MC-LR-induced liver injury has not been determined. The aim of the present study was to determine whether miRNAs are regulated in MC-LR-induced liver toxicity by using high-throughput sequencing. Our research demonstrated that 53 miRNAs and 319 miRNAs were significantly changed after 24 h of treatment with MC-LR (50 and 200 μg/kg, respectively) compared with the control group. GO enrichment analysis revealed that these target genes were related to cellular, metabolic, and single-organism processes. Furthermore, KEGG pathway analysis demonstrated that the target genes of differentially expressed miRNAs in fish liver were primarily involved in the insulin signaling pathway, PPAR signaling pathway, Wnt signaling pathway, and transcriptional misregulation in cancer. Moreover, we hypothesized that 4 miRNAs (miR-16, miR-181a-3p, miR-451, and miR-223) might also participate in MC-LR-induced toxicity in multiple organs of the fish and play regulatory roles according to the qPCR analysis results. Taken together, our results may help to elucidate the biological function of miRNAs in MC-LR-induced toxicity.

Assessing the effect of abiotic variables and zooplankton on picocyanobacterial dominance in two tropical mesotrophic reservoirs by means of evolutionary computation

Evolutionary computation has been applied to predict the occurrence of massive cyanobacteria proliferations; in the present study, this tool was further used to explore the factors responsible for maintaining picocyanobacterial dominance. Aiming to increase the understanding of factors that promote dominance of picocyanobacteria in tropical reservoirs, we chose two reservoirs used for water supplies located in different regions of Brazil and subjected to climate changes such as warmer winters that intensify water column stratification and prolonged dry seasons that cause water level decreases. This study focused on the diagnosis of the relationships among picocyanobacteria (1-2 μm), zooplankton and environmental variables using evolutionary computation. The integrated data analysis performed here was very successful in elucidating the dynamics of picocyanobacterial density variation influenced by both abiotic and biotic factors by the modeling approach. Relative water column stability - RWCS and electrical conductivity were highlighted as the most important environmental drivers for picocyanobacterial peaks. Hybrid Evolutionary Analysis (HEA) models for the two reservoirs indicated for the first time in the literature that rotifers, small-sized cladocerans and copepods (mainly nauplii) can directly or indirectly control picocyanobacteria in tropical mesotrophic reservoirs, depending on RWCS conditions and electrical conductivity. However, this control is modulated by pH, water transparency and water temperature thresholds.

Using Non-supervised Artificial Neural Network for Determination of Anthropogenic Disturbance in a River System

The study of river water quality plays an important role in assessing the pollution status and health of the water bodies. Human-induced activities such as domestic activities, aquaculture, agriculture and industries have detrimentally affected the river water quality. Pinang River is one of the important rivers in Balik Pulau District that supplies freshwater for human consumption. A total of 442 physical and chemical parameters data of the Pinang River, Balik Pulau catchment were analysed to determine the sources of pollutants entering the river. Non-supervised artificial neural network (ANN) was employed to classify and cluster the river into upstream, middle-stream and downstream zones. The monitored data and non-supervised ANN analysis demonstrated that the source of nitrate was derived from the upper part of the Pinang River, Balik Pulau while the sources of nitrite, ammonia and ortho-phosphate are predominant at the middle-stream of the river system. Meanwhile, the sources of high total suspended solid and biological oxygen demand were concentrated at the downstream of the river.

Associations between chlorophyll a and various microcystin health advisory concentrations

Cyanobacteria harmful algal blooms (cHABs) are associated with a wide range of adverse health effects that stem mostly from the presence of cyanotoxins. To help protect against these impacts, several health advisory levels have been set for some toxins. In particular, one of the more common toxins, microcystin, has several advisory levels set for drinking water and recreational use. However, compared to other water quality measures, field measurements of microcystin are not commonly available due to cost and advanced understanding required to interpret results. Addressing these issues will take time and resources. Thus, there is utility in finding indicators of microcystin that are already widely available, can be estimated quickly and in situ, and used as a first defense against high levels of microcystin. Chlorophyll a is commonly measured, can be estimated in situ, and has been shown to be positively associated with microcystin. In this paper, we use this association to provide estimates of chlorophyll a concentrations that are indicative of a higher probability of exceeding select health advisory concentrations for microcystin. Using the 2007 National Lakes Assessment and a conditional probability approach, we identify chlorophyll a concentrations that are more likely than not to be associated with an exceedance of a microcystin health advisory level. We look at the recent US EPA health advisories for drinking water as well as the World Health Organization levels for drinking water and recreational use and identify a range of chlorophyll a thresholds. A 50% chance of exceeding one of the specific advisory microcystin concentrations of 0.3, 1, 1.6, and 2 μg/L is associated with chlorophyll a concentration thresholds of 23, 68, 84, and 104 μg/L, respectively. When managing for these various microcystin levels, exceeding these reported chlorophyll a concentrations should be a trigger for further testing and possible management action.

Associations between chlorophyll a and various microcystin health advisory concentrations

Cyanobacteria harmful algal blooms (cHABs) are associated with a wide range of adverse health effects that stem mostly from the presence of cyanotoxins. To help protect against these impacts, several health advisory levels have been set for some toxins. In particular, one of the more common toxins, microcystin, has several advisory levels set for drinking water and recreational use. However, compared to other water quality measures, field measurements of microcystin are not commonly available due to cost and advanced understanding required to interpret results. Addressing these issues will take time and resources. Thus, there is utility in finding indicators of microcystin that are already widely available, can be estimated quickly and in situ, and used as a first defense against high levels of microcystin. Chlorophyll a is commonly measured, can be estimated in situ, and has been shown to be positively associated with microcystin. In this paper, we use this association to provide estimates of chlorophyll a concentrations that are indicative of a higher probability of exceeding select health advisory concentrations for microcystin. Using the 2007 National Lakes Assessment and a conditional probability approach, we identify chlorophyll a concentrations that are more likely than not to be associated with an exceedance of a microcystin health advisory level. We look at the recent US EPA health advisories for drinking water as well as the World Health Organization levels for drinking water and recreational use and identify a range of chlorophyll a thresholds. A 50% chance of exceeding one of the specific advisory microcystin concentrations of 0.3, 1, 1.6, and 2 μg/L is associated with chlorophyll a concentration thresholds of 23, 68, 84, and 104 μg/L, respectively. When managing for these various microcystin levels, exceeding these reported chlorophyll a concentrations should be a trigger for further testing and possible management action.

Multi-Year Assessment of Toxic Genotypes and Microcystin Concentration in Northern Lake Taihu, China

Lake Taihu is the third-largest freshwater lake in China and has been suffering from cyanobacterial blooms for over two decades. The northern part of the lake, Meiliang Bay, is known to be at high risk of dense and sustained Microcystis blooms and toxins. This study aimed to investigate and record the annual and seasonal dynamics of toxic genotype, Microcystis morphospecies succession and microcystin variation. It also aimed to find out the underlying driving factors influencing the dynamic changes. Microcystin (MC) and the Microcystis genotype were quantified using HPLC and quantitative real-time PCR, respectively. Our study, over three consecutive years, showed that the pattern of morphospecies succession was seasonally distinct and annually consistent. During the same period in 2012, 2013 and 2014, the average MC were, on dry weight basis, 733 μg·g⁻¹, 844 μg·g⁻¹, 870 μg·g⁻¹, respectively. The proportion of toxic Microcystis accounted for 41%, 44% and 52%, respectively. Cell bound microcystin was found to correlate with the percentage of toxic Microcystis. Based on historical and current data, we conclude that annual bloom toxicity was relatively stable or possibly increased over the last decade.

Inductive reasoning and forecasting of population dynamics of Cylindrospermopsis raciborskii in three sub-tropical reservoirs by evolutionary computation

Seven-day-ahead forecasting models of Cylindrospermopsis raciborskii in three warm-monomictic and mesotrophic reservoirs in south-east Queensland have been developed by means of water quality data from 1999 to 2010 and the hybrid evolutionary algorithm HEA. Resulting models using all measured variables as inputs as well as models using electronically measurable variables only as inputs forecasted accurately timing of overgrowth of C. raciborskii and matched well high and low magnitudes of observed bloom events with 0.45≤r²>0.61 and 0.4≤r²>0.57, respectively. The models also revealed relationships and thresholds triggering bloom events that provide valuable information on synergism between water quality conditions and population dynamics of C. raciborskii. Best performing models based on using all measured variables as inputs indicated electrical conductivity (EC) within the range of 206-280mSm^-1 as threshold above which fast growth and high abundances of C. raciborskii have been observed for the three lakes. Best models based on electronically measurable variables for the Lakes Wivenhoe and Somerset indicated a water temperature (WT) range of 25.5-32.7°C within which fast growth and high abundances of C. raciborskii can be expected. By contrast the model for Lake Samsonvale highlighted a turbidity (TURB) level of 4.8 NTU as indicator for mass developments of C. raciborskii. Experiments with online measured water quality data of the Lake Wivenhoe from 2007 to 2010 resulted in predictive models with 0.61≤r²>0.65 whereby again similar levels of EC and WT have been discovered as thresholds for outgrowth of C. raciborskii. The highest validity of r²=0.75 for an in situ data-based model has been achieved after considering time lags for EC by 7 days and dissolved oxygen by 1 day. These time lags have been discovered by a systematic screening of all possible combinations of time lags between 0 and 10 days for all electronically measurable variables. The so-developed model performs seven-day-ahead forecasts and is currently implemented and tested for early warning of C. raciborskii blooms in the Wivenhoe reservoir.

Unraveling associations between cyanobacteria blooms and in-lake environmental conditions in Missisquoi Bay, Lake Champlain, USA, using a modified self-organizing map

Exploratory data analysis on physical, chemical, and biological data from sediments and water in Lake Champlain reveals a strong relationship between cyanobacteria, sediment anoxia, and the ratio of dissolved nitrogen to soluble reactive phosphorus. Physical, chemical, and biological parameters of lake sediment and water were measured between 2007 and 2009. Cluster analysis using a self-organizing artificial neural network, expert opinion, and discriminant analysis separated the data set into no-bloom and bloom groups. Clustering was based on similarities in water and sediment chemistry and non-cyanobacteria phytoplankton abundance. Our analysis focused on the contribution of individual parameters to discriminate between no-bloom and bloom groupings. Application to a second, more spatially diverse data set, revealed similar no-bloom and bloom discrimination, yet a few samples possess all the physicochemical characteristics of a bloom without the high cyanobacteria cell counts, suggesting that while specific environmental conditions can support a bloom, another environmental trigger may be required to initiate the bloom. Results highlight the conditions coincident with cyanobacteria blooms in Missisquoi Bay of Lake Champlain and indicate additional data are needed to identify possible ecological contributors to bloom initiation.

Oxidative stress response in zebrafish (Danio rerio) gill experimentally exposed to subchronic microcystin-LR

The worldwide occurrence of cyanobacterial blooms makes it necessary to perform environmental risk assessment procedures to monitor the effects of microcytins on fish. Oxidative stress biomarkers are valuable tools in this regard. Considering that zebrafish (Danio rerio) is a common model species in fish toxicology and the zebrafish gill is potentially useful in screening waterborne pollutants, this study investigated the oxidative stress response in zebrafish gill exposed to subchronic microcystin-LR (MCLR) concentrations (2 or 20 μg/l) via measurement of toxin accumulation, protein phosphatase (PP) activity, and the antioxidant parameters (glutathione-S-transferase-GST; glutathione-GSH; superoxide dismutase-SOD; catalase-CAT; glutathione peroxide-GPx; glutathione reductase-GR), as well as levels of hydroxyl radical (OH) and lipid peroxidation (LPO). The results showed that after 30 days exposure, MCLR accumulated in zebrafish gill and MCLR exposure induced PP activity in gill. A linear inhibition of GST activity and GSH content was observed in the gills, revealing that they were involved in the first step of MCLR detoxification. The 2 μg/l MCLR treatment neglectably affected OH content and the antioxidant enzymes (SOD, CAT, GPx, and GR), however oxidative stress was induced under the 20 μg/l MCLR treatment in which an enhanced OH content and alterations of the antioxidant enzymes were observed in the treated gills, although both treatments exerted little effect on LPO level. The principal component analysis results indicated that the most sensitive biomarkers of MCLR exposure were GST and GSH in zebrafish gill. So, D. rerio could be regarded as a suitable bioindicator of MCLR exposure by measuring CAT, GR, GST, and GSH as biomarkers.