Satellite and in situ cyanobacteria monitoring: Understanding the impact of monitoring frequency on management decisions

Systematic Review on CyanoHABs in Central Asia and Post-Soviet Countries (2010-2024)

Cyanobacterial harmful blooms (CyanoHABs) in lakes, estuaries, and freshwater reser-voirs represent a significant risk to water authorities worldwide due to their cyanotoxins and economic impacts. The duration, spread, and severity of CyanoHABs have markedly increased over the past decades. The article addresses CyanoHABs, cyanotoxins, and monitoring methodologies in post-Soviet and Central Asian countries. This particular region was selected for the systematic review due to its relative lack of representation in global CyanoHABs reporting, particularly in Central Asia. The main aim of this systematic review was to analyze the primary literature available from 2010-2024 to examine the current situation of CyanoHAB detection, monitoring, and management in Central Asia and post-Soviet countries. Following a detailed database search in several selected data-bases (Google Scholar, Pubmed, Web of Science (WOS), Scopus, Elibrary, ENU, and KazNU) along with additional hand searching and citation searching, 121 primary articles reporting 214 local cyanobacterial bloom cases were selected for this review. Aquatic cyanotoxins were reported in water bodies of eight countries, including high concentrations of microcystins that often exceeded reference values established by the World Health Organization (WHO). Advancing monitoring efforts in Baltic countries, Belarus, and the Russian Federation differed from only a few Central Asian reports. However, Central Asian aquatic ecosystems are especially threatened by rising anthropogenic pressures (i.e., water use, intensive agriculture, and pollution), climate change, and the lack of adequate ecological surveillance. We hypothesize that recent Caspian seal mass mortality events have been caused by a combination of infection (viral or bacterial) and exposure to algal neurotoxins resulting from harmful algal blooms of Pseudo-nitzschia. We conclude that there is an urgent need to improve the assessment of cyanobacterial blooms in Central Asia and post-Soviet countries.

Effective Nutrient Management of Surface Waters in the United States Requires Expanded Water Quality Monitoring in Agriculturally Intensive Areas

The U.S. Clean Water Act is believed to have driven widespread decreases in pollutants from point sources and developed areas, but has not substantially affected nutrient pollution from agriculture. Today, the highest nutrient concentrations in surface waters are often associated with agricultural production. In this Perspective, we explore whether challenges stemming from the Clean Water Act's inability to mitigate agricultural nutrient pollution are also exacerbated by coarse nutrient monitoring. We evaluate the current state of nutrient monitoring in surface waters of the contiguous U.S. relative to agricultural nutrient inputs to assess how monitoring effort varies across agriculturally intensive areas. The locations of nutrient monitoring stations with approximately seasonal sampling frequency (4 samples per year, on average) from 2012 to 2021 were compiled from the U.S. Water Quality Portal. Monitoring station locations were then compared to watershed-scale (HUC-8) nutrient inventory estimates for agricultural fertilizer and livestock manure inputs. From this assessment, we found that many, but not all, of the nation's most agriculturally intensive areas are under-monitored, and often unmonitored. While it is well-known that the Midwest is the epicenter of agricultural production in the U.S., our results reveal it is poorly monitored relative to its agricultural nutrient inputs. Other regions, like the California Central Valley and parts of the southeastern Coastal Plain were also coarsely monitored relative to nutrient inputs. Conversely, some agriculturally intensive watersheds were moderately-to-well monitored (e.g., western Lake Erie basin, eastern North Carolina, and the Delmarva Peninsula), with these basins largely having established Total Maximum Daily Loads and discharging to prominent waterways. In closing, we argue that sparse monitoring across many of the nation's most agriculturally intensive areas motivate a need to re-envision nutrient monitoring networks, and that increased resources and advanced technologies are likely required to enable effective nutrient source identification throughout the nation.

Development of a deep learning-based feature stream network for forecasting riverine harmful algal blooms from a network perspective

Global increases in the occurrence of harmful algal blooms (HABs) are of major concern in water quality and resource management. A predictive model capable of quantifying the spatiotemporal associations between HABs and their influencing factors is required for effective preventive management. In this study, a feature stream network (FSN) model is proposed to provide daily forecasts of cyanobacteria abundance at multiple monitoring sites simultaneously in a river network. The spatial connectivity between monitoring sites was expressed as a directed acyclic graph comprising edges and nodes representing flows and monitoring sites, respectively. Furthermore, a segment-wise node connection structure was developed to extract the latent features of a river segment comprising individual nodes and sequentially transfer them to the downstream segment(s). In addition, a feature engineering-attention hybrid mechanism was employed to address temporal mismatches among different monitoring schemes while adding explainability to the model. Consequently, the FSN showed improved predictive performance, temporal resolution, and explainability for multi-site forecasts of HAB in a single model framework. The developed model was applied to a bloom-prone middle course of the Nakdong River, South Korea. Various hydrological, environmental, and biological factors were utilized for forecasting the cyanobacteria abundance. The FSN exhibited a high degree of accuracy across the sites for the test data with a coefficient of determination in the range of 0.64-0.71 and root mean square error in the range of 2.06-2.26 cells/mL on natural log scales. Although the relative importance of input features varied across the sites, the features extracted from nearby nodes consistently exhibited high importance in forecasting the cyanobacteria abundance. These explanations indicate that the proposed model can successfully characterize the spatial hierarchy of a river network. A scenario analysis suggested that reduced total nitrogen loads in the effluents from the wastewater treatment plant and the combined operations of upstream and downstream weirs were effective in managing HABs.

Leveraging explainable machine learning for enhanced management of lake water quality

Freshwater lakes worldwide suffer from eutrophication caused by excessive nutrient loads, particularly nitrogen (N) and phosphorus (P) from wastewater and runoff, affecting aquatic life and public health. Using a large (1800 km2) subtropical lake as an example (Lake Okeechobee, Florida, USA), this study aims to (1) predict key water quality parameters using machine learning (ML) algorithms based on easily measurable variables, (2) identify spatial patterns of these parameters, and (3) determine environmental drivers influencing turbidity levels. The study employs four ML algorithms-Extreme Gradient Boosting (XGB), Light Gradient-Boosting Machine (LGBM), Support Vector Regression (SVR), and Random Forests (RFs)-to predict total phosphorus (TP), total nitrogen (TN), nitrate + nitrite (NOx-N), and turbidity, via station-specific and lake-wide modeling approaches. The station-specific models uncover spatial patterns, while the lake-wide models support operational decision-making. Results indicated that lake stage (water level), water temperature, and, most notably, turbidity were the main nutrient predictors, with XGB demonstrating superior prediction performance. Spatial analysis using K-means clustering identified three distinct lake regions based on nutrient levels and turbidity. Due to its importance, SHapley Additive exPlanations (SHAP) were employed to identify and quantify environmental factors affecting turbidity. Inflows and lake stage were found as primary drivers of turbidity near lake inlets, while wind speed and air temperature affected turbidity in the middle of the lake. This research advances the understanding of lake water quality dynamics, emphasizing the importance of frequent monitoring of turbidity and its environmental drivers for enhanced management and future mitigation efforts.

Assessment of in-situ monitoring and tracking the vertical migration of cyanobacterial blooms using LISST-HAB

Cyanobacterial harmful algal blooms (cyanoHABs) are becoming increasingly common in aquatic ecosystems worldwide. However, their heterogeneous distributions make it difficult to accurately estimate the total algae biomass and forecast the occurrence of surface cyanoHABs by using traditional monitoring methods. Although various optical instruments and remote sensing methods have been employed to monitor the dynamics of cyanoHABs at the water surface (i.e., bloom area, chlorophyll a), there is no effective in-situ methodology to monitor the dynamic change of cell density and integrated biovolume of algae throughout the water column. In this study, we propose a quantitative protocol for simultaneously measurements of multiple indicators (i.e., biovolume concentration, size distribution, cell density, and column-integrated biovolume) of cyanoHABs in water bodies by using the laser in-situ scattering and transmissometry (LISST) instrument. The accuracy of measurements of the biovolume and colony size of algae was evaluated and exceeded 95% when the water bloom was dominated by cyanobacteria. Furthermore, the cell density of cyanobacteria was well estimated based on total biovolume and mean cell volume measured by the instrument. Therefore, this methodology has the potential to be used for broader applications, not only to monitor the spatial and temporal distribution of algal biovolume concentration but also monitor the vertical distribution of cell density, biomass and their relationship with size distribution patterns. This provides new technical means for the monitoring and analysis of algae migration and early warning of the formation of cyanoHABs in lakes and reservoirs.

Satellite mapping reveals phytoplankton biomass's spatio-temporal dynamics and responses to environmental factors in a eutrophic inland lake

Chlorophyll a (Chla) concentration can be used as an indicator of algal biomass, and the accumulation of algal biomass in water column is essential for the emergence of surface blooms. By using Moderate Resolution Imaging Spectrometer (MODIS) data, a machine learning algorithm was previously developed to assess algal biomass within the euphotic depth (Beu). Here, a long-term Beu dataset of Lake Taihu from 2003 to 2020 was generated to examine its spatio-temporal dynamics, sensitivity to environmental factors, and variations in comparison to the surface algal bloom area. During this period, the daily Beu (total Beu within the whole lake) exhibited temporal fluctuations between 40 and 90 t Chla, with an annual average of 63.32 ± 5.23 t Chla. Notably, it reached its highest levels in 2007 (72.34 t Chla) and 2017 (73.57 t Chla). Moreover, it demonstrated a clear increasing trend of 0.197 t Chla/y from 2003 to 2007, followed by a slight decrease of 0.247 t Chla/y after 2017. Seasonal variation showed a bimodal annual cycle, characterized by a minor peak in March ∼ April and a major peak in July ∼ September. Spatially, the average pixel-based Beu (total Beu of a unit water column) ranged from 21.17 to 49.85 mg Chla, with high values predominantly distributed in the northwest region and low values in the central region. The sensitivity of Beu to environmental factors varies depending on regions and time scales. Temperature has a significant impact on monthly variation (65.73%), while the level of nutrient concentrations influences annual variation (55.06%). Wind speed, temperature, and hydrodynamic conditions collectively influence the spatial distribution of Beu throughout the entire lake. Algal bloom biomass can capture trend changes in two mutant years as well as bimodal phenological changes compared to surface algal bloom area. This study can provide a basis for scientific evaluation of water environment and a reference for monitoring algal biomass in other similar eutrophic lakes.

Forecasting freshwater cyanobacterial harmful algal blooms for Sentinel-3 satellite resolved U.S. lakes and reservoirs

This forecasting approach may be useful for water managers and associated public health managers to predict near-term future high-risk cyanobacterial harmful algal blooms (cyanoHAB) occurrence. Freshwater cyanoHABs may grow to excessive concentrations and cause human, animal, and environmental health concerns in lakes and reservoirs. Knowledge of the timing and location of cyanoHAB events is important for water quality management of recreational and drinking water systems. No quantitative tool exists to forecast cyanoHABs across broad geographic scales and at regular intervals. Publicly available satellite monitoring has proven effective in detecting cyanobacteria biomass near-real time within the United States. Weekly cyanobacteria abundance was quantified from the Ocean and Land Colour Instrument (OLCI) onboard the Sentinel-3 satellite as the response variable. An Integrated Nested Laplace Approximation (INLA) hierarchical Bayesian spatiotemporal model was applied to forecast World Health Organization (WHO) recreation Alert Level 1 exceedance >12 μg L-1 chlorophyll-a with cyanobacteria dominance for 2192 satellite resolved lakes in the United States across nine climate zones. The INLA model was compared against support vector classifier and random forest machine learning models; and Dense Neural Network, Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Gneural Network (GNU) neural network models. Predictors were limited to data sources relevant to cyanobacterial growth, readily available on a weekly basis, and at the national scale for operational forecasting. Relevant predictors included water surface temperature, precipitation, and lake geomorphology. Overall, the INLA model outperformed the machine learning and neural network models with prediction accuracy of 90% with 88% sensitivity, 91% specificity, and 49% precision as demonstrated by training the model with data from 2017 through 2020 and independently assessing predictions with data from the 2021 calendar year. The probability of true positive responses was greater than false positive responses and the probability of true negative responses was less than false negative responses. This indicated the model correctly assigned lower probabilities of events when they didn't exceed the WHO Alert Level 1 threshold and assigned higher probabilities when events did exceed the threshold. The INLA model was robust to missing data and unbalanced sampling between waterbodies.