Quantitative discrimination of algae multi-impacts on N2O emissions in eutrophic lakes: Implications for N2O budgets and mitigation

Authors

Yiping Wang, Yu Peng, Chengxu Lv, Xiaoguang Xu, Han Meng, Yiwen Zhou, Guoxiang Wang, Yongjun Lu

It is generally accepted that eutrophic lakes significantly contribute to nitrous oxide (N₂O) emissions. However, how these emissions are affected by the formation, disappearance, and mechanisms of algal blooms in these lakes has not been systematically investigated. This study examined and determined the relative contribution of spatiotemporal N₂O production pathways in hypereutrophic Lake Taihu. Synchronously, the multi-impacts of algae on N₂O production and release potential were measured in the field and in microcosms using isotope ratios of oxygen (δ¹⁸O) and bulk nitrogen (δ¹⁵N) to N₂O and to intramolecular ¹⁵N site preference (SP). Results showed that N₂O production in Lake Taihu was derived from microbial effects (nitrification and incomplete denitrification) and water air exchanges. N₂O production was also affected by the N₂O reduction process. The mean dissolved N₂O concentrations in the water column during the pre-outbreak, outbreak, and decay stages of algae accumulation were almost the same (0.05 μmol·L^–1), which was 2–10 times higher than in lake areas algae was not accumulating. However, except for the central lake area, all surveyed areas (with and without accumulated algae) displayed strong release potential and acted as the emission source because of dissolved N₂O supersaturation in the water column. The mean N₂O release fluxes during the pre-outbreak, outbreak, and decay stages of algae accumulation areas were 17.95, 26.36, and 79.32 μmol·m^–2·d^–1, respectively, which were 2.0–7.5 times higher than the values in the non-algae accumulation areas. In addition, the decay and decomposition of algae released large amounts of nutrients and changed the physiochemical properties of the water column. Additionally, the increased algae biomass promoted N₂O release and improved the proportion of N₂O produced via denitrification process to being 9.8–20.4% microbial-derived N₂O. This proportion became higher when the N₂O consumption during denitrification was considered as evidenced by isotopic data. However, when the algae biomass was excessive in hypereutrophic state, the algae decomposition also consumed a large amount of oxygen, thus limiting the N₂O production due to complete denitrification as well as due to the limited substrate supply of nitrate by nitrification in hypoxic or anoxic conditions. Further, the excessive algae accumulation on the water surface reduced N₂O release fluxes via hindering the migration of the dissolved N₂O into the atmosphere. These findings provide a new perspective and understanding for accurately evaluating N₂O release fluxes driven by algae processes in eutrophic lakes.

Comparative assessment of algaecide performance on freshwater phytoplankton: Understanding differential sensitivities to frame cyanobacteria management

Authors

Malihe Mehdizadeh Allaf, Kevin J. Erratt, Hassan Peerhossaini

Cyanobacterial bloom represent a growing threat to global water security. With fast proliferation, they raise great concern due to potential health and socioeconomic concerns. Algaecides are commonly employed as a mitigative measure to suppress and manage cyanobacteria. However, recent research on algaecides has a limited phycological focus, concentrated predominately on cyanobacteria and chlorophytes. Without considering phycological diversity, generalizations crafted from these algaecide comparisons present a biased perpective. To limit the collateral impacts of algaecide interventions on phytoplankton communities it is critical to understand differential phycological sensitivities for establishing optimal dosage and tolerance thresholds. This research attempts to fill this knowledge gap and provide effective guidelines to frame cyanobacterial management. We investigate the effect of two common algaecides, copper sulfate (CuSO₄) and hydrogen peroxide (H₂O₂), on four major phycological divisions (chlorophytes, cyanobacteria, diatoms, and mixotrophs). All phycological divisions exhibited greater sensitivity to copper sulfate, except chlorophytes. Mixotrophs and cyanobacteria displayed the highest sensitivity to both algaecides with the highest to lowest sensitivity being observed as follows: mixotrophs, cyanobacteria, diatoms, and chlorophytes. Our results suggest that H₂O₂ represents a comparable alternative to CuSO₄ for cyanobacterial control. However, some eukaryotic divisions such as mixotrophs and diatoms mirrored cyanobacteria sensitivity, challenging the assumption that H₂O₂ is a selective cyanocide. Our findings suggest that optimizing algaecide treatments to suppress cyanobacteria while minimizing potential adverse effects on other phycological members is unattainable. An apparent trade-off between effective cyanobacterial management and conserving non-targeted phycological divisions is expected and should be a prime consideration of lake management.

Prediction of algal blooms via data-driven machine learning models: an evaluation using data from a well-monitored mesotrophic lake

Authors

Shuqi Lin, Donald C. Pierson, and Jorrit P. Mesman

With increasing lake monitoring data, data-driven machine learning (ML) models might be able to capture the complex algal bloom dynamics that cannot be completely described in process-based (PB) models. We applied two ML models, the gradient boost regressor (GBR) and long short-term memory (LSTM) network, to predict algal blooms and seasonal changes in algal chlorophyll concentrations (Chl) in a mesotrophic lake. Three predictive workflows were tested, one based solely on available measurements and the others applying a two-step approach, first estimating lake nutrients that have limited observations and then predicting Chl using observed and pre-generated environmental factors. The third workflow was developed using hydrodynamic data derived from a PB model as additional training features in the two-step ML approach. The performance of the ML models was superior to a PB model in predicting nutrients and Chl. The hybrid model further improved the prediction of the timing and magnitude of algal blooms. A data sparsity test based on shuffling the order of training and testing years showed the accuracy of ML models decreased with increasing sample interval, and model performance varied with training–testing year combinations.

Clarifying water clarity: A call to use metrics best suited to corresponding research and management goals in aquatic ecosystems

Authors

Jessica S. Turner, Kelsey A. Fall, Carl T. Friedrichs

Water clarity is a subjective term and can be measured multiple ways. Different metrics such as light attenuation and Secchi depth vary in effectiveness depending on the research or management application. In this essay, we argue that different questions merit different water clarity metrics. In coastal and inland waters, empirical relationships to estimate light attenuation can yield clarity estimates that either under- or overestimate the underwater light climate for restoration goals, such as potential habitat available for submerged aquatic vegetation. Best practices in reporting water clarity measurements include regionally specific, temporally representative calibrations and communicating the metric that was actually measured. An intentional choice of the water clarity metric best suited to the research or management question yields the most useful results.

Comparison of organic matter (OM) pools in water, suspended particulate matter, and sediments in eutrophic Lake Taihu, China: Implication for dissolved OM tracking, assessment, and management

Authors

Zhipeng Duan, Xiao Tan, Imran Ali, Xiaoge Wu, Jun Cao, Yangxue Xu, Lin Shi, Wanpeng Gao, Yinlan Ruan, Chen Chen

Suspended particulate matter (SPM) and sediments are important sources of dissolved organic matter (DOM) in lake water. However, studies on what extent and how both sources affect DOM composition are lacking, which hampers DOM management. Herein, DOM, SPM-extracted particulate organic matter (POM), and sediment-extracted organic matter (SOM) were characterized and compared in terms of absorption spectral properties and chemical composition in Lake Taihu, a large cyanobacterial bloom-affected shallow lake. A statistical method was proposed to quantify the similarity of organic matter (OM) in the different states and to evaluate the potential effects of SPM and sediments on DOM. Results showed that POM and DOM were mainly composed of small-molecular-size and low-humified organic components (i.e., 27 %–38 % tryptophan-like and ~30 % protein-like substances), and most of them were derived from autochthonous sources. While tyrosine-like (57 %) and humic-like (27 %) substances were dominant in SOM. The OM similarity between POM and DOM was approximately 1.5 times higher than that between SOM and DOM, indicating the greater effect of SPM than sediments on DOM composition. High pH and low nitrogen (e.g., nitrate and ammonia) were positively correlated to the OM similarity between POM and DOM. Further, the findings indicated that nitrogen limitation enhanced the OM exchange between POM and DOM by promoting the production of extracellular polymeric substances (EPS) in cyanobacterial aggregates. The obtained findings highlighted the importance of SPM in shaping the DOM composition relative to sediments and facilitating the DOM management in bloom-affected lakes.

Co-occurrence of multiple cyanotoxins and taste-and-odor compounds in the large eutrophic Lake Taihu, China: Dynamics, driving factors, and challenges for risk assessment

Authors

Hongmin Li, Xiaohong Gu, Huihui Chen, Zhigang Mao, Ruijie Shen, Qingfei Zeng, You Ge

Cyanobacterial blooms producing toxic metabolites occur frequently in freshwater, yet the environmental behaviors of complex cyanobacterial metabolites remain largely unknown. In this study, the seasonal and spatial variations of several classes of cyanotoxins (microcystins, cylindrospermopsins, saxitoxins) and taste-and-odor (T&O) compounds (β-cyclocitral, β-ionone, geosmin, 2-methylisoborneol) in Lake Taihu were simultaneously investigated for the first time. The total cyanotoxins were dominated by microcystins with concentrations highest in November (mean 2209 ng/L) and lowest in February (mean 48.7 ng/L). Cylindrospermopsins were abundant in May with the highest content of 622.8 ng/L. Saxitoxins only occurred in May (mean 19.2 ng/L) and November (mean 198.5 ng/L). Extracellular T&O compounds were most concentrated in August, the highest being extracellular β-cyclocitral (mean 240.6 ng/L) followed by 2-methylisoborneol (mean 146.6 ng/L). Environment variables play conflicting roles in modulating the dynamics of different groups of cyanotoxins and T&O compounds. Total phosphorus (TP), total nitrogen (TN), chlorophyll-a and cyanobacteria density were important factors affecting the variation of total microcystins, β-cyclocitral and β-ionone concentrations. In contrast, total cylindrospermopsins, 2-methylisoborneol and geosmin concentrations were significantly influenced by water temperature and TP. There was a significant and linear relationship between microcystins and β-cyclocitral/β-ionone, while cylindrospermopsins were positively correlated with 2-methylisoborneol and geosmin. The perceptible odors may be good indicators for the existence of cyanotoxins. Hazard quotients revealed that potential human health risks from microcystins were high in August and November. Meanwhile, the risks from cylindrospermopsins were at moderate levels. Cylindrospermopsins and saxitoxins were first identified in this lake, suggesting that diverse cyanotoxins might co-occur more commonly than previously thought. Hence, the risks from other cyanotoxins beyond microcystins shouldn’t be ignored. This study also highlights that the necessity for further assessing the combination effects of these complex metabolites.

Novel Algicides against Bloom-Forming Cyanobacteria from Allelochemicals: Design, Synthesis, Bioassay, and 3D-QSAR Study

Authors

Yin Luo,Yushun Yang, Wenguang Hou, and Jie Fu

Cyanobacteria bloom caused by water eutrophication has threatened human health and become a global environmental problem. To develop green algicides with strong specificity and high efficiency, three series of ester and amide derivatives from parent allelochemicals of caffeic acid (CA), cinnamic acid (CIA), and 3-hydroxyl-2-naphthoic acid (HNA) were designed and synthesized. Their inhibitory effects on the growth of five harmful cyanobacterial species, Microcystis aeruginosa (M. aeruginosa), Microcystis wesenbergii (M. wesenbergii), Microcystis flos-aquae (M. flos-aquae), Aphanizomenon flos-aquae (Ap. flos-aquae), and Anabaena flos-aquae (An. flos-aquae), were evaluated. The results revealed that CIA esters synthesized by cinnamic acid and fatty alcohols showed the best inhibition effect, with EC₅₀ values ranging from 0.63 to >100 µM. Moreover, some CIA esters exhibited a good selectivity in inhibiting cyanobacteria. For example, the inhibitory activity of naphthalen-2-yl cinnamate was much stronger on Ap. flos-aquae (EC₅₀ = 0.63 µM) than other species (EC₅₀ > 10 µM). Three-dimensional quantitative structure–activity relationship (3D-QSAR) analysis was performed and the results showed that the steric hindrance of the compounds influenced the algicidal activity. Further mechanism study found that the inhibition of CIA esters on the growth of M. aeruginosa might be related to the accumulation of malondialdehyde (MDA).

Dark side of cyanobacteria: searching for strategies to blooms control

Authors

María José Huertas, Manuel J. Mallén-Ponce

Cyanobacteria are ecologically one of the most prolific groups of photosynthetic prokaryotes in marine and freshwater habitats. They are primary producer microorganisms and are involved in the production of important secondary metabolites, including toxic compounds such as cyanotoxins. Environmental conditions promote massive growth of these microbes, causing blooms that can have critical ecological and public health implications. In this highlight, we discuss some of the approaches being addressed to prevent these blooms, such as control of nutrient loading, treatments to minimize growth or monitoring interactions with other species.

Combining indicators for better decisions – Algorithms vs experts on lakes ecological status assessment

Authors

Grzegorz Chrobak, Tomasz Kowalczyk, Thomas B. Fischer, Katarzyna Chrobak, Szymon Szewrański, Jan K. Kazak

The results of ecological condition assessments of ecosystems are related to key decisions taken for the purpose of remedial measures or maintaining their current state. In the assessment process, experts come across extensive datasets, the quality, and completeness of which do not always allow for a reliable evaluation, especially if a single empirical approach is used. In this paper, results of machine learning algorithms are presented, with a focus on Self-Organizing Maps. In this context, measurements of component parameters for the assessment of the ecological state of lake ecosystems were subjected to the process of unsupervised machine learning with the aim to create an alternative assessment approach based on the capabilities of neural networks. Results are mapped and compared with expert evaluations, allowing to extend knowledge about sub-clusters present in the data. The primary target of this paper is the ecological assessment expert. At an early stage, information was obtained about the presence of ecological outliers that may be subject to separate monitoring or verification of environmental activities and objectives. In the back-mapping process, the presented technique of map construction and clustering with various versions of the division was referenced to a set of expert classification findings, revealing the underlying structure of the results when addressed with an unsupervised data comprehension. The approach introduced here does not intend to interfere with the format of an original assessment methodology. Rather it aims at obtaining useful additional information which may help in making better decisions.

Managing taste and odour metabolite production in drinking water reservoirs: The importance of ammonium as a key nutrient trigger

Authors

R.G. Perkins, E.I. Slavin, T.M.C. Andrade, C. Blenkinsopp, P. Pearson, T. Froggatt, G. Godwin, J. Parslow, S. Hurley, R. Luckwell, D.J. Wain

Taste and odour (T&O) compounds (most commonly 2-MIB and Geosmin) in drinking water are becoming an increasingly global problem for water management. Here, the trigger(s) for 2-MIB and Geosmin production were investigated in Plas Uchaf reservoir (North Wales, UK) with detailed water sample analysis between 2015 and 2016. Historical abstraction data from this reservoir and 4 reservoirs in Somerset (England, UK) were compared statistically using Self-Organising Map (SOM) analysis. In-reservoir measurements (2015–2016) revealed an 85% reduction in ammonium from the primary external loading source led to lower 2-MIB and Geosmin concentrations, with peak concentrations of 2-MIB declining from 60 to 21  ng l⁻¹ and Geosmin declining from 140 to 18  ng l⁻¹. No other measured water chemistry parameter showed a significant difference between years. The SOM results support the in-reservoir findings, revealing 2-MIB and Geosmin to be associated with high ammonium relative to nitrate for all 5 reservoirs. We conclude that ammonium is key for stimulating cyanobacterial productivity and production of T&O compounds. Whilst it is well understood that adequate availability of phosphorus is required for rapid growth in cyanobacteria, and hence should still be considered in management decisions, we suggest that monitoring sources and concentrations of ammonium is key for managing T&O outbreaks in drinking water reservoirs.