Multivariable integrated risk assessment for cyanobacterial blooms in eutrophic lakes and its spatiotemporal characteristics

Microplastics enhanced the allelopathy of pyrogallol on toxic Microcystis with additional risks: Microcystins release and greenhouse gases emissions

Cyanobacteria blooms (CBs) caused by eutrophication pose a global concern, especially Microcystis aeruginosa (M. aeruginosa), which could release harmful microcystins (MCs). The impact of microplastics (MPs) on allelopathy in freshwater environments is not well understood. This study examined the joint effect of adding polystyrene (PS-MPs) as representative MPs and two concentrations (2 and 8 mg/L) of pyrogallol (PYR) on the allelopathy of M. aeruginosa. The results showed that the addition of PS-MPs intensified the inhibitory effect of 8 mg/L PYR on the growth and photosynthesis of M. aeruginosa. After a 7-day incubation period, the cell density decreased to 69.7 %, and the chl-a content decreased to 48 % compared to the condition without PS-MPs (p < 0.05). Although the growth and photosynthesis of toxic Microcystis decreased with the addition of PS-MPs, the addition of PS-MPs significantly resulted in a 3.49-fold increase in intracellular MCs and a 1.10-fold increase in extracellular MCs (p < 0.05). Additionally, the emission rates of greenhouse gases (GHGs) (carbon dioxide, nitrous oxide and methane) increased by 2.66, 2.23 and 2.17-fold, respectively (p < 0.05). In addition, transcriptomic analysis showed that the addition of PS-MPs led to the dysregulation of gene expression related to DNA synthesis, membrane function, enzyme activity, stimulus detection, MCs release and GHGs emissions in M. aeruginosa. PYR and PS-MPs triggered ROS-induced membrane damage and disrupted photosynthesis in algae, leading to increased MCs and GHG emissions. PS-MPs accumulation exacerbated this issue by impeding light absorption and membrane function, further heightening the release of MCs and GHGs emissions. Therefore, PS-MPs exhibited a synergistic effect with PYR in inhibiting the growth and photosynthesis of M. aeruginosa, resulting in additional risks such as MCs release and GHGs emissions. These results provide valuable insights for the ecological risk assessment and control of algae bloom in freshwater ecosystems.

Functional trait-based phytoplankton biomass and assemblage analyses in the pre-growing season for comprehensive algal bloom risk assessment

Algal bloom (AB) risk assessment is critical for maintaining ecosystem health and human sustainability. Previous AB risk assessments have focused on the potential occurrence of ABs and related factors in the growing season, whereas their hazards, especially in the pre-growing season, have attracted less attention. Here, we performed a comprehensive AB risk assessment, including water trophic levels, phytoplankton biomass, functional trait-based assemblages, and related environmental factors, in the pre-growing season in Dongting Lake, China. Although mesotrophic water and low phytoplankton biomass suggested low AB potential, toxic taxa, which constituted 13.28% of the phytoplankton biomass, indicated non-negligible AB hazards. NH4+ and water temperature were key factors affecting phytoplankton motility and toxicity. Our study establishes a new paradigm for quantitative AB risk assessment, including both potential AB occurrence and hazards. We emphasize the importance of phytoplankton functional traits for early AB warning and NH4+ reduction for AB control in the pre-growing season.

Integrated physio-biochemical and transcriptomic analysis reveals the joint toxicity mechanisms of two typical antidepressants fluoxetine and sertraline on Microcystis aeruginosa

Selective serotonin reuptake inhibitor (SSRI) antidepressants are of increasing concern worldwide due to their ubiquitous occurrence and detrimental effects on aquatic organisms. However, little is known regarding their effects on the dominant bloom-forming cyanobacterium, Microcystis aeruginosa. Here, we investigated the individual and joint effects of two typical SSRIs fluoxetine (FLX) and sertraline (SER) on M. aeruginosa at physio-biochemical and molecular levels. Results showed that FLX and SER had strong growth inhibitory effects on M. aeruginosa with the 96-h median effect concentrations (EC50s) of 362 and 225 μg/L, respectively. Besides, the mixtures showed an additive effect on microalgal growth. Meanwhile, both individual SSRIs and their mixtures can inhibit photosynthetic pigment synthesis, cause oxidative damage, destroy cell membrane, and promote microcystin-leucine-arginine (MC-LR) synthesis and release. Moreover, the mixtures enhanced the damage to photosynthesis, antioxidant system, and cell membrane and facilitated MC-LR synthesis and release compared to individuals. Furthermore, transcriptomic analysis revealed that the dysregulation of the key genes related to transport, photosystem, protein synthesis, and non-ribosomal peptide structures was the fundamental molecular mechanism underlying the physio-biochemical responses of M. aeruginosa. These findings provide a better understanding of the toxicity mechanisms of SSRIs to microalgae and their risks to aquatic ecosystems.

Prediction of hydrological and water quality data based on granular-ball rough set and k-nearest neighbor analysis

Hydrological and water quality datasets usually encompass a large number of characteristic variables, but not all of these significantly influence analytical outcomes. Therefore, by wisely selecting feature variables with rich information content and removing redundant features, it not only can the analysis efficiency be improved, but the model complexity can also be simplified. This paper considers introducing the granular-ball rough set algorithm for feature variable selection and combining it with the k-nearest neighbor method and back propagation network to analyze hydrological and water quality data, thus promoting overall and fused inspection. The results of hydrological water quality data analysis show that the proposed method produces better results compared to using a standalone k-nearest neighbor regressor.

The effects of wind-wave disturbances on sediment resuspension and phosphate release in Lake Chao

As a typical shallow lake with a wind-generated flow, the resuspension state of sediment and phosphorus release under wind field disturbance plays an important role in controlling lake eutrophication in Lake Chao. In this study, we proposed a combination of experimental analysis of dynamic disturbances, wind-wave disturbance shear stress calculation, and model simulation (experimental-calculative-modeling) to quantitatively investigate the effects of wind-wave disturbances on the resuspension state of Lake Chao bottom sediment and phosphorus release and distribution. The results showed that the release rate of phosphorus from the Lake Chao bottom sediment was affected by the wind field and bottom sediment content, which varied significantly spatially and showed some difference between different seasons. Under the condition of sufficient water body disturbance, the substrate in the Western Lake area of Lake Chao mainly adsorbed phosphate from the water body, while the substrate in the Central Lake area and the Eastern Lake area adsorbed phosphate along with the release. The magnitude of the phosphorus release rate due to sediment resuspension was mainly affected by wind speed, and the distribution of phosphorus content was influenced by the circulation generated by different dominant wind directions. The wind-wave disturbances have a significant effect on the spatial and temporal distribution of phosphorus in Lake Chao, and the proposed experimental-calculative-modeling ensemble can provide relevant technical support for the study of water pollution control strategies and comprehensive remediation and management of Lake Chao.

Temporal characteristics and trends of nitrogen loadings in lake Taihu, China and its influencing mechanism at multiple timescales

Climate warming impact on excessive nitrogen (N) load in sediment favours cyanobacterial blooms in eutrophic waters. The nitrate (NO3--N) and ammonium (NH4+-N) are two forms of N loads that contribute to algae blooms. However, little attention is paid to the impact of environmental factors on N loads variations at different time scales. This paper used a well-calibrated and validated EFDC model to investigate the temporal patterns and trends of ammonium and nitrate from June 2016 to June 2017. This paper presented the relationship and effects between these variations and environmental factors using data from satellite and reanalysis-based observations obtained for six meteorological parameters. The relationship and effects between these variations and environmental factors were also examined at different timescales (i.e., daily, monthly and seasonal scales). Model calibration results indicated that measured values reasonably matched simulated values. The validation results revealed that relative error (RE) values were within an acceptable range. The REs of ammonium at East Taihu (S12) and Xu Lake (S23) sampling sites were 55.83% and 57.61%, while that of nitrate was 24.37% (S12) and 41.08%, respectively. The daily analysis of NH4+-N and NO3--N variations was 7.318 ± 3.876 (g/m2/day) and 0.0275 ± 0.222 (g/m2/day), respectively. The monthly analysis showed NH4+-N and NO3-N range from 2.04 to 12.04 (g/m2/day) and 0.0008 to 0.064 (g/m2/day), respectively. The magnitude NH4+-N and NO3--N varied and showed distinct inter-monthly variations. , The relationship between sediment fluxes and meteorological parameters showed the magnitude of correlation coefficient (r) and strength of correlation varied significantly. At daily scales, the relationship of NH4+-N and NO3--N had a significant positive correlation with all meteorological parameters. At monthly, the correlation coefficient (r) of NH4+-N and NO3-N were heterogenous. At daily and monthly scales, air temperature and wind speed are the main drivers affecting sediment N loads' dynamics; however, the influence of relative humidity, precipitation, and evaporation on N loads are smaller. The study demonstrates the contribution of meteorological conditions to the magnitude and timing of N loadings variability in water bodies. The findings provide more insight into lake ecosystem protection and environmental remediation.

Solar/periodate-triggered rapid inactivation of Microcystis aeruginosa by interrupting the Calvin-Benson cycle

Frequent outbreak of cyanobacteria is a serious problem for drinking water treatment. The microcystins released from Microcystis aeruginosa (M. aeruginosa) could cause irreversible damage to human health. Catalyst-free solar/periodate (PI) system has recently presented great potential for bacterial inactivation, whereas the application potential and underlying mechanisms of the effective M. aeruginosa control remain unclear. Our work delineated the key role of ROS that inactivating/harmless disposing M. aeruginosa in the simulated sunlight (SSL)/PI system. Singlet oxygen may specifically cause DNA damage but maintain membrane integrity, preventing the risk of microcystins leakage. The SSL/PI 300 μM system could also effectively inhibit M. aeruginosa recovery for >7 days and completely degrade microcystin-LR (50.0 μg/L) within 30 min. Non-targeted metabolomic analysis suggested that the SSL/PI system inactivated M. aeruginosa mainly by interrupting the Calvin-Benson cycle, which damaged the metabolic flux of glycolysis and its downstream pathways such as the oxidative PPP pathway and glutathione metabolism. Furthermore, the activated PI system exhibited an even better algal inhibition under natural sunlight irradiation, evidenced by the seriously damaged cell membrane of M. aeruginosa. Overall, this study reported the comprehensive mechanisms of algal control and application potentials of solar/PI systems. The findings facilitated the development of emerging algicidal technology and its application in controlling environmental harmful algae.

Improving lake chlorophyll-a interpreting accuracy by combining spectral and texture features of remote sensing

Cyanobacterial blooms in lakes fueled by increasing eutrophication have garnered global attention, and high-precision remote sensing retrieval of chlorophyll-a (Chla) is essential for monitoring eutrophication. Previous studies have focused on the spectral features extracted from remote sensing images and their relationship with chlorophyll-a concentrations in water bodies, ignoring the texture features in remote sensing images which is beneficial to improve interpreting accuracy. This study explores the texture features in remote-sensing images. It proposes a retrieval method for estimating lake Chla concentration by combining spectral and texture features of remote sensing images. Remote sensing images from Landsat 5 TM and 8 OLI were used to extract spectral bands combination. The gray-level co-occurrence matrix (GLCM) of remote sensing images was used to obtain a total of 8 texture features; then, three texture indices were calculated using texture features. Finally, a random forest regression was used to establish a retrieval model of in situ Chla concentration from texture and spectral index. Results showed that texture features are significantly correlated with lake Chla concentration, and they can reflect the temporal and spatial distribution change of Chla. The retrieval model combining spectral and texture indices performs better (MAE = 15.22 μg·L-1, bias = 9.69%, MAPE = 47.09%) than the model without texture features (MAE = 15.76 μg·L-1, bias = 13.58%, MAPE = 49.44%). The proposed model performance varies in different Chla concentration ranges and is excellent in predicting higher concentrations. This study evaluates the potential of incorporating texture features of remote sensing images in lake water quality estimation and provides a novel remote sensing method to better estimate lake Chla concentration.