Temporal continuous monitoring of cyanobacterial blooms in Lake Taihu at an hourly scale using machine learning

Wind-driven post-bloom dispersion of Microcystis in a large shallow eutrophic lake: A case study in Lake Taihu

To clarify the wind-driven post-bloom dispersion range of Microcystis, which originally clustered on the water surface, an Individual-Based Model (IBM) of Microcystis movement considering the combined effects of wind and light was developed based on actual hydrodynamic data and Microcystis biomass. After calibrating the effects of hydrodynamics and light, 66 cases of short-term (within a week) post-bloom with satellite images from 2011 to 2017 were simulated. The results showed that there were three short-term post-bloom types: vertical reduction (VR), horizontal reduction (HR) and mixed reduction (MR). For VR type, the cyanobacterial bloom reduction rate was rapid (>160 km2/day), but the dispersion range of Microcystis was limited (<2 km/day), and a larger bloom area was likely to form in the original location when wind speed decreased. For HR type, the cyanobacterial bloom reduction rate was slow (<10 km2/day), but Microcystis exhibited a broad dispersion range (>4 km/day), often leading to smaller, thicker, and longer-lasting cyanobacterial blooms downwind, albeit with a lower probability of occurrence. The characteristics of MR lay between the two aforementioned types.

The occurrence of positive selection on BicA transporter of Microcystis aeruginosa

The rapid growth of cyanobacteria, particularly Microcystis aeruginosa, poses a significant threat to global water security. The proliferation of toxic Microcystis aeruginosa raises concerns due to its potential harm to human health and socioeconomic impacts. Dense blooms contribute to spatiotemporal inorganic carbon depletion, promoting interest in the roles of carbon-concentrating mechanisms (CCMs) for competitive carbon uptake. Despite the importance of HCO3- transporters, genetic evaluations and functional predictions in M. aeruginosa remain insufficient. In this study, we explored the diversity of HCO3- transporters in the genomes of 46 strains of M. aeruginosa, assessing positive selection for each. Intriguingly, although the Microcystis BicA transporter became a partial gene in 23 out of 46 genomic strains, we observed significant positive sites. Structural analyses, including predicted 2D and 3D models, confirmed the structural conservation of the Microcystis BicA transporter. Our findings suggest that the Microcystis BicA transport likely plays a crucial role in competitive carbon uptake, emphasizing its ecological significance. The ecological function of the Microcystis BicA transport in competitive growth during cyanobacterial blooms raises important questions. Future studies require experimental confirmation to better understand the role of the Microcysits BicA transporter in cyanobacterial blooms dynamics.

Enhanced forecasting of chlorophyll-a concentration in coastal waters through integration of Fourier analysis and Transformer networks

The accurate prediction of chlorophyll-a (chl-a) concentration in coastal waters is essential to coastal economies and ecosystems as it serves as the key indicator of harmful algal blooms. Although powerful machine learning methods have made strides in forecasting chl-a concentrations, there remains a gap in effectively modeling the dynamic temporal patterns and dealing with data noise and unreliability. To wiggle out of quagmires, we introduce an innovative deep learning prediction model (termed ChloroFormer) by integrating Transformer networks with Fourier analysis within a decomposition architecture, utilizing coastal in-situ data from two distinct study areas. Our proposed model exhibits superior capabilities in capturing both short-term and middle-term dependency patterns in chl-a concentrations, surpassing the performance of six other deep learning models in multistep-ahead predictive accuracy. Particularly in scenarios involving extreme and frequent blooms, our proposed model shows exceptional predictive performance, especially in accurately forecasting peak chl-a concentrations. Further validation through Kolmogorov-Smirnov tests attests that our model not only replicates the actual dynamics of chl-a concentrations but also preserves the distribution of observation data, showcasing its robustness and reliability. The presented deep learning model addresses the critical need for accurate prediction on chl-a concentrations, facilitating the exploration of marine observations with complex dynamic temporal patterns, thereby supporting marine conservation and policy-making in coastal areas.

Sporadic diurnal fluctuations of cyanobacterial populations in oligotrophic temperate systems can prevent accurate characterization of change and risk in aquatic systems

Cyanobacteria increasingly threaten recreational water use and drinking water resources globally. They require dynamic monitoring to account for variability in their distribution arising from diel cycles associated with oscillatory vertical migration. While this has been discussed in marine and eutrophic freshwater contexts, reports of diurnal vertical migration of cyanobacteria in oligotrophic freshwater lakes are scant. Typical monitoring protocols do not reflect these dynamics and frequently focus only on surface water sampling approaches, and either ignore sampling time or recommend large midday timeframes (e.g., 10AM-3PM), thereby preventing accurate characterization of cyanobacterial community dynamics. To evaluate the impact of diurnal migrations and water column stratification on cyanobacterial abundance and composition, communities were characterized in a shallow well-mixed lake interconnected to a thermally stratified lake in the Turkey Lakes Watershed (Ontario, Canada) using amplicon sequencing of the 16S rRNA gene across a multi-time point sampling series in 2018 and 2022. This work showed that cyanobacteria are present in oligotrophic lakes and their community structure varies (i) diurnally, (ii) across the depth of the water column, (iii) interannually within the same lake and (iv) between different lakes that are closely interconnected within the same watershed. It underscored the need for integrating multi-timepoint, multi-depth discrete sampling guidance into lake and reservoir monitoring programs to describe cyanobacteria community dynamics and signal change to inform risk management associated with the potential for cyanotoxin production. Ignoring variability in cyanobacterial community dynamics (such as that reported herein) and reducing sample numbers can lead to a false sense of security and missed opportunities to identify and mitigate changes in trophic status and associated risks such as toxin or taste and odor production, especially in sensitive, oligotrophic systems.

One to twelve-month-ahead forecasting of MODIS-derived Qinghai Lake area, using neuro-fuzzy system hybridized by firefly optimization

Lakes, as the main sources of surface water, are of great environmental and ecological importance and largely affect the climatic conditions of the surrounding areas. Lake area fluctuations are very effective on plant and animal biodiversity in the areas covered. Hence, accurate and reliable forecasts of ​lake area might provide the awareness of water and climate resources and the survival of various species dependent on area fluctuations. Using machine learning methods, the current study numerically predicted area fluctuations of ​China's largest lake, Qinghai, over 1 to 12 months ahead of lead time. To this end, Moderate Resolution Imaging Spectroradiometer (MODIS) sensor images were used to monitor the monthly changes in the area of ​the lake from 2000 to 2021. Predictive inputs included the MODIS-derived lake area time latency specified by the autocorrelation function. The data was divided into two periods of the train (initial 75%) and test (final 25%), and the input combinations were arranged so that the model in the test period could be used to predict 12 scenarios, including forecast horizons for the next 1 to 12 months. The adaptive neuro-fuzzy inference system (ANFIS) was utilized as a predictive model. The firefly algorithm (FA) was also used to optimize ANFIS and improve its accuracy, as a hybrid model ANFIS-FA. Based on evaluation criteria such as root mean square error (RMSE) (477-594 km2) and R2 (88-92%), the results confirmed the acceptable accuracy of the models in all forecast horizons, even long-term horizons (10 months, 11 months, and 12 months). Based on the normalized RMSE criterion (0.095-0.125), the models' performance was reported to be appropriate. Furthermore, the firefly algorithm improved the prediction accuracy of the ANFIS model by an average of 16.9%. In the inter-month survey, the models had fewer forecast errors in the dry months (February-March) than in the wet months (October-November). Using the current method can provide remarkable information about the future state of lakes, which is very important for managers and planners of water resources, environment, and natural ecosystems. According to the results, the current approach is satisfactory in predicting MODIS-derived fluctuations of Qinghai Lake area and has research value for other lakes.

A study on identifying synergistic prevention and control regions for PM2.5 and O3 and exploring their spatiotemporal dynamic in China

Air pollutants, notably ozone (O3) and fine particulate matter (PM2.5) give rise to evident adverse impacts on public health and the ecotope, prompting extensive global apprehension. Though PM2.5 has been effectively mitigated in China, O3 has been emerging as a primary pollutant, especially in summer. Currently, alleviating PM2.5 and O3 synergistically faces huge challenges. The synergistic prevention and control (SPC) regions of PM2.5 and O3 and their spatiotemporal patterns were still unclear. To address the above issues, this study utilized ground monitoring station data, meteorological data, and auxiliary data to predict the China High-Resolution O3 Dataset (CHROD) via a two-stage model. Furthermore, SPC regions were identified based on a spatial overlay analysis using a Geographic Information System (GIS). The standard deviation ellipse was employed to investigate the spatiotemporal dynamic characteristics of SPC regions. Some outcomes were obtained. The two-stage model significantly improved the accuracy of O3 concentration prediction with acceptable R2 (0.86), and our CHROD presented higher spatiotemporal resolution compared with existing products. SPC regions exhibited significant spatiotemporal variations during the Blue Sky Protection Campaign (BSPC) in China. SPC regions were dominant in spring and autumn, and O3-controlled and PM2.5-dominated zones were detected in summer and winter, respectively. SPC regions were primarily located in the northwest, north, east, and central regions of China, specifically in the Beijing-Tianjin-Hebei urban agglomeration (BTH), Shanxi, Shaanxi, Shandong, Henan, Jiangsu, Xinjiang, and Anhui provinces. The gravity center of SPC regions was distributed in the BTH in winter, and in Xinjiang during spring, summer, and autumn. This study can supply scientific references for the collaborative management of PM2.5 and O3.

Does water temperature influence in microcystin production? A case study of Billings Reservoir, São Paulo, Brazil

We investigated the relationship between some water quality parameters and microcystin, chlorophyll-a, and cyanobacteria in different conditions of water temperature. We also proposed to predict chlorophyll-a concentration in the Billings Reservoir using three machine learning techniques. Our results indicate that in the condition of higher water temperatures with high density of cyanobacteria, microcystin concentration can increase severely (>10² μg/L). Besides the magnitude observed in higher concentrations, in water temperatures above 25.3 °C (classified as high extreme event), higher frequencies of inadequate values of microcystin (87.5%), chlorophyll-a (70%), and cyanobacteria (82.5%) compared to cooler temperatures (<19.6 °C) were observed. The prediction of chlorophyll-a in Billings Reservoir presented good results (0.76 ≤ R² ≤ 0.82; 0.17 ≤ RMSE≤0.20) using water temperature, total phosphorus, and cyanobacteria as predictors, with the best result using Support Vector Machine.