Effects of typhoon events on coastal hydrology, nutrients, and algal bloom dynamics: Insights from continuous observation and machine learning in semi-enclosed Zhanjiang Bay, China

Typhoons can induce variations in hydrodynamic conditions and biogeochemical processes, potentially escalating the risk of algal bloom occurrences impacting coastal ecosystems. However, the impacts of typhoons on instantaneous changes and the mechanisms behind typhoon-induced algal blooms remain poorly understood. This study utilized high-frequency in situ observation and machine learning model to track the dynamic variations in meteorological, hydrological, physicochemical, and Chlorophyll-a (Chl-a) levels through the complete Typhoon Talim landing in Zhanjiang Bay (ZJB) in July 2023. The results showed that a delayed onset of algal bloom occurring 10 days after typhoon's arrival. Subsequently, as temperatures reached a suitable range, with an ample supply of nutrients and water stability, Chl-a peaked at 121.49 μg L-1 in algal bloom period. Additionally, water temperature and air temperature decreased by 1.61 °C and 2.8 °C during the typhoon, respectively. In addition, wind speed and flow speed increased by 1.34 and 0.015 m s-1 h-1 to peak values, respectively. Moreover, the slow decline of 8.2 % in salinity suggested a substantial freshwater input, leading to an increase in nutrients. For instance, the mean DIN and DIP were 2.2 and 8.5 times higher than those of the pre-typhoon period, resulting in a decrease in DIN/DIP (closer to16) and the alleviation of P limitation. Furthermore, pH and dissolved oxygen (DO) were both low during the typhoon period and then peaked at 8.93 and 19.05 mg L-1 during the algal bloom period, respectively, but subsequently decreased, remaining lower than those of the pre-typhoon period. A preliminary learning machine model was established to predict Chl-a and exhibited good accuracy, with R2 of 0.73. This study revealed the mechanisms of eutrophication status formation and algal blooms occurrence in the coastal waters, providing insights into the effects of typhoon events on tropical coastal biogeochemistry and ecology.

An Intelligent Early Warning System for Harmful Algal Blooms: Harnessing the Power of Big Data and Deep Learning

Harmful algal blooms (HABs) pose a significant ecological threat and economic detriment to freshwater environments. In order to develop an intelligent early warning system for HABs, big data and deep learning models were harnessed in this study. Data collection was achieved utilizing the vertical aquatic monitoring system (VAMS). Subsequently, the analysis and stratification of the vertical aquatic layer were conducted employing the "DeepDPM-Spectral Clustering" method. This approach drastically reduced the number of predictive models and enhanced the adaptability of the system. The Bloomformer-2 model was developed to conduct both single-step and multistep predictions of Chl-a, integrating the " Alert Level Framework" issued by the World Health Organization to accomplish early warning for HABs. The case study conducted in Taihu Lake revealed that during the winter of 2018, the water column could be partitioned into four clusters (Groups W1-W4), while in the summer of 2019, the water column could be partitioned into five clusters (Groups S1-S5). Moreover, in a subsequent predictive task, Bloomformer-2 exhibited superiority in performance across all clusters for both the winter of 2018 and the summer of 2019 (MAE: 0.175-0.394, MSE: 0.042-0.305, and MAPE: 0.228-2.279 for single-step prediction; MAE: 0.184-0.505, MSE: 0.101-0.378, and MAPE: 0.243-4.011 for multistep prediction). The prediction for the 3 days indicated that Group W1 was in a Level I alert state at all times. Conversely, Group S1 was mainly under an Level I alert, with seven specific time points escalating to a Level II alert. Furthermore, the end-to-end architecture of this system, coupled with the automation of its various processes, minimized human intervention, endowing it with intelligent characteristics. This research highlights the transformative potential of integrating big data and artificial intelligence in environmental management and emphasizes the importance of model interpretability in machine learning applications.

A new approach to quantify chlorophyll-a over inland water targets based on multi-source remote sensing data

Chlorophyll-a (Chl-a) concentration is a reliable indicator of phytoplankton biomass and eutrophication, especially in inland waters. Remote sensing provides a means for large-scale Chl-a estimation by linking the spectral water-leaving signal from the water surface with in situ measured Chl-a concentrations. Single-sensor images cannot meet the practical needs for long-term monitoring of Chl-a concentrations due to cloud cover and satellite operational lifetimes. However, quantifying long-term inland water Chl-a concentrations using multi-source remote sensing data remains a problem, as improper input of satellite reflectance products will affect the accuracy of Chl-a over inland waters, as well as existing models cannot meet the need for multi-source remote sensing data to retrieve high precision Chl-a. To explore these problems towards a solution, four reflectance data derived from Ocean and Land Colour Instrument (OLCI), MultiSpectral Instrument (MSI), and Operational Land Imager (OLI) were evaluated against in situ measurements of Erhai Lake. Reflectance data from these sensors were assessed to determine their consistency. Results indicate that R_rhos products (i.e., surface reflectance, a semi-atmospheric correction reflectance) that controlled for the atmospheric diffuse transmittance were highly correlated with the measured reflectance values. The in situ reflectance also confirmed the higher fidelity of satellite reflectance in the green-red band. Subsequently, a new extreme gradient boosting (XGB) model applied to multi-source remote sensing data is proposed to estimate long-term inland water Chl-a concentrations. Comparative experiments showed the XGB model with R_rhos products outperformed other solutions, providing accurate estimates for daily, monthly, and long-term trends in Erhai Lake. The XGB model was finally processed 3954 R_rhos reflectance data derived from OLCI, ENVISAT Medium Resolution Imaging Spectrometer (MERIS), MSI, and OLI sensors, mapping Chl-a concentrations in Erhai Lake over a 20-year period. This study could serve as a reference for the long-term Chl-a monitoring using multi-source remote sensing data to support inland lake management and future water quality evaluation.

Dynamics of environmental variables during the incidence of algal bloom in the coastal waters of Gujarat along the northeastern Arabian Sea

The dynamics of physico-chemical, nutrient, and chlorophyll-a variables were studied in the bloom and non-bloom locations along the off-Gujarat coastal waters to understand the variability in biogeochemistry using multivariate analytical tests. The dissolved oxygen was significantly lower in the bloom stations (3.89 ± 0.44 mgL-1) than in the non-bloom stations (5.50 ± 0.70 mg L-1), due to the biological degradation of organic matter in addition to anaerobic microbial respiration. Nutrients (PO4 and NO3) and Chl-a concentrations were recorded higher in the bloom locations at 0.83 ± 0.21 µmol L-1, 4.47 ± 0.69 µmol L-1, 4.14 ± 1.49 mg m-3, respectively. PO4 and NO3 have shown a significantly higher positive correlation of r = 0.73 and r = 0.69 with Chl-a for bloom data than the non-bloom data. The percentage variance contributed by PC1 and PC2 for both bloom and non-bloom locations were estimated at 52.33%. The variable PO4 explains the highest 24.19% variability in PC1, followed by Chl-a (19.89%). The PO4 triggers the bloom formation and also correlates to the higher concentrations of Chl-a in the bloom locations. The bloom concentration ranges from 9553 to 12,235 trichomes L-1. The bloom intensity has shown a significant positive correlation with Chl-a (r = 0.77), NO3 (r = 0.56), and PO4 (r = 0.30), but a negative correlation was noticed with DO (r =  - 0.63) and pH (r =  - 0.49). The study also initiates a way forward research investigation on ocean-color technologies to identify and monitor blooms and climate change-driven factors for bloom formation. The occurrence of bloom and its influence on fishery resources and other marine biotas will open many research windows in marine fisheries, oceanography, remote sensing, marine biology, and trophodynamics.

Agricultural non-point sources and their effects on chlorophyll-a in a eutrophic lake over three decades (1985-2020)

Erhai Lake is the second largest freshwater lake in Yunnan Province but suffers from the deterioration of water quality and agricultural non-point source pollution (ANPSP). However, little is known about the influence of ANPSP on the water quality of Erhai Lake. The export coefficient model (ECM) was used to obtain the total nitrogen (TN) and total phosphorus (TP) loads from ANPSP in Erhai Lake Basin (ELB). The trophic status of Erhai Lake as influenced by such sources of nutrient input was also been assessed. Results indicated that the TN and TP loads in ELB increased from 1985 to 2005 due to sustainable agricultural development; thereafter, the TN and TP loads decreased from 2005 to 2020, indicating that agricultural pollution prevention improved in ELB. The northern part of ELB had higher pollution intensity than the southern part and the central part, indicating that the ecosystem in the northern part of ELB appeared to be vulnerable. Driving force analysis showed that cattle breeding was the main reason for the exported TN loads in most watersheds, and intensive agricultural planting was the major contributor to TP loads. The mean annual Chl-a concentration had a strong correlation with the TN and TP loads exported from north of ELB, and this finding suggested that ANPSP could lead to eutrophication. The results of this study demonstrate the impacts of agricultural activities on water quality at the watershed scale and provide a scientific foundation for lake management decision-making.

Long-term prediction of sea surface chlorophyll-a concentration based on the combination of spatio-temporal features

Harmful algal blooms (HABs) events have a serious impact on marine fisheries and marine management. They occur globally with high frequency and are characterized by a long duration and difficult governance. HABs incidents have occurred in the South China Sea (SCS), and the frequency of occurrence has been on the rise in recent decades. Predicting the long-term chlorophyll-a (Chl-a) concentration has the potential to facilitate long-term monitoring and early warning of HABs events. Currently, long-term predictions of ocean circulation and temperature are common, while long-term predictions of marine biochemistry are still in their infancy. Traditional Chl-a prediction methods have problems, such as low accuracy and the inability to carry out long-term predictions. This research improved the CNN-LSTM model by combining spatio-temporal features to predict Chl-a concentrations. This model can extract both the temporal and spatial features of Chl-a, expand the dataset, and improve the prediction accuracy and training speed. The predictions were made using a Chl-a dataset for the Reed Tablemount in the SCS. The time series of Chl-a used was the satellite data of NASA's official website from January 2002 to June 2020. The results indicate that the predictions of the CNN-LSTM model are better than those of the LSTM and SARIMA models. The five-year long-term rolling prediction of Chl-a was carried out, and the three-year Pearson correlation coefficient reached 0.5. The novelty of this study is the realization of a three-year long-term prediction of Chl-a concentrations. The Mann-Kendall trend test method and the least square method were used to fit the straight line to detect the trend of the five-year predicted value and the true value, respectively. The results indicated that the prediction value and true value of the sea surface Chl-a from 2015 to 2020 both exhibited an overall upward trend. In addition, the prediction performance of the model in large-scale prediction is better than that in small-scale prediction.

Relevant factors in the eutrophication of the Uruguay River and the Río Negro

In recent decades, there has been increasing eutrophication of rivers and lagoons in Uruguay and solutions leading to water purification are being sought. The growing pollution has been attributed to nitrogen and phosphorus compounds exported from the river basins with intensification of agricultural production and the absence of tertiary treatment for urban and industrial effluents. Although nitrogen and phosphorus are relevant to eutrophication, there are also other factors that can promote eutrophication and algal blooms. This paper reports a broad analysis of water quality variables recorded over 9 years (2009-2018) at 17 sampling stations on the Uruguay River and 16 sampling stations on the Río Negro, and explores their relationship with the changes of chlorophyll a (Chl-a) concentrations using a generalized linear model and a neural network simulation (NNS). The input variables were total phosphorus; total suspended solids; electrical conductivity of water (EC_w); alkalinity; water temperature (T); water pH (pH) and sampling month. The NNS explained 79% of Chl-a variations and showed the most relevant variables to be T, EC_w, and pH. Moreover, the NNS showed that replacement of current land uses by natural prairie would not significantly reduce Chl-a concentrations. The results showed that the main factors that drive Chl-a concentrations (i.e., algae) are not directly linked to agriculture land use.

Growth inhibition of harmful algae using TiO2-embedded expanded polystyrene balls in the hypereutrophic stream

TiO₂-embedded expanded polystyrene (TiO₂EPS) balls were produced in large quantities using continuous manufacturing plants and four processes (i.e., fixing-coating-freeze drying-air drying). Two mesocosms [i.e., a photocatalytic zone (PZ) vs. a control zone (CZ)] and in-situ real-time monitoring device were installed to monitor the water quality within the hypereutrophic stream and to elucidate the long-term effect of TiO₂EPS balls on the growth inhibition of harmful algae for two years (i.e., 2016-2017). The difference in the concentrations of both Chl-a and phycocyanin between CZ and PZ was temporally variable and was significant from August to October with higher solar irradiation levels. Although the effective depth of TiO₂EPS balls varied as a complex function of the photocatalytic reactions of TiO₂EPS balls and various environmental factors, the photooxidation effect of TiO₂EPS balls decreased with the increase in water depth and the effective water depth for growth inhibition was less than 0.5 m. Compare to bimodal distributions in concentrations of both Chl-a and phycocyanin in CZ, both shifted and lowered peaks were mainly due to the photocatalytic degradation and irradiation shading under natural solar irradiation by applying TiO₂EPS balls in hypereutrophic water body. Consequently, the TiO₂EPS balls developed in this study can prevent both growth and reproduction of harmful algae in hypereutrophic water body, regardless of the environmental factors.

Assessment of the Number of Valid Observations and Diurnal Changes in Chl-a for GOCI: Highlights for Geostationary Ocean Color Missions

The first geostationary ocean color satellite mission (geostationary ocean color imager, or GOCI) has provided eight hourly observations per day over the western Pacific region since June 2010. GOCI imagery has been widely used to track the short-term dynamics of coastal and inland waters. Few studies have been performed to comprehensively assess the advantages of GOCI images in obtaining valid observations and estimating diurnal changes within the water column. Using the entire mission dataset between 2011 and 2017, these knowledge gaps were filled by comparing the daily percentages of valid observations (DPVOs) between GOCI and MODIS Aqua (MODISA) and by examining the diurnal changes in Chl-a over the East China Sea. The mean DPVOs of GOCI was 152.6% over the clear open ocean, suggesting that a daily valid coverage could be expected with GOCI. The GOCI DPVOs were ~26 times greater than the MODISA DPVOs; this pronounced difference was caused by the combined effects of their different observational frequencies and the more conservative quality flag system for MODISA. Diurnal changes in the GOCI-derived Chl-a were also found, with generally higher Chl-a in the afternoon than the morning and pronounced heterogeneities in the temporal and spatial domains. However, whether such diurnal changes are due to the real dynamics of the oceanic waters or artifacts of the satellite retrievals remains to be determined. This study provides the first comprehensive quantification of the unparalleled advantages of geostationary ocean color missions over polar orbiters, and the results highlights the importance of geostationary ocean color missions in studying coastal and inland waters.

Spectral and spatial kernel water quality mapping

An empirical approach through remote sensing generally produces a robust data model of water quality for inland and coastal water. Traditional regressions in water quality mapping fail because the bio-optical relationship of turbid water exhibits nonlinear and heterogeneous patterns. In addition, in situ data are generally insufficient in the water quality mapping. Mapping based on a relatively small amount of water quality samples is considered a practical issue in environmental monitoring. Learning-based algorithms that require a large amount of data are inapplicable in this case. According to the concept of Nadaraya-Watson estimator, the kernel model can estimate nonlinear and spatially varying water quality maps effectively in turbid water.Experiments indicate that the kernel estimator provides better goodness-of-fit between the observed and derived concentrations of water quality parameter, e.g., chlorophyll-a in turbid water. The kernel estimator is feasible for a relatively small size of ground observations. Approximately 30% improvement of cross-validation error was identified in this approach when compared with traditional regressions. The model offers a robust approach without further calibrations for estimating the spatial patterns of water quality by using remote sensing reflectance and a small set of observations, considering spatial and spectral information, e.g., multiple bands and band ratios.

Hyperspectral Fluorescence LIDAR Based on a Liquid Crystal Tunable Filter for Marine Environment Monitoring

An innovative hyperspectral LIDAR instrument has been developed for applications in marine environment monitoring research activities, remotely detecting the fluorescence spectra produced in the spectral interval between 400 nm and 720 nm. The detection system is composed by a custom made photomultiplier charge integrating and measuring (CIM) unit, which makes automatic background signal subtraction, and a liquid crystal tunable filter (LCTF). The new instrument therefore has hyperspectral resolution and allows automatic background subtraction; it is compact and automated by custom software that permit to adapt the instrument properties depending on the environmental conditions. Laboratory tests to characterize the instrument performance have been carried out, concluding that this sensor can be employed in remote sites for Chl-a detection.

Tuning antenna function through hydrogen bonds to chlorophyll a

We describe a molecular mechanism tuning the functional properties of chlorophyll a (Chl-a) molecules in photosynthetic antenna proteins. Light-harvesting complexes from photosystem II in higher plants - specifically LHCII purified with α- or β-dodecyl-maltoside, along with CP29 - were probed by low-temperature absorption and resonance Raman spectroscopies. We show that hydrogen bonding to the conjugated keto carbonyl group of protein-bound Chl-a tunes the energy of its Soret and Q_y absorption transitions, inducing red-shifts that are proportional to the strength of the hydrogen bond involved. Chls-a with non-H-bonded keto C13¹ groups exhibit the blue-most absorption bands, while both transitions are progressively red-shifted with increasing hydrogen-bonding strength - by up 382 & 605 cm^-1 in the Q_y and Soret band, respectively. These hydrogen bonds thus tune the site energy of Chl-a in light-harvesting proteins, determining (at least in part) the cascade of energy transfer events in these complexes.

Pigment configuration in the light-harvesting protein of the xanthophyte alga Xanthonema debile

The soil chromophyte alga Xanthonema (X.) debile contains only non-carbonyl carotenoids and Chl-a. X. debile has an antenna system denoted Xanthophyte light-harvesting complex (XLH) that contains the carotenoids diadinoxanthin, heteroxanthin, and vaucheriaxanthin. The XLH pigment stoichiometry was calculated by chromatographic techniques and the pigment-binding structure studied by resonance Raman spectroscopy. The pigment ratio obtained by HPLC was found to be close to 8:1:2:1 Chl-a:heteroxanthin:diadinoxanthin:vaucheriaxanthin. The resonance Raman spectra suggest the presence of 8-10 Chl-a, all of which are 5-coordinated to the central Mg, with 1-3 Chl-a possessing a macrocycle distorted from the relaxed conformation. The three populations of carotenoids are in the all-trans configuration. Vaucheriaxanthin absorbs around 500-530 nm, diadinoxanthin at 494 nm and heteroxanthin at 487 nm at 4.5 K. The effective conjugation length of heteroxanthin and diadinoxanthin has been determined as 9.4 in both cases; the environment polarizability of the heteroxanthin and diadinoxanthin binding pockets is 0.270 and 0.305, respectively.

Concentration Effect on Quenching of Chlorophyll a Fluorescence by All-Trans-β-Carotene in Photosynthesis

Absorption, fluorescence spectra of chlorophyll a (Chl-a) and all-trans-β-carotene (β-Car) mixing solution are investigated in different polarity and polarizability solvents. The carotenoids regulate the energy flow in photosynthesis by interaction with chlorophyll, leading to an observable reduction of Chl-a fluorescence. The fluorescence red shifts with the increasing solvent polarizability. The energy transfer in the Chl-a and β-Car system is proposed. The electron transfer should be dominant in quenching Chl-a fluorescence rather than the energy transfer in this system. Polar solvent with large polarizability shows high quenching efficiency. When dissolved in carbon tetrachloride, Chl-a presents red shift of absorption and blue shift of fluorescence spectra with increasing β-Car concentration, which implies a Chl-a conformational change.

Enhancement of Taihu blue algae anaerobic digestion efficiency by natural storage

Taihu blue algae after different storage time from 0 to 60 d were anaerobic fermented to evaluate their digestibility and process stability. Results showed that anaerobic digestion (AD) of blue algae under 15 d natural storage led to the highest CH4 production of 287.6 mL g(-1) VS at inoculum substrate ratio 2.0, demonstrating 36.69% improvement comparing with that from fresh algae. Storage of blue algae led to cell death, microcystins (MCs) release and VS reduction by spontaneous fermentation. However, it also played an important role in removing algal cell wall barrier, pre-hydrolysis and pre-acidification, leading to the improvement in CH4 yield. Closer examination of volatile fatty acids (VFA) variation, VS removal rates and key enzymes change during AD proved short storage time (≤ 15 d) of blue algae had higher efficiencies in biodegradation and methanation. Furthermore, AD presented significant biodegradation potential for MCs released from Taihu blue algae.

Transmembrane signaling and assembly of the cytochrome b6f-lipidic charge transfer complex

Structure-function properties of the cytochrome b6f complex are sufficiently unique compared to those of the cytochrome bc1 complex that b6f should not be considered a trivially modified bc1 complex. A unique property of the dimeric b6f complex is its involvement in transmembrane signaling associated with the p-side oxidation of plastoquinol. Structure analysis of lipid binding sites in the cyanobacterial b6f complex prepared by hydrophobic chromatography shows that the space occupied by the H transmembrane helix in the cytochrome b subunit of the bc1 complex is mostly filled by a lipid in the b6f crystal structure. It is suggested that this space can be filled by the domain of a transmembrane signaling protein. The identification of lipid sites and likely function defines the intra-membrane conserved central core of the b6f complex, consisting of the seven trans-membrane helices of the cytochrome b and subunit IV polypeptides. The other six TM helices, contributed by cytochrome f, the iron-sulfur protein, and the four peripheral single span subunits, define a peripheral less conserved domain of the complex. The distribution of conserved and non-conserved domains of each monomer of the complex, and the position and inferred function of a number of the lipids, suggests a model for the sequential assembly in the membrane of the eight subunits of the b6f complex, in which the assembly is initiated by formation of the cytochrome b6-subunit IV core sub-complex in a monomer unit. Two conformations of the unique lipidic chlorophyll a, defined in crystal structures, are described, and functions of the outlying β-carotene, a possible 'latch' in supercomplex formation, are discussed. This article is part of a Special Issue entitled: Respiratory complex III and related bc complexes.