Inversion Method for Chlorophyll-a Concentration in High-Salinity Water Based on Hyperspectral Remote Sensing Data.

As one of the important lakes in the "One Lake and Two Seas" of the Inner Mongolia Autonomous Region, the monitoring of water quality in Lake Daihai has attracted increasing attention, and the concentration of chlorophyll-a directly affects the water quality, making the monitoring of chlorophyll-a concentration in Lake Daihai particularly crucial. Traditional methods of monitoring chlorophyll-a concentration are not only inefficient but also require significant human and material resources. Remote sensing technology has the advantages of wide coverage and short update cycles. For lakes such as Daihai with a high salinity content, salinity is considered a key factor when inverting the concentration of chlorophyll-a. In this study, machine learning models, including model stacking from ensemble learning, a ridge regression model, and a random forest model, were constructed. After comparing the training accuracy of the three models on Zhuhai-1 satellite data, the random forest model, which had the highest accuracy, was selected as the final training model. By comparing the accuracy changes before and after adding salinity factors to the random forest model, a high-precision model for inverting chlorophyll-a concentration in hypersaline lakes was obtained. The research results show that, without considering the salinity factor, the root mean square error (RMSE) of the model was 0.056, and the coefficient of determination (R2) was 0.64, indicating moderate model performance. After adding the salinity factor, the model accuracy significantly improved: the RMSE decreased to 0.047, and the R2 increased to 0.92. This study provides a solid basis for the application of remote sensing technology in hypersaline aquatic environments, confirming the importance of considering salinity when estimating chlorophyll-a concentration in hypersaline waters. This research helps us gain a deeper understanding of the water quality and ecosystem evolution in Daihai Lake.

Spatiotemporal variability of lake surface water temperature and water quality parameters and its interrelationship with water hyacinth biomass in Lake Tana, Ethiopia.

Urbanization, agriculture, and climate change affect water quality and water hyacinth growth in lakes. This study examines the spatiotemporal variability of lake surface water temperature, turbidity, and chlorophyll-a (Chl-a) and their association with water hyacinth biomass in Lake Tana. MODIS Land/ Lake surface water temperature (LSWT), Sentinel 2 MSI Imagery, and in-situ water quality data were used. Validation results revealed strong positive correlations between MODIS LSWT and on-site measured water temperature (R = 0.90), in-situ turbidity and normalized difference turbidity index (NDTI) (R = 0.92), and in-situ Chl-a and normalized difference chlorophyll index (NDCI) (R = 0.84). LSWT trends varied across the lake, with increasing trends in the northeastern, northwestern, and southwestern regions and decreasing trends in the western, southern, and central areas (2001-2022). The spatial average LSWT trend decreased significantly in pre-rainy (0.01 ℃/year), rainy (0.02 ℃/year), and post-rainy seasons (0.01℃/year) but increased non-significantly in the dry season (0.00 ℃/year) (2001-2022, P < 0.05). Spatial average turbidity decreased significantly in all seasons, except in the pre-rainy season (2016-2022). Likewise, spatial average Chl-a decreased significantly in pre-rainy and rainy seasons, whereas it showed a non-significant increasing trend in the dry and post-rainy seasons (2016-2022). Water hyacinth biomass was positively correlated with LSWT (R = 0.18) but negatively with turbidity (R = -0.33) and Chl-a (R = -0.35). High spatiotemporal variability was observed in LSWT, turbidity, and Chl-a, along with overall decreasing trends. The findings suggest integrated management strategies to balance water hyacinth eradication and its role in water purification. The results will be vital in decision support systems and preparing strategic plans for sustainable water resource management, environmental protection, and pollution prevention.

Examining chlorophyll-a concentrations in tropical reservoirs under various land use changes using Sentinel - 2 and Google Earth engine - Bhadra and Tungabhadra, India.

The understanding of spatio-temporal variation in land use and land cover (LULC) patterns is crucial for managing catchment land use planning, as it directly influences of tropical reservoir water quality and the subsequent Nutrient Contamination (NC) of unmonitored water bodies. The current research attempts to accurately measure the influence of LULC and its associated determinants on the quantities of NC loads by using Chl-a as a proxy, within tropical reservoirs, i.e. Bhadra and Tungabhadra, located in same river catchment. This Chl-a spread calculated by the Maximum Chlorophyll Index (MCI) derived from Sentinel 2 satellite data products covering the period from July 2016 to June 2021 were done using Google Earth Engine (GEE) platform. The validation analysis confirms the robustness of the methodology with a strong correlation between MCI-calculated values and EOMAP (Earth Observation and Environmental Services Mapping) Chl-a (µg/L) data points for both reservoirs, Bhadra (R2 = 0.64) and Tungabhadra (R2 = 0.68). The findings reveal that, Tungabhadra reservoir consistently exhibits an excessive spatial distribution of Chl-a spread area (17 km2 to 335 km2), reflecting nutrient-rich water inflows, particularly evident during the post-monsoon period. This notable rise could be linked to harvesting the Kharif crop, resulting in elevated nutrient concentrations. In contrast Bhadra reservoir, dominated by forested areas, maintains relatively lower Chl-a spread areas (<20 km2), highlighting its pivotal role in maintaining water cleanliness and serves as a riparian boundary. In addition, the changes in LULC classes show a strong relationship with variation in Chl-a during the studied period, for the Bhadra Reservoir R2 = 0.51 (F- statistics = 3.983, p = 0.021), and the Tungabhadra Reservoir R2 = 0.802 (F- statistics = 7.489, p = 0.0143). This highlights how changes in land use significantly shape contamination dynamics, deepening our understanding of nutrient inputs and contamination drivers in tropical reservoirs.

Comparative analysis of machine learning methods for prediction of chlorophyll-a in a river with different hydrology characteristics: A case study in Fuchun River, China.

Eutrophication is a serious threat to water quality and human health, and chlorophyll-a (Chla) is a key indicator to represent eutrophication in rivers or lakes. Understanding the spatial-temporal distribution of Chla and its accurate prediction are significant for water system management. In this study, spatial-temporal analysis and correlation analysis were applied to reveal Chla concentration pattern in the Fuchun River, China. Then four exogenous variables (wind speed, water temperature, dissolved oxygen and turbidity) were used for predicting Chla concentrations by six models (3 traditional machine learning models and 3 deep learning models) and compare the performance in a river with different hydrology characteristics. Statistical analysis shown that the Chla concentration in the reservoir river segment was higher than in the natural river segment during August and September, while the dominant algae gradually changed from Cyanophyta to Cryptophyta. Moreover, air temperature, water temperature and dissolved oxygen had high correlations with Chla concentrations among environment factors. The results of the prediction models demonstrate that extreme gradient boosting (XGBoost) and long short-term memory neural network (LSTM) were the best performance model in the reservoir river segment (NSE = 0.93; RMSE = 4.67) and natural river segment (NSE = 0.94; RMSE = 1.84), respectively. This study provides a reference for further understanding eutrophication and early warning of algal blooms in different type of rivers.

Freshwater spreading far offshore the Japanese coast.

River discharge to the ocean influences the transport of salts and nutrients and is a source of variability in water mass distribution and the elemental cycle. Recently, using an underwater glider, we detected thick, low-salinity water offshore for the first time, probably derived from coastal waters, in the central-eastern Sea of Japan, whose primary productivity is comparable to that of the western North Pacific. Thereafter, we aimed to investigate the offshore advection and diffusion of coastal water and its variability and assess their impact. We examined the effects of river water discharge on the flow field and biological production. Numerical experiments demonstrated that low-salinity water observed by the glider in spring was discharged from the Japanese coast to offshore regions. The water is discharged offshore because of its interaction with mesoscale eddies. A relationship between the modeled low-salinity water transport to the offshore region and the observed chlorophyll-a in the offshore region was also observed, indicating the influence of river water on offshore biological production. This study contributes to understanding coastal-offshore water exchange, ocean circulation, elemental cycles, and biological production, which are frontiers in the Sea of Japan and throughout the world.

Retrieval of subsurface dissolved oxygen from surface oceanic parameters based on machine learning.

Oceanic dissolved oxygen (DO) is crucial for oceanic material cycles and marine biological activities. However, obtaining subsurface DO values directly from satellite observations is limited due to the restricted observed depth. Therefore, it is essential to develop a connection between surface oceanic parameters and subsurface DO values. Machine learning (ML) methods can effectively grasp the complex relationship between input attributes and target variables, making them a valuable approach for estimating subsurface DO values based on surface oceanic parameters. In this study, the potential of ML methods for subsurface DO retrieval is analyzed. Among the selected ML methods, namely support vector regression (SVR), random forest (RF) regression, and extreme gradient boosting (XGBoosting) regression, the RF method generally demonstrates superior performance. As the depth increases, the accuracy of DO estimates tends to initially decrease, then gradually improve, with the poorest performance occurring at the depth of 600 dbar. The range of determination coefficients (R2) and root mean square error (RMSE) values based on the test dataset at different depths lies between 0.53 and 47.59 µmol/kg to 0.99 and 4.01 µmol/kg. In addition, compared to sea surface salinity (SSS) and sea surface chlorophyll-a (SCHL), sea surface temperature (SST) plays a more significant role in DO retrieval. Finally, compared to the pelagic interactions scheme for carbon and ecosystem studies (PISCES) model, the RF method achieves higher retrieval accuracies at depths above 700 dbar. In the deep ocean, the primary differences in DO values obtained from the RF method and the PISCES model-based method are noticeable in the vicinity of the equatorial region.

Seasonal patterns and bloom dynamics of phytoplankton based on satellite-derived chlorophyll-a in the eastern yellow sea.

Satellite-derived chlorophyll-a concentration (Chl-a) is essential for assessing environmental conditions, yet its application in the optically complex waters of the eastern Yellow Sea (EYS) is challenged. This study refines the Chl-a algorithm for the EYS employing a switching approach based on normalized water-leaving radiance at 555 nm wavelength according to turbidity conditions to investigate phytoplankton bloom patterns in the EYS. The refined Chl-a algorithm (EYS algorithm) outperforms prior algorithms, exhibiting a strong alignment with in situ Chl-a. Employing the EYS algorithm, seasonal and bloom patterns of Chl-a are detailed for the offshore and nearshore EYS areas. Distinct seasonal Chl-a patterns and factors influencing bloom initiation differed between the areas, and the peak Chl-a during the bloom period from 2018 to 2020 was significantly lower than the average year in both areas. Specifically, bimodal and unimodal peak patterns in Chl-a were observed in the offshore and nearshore areas, respectively. By investigating the relationships between environmental factors and bloom parameters, we identified that major controlling factors governing bloom initiation were mixed layer depth (MLD) and suspended particulate matter (SPM) in the offshore and nearshore areas, respectively. Additionally, this study proposed that the recent decrease in the peak Chl-a might be caused by rapid environmental changes such as the warming trend of sea surface temperature (SST) and the limitation of nutrients. For example, external forcing, phytoplankton growth, and nutrient dynamics can change due to increased SST and limitation of nutrients, which can lead to a decrease in Chl-a. This study contributes to understanding phytoplankton dynamics in the EYS, highlighting the importance of region-specific considerations in comprehending Chl-a patterns and bloom dynamics.

Change in water column total chlorophyll-a in the Mediterranean revealed by satellite observation.

Chlorophyll-a (Chl-a) is a crucial pigment in algae and macrophytes, which makes the concentration of total Chl-a in the water column (total Chl-a) an essential indicator for estimating the primary productivity and carbon cycle of the ocean. Integrating the Chl-a concentration at different depths (Chl-a profile) is an important way to obtain the total Chl-a. However, due to limited cost and technology, it is difficult to measure Chl-a profiles directly in a spatially continuous and high-resolution way. In this study, we proposed an integrated strategy model that combines three different machine learning methods (PSO-BP, random forest and gradient boosting) to predict the Chl-a profile in the Mediterranean by using several sea surface variables (photosynthetically active radiation, spectral irradiance, sea surface temperature, wind speed, euphotic depth and KD490) and subsurface variables (mixed layer depth) observed by or estimated from satellite and BGC-Argo float observations. After accuracy estimation, the integrated model was utilized to generate the time series total Chl-a in the Mediterranean from 2003 to 2021. By analysing the time series results, it was found that seasonal fluctuation contributed the most to the variation in total Chl-a. In addition, there was an overall decreasing trend in the Mediterranean phytoplankton biomass, with the total Chl- decreasing at a rate of 0.048 mg/m2 per year, which was inferred to be related to global warming and precipitation reduction based on comprehensive analysis with sea surface temperature and precipitation data.

Impacts of a newly formed lava delta on the marine environment: Lava-induced upwelling and abrupt chlorophyll depletion during the Tajogaite eruption (La Palma, 2021).

The 2021 Tajogaite eruption in La Palma (Canary Islands, Spain) emitted vast volumes of lava during 85 days, which reached the ocean in several occasions at the western flank of the island. Most of these flows merged to create a primary lava delta, covering an area of 48 ha, with an additional 30 ha underwater. Here we characterize the effects of the lava-seawater interaction on the surrounding marine environment. The area was sampled during two multidisciplinary oceanographic cruises: the first one comprised the days before the lava reached the ocean and after the first contact; and the second took place a month later, when the lava delta was already formed but still receiving lava inputs. Physical-chemical anomalies were found in the whole water column at different depths up to 300 m in all measured parameters, such as turbidity (+9 NTU), dissolved oxygen concentration (-17.17 µmol kg-1), pHT25 (-0.1), and chlorophyll-a concentration (-0.33 mg m-3). Surface temperature increased up to +2.3 °C (28.5 °C) and surface salinity showed increases and decreases of -1.01 and +0.70, respectively, in a radius of 4 km around the lava delta. In the water column, the heated waters experimented a lava-induced upwelling, bringing deeper, nutrient-rich waters to shallower depths; however, this feature did not trigger any phytoplankton bloom. In fact, integrated chlorophyll-a showed an abrupt decrease of -41 % in just two days and -69 % a month later, compared to prior conditions. The chlorophyll-a depletion reached a distance larger than 2.5 km (not delimited).

Classification of eco-zones from the factors and processes controlling phytoplankton biomass.

Phytoplankton is of utmost importance to the marine ecosystem and, subsequently, to the Blue Economy. This study aims to explain the reasons for variability of phytoplankton by estimating the dependency of Chlorophyll-a (Chl-a) on various limiting factors using statistics. The global oceans are classified into coherent units that display similar sensitivity to changing parameters and processes using the k-means algorithm. The resulting six clusters are based on the limiting factors (PAR, iron, or nitrate) that modulate Chl-a yield divisions of the oceans, similar to regions of different trophic statuses. The clusters range from the polar and equatorial regions with high nutrient values limited by light, to open oceanic regions in downwelling gyres limited by nutrients. Some clusters also show a high dependency on marine dissolved iron. Further, oceans are also divided into eight clusters based on the processes (stratification, upwelling, topography, and solar insolation) that impact ocean productivity. The study shows that considering temporal variations is crucial for segregating oceans into ecological zones by utilizing correlation of time-series data into classification. Our results provide valuable insights into the regulation of phytoplankton abundance and its variability, which can help in understanding the implications of climate change and other anthropogenic effects on marine biology.

Effects of lipids on the rate-limiting steps in the dark-to-light transition of Photosystem II core complex of Thermostichus vulcanus.

In our earlier works, we have shown that the rate-limiting steps, associated with the dark-to-light transition of Photosystem II (PSII), reflecting the photochemical activity and structural dynamics of the reaction center complex, depend largely on the lipidic environment of the protein matrix. Using chlorophyll-a fluorescence transients (ChlF) elicited by single-turnover saturating flashes, it was shown that the half-waiting time (Δτ 1/2) between consecutive excitations, at which 50% of the fluorescence increment was reached, was considerably larger in isolated PSII complexes of Thermostichus (T.) vulcanus than in the native thylakoid membrane (TM). Further, it was shown that the addition of a TM lipid extract shortened Δτ 1/2 of isolated PSII, indicating that at least a fraction of the 'missing' lipid molecules, replaced by detergent molecules, caused the elongation of Δτ 1/2. Here, we performed systematic experiments to obtain information on the nature of TM lipids that are capable of decreasing Δτ 1/2. Our data show that while all lipid species shorten Δτ 1/2, the negatively charged lipid phosphatidylglycerol appears to be the most efficient species - suggesting its prominent role in determining the structural dynamics of PSII reaction center.

Chlorophyll-a unveiled: unlocking reservoir insights through remote sensing in a subtropical reservoir.

Effective water resources management and monitoring are essential amid increasing challenges posed by population growth, industrialization, urbanization, and climate change. Earth observation techniques offer promising opportunities to enhance water resources management and support informed decision-making. This study utilizes Landsat-8 OLI and Sentinel-2 MSI satellite data to estimate chlorophyl-a (chl-a) concentrations in the Nandoni reservoir, Thohoyandou, South Africa. The study estimated chl-a concentrations using random forest models with spectral bands only, spectral indices only (blue difference absorption (BDA), fluorescence line height in the violet region (FLH_violet), and normalized difference chlorophyll index (NDCI)), and combined spectral bands and spectral indices. The results showed that the models using spectral bands from both Landsat-8 OLI and Sentinel-2 MSI performed comparably. The model using Sentinel-2 MSI had a higher accuracy of estimating chl-a when spectral bands alone were used. Sentinel-2 MSI's additional red-edge spectral bands provided a notable advantage in capturing subtle variations in chl-a concentrations. Lastly, the -chl-a concentration was higher at the edges of the Nandoni reservoir and closer to the reservoir wall. The findings of this study are crucial for improving the management of water reservoirs, enabling proactive decision-making, and supporting sustainable water resource management practices. Ultimately, this research contributes to the broader understanding of the application of earth observation techniques for water resources management, providing valuable information for policymakers and water authorities.

Satellite-based estimation of total suspended solids and chlorophyll-a concentrations for the Gold Coast Broadwater, Australia.

Satellite retrieval of total suspended solids (TSS) and chlorophyll-a (chl-a) was performed for the Gold Coast Broadwater, a micro-tidal estuarine lagoon draining a highly developed urban catchment area with complex and competing land uses. Due to the different water quality properties of the rivers and creeks draining into the Broadwater, sampling sites were grouped in clusters, with cluster-specific empirical/semi-empirical prediction models developed and validated with a leave-one-out cross validation approach for robustness. For unsampled locations, a weighted-average approach, based on their proximity to sampled sites, was developed. Confidence intervals were also generated, with a bootstrapping approach and visualised through maps. Models yielded varying accuracies (R2 = 0.40-0.75). Results show that, for the most significant poor water quality event in the dataset, caused by summer rainfall events, elevated TSS concentrations originated in the northern rivers, slowly spreading southward. Conversely, high chl-a concentrations were first recorded in the southernmost regions of the Broadwater.

Algae development in rivers with artificially constructed weirs: Dominant influence of discharge over temperature.

Algal blooms contribute to water quality degradation, unpleasant odors, taste issues, and the presence of harmful substances in artificially constructed weirs. Mitigating these adverse effects through effective algal bloom management requires identifying the contributing factors and predicting algal concentrations. This study focused on the upstream region of the Seungchon Weir in Korea, which is characterized by elevated levels of total nitrogen and phosphorus due to a significant influx of water from a sewage treatment plant. We employed four distinct machine learning models to predict chlorophyll-a (Chl-a) concentrations and identified the influential variables linked to local algal bloom events. The gradient boosting model enabled an in-depth exploration of the intricate relationships between algal occurrence and water quality parameters, enabling accurate identification of the causal factors. The models identified the discharge flow rate (D-Flow) and water temperature as the primary determinants of Chl-a levels, with feature importance values of 0.236 and 0.212, respectively. Enhanced model precision was achieved by utilizing daily average D-Flow values, with model accuracy and significance of the D-Flow amplifying as the temporal span of daily averaging increased. Elevated Chl-a concentrations correlated with diminished D-Flow and temperature, highlighting the pivotal role of D-Flow in regulating Chl-a concentration. This trend can be attributed to the constrained discharge of the Seungchon Weir during winter. Calculating the requisite D-Flow to maintain a desirable Chl-a concentration of up to 20 mg/m3 across varying temperatures revealed an escalating demand for D-Flow with rising temperatures. Specific D-Flow ranges, corresponding to each season and temperature condition, were identified as particularly influential on Chl-a concentration. Thus, optimizing Chl-a reduction can be achieved by strategically increasing D-Flow within these specified ranges for each season and temperature variation. This study highlights the importance of maintaining sufficient D-Flow levels to mitigate algal proliferation within river systems featuring weirs.

Past environmental changes: using sedimentary photosynthetic pigments to enhance subtropical reservoir management.

The historical impacts of eutrophication processes were investigated in six subtropical reservoirs (São Paulo, Brazil) using a paleolimnological approach. We questioned whether the levels of pigment indicators of algal biomass could provide information about trophic increase and whether carotenoid pigments could offer additional insights. The following proxies were employed: organic matter, total phosphorus, total nitrogen, photosynthetic pigments (by high-performance liquid chromatography), sedimentation rates, and geochronology (by 210 Pb technique). Principal component analysis indicated a gradient of eutrophication. In eutrophic reservoirs (e.g., Rio Grande and Salto Grande), levels of lutein and zeaxanthin increased over time, suggesting growth of Chlorophyta and Cyanobacteria. These pigments were significantly associated with algal biomass, reflecting their participation in phytoplankton composition. In mesotrophic reservoirs, Broa and Itupararanga, increases and significative linear correlations (r > 0.70) between pigments and nutrients are mainly linked to agricultural and urban activities. In the oligotrophic reservoir Igaratá, lower pigment and nutrient levels reflected lesser human impact and good water quality. This study underscores eutrophication's complexity across subtropical reservoirs. Photosynthetic pigments associated with specific algal groups were informative, especially when correlated with nutrient data. The trophic increase, notably in the 1990s, may have been influenced by neoliberal policies. Integrated pigment and geochemical analysis offers a more precise understanding of eutrophication changes and their ties to human factors. Such research can aid environmental monitoring and sustainable policy development.

Daily impact of the simultaneous passage of binary typhoons on sea surface chlorophyll-a concentration dynamics in the Northwestern Pacific.

Typhoons are recognized as one of the most destructive meteorological phenomena, exerting significant influences on marine ecosystems. Sea surface chlorophyll-a concentration (CHL)an essential indicator of phytoplankton biomass, can be utilized to characterize the disturbances of typhoons on the marine ecosystem. However, it is challenging to investigate this impact at a daily scale due to the missing CHL remote sensing data caused by cloud cover. Given that concurrent passing typhoons may interact with CHL, this study analyzes the effect of the simultaneous passage of binary typhoons Tembin and Bolaven on CHL by using daily CHL reconstruction data, and investigates the role of ocean environmental factors in driving the dynamics of CHL, including sea surface temperature (SST), mixed layer depth (MLD), and sea surface height anomaly (SSHA). The results show that typhoons Tembin and Bolaven increase CHL with the maximum increment of ∼3.2 mg∙m-3 during 4-6 days after typhoons passage. The maximum change areas of CHL are distributed near the intersection of typhoon track of (32°N, 125.2°E), corresponding to the regions of greater variation in SST and MLD. During 15 days before and after typhoons (i.e., from 15 August to 15 September 2012), SST is negatively correlated with CHL (the correlation coefficient of -0.85) and MLD is positively correlated with CHL (the correlation coefficient of -0.80). SST immediately declines after typhoons with a maximum cooling of 7.8 deg. C, showing the decreased SST from ∼28 deg. C to ∼23 deg. C can promote phytoplankton growth. MLD deepens from 10 m to >25 m caused by typhoon-induced strong winds, allowing more nutrients to be transported from the subsurface layer to the euphotic layer for phytoplankton blooms. Furthermore, oceanic eddies captured by SSHA change from cyclonic to anticyclonic eddies accompanied by the beginning of CHL increases, and the largest CHL increases correspond to the distribution of pre-existing cyclonic eddies. It suggests that Tembin and Boravin promote phytoplankton growth to increase CHL by enhancing vertical mixing and upwelling to transport nutrients to the sea surface. These findings inspire us to rethink the daily effects of typhoons on CHL, with critical importance for predicting and managing the ecological consequences of typhoons in the ocean.

Compensatory growth of Microcystis aeruginosa after copper stress and the characteristics of algal extracellular organic matter (EOM).

Cyanobacterial blooms can impair drinking water quality due to the concomitant extracellular organic matter (EOM). As copper is often applied as an algicide, cyanobacteria may experience copper stress. However, it remains uncertain whether algal growth compensation occurs and how EOM characteristics change in response to copper stress. This study investigated the changes in growth conditions, photosynthetic capacity, and EOM characteristics of M. aeruginosa under copper stress. In all copper treatments, M. aeruginosa experienced a growth inhibition stage followed by a growth compensation stage. Notably, although chlorophyll-a fluorescence parameters dropped to zero immediately following high-intensity copper stress (0.2 and 0.5 mg/L), they later recovered to levels exceeding those of the control, indicating that photosystem II was not destroyed by copper stress. Copper stress influenced the dissolved organic carbon (DOC) content, polysaccharides, proteins, excitation-emission matrix spectra, hydrophobicity, and molecular weight (MW) distribution of EOM, with the effects varying based on stress intensity and growth stage. Principal component analysis revealed a correlation between the chlorophyll-a fluorescence parameters and EOM characteristics. These results imply that copper may not be an ideal algicide. Further research is needed to explore the dynamic response of EOM characteristics to environmental stress.

Assessing sandy beach macrofaunal assemblages across geographically diverse morphodynamic environments.

While the physical characteristics of sandy beaches play a significant role in shaping the macrofaunal community features through morphodynamics, regional environmental factors may also account for deviations from the expected patterns. Here, we assess the concurrent effects of local morphodynamic factors and regional variables, such as sea surface temperature (SST), salinity, and chlorophyll-a (chl-a), on species richness and abundance of intertidal macrofaunal assemblages in four sandy beaches located along the estuarine gradient generated by the Río de la Plata (RdlP) in the southwestern Atlantic Ocean. Species richness was higher in dissipative beaches compared to intermediate ones, consistent with the predictions of the Swash Exclusion Hypothesis. However, this trend was not observed for total abundance, which significantly increased with chl-a. Both local and regional-scale environmental factors, such as salinity and chl-a, proved to be significant predictors in the arrangement of these communities. These results further support previous findings that highlight the critical role of the estuarine gradient of the RdlP in shaping life-history traits, population structure, and abundance of the resident intertidal macrofauna at both local and regional scales. The study underscores the importance of integrating environmental factors operating at different spatial scales to decipher community patterns in these physically-controlled environments.

Knowledge-guided mixture density network for chlorophyll-a retrieval and associated pixel-by-pixel uncertainty assessment in optically variable inland waters.

Machine learning has been increasingly used to retrieve chlorophyll-a (Chl-a) in optically variable waters. However, without the guidance of physical principles or expert knowledge, machine learning may produce biased mapping relationships, or waste considerable time searching for physically infeasible hyperparameter domains. In addition, most Chl-a retrieval models cannot evaluate retrieval uncertainty when ground observations are not available, and the retrieval uncertainty is crucial for understanding the model limitations and evaluating the reliability of retrieval results. In this study, we developed a novel knowledge-guided mixture density network to retrieve Chl-a in optically variable inland waters based on Sentinel-3 Ocean and Land Color Instrument (OLCI) imagery. The proposed method embedded prior knowledge derived from spectral shape classification into the mixture density network. Compared to another deterministic model, the knowledge-guided mixture density network outputted the conditional distribution of Chl-a given an input spectrum, enabling us to estimate the optimal retrieval and the associated uncertainty. The proposed method showed favorable correspondence with the field Chl-a, with root mean square error (RMSE) of 6.56 µg/L, and mean absolute percentage error (MAPE) of 43.64 %. Calibrated against Sentinel-3 OLCI spectrum, the proposed method also performed well when applied to field spectrum (RMSE = 4.58 µg/L, MAPE = 72.70 %), suggesting its effectiveness and good generalization. The proposed method provided the standard deviation of each estimated Chl-a, which enabled us to inspect the reliability of the estimated results and understand the model limitations. Overall, the proposed method improved the Chl-a retrieval in terms of model accuracy and uncertainty evaluation, providing a more comprehensive Chl-a observation of inland waters.

Impact of COVID-19 emission reduction on dust aerosols and marine chlorophyll-a concentration.

The long-range transport of dust aerosols plays a crucial role in biogeochemical cycling, and dust deposition is an important source of nutrients for marine phytoplankton growth. To study the impact of COVID-19 emission reduction on dust aerosols and marine chlorophyll-a (Chl-a) concentration, we selected two similar dust processes from the COVID-19 period (10-15 March 2020) and the non-COVID-19 period (15-20 March 2019) using the Euclidean distance calculation method in combination with the HYSPLIT model and multiple satellite data. During the non-COVID-19 period, the proportion of dust was 6.68 %, approximately half that of the COVID-19 period. Meanwhile, the proportion of polluted dust during the non-COVID-19 period was 4.95 %, which was more than tenfold compared to the COVID-19 period. Furthermore, noticeable discrepancies in Chl-a concentration were observed between the two periods. In the non-COVID-19 period, the maximum daily deposition of dust aerosols can reach 16.23 mg/m2, resulting in a 39-85 % increase in Chl-a concentration. However, during COVID-19 period, the maximum daily dust deposition can reach 33.33 mg/m2, while the increase in Chl-a concentration was <30 %. This conclusion suggests that reductions in anthropogenic emissions during the COVID-19 period have influenced the nutrient content of dust aerosols, resulting in a lesser impact on Chl-a concentrations in the ocean.