Variations in dissolved oxygen and aquatic biological responses in China's coastal seas

Records of biogeochemical variables for Semarang Bay, Indonesia, facing potential coastal deoxygenation

Coastal areas worldwide, including the Indonesian seas, are experiencing a decline in dissolved oxygen (DO) concentration, leading to deoxygenation. Semarang Bay, due to its semi-enclosed nature and significant terrestrial input, is particularly vulnerable to this phenomenon. We analyzed multi-annual records of biogeochemical variables, including nutrient concentrations, chlorophyll-a (Chl-a), and both surface and bottom dissolved oxygen (DO), to assess the possibility of coastal deoxygenation. The study focuses specifically on bottom DO to understand its variability and potential impact on the sediment-water interface. It aims to evaluate the potential for coastal deoxygenation in the bay region by examining sediment stable isotope signatures and biogeochemical variable records in connection with their potential influence on DO at the sediment-water interface. The analysis reveals contrasting trends in sea surface temperature, bottom temperature, and DO concentrations. Spatial analysis uncovers distinct patterns, emphasizing the influence of monsoon seasons on temperature, DO, and Chl-a concentrations. The study notes a declining trend in DO and bottom DO (i.e., -0.055 and -0.048 mmol/m3 per year, respectively), underscoring the need to monitor dissolved oxygen dynamics at the sediment-water interface. Isotope analysis of surface sediment suggests potential sediment deoxygenation at specific sampling sites, irrespective of proximity to the shoreline or bathymetric depth, with similar indications in flood channels. This research offers valuable insights into the complex dynamics of coastal biogeochemistry in Semarang Bay. The findings underscore the need for further research to refine models and explore alternative approaches to address the identified limitations, thereby contributing to enhanced environmental monitoring and assessment in the study site.

Implications of seawater characteristics on dissolved heavy metals in near-shore surface waters of the Yellow Sea

To explore the response of dissolved heavy metals (HMs) along the Yellow Sea coast to seawater characteristics, we assessed the temporal and spatial variations as well as pollution levels of six dissolved HMs (Cu, Zn, Pb, Cd, Hg, and As) in surface seawater from 2021 to 2023. The results indicated that the average concentrations of HMs in the seawater were ranked as follows: Zn > Cu > As > Pb > Cd > Hg, with specific concentration ranges of 4.70-7.01, 0.64-2.83, 0.02-2.83, 0.05-1.37, 0.14-0.55, and 0.02-0.20 μg L-1, respectively. Spatial distribution revealed irregular fluctuations in HM concentrations between nearshore and offshore regions. Analyses of the metal sources suggested that HMs were diffusing from nearshore to offshore, indicating that land-based inputs were the primary source of dissolved HMs along the coast of the Yellow Sea. During the study period, Hg exceeded environmental safety threshold in the study area, indicating a potential ecological risk from Hg pollution. Furthermore, Pb showed signs of pollution in certain seasons. Regarding the correlation of variables, Cu showed the strongest correlation with temperature and dissolved oxygen (DO), but Zn showed a significant correlation with temperature and salinity. Differently, Pb, Cd, Hg, and As were markedly associated with DO and salinity. The complex interactions between environmental variables significantly influenced HM concentrations, and no single factor could adequately explain these changes. Taken together, the alterations in seawater characteristics along the coast of the Yellow Sea had significantly impacted the dissolved HMs during the study period.

Environmental and hydrological synergies shaping phytoplankton diversity in the Hetao irrigation district

Phytoplankton are crucial primary producers in freshwater ecosystems, driving matter and energy flow across trophic levels, essential for biodiversity and ecological balance. Most research emphasizes environmental factors shaping their diversity, while the role of hydrological connectivity remains poorly understood. This study collected 81 phytoplankton samples from the Hetao Irrigation District along a gradient from upstream to downstream and utilized high-throughput sequencing to evaluate the spatial distribution patterns of phytoplankton diversity. The study analyzed the impacts of environmental factors, hydrological connectivity (water surface ratio, Wp), and human activities (land-use intensity, LUI) on phytoplankton diversity. The results revealed that the phytoplankton community comprised 9 phyla, 158 families, 378 genera, and 1189 species. There were significant differences in phytoplankton diversity among different water bodies, with a gradual increase in phytoplankton diversity from west to east across the five major irrigation areas. Lake Ulansuhai had relatively low phytoplankton diversity. The ASV number, Chao1 index, and ACE index showed significant positive correlations with dissolved oxygen (DO), pH, and water temperature (WT). The Shannon index and Pielou'e evenness (Pielou_e) index showed significant positive correlations with the water surface ratio (Wp). The partial least squares model indicated that environmental factors directly influenced phytoplankton diversity. Hydrological connectivity indirectly affected phytoplankton diversity by altering environmental factors. We emphasize that hydrological connectivity is as important as environmental factors in driving phytoplankton diversity in the Hetao Irrigation District. This study provides key insights for water quality assessment and biodiversity conservation in the region.

Dynamics controlling seasonal variability of the dissolved oxygen in the Bohai Sea: A numerical study

A coupled physical-biogeochemical model is used with numerical experiments to investigate the dynamics controlling the spatiotemporal variability of dissolved oxygen (DO) distribution in the Bohai Sea. The model reproduces distinct seasonal variability of DO concentration in the Bohai Sea as characterized by low in summer with large vertical gradient and high in winter with enhanced ventilation. The evolution of bottom DO-depleted zones at northern and southern depressions is primarily controlled by water stratification and topography but also modulated by lateral transport. High bottom turbulence kinetic energy and weak stratification above the Central Bank of the Bohai Sea induce relatively high DO concentration. A comparison between the model and no-resuspension experiment suggests a slight (<5 %) impact of resuspension on DO concentration variation in the Bohai Sea. The input of terrestrial materials from the Yellow River may provide additional nutrients and phytoplankton, sustaining a relatively weak but stable oxygen production along the transport pathway.

Pollution level and ecological risk assessment of triazine herbicides in Laizhou Bay and derivation of seawater quality criteria

Triazine herbicides are widely used in agriculture and have become common pollutants in marine environments. However, the spatiotemporal distribution characteristics and water quality criteria (WQC) of triazine herbicides are still unclear. This study found that triazine herbicides had a high detection rate of 100 % in surface seawater of Laizhou Bay, China, with average concentrations of 217.61, 225.13, 21.97, and 1296.72 ng/L in March, May, August, and October, respectively. Moreover, estuaries were important sources, and especially the Yellow River estuary exhibited the highest concentrations of 16,115.86 ng/L in October. The 10 triazine herbicides were detected in the sediments of Laizhou Bay, with a concentration ranging from 0.14-1.68 μg/kg. Atrazine and prometryn accounted for 33.41 %-59.10 % and 28.93 %-50.06 % of the total triazine herbicides in the seawater, and prometryn had the highest proportion (63.50 %) in the sediments. Correlation analysis revealed that triazine herbicides led to the loss of plankton biodiversity, which further decreased the dissolved oxygen. In addition, this study collected 45 acute toxicity data and 22 chronic toxicity data of atrazine, 16 acute toxicity data of prometryn, and supplemented with toxicity experiments of prometryn on marine organisms. Based on the toxicity database, the WQCs of atrazine and prometryn were derived using species sensitivity distribution. The overall risk probability of atrazine and prometryn were both less than 1.75 % in the Laizhou Bay, indicating an acceptable risk. This study not only clarified the pollution status and ecological risk of triazine herbicides, but also provided scientific basis for their environmental management standards.

An integrated deep learning approach for modeling dissolved oxygen concentration at coastal inlets based on hydro-climatic parameters

Climate change has a significant impact on dissolved oxygen (DO) concentrations, particularly in coastal inlets where numerous human activities occur. Due to the various water quality (WQ), hydrological, and climatic parameters that influence this phenomenon, predicting and modeling DO variation is a challenging process. Accordingly, this study introduces an innovative Deep Learning Neural Network (DLNN) methodology to model and predict DO concentrations for the Egyptian Rashid coastal inlet, leveraging field-recorded WQ and hydroclimatic datasets. Initially, statistical and exploratory data analyses are performed to provide a thorough understanding of the relationship between DO fluctuations and associated WQ and hydroclimatic stressors. As an initial step towards developing an effective DO predictive model, conventional Machine Learning (ML) approaches such as Gaussian Process Regression (GPR), Support Vector Regression (SVR), and Decision Tree Regressor (DTR) are employed. Subsequently, a DLNN approach is utilized to validate the prediction capabilities of the investigated conventional ML approaches. Finally, a sensitivity analysis is conducted to evaluate the impact of WQ and hydroclimatic parameters on predicted DO. The outcomes demonstrate that DLNN significantly improves DO prediction accuracy by 4% compared to the best-performing ML approach, achieving a Correlation Coefficient of 0.95 with a root mean square error (RMSE) of 0.42 mg/l. Solar radiation (SR), pH, water levels (WL), and atmospheric pressure (P) emerge as the most significant hydroclimatic parameters influencing DO fluctuations. Ultimately, the developed models could serve as effective indicators for coastal authorities to monitor DO changes resulting from accelerated climate change along the Egyptian coast.

Spatiotemporal distribution patterns and coupling effects of aquatic environmental factors in the dry-wet season over a decade from the Beibu Gulf, South China Sea

Since the 21st century, the Beibu Gulf area has been affected by increasing anthropogenic activities, which makes the coastal aquatic ecosystem extremely concerning. However, the comprehensive exploration and analysis of the long-term scale behavior change characteristics of various water quality environment factors is still limited. Through comprehensively detecting coastal surface water environmental behavior information from 33 locations in the Beibu Gulf from 2005 to 2015, we revealed and quantified mutual response characteristics and patterns of various environmental indicators. The main environmental pollution indicators (e.g., COD, NH4+, NO3-, and DIP) showed a gradual decrease in concentration from the coast to the offshore sea area, and significantly increases during the wet season. The semi-enclosed Maowei Sea exhibited the most prominent performance with significant differences compared to other regions in Beibu Gulf. The average Chlorophyll-a (Chla) content in the coastal area of the Beibu Gulf during the wet season was more than twice that of the dry season, yet the interaction pattern between Chla and environmental factors in the two seasons was opposite to its concentration behavior, accompanied by a closely significant relationship with thermohaline structure and the input of nitrogen and phosphorous nutrients. The multivariate statistical analysis results of total nutrient dynamics suggested that the Beibu Gulf was clearly divided into different regions in both dry and wet season clusters. The present study can provide a comprehensive perspective for the spatial and temporal migration patterns and transformation laws of coastal water environmental factor, which should contribute to improve the prevention countermeasure of nutrient pollution in coastal environment.

Deciphering variations in the surficial bacterial compositions and functional profiles in the intersection between North and South Yellow Sea

The coastal ocean systems play paramount role in the nutrient biogeochemistry because of its interconnected environment. To gain a novel insight into coupling relationships between bacterial community, functioning properties and nutrient metabolism, we conducted analysis on the patterns and driving factors of planktonic bacterial functional community across subsurface water of marine ranching near the Yellow Sea in both summer and winter. Illumina HiSeq Sequencing and a corresponding set of biogeochemical data were used to assess distribution patterns of taxa, adaptive mechanism and metabolic function. Results demonstrated that Proteobacteria, Cyanobacteria, Actinobacteriota and Bacteroidota were dominant phyla both in summer and winter. Taxonomic profiles related to nutrient variation were found to be highly correlated with Dissolved Oxygen (DO) and Chlorophyll fluorescence (FLUO), and distinct diversity differences were also found between summer and winter samples. Functional activity in summer associated with the relative abundance of phototrophy and photoautotrophy were the highest in the subsurface water, while in winter the dominant functional properties were mainly include chemoheterotrophy and aerobic_ chemoheterotrophy. A significant difference related to functional activity between summer and winter, mainly representing ligninolysis and iron_respiration. In general, our study provides a framework for understanding the relative importance of environmental factors, temperature variation and nutrient availability in shaping the metabolic processes of aquatic microorganisms, particularly in ocean mariculture systems.

Environmental DNA metabarcoding reveals the effect of environmental selection on phytoplankton community structure along a subtropical river

The Dongjiang River, a major tributary of the Pearl River system that supplies water to more than 40 million people in Guangdong Province and neighboring regions of China, harbors rich biodiversity, including many endemic and endangered species. However, human activities such as urbanization, agriculture, and industrialization have posed serious threats to its water quality and biodiversity. To assess the status and drivers of phytoplankton diversity, which is a key indicator of aquatic ecosystem health, this study used Environmental DNA (eDNA) metabarcoding combined with machine learning methods to explore spatial variations in the composition and structure of phytoplankton communities along the Dongjiang River, including its estuary. The results showed that phytoplankton diversity exhibited spatial distribution patterns, with higher community structure similarity and lower network complexity in the upstream than in the downstream regions. Environmental selection was the main mechanism shaping phytoplankton community composition, with natural factors driving the dominance of Pyrrophyta, Ochrophyta, and Cryptophyta in the upstream regions and estuaries. In contrast, the downstream regions was influenced by high concentrations of pollutants, resulting in increased abundance of Cryptophyta. The random forest model identified temperature, dissolved oxygen, chlorophyll a, NO2-, and NH4+ as the main factors influencing the primary phytoplankton communities and could be used to predict changes during wet periods. This study provides valuable insights into the factors influencing phytoplankton diversity and community composition in the Dongjiang River, and demonstrates the application value of eDNA metabarcoding technique in large-scale, long-distance river biodiversity monitoring.

The correlation between echinoderms diversity and physicochemical parameters in marine pollution: A case study of the Persian Gulf coastline

This study was conducted with the aim of investigating the correlation between echinoderms diversity and physicochemical parameters in the Persian Gulf coastline in Bushehr province in 4 seasons from March to December 2017. The physicochemical parameters including water temperature, dissolved oxygen (DO), electrical conductivity (EC), salinity, pH and turbidity were measured at each sampling location. The results showed a significant correlation between echinoderms diversity and physicochemical parameters. The correlation coefficient of the Astropecten polyacanthus species with the parameters of temperature, DO, EC, salinity and turbidity was reported as -0.41, 0.64, -0.25, -0.44 and 0.60 respectively. This coefficient for the Ophiothrix fragilis species was reported as -0.68, 0.70, -0.21, -0.36 and -0.55 respectively. The results demonstrated that the most sensitive species were Astropecten polyacanthus and Ophiothrix fragilis respectively. The different species of echinoderms can be used as biological indicators of pollution in evaluating the physicochemical quality of marine environments.

Ecosystem health evaluation of urban rivers based on multitrophic aquatic organisms

The ecosystem health evaluation method of urban rivers is significantly different from natural rivers, because of intensive human interferences and ecological restoration measures. Biotic integrity index (IBI) provides a method to quantify the response of aquatic organisms to environmental stress. Multi-trophic aquatic organisms may exhibit different responses and sensitivities to stress factors, which affects the reliability of the IBIs. This study proposed a hypothesis that the biota with the higher trophic level (whose habitat was not completely destroyed) or that of the biota with the shorter life cycle would be more sensitive in urban rivers. To prove the above hypothesis, the ecosystem health status of urban rivers was evaluated by the IBIs across multitrophic groups, including benthic invertebrates, zooplankton, phytoplankton, periphyton algae and microorganisms. The reliability of the IBIs was assessed by estimating their relationship with water quality index (WQI). The spatial distribution differences of the IBIs were distinguished by spatial autocorrelation analysis. The results showed that the IBI based on benthic invertebrates cannot mask the effects of dredging. Compared with the IBIs from other trophic groups, the correlation coefficients between the IBIs based on zooplankton and microorganisms and WQI were higher. Moreover, the evaluation results of Z (Zooplankton)-IBI and M (Microorganism)-IBI were able to discriminate the least, medium and highly impaired site groups divided by WQI. For the spatial response mode, Z-IBI and M-IBI could identify the high-value river sections under ecosystem restoration projects, and Z-IBI could also identify the low-value river sections under intensive human interferences. Therefore, Z-IBI and M-IBI could be recommended as the priority application in urban rivers. The constructed ecosystem health evaluation framework for urban rivers would play a guiding role in reducing impairments and restoring water ecosystem quality.

Spatiotemporal variation of dissolved oxygen concentration in the northern Gulf of Mexico during the period of 1992-2017

Dissolved oxygen (DO) concentration is an important index of ocean systems. In this study, spatiotemporal variations in DO were analyzed in the northern Gulf of Mexico (NGM) for the period of 1992-2017. Temporal variation involves annual, monthly, and seasonal variations. The spatial scope ranged from the sea surface to 1000 m depth. The results show that: (1) DO decreased from 1992 to 2017 with a brief increase; (2) clear seasonal variation characteristics of DO were presented, and the seasonal average DO was relatively low in summer; (3) the monthly average trends of sea surface DO in different years were relatively consistent; and (4) with increasing water depth from seawater surface to 1000 m, the average DO of 1992 to 2017 tended to decrease and then increase. The results aim at shedding some light on the assessment of DO change in the NGM over the past quarter century.