A change in phytoplankton community index with water quality improvement in Tolo Harbour, Hong Kong

Remotely sensed estimates of long-term biochemical oxygen demand over Hong Kong marine waters using machine learning enhanced by imbalanced label optimisation

In many coastal cities around the world, continuing water degradation threatens the living environment of humans and aquatic organisms. To assess and control the water pollution situation, this study estimated the Biochemical Oxygen Demand (BOD) concentration of Hong Kong's marine waters using remote sensing and an improved machine learning (ML) method. The scheme was derived from four ML algorithms (RBF, SVR, RF, XGB) and calibrated using a large amount (N > 1000) of in-situ BOD5 data. Based on labeled datasets with different preprocessing, i.e., the original BOD5, the log10(BOD5), and label distribution smoothing (LDS), three types of models were trained and evaluated. The results highlight the superior potential of the LDS-based model to improve BOD5 estimate by dealing with imbalanced training dataset. Additionally, XGB and RF outperformed RBF and SVR when the model was developed using log10(BOD5) or LDS(BOD5). Over two decades, the BOD5 concentration of Hong Kong marine waters in the autumn (Sep. to Nov.) shows a downward trend, with significant decreases in Deep Bay, Western Buffer, Victoria Harbour, Eastern Buffer, Junk Bay, Port Shelter, and the Tolo Harbour and Channel. Principal component analysis revealed that nutrient levels emerged as the predominant factor in Victoria Harbour and the interior of Deep Bay, while chlorophyll-related and physical parameters were dominant in Southern, Mirs Bay, Northwestern, and the outlet of Deep Bay. LDS provides a new perspective to improve ML-based water quality estimation by alleviating the imbalance in the labeled dataset. Overall, the remotely sensed BOD5 can offer insight into the spatial-temporal distribution of organic matter in Hong Kong coastal waters and valuable guidance for the pollution control.

Phytoplankton Community Integrity Index (PCII) - A potential supplementary tool for evaluating nutrient enrichment status of Hong Kong marine waters

Diagnosis of eutrophication requires evidence of disturbance to the balance of organisms. We describe a tool, the Plankton Community Integrity Index (PCII), derived from the Plankton Index (PI) for tracking change in the seasonal patterns of abundance of diatom and dinoflagellate lifeforms when plotted in state space. The tool uses a nutrient-minimum reference period to interpret PCII values as status indicators, with values close to 1 indicating "High" status and 0.6 a Biological Water Quality Criterion (BioWQC) target set at the "Fair"/"Good" status boundary. It has been applied to Hong Kong marine waters, using data from monthly samples from 1995 through 2021. A preliminary analysis, required for the PI method, confirmed monsoonal seasonality in the diatom lifeform. In 5 of the 9 water bodies examined, PCII time series correlated with those of Total Inorganic Nitrogen (TIN). Since 2020, all Water Control Zones met the operationally defined BioWQC target.

The relationship between salmon (Salmo salar) farming and cell abundance of harmful algal taxa

The effects of nutrient effluents from fish cage aquaculture are an important eutrophication concern. It has been proposed that marine fish farm derived nutrients have the potential to increase phytoplankton abundance and lead to intensification of Harmful Algal Blooms (HABs), and that these blooms may negatively impact both the finfish and the shellfish industry. This study addressed this hypothesis using farmed salmon biomass in Scottish marine waters (as a proxy for nutrient load added to the water column as a consequence of fish farming) cell abundance of HAB taxa that most frequently impact shellfish farms and human health in the region (Dinophysis spp., Alexandrium spp. and Pseudo-nitzschia spp.), and cell abundance of one phytoplankton species of particular concern to the salmon farming industry (Karenia mikimotoi). Data from a 15-year weekly HAB monitoring programme and parallel national monitoring data relating to salmon farm stocking biomass were summarised in 5 km per 5 km aggregation boxes. Linear regression models were used to assess (i) inter-annual variation in cell abundance and total annual farmed salmon biomass; (ii) intra-annual (monthly) variation in harmful phytoplankton cell abundance and salmon biomass; (iii) a further analysis included seasonal effects within the intra-annual analysis. Farmed salmon biomass alone had a non-significant effect on cell abundance of any of the studied phytoplankton taxa. In contrast, a significant effect on cell abundance was found when using location, month or season as the predictive variable. Despite the non-significant impact of fish biomass on phytoplankton counts, the relationship varied seasonally, with a different response of Dinophysis spp. indicating a taxa specific interaction. A possible explanation for the lack of a significant relationship between farmed salmon and harmful phytoplankton cell abundance is that aquaculture farms are generally located in hydrodynamically energetic locations where recurrent flushing likely allows efficient dilution of nutrients. Overall, the analyses suggest that current levels of salmon farming activities do not markedly impact the abundance of routinely monitored biotoxin producing or fish killing phytoplankton taxa in Scottish waters.

Assessment of red tide risk by integrating CRITIC weight method, TOPSIS-ASSETS method, and Monte Carlo simulation

This study proposes a red tide risk assessment method based on intercriteria correlation (CRITIC), technique for order preference by similarity to an ideal solution (TOPSIS), assessment of estuarine trophic status (ASSETS) methods and Monte Carlo simulation (MCS) to calculate the probability of each risk level. The integrated TOPSIS-ASSETS method is used to calculate the risk levels of each year, where index weight is determined by CRITIC method. MCS method is employed to calculate the probability of each risk level. The results showed that level III to level V indicates high possibility of red tides in the case study area (Tolo Harbor). The highest risk rating was level V in 1988. The change of the risk level of red tide is consistent with the real situation of the occurrence of red tide. Another case of the east part of Skagerrak Strait shows that the results of this method are consistent with field situation. When there is an error between the evaluation results and the real situation, MCS can further suggest the probability of error in the evaluation results. Meanwhile, sensitivity analysis was used to test the performance of the evaluation model and two comparative methods. The results show that the proposed risk assessment method has better performance than other methods and can provide an effective risk evaluation for red tide management.

Spatiotemporal variations of retinoic acids and their metabolites in the marine environment of Hong Kong

Excessive intake of retinoic acids (RAs) and the oxidative metabolites, 4-oxo-RAs, can lead to abnormal morphological development in animals. This study investigated spatiotemporal variations of concentrations and compositions of these compounds in Hong Kong's seawater and during algal blooms. Total concentrations of the studied compounds in seawater were up to 0.790 and 0.427 ng/L in dry and wet seasons, respectively, though no significant seasonal variation was observed. Spatially, the Deep Bay Water Control Zone was the most enriched area with the studied compounds owing to its semi-enclosed nature and influence from the Pearl River discharge. During algal blooms, the studied compounds were detected up to 4.74 ng/L. Based on calculated risk quotients, the ecological risk of the studied compounds to Hong Kong's marine ecosystems was low. Nevertheless, the occurrence and distribution of these chemicals in the marine environment should be closely monitored where algal blooms frequently occur.

Phytoplankton Communities and Their Relationship with Environmental Factors in the Waters around Macau

An investigation of the waters around Macau collected 43 phytoplankton species belonging to 29 genera and 5 phyla, including 32 species from 22 genera of Bacillariophyta, 7 species from 3 genera of Pyrrophyta, 2 species from 2 genera of Cyanophyta, and 1 genus and 1 species from both Euglenophyta and Chromophyta. The dominant phytoplankton species in the study areas were Skeletonema costatum (Greville) Cleve, Aulacoseira granulata (Ehrenberg) Simonsen, Thalassiothrix frauenfeidii Grunow, and Thalassionema nitzschioides Grunow. The phytoplankton abundance in the waters around Macau was between 46,607.14 and 1,355,000 cells/m3, with the highest abundance noted in station S8. Diatoms were the main contributor to phytoplankton abundance in station S8, accounting for 96.2% of the total abundance. Station S4 exhibited the lowest phytoplankton abundance of 46,607.1 cells/m3, with diatoms and Chromophytaaccounting for 58.6% and 29.9% of the total phytoplankton abundance, respectively. Biodiversity analysis results showed that the phytoplankton richness index was 1.18−3.61, the uniformity index was 0.24−0.78, and the Shannon−Wiener index was 0.94−3.41. Correlation analysis revealed that ammonia nitrogen was significantly negatively correlated with the phytoplankton richness, uniformity, and Shannon−Wiener indices. Nitrite nitrogen, nitrate nitrogen, inorganic nitrogen, salinity, turbidity, and pH were positively correlated with the phytoplankton evenness index and Shannon−Wiener index. Cluster and non-metric multidimensional scaling analyses demonstrated that the phytoplankton community structure in the waters around Macau could be divided into three groups, with A. granulata, S. costatum, T. frauenfeidii, T. nitzschioides, Chaetoceros curvisetus Cleve, and Chaetoceros diadema (Ehrenberg) Gran being predominant in different grouping communities (contribution% > 10%). Biota-Environment Stepwise Analysis (BIOENV) showed a significant correlation between the phytoplankton community and nitrite nitrogen content in the waters around Macau (correlation: 0.5544, Mantel test: statistic 0.4196, p = 0.009), which was consistent with the results of the canonical correspondence analysis.

Determination of microplastics in the edible green-lipped mussel Perna viridis using an automated mapping technique of Raman microspectroscopy

Microplastics are prevalent in marine environments and seafood and thus can easily end up in human diets. This has raised serious concerns worldwide, particularly in Hong Kong where the seafood consumption per capita can be three times higher than the global average. This study focused on the green-lipped mussel Perna viridis, a popular seafood species which is subject to a high risk of contamination by microplastics due to its filter-feeding nature. P. viridis was collected from five mariculture sites in Hong Kong and assessed for its body load of microplastics using an automated Raman mapping approach. Microplastics were found in all sites, with an average of 1.60-14.7 particles per mussel per site, or 0.21-1.83 particles per g wet weight. Polypropylene, polyethylene, polystyrene and polyethylene terephthalate were detected among the microplastics, mainly as fragments or fibres in the size range of 40-1000 µm. It was estimated that through consumption of P. viridis, the population in Hong Kong could ingest up to 10,380 pieces of microplastics per person per year. These estimated rates were high compared to the values reported worldwide, suggesting the potential human health risk of microplastics in Hong Kong and adjacent areas.

Long-term changes of nutrients and biocenoses indicating the anthropogenic influences on ecosystem in Jiaozhou Bay and Daya Bay, China

Long-term changes of nutrients, plankton and macrobenthos were studied to research the transformation of ecosystem in Jiaozhou Bay and Daya Bay in the past 30 years. Concentrations of dissolved inorganic nitrogen and phosphate increased with significant changes in nutrient compositions and ratios. Concentrations of Chl a slightly decreased in Jiaozhou Bay but increased in Daya Bay. Phytoplankton abundances increased and diatoms were dominant, however, dinoflagellate gradually had the competitive advantage under high N/P and N/Si in the two bays. Zooplankton biomass significantly increased in Jiaozhou Bay, but only increased slightly in Daya Bay over the past years. Polychaetes were dominant in macrobenthos in the bays, indicating their adaptation to the changing benthic environments. The long-time variations of biocenoses and nutrients reflected that the ecological environments have changed under the influence of anthropogenic activities in the two bays.

Tracing the potential pollution sources of the coastal water in Hong Kong with statistical models combining APCS-MLR

In this study, variety of statistical methods were performed to reveal the spatiotemporal distribution characteristics of pollutants and parsing pollution sources of the coastal water in Hong Kong. The temporal-spatial distribution characteristics of the water pollution were various among the three distinct areas, which might be ascribed to the different dominant pollution sources. Cluster and network analysis showed preliminary pollution sources in these areas, and also indicated the temporal characteristics of Deep Bay water pollution, which could divided into two parts before and after 2010. According to the principal component analysis/factor analysis results, three factors in Deep Bay, Tolo Harbour and Victoria Harbour could explained 68.72%, 54.87% and 72.28% of the total variances, respectively. The contribution rate of different pollution source on water quality variables in each area had calculated by absolute principal component score-multiple linear regression model. The contribution rate was roughly ranked as: point source pollution > non-point source pollution > overland runoff > river input. It is the first time to combine multivariate statistical methods, network analysis and regression model to profoundly analyze spatiotemporal variation of seawater quality and parsing the pollution sources. This novel analysis method can provide reference for the water quality evaluation and management of other water bodies.