Search Results (32)

In recent years, China has experienced growing impacts from extreme weather events, emphasizing the importance of understanding regional atmospheric moisture dynamics, particularly Precipitable Water Vapor (PWV), to support sustainable environmental and urban planning. This study utilizes ten years (2013–2022) of Global Navigation Satellite System (GNSS) observations in typical cities in eastern China and proposes a comprehensive multiscale frequency-domain analysis framework that integrates the Fourier transform, Bayesian spectral estimation, and wavelet decomposition to extract the dominant PWV periodicities. Time-series analysis reveals an overall increasing trend in PWV across most regions, with notably declining trends in Beijing, Wuhan, and southern Taiwan, primarily attributed to groundwater depletion, rapid urban expansion, and ENSO-related anomalies, respectively. Frequency-domain results indicate distinct latitudinal and coastal–inland differences in the PWV periodicities. Inland stations (Beijing, Changchun, and Wuhan) display annual signals alongside weaker semi-annual components, while coastal stations (Shanghai, Kinmen County, Hong Kong, and Taiwan) mainly exhibit annual cycles. High-latitude stations show stronger seasonal and monthly fluctuations, mid-latitude stations present moderate-scale changes, and low-latitude regions display more diverse medium- and short-term fluctuations. In the short-term frequency domain, GNSS stations in most regions demonstrate significant PWV periodic variations over 0.5 days, 1 day, or both timescales, except for Changchun, where weak diurnal patterns are attributed to local topography and reduced solar radiation. Furthermore, ERA5-derived vertical temperature profiles are incorporated to reveal the thermodynamic mechanisms driving these variations, underscoring region-specific controls on surface evaporation and atmospheric moisture capacity. These findings offer novel insights into how human-induced environmental changes modulate the behavior of atmospheric water vapor. Full article

The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is one of the most urbanized and industrialized coastal regions in China, where intense human activities contribute to substantial terrestrial sediment discharge into the adjacent marine environment. However, complex hydrodynamic conditions and high spatiotemporal variability pose challenges for accurate suspended sediment concentration (SSC) retrieval. Developing water quality retrieval models based on different classifications of water bodies could enhance the accuracy of SSC inversion in coastal waters. Therefore, this study classified the coastal waters of the GBA into clear and turbid zones based on Hue angle α, and established retrieval models for SSC using a single-scattering approximation model for clear zones and a secondary-scattering approximation model for turbid zones based on radiative transfer processes. Model validation with in-situ data shows a coefficient of determination (R²) of 0.73, a root mean square error (RMSE) of 8.30, and a mean absolute percentage error (MAPE) of 42.00%. Spatial analysis further reveals higher SSC in the waters around Qi’ao Island in the Pearl River Estuary (PRE) and along the coastline of Guanghai Bay, identifying these two areas as priorities for attention. This study aims to offer valuable insights for SSC management in the coastal waters of the GBA. Full article

A type of aquaculture pond called a dike-pond system is distributed in the low-lying river delta of China’s eastern coast. Along with the swift growth of the coastal economy, the water surfaces of the dike-pond system (WDPS) play a major role attributed to pond aquaculture yielding more profits than dike agriculture. This study aims to explore the performance of deep learning methods for extracting WDPS from high spatial resolution remote sensing images. We developed three fully convolutional network (FCN) models: SegNet, UNet, and UNet++, which are compared with two traditional methods in the same testing regions from the Guangdong–Hong Kong–Macao Greater Bay Area. The extraction results of the five methods are evaluated in three parts. The first part is a general comparison that shows the biggest advantage of the FCN models over the traditional methods is the P-score, with an average lead of 13%, but the R-score is not ideal. Our analysis reveals that the low R-score problem is due to the omission of the outer ring of WDPS rather than the omission of the quantity of WDPS. We also analyzed the reasons behind it and provided potential solutions. The second part is extraction error, which demonstrates the extraction results of the FCN models have few connected, jagged, or perforated WDPS, which is beneficial for assessing fishery production, pattern changes, ecological value, and other applications of WDPS. The extracted WDPS by the FCN models are visually close to the ground truth, which is one of the most significant improvements over the traditional methods. The third part is special scenarios, including various shape types, intricate spatial configurations, and multiple pond conditions. WDPS with irregular shapes or juxtaposed with other land types increases the difficulty of extraction, but the FCN models still achieve P-scores above 0.95 in the first two scenarios, while WDPS in multiple pond conditions causes a sharp drop in the indicators of all the methods, which requires further improvement to solve it. We integrated the performances of the methods to provide recommendations for their use. This study offers valuable insights for enhancing deep learning methods and leveraging extraction results in practical applications. Full article

One of the manifests of air-sea interactions is the change in sea level due to meteorological forcing through wind stress and atmospheric pressure. When meteorological conditions conducive to water level increase coincide with high tides during spring tides, the sea level may rise higher than expected and pose a flood risk to coastal low-lying areas. In Hong Kong, specifically when the northeast monsoon coincides with the higher spring tides in late autumn and winter, and sometimes even compounded by the storm surge brought by late-season tropical cyclones (TCs), the result may be coastal flooding or sea inundation. Aiming at forecasting such sea level anomalies on the scale of hours and days with local tide gauges using a flexible and computationally efficient method, this study adapts a data-driven method based on empirical orthogonal functions (EOF) regression of non-uniformly lagged regional wind field from ECMWF Reanalysis v5 (ERA5) to capture the effects from synoptic weather evolution patterns, excluding the effect of TCs. Local atmospheric pressure and winds are also included in the predictors of the regression model. Verification results show good performance in general. Hindcast using ECMWF forecasts as input reveals that the reduction of mean absolute error (MAE) by adding the anomaly forecast to the existing predicted astronomical tide was as high as 30% in February on average over the whole range of water levels, as well as that compared against the Delft3D forecast in a strong northeast monsoon case. The EOF method generally outperformed the persistence method in forecasting water level anomaly for a lead time of more than 6 h. The performance was even better particularly for high water levels, making it suitable to serve as a forecast reference tool for providing high water level alerts to relevant emergency response agencies to tackle the risk of coastal inundation in non-TC situations and an estimate of the anomaly contribution from the northeast monsoon under its combined effect with TC. The model is capable of improving water level forecasts up to a week ahead, despite the general decreasing model performance with increasing lead time due to less accurate input from model forecasts at a longer range. Some cases show that the model successfully predicted both positive and negative anomalies with a magnitude similar to observations up to 5 to 7 days in advance. Full article

While mangrove ecosystems are rich in biodiversity, they are increasingly impacted by climate change and urban pollutants. The current study provides first insights into the emergence of potentially pathogenic yeasts in Hong Kong’s mangroves. Sediment and water samples were collected from ten urban and rural mangroves sites. Initial CHROMagar^TM Candida Plus screening, representing the first application of this differential medium for water and soil samples collected from a non-clinical environment, enabled the rapid, preliminary phenotypic identification of yeast isolates from mangroves. Subsequent molecular profiling (ITS and/or 28S nrDNA sequencing) and antifungal drug susceptibility tests were conducted to further elucidate yeast diversity and drug resistance. A diversity of yeasts, including 45 isolates of 18 distinct species across 13 genera/clades, was isolated from sediments and waters from Hong Kong mangroves. Molecular profiling revealed a dominance of the Candida/Lodderomyces clade (44.4%), a group of notorious opportunistic pathogens. The findings also reveal a rich biodiversity of non-Candida/Lodderomyces yeasts in mangroves, including the first reported presence of Apiotrichum domesticum and Crinitomyces flavificans. A potentially novel Yamadazyma species was also discovered. Remarkably, 14.3% of the ubiquitous Candida parapsilosis isolates displayed resistance to multiple antifungal drugs, suggesting that mangroves may be reservoirs of multi-drug resistance. Wildlife, especially migratory birds, may disseminate these hidden threats. With significant knowledge gaps regarding the environmental origins, drug resistance, and public health impacts of pathogenic yeasts, urgent surveillance is needed from a One Health perspective. This study provides an early warning that unrestrained urbanization can unleash resistant pathogens from coastal ecosystems globally. It underscores the necessity for enhanced surveillance studies and interdisciplinary collaboration between clinicians, ornithologists, and environmental microbiologists to effectively monitor and manage this environmental health risk, ensuring the maintenance of ‘One Health’. Full article

The Hong Kong Observatory has been using a parametric storm surge model to forecast the rise of sea level due to the passage of tropical cyclones. This model includes an offset parameter to account for the rise in sea level due to other meteorological factors. By adding the sea level rise forecast to the astronomical tide prediction using the harmonic analysis method, coastal sea level prediction can be produced for the sites with tidal observations, which supports the high water level forecast operation and alert service for risk assessment of sea flooding in Hong Kong. The Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) Modelling System, which comprises the Weather Research and Forecasting (WRF) Model and Regional Ocean Modelling System (ROMS), which in itself is coupled with wave model WaveWatch III and nearshore wave model SWAN, was tested with tropical cyclone cases where there was significant water level rise in Hong Kong. This case study includes two super typhoons, namely Hato in 2017 and Mangkhut in 2018, three cases of the combined effect of tropical cyclone and northeast monsoon, including Typhoon Kompasu in 2021, Typhoon Nesat and Severe Tropical Storm Nalgae in 2022, as well as two cases of monsoon-induced sea level anomalies in February 2022 and February 2023. This study aims to evaluate the ability of the WRF-ROMS-SWAN model to downscale the meteorological fields and the performance of the coupled models in capturing the maximum sea levels under the influence of significant weather events. The results suggested that both configurations could reproduce the sea level variations with a high coefficient of determination (R²) of around 0.9. However, the WRF-ROMS-SWAN model gave better results with a reduced RMSE in the surface wind and sea level anomaly predictions. Except for some cases where the atmospheric model has introduced errors during the downscaling of the ERA5 dataset, bias in the peak sea levels could be reduced by the WRF-ROMS-SWAN coupled model. The study result serves as one of the bases for the implementation of the three-way coupled atmosphere–ocean–wave modelling system for producing an integrated forecast of storm surge or sea level anomalies due to meteorological factors, as well as meteorological and oceanographic parameters as an upgrade to the two-way coupled Operational Marine Forecasting System in the Hong Kong Observatory. Full article

The application of remote sensing technology for water quality monitoring has attracted much attention recently. Remote sensing inversion in coastal waters with complex hydrodynamics for non-optically active parameters such as total nitrogen (TN) and total phosphorus (TP) remains a challenge. Existing studies build the relationships between remote sensing spectral data and TN/TP directly or indirectly via the mediation of optically active parameters (e.g., total suspended solids). Such models are often prone to overfitting, performing well with the training set but underperforming with the testing set, even though both datasets are from the same region. Using the Hong Kong coastal region as a case study, we address this issue by incorporating spatial covariates such as hydrometeorological and locational variables as additional input features for machine learning-based inversion models. The proposed model effectively alleviates overfitting while maintaining a decent level of accuracy (R² exceeding 0.7) during the training, validation and testing steps. The gap between model R² values in training and testing sets is controlled within 7%. A bootstrap uncertainty analysis shows significantly improved model performance as compared to the model with only remote sensing inputs. We further employ the Shapely Additive Explanations (SHAP) analysis to explore each input’s contribution to the model prediction, verifying the important role of hydrometeorological and locational variables. Our results provide a new perspective for the development of remote sensing inversion models for TN and TP in similar coastal waters. Full article

This research delves into the molecular and morphological characteristics of myzostomid worms associated with common shallow-water feather stars (Echinodermata: Crinoidea: Comatulidae) in the coastal waters near Sanya, Hainan Island. Through the examination of specimens collected at depths of up to 10 m using scuba diving techniques, we describe three new species (Myzostoma ordinatum sp. nov., M. scopus sp. nov., and M. solare sp. nov.) and report the first record of Myzostoma polycyclus Atkins, 1927 in the South China Sea. The absence of overlap with the seven previously documented Myzostomida species in the shallow waters of Hong Kong and Shenzhen reveals significant gaps in our understanding of marine biodiversity in the South China Sea. These findings, combined with an analysis of available molecular data, underscore the potential existence of unexplored and diverse symbiotic relationships among marine invertebrates within the region. Full article

Chlorophyll-a (Chl-a) concentration is a key indicator for assessing the eutrophication level in water bodies. However, accurately inverting Chl-a concentrations in optically complex coastal waters presents a significant challenge for traditional models. To address this, we employed Sentinel-2 MSI sensor data and leveraged the power of five machine learning models, including a convolutional neural network (CNN), to enhance the inversion process in the coastal waters near Hong Kong. The CNN model demonstrated superior performance with on-site data validation, outperforming the other four models (R² = 0.810, RMSE = 1.165 μg/L, MRE = 35.578%). The CNN model was employed to estimate Chl-a concentrations from images captured over the study area in April and October 2022, resulting in the creation of a thematic map illustrating the spatial distribution of Chl-a levels. The map indicated high Chl-a concentrations in the northeast and southwest areas of Hong Kong Island and low Chl-a concentrations in the southeast facing the open sea. Analysis of patch size effects on CNN model accuracy indicated that 7 × 7 and 9 × 9 patches yielded the most optimal results across the tested sizes. Shapley additive explanations were employed to provide post-hoc interpretations for the best-performing CNN model, highlighting that features B6, B12, and B8 were the most important during the inversion process. This study can serve as a reference for developing machine learning models to invert water quality parameters. Full article

Effluent discharged from urban wastewater treatment plants (WWTPs) is a major source of emerging contaminants (ECs) requiring effective regulation. To this end, we collected discharge datasets of pharmaceuticals (PHACs) and endocrine-disrupting chemicals (EDCs), representing two primary categories of ECs, from Chinese WWTP effluent from 2012 to 2022 to establish an exposure database. Moreover, high-risk ECs’ long-term water quality criteria (LWQC) were derived using the species sensitivity distribution (SSD) method. A total of 140 ECs (124 PHACs and 16 EDCs) were identified, with concentrations ranging from N.D. (not detected) to 706 μg/L. Most data were concentrated in coastal regions and Gansu, with high ecological risk observed in Gansu, Hebei, Shandong, Guangdong, and Hong Kong. Using the assessment factor (AF) method, 18 high-risk ECs requiring regulation were identified. However, only three of them, namely carbamazepine, ibuprofen, and bisphenol-A, met the derivation requirements of the SSD method. The LWQC for these three ECs were determined as 96.4, 1010, and 288 ng/L, respectively. Exposure data for carbamazepine and bisphenol-A surpassed their derived LWQC, indicating a need for heightened attention to these contaminants. This study elucidates the occurrence and risks of ECs in Chinese WWTPs and provides theoretical and data foundations for EC management in urban sewage facilities. Full article

Chlorophyll-a (Chl-a) concentration monitoring is very important for managing water resources and ensuring the stability of marine ecosystems. Due to their high operating efficiency and high prediction accuracy, backpropagation (BP) neural networks are widely used in Chl-a concentration inversion. However, BP neural networks tend to become stuck in local optima, and their prediction accuracy fluctuates significantly, thus posing restrictions to their accuracy and stability in the inversion process. Studies have found that metaheuristic optimization algorithms can significantly improve these shortcomings by optimizing the initial parameters (weights and biases) of BP neural networks. In this paper, the adaptive nonlinear weight coefficient, the path search strategy “Levy flight” and the dynamic crossover mechanism are introduced to optimize the three main steps of the Artificial Ecosystem Optimization (AEO) algorithm to overcome the algorithm’s limitation in solving complex problems, improve its global search capability, and thereby improve its performance in optimizing BP neural networks. Relying on Google Earth Engine and Google Colaboratory (Colab), a model for the inversion of Chl-a concentration in the coastal waters of Hong Kong was built to verify the performance of the improved AEO algorithm in optimizing BP neural networks, and the improved AEO algorithm proposed herein was compared with 17 different metaheuristic optimization algorithms. The results show that the Chl-a concentration inversion model based on a BP neural network optimized using the improved AEO algorithm is significantly superior to other models in terms of prediction accuracy and stability, and the results obtained via the model through inversion with respect to Chl-a concentration in the coastal waters of Hong Kong during heavy precipitation events and red tides are highly consistent with the measured values of Chl-a concentration in both time and space domains. These conclusions can provide a new method for Chl-a concentration monitoring and water quality management for coastal waters. Full article

As a highly productive and biologically diverse ecosystem, wetlands provide unique habitat for a wide array of plant and animal species. Owing to the strong disturbance by human activities and climate change, wetland degradation and fragmentation have become a common phenomenon across the globe. The Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is a typical case. The GBA has experienced explosive growth in the population and economy since the early 1980s, which has resulted in complicated transitions between wetlands and non-wetlands. However, our knowledge about the transformation paths, associated drivers, and ecological influence of the GBA’s wetlands is still very limited. Taking advantage of the land use maps generated from Landsat observations over the period of 1980–2020, here, we quantified the spatiotemporal transformation paths of the GBA’s wetlands and analyzed the associated drivers and ecological influence. We found that the dominant transformation path between wetland and non-wetland was from wetland to built-up land, which accounted for 98.4% of total wetland loss. The primary transformation path among different wetland types was from coastal shallow water and paddy land to reservoir/pond, with the strongest transformation intensity in the 1980s. The driving forces behind the wetland change were found to vary by region. Anthropogenic factors (i.e., population growth and urbanization) dominated in highly developed cities, while climate factors and aquaculture had a greater influence in underdeveloped cities. The findings presented in this study will provide a reference for wetland management and planning in the GBA. Full article

Mangrove wetlands are vulnerable coastal ecosystems that provide critical habitats for aquatic life. Tai O is a popular tourist village on Lantau Island, Hong Kong, which is surrounded by mangrove wetlands with rich biodiversity; and this village is also famous for its traditional stilt houses. However, the untreated municipal sewage from some stilt houses is directly discharged into nearby tidal channels, potentially threatening health of the adjacent mangrove wetlands. In order to evaluate the anthropogenic impact on these wetlands and identify the potential sources of their pollution, this study aimed to evaluate spatial (at the sampling points) and temporal (during weekdays and weekends) differences in the quality of their tidal water, and examine relationships between the water quality and the density of the stilt houses. The results indicated that the water quality was worse during weekends. The ammonia concentrations in most samples exceeded the limits of the Hong Kong Water Quality Objectives, China’s Sea Water Quality Standards, and even the U.S. EPA criterion for fish reproduction. This high ammonia input could potentially adversely affect the mangrove ecosystem, underscoring the need for further comprehensive studies. Moreover, some of the weekend water samples had lower dissolved oxygen levels and were polluted by phosphate. Our Principal Component Analysis revealed that water quality was correlated with stilt house density, suggesting that anthropogenic inputs of untreated sewage was the major source of pollution. These findings highlight that nutrients released from human activities, particularly ammonia and phosphate, must be controlled for a better protection of mangrove wetland ecosystems. Full article

Total suspended solids (TSS) and chlorophyll-a (Chl-a) are critical water quality parameters. Focusing on the Pearl River Estuary and its coastal waters, this study compared the performance of XGBoost- and BPNN-based algorithms in estimating TSS and Chl-a levels. The XGBoost-based algorithm demonstrated better performance and was then used to estimate TSS and Chl-a in the Pearl River Estuary and coastal waters from 2000 to 2021. According to our results, TSS and Chl-a were relatively high mainly in the northwest and low in the southeast. Furthermore, values were high in spring and summer and low in fall and winter, with high values emerging near the estuary of the Pearl River. In summer, a band zone with high Chl-a was observed from south of Yamen to south of Hong Kong. In terms of trends, TSS and Chl-a concentrations in the area around the Hong Kong–Zhuhai–Macao Bridge tended to decrease from 2000 to 2021. As the construction of the bridge began, changes in water flow caused by the bridge piers and artificial islands were influenced, the change in the rate of TSS in the west area of the bridge was greater than 0, and the TSS in the upstream area of the west side changed from decreasing to increasing trends. Concerning Chl-a concentrations, the change in the rate in the downstream area of the west side of the bridge was greater than 0. The study may provide a helpful example for similar estuarine and coastal waters in other coastal areas. Full article

Shenzhen is a crucial city in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA). With high-intensity land development and rapid population growth, the ocean has become an essential space for expansion, leading to significant variations in water quality in the coastal area of Shenzhen. Water clarity (Z_sd) is a key indicator for evaluating water quality. We applied the quasi-analytical algorithm (QAA) to Sentinel-2 data and retrieved the Z_sd of the coastal area of Shenzhen. By adjusting the red band for distinguishing water types, we avoided underestimating Z_sd for clear water. This study pioneered the production of a 10 m Z_sd product for the coastal area of Shenzhen from 2016 to 2021. The results showed that the coastal area of Shenzhen exhibited a spatial distribution pattern with low Z_sd in the west and high in the east, with Pearl River Estuary (PRE: 0.41–0.67 m) and Shenzhen Bay (SZB: 0.30–0.58 m) being lower than Dapeng Bay (DPB: 2.7–2.9 m) and Daya Bay (DYB: 2.5–2.9 m). We analyzed the seasonal and interannual variations and driving factors of the four areas, where PRE and SZB showed similar variation patterns, while DPB and DYB showed similar variation patterns. PRE and SZB are important estuaries in southern China, significantly affected by anthropogenic activities. DPB and DYB are important marine aquaculture areas, mainly affected by natural factors (wind speed, precipitation, and sea level). The Z_sd of the coastal area of Shenzhen, along with the analysis of its results and driving factors, contributes to promoting local water resource protection and providing a reference for formulating relevant governance policies. It also provides a practical method for assessing and monitoring near-shore water quality. Full article