Pub Date : 2024-11-16DOI: 10.1016/j.marpolbul.2024.117261
Rong Zhu , Yan-Yan Zeng , Li-Min Liu , Lu Yin , Kai-Ping Xu , Wei-Feng Chen , Shang-Chun Li , Xiao-Feng Zhou
Hangzhou Bay, one of the fastest economy and population growth region in China, was heavily polluted by a large amounts of industrial waste water and domestic sewage containing harmful heavy metal pollutants. To investigate the status of heavy metals pollution and assess the ecological risks in Hangzhou Bay, seven heavy metals (Cu, Zn, Pb, Cd, Cr, Hg and As) concentrations of water and sediments were analyzed. Heavy metals concentrations in sediments close to the estuarine coast and nearshore area were higher than that in other areas. Cu, Zn, Pb, Cd, Cr and As in sediments might have extensive homologies and originate from the petroleum industry. The pollutions of Cu, Zn, Pb, Cd, Cr and As in seawater and sediment were very light or no pollution. Both in seawater and sediments, the Hg contamination was the most serious among the measured seven heavy metals and should be paid more attention.
{"title":"Pollution status and assessment of seven heavy metals in the seawater and sediments of Hangzhou Bay, China","authors":"Rong Zhu , Yan-Yan Zeng , Li-Min Liu , Lu Yin , Kai-Ping Xu , Wei-Feng Chen , Shang-Chun Li , Xiao-Feng Zhou","doi":"10.1016/j.marpolbul.2024.117261","DOIUrl":"10.1016/j.marpolbul.2024.117261","url":null,"abstract":"<div><div>Hangzhou Bay, one of the fastest economy and population growth region in China, was heavily polluted by a large amounts of industrial waste water and domestic sewage containing harmful heavy metal pollutants. To investigate the status of heavy metals pollution and assess the ecological risks in Hangzhou Bay, seven heavy metals (Cu, Zn, Pb, Cd, Cr, Hg and As) concentrations of water and sediments were analyzed. Heavy metals concentrations in sediments close to the estuarine coast and nearshore area were higher than that in other areas. Cu, Zn, Pb, Cd, Cr and As in sediments might have extensive homologies and originate from the petroleum industry. The pollutions of Cu, Zn, Pb, Cd, Cr and As in seawater and sediment were very light or no pollution. Both in seawater and sediments, the Hg contamination was the most serious among the measured seven heavy metals and should be paid more attention.</div></div>","PeriodicalId":18215,"journal":{"name":"Marine pollution bulletin","volume":"209 ","pages":"Article 117261"},"PeriodicalIF":5.3,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142648615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-16DOI: 10.1016/j.marpolbul.2024.117247
Jie Li , Wanting Wang , Xinlei Li , Sen Liu , Xuming Xu , Yinglan A. , Shilong Ren
Present study investigated heavy metal pollution in the continuous upper river−estuary−sea systems of the Yellow River Delta (YRD). Significant seasonal differences (p < 0.05) for the heavy metal overall profile were observed, although there were no significant spatial variations among the different water bodies. Positive matrix factorization indicated that heavy metals primarily originated from anthropogenic activities (e.g., oil field development, mining, and agricultural activities). Chemical oxygen demand, water temperature, electrical conductivity, dissolved oxygen, pH, and salinity influenced the distribution of heavy metals in water. The NO3− and total phosphorus concentrations were the main influencing factors in sediment, with both showing positive correlations with all heavy metals. Furthermore, low ecological risks were observed for sediment based on the values of the ecological risk and potential ecological risk indexes in the YRD. This study will assist with the effective control and management of heavy metal pollution in a continuous river−estuary−sea system.
{"title":"Heavy metals in the continuous river−estuary−sea system of the Yellow River Delta, China: Spatial patterns, potential sources, and influencing factors","authors":"Jie Li , Wanting Wang , Xinlei Li , Sen Liu , Xuming Xu , Yinglan A. , Shilong Ren","doi":"10.1016/j.marpolbul.2024.117247","DOIUrl":"10.1016/j.marpolbul.2024.117247","url":null,"abstract":"<div><div>Present study investigated heavy metal pollution in the continuous upper river−estuary−sea systems of the Yellow River Delta (YRD). Significant seasonal differences (<em>p</em> < 0.05) for the heavy metal overall profile were observed, although there were no significant spatial variations among the different water bodies. Positive matrix factorization indicated that heavy metals primarily originated from anthropogenic activities (e.g., oil field development, mining, and agricultural activities). Chemical oxygen demand, water temperature, electrical conductivity, dissolved oxygen, pH, and salinity influenced the distribution of heavy metals in water. The NO<sub>3</sub><sup>−</sup> and total phosphorus concentrations were the main influencing factors in sediment, with both showing positive correlations with all heavy metals. Furthermore, low ecological risks were observed for sediment based on the values of the ecological risk and potential ecological risk indexes in the YRD. This study will assist with the effective control and management of heavy metal pollution in a continuous river−estuary−sea system.</div></div>","PeriodicalId":18215,"journal":{"name":"Marine pollution bulletin","volume":"209 ","pages":"Article 117247"},"PeriodicalIF":5.3,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142644463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-16DOI: 10.1016/j.marpolbul.2024.117254
Zhuangming Zhao , Min Xu , Yu Yan , Shibo Yan , Qiaoyun Lin , Juan Xu , Jing Yang , Zhonghan Chen
This study developed an intelligent method for identifying and quantifying water pollution sources in estuarine areas. It characterized the excitation-emission matrix (EEM) fluorescence spectra from seven end-members, including seawater, rainwater, and five pollution sources typical of these areas. A deep learning model was established to identify and quantify these pollution sources in mixed water bodies. The model was fed either the original EEM or a combined EEM and gradient input. The results indicated that the combined input enhanced classification and quantification accuracy; Although model accuracy declined with an increasing number of mixed pollution sources, the combined input still improved classification accuracy by 3.1 % to 6.8 %; When the proportion of rainwater and seawater was below 70 %, the model maintained a classification accuracy of 57.4 % with original input and 61.3 % with combined input, with root mean square error values for the pollution source proportion being 12.2 % and 11.4 %, respectively.
{"title":"Identifying and quantifying multiple pollution sources in estuaries using fluorescence spectra and gradient-based deep learning","authors":"Zhuangming Zhao , Min Xu , Yu Yan , Shibo Yan , Qiaoyun Lin , Juan Xu , Jing Yang , Zhonghan Chen","doi":"10.1016/j.marpolbul.2024.117254","DOIUrl":"10.1016/j.marpolbul.2024.117254","url":null,"abstract":"<div><div>This study developed an intelligent method for identifying and quantifying water pollution sources in estuarine areas. It characterized the excitation-emission matrix (EEM) fluorescence spectra from seven end-members, including seawater, rainwater, and five pollution sources typical of these areas. A deep learning model was established to identify and quantify these pollution sources in mixed water bodies. The model was fed either the original EEM or a combined EEM and gradient input. The results indicated that the combined input enhanced classification and quantification accuracy; Although model accuracy declined with an increasing number of mixed pollution sources, the combined input still improved classification accuracy by 3.1 % to 6.8 %; When the proportion of rainwater and seawater was below 70 %, the model maintained a classification accuracy of 57.4 % with original input and 61.3 % with combined input, with root mean square error values for the pollution source proportion being 12.2 % and 11.4 %, respectively.</div></div>","PeriodicalId":18215,"journal":{"name":"Marine pollution bulletin","volume":"209 ","pages":"Article 117254"},"PeriodicalIF":5.3,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142648613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-16DOI: 10.1016/j.marpolbul.2024.117255
Taison Ka Tai Chang , Billy Chun Ting Cheung , Justin Chi Ho Leong , Gerard F. Ricardo , Jenny Tsz Ching Chan , James Kar Hei Fang , Peter J. Mumby , Apple Pui Yi Chui
Suspended sediment and salinity stresses may escalate under climate change in inshore turbid habitats. We test whether fertilization and embryonic development of Acropora tumida and Platygyra carnosa are less prone to both stressors in turbid coral habitats compared to thresholds reported in literature for species found in clear water reefs. Under optimal sperm concentration (106 sperm mL−1), fertilization of A. tumida declined by 50 % when exposed to combined sediment (92 mg L−1) and salinity stresses. However, these stressors had no significant impact on P. carnosa. We found ∼20- and ∼ 7-fold increases in abnormal embryos for A. tumida and P. carnosa, respectively, under combined stressors. Furthermore, silicon-rich terrestrial-originated sediment caused 50 % larval mortality for A. tumida at a lower concentration of 53 mg L−1. We showed that climate change-related salinity and sediment stresses may hinder coral reproduction and challenge coral recovery, questioning the coral survival in nearshore turbid habitats.
{"title":"Suspended sediment and reduced salinity decrease development success of early stages of Acropora tumida and Platygyra carnosa in a turbid coral habitat, Hong Kong","authors":"Taison Ka Tai Chang , Billy Chun Ting Cheung , Justin Chi Ho Leong , Gerard F. Ricardo , Jenny Tsz Ching Chan , James Kar Hei Fang , Peter J. Mumby , Apple Pui Yi Chui","doi":"10.1016/j.marpolbul.2024.117255","DOIUrl":"10.1016/j.marpolbul.2024.117255","url":null,"abstract":"<div><div>Suspended sediment and salinity stresses may escalate under climate change in inshore turbid habitats. We test whether fertilization and embryonic development of <em>Acropora tumida</em> and <em>Platygyra carnosa</em> are less prone to both stressors in turbid coral habitats compared to thresholds reported in literature for species found in clear water reefs. Under optimal sperm concentration (10<sup>6</sup> sperm mL<sup>−1</sup>), fertilization of <em>A. tumida</em> declined by 50 % when exposed to combined sediment (92 mg L<sup>−1</sup>) and salinity stresses. However, these stressors had no significant impact on <em>P. carnosa</em>. We found ∼20- and ∼ 7-fold increases in abnormal embryos for <em>A. tumida</em> and <em>P. carnosa</em>, respectively, under combined stressors. Furthermore, silicon-rich terrestrial-originated sediment caused 50 % larval mortality for <em>A. tumida</em> at a lower concentration of 53 mg L<sup>−1</sup>. We showed that climate change-related salinity and sediment stresses may hinder coral reproduction and challenge coral recovery, questioning the coral survival in nearshore turbid habitats.</div></div>","PeriodicalId":18215,"journal":{"name":"Marine pollution bulletin","volume":"209 ","pages":"Article 117255"},"PeriodicalIF":5.3,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142648616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
<div><div>Accurate estimation of coastal and in-land water quality parameters is important for managing water resources and meeting the demand of sustainable development goals. The water quality monitoring based on discrete water sample analysis is limited to specific locations and becomes less effective to offer a synoptic view of the water quality variability at different spatial and temporal scales. The optical remote sensing techniques have proved their ability to provide a comprehensive and synoptic view of water quality parameters. In conjugation with other products, the optical remote sensing data products can be utilized for the effective management of water bodies while addressing the socio-economic issues faced by local governments and states. In recent years, multiple machine-learning (ML) models have been reported on the estimation of water quality using remote sensing data, but their performance is limited when extended to diverse water types within coastal and inland water environments. In this study, we present an ensemble machine-learning model for estimating the primary water quality parameters in coastal and inland waters, such as Chlorophyll-a (Chl-<em>a</em>) concentration, colored dissolved organic matter (<span><math><msub><mi>a</mi><mi>CDOM</mi></msub><mspace></mspace><mfenced><mn>440</mn></mfenced></math></span>), and Turbidity. It utilizes the in-situ measurements to train and optimize the ensemble machine-learning models for the spectral measurements data (400–700 nm) provided by MODIS-Aqua, Sentinel-2 Multi Spectral Instrument (MSI), and PlanetScope (Planet). To develop the prediction models, these in-situ measurements data were split into two parts: a training dataset (70 %) and a testing dataset (30 %). The ensemble machine-learning models were validated using the 5-fold cross-validation method. These models were trained and tested against distinct datasets encompassing a broad range of variations in water quality parameters collected from open ocean, coastal and inland waters. The validation results demonstrated a superior performance of the present ensemble ML models compared to other ML models (Chl-<em>a</em>: R<sup>2</sup> = 0.96, RMSE = 4.93, MAE = 2.89; <span><math><msub><mi>a</mi><mi>CDOM</mi></msub><mspace></mspace><mfenced><mn>440</mn></mfenced></math></span>: R<sup>2</sup> = 0.93, RMSE = 0.057, MAE = 0.025; Turbidity: R<sup>2</sup> = 0.95, RMSE = 4.52, MAE = 1.009). To realize the importance of this study, the ensemble ML models were applied to MODIS-Aqua monthly composite measurements from 2003 to 2022 and captured pronounced seasonal variations in water quality parameters (WQP) and Water Quality Index (WQI). For instance, in the Gulf of Khambhat, turbidity decreased at an annual average rate of ∼0.08 NTU and Chl-<em>a</em> increased at an annual average rate of ∼0.004 mg m<sup>−3</sup> for the past 20 years. Furthermore, we investigated the occurrences of <em>Noctiluca scintillans</em> (here after <em>N. s
{"title":"Long-term water quality assessment in coastal and inland waters: An ensemble machine-learning approach using satellite data","authors":"Murugan Karthick , Palanisamy Shanmugam , Gurunathan Saravana Kumar","doi":"10.1016/j.marpolbul.2024.117036","DOIUrl":"10.1016/j.marpolbul.2024.117036","url":null,"abstract":"<div><div>Accurate estimation of coastal and in-land water quality parameters is important for managing water resources and meeting the demand of sustainable development goals. The water quality monitoring based on discrete water sample analysis is limited to specific locations and becomes less effective to offer a synoptic view of the water quality variability at different spatial and temporal scales. The optical remote sensing techniques have proved their ability to provide a comprehensive and synoptic view of water quality parameters. In conjugation with other products, the optical remote sensing data products can be utilized for the effective management of water bodies while addressing the socio-economic issues faced by local governments and states. In recent years, multiple machine-learning (ML) models have been reported on the estimation of water quality using remote sensing data, but their performance is limited when extended to diverse water types within coastal and inland water environments. In this study, we present an ensemble machine-learning model for estimating the primary water quality parameters in coastal and inland waters, such as Chlorophyll-a (Chl-<em>a</em>) concentration, colored dissolved organic matter (<span><math><msub><mi>a</mi><mi>CDOM</mi></msub><mspace></mspace><mfenced><mn>440</mn></mfenced></math></span>), and Turbidity. It utilizes the in-situ measurements to train and optimize the ensemble machine-learning models for the spectral measurements data (400–700 nm) provided by MODIS-Aqua, Sentinel-2 Multi Spectral Instrument (MSI), and PlanetScope (Planet). To develop the prediction models, these in-situ measurements data were split into two parts: a training dataset (70 %) and a testing dataset (30 %). The ensemble machine-learning models were validated using the 5-fold cross-validation method. These models were trained and tested against distinct datasets encompassing a broad range of variations in water quality parameters collected from open ocean, coastal and inland waters. The validation results demonstrated a superior performance of the present ensemble ML models compared to other ML models (Chl-<em>a</em>: R<sup>2</sup> = 0.96, RMSE = 4.93, MAE = 2.89; <span><math><msub><mi>a</mi><mi>CDOM</mi></msub><mspace></mspace><mfenced><mn>440</mn></mfenced></math></span>: R<sup>2</sup> = 0.93, RMSE = 0.057, MAE = 0.025; Turbidity: R<sup>2</sup> = 0.95, RMSE = 4.52, MAE = 1.009). To realize the importance of this study, the ensemble ML models were applied to MODIS-Aqua monthly composite measurements from 2003 to 2022 and captured pronounced seasonal variations in water quality parameters (WQP) and Water Quality Index (WQI). For instance, in the Gulf of Khambhat, turbidity decreased at an annual average rate of ∼0.08 NTU and Chl-<em>a</em> increased at an annual average rate of ∼0.004 mg m<sup>−3</sup> for the past 20 years. Furthermore, we investigated the occurrences of <em>Noctiluca scintillans</em> (here after <em>N. s","PeriodicalId":18215,"journal":{"name":"Marine pollution bulletin","volume":"209 ","pages":"Article 117036"},"PeriodicalIF":5.3,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142648614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-16DOI: 10.1016/j.marpolbul.2024.117267
Jin Young Choi , Jae Seong Lee , Kyung-Tae Kim , Geun-Ha Park , Jun-Mo Jung , Gi Hoon Hong , Kongtae Ra , Sangmin Hyun , Chang Eon Lee , Eun-Ji Won
This study investigated the spatial distribution and chemical characteristics of potentially toxic elements (PTEs) in road-deposited sediments (RDS) at the Port of Busan by size fraction. Enrichment factor (EF) values for Zn, Cd, and Sb in fine RDS <250 μm were 52–69, 49–78, and 46–44, respectively, indicating ‘extremely high enrichment’. Various statistical analyses, including PCA and PMF models, revealed a strong correlation between pollution levels in RDS <250 μm and vehicle type, identifying non-exhaust emissions (NEE) of vehicles as a primary source of PTEs in RDS from the port. The risk index (RI) value of fine RDS ranged from 649 to 2238, indicating that the entire study area could be classified as having a ‘significant ecological risk,’ with higher values observed in heavy-duty vehicles (HDV) areas. The study underscores the need for effective NEE management to mitigate the environmental impact of ports on marine ecosystems.
{"title":"Characteristics and sources of potentially toxic elements in road-deposited sediments at the Port of Busan, South Korea: A key contributor to port sediments pollution","authors":"Jin Young Choi , Jae Seong Lee , Kyung-Tae Kim , Geun-Ha Park , Jun-Mo Jung , Gi Hoon Hong , Kongtae Ra , Sangmin Hyun , Chang Eon Lee , Eun-Ji Won","doi":"10.1016/j.marpolbul.2024.117267","DOIUrl":"10.1016/j.marpolbul.2024.117267","url":null,"abstract":"<div><div>This study investigated the spatial distribution and chemical characteristics of potentially toxic elements (PTEs) in road-deposited sediments (RDS) at the Port of Busan by size fraction. Enrichment factor (EF) values for Zn, Cd, and Sb in fine RDS <250 μm were 52–69, 49–78, and 46–44, respectively, indicating ‘extremely high enrichment’. Various statistical analyses, including PCA and PMF models, revealed a strong correlation between pollution levels in RDS <250 μm and vehicle type, identifying non-exhaust emissions (NEE) of vehicles as a primary source of PTEs in RDS from the port. The risk index (RI) value of fine RDS ranged from 649 to 2238, indicating that the entire study area could be classified as having a ‘significant ecological risk,’ with higher values observed in heavy-duty vehicles (HDV) areas. The study underscores the need for effective NEE management to mitigate the environmental impact of ports on marine ecosystems.</div></div>","PeriodicalId":18215,"journal":{"name":"Marine pollution bulletin","volume":"209 ","pages":"Article 117267"},"PeriodicalIF":5.3,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142648611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-16DOI: 10.1016/j.marpolbul.2024.117273
Jie Li , Junxiang Lai , Guilin Xu , Mingben Xu , Man Wu , Xiaomin Yan , Zihan Pan , Jing Guo
Phaeocystis globosa is the most common species making up harmful algal blooms. For better detect P. globosa bloom, a multispectral approach was developed based on extensive in-situ investigation and MODIS remote sensing reflectance (Rrs) dataset. A novel proxy RPG was created based on the feature of Rrs spectral shape and P. globosa bloom was identified when RPG was >1.6. Normalized Fluorescence Line Height (nFLH) was applied to discriminate the bloom events and nFLH of bloom waters was almost higher than 0.095 Wm−2μm−1sr−1. The RPG associated with nFLH exhibited the P. globosa bloom areas comparable to that in field investigation, which indicated this practical method was successful on the spatial and temporal distribution of P. globosa blooms. Several environmental factors derived from MODIS products and field survey were analyzed to characterize the bloom conditions. Redundancy analysis suggested that nutrients and temperature are vital for triggering P. globosa bloom.
球囊藻是构成有害藻华的最常见物种。为了更好地检测球藻水华，在广泛的现场调查和 MODIS 遥感反射率（Rrs）数据集的基础上开发了一种多光谱方法。根据 Rrs 光谱形状特征创建了一种新的替代 RPG，当 RPG >1.6 时，就能识别出球藻花。应用归一化荧光线高（nFLH）来区分水华事件，水华水域的 nFLH 几乎高于 0.095 Wm-2μm-1sr-1。与 nFLH 相关的 RPG 所显示的球藻藻华区域与实地调查的区域相当，这表明这种实用的方法在球藻藻华的时空分布方面是成功的。分析了从 MODIS 产品和实地调查中得出的若干环境因素，以确定水华的特征。冗余分析表明，营养物质和温度对引发球藻藻华至关重要。
{"title":"Detecting the Phaeocystis globosa bloom and characterizing its bloom condition in the northern Beibu Gulf using MODIS measurements","authors":"Jie Li , Junxiang Lai , Guilin Xu , Mingben Xu , Man Wu , Xiaomin Yan , Zihan Pan , Jing Guo","doi":"10.1016/j.marpolbul.2024.117273","DOIUrl":"10.1016/j.marpolbul.2024.117273","url":null,"abstract":"<div><div><em>Phaeocystis globosa</em> is the most common species making up harmful algal blooms. For better detect <em>P. globosa</em> bloom, a multispectral approach was developed based on extensive in-situ investigation and MODIS remote sensing reflectance (R<sub>rs</sub>) dataset. A novel proxy R<sub>PG</sub> was created based on the feature of R<sub>rs</sub> spectral shape and <em>P. globosa</em> bloom was identified when R<sub>PG</sub> was >1.6. Normalized Fluorescence Line Height (nFLH) was applied to discriminate the bloom events and nFLH of bloom waters was almost higher than 0.095 Wm<sup>−2</sup>μm<sup>−1</sup>sr<sup>−1</sup>. The R<sub>PG</sub> associated with nFLH exhibited the <em>P. globosa</em> bloom areas comparable to that in field investigation, which indicated this practical method was successful on the spatial and temporal distribution of <em>P. globosa</em> blooms. Several environmental factors derived from MODIS products and field survey were analyzed to characterize the bloom conditions. Redundancy analysis suggested that nutrients and temperature are vital for triggering <em>P. globosa</em> bloom.</div></div>","PeriodicalId":18215,"journal":{"name":"Marine pollution bulletin","volume":"209 ","pages":"Article 117273"},"PeriodicalIF":5.3,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142648612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-16DOI: 10.1016/j.marpolbul.2024.117245
Yu Lee Jang , Soeun Eo , Gi Myung Han , Sung Yong Ha , Sang Hee Hong , Won Joon Shim
Visual observation surveys from ships are commonly used for monitoring floating marine debris, but their detection performance has not yet been fully verified. Here, simultaneous visual observation surveys and surface trawling were conducted in three coastal areas of South Korea, each with distinct characteristics. The extent of floating debris missed by visual observations was assessed, and the characteristics of overlooked debris were identified. The mean density of floating debris observed visually was five-fold lower than that obtained from surface trawling. Loss of buoyancy and transparent colour of debris were identified as major factors contributing to the significant difference in density between the two survey methods. Our findings suggest that visual observation can underestimate the density of floating debris, especially in areas with abundant plastic bags and sheets. Supplementary methods such as surface trawls with macro-sized mesh are recommended to accurately assess the level of contamination from floating debris.
{"title":"Ship-based visual observation underestimates plastic debris in marine surface water","authors":"Yu Lee Jang , Soeun Eo , Gi Myung Han , Sung Yong Ha , Sang Hee Hong , Won Joon Shim","doi":"10.1016/j.marpolbul.2024.117245","DOIUrl":"10.1016/j.marpolbul.2024.117245","url":null,"abstract":"<div><div>Visual observation surveys from ships are commonly used for monitoring floating marine debris, but their detection performance has not yet been fully verified. Here, simultaneous visual observation surveys and surface trawling were conducted in three coastal areas of South Korea, each with distinct characteristics. The extent of floating debris missed by visual observations was assessed, and the characteristics of overlooked debris were identified. The mean density of floating debris observed visually was five-fold lower than that obtained from surface trawling. Loss of buoyancy and transparent colour of debris were identified as major factors contributing to the significant difference in density between the two survey methods. Our findings suggest that visual observation can underestimate the density of floating debris, especially in areas with abundant plastic bags and sheets. Supplementary methods such as surface trawls with macro-sized mesh are recommended to accurately assess the level of contamination from floating debris.</div></div>","PeriodicalId":18215,"journal":{"name":"Marine pollution bulletin","volume":"209 ","pages":"Article 117245"},"PeriodicalIF":5.3,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142644465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-15DOI: 10.1016/j.marpolbul.2024.117251
Jeancarlo M. Fajardo-Urbina , Yang Liu , Sonja Georgievska , Ulf Gräwe , Herman J.H. Clercx , Theo Gerkema , Matias Duran-Matute
Several coastal regions require operational forecast systems for predicting the transport of pollutants released during marine accidents. In response to this need, surrogate models offer cost-effective solutions. Here, we propose a surrogate modeling method for predicting the residual transport of particle patches in coastal environments. These patches are collections of passive particles equivalent to Eulerian tracers but can be extended to other particulates. By only using relevant forcing, we train a deep learning model (DLM) to predict the displacement (advection) and spread (dispersion) of particle patches after one tidal period. These quantities are then coupled into a simplified Lagrangian model to obtain predictions for larger times. Predictions with our methodology, successfully applied in the Dutch Wadden Sea, are fast. The trained DLM provides predictions in a few seconds, and our simplified Lagrangian model is one to two orders of magnitude faster than a traditional Lagrangian model fed with currents.
{"title":"Efficient deep learning surrogate method for predicting the transport of particle patches in coastal environments","authors":"Jeancarlo M. Fajardo-Urbina , Yang Liu , Sonja Georgievska , Ulf Gräwe , Herman J.H. Clercx , Theo Gerkema , Matias Duran-Matute","doi":"10.1016/j.marpolbul.2024.117251","DOIUrl":"10.1016/j.marpolbul.2024.117251","url":null,"abstract":"<div><div>Several coastal regions require operational forecast systems for predicting the transport of pollutants released during marine accidents. In response to this need, surrogate models offer cost-effective solutions. Here, we propose a surrogate modeling method for predicting the residual transport of particle patches in coastal environments. These patches are collections of passive particles equivalent to Eulerian tracers but can be extended to other particulates. By only using relevant forcing, we train a deep learning model (DLM) to predict the displacement (advection) and spread (dispersion) of particle patches after one tidal period. These quantities are then coupled into a simplified Lagrangian model to obtain predictions for larger times. Predictions with our methodology, successfully applied in the Dutch Wadden Sea, are fast. The trained DLM provides predictions in a few seconds, and our simplified Lagrangian model is one to two orders of magnitude faster than a traditional Lagrangian model fed with currents.</div></div>","PeriodicalId":18215,"journal":{"name":"Marine pollution bulletin","volume":"209 ","pages":"Article 117251"},"PeriodicalIF":5.3,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142639262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Marine plastic pollution is a global issue affecting ecosystems and various aspects of human life. The scientific community is exploring new monitoring and containment approaches. Because in-situ sampling campaigns are time and resource demanding, there is a focus on integrating different approaches for marine litter monitoring. Data of two in-situ surveys (using a manta net) were compared to sea surface currents data and derived products with the aim to find a proxy variable of the plastic occurrence. Sea surface currents data were provided by the CALYPSO HF network (operating in the Sicily Channel since 2012). Notably, the occurrence of fragment items is inversely correlated with the total kinetic energy (r2 ~ 0.85). This result was confirmed by a Lagrangian tracking model considering the deployment of virtual drifters around each in-situ measurement point. The proposed method applied to a wider domain using Copernicus Marine Service (CMS) data revealed that high plastic accumulation areas could be located at the centre of eddies often occurring in the winter period. However, uncertainties arise by the moderate-low correlation found between HF CALYPSO and CMS sea current data.
{"title":"Towards microplastic hotspots detection: A comparative analysis of in-situ sampling and sea surface currents derived by HF radars","authors":"Fulvio Capodici , Laura Corbari , Adam Gauci , Gualtiero Basilone , Angelo Bonanno , Salvatore Campanella , Giuseppe Ciraolo , Angela Candela , Daniela D'Amato , Rosalia Ferreri , Ignazio Fontana , Simona Genovese , Giovanni Giacalone , Giuseppina Marino , Salvatore Aronica","doi":"10.1016/j.marpolbul.2024.117237","DOIUrl":"10.1016/j.marpolbul.2024.117237","url":null,"abstract":"<div><div>Marine plastic pollution is a global issue affecting ecosystems and various aspects of human life. The scientific community is exploring new monitoring and containment approaches<em>.</em> Because <em>in-situ</em> sampling campaigns are time and resource demanding, there is a focus on integrating different approaches for marine litter monitoring. Data of two <em>in-situ</em> surveys (using a manta net) were compared to sea surface currents data and derived products with the aim to find a proxy variable of the plastic occurrence. Sea surface currents data were provided by the CALYPSO HF network (operating in the Sicily Channel since 2012). Notably, the occurrence of fragment items is inversely correlated with the total kinetic energy (r<sup>2</sup> ~ 0.85). This result was confirmed by a Lagrangian tracking model considering the deployment of virtual drifters around each <em>in-situ</em> measurement point. The proposed method applied to a wider domain using Copernicus Marine Service (CMS) data revealed that high plastic accumulation areas could be located at the centre of eddies often occurring in the winter period. However, uncertainties arise by the moderate-low correlation found between HF CALYPSO and CMS sea current data.</div></div>","PeriodicalId":18215,"journal":{"name":"Marine pollution bulletin","volume":"209 ","pages":"Article 117237"},"PeriodicalIF":5.3,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142644466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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