A two-year(2020–2021)observation of marine phycotoxins in phytoplankton in typical mariculture areas of East China Sea

To learn the marine phycotoxins(MPTs)contamination status in mariculture areas in the East China Sea(ECS),from May 2020 to October 2021,80 net-concentrated phytoplankton samples were collected from 12 stations in three typical areas with high incidence of red tides,namely,Gouqi Island,Sandu’ao-Lianjiang,and Zhangzhou-Dongshan Island in ECS,and MPTs of the samples were detected.Six types of toxins were detected in 24 samples from 9 stations.Lipophilic marine toxins(LMTs)were more common and diverse in these areas.Pectenotoxin 2(PTX2)was the main lipophilic marine toxin(LMT)in the concentrated phytoplankton samples and the occurrence showed seasonal differences from north to south.According to the potential risks of pectenotoxin(PTXs)to seafood safety,it is suggested to execute regular monitoring on PTXs in ECS and a mandatory standard should be formulated based on the comprehensive analysis of in-situ monitoring and lab research.Meanwhile,contamination risks of cyclic imine toxins in the north and domoic acid(DA)in the south of ECS should also be paid with attention to.Only 2 paralytic shellfish toxins(PSTs),N-sulfocarbamoyl toxin C2(C2),and decarbamoyl gonyatoxin 3(dcGTX3),were found from spring samples in the north of ECS.As the biggest mussel culture county of China,Gouqi Island showed higher toxin diversity and the toxin detection rate was higher than the other two areas.In Gouqi Island area,PSTs were a serious potential threat in spring,and LMTs instead of PSTs became the main risk in summer-autumn seasons.To ensure the safety of seafood and marine environmental health,it is recommended to conduct long-term targeted tracking and monitoring of MPTs in this and similar mariculture areas.

Relationship between toxic and harmful microalgae and environmental factors in typical mariculture areas of East China Sea

Toxic and harmful algal blooms are usually more frequent in mariculture areas due to the abundant trophic conditions.To investigate the relationship between toxic and harmful microalgae and environmental factors,we set up 12 stations near three mariculture regions(Gouqi Island,Sandu Bay,and Dongshan Bay)in the East China Sea.We collected samples from all four seasons starting from May 2020 to March 2021.We identifi ed 199 species belonging to 70 genera,of which 38 species were toxic and harmful,including 24 species of Dinophyceae,13 species of Bacillariophyceae,and 1 species of Raphidophyceae.The species composition of toxic and harmful microalgae showed a predominance of diatoms in the summer(August),and dinofl agellates in the spring(May),autumn(November),and winter(March).The cell densities of toxic and harmful microalgae were higher in summer(with an average value of 15.34×10^(3) cells/L)than in other seasons,3.53×10^(3) cells/L in spring,1.82×10^(3) cells/L in winter,and 1.0×10^(3) cells/L in autumn.Pseudo-nitzschia pungens,Prorocentrum minimum,Paralia sulcata,and Prorocentrum micans were the dominant species and were available at all 12 stations in the three mariculture areas.We selected 10 toxic and harmful microalgal species with frequency>6 at the survey stations for the redundancy analysis(RDA),and the results show that NO^(-)_(3),water temperature(WT),pH,DO,and NO^(-)_(2) were the main factors on distribution of toxic and harmful microalgae.We concluded that the rich nutrient conditions in the East China Sea mariculture areas increased the potential for the risk of toxic and harmful microalgal bloom outbreaks.

Distribution and Ecological Risk Assessment of HCHs and DDTs in Surface Seawater and Sediment of the Mariculture Area of Jincheng Bay,China

The distribution of hexachloroeyclohexanes （HCHs） and dichlorodiphenyltrichloroethanes （DDTs） in the surface sea- water and sediment of Jincheng Bay mariculture area were investigated in the present study. The concentration of total HCHs and DDTs ranged from 2.98 to 14.87ngL-l and were〈0.032ngL-1, respectively, in surface seawater, and ranged from 5.52 to 9.43 and from 4.11 to 6.72 ng g-l, respectively, in surface sediment. It was deduced from the composition profile of HCH isomers and DDT congeners that HCH residues derived from a mixture of technical-grade HCH and lindane whereas the DDT residues derived from technical-grade DDT and dicofol. Moreover, both HCH and DDT residues may mainly originate from historical inputs. The hazard quotient of α-HCH, β-HCH, γ-HCH and δ-HCH to marine species was 0.030, 0.157, 3.008 and 0.008, respectively. It was estimated that the overall probability of adverse biological effect from HCHs was less than 5%, indicating that its risk to seawater column spe- cies was low. The threshold effect concentration exceeding frequency of γ-HCH, p,p＇-DDD, p,p＇-DDE and p,p＇-DDT in sediment ranged from 8.3% to 100%, and the relative concentration of the HCH and DDT mixture exceeded their probable effect level in sediment. These findings indicated that the risk to marine benthos was high and potentially detrimental to the safety of aquatic prod- ucts, e.g., sea cucumber and benthic shellfish.

Marine aquaculture mapping using GF-1 WFV satellite images and full resolution cascade convolutional neural network

Growing demand for seafood and reduced fishery harvests have raised intensive farming of marine aquaculture in coastal regions,which may cause severe coastal water problems without adequate environmental management.Effective mapping of mariculture areas is essential for the protection of coastal environments.However,due to the limited spatial coverage and complex structures,it is still challenging for traditional methods to accurately extract mariculture areas from medium spatial resolution(MSR)images.To solve this problem,we propose to use the full resolution cascade convolutional neural network(FRCNet),which maintains effective features over the whole training process,to identify mariculture areas from MSR images.Specifically,the FRCNet uses a sequential full resolution neural network as the first-level subnetwork,and gradually aggregates higher-level subnetworks in a cascade way.Meanwhile,we perform a repeated fusion strategy so that features can receive information from different subnetworks simultaneously,leading to rich and representative features.As a result,FRCNet can effectively recognize different kinds of mariculture areas from MSR images.Results show that FRCNet obtained better performance than other classical and recently proposed methods.Our developed methods can provide valuable datasets for large-scale and intelligent modeling of the marine aquaculture management and coastal zone planning.