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Water Environment Carrying Capacity Of Haizhou Bay Marine Ranching

Posted on:2021-04-26Degree:MasterType:Thesis
Country:ChinaCandidate:J C YuFull Text:PDF
GTID:2381330611461646Subject:Environmental Science and Engineering
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At present,China was setting off a wave of construction of marine pastures.Various coastal provinces and cities have also issued relevant supporting policies.They have carried out the maintenance of marine biological resources in the main form of marine pastures construction in the coastal areas of China,improved the marine ecological environment and adjusted the fishery industrial structure,and promoted the construction of marine ecological civilization and the industrial upgrading of marine economy.With the increasing scale of modern marine ranching construction in China,economic,ecological and social benefits are increasingly significant,but there was a lack of scientific and systematic evaluation,prediction and management of marine ranching related system and mechanism.Although the marine ranching can improve the ecological environment,if the exploitation and utilization exceed the limit for a long time,it will cause the deterioration of the ecological environment and eventually lead to the decline of fishery resources.In order to ensure the sustainable and healthy development of the marine ranching,it was necessary to monitor the ecological environment of the marine ranching for a long time,ensure its ecological environment safety,evaluate its environmental carrying capacity,measure the ecological environment health of the marine ranching,give early warning to the improper human development activities and carry out scientific regulation and control,and restrict all kinds of development activities within the environmental carrying capacity,so as to promote the economy Resources and environment are balanced.Based on the analysis of the environmental monitoring data of marine pastures in Haizhou Bay over the years,this paper studies the temporal and spatial variation characteristics of various water environmental factors and their organic pollution and eutrophication status,constructs the water environmental carrying capacity evaluation system by principal component analysis and frequency distribution method,and evaluates the water environmental carrying capacity of marine pastures in Haizhou Bay with the help of BP neural network model The relationship between water environment carrying capacity and plankton index was analyzed.The conclusion was as follows:?1?The dissolved oxygen saturation of Haizhou Bay marine ranching area was relatively high,belonging to the oxygen rich area;the COD concentration was greatly affected by the internal pollution,meeting the water quality requirements of the functional area,but there was an upward trend;the concentration of active phosphate and active silicate was greatly affected by the river runoff;the inorganic nitrogen mainly exists in the form of nitrate nitrogen,and a small part of the quarterly pollution was relatively heavy,belonging to the fourth type of water quality;chlorophyll The concentration of a fluctuated greatly in different seasons,and the primary productivity in the reef area was significantly higher than that in the control area.?2?The eutrophication level of the study area was in good condition for most of the time,and some of the eutrophication occurred in spring and autumn;the region was heavily polluted by organic pollutants,and most of the organic pollutants appeared in different degrees in most of the quarters,among which DIN was the largest contributor to the organic pollution index,followed by COD.?3?Through principal component analysis,objective and reasonable evaluation indexes of water environment bearing capacity such as do,DO?CODMn?BOD5?SiO32-–Si?PO43-–P?DIN were selected,and the threshold values of each index interval were determined by combining the sea water quality standard and frequency step-by-step method,the bearing capacity grade was divided,and the evaluation system of water environment bearing capacity was constructed.The model of water environment carrying capacity of Haizhou Bay was established by using MATLAB software.The result shows that the model of BP neural network fits well and has certain credibility.It can be applied to the study of water environment carrying capacity of marine pastures.?4?The model was used to evaluate the marine pastures in Haizhou Bay from 2009 to2015 and 2017.The results show that the water environment carrying capacity index has a downward trend.Generally speaking,it can meet the functional requirements of the aquaculture area within the bearing range.In the spring of 2013,summer and autumn of2014,it was a light overload state,and the excessive concentration of DIN,BOD and COD was the main reason for the overload;the near shore control area and marine ranching area had different degrees of light overload,which was related to land-based pollution,excessive feeding and fertilization in aquaculture.?5?The correlation analysis results of water environment carrying capacity index and plankton index in 2017 show that WECCI in spring was significantly correlated with phytoplankton diversity index,WECCI in summer was significantly correlated with zooplankton biomass and abundance,while WECCI in autumn was not.It shows that WECCIhas a certain response to plankton index,but the seasonal difference was obvious.The diversity and abundance index of plankton in aquaculture area are affected by many factors,so we can't judge the water quality simply by the index.Based on the theory of water environment carrying capacity,combined with the actual situation of Haizhou Bay marine ranching,this paper uses BP neural network model to evaluate the water environment carrying capacity,which provides a scientific basis for the development and utilization of marine ranching and ecological environment protection,and provides feasible technical methods and demonstration cases for the study of water environment carrying capacity in other sea areas.
Keywords/Search Tags:Haizhou Bay, marine ranching, water environment carrying capacity, BP neural network
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