Eutrophication Assessment And Modelling Of Water Quality

Eutrophication is a serious environmental problem facing society today, to carry out quality assessment, the waters of lakes and reservoirs simulate different characteristics of space pollution and eutrophication of effective prevention and control of eutrophication and to improve the water quality of the environment is important. Tiegang Reservoir is an important source of drinking water reservoir in Baoan District of Shenzhen, it is known as ‘Shenzhen Water Tank’, due to huge water supply demand, the good or bad of its water quality will directly affect local development of people’s livelihood. To know the characteristics and eutrophication state of phytoplankton in Tiegang Reservoir, the author carried through investigation and analysis on phytoplankton, physical and chemical indexes of 10 sampling points in Tiegang Reservoir in June 2013 to May 2014. Firstly, the author evaluates the eutrophication level of reservoir by means of gray clustering method, explores key factors affecting phytoplankton community structure change and the relationship between wood plant and chlorophyll a using canonical correspondence analysis and correlation analysis, establishes genetic algorithm to improve the BP neural network model, simulates and predicts the dynamic condition of chlorophyll a which is the key factor of eutrophication, and puts forward suggestions for preventing and controlling. The main research conclusion are as follows:1. The eutrophication level of various points throughout the year in Tiegang Reservoir is in mild eutrophication, water quality in space, water inlet and outlet of reservoir presents better and better, eutrophication level in the tail of water reservoir is higher than other areas due to exterior pollution.2. There are 56 genera of phytoplankton being identified throughout the year in Tiegang Reservoir, including 25 genera of chlorophyta, 15 genera of bacillariophyta, 16 genera of cyanophyta, 2 genera of pyrrhophyta and 1 genus of euglenophyta. The survey of phytoplankton abundance shows that cyanophyta accounts for absolute advantage in most of months in reservoir, in terms of genus level, lyngbya is dominant species in spring and autumn, pseudanabaena mucicola is dominant species in summer and melosira has higher degree of dominance in winter, abundance variation presents the trend of high in winter and low in summer. Correlation analysis shows that the overall abundance variation of phytoplankton in Tiegang Reservoir has higher correlation with chlorophyll a. CCA results showed that the phytoplankton community dynamics is mainly affected by temperature, TOC, p H, influence of ammonia.3. Use BP neural network to carry through simulation and prediction on the key factor of eutrophication-chlorophyll a, establish the BP neural network of key factors TOC, TN, TP screened through gray correlation method and chlorophyll a, short-term forecasting effect achieves ideal goal, simulate the pressure response of chlorophyll a to its driving factors and conclude that cutting down TN is the fastest and most effective means to control eutrophication process in Tiegang Reservoir.The main conclusions:Grey clustering method is accurate, objective and practical in evaluating water eutrophication. each category makes the highest contribution to chlorophyll a when becoming dominant species. The key environmental factors influence phytoplankton community dynamics Tiegang temperature, followed by total organic carbon, etc. BP neural network better short-term forecasting, TN reduce eutrophication process is an iron post the most rapid and effective means of control.