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Variations in the Distribution of Chl- a and Simulation Using a Multiple Regression Model

Author

Listed:
  • Jiancai Deng

    (State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China)

  • Fang Chen

    (Monitoring Center of Hydrology and Water Resources of Taihu Basin, Wuxi 214024, China)

  • Weiping Hu

    (State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China)

  • Xin Lu

    (Institute of Agricultural Resources and Environment, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China)

  • Bin Xu

    (Monitoring Center of Hydrology and Water Resources of Taihu Basin, Wuxi 214024, China)

  • David P. Hamilton

    (Australian Rivers Institute, Griffith University, Queensland 4111, Australia)

Abstract

Chlorophyll a (Chl- a ) is an important indicator of algal biomass in aquatic ecosystems. In this study, monthly monitoring data for Chl- a concentration were collected between 2005 and 2015 at four stations in Meiliang Bay, a eutrophic bay in Lake Taihu, China. The spatiotemporal distribution of Chl- a in the bay was investigated, and a statistical model to relate the Chl- a concentration to key driving variables was also developed. The monthly Chl- a concentration in Meiliang Bay changed from 2.6 to 330.0 μg/L, and the monthly mean Chl- a concentration over 11 years was found to be higher at sampling site 1, the northernmost site near Liangxihe River, than at the three other sampling sites. The annual mean Chl- a concentration fluctuated greatly over time and exhibited an upward trend at all sites except sampling site 3 in the middle of Meiliang Bay. The Chl- a concentration was positively correlated with total phosphorus (TP; r = 0.57, p < 0.01), dissolved organic matter (DOM; r = 0.73, p < 0.01), pH ( r = 0.44, p < 0.01), and water temperature (WT; r = 0.37, p < 0.01), and negatively correlated with nitrate (NO 3 − -N; r = −0.28, p < 0.01), dissolved oxygen (DO; r = −0.12, p < 0.01), and Secchi depth (ln(SD); r = −0.11, p < 0.05). A multiple linear regression model integrating the interactive effects of TP, DOM, WT, and pH on Chl- a concentrations was established ( R = 0.80, F = 230.7, p < 0.01) and was found to adequately simulate the spatiotemporal dynamics of the Chl- a concentrations in other regions of Lake Taihu. This model provides lake managers with an alternative for the control of eutrophication and the suppression of aggregations of phytoplankton biomass at the water surface.

Suggested Citation

  • Jiancai Deng & Fang Chen & Weiping Hu & Xin Lu & Bin Xu & David P. Hamilton, 2019. "Variations in the Distribution of Chl- a and Simulation Using a Multiple Regression Model," IJERPH, MDPI, vol. 16(22), pages 1-16, November.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:22:p:4553-:d:288061
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    References listed on IDEAS

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