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Exploring the influence of lake water chemistry on chlorophyll a: A multivariate statistical model analysis

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  • Liu, Yong
  • Guo, Huaicheng
  • Yang, Pingjian

Abstract

A multivariate statistical approach integrating the absolute principal components score (APCS) and multivariate linear regression (APCS-MLR), along with structural equation modeling (SEM), was used to model the influence of water chemistry variables on chlorophyll a (Chl a) in Lake Qilu, a severely polluted lake in southwestern China. Water quality was surveyed monthly from 2000 to 2005. APCS-MLR was used to identify key water chemistry variables, mine data for SEM, and predict Chl a. Seven principal components (PCs) were determined as eigenvalues >1, which explained 68.67% of the original variance. Four PCs were selected to predict Chl a using APCS-MLR. The results showed a good fit between the observed data and modeled values, with R2=0.80. For SEM, Chl a and eight variables were used: NH4-N (ammonia-nitrogen), total phosphorus (TP), Secchi disc depth (SD), cyanide (CN), arsenic (As), cadmium (Cd), fluoride (F), and temperature (T). A conceptual model was established to describe the relationships among the water chemistry variables and Chl a. Four latent variables were also introduced: physical factors, nutrients, toxic substances, and phytoplankton. In general, the SEM demonstrated good agreement between the sample covariance matrix of observed variables and the model-implied covariance matrix. Among the water chemistry factors, T and TP had the greatest positive influence on Chl a, whereas SD had the largest negative influence. These results will help researchers and decision-makers to better understand the influence of water chemistry on phytoplankton and to manage eutrophication adaptively in Lake Qilu.

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  • Liu, Yong & Guo, Huaicheng & Yang, Pingjian, 2010. "Exploring the influence of lake water chemistry on chlorophyll a: A multivariate statistical model analysis," Ecological Modelling, Elsevier, vol. 221(4), pages 681-688.
    • Handle: RePEc:eee:ecomod:v:221:y:2010:i:4:p:681-688
    DOI: 10.1016/j.ecolmodel.2009.03.010
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    References listed on IDEAS

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    1. Bayard, Budry & Jolly, Curtis, 2007. "Environmental behavior structure and socio-economic conditions of hillside farmers: A multiple-group structural equation modeling approach," Ecological Economics, Elsevier, vol. 62(3-4), pages 433-440, May.
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    1. Gkargkavouzi, Anastasia & Halkos, George & Matsiori, Steriani, 2019. "How do motives and knowledge relate to intention to perform environmental behavior? Assessing the mediating role of constraints," Ecological Economics, Elsevier, vol. 165(C), pages 1-1.
    2. Deutsch, Eliza S. & Alameddine, Ibrahim & Qian, Song S., 2020. "Using structural equation modeling to better understand microcystis biovolume dynamics in a mediterranean hypereutrophic reservoir," Ecological Modelling, Elsevier, vol. 435(C).
    3. Yu Liu & Du-Gang Xi & Zhao-Liang Li, 2015. "Determination of the Optimal Training Principle and Input Variables in Artificial Neural Network Model for the Biweekly Chlorophyll-a Prediction: A Case Study of the Yuqiao Reservoir, China," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-16, March.
    4. Yaohuan Huang & Dong Jiang & Dafang Zhuang & Jingying Fu, 2010. "Evaluation of Hyperspectral Indices for Chlorophyll- a Concentration Estimation in Tangxun Lake (Wuhan, China)," IJERPH, MDPI, vol. 7(6), pages 1-15, May.
    5. Mark H Holmes & Michael Caiola, 2018. "Invariance properties for the error function used for multilinear regression," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-25, December.
    6. Halkos, George & Leonti, Aikaterini & Sardianou, Eleni, 2021. "Activities, motivations and satisfaction of urban parks visitors: A structural equation modeling analysis," Economic Analysis and Policy, Elsevier, vol. 70(C), pages 502-513.
    7. 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.

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