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Correlations Between Algae and Water Quality: Factors Driving Eutrophication in Lake Taihu, China

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Correlations Between Algae and Water Quality: Factors Driving Eutrophication in Lake Taihu, China Int. J. Environ. Sci. Technol. (2014) 11:169–182 DOI 10.1007/s13762-013-0436-4 ORIGINAL PAPER Correlations between algae and water quality: factors driving eutrophication in Lake Taihu, China Y. P. Li • C. Y. Tang • Z. B. Yu • K. Acharya Received: 29 September 2011 / Revised: 3 June 2012 / Accepted: 28 December 2012 / Published online: 10 December 2013 Ó Islamic Azad University (IAU) 2013 Abstract Rapid population increase and economic another important controlling factor for algal growth in growth in eastern China has lead to the degradation of the lake. Using principal component analysis, nutrient many water bodies in the region, such as Lake Taihu, contaminations from anthropogenic and natural inputs the third largest freshwater lake in China. Using data were identified as the key driving factor for the water from recent investigations, the correlations between quality problems of the lake. Moreover, five principal algae (measured as chlorophyll-a) and water quality components were extracted and characterized with high indices in Lake Taihu were described by multivariate spatial and seasonal variations in Lake Taihu. The key statistical analyses, and the key driving factors for the driving factors were believed to influence spatial vari- lake eutrophication were identified by principal com- ations including heavily polluted areas located in the ponent analysis. Results revealed strong spatiotemporal northern and northwestern parts of the lake, where many variation in the correlations between algae and water manufacturing factories were built and wastewater from quality indices, suggesting that the limiting factor for domestic and industrial plants was discharged. Based on the dominant algae growth depends on seasonality and this analysis, attention should be paid to effective land location and it is necessary to reduce both nitrogen and management, industrial wastewater treatment, and phosphorus inputs for a long-term eutrophication control macrophytic vegetation restoration to reduce the pollu- in this hyper-eutrophic system. Water temperature was tant loads and improve water quality. Principal com- ponent analysis was found to be a useful and effective method to reduce the number of analytical parameters Y. P. Li Á C. Y. Tang without notably impairing the quality of information in Key Laboratory of Integrated Regulation and Resource this study. Development on Shallow Lakes, Ministry of Education, Hohai University, Nanjing 210098, China Keywords Cyanobacteria bloom Á Large shallow lakes Á Y. P. Li (&) Á C. Y. Tang Nutrient limitation Á Principal component analysis College of Environment, Hohai University, Nanjing 210098, China e-mail: [email protected] Introduction Z. B. Yu Department of Geoscience, University of Nevada, Las Vegas, Taihu Basin has become the most industrialized and NV 89119, USA urbanized area in China (Qin et al. 2007). By 2000, Z. B. Yu 10 % of the gross domestic product (GDP) of China was College of Hydrology and Water Resource, Hohai University, generated in this basin, while it accounted for only Nanjing 210098, China 0.4 % of the land surface and 3 % of the population (NSB 2000). Lake Taihu, located in the Taihu Basin, is K. Acharya Division of Hydrologic Sciences, Desert Research Institute, the third largest lake in China with a water surface area Las Vegas, NV 89119, USA of 2,338 km2, an annual average water depth of 1.9 m, 123 170 Int. J. Environ. Sci. Technol. (2014) 11:169–182 and a mean hydraulic retention time of 300 days (Mao clarify the main principal components from all of the et al. 2008). It provides critical services to China’s water quality parameters and characterize the spatio- economy. However, Lake Taihu has been subject to temporal distribution of those indices, (3) identify the pollutant loading from an array of nonpoint and point key driving factors for the lake eutrophication due to sources, and experienced severe water quality degrada- natural or anthropogenic influences of point or non- tion over the past several decades. The lake water point sources of pollution via the results of PCA. quality is continuing to deteriorate; eventually, the water quality could decline to a level where the lake will no longer provide potable drinking water or serve Materials and methods as a habitat for fish and birds (Dong et al. 2008;Hu et al. 2008). For example, the algal bloom events that To address the objectives listed above, lake and watershed occurred during the summer of 2007 led to the restric- data were collected from a wide range of sources including tion of water supplies for approximately five million lake monitoring efforts, publications, and local agencies residents in Wuxi city (CD 2007). (Tables 1, 2). Hydrological and water quality data were The worsening condition of Lake Taihu has prompted collected from 58 monitoring stations with 32 in tributaries researchers and government officials to seek mitigation and 26 in the lake (Fig. 1). The runoff from the 32 tribu- strategies. The large regions of the lake and its water- taries accounted for approximately 90 % of the total runoff shed have limited previous research investigations to input to and output from the lake (Qin et al. 2007), while local scales not encompassing the entire watershed the 26 stations in the lake covered most of the lake regions (Dong et al. 2008; James et al. 2009), to limited water from the littoral area to open water, including Zhushan quality indices using data from a small number of Bay, Meiliang Bay, Gonghu Bay, Central lake, Western monitoring stations (Chen et al. 2003;Zhu2008), or to lake, South lake and East Taihu Bay. Hydrologic data for remote sensing analyses over short time periods (Wang tributaries included average monthly flow discharge, water et al. 2008;WangandShi2008). The spatiotemporal level, flow velocity and direction. Water quality samples distributions of and interactions between algal concen- were collected monthly for the tributary and in-lake mon- trations and water quality indices based on long-term itoring stations from 2000 to 2005 which included dis- monitoring data that encompass the entire watershed solved oxygen (DO), pH, water temperature (WT), Secchi area and the key driving factors over season need to be depth (SD), total nitrogen (TN), total phosphorus (TP), further studied (Paerl et al. 2011a). phytoplankton biomass and Chl-a. In-lake water quality Evaluating multiple pollutants at different monitoring samples were collected at 0.5 m depth, which is considered stations in a watershed concurrently is a complex task to be representative of the water column due to the shallow that can help in understanding the mechanisms behind and well-mixed nature of the lake (Li et al. 2011b). A YSI- the spatial variations of those pollutants (Razmkhah 6600 multi probe was used to measure DO and WT. TN, et al. 2010). Various external factors impact water TP, and Chl-a were measured using the Chinese standard quality; alternatively, these factors may have annual methodology for lake eutrophication surveys (Jin and Tu trends and fluctuations depending upon local conditions, 1990). This method is similar to the American standard seasonal variations, and hydrodynamic circulation, as methods (APHA 1998) for those parameters (James et al. well as chemical and biological processes. The volume 2009). of monitoring data and the complexity of relationships To address the roles of water quality parameters and among variables poses a barrier to comprehensive limiting factors in algal growth in different seasons and evaluation of pollutants (Fan et al. 2010). Multivariate lake regions, temporal trends between monthly phyto- statistical techniques, such as principal component plankton biomass and TN, TP, air temperature were analysis (PCA), are effective tools to reduce the com- analyzed. In addition, the spatial distributions of correla- plexity of large-scale data sets to enable their evalua- tions between Chl-a and water quality were analyzed for tion, spatial analysis, and temporal analysis (Unmesh the warm (March to September) and cold seasons et al. 2006). The PCA technique is widely used in (October to February) using Microsoft Excel 2003. Cor- environmental management studies and is useful to relation coefficients between parameters for each moni- identify characteristics of the major factors that affect toring station in the lake were calculated using water quality (Xu et al. 2009). multivariate statistical analyses. Each correlation coeffi- The objectives of this study were to (1) investigate cient was calculated based on 72 data sets of monthly the spatiotemporal distributions of correlations between observed values from 2000 to 2005 (12 9 6). The equa- chlorophyll-a (Chl-a) and water quality indices, (2) tion for the correlation coefficient is: 123 Int. J. Environ. Sci. Technol. (2014) 11:169–182 171 Table 1 Water quality parameter annual means with standard deviations for Lake Taihu from 2000 to 2008 Year TN (mg/L) TP (mg/L) Chl-a (lg/L) Phytoplankton SD (cm) pH DO (mg/L) WT (C) biomass (mg/L) 2000 2.68 ± 2.23 0.102 ± 0.080 21.87 ± 29.7 6.80 ± 7.34 42.3 ± 24.7 7.88 ± 0.43 9.96 ± 2.23 16.7 ± 8.5 2001 2.49 ± 2.17 0.096 ± 0.095 12.13 ± 14.6 10.02 ± 8.57 41.9 ± 25.1 8.02 ± 0.73 9.09 ± 2.83 18.3 ± 8.7 2002 2.91 ± 2.55 0.100 ± 0.093 17.16 ± 22.1 6.45 ± 4.47 42.9 ± 24.2 8.00 ± 0.38 8.42 ± 2.75 19.0 ± 7.5 2003 3.39 ± 2.76 0.104 ± 0.090 21.31 ± 24.2 7.16 ± 4.26 37.3 ± 23.6 7.94 ± 0.39 8.65 ± 2.76 17.3 ± 9.0 2004 3.57 ± 2.87 0.121 ± 0.118 29.32 ± 41.7 7.77 ± 6.30 33.5 ± 21.6 8.10 ± 0.45 8.82 ± 2.58
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