Temporal and spatial trends in water quality of Lake ...

Temporal and spatial trends in water quality of Lake Taihu, China: analysis from a north to mid-lake transect, 1991–2011 Dilek Eren Akyuz, Liancong Luo & David P. Hamilton

Environmental Monitoring and Assessment An International Journal Devoted to Progress in the Use of Monitoring Data in Assessing Environmental Risks to Man and the Environment ISSN 0167-6369 Environ Monit Assess DOI 10.1007/s10661-014-3666-0

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Author's personal copy Environ Monit Assess DOI 10.1007/s10661-014-3666-0

Temporal and spatial trends in water quality of Lake Taihu, China: analysis from a north to mid-lake transect, 1991–2011 Dilek Eren Akyuz & Liancong Luo & David P. Hamilton

Received: 24 May 2013 / Accepted: 9 January 2014 # Springer International Publishing Switzerland 2014

Abstract Interpretations of state and trends in lake water quality are generally based on measurements from one or more stations that are considered representative of the response of the lake ecosystem. The objective of this study is to examine how these interpretations may be influenced by station location in a large lake. We addressed this by analyzing trends in water quality variables collected monthly from eight monitoring stations along a transect from the central lake to the north in Lake Taihu (area about 2,338 km2), China, from October 1991 to December 2011. The parameters examined included chlorophyll a (Chl a), total nitrogen (TN), and total phosphorus (TP) concentrations, and Secchi disk depth (SD). The individual variables were increasingly poorly correlated among stations along the transect from the central lake to the north, particularly for Chl a and

D. E. Akyuz Department of Civil Engineering, Istanbul Technical University (ITU), Istanbul 34460, Turkey

TP. The timing of peaks in individual variables was also dependent on station location, with spectral analysis revealing a peak at annual frequency for the central lake station but absence of, or much reduced signal, at this frequency for the near-shore northern station. Percentage annual change values for each of the four variables also varied with station and indicated general improvement in water quality at northern stations, particularly for TN, but little change or decline at central lake stations. Sediment resuspension and tributary nutrient loads were considered to be responsible for some of the variability among stations. Our results indicate that temporal trends in water quality may be station specific in large lakes and that calculated whole-lake trophic status trends or responses to management actions may be specific to the station(s) selected for monitoring and analysis. These results have important implications for efficient design of monitoring programs that are intended to integrate the natural spatial variability of large lakes.

D. E. Akyuz : L. Luo (*) Department of Ecology, Jinan University, Guangzhou 510632, China e-mail: [email protected]

Keywords Spatio-temporal variation . Lake Taihu . Nitrogen . Phosphorus . Chlorophyll a . Secchi disk

L. Luo : D. P. Hamilton Nanjing Institute of Geography & Limnology, Chinese Academy of Sciences, Nanjing 210098, China

Introduction

D. P. Hamilton Environmental Research Institute, University of Waikato, Private Bag 3105, Hamilton 3240, New Zealand

Eutrophication remains one of the most pervasive and difficult environmental problems to address on global scale (Koelmans et al. 2001), and there is mounting evidence that it may be difficult to reverse (Davis et al. 2010). In China, the biodiversity and functionality of

Author's personal copy Environ Monit Assess

many large lake ecosystems have already been seriously impaired. Ecological function was either seriously impaired or totally collapsed in 80 % of 67 lakes surveyed in China, including some of the largest lakes such as Taihu, Chaohu, and Dianchi (Le et al. 2010). For example, approximately 41 fish, 65 zooplankton, and 16 macrophyte species have disappeared from Lake Taihu since the 1980s (Guan et al. 2011). Collection of suitable data can help to separate anthropogenic and natural (baseline) variations in water quality and enable realistic goal setting for maintaining or restoring water quality and biodiversity (UNEP-IETC 2000). Monitoring plays a key role in assessing the spatiotemporal variability of lake ecosystems. Large lakes present a particular challenge for monitoring, as it may be difficult to synchronize sample collection over large distances across multiple stations (Qin and Hu 2010). Remote sensing methods such as MODIS offer an opportunity to obtain representative “snapshots” of selected optically active variables in surface waters of large lakes such as Taihu (e.g., Hu et al. 2010a; Wang et al. 2011). However, these methods are still reliant on in situ validation data and optically favorable conditions, for example, absence of cloud cover, for satisfactory image collection and derivation of water quality variables (Vos et al. 2003). A major environmental monitoring and protection program in China is now in its twelfth 5-year phase and recognizes that environmental pollution is an issue of national concern. The program includes restoration of three major lakes: Taihu, Chaohu, and Dianchi. Effective monitoring of these lakes is an essential component of documenting whether restoration initiatives will be successful. Lake Taihu has undergone extreme eutrophication over the past 40 years (e.g., Qin 2008; Guan et al. 2011). Water quality is poorest in the north-west region of the lake, particularly in Meiliang Bay where there has been widespread input of untreated wastewater from domestic sewage and factories, as well as from diffuse agricultural sources (Jin and Hu 2003; Li et al. 2011a). In Lake Taihu, there may be large spatial gradients in nitrogen, driven by variations in nitrogen fixation and denitrification (McCarthy et al. 2007) and inputs of land-derived sources of nitrate (Li et al. 2011b; Chen et al. 2012). Releases of nutrients from the bottom sediments to the water vary across the lake (Jiang et al. 2008), partly due to variations in sediment composition (e.g., nitrogen, phosphorus, and organic content) across the lake (Trolle et al. 2009) and also due to wind

resuspension (e.g., Hu et al. 2011). In addition, Chl a concentrations are highly variable, with tributary loadings and sediment resuspension being highly dynamic and both nitrogen and phosphorus playing a key role in limiting phytoplankton growth at different times and locations (Paerl et al. 2011; Wilhelm et al. 2011). Several studies have addressed spatial variations in water quality of Lake Taihu (e.g., Zhang et al. 2007, 2011; Wang et al. 2007; Song et al. 2009; Li et al. 2011a; Zhao et al. 2011; Otten et al. 2012), but to date, none has provided an integrated spatio-temporal analysis of trends on long-term monitoring data. Moreover, there has been no clear agreement on the trends observed within the lake. For example, TN concentrations were considered to have increased almost continuously from 1960 to 2005 (Guan et al. 2011), but Chen et al. (2003) found that concentrations had decreased from 1991 to 1999, particularly after 1996. Analysis of spatiotemporal trends is potentially useful to contribute information on the relative effectiveness of different eutrophication management methods currently being applied to Lake Taihu, such as dredging, submerged plant reestablishment, and water transfers including partial diversion of Yangtze River into Lake Taihu. In this study, we hypothesized that the influences of tributary nutrient loads, predominantly from Meiliang Bay in the northern area of Lake Taihu (e.g., McCarthy et al. 2007), create spatial and temporal variations in key variables related to assessment of eutrophication between near-shore and central lake stations. A key objective of this study is to examine differences in the magnitude and frequency of changes of these selected key variables [total nitrogen (TN), total phosphorus (TP), chlorophyll a (Chl a), and Secchi disk depth (SD)] and determine how trends are manifested by year, month, and station location over a study period from October 1991 to December 2011. This information will help to better understand the way in which station locations affect perceived trends in water quality in large lakes.

Materials and methods Study site Lake Taihu is a large, subtropical, shallow, polymictic lake, which has undergone extreme eutrophication. It is one of the most studied lakes in the world because of its location in a highly populated region (1,333 people/


km2; see Fig. 1 and Table 1) and its significance to gross domestic product and provision of essential functions such as flood control, transportation, tourism, and water supply for industries and domestic use (Qin 2008). It is the only available water source for approximately 30 million people (Bi and Liu 2009) and supports a key fishery containing more than 30 economically important fish and shellfish species such as black carp, white fish, ice fish, and white shrimps (Peng et al. 2010). Sustainability of the lake ecosystem and its capacity to continue to support a productive fishery and agricultural and industrial activities are of national importance. Periods of algal blooms in Lake Taihu, such as in July 1990, have been estimated to cost up to 130 million RMB (Huang and Zhu 1996). The catchment area of Lake Taihu of 36,900 km2 is becoming increasingly urbanized and has a high density of tributaries (3.3 km km−2). The lake surface area was about 2,550 km2 in the early 1950s but has decreased due to high in-lake sedimentation and expansion of farm land, to its present size of about 2,338 km2 (An 2009). Because of its large size, the catchment is divided into major pollution control regions for management purposes, including Jiangsu Province (53 %), the municipality of Shanghai (13.5 %), Zhejiang Province (33.4 %), and Anhui Province (0.1 %; Jin and Hu 2003). Highly polluted Lake Wuli was separated from Meiliang Bay with a gate in 1991 to prevent its water being discharged into Taihu (Qin 2008). Several other methods have been used in Taihu to attempt to arrest eutrophication including gate construction on rivers, flood control; solid waste and wastewater management, nonpoint source pollution reduction, discharge regulation, establishment of buffers and riparian areas, dredging in tributaries and the lake itself, water diversion (flushing) from Yangtze River, establishment of an early warning and emergency response system for algal blooms, lake bank and shoreline restoration, wetland construction on the shores of the lake, organic farming, restructuring of industries in the catchment, direct phytoplankton removal via filtration, enhancement of plant-eating fish, fishery regulation (pen-fish and fish farming prohibition), and wave barriers to reduce sediment resuspension (Huang and Zhu 1996; Jin and Hu 2003; Zhang et al. 2008; Qin 2009; Wang et al. 2010; Qian 2012).

Drinking water crises have occurred as a result of cyanobacterial blooms in Lake Taihu in 1990, 1995, 1998, and 2007 (Xin 2009). After the water crisis in 2007, the Jiangsu government strengthened protection and control within the basin by closing down about 1,000 factories and more than 4,300 small chemical firms, implementing an emission control policy, introducing chemical regulations, and increasing urban sewage treatment (Wu and Hu 2008; Xin 2009). Sample collection The data set used in this study was made available from Taihu Laboratory for Lake Ecosystem Research (TLLER; Qin and Hu 2010). It consisted of stations monitored monthly from October 1991 to December 2011 along a transect from near shore in the northern part of the lake to a central lake location (Fig. 1). Water samples for TN, TP, and Chl a were collected only at a depth of 0.5 m until December 2004 after which time samples were taken at 0.5 m below the water surface, at 0.5 m above the bottom of the lake, and often at middepth. The samples were pooled to obtain an “average” water column sample at each of the seven stations (THL00, THL01, THL03, THL04, THL05, THL07, and THL08). These stations are not in numerical order as they follow naming conventions and primary sampling stations of TLLER. The change in water column sampling methodology was not considered to have affected concentrations of TN, TP, and Chl a because of the typically well-mixed nature of the lake (Qin 2008). Water samples were immediately divided into two subsamples. One subsample, for Chl a analysis, was filtered (GF/C, nominal pore size 1.2 μm) and frozen prior to ethanol extraction of Chl a from thawed filters. The extract was analyzed at wavelengths of 665 and 750 nm to determine Chl a concentration (Jin and Tu 1990). The other subsample, for TN and TP analysis, was frozen upon return to TLLER, and subsequently thawed, digested with a combined potassium persulphate solution, and then analyzed spectrophotometrically (Jin and Tu 1990). Visual and statistical analysis Visualization of TN, TP, and Chl a concentrations, and SD were made using color contour plots with time as the x-axis, station location on the y-axis,


Fig. 1 Map of Lake Taihu sampling station seven sampling locations from north (THL00) to central lake (THL08), depth contours, major inflow, and outflow rivers. Pumped water transfers (inflow and outflow) are denoted by red arrows

and the values of each variable scaled in color. SD was plotted with an inverse color scale. All calculations and visualizations were performed using the MATLAB® (Version 7.8). Variations between months were examined at each station using a relative monthly average (RMm,s,p), calculated for each parameter (Chl a, TN, and TP concentrations and SD) as: RMm;s;p ¼

X m;s;p X s;p

ð1Þ

where X m;s;p is the average parameter (p) value for each month (m) of the study period at each station (s) and X s;p is the average at that station over the entire study period (1991–2011). The latter variable was divided by the average over

all stations (X p ) and a value of one was subtracted as follows: RSs;p ¼

X s;p Xp

−1

ð2Þ

where RSs,p then defined the variations between stations. If RSs,p >0, then the station mean is higher than the mean over all stations and vice-versa for RSs,p