1 Introduction

Water is vital to all organisms and its availability and quality are important issues, especially in light of global warming and human population growth (Hanjra and Qureshi 2010; Franchini et al. 2011). Water scarcity is already a critical concern in parts of the world (Schindler 1974; Fedoroff et al. 2010) and there are growing public concerns (Postel et al. 1996; Franczyk and Chang 2009; Khan and Hanjra 2009; Khan et al. 2009a,b). Anthropogenic water eutrophication and pollution, in association with industrialization and agricultural activities such as mining, metallurgy, fuel combustion, waste disposal, fertilizer application, have exacerbated water resource scarcity. Nutrient enrichment seriously degrades aquatic ecosystems, impairing the use of water for drinking, industry, agriculture, recreation and other purposes (Carpenter et al. 1998). Heavy metal residues may accumulate in aquatic ecosystem, which, in turn, may enter the human food chain and cause health problems (Deniseger et al. 1990; Sin et al. 2001). Sediments may act as sinks for contaminants, but are also potential sources of contaminants in aquatic systems (Smol 2002; Battarbee et al. 2005). Therefore, analysis of river and lake sediments is a useful method to study metal and nutrient pollution in an area. Many studies have been carried out worldwide to assess nutrient and heavy metal contamination in surface water and/or sediments, with the objective of improving local water quality (e.g. Galan et al. 2003; Loska et al. 2004; Rose et al. 2004; Alexakis 2010) and thereby mitigating the issue of limited water resources. China is facing increasingly severe water scarcity. Overexploitation of water resources has led to serious environmental consequences, such as ground subsidence, salinity intrusion, and ecosystem deterioration (Liu and Yu 2001; Qin 2002; Han 2003; Foster et al. 2004; Liu and Xia 2004; Fan et al. 2006; Cai and Ringler 2007; Xia et al. 2007; Jiang 2009). Poor water quality caused by pollution is now observed in both areas where water is scarce and in areas where water is abundant (Zhu et al. 2001; Liu and Diamond 2005). Water shortages and poor water quality interact to threaten China’s food security, economic development, and quality of life. Environmental scientists, citizens and governments must face this serious problem (Kuang et al. 2004; Khan and Ansari 2005; James et al. 2008; Shao 2010).

The Yangtze River, the longest river in China, is located in Southeast Asia and discharges into the western Pacific Ocean. The river plays an important role in regional economic and social development and is used for shipping and irrigation, as well as for domestic and industrial water supply. Historical processes along the river have given rise to numerous lakes that store great volumes of fresh water. These waterbodies play a vital role in the lives of local inhabitants and have stimulated regional economic development (Wang and Dou 1999). Many of these lakes, however, experience cyanobacterial blooms in the warm season because of high nutrient levels (Chen et al. 2002; Qin 2002; Cheng and Li 2006). Most of the lakes in the middle and lower reaches of the Yangtze River region are mesotrophic or eutrophic as a consequence of excessive agricultural fertilizer use and discharge of municipal sewage, associated with large-scale cultivation and high-density populations in their watersheds, respectively. These waterbodies constitute the main eutrophic lake region in China, and their condition now affects the economic development of this region. Lake eutrophication and pollution in this region have become serious concerns (Qin 2002).

We present results of water and sediment analyses from a limnologically diverse group of lakes along the middle-lower Yangtze River. Many of these waterbodies were previously unstudied. We characterize the physical and chemical limnology of these lakes and address the effects of river regulation and human influences on water quality. In addition to examining the current status of lake water quality in this region, this study provides information that future studies can use to evaluate further changes in water quality. Our analysis can be used to develop a scientific basis for environmental protection in the region and a policy for rational water utilization.

2 Materials and Methods

2.1 Study Area

The Yangtze River is 6300 km long and has a catchment area of 1.8 million km2, which is equivalent to ~20 % of the total land area of China. The river can be divided by topographic and hydrologic characteristics. The upper stream runs from the source to Yichang in Hubei Province, a distance of 4510 km. The famous “Three Gorges” stretch is located in this section of the river and is 209 km long. From Yichang to Hukou, in Jiangxi Province, the river enters the middle reaches, which are 940 km long. From Hukou to the lower reaches is a distance of 850 km (Fig. 1).

Fig. 1
figure 1

Location of the study region in southeast China (a and b) and sampled lakes along the middle and lower Yangtze River (c)

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The middle and lower reaches of the Yangtze River cross the central and eastern parts of China and lie in the most important economic area of the country. They are located in the subtropical monsoon zone. The seasonal climate is warm and wet in summer, and cold and dry in winter. The basin has a mean annual temperature of between 15 °C and 20 °C and receives more than 1,000 mm of precipitation annually, 40–60 % of which falls between June and August under the influence of a strong monsoon that originates in the Pacific Ocean. Evaporation approximates or slightly exceeds rainfall in the basin during an average year (Wang and Dou 1999; Chen et al. 2001; Shi et al. 2007; Xu et al. 2010).

The middle and lower Yangtze River crosses the Dongting and Poyang lacustrine plains, and receives inflows from the Dongting and Poyang lake systems and the large Han River tributary, forming a flatland river reach with a wide channel and slow flow. Downstream, the channel is wider and lacks large tributary inflows. On the Anhui-Jiangsu plain the river is a flatland-tidal reach as a consequence of tidal backwater flow. Topographic and climate characteristics, combined with evolutionary processes in the Yangtze, have contributed to formation of three lake groups along the middle and lower reaches of the river, the Tongting-Jianghan lake group, the Poyang-Huayang lake group and Taihu-Yangtze Delta lake group. There are more than 600 lakes >1 km2 in this region, including many large lakes such as Dongting, Poyang, Caohu and Taihu, which cover a combined area of 18,400 km2 and represent 25 % of lake surface area in China (Wang and Dou 1999). The 45 shallow lakes in this study belong, for the most part, to the three lake groups and are scattered across six provinces (Hubei, Hunan, Jiangxi, Anhui, Jiangsu and Shanghai) along the middle and lower reaches of the Yangtze River (Fig. 1) (Table 1). Most of these lakes are eutrophic or hypereutrophic.

Table 1 Lake location (lake name, latitude, longitude), surface area, mean depth and lake elevation
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2.2 Sampling, Sample Preparation and Analysis

Two or three sites were sampled in each lake to account for spatial heterogeneity and the large area of the lakes. Sampling positions were recorded using a GPS system. Every lake was sampled over one year during each of the four seasons. Sampling of all lakes required two years (2003 and 2004) to complete because of the wide geographic distribution of the sampled water bodies (Yang et al. 2005).

Water samples for analysis of total nitrogen (TN), total phosphorus (TP), NO −3 , NO −2 and chlorophyll a (Chl-a) were collected using Tygon tubing fitted with a one-way valve. A sediment sample was taken from the topmost (0–1 cm) mud in the lake bottom at each site. Samples were refrigerated under dark conditions until laboratory analysis. Lake water was filtered through a 0.45-μ-membrane filter for analysis of NO −3 , NH +4 and NO −2 . TN was determined by the alkaline potassium persulphate digestion-UV spectrophotometric method. TP was determined by the ammonium molybdate method after potassium persulphate digestion. Ammonium (NH +4 -N) was measured by the Nessler method, nitrite by the a-naphthylamine method and nitrate by the UV spectrophotometry method (Eaton et al. 1995). Chl-a was measured spectrophotometrically (Zhang et al. 1991). Mean water temperature was measured in situ using a thermometer and Secchi depth (SD) was recorded at one central site.

All samples for δ18O were stored in 25-ml glass bottles free of headspace and refrigerated at 4 ºC before analysis. The δ18O analysis involved the CO2–H2O equilibration technique and used a GAS Bench II (Finnigan MAT) connected to a Delta plus mass spectrometer (Finnigan MAT) (Laudon et al. 2007). δ18O values were determined by analysis relative to an internal standard that was calibrated to the SMOW standard and data are reported in standard δ-‰ notation. Analytical reproducibility was better than 0.2‰.

Sediment samples were oven-dried at 40 °C, ground in a mortar and pestle to <74 μm grain size, and then dried again at 105 °C prior to laboratory analyses. An accurately weighed sediment sample (~125 mg) was placed in a Teflon nitrification tank, 6.0 ml HNO3, 0.5 ml HCl, and 3.0 ml HF were added. The sealed tank was then placed in a microwave oven (Berghof MWS-3 Digester) and nitrified at 180 ± 5 °C for 15 min. The residue from the tank was then transferred into a Teflon breaker and dissolved with 0.5 ml HClO4 in a heating block at about 200 °C and diluted to 25 ml with double-distilled de-ionized water. The solution was then analyzed for elements by inductively coupled plasma-atomic emission spectrometry (ICP-AES). Standard EPA method 3052 was used for the determination of element concentrations (USEPA 1996). The accuracy of the analytical determination was established using the reference material GSD-9, supplied by the Chinese Academy of Geological Sciences. The analytical results for all elements were found to be in agreement with the certified values, with measurement errors <10 % (Zeng and Wu 2009). The degree of anthropogenic enrichment was estimated with the index of geoaccumulation (Igeo) (Loska et al. 2004), which was calculated as follows:

$$ {{\text{I}}_{\text{geo}}} = {\text{lo}}{{\text{g}}_2}\left( {{\text{Cn}}/1.5{\text{Bn}}} \right) $$

Where Cn is the measured concentration of element n in the sediment and Bn is the background concentration of element n. The factor 1.5 is introduced to allow for possible differences in background values as a consequence of lithologic variability (Müller 1969). Igeo assesses the degree of metal pollution, defining seven classes of sediment quality. Class 0 (practically unpolluted): Igeo ≤ 0; Class 1 (unpolluted to moderately polluted): 0 < Igeo < 1; Class 2 (moderately polluted): 1 < Igeo < 2; Class 3 (moderately to heavily polluted): 2 < Igeo < 3; Class 4 (heavily polluted): 3 < Igeo < 4; Class 5 (heavily to extremely polluted): 4 < Igeo < 5; Class 6 (extremely polluted): 5 < Igeo (Bhuiyan et al. 2010). For this study, we considered the geographic location of each lake and used the soil background values of Jiangsu, Anhui, Jiangxi, Hubei, and Hunan Provinces (Table 2), as proposed by China National Environmental Monitoring Center (1990), as the reference values to calculate the geoaccumulation index.

Table 2 The background values of heavy metals (mg kg−1) in each Province in the study area
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STATISTIC for Windows statistical software (version 6.0) was used for all statistical analyses, including means, correlations, and cluster analyses. Cluster analyses were performed using euclidean distances and the average clustering methods (Krzysztof and Danuta 2003).

3 Results

3.1 Physical and Chemical Characteristics of the Lake Water

Physical and chemical characteristics of the 45 lakes are given in Table 3. Concentration ranges for cations were 0.03–0.30 meq/L for K+, 0.21–2.76 meq/L for Na+, 0.38–3.16 meq/L for Ca2+ and 0.19–0.83 meq/L for Mg2+. For anions, the concentration range was 0.13–2.04 meq/L for Cl−, 0.12–1.47 meq/L for SO 2−4 , 0.00–0.18 meq/L for CO 2−3 and 0.66–2.54 meq/L for HCO −3 . Nutrient concentration ranges were 0.02–0.73 mg/L for TP, 0.21–3.54 mg/L for TN, 2.14–93.50 μg/L for Chl-a and 1.25–8.99 mg/L for chemical oxygen demand (COD). The pH values ranged from 7.6 to 8.9 and Secchi depth (SD) varied between 0.3 and 3.4 m. The δ18O values ranged from −8.0‰ to −4.2‰ (Table 3).

Table 3 Mean values of surface water quality parameters of the 45 lakes sampled in this study
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Lakes in this region are diverse with respect to nutrient status and ion composition. Concentrations of TP, TN, Chl-a, COD and SD depth vary widely. The major ions in the lake water are Ca2+, Mg2+, Na++ K+, Cl−, SO 2−4 , CO 2−3 , and HCO −3 . HCO −3 represents 36.4 %–83.2 % of anions in all the lakes. Na+ + K+ represent 44.1 %–52.9 % of cations in lakes with codes 1 to 8, whereas Ca2+ represents 39.2 %–77.4 % of cations in the lakes of codes 9 to 49. The total ion concentrations of the lake waters of the lower region were higher than those of the upper and middle regions. Moreover, the concentration of K+, Na+, Ca2+, Mg2+, Cl− and SO 2−4 in lake water increased toward Shanghai (Table 3).

3.2 Trace Element Concentrations in Sediments

Trace metal concentrations in sediments from the sampled lakes are shown in Table 4. The concentration range was 51.0–104.0 mg/g for Al, 2.4–164.0 mg/g for Ca, 26.2–84.6 mg/g for Fe, 10.3–25.6 mg/g for K, 4.0–18.6 mg/g for Mg, 0.9–14.0 mg/g for Na, 2.3–6.8 mg/g for Ti, 291–805 mg/kg for Ba, 1.4–3.5 mg/kg for Be, 5–52 mg/kg for Co, 50–159 mg/kg for Cr, 21–1462 mg/kg for Cu, 508–2365 mg/kg for Mn, 20.4–81.0 mg/kg for Ni, 24–116 mg/kg for Pb, 41–658 mg/kg for Sr, 62–185 mg/kg for V and 78–1182 mg/kg for Zn (Table 4).

Table 4 Major and trace element contents of lake sediments of the study area
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Mean concentrations of trace elements are in the order Al > Fe > K > Ca > Mg > Na > Ti > Mn > Ba > Zn > V > Sr > Cr > Cu > Pb > Ni > Be. Skewness and K-S tests indicate that Al, Ba, Be, Co, Cr, Fe, K, Mg, Mn, Na, Ni, Ti and V approach a normal distribution. On the other hand, elements Ca, Cu, Pb, Sr and Zn are positively skewed toward lower concentrations. This is confirmed by the fact that the median concentration for each of the elements in this latter group is much lower than the mean concentration. The variability coefficients (VCs) of the concentrations of the trace elements can be used to classify them into three groups: Al, Ba, Be, Co, Cr, Fe, K, Mg, Mn, Ni, Na, Ti and V with VCs <50 %; Pb and Sr with VCs between 50 % and 100 %; Cu and Zn with VCs >100 %. Elements dominated by natural sources usually have low VCs, whereas elements affected by anthropogenic sources are associated with high VCs (Huang et al. 2009). Results of skewness, K-S test and VCs calculations indicate that some elements in the sediments of lakes in the study area exhibit great spatial variability and are affected by anthropogenic sources.

4 Discussion

4.1 Classification

Cluster analysis of lakes, using mean annual water quality variables, produced three clusters (Fig. 2). All water quality variables were used in this analysis. Cluster A consists of nine lakes in the Huayan-Poyan lake group (Fig. 1). Lake Hualiang (25), a man-made reservoir, is strongly differentiated from the other lakes in the cluster because of its low K+, Na+, Ca2+, conductivity, and TP and TN values. Lake Poyang (27), a section of the Han River, is connected with the Yangtze River seasonally, and is differentiated from other lakes by virtue of its low Ca2+ and SO 2−4 concentrations. Cluster B is composed of 15 lakes, including all the lakes in the Taihu Delta and some lakes in the Dongting-Honghu lake group, i.e. lakes along the middle Yangtse River. Lakes 38 (Lake Ci), 39 (Lake Daye), 40 (Lake Baoan), and 42 (Lake Dong) are located in the middle Yangtze River where lake water is greatly affected by sewage, e.g. Lake Dong by Wuhan City and Lake Daye by Huangshi, etc. Lake Mochou (16) is situated in Nanjing City. All these lakes have high Ca2+ and SO 2−4 concentrations and receive high nutrient loads. Cluster C consists of 21 lakes with median values of water quality variables distributed across the whole study region, except the Taihu Delta. Most of the lakes in this cluster are affected by the Yangtze River seasonally or by temporary flooding. Lake Xuanwu (15) is an urban lake in Nanjing that is differentiated from the other lakes by its high Ca2+ and SO 2−4 concentrations. Values are, however, lower than those of another waterbody in Nanjing City, Lake Mucou, which belongs to cluster B. This is because Lake Xuanwu was inundated by water from the Yangtze River just before samples were collected.

Fig. 2
figure 2

Dendrograms produced by hierarchical clustering of water quality variables from selected lakes. Lake codes and parameters of lake water quality are indicated in Table 2. Cluster A: group of nine lakes 25, 27, 47, 19, 36, 30, 20, 24, 21; Cluster B: group of 15 lakes 4, 2, 1, 8, 7, 3, 6, 5, 40, 39, 10, 9, 42, 38, 16; Cluster C: group of 21 lakes 15, 48, 46, 49, 45, 41, 17, 12, 14, 44, 11, 43, 32, 26, 13, 18, 35, 29, 37, 23, 22

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4.2 Water Quality

Lakes of cluster A have the lowest trophic state, inferred from the lowest mean values of TP, TN, Chl-a, and COD in the water column (Fig. 3). Cluster B lakes have the highest values of TP, TN, Chl-a, and COD, indicating the highest trophic states. Cluster C is of intermediate status. Generally, lakes in cluster B are eutrophic to hypereutrophic, judging from their TP, TN, Chl-a, and COD values. Lake Taihu (7), for example, has cyanobacteria blooms in summer, indicating poor water quality (Chen et al. 2002). Likewise, Lake Donghu (42) experiences such blue-green blooms. Lakes in cluster A, with the lowest average TP, TN, Chl-a, and COD, range from oligotrophic to mesotrophic. These lakes are located farther upstream and receive more precipitation from drainage of the mountain region. Lower δ18O values in the water column of these lakes are probably indicative of their relatively short residence times. For instance, Lake Hua (25) is situated at the base of mountains and yielded a δ18O value of −6.9‰. Lake Poyang (27), with a δ18O value of −7.6‰ receives a large amount of water from the Han River, the largest tributary of the Yangtze River, which originates in a nearby mountain region.

Fig. 3
figure 3

Water quality variables among lake clusters A, B, and C. The value above each bar indicates the mean value for the variable in lakes of the cluster

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Cluster C contains the largest number of lakes studied. They mostly range in trophic state from mesotrophic to eutrophic. Topographically, they are located nearer the Yangtze River and have a connection with the river, especially during the flood season. For example, Lake Dongting (49) is part of the Yangtze River and has a δ18O value of −8.0‰. Lake Longgan (26) often receives river water during the flood season. In this cluster, some lakes stand out. For example, Lake Tianmu (11) lies far from the river and receives much of its hydrologic input from direct precipitation. Nevertheless, tourism activities have influenced this relatively pristine lake, and it now displays mesotrophic conditions.

Generally, nutrient status increases from the middle Yangtze River to the Delta region, reflecting the impact of cities and economic development. Upstream lakes in the central part, however, show signs of rapid eutrophication in recent years (Cheng and Li 2006).

4.3 Heavy Metals

Cluster analysis of lakes, using concentrations of metals in the sediments, produced three clusters (Fig. 4). Cluster A contained Cu, Zn, Pb, Co, Fe, Mg and Mn. These elements probably are attributed to anthropogenic sources in the lake basins, and reflect heavy metal pollution. Cluster B contained Cr, Ni, Be, V, Al, Ba, K and Ti. The elements may originate from natural parent materials in watershed rocks and soils. Cluster C contained Ca, Sr and Na.

Fig. 4
figure 4

Dendrograms produced by hierarchical clustering of trace metals in the sediments from selected lakes. Lake codes and concentrations of trace metals are shown in Table 3. Cluster A contains metals Cu, Zn, Pb, Co, Fe, Mg, and Mn. Cluster B contains metals Cr, Ni, Be, V, Al, Ba, K and Ti. Cluster C possesses metals Ca, Sr and Na

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Heavy metal elements Cu, Zn, Pb, Co, Fe, Mg and Mn were used in a cluster analysis that yielded three lake groups (Fig. 5) and Fig. 6 shows the distribution of each heavy metal Igeo values in the different lake groups. Metal pollution displays a sequence, with group 1 > group 2 > group 3. Group 1 consists of three lakes, 5, 39 and 30. The lake sediments in the group can be characterized as moderately polluted with Pb and Zn (1 ≤ mean Igeo ≤ 2), strongly polluted with Cu (3 ≤ mean Igeo ≤ 4), and unpolluted to moderately polluted with Co, Cr and Ni (0 ≤ Igeo ≤ 1). Lake Jinji (5), with higher Igeo values for Cd, Cu, Zn and Pb of 3.78, 3.47, 2.2, 1.55, respectively, is located in a special industrial zone in Suzhou City that is surrounded by factories from which it receives waste water. Lake Daye (39) displays the highest Igeo values for these metals, among all the lake sediments. The Igeo values for Cd, Pb, Cu and Zn from Daye Lake sediment are 8.28, 2.03, 3.24, 4.99, respectively, reflecting moderate to extreme pollution by the different heavy metals. Lake Bali (30) has high Cu content, with an Igeo value 3.17. The Igeo values for other metals were <1 in the sediment of Lake Bali, which generally fell into the “unpolluted to moderately polluted” class. Group 2 contains eight lakes: 9, 29, 16, 38, 6, 15, 17 and 49, which receive pollutants from urban runoff. For example, lakes 15 and 16 are located in Nanjing City, lake 17 is near Hefei City, the capital of Anhui Province, and Lake 49 is affected by Yueyang City. The lake sediments from the group can be categorized as unpolluted to moderately polluted with Pb, Cu, Cr, Ni and Co(0 ≤ mean Igeo ≤ 1), and as moderately polluted with Zn (1 ≤ mean Igeo ≤ 2). The other lakes belong to group 3, which is characterized by normal heavy metal concentrations. Lakes in the group are distributed across the whole study area, including near cities. The lake sediments from the group are unpolluted with Co, Cr, Ni and Pb, and are unpolluted to moderately polluted with Cu and Zn (0 ≤ mean Igeo ≤ 1). In this group, a few lakes display exceptional heavy metal content. Lake Wushan (35) has high Cd, with an Igeo value of 2.66, a consequence of chemical and medical industries.

Fig. 5
figure 5

Dendrograms produced by hierarchical clustering of trace metals Cu, Zn, Pb, Co, Fe, Mg, Mn from selected lakes. Lake codes and parameters of lake water quality are indicated in Table 2. Cluster A: group of three lakes—5, 39, 30; Cluster B: group of eight lakes—9, 29, 16, 38, 6, 15, 17, 49; Cluster C: group of 34 lakes—40, 24, 4, 3, 10, 7, 2, 1, 37, 35, 23, 20, 44, 36, 48, 46, 42, 47, 8, 14, 45, 13, 19, 11, 27, 18, 43, 41, 26, 22, 25, 12, 32, 21

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Fig. 6
figure 6

Distribution of index of geoaccumulation of heavy metals in the sediments of the study. The lines extending from the top of each column mark the standard deviation

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Geographically, lakes polluted by heavy metals are mainly concentrated along the upper part of the lower Yangtze River, in Anhui and Hubei Provinces, in the centre of China. Lake Daye is polluted by mining activity near the city of Huangshi and Lake Wushan is affected by factories along the lake shore (Deng and Xie 1995). Lakes along the middle of the Yangtze River are unpolluted to moderately polluted with metals, except for those near urban areas. For example, Lake 49 is affected by a yacht dock, a famous tourist destination near Yueyang City. Lakes in the Taihu Delta or within urban areas are polluted mainly from municipal drainage or industries that discharge waste with relatively high heavy metal concentrations (Lin and Wu 2008). For example, lakes 15 and 16 are located in Nanjing City, lake 17 is at Hefei City, the capital of Anhui Province and Lake 5 is at Suzhou City.

5 Conclusions

Nutrient pollution in lakes of the Yangtze River region, China, is marked by high TP, TN, COD, and Chl-a in the water column, and is associated with high δ18O of lake waters. High δ18O values in the lake water indicate the effect of evaporation and hence the relatively long residence time of water in the basin. Urbanization is the main source of nutrient loads. Industries also contribute to pollution. Lakes with heavy metal pollution display high concentrations of Cu, Zn, Pb, Co, Fe, Mg and Mn in their sediment. Such lakes receive untreated waste water from industries in their catchments. Other anthropogenic activities in urban areas can also serve as heavy metal sources. Water residence time has a greater impact on nutrient pollution than it does on heavy metal pollution of lake sediments, e.g. Lake Tongtin. Whereas increased water exchange can improve water quality by lowering nutrient concentrations, it has little impact on heavy metal accumulation in lake sediments.

Lakes affected by the Yangtze River are in relatively good condition. Most of them, such as Lakes Taibai and Longgan are mesotrophic. Most of these lakes are found in the interior part of China, in the upper portion of the lower Yangtze River. Lakes in this region, however, are polluted by heavy metals. The pollution is caused by industries that discharge untreated waste water into the rivers and lakes. Most of the lakes serve as temporary holding basins for water that then flows into the Yangtze River. Over time, these lake sediments accumulate a great amount of pollutants. High heavy metal concentrations in lake sediments are a serious threat to these lakes. Because these lakes exchange water seasonally with the Yangtze River, nutrient concentrations do not reach high levels, but heavy metals accumulate in the sediments.

Lakes in the Delta area or urban regions display the highest nutrient levels. In recent years, as a result of changing industrial practices and more environmental protection from local authorities, these lakes have not been polluted by heavy metals. Nutrient pollution, however, is a serious problem for lakes in these areas as a consequence of development of modern cities and industries. Lakes in the middle reach of the river or upstream have large freshwater supplies, with low nutrient levels and heavy metal concentrations. Only a few of the lakes, however, belong to this group, and even some of them are starting to show evidence of anthropogenic impacts, for instance the effect of tourism on Lake Tianmu.

Current lake status in the middle and lower reaches of the Yangtze River is related to the policy of regional economic development. The Delta region of China is developed and includes the cities of Shanghai, Suzhou, and others. Heavy industries have recently moved into the interior region of China, accompanied by the development of modern cities. Despite environmental protection efforts, lakes in the central region are faced with high nutrient loading from industry. This has occurred as a consequence of efforts to improve the economic conditions of citizens, sometimes at the expense of environmental quality. Lakes in the central part of China require immediate attention and efforts should be made to implement management plans to prevent further degradation of water quality in these lakes. If such steps are not taken in these upstream aquatic ecosystems, there may also be adverse impacts on the downstream water bodies.