Analysis of the Temporal and Spatial Distribution of Lake and Reservoir Water Quality in China

Sustainability 2015, 7, 2000-2027; doi:10.3390/su7022000 sustainability

ISSN 2071-1050

http://wendang.chazidian.com/journal/sustainability

Article OPEN ACCESS

Analysis of the Temporal and Spatial Distribution of Lake and Reservoir Water Quality in China and Changes in Its Relationship with GDP from 2005 to 2010

Xiaojie Meng 1,2, Yan Zhang 1,*, Xiangyi Yu 3, Jinyan Zhan 1,*, Yingying Chai 2, Andrea Critto 4, Yating Li 2 and Jinjian Li 1

1 State Key Joint Laboratory of Environmental Simulation and Pollution Control,

School of Environment, Beijing Normal University, Xinjiekouwai Street No. 19,

Beijing 100875, China; E-Mails: mengxj@http://wendang.chazidian.com (X.M.); jinjianljj@http://wendang.chazidian.com (J.L.)

2 State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, No. 8 Dayangfang, Beiyuan Street, Beijing 100012, China;

E-Mails: chaiyy@http://wendang.chazidian.com (Y.C.); liyt@http://wendang.chazidian.com (Y.L.)

3 Solid Waste and Chemical Management Center of MEP, No. 1 Yuhui South Road, Beijing 100029, China; E-Mail: yuxiangyi@http://wendang.chazidian.com

4 Department of Environmental Sciences, Informatics and Statistics, University Ca’ Foscari Venice, Calle Larga S. Marta 2137, Venice 30123, Italy; E-Mail: critto@unive.it

* Authors to whom correspondence should be addressed; E-Mails: yzhang@http://wendang.chazidian.com (Y.Z.); zhanjy@http://wendang.chazidian.com (J.Z.); Tel.: +86-10-5880-7280 (Y.Z.); +1-352-107-1561 (J.Z.)

Academic Editor: Ram Babu Singh

Received: 22 October 2014 / Accepted: 23 January 2015 / Published: 12 February 2015

Abstract: We analyzed the spatial distribution of lake and reservoir water quality in China, and the trends from 2005 to 2010, based on monitoring data from 28 large Chinese lakes and reservoirs. We used a comprehensive water pollution index (WPI) to describe water quality and also identified the major pollutants. Using GDP data, we analyzed the relationships between economic factors and water quality. We found that although the water quality of large reservoirs is improving or remaining stable, despite economic growth, the water quality of most lakes either did not change or worsened. The outlook is pessimistic, as water quality in most lakes has decreased to Grade V or worse. The water quality was lowest for northern lakes and highest for southern lakes due to a combination of the local industrial structure and lower rainfall in the north. The primary pollutants generally remained stable during the study period. For some lakes, fluoride and volatile phenols became the

Sustainability 2015, 7 2001

primary pollutants, indicating more diverse sources of contamination. We divided the

28 bodies of water into four types based on the median WPI and GDP. The dominant

combinations were low WPI with low GDP and high WPI with high GDP, as a result of the

balance among economic development, the natural environment and environmental policy.

Keywords: aquatic environment; water quality; temporal and spatial distribution; water

pollution index; correlation analysis

1. Introduction

Lakes and other water bodies serve as the focus of interactions among various components of the

terrestrial system, and in many areas, they are the most important freshwater resources. Lakes play

essential roles in maintaining the ecological balance of watersheds, meeting the water needs of residents

and preventing flooding. According to Chinese Lakes [1], China has 2759 lakes larger than 1.0 km2, and

these lakes cover a total area of more than 91,000 km2.

After the 1970s, rapid socioeconomic development in and near lake basins has led to intensive human

activities that have increasingly damaged the water quality in most lakes, especially in China’s densely

populated and highly industrialized eastern plains. For example, frequent algal blooms occurred in

Chaohu Lake in the late 1980s [2] and in Taihu Lake in nearly every year, especially in 2007 [3], and a

continuous algal bloom affected Dianchi Lake in 1998 and 1999 [4]. These events provide clear proof

that the water quality of Chinese lakes has become severely degraded. To control water pollution, the

Chinese government has implemented many regulations, such as the “Control Program for Water

Pollution in Dianchi Lake Basin (2006–2010)” [5], the “Control Program for Water Pollution of Chaohu

Lake Basin (2006–2010)” [6] and the “General Planning for Comprehensive Water Treatment in the

Taihu Lake Basin” [7]. The government invested 1.278 × 1011 RMB during the period of 2006 to 2010 to

control industrial pollution of water, build sewage treatment plants and implement comprehensive

regional control of the pollution levels in three major lakes [8]. The water quality in these regions has

improved to some extent or has at least not worsened.

However, despite these efforts, the rapid development of China’s economy and the implementation of

regional development plans, such as the Western China Development Strategy, have caused a

continuously decrement of the overall water quality in lakes. Since the announcement of the water

environment quality guidelines by the Chinese Ministry of Environmental Protection in 2010, the water

quality in eight of the 28 key state-controlled lakes and reservoirs (i.e., water bodies identified and

prioritized by China’s central government because of their large size and the environmental and

socio-economic importance) has degraded to below Grade V: Taihu Lake, Dianchi Lake, Dalai Lake, the

Dahuofang Reservoir, Baiyang Lake, the Menlou Reservoir, the Laoshan Reservoir and Dongting Lake.

Except the last one, in central China, the other lakes and reservoirs are located in the north, the northeast

and the Inner Mongolia-Xinjiang Plateau. Such a level of water quality degradation means that the water

is unsafe for human consumption and cannot be used by industry and clearly shows that the scope of

China’s lake and reservoir pollution problem has expanded far beyond the “three lakes” region, which

includes Taihu, Chaohu and Dianchi lakes. In particular, it has begun to affect the northern and

Sustainability 2015, 7 2002

northwestern regions, which are characterized by low precipitation (thus, low self-purification capacity),

a fragile ecological environment and difficulty in ecological restoration.

Efforts to decrease pollution in Chaohu, Dianchi and Taihu lakes have consumed large amounts of

labor, time and economic resources. To reduce the need for such expenditures in other lake and reservoir

basins, measures should be taken to avoid damaging water quality in the central and northern regions.

In order to understand the problem and prioritize mitigation efforts, it is necessary to comprehensively

analyze the temporal and spatial variation of water quality in key Chinese lakes and reservoirs and to

determine the relationship between the water pollution characteristics and socioeconomic development.

This knowledge will provide scientific guidance to adjust economic development strategies, develop

sound strategies for protecting the water environment of Chinese lakes and reservoirs and implement

more sustainable socioeconomic development to protect the environment.

In the present study, monitoring data from 2005 to 2010 were used, concerning 24 water quality

parameters collected by the Chinese Ministry of Environmental Protection in the 28 key lakes and

reservoirs in China. This study period was selected because it represented the longest period during

which data were available for all of the investigated key lakes and reservoirs. Moreover, considering the

different socioeconomic development conditions in these 28 key lakes and reservoirs, the temporal and

spatial variations in water quality and the relation with socioeconomic development was analyzed. The

main objective was to identify the temporal distribution of water quality at the national scale and the

relationship between economic development and water quality, in order to provide a more scientific

basis for developing strategies for the protection and sustainable utilization of Chinese lakes and reservoirs.

2. Data and Methodology

2.1. Data

The utilized dataset includes monitoring data for the 28 key water bodies (Figure 1): 10 freshwater

lakes (i.e., where the mineral concentration of lake water is less than 1 g/L), 5 municipal lakes (i.e.,

lakes located in big and medium-sized city, where the urban development conditions are related to the

natural and social functions of the lakes), 10 large reservoirs (i.e., more than 0.1 million cubic meter

storage) and 3 large lakes (Chaohu, Taihu and Dianchi). The data were obtained from 262 monitoring

sections in these water bodies from 2005 to 2010. The data included the following 24 water quality

parameters: water temperature, water level, pH, dissolved oxygen content (DOC), permanganate index

(CODMn), biological oxygen demand (BOD5, after 5 days of incubation), chemical oxygen demand

(CODCr), NH4-N (ammonium nitrogen), petroleum compounds (hereafter, “oils”), total nitrogen (TN),

total phosphorus (TP), volatile phenolic compounds (hereafter, “phenolics”), mercury (Hg), lead (Pb),

copper (Cu), zinc (Zn), fluoride (F), selenium (Se), arsenic (As), cadmium (Cd), chromium (Cr6+),

cyanide (CN), anionic surfactants and sulfides. Due to the large amount of data for 2005 and the

impossibility of reporting the whole dataset in this paper, Xuanwuhu Lake was selected as the example,

and the original data and the calculated results of water pollution index (WPI) and K are shown in the

Table A1. Unless otherwise noted, all data were obtained from the Chinese Ministry of Environmental

Protection. In each monitoring section the parameters were detected quarterly and the yearly average

calculated, in order to be in line with the annual values of GDP. The caption of Figure 1 shows the

Sustainability 2015, 7 2003

number of monitoring sections in each lake: if the number of monitoring sections in each lake is more

than 3, these sections include estuary, bayou and the central part of the water body; if the number is

less than 2, the monitoring sections include only estuary and bayou.

Figure 1. Locations of the key lakes and reservoirs included in the present study. 1. Xingkai

Lake (Mishan City, Heilongjiang Province, monitoring data from 3 sections); 2. Jingbo Lake

(Mudanjiang City, Heilongjiang Province, 3 sections); 3. Songhua Reservoir (Jilin City, Jilin

Province, 5 sections); 4. Dahuofang Reservoir (Fushun City, Liaoning Province, 5 sections);

5. Miyun Reservoir (Miyun County, Beijing City, 1 section); 6. Kunming Lake (Haidian

District, Beijing City, 1 section); 7. Yuqiao Reservoir (Ji County, Tianjin City, 3 sections);

8. Baiyang Lake (Baoding City, Hebei Province, 9 sections); 9. Daminghu Lake (Jinan City,

Shandong Province, 3 sections); 10. Nansi Lake (Jining City, Shandong Province, 5 sections);

11. Menlou Reservoir (Yantai City, Shandong Province, 2 sections); 12. Laoshan Reservoir

(Qingdao City, Shandong Province, 3 sections); 13. Hongze Lake (Huai’an City, Jiangsu

Province, 6 sections); 14. Xuanwuhu Lake (Nanjing City, Jiangsu Province, 2 sections);

15. Dongpu Reservoir (Heifei City, Anhui Province, 2 sections); 16. Chaohu Lake (Heifei

City, Anhui Province, 24 sections); 17. Danjiangkou Reservoir (Danjiangkou City, Hubei

Province, Nanyang City, Henan Province, 4 sections); 18. Donghu Lake (Wuhan City,

Hubei Province, 5 sections); 19. Taihu Lake (Shanghai City; Hangzhou City, Jiaxing City,

Huzhou City, Pinghu City, Zhejiang Province; Zhenjiang City, Wuxi City, Wujin City,

Yixing City, Wujiang City, Danyang City, Changzhou City, Suzhou City, Jintan City,

Liyang City, Kunshan City, Taicang City, Changshu City, Jiangyin City, Zhangjiagang City,

Tongxiang City, Zhejiang Province; 110 sections); 20. Xihu Lake (Hangzhou City, Zhejiang

Province, 3 sections); 21. Qiandao Reservoir (Chun’an County, Zhejiang Province, 3 sections);

22. Dongting Lake (Yueyang City, Yiyang City, Changde City, Jin City, Changsha City,

Yuanjiang City, Hunan Province, 12 sections); 23. Erhai Lake (Dali Prefecture, Yunnan

Province, 9 sections); 24. Dianchi Lake (Kunming City, Yunnan Province, 18 sections);

25. Shimen Reservoir (Hanzhong City, Shanxi Province, 1 section); 26. Dalai Lake

(Manzhouli City, Inner Mongolia Autonomous Region, 2 sections); 27. Bositeng Lake

(Bazhou City, Xinjiang Province, 14 sections); 28. Poyang Lake (Jiujiang City, Nanchang

City, Jiangxi Province, 4 sections).

Sustainability 2015, 7 2004

2.2. Methodology

2.2.1. Water Quality Assessment

Various methodologies have been developed to assess water quality, including specific applications

to Chinese case studies [9]. Shi et al. [10] analyzed the temporal and spatial distribution of 4 monitoring

sections of Qujiang River using the grey system analysis method, providing a useful method when a

lack of data is the main issue. Zhou et al. [11] analyzed the trend of water quality in Dianchi Lake

adopting wavelet analysis, a method based on a complex calculation and not suitable for a large

amount of data. Chang et al. [12,13] used fuzzy mathematical analysis, considering uncertainty factors

in the water system and applying the linear weighted average method, which could result in some

ineffectiveness, homogenization and inaccuracy. Li et al. [14] adopted the artificial neural network

method for sea water assessment, simulating the interactions of a biological nerve system with the real

world and providing a tool that can only categorize water quality, without reflecting its temporal

changes. Feng et al. [15] assessed groundwater quality using the matter element analysis method,

which is easier than the artificial neural network method, but still presents the same limitations.

Chen et al. [16] applied the analytical hierarchy process method (AHP), a flexible and practical method

to combine qualitative and quantitative determination, but showing high subjectivity in the allocation

of weights based on the knowledge and experience of the experts. At the international level, in 2007,

the Great Lakes Environmental Indicators (GLEI) project was implemented in the U.S. Subsequently,

various investigators have related the GLEI indicator to various fields to assess the link between

anthropogenic activities and water quality using classification and regression tree analysis (CART) [17],

special ecological indices [18], the cumulative stress index [19] and hierarchical partitioning and

all-subsets regression analyses [20]. Besides, several predictive models based on input-output [21,22],

multivariate statistical techniques [23,24], the three-dimensional hydrodynamic and water quality

model [25] and the continuous wavelet time series analysis [12] have been developed to evaluate water

quality. However, many of them are not widely used, because of the complicated calculation

processes [26]. Index evaluation is simpler and can be effective for analyzing water quality and its

trend [27–29]. This approach was used in water quality evaluation for many lakes and rivers, such as the

Odzi River [30] and the Suquia River [31]. This approach also allows a flexible comparison of water

quality and trends among different water systems and an examination of temporal variation in a given

water system [32]. Accordingly, in the present work, a comprehensive water pollution index method and

a pollution weight method (based on the proportion of the total pollution) was adopted to evaluate the

water quality of lakes in China. Dealing with a large amount of data and trying to reduce the data

processing work effectively, the proposed method uses the following equations to calculate the pollution indices [31,33–35]:

WPIi??j?1

mncjcj0

i (1)

WPI??WPIi?1(2)

m