Based on water sample data collected from the Yangtze River Estuary (YRE) during four sampling periods in 2010 and 2011, the total nitrogen (TN) and total phosphorus (TP) content were evaluated using the traditional single-factor evaluation (TSE) and the fuzzy comprehensive evaluation (FCE). Statistical analyses showed that the average TN and TP for the four periods were 2.60 mg/L and 0.11 mg/L, respectively. August 2010 showed the lowest TN (1.57 mg/L), and February 2011 showed the highest TP (0.15 mg/L). The annual spatial distribution results indicated that an area of high TN concentration (TN ≥ 3.0 mg/L) occurred in the adjacent sea and increased on an eastward gradient. An area of high TP concentration (TP ≥0.10 mg/L) occurred in the inner YRE and decreased on an eastward gradient. There were significant differences in the results of TSE and FCE. The TSE results only reflected the TN evaluation results for certain locations of the YRE. The FCE method combined the effects of the TN and TP factors, and the results indicate that the Chinese water quality classification of Class 5 was dominant in the YRE.

INTRODUCTION

The Yangtze River Estuary (YRE) is a densely populated and economically developed area that is an important industrial and economic center of East China. The YRE is a large and partially mixed estuary on the eastern coast of China and extends from Xuliujing (121°E) in the west to the adjacent sea in the east. The average water discharge in the YRE from the Yangtze River to the East China Sea is 0.9 × 1012 m3/a (Edmond et al. 1985; Dai et al. 2011). The Taiwan Warm Current from the Taiwan Strait and the Yellow Sea Coastal Current along mainland China also have noticeable impacts on the YRE (Zhao 1993; Chen et al. 2001; Zhou et al. 2008).

In the Yangtze River watershed, the impact of the human population and associated activities, such as industrial emissions, construction within aquatic areas and fertilizer use, has increased in recent decades and produced noticeable changes to the water quality of the YRE, and the YRE has now become an ecologically sensitive area (Li et al. 2007). The annual suspended sediment discharged from the Yangtze River was 4.2 × 108 t, carrying many pollutants such as polycyclic aromatic hydrocarbons (Bouloubassi et al. 2001; Liu et al. 2008; Li et al. 2012), polychlorinated biphenyls (Liu et al. 2004; Hui et al. 2009; Zhang et al. 2011), heavy metals (Lin et al. 2002a; Chen et al. 2004; An et al. 2010) and nutrients (Zhang 1996; Chai et al. 2009; Chen et al. 2012) from the Yangtze River watershed to the YRE.

Nutrients play an important role in the ecological cycle of a water body; however, nutrients are also closely related to estuary pollution and may result in severe eutrophication. Previous studies have shown that increased nutrient levels in estuaries and coastal oceans have been linked to eutrophication, seasonal hypoxia and red tides (Rabalais et al. 1996; McIsaac et al. 2001; Li et al. 2010). Compared to rivers in other countries, Chinese rivers have higher concentrations of nutrients (Zhang 1996). Research related to estuary pollution has shown that total nitrogen (TN) and total phosphorus (TP) are two important indicators of water quality, and both nitrogen and phosphorus are common causes of eutrophication in the water body of the YRE (Wang 2006; Gao et al. 2008). Inorganic nitrogen and active phosphate were major control pollutants in the YRE (Duan et al. 2008). Dissolved inorganic nitrogen and soluble reactive phosphorus in the water body of the YRE were higher than in estuarine systems of under-developed countries, and their concentrations were close to that of European countries (Chai et al. 2009). In sediments, TP showed irregular variation in its spatial distribution whereas TN concentrations were highest in the high marsh zones and lowest in the bare flat areas (Zhou et al. 2007; Deng et al. 2010).

Evaluation is necessary during environmental quality analyses to control and manage pollution. The most frequently used method is the traditional single-factor evaluation (TSE) method. The application of the TSE method is simple because only one parameter is considered (Lin et al. 2002b). In the Taihu Lake region of China, an integrated evaluation value was obtained for the whole Taihu Lake region after geostatistical analysis of the spatial data (Wang 2010). In the Chesapeake Bay area of the USA, kriging methods generally outperformed inverse distance weighting for all parameters and depths (Murphy et al. 2010). A spatial statistical measurement of aquatic pollutant distribution was estimated using a 3D kriging model (Chehata et al. 2007; Murphy et al. 2010).

However, the results of the TSE method are absolute because other parameters are not considered. Fuzzy comprehensive evaluation (FCE) is a multi-factor method that uses the theory of fuzzy mathematics. It can ascertain the probability of evaluation results by utilizing analysis criteria in a weighted manner. FCE is characterized by a fuzzy set of evaluation results instead of an absolute set. Presently, there is an increasing number of published papers about FCE (Feng & Xu 1999; Onkal-Engin et al. 2004). In recent years, researchers have begun to discuss the spatial characteristics of evaluation results by coupling FCE and geographical information system (GIS) techniques, and the results showed increased detail over the single-factor evaluation method (Sui 1992; Chang et al. 2008). However, studies regarding water quality evaluation using FCE coupled with GIS and geostatistics are limited.

In this paper, the spatial and temporal variations of pollutants found in water samples collected from the YRE during four sampling periods in 2010 and 2011 were analyzed using the ordinary kriging (OK) spatial-interpolation method and FCE.

MATERIALS AND METHODS

Study area and sampling

The YRE is a large and partially mixed estuary on the eastern coast of China. The width of the YRE's mouth is over 90 km. Utilizing data from previous studies (Chai 2006), water discharge characteristics and the marine digital elevation model, we limited our study area to 121°E–122.7°E, 30.8°N–31.8°N, which has an area of 11,000 km2 (Figure 1). Using the most easterly position of Jiuduansha as a marker, the study area can be divided into two parts: the inner estuary of the Yangtze River and the adjacent sea of the YRE. The inner estuary is separated into north and south branches by Chongming Island. The lower south branch is separated into north and south channels by Changxing Island and Hengsha Island (to the east of Changxing Island).
Figure 1

Study area and location of sample sites in the YRE.

Figure 1

Study area and location of sample sites in the YRE.

In the presented research, 30 sample sites were chosen in the region of the YRE: 19 in the inner estuary and 11 in the adjacent sea. Based on the Technical Regulation of Water Quality Sample in China, water samples were collected from surface water (depth of 0.5 m) using 1 L sampling bottles. The concentrations of TN and TP were measured using a UV-Vis spectrophotometer, analytical balance, etc. All of the measurements were completed within 2 weeks. Sampling and experiments were executed in August 2010, November 2010, February 2011 and May 2011.

Methodology

Ordinary kriging

In GIS, kriging is an interpolation method based on the assumption that the interpolated parameter can be treated as a regionalized variable. The estimator is given by a linear combination of the observed values with weights. Depending on the stochastic properties of random fields, there are different types of kriging, and the type of kriging determines the linear constraint on the weights implied by the unbiased condition (Cressie 1993; Webster & Oliver 2007; Sollitto et al. 2010; Xie et al. 2011).The weights of OK are derived from kriging equations using a semivariance function. The parameters of the semivariance function and the nugget effect can be estimated by an empirical semivariance function. An unbiased estimator of the semivariance function is equal to half the average squared difference between paired data values: 
formula
1
where γ(h) is the semivariance value at distance interval h; N(h) is the number of sample pairs within the distance interval h; and z(xi + h) and z(xi) are sample values at two points separated by the distance interval h.

Fuzzy comprehensive evaluation

Fuzzy comprehensive evaluation (FCE) is a multi-factor method for decision making that is often used in multi-factor analysis (Chang et al. 2008). The two main steps of FCE are the normalization function and the membership grade function. The membership grade function is the foundation of FCE.

In the normalization function, the individual variables have different weights; so the set of normalization weights can be expressed as the following vector: 
formula
2
where i=1, 2n represents the weight of each individual variable (n variables), and 
formula
3
where the total weight of each individual variable is represented as 1. The following formula stands for the normalization function: 
formula
4
where Vi, i=1, 2n represents each individual variable.
In the membership grade function, the membership weight of each individual variable is assigned to a certain class, and the membership weight vector of each individual variable is assigned to all of the classes: 
formula
5
where Cj, j=1, 2m represents the membership weight of all the classes (m classes) in the evaluation criteria Cj ∈ [0,1].
Thus, the membership grade matrix can be summarized: 
formula
6
In the above matrix, there are n variables and m classes, and Ci,j represents the i-th variable's membership weight of the j-th class.
After the normalization function and the membership grade function, the FCE result is 
formula
7
based on the maximum membership grade principle, if is the maximum of the vector, then class j is the dominant class.

Water quality criteria

Estuary regions are found at the intersection of surface water and saltwater; however, there are no special water quality criteria for estuary regions. In China, there are surface water quality criteria and sea water quality criteria (PR China Ministry of Environmental Protection 1997). The water quality criteria consist of five classes; the classes according to the concentration of TN and TP are given in Table 1.

Table 1

Water quality criteria in China (mg/L)

Class 
TN ≤ 0.2 0.5 1.0 1.5 2.0 
TP ≤ 0.02 0.10 0.20 0.30 0.40 
Class 
TN ≤ 0.2 0.5 1.0 1.5 2.0 
TP ≤ 0.02 0.10 0.20 0.30 0.40 

RESULTS AND DISCUSSION

Basic statistics

There are noticeable differences in the different seasons. The average TN of the four seasons was 2.60 mg/L, which is higher than the Class 5 level (2.0 mg/L), and the average TP of the four seasons was 0.11 mg/L (Table 2). The mean TN in August 2010 (1.57 mg/L) was much lower than the average for the other three seasons, and the mean TP in February 2011 (0.15 mg/L) was higher than the average for the other three seasons. Based on the water quality criteria in China, 10, 16, 30 and 23 out of 30 TN samples had higher concentrations than the Class 5 level (2.0 mg/L) in August 2010, November 2010, February 2011 and May 2011, respectively. However, all of the TP samples had lower concentrations than the Class 5 level (0.40 mg/L) for the four sampling periods.

Table 2

Statistics of sample data in YRE (mg/L)

Periods Nutrients Mean Max Min SD 
Aug 2010 TN 1.57 4.13 0.09 0.98 
TP 0.10 0.36 0.02 0.09 
Nov 2010 TN 2.26 4.48 1.21 0.76 
TP 0.13 0.34 0.04 0.10 
Feb 2011 TN 3.28 7.76 2.27 1.16 
TP 0.15 0.37 0.04 0.08 
May 2011 TN 3.27 8.55 0.44 2.08 
TP 0.06 0.21 0.00 0.04 
Average TN 2.60 5.03 1.27 0.91 
TP 0.11 0.23 0.04 0.05 
Periods Nutrients Mean Max Min SD 
Aug 2010 TN 1.57 4.13 0.09 0.98 
TP 0.10 0.36 0.02 0.09 
Nov 2010 TN 2.26 4.48 1.21 0.76 
TP 0.13 0.34 0.04 0.10 
Feb 2011 TN 3.28 7.76 2.27 1.16 
TP 0.15 0.37 0.04 0.08 
May 2011 TN 3.27 8.55 0.44 2.08 
TP 0.06 0.21 0.00 0.04 
Average TN 2.60 5.03 1.27 0.91 
TP 0.11 0.23 0.04 0.05 

Spatial–temporal distribution of nutrients

In order to investigate spatial structure of variables, omnidirectional variograms of the average TN and TP were calculated. The fitted variogram plots are shown in Figures 2(a) and 2(b). Variograms were computed in different directions to detect any anisotropy of the spatial variability, and the variables did not show a considerable anisotropy.
Figure 2

Variogram and spatial distribution of annual average of TN and TP in the YRE.

Figure 2

Variogram and spatial distribution of annual average of TN and TP in the YRE.

Based on OK, the spatial distributions of the average TN and TP are shown in Figures 2(c) and 2(d). The grid resolution was 100 m. The uncertainty of kriging results was lower compared with other methods (Liu et al. 2014). To simplify the explanation of the distribution characteristics, the contour lines 1.5, 2.0, 3.0 and 5.0 mg/L of TN and the contour line 0.10 mg/L of TP were marked on the maps.

In the north branch of the YRE, the average TN showed higher values and well-defined spatial structures. The average TN was lower in the inner YRE, especially in the inner south branch. A contour line (TN = 3.0 mg/L) extended southeastward from the north branch entrance. In contrast, the average TP was higher in the south branch of the YRE but lower in the adjacent sea. The south branch area was surrounded by a contour line (TP = 0.10 mg/L), and the highest value appeared in the vicinity of Shanghai downstream of the Huangpu River entrance.

According to the results from the four seasons, the interpolation results for TN in the YRE showed that an obviously well-defined high-concentration region occurred downstream of the north branch in November 2010 and February 2011 (Figure 3). In May 2011, a contour line (TN = 3.0 mg/L) extended southeastward from the north branch and surrounded nearly the entire adjacent sea. In August 2010, however, the region of high concentration occurred in the south part of the adjacent sea area. In all four sampling periods, the low-concentration zones mainly occurred in the inner YRE along the south branch.
Figure 3

Seasonal spatial distribution of TN and TP in the YRE.

Figure 3

Seasonal spatial distribution of TN and TP in the YRE.

The interpolation results for TP in the YRE showed that a high concentration region occurred in the south branch in August, November and February. And the highest value appeared downstream of the Huangpu River entrance. The high concentration region of TP in May occurred near the north branch. In all four sampling periods, low-concentration areas were mainly distributed in the adjacent sea, and the TP concentration decreased on an eastward gradient in the YRE.

Water quality evaluation based on FCE

According to the environmental quality standards of China, nitrogen pollution is more significant than phosphorus; thus, the TSE results accurately reflected the TN evaluation results for certain locations of the YRE (Figure 4). The result of the annual average TSE indicated that the water quality in the inner estuary was higher than the quality in the adjacent sea area. Class 4 and Class 5 water quality was found in the south branch, which had a higher water quality than the north branch.
Figure 4

FCE results of water quality in YRE.

Figure 4

FCE results of water quality in YRE.

The results of the annual average FCE indicated that the water quality in the inner estuary was higher than the quality in the adjacent sea area (Figure 5). The Class 4 areas were mainly distributed in the south branch of the YRE. Furthermore, there was a small area of Class 3 quality in the south branch, which was the area with the lowest pollution in the YRE. In the north branch, the water quality was higher in November 2010 than in May 2011; in the south branch, however, the water quality was higher in May 2011 than in November 2010.
Figure 5

TSE of water quality in YRE.

Figure 5

TSE of water quality in YRE.

The pollution was most serious in February 2011 and the area of Class 5 covered the entire YRE (Table 3). In August 2010, 65.86% of the YRE was considered Class 5, which was the lowest percentage of the four periods. Nearly the entire inner estuary was reclassified as Class 3 and Class 4 in August 2010, and there was a large Class 4 area in the adjacent sea. There were also similar pollution levels in November 2010 and May 2011.

Table 3

Area percentages of FCE method (%)

Periods Class 1 Class 2 Class 3 Class 4 Class 5 
Aug 2010 0.00 0.00 5.37 28.77 65.86 
Nov 2010 0.00 0.00 2.64 8.46 88.90 
Feb 2011 0.00 0.00 0.00 0.00 100.00 
May 2011 0.00 0.40 7.28 4.20 88.12 
Average 0.00 0.00 0.04 8.33 91.63 
Periods Class 1 Class 2 Class 3 Class 4 Class 5 
Aug 2010 0.00 0.00 5.37 28.77 65.86 
Nov 2010 0.00 0.00 2.64 8.46 88.90 
Feb 2011 0.00 0.00 0.00 0.00 100.00 
May 2011 0.00 0.40 7.28 4.20 88.12 
Average 0.00 0.00 0.04 8.33 91.63 

The FCE framework and implementation has shown substantial potential to support marine protected areas planning and management (Wood & Dragicevic 2007), because in the FCE method, the effects of different assessment factors on water quality are combined, avoiding the one-sidedness of the TSE method (Chang et al. 2008; Sowlat et al. 2011).

Comparison of TSE and FCE

There were similarities between the results of the TSE and FCE methods. The results of the annual average and the four periods showed the same trends and similar distributions, indicating that the water quality in the inner estuary was higher than in the adjacent sea area. In February 2011, the results from both methods demonstrated acute pollution in the YRE.

However, there were significant differences in the results of the two methods. In the FCE method, the Over Class 5 area was reclassified as Class 5 with 100% membership weight. In addition, the water quality grade of FCE was lower than that of TSE. The FCE results even included a region of Class 2 water quality in May 2011.

The reason for the differences between the methods is that TN and TP are considered aggregate impact factors, and they are synthetically analyzed in FCE. The normalization weights of TN and TP were taken into consideration in FCE, and the TP pollution was lower than that of TN overall in the YRE. In the TSE method, however, TN was used to describe the pollution. The FCE method can reflect the combined effects of different assessment factors on water quality and avoids the one-sidedness of the TSE method. Therefore, the FCE is a more reliable approach for assessing water quality in estuaries.

CONCLUSION

Generally, the average TN of the four seasons was 2.60 mg/L, and the mean TP of the four seasons was 0.11 mg/L based on sample data. The mean TN in August 2010 (1.57 mg/L) was much lower than that in the other three seasons, and the mean TP in February 2011 (0.15 mg/L) was higher than in the other seasons.

The results of spatial distribution showed a high annual TN-concentration area in the adjacent sea, with an increasing eastward TN gradient in the YRE, and a high annual TP concentration area in the inner YRE, with a decreasing eastward TP gradient in the YRE. There are multiple factors, such as Yangtze River discharge, ocean current and atmospheric deposition, that influence the distribution of nutrients in the YRE. High TN-concentration regions may be caused by drastic salt water invasions, and the high TP-concentration regions demonstrated that the distribution of TP was significantly impacted by the input of runoff.

The two different evaluation methods produced similar pollution classification maps. They both showed that the water quality in the inner YRE was higher than in the adjacent sea. However, the water quality result of the FCE method was lower than that of the TSE method. The results of the TSE method showed that the Over Class 5 area was dominant in the YRE for all periods except August 2010 whereas the FCE method combined the effects of the TN and TP factors and showed that the Class 5 area was dominant in annual averages and the four seasons. The reason for this result is because only TN was used in the classification of pollution in the TSE method while TN and TP were both comprehensively used in the classification of pollution in the FCE method.

In further studies, hydraulic models with water quality modules in the upstream Yangtze River combined with similar models for the coastal areas, calibrated and verified by the samples of this study, will be carried out, which can temporally present higher resolution with shorter time steps and spatial–temporal dynamic result presentation. The hydraulic models will include the significant process of advection–dispersion, and can also be 3D models to investigate water quality in different depths under water surface.

ACKNOWLEDGEMENTS

The research was funded by the Ministry of Education and Social Science Fund (14YJAZH048), CRSRI Open Research Program (CKWV2014223/KY) and the National Basic Research Program of China (973 Project, 2010CB429003). The authors would like to thank the editors and the anonymous reviewers for their valuable comments and suggestions.

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