Identification of Coastal Water Quality by Statistical Analysis Methods in Daya Bay, South China

ARTICLE IN PRESS

Marine Pollution Bulletin xxx (2010) xxx–xxx

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Marine Pollution Bulletin

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Identiﬁcation of coastal water quality by statistical analysis methods in Daya Bay, South China Sea

Mei-Lin Wu a,b, You-Shao Wang a,b,*, Cui-Ci Sun a,b, Haili Wang c, Jun-De Dong a, Jian-Ping Yin a, Shu-Hua Han d a Key Laboratory of Tropical Marine Environmental Dynamics, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China b Marine Biology Research Station at Daya Bay, Chinese Academy of Sciences, Shenzhen 518121, China c Scripps Institution of Oceanography, University of California, San Diego, CA 92093-0218, USA d College of Chemistry, Shandong University, Jinan 265200, China article info abstract

Keywords: In this paper, cluster analysis (CA), principal component analysis (PCA) and the fuzzy logic approach were Principal component analysis employed to evaluate the trophic status of water quality for 12 monitoring stations in Daya Bay in 2003. Fuzzy logic approach CA grouped the four seasons into four groups (winter, spring, summer and autumn) and the sampling Cluster analysis sites into two groups (cluster DA: S1, S2, S4–S7, S9 and S12 and cluster DB: S3, S8, S10 and S11). PCA iden- Trophic status tiﬁed the temporal and spatial characteristics of trophic status in Daya Bay. Cluster DB, with higher con- Water quality centrations of TP and DIN, is located in the western and northern parts of Daya Bay. Cluster DA, with the Daya Bay low Secchi, is located in the southern and eastern parts of Daya Bay. The fuzzy logic approach revealed more information about the temporal and spatial patterns of the trophic status of water quality. Chloro- phyll a, TP and Secchi may be major factors for deteriorating water quality. Ó 2010 Elsevier Ltd. All rights reserved.

1. Introduction yses are necessary for effective management of coastal water quality. Coastal water has become a major concern, because of its values Conventional water quality regulations contain quality classes, for socioeconomic development and human health. With the which use crisp sets, and the limits between different classes have growth of human populations and commercial industries, marine inherent imprecision (Silvert, 1998). A parameter being close to or water has received large amounts of pollution from a variety of far from the limit has equal importance for the evaluation of the sources such as recreation, fish culture, toilet flushing, and the concentration of the parameter. Each quality parameter may be- assimilation and transport of pollution effluents (Zhou et al., long to one of several classes. That is, not all of the parameters 2007a). Human activities have already negatively influenced water may be included in a single class. These established various quality quality and aquatic ecosystem functions. This situation has gener- classes in one sampling location may cause confusion (ambiguity) ated great pressure on these ecosystems, resulting in a decrease of in the quality definition of that sampling location (Icaga, 2007). water quality and biodiversity, loss of critical habitats, and an over- In recent years, multivariate statistical analysis, such as cluster all decrease in the quality of life of local inhabitants (Herrera-Silve- analysis (CA), principal component analysis (PCA) and the fuzzy lo- ira and Morales-Ojeda, 2009). It is therefore essential to prevent gic approach have been effectively employed to evaluate the tem- and control marine water pollution and to implement regular poral and spatial characteristics of coastal water quality and river monitoring programs, which help us to understand the temporal water (Kuppusamy and Giridhar, 2006; Zhou et al., 2007a,b; Yung and spatial variations in marine water quality (Simeonov et al., et al., 2001; Yeung, 1999; Wang et al., 2006; Kotti et al., 2005; Wu 2003; Singh et al., 2004) and diagnose the present condition of and Wang, 2007; Chau and Muttil, 2007; Simeonov et al., 2003; coastal water quality. Coastal water quality changes with time Singh et al., 2005; Brodnjak-Voncina et al., 2002; Icaga, 2007; and space, and continuous water quality measurements and anal- Ocampo-Duque et al., 2006; Chen and Mynett, 2006). Cluster analysis (CA) is an unsupervised pattern recognition method that groups objects into classes (clusters) such that objects within a class are similar to each other but different from those in other * Corresponding author. Address: Key Laboratory of Tropical Marine Environ- classes (Alberto et al., 2001). mental Dynamics, South China Sea Institute of Oceanology, Chinese Academy of PCA and CA find groups and sets of variables with similar prop- Sciences, Guangzhou 510301, China. Tel./fax: +86 20 89023102. E-mail address: [email protected] (Y.-S. Wang). erties, thus potentially allowing us to simplify our description of

Please cite this article in press as: Wu, M.-L., et al. Identiﬁcation of coastal water quality by statistical analysis methods in Daya Bay, South China Sea. Mar. Pollut. Bull. (2010), doi:10.1016/j.marpolbul.2010.01.007 ARTICLE IN PRESS

2 M.-L. Wu et al. / Marine Pollution Bulletin xxx (2010) xxx–xxx observations by finding the structure or patterns in the presence of 54' chaotic or confusing data (Ragno et al., 2007). The fuzzy logic approach can be viewed as a language that allows one to translate 51' sophisticated statements from natural language into a mathemat- Huizhou Renshan ical formalism. The classification criteria used and the boundaries 48' between different classes should be somewhat fuzzy, and it is nec- Xi ac hong S11 essary to use membership degree to describe it (Wang, 2002). 45' Xiaojin Wan In this study, statistical analysis methods (CA, PCA and fuzzy lo- Aotou S10 Tieyong Danshui gic approach) were employed to evaluate the trophic status of 42' water quality in Daya Bay, South China Sea. Cluster analysis was S8 Xunliao o S9 used to identify similar groups of temporal and spatial variations 22 N Yaling Bay S12 of water quality, the temporal and spatial patterns of trophic status 39.00' Shenzhen S7 were also determined by principal component analysis and the LNPP S5 36' Dapeng S6 fuzzy logic approach identified the temporal and spatial patterns DNPP S4 S3 of trophic status and the corresponding driving factors. S2 33' S1 Nan ao 2. Materials and methods 30'

2.1. Study area 27' Sanmen Island Northern South China Sea 0 00 Daya Bay lies along the southern coast of China (from 22.31 12 24' to 22.5000000 N, 114.2904200 to 114.4904200 E) and is door shaped. 24' 30' 114oE 42' 48' 54' There are two towns (Dapeng town and Nan’ao) in the western 36.00' coastal area. The northern and eastern coastal areas belong to Huizhou, Guangdong Province, China, and there are seven towns: Fig. 1. Monitoring stations in Daya Bay. Dapeng, Xiachong, Aotou, Renshan, Danshui, Xunliao and Nan’ao. In recent years, the rapid economic development and anthropo- na). Water samples from various depths were analyzed for nitrate, genic activities from Shenzhen and Huizhou have had a great influ- nitrite and silicate with a SKALAR auto-analyzer (Skalar Analytical ence on the environment of the bay. In addition, two nuclear power B.V. SanPlus, Holand). Ammonium and total phosphorus (TP) were stations, Daya Bay Nuclear Power Plant (DNPP) and Lingao nuclear analyzed with methods of oxidized by hypobromite and molybdo- Power Plant (LNPP), have been in operation since 1993 and 2003, phosphoric blue, respectively, and analyzed with a UV1601 spec- respectively. The marine aquaculture industry has been one of trophotometer (SHIMADZU Corporation). Dissolved oxygen (DO), the important industries in this area. The stronger northeast mon- 5 day biochemical oxygen demand (BOD ) and transparency (Sec- soon prevails from October to April; and the Southeast Asian 5 chi) were tested according to ‘‘The specialties for marine monitor- southwesterly monsoons occur from May to September. In con- ing” (GB17378.4-1998, China) and Wang et al. (2006, 2008). The trast, it is interesting that the east coastal upwelling of Guangdong water quality parameters, their units and the analytical methods Province may bring nutrients from the northern South China Sea in are summarized in Table 1. summer (Xu, 1989). The freshwater from the Pearl River Estuary Two replicates of 1.5 L samples from the depths mentioned input diminishes the surface salinity, and water of a lower temper- above were filtered through 47 mm GF/F filters and frozen imme- ature and high salinity intrudes into the bay along the bottom from diately at 20 °C. At the end of the cruise, all filters were trans- the South China Sea under the influence of the weak southwesterly ported to the shore laboratory in liquid nitrogen. Within a week, monsoons from May to September (Han, 1998; Ji and Huang, the chlorophyll a was extracted in 10 ml 90% acetone in the dark 1990). In order to evaluate the anthropogenic and natural effects for 24 h in a refrigerator, and its concentration was determined in the bay, the survey stations were as follows: with a 10-AU Fluorometer (Turner Designs, USA). Stations 1 and 2 are monitoring stations located in the mouth of the bay. The hydrological environment can elucidate important information about water exchange between Daya Bay and the 2.3. Data treatment South China Sea. Stations 3 and 8 are located in Dapeng Cove and Aotou, respec- All data for this study are the mean data obtained from surface tively. They are in an aquaculture area in order to assess the influ- and bottom water. The standardized skewness and standardized ence of aquaculture. kurtosis were determined to assess whether the sample came from Stations 4 and 5 are near the nuclear power plants (DNPP and a normal distribution. Values of these statistics outside the range of LNPP). 2 to + 2 indicated significant departures from normality. The sta- Stations 6, 7, 9 and 12 are present in areas from the central to tistical analysis of data showed that all variables in the original eastern area of the bay for evaluating the South China Sea and dataset. anthropogenic influence. Stations 10 and 11 are located in the northern part of the bay. 2.3.1. Cluster analysis (CA) Twelve monitoring stations are located in Daya Bay (Fig. 1). CA is a group of multivariate techniques of which the primary purpose is to assemble objects based on the characteristics they 2.2. Sampling and analytical methods possess. CA classifies objects such that each object is similar to the others in the cluster with respect to a predetermined selection Seawater samples for analysis of nutrients and chlorophyll a criterion (Shrestha and Kazama, 2007; Iscen et al., 2008). Hierar- (Chl-a) were taken using 5 L GO FLO bottles at the surface and bot- chical agglomerative clustering is the most common approach tom layers of all stations in January (winter), April (spring), August and provides intuitive similarity relationships between any one (summer) and November (autumn) in 2003 (GB12763-1991, Chi- sample and the entire data set; it is typically illustrated by a den-

M.-L. Wu et al. / Marine Pollution Bulletin xxx (2010) xxx–xxx 3

Table 1 Physical–chemical and biological parameters determined and analytical methods used.

Parameters Abbreviation Units Analytical analysis Limit of detection Dissolved oxygen DO mg L1 Winkler titration 0.08 mg L1 (5.30 lmol L1) 1 5-day biochemical oxygen BOD5 mg L 5-Day incubation, 20 °C demand 1 1 Nitrite NO2–N lmol L The sulfanilamide and N-(1-naphthyl) ethylenediamine 0.02 lmol L dihydrochloride method 1 1 Nitrate NO3–N lmol L The cadmium-copper reduction method 0.05 lmol L 1 1 Ammonia NH4–N lmol L The indophenol blue method 0.03 lmol L Chlorophyll a Chl-a mg L1 Spectrophotometry 0.02 mg L1 Total phosphate TP lmol L1 Potassium peroxodisufate oxidation colorimetry 0.09 lmol L1

Table 2

Trophic status scaling based on water quality parameters (Secchi, DO, BOD5, COD, Chl-a, DIN and TP) and FQWI.

a b b b b a Secchi (m) DO (mg/L) BOD5 (mg/L) Chl-a (mg/L) DIN (lg/L) TP (lg/L) Water quality FWQI 2.5 3 5 5 500 31 The fourth class(IV): eutrophic(d) 0–25 3.4 4 4 3.2 400 24 The third class(III): upper-mesotrophic(c) 26–50 4.0 5 3 2.2 300 19 The second class(II): lower-mesotrophic(b) 51–75 5.4 6 1 1.5 200 15 The ﬁrst class(I): oligotrophic(a) 76–100

a Based on the standard (Lundberg et al., 2005). b Based on the standard (GB 3097-1997, China). drogram (tree diagram). The dendrogram provides a visual sum- by a set of formulae of membership functions as follows (see Eqs. mary of the clustering processes, presenting a picture of the groups (1) and (2)): and their proximity, with a dramatic reduction in the dimensional- 8 > > 1 for x > a ity of the original data (Alberto et al., 2001). The Euclidean distance > usually provides the similarity between two samples, and a dis- <> 2=3 þðx bÞ=3ða bÞ for b x a tance can be represented by the difference between analytical val- fxðxÞ¼> 1=3 þðx cÞ=3ðb cÞ for c x b ð1Þ > ues of the samples (Otto, 1998). In this study, hierarchical > ðx dÞ=3ðc dÞ for d x c :> agglomerative CA was performed on the normalized data set by 0 for x < d means of the Ward’s method, using squared Euclidean distances 8 as a measure of similarity (Simeonov et al., 2003; Boyacioglu, > > 1 for x < a 2008; Mendiguchia et al., 2007; Shrestha and Kazama, 2007). > <> 2=3 þðb xÞ=3ðb aÞ for a x b 2.3.2. Principal component analysis (PCA) fyðxÞ¼> 1=3 þðc xÞ=3ðc bÞ for b x c ð2Þ > PCA is designed to transform the original variables into new, > ðd xÞ=3ðd cÞ for c x d :> uncorrelated variables (axes), called the principal components, 0 for x > d which are linear combinations of the original variables. The new axes lie along the directions of maximum variance (Shrestha and where x is the actual monitoring data of any assessment parameter Kazama, 2007). It reduces the dimensionality of the data set by for DO and Secchi in fx(x) and for BOD5, DIN (NH4–N + NO3– explaining the correlation amongst a large number of variables in terms of a smaller number of underlying factors (principal components or PCs), without losing much information (Vega et al., 1998; Alberto et al., 2001; Helena et al., 2000). The principal component (PC) can be expressed as zij ¼ pci1x1j þ pci2x2j þþpcimxmj; where z is the component score, pc is the component loading, x is the measured value of the variable, i is the component number, j is the sample number and m is the total number of variables.

2.3.3. The fuzzy logic approach Fuzzy logic usually contains fuzzification, application of the rule base to fuzzy data, inference of fuzzy results and defuzzification of fuzzy results stages. Fuzzification is a process that transforms the observed real data to fuzzy form using membership functions defined for problem features (Icaga, 2007). The steps of the fuzzy logic approach might simply be described by the following equations. For details, please refer to (Liou et al., 2003; Icaga, 2007). The membership degree of each assessment parameter to the Fig. 2. Dendrogram based on Ward’s method for four seasonal monitoring in Daya assessment criteria at each level can be described quantitatively Bay (2003).

4 M.-L. Wu et al. / Marine Pollution Bulletin xxx (2010) xxx–xxx hi 2=ðm1Þ N+NO2–N), Chl-a and TP in fy(x), and fx(x) and fy(x) are its member- 1= kkfk ei A ship function to the assessment criterion at the jth level. Trophic sta- lik ¼ P ; ð3Þ c 1 tus scaling based on water quality parameters (Secchi, DO, BOD5, j¼1 2=ðm1Þ f e COD, Chl-a, DIN and TP) is shown in Table 2. kkk j A The membership degree of each sample is redeﬁned as follows where f represents the data point transformed from the concentra- in quality evaluating application (see Eq. (3)) k tion to the memberships of quality for a given use. The prototype ei

a 9.5 b 4.5 9 4 8.5 3.5 ) -1 8 3

DO (mg L 7.5 2.5 Secchi (m)

7 2

6.5 1.5

6 1

Group A Group B Group C Group D Group A Group B Group C Group D

14 3.5 c d 12 3

) 10 ) -1 -1 2.5 8 (mg L 5 2 Chl-a (mgL BOD 6

1.5 4

1 2

0.5 0 Group A Group B Group C Group D Group A Group B Group C Group D

180 e 60 f

55 160 50 140 ) 45 -1 )

g L 120 -1

µ 40 g L

100 µ 35 DIN (

80 TP ( 30 25 60 20 40 15

20 10

Group A Group B Group C Group D Group A Group B Group C Group D

Fig. 3. Box-plot for Secchi (a), DO (b), BOD5 (c), Chl-a (d), DIN (e), and TP (f) in four groups.

M.-L. Wu et al. / Marine Pollution Bulletin xxx (2010) xxx–xxx 5 is defined as a specific quality-ordered level and is assigned in ad- and northern coastal parts of Daya Bay. S3 and S8 lay in the cage vance, and lik = [0, 1] is the similarity degree of data point fk to culture areas of Dapeng Cove and the northwestern part near Ao- the ith specific quality level. tou Harbor, respectively. S10 was close to Xiachong and a shellfish

The weighting points of quality levels qi = [0, 1] (for i =1 to c) culture area in Xiaojing Wan. S11 was primarily impacted by the are registered into equal parts according to the number of specific industrial wastewater, agricultural runoff, municipal sewage and quality-ordered levels for having a general formula, which could be culture from Fanhe Port. applied in any number of quality levels. By accumulating one set of weighted similarity degrees, the FQWI of observation x is derived (as Eq. (4)). 3.2. Principal component analysis ! Xc Before applying PCA, correlation analysis was carried out. This FWQIk ¼ lik qi 100 ð4Þ was utilized to find an internal structure and assist in the identifi- i¼1 cation of pollutant sources not accessible at first glance. The high-

The relationship between FQWI and water quality is shown in est correlation existed between DIN and Secchi, BOD5 and Secchi, Table 2. Chl-a and DIN, and Chl-a and TP (Fig. 5). Due to the complexity of the relationships, it was difﬁcult to draw more clear conclusions directly. However, principal compo- 3. Results nent analysis can extract the latent information and explain the structure of the data in detail. 3.1. Cluster analysis

Cluster analysis (CA) was used to detect similar groups between BOD the sampling sites in four seasons. CA generated a dendrogram Secchi DO 5 Chl-a DIN TP grouping the sampling sites into four groups at (Dlink/ ...... Dmax) 100 < 45, and the difference between the clusters was sig- -1 1 . . niﬁcant (Fig. 2). Group A included WA1–WA12, WS2 and WS5 -0.9 0.9 . . . . .

(most of the sampling sites in autumn); Group B included DW1– DO Secchi . . -0.8 0.8 DW12, WS6, DS3, DS6 and DS8 (most of the sampling sites in win- 5 . . ter); Group C included DS1, DS2, DS4, DS5, DS7 and DS9–DS12 -0.7 0.7 ...... (most of the sampling sites in spring); and Group D included most BOD -0.6 0.6 . . of the sampling sites in summer (WS1, WS3, WS4 and WS7– -0.5 0.5 . . . . . WS12). -0.4 0.4 As identiﬁed by CA, box and whisker plots of the parameters . . -0.3 0.3 showing group differences are presented in Fig. 3. The concentra- . . . . tions of DIN and TP in Group D (summer) were higher than those -0.2 0.2 . . in other groups (other seasons). -0.1 0.1 . .

TP DIN Chl-a . . CA was employed to detect the spatial similarity in Daya Bay. 0 0 CA generated a dendrogram grouping the sampling sites into two groups at (Dlink/Dmax) 100 < 30 (Fig. 4), and the difference be- :Significant level p<=0.01 tween the clusters was signiﬁcant. Cluster DA (S1, S2, S4–S7, S9 :Significant level p<=0.05 and S12) and cluster DB (S3, S8, S10 and S11) corresponded to lowly and highly polluted regions, respectively. The stations in Fig. 5. Linear correlation coefﬁcients of six parameters. cluster DA were located in the center, east and southern parts of Daya Bay. The stations in cluster DB were located in the west

3 WA 6 WA10BOD 5 WS 2 Cluster A WA11 2 WS 5 WA 1 WA 5 WA 9WA 7WA 2WA 8 WAWA 412 WA 3 1 DIN DS12 DS 3 WS 7 WS 9 DS 4 WS 1 0 DS 1 DS11 WS 6 WS12 DS 9DS 7 DS 6 DS10 WS11 DW 2 WS 8 DS 5 Cluster C PC(28.36%) DS 2 Cluster D DW 1 DS 8 -1 DW 6 DW 8 WS10 Secchi DW 5 DW 3 WS 4 DW12 DW 4 Cluster B DWDW10 11DW 7 DW 9 Chl-a -2 TP WS 3 -3 DO

-4 -4 -3 -2 -1 0 1 2 3 4 PC(37.33%)

Fig. 6. The loadings of variables and scores of four season monitoring sites for the Fig. 4. Dendrogram based on Ward’s clustering method for 12 samples in Daya Bay ﬁrst two PCs. The number denotes the station number; the font denotes the in 2003. variable.

6 M.-L. Wu et al. / Marine Pollution Bulletin xxx (2010) xxx–xxx

1 temporal characteristics by clustering the samples, but also can describe their different characteristics and help to elucidate the rela- 0.8 tionship between different variables by the variable lines. The variable lines were obtained from the factor loadings of the Cluster DA 0.6 S 2 original variables. They stand for the contribution of the variables BOD 5 to the samples. The closer together were two variable lines, the 0.4 S 5 S 6 stronger was the mutual correlation (Qu and Kelderman, 2001). 0.2 S 1 In Fig. 6, the highest positive correlation coefﬁcient was observed DIN between TP and Chl-a. S12 S11 0 In Fig. 6, the temporal characteristics of trophic status in Daya S 7 PC(28.36%) S 9 S10 Bay can be observed clearly. Cluster A was characterized by high -0.2 Cluster DB Secchi contents of BOD5, Cluster B was characterized by Secchi, Cluster S 8 C was characterized by DO and Cluster D was characterized by S 4 Chl-a -0.4 TP TP and Chl-a. S 3 In Fig. 7, the spatial characteristics of trophic status in Daya Bay -0.6 DO can be observed clearly. It was intuitive and clear that the loading

-0.8 of some variables contributed more to the score of some stations -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 1.2 (Fig. 7). The data were distributed in a limited region of space PC(37.33%) spanned by the two PC well-defined axes. The scores of stations S3, S8, S10 and S11 in cluster DB had positive and negative values Fig. 7. The loadings of variables and scores of 12 monitoring sites for the first two PCs. The number denotes the station number; the font denotes the variable. in PC1and PC2, respectively. These stations were located in the western and northern parts of Daya Bay. They had higher concentrations of TP and DIN than did cluster DA. The scores of other sta- To examine the suitability of the data for principal component tions (S1, S2, S4-S7, S9 and S12) in cluster DA had negative values analysis, Kaiser–Meyer–Olkin (KMO) measure was performed. in PC1. These stations were located in the southern and eastern KMO is a measure of sampling adequacy that indicates the propor- parts of Daya Bay. tion of variance, and the KMO value must be more than 0.5. In this study, KMO (equal to 0.6) indicated that PCA could achieve a significant reduction of the dimensionality of the original data set. 3.3. The fuzzy logic approach PCA rendered six significant PCs (eigenvalue > 1) that explained 65% of the total variance of the data set. The loadings and scores The trophic status for Daya Bay was assessed with the FWQ in- plots of the first two PCs are presented in Fig. 6. The loadings plot dex. Weights for water quality indicators were calculated with the (Fig. 6) showed grouping and relationship between the variables. fuzzy logic method, which is described in Materials and methods. The group of variables and their close relation was visible in the Evaluation results for the water quality of Daya Bay in 2003 are loadings plot. The principal component can not only interpret the summarized in Fig. 8.

Fig. 8. The trophic status of water quality of the fuzzy water quality index in four seasons.

M.-L. Wu et al. / Marine Pollution Bulletin xxx (2010) xxx–xxx 7

The index of FWQI showed differences among the four seasons. autumn (Fig. 8). The water quality of Daya Bay in spring was the The FWQI scores of monitoring sites in winter ranged from 50 to best among the four seasons, and some areas may reach the ﬁrst 63. The seawater belonged to the second cluster in the standard cluster in the standard of Chinese seawater quality. The water of Chinese seawater quality during this period. The water quality quality of Daya Bay in summer was the worst among the four sea- in S3 was the worst compared to the other monitoring sites sons, and some areas may reach the third cluster in the standard of (Fig. 8). The similarity memberships of Chl-a, TP and Secchi of Chinese seawater quality. A one-way ANOVA was used to compare the monitoring sites were close to 0 (Fig. 8), representing the sim- trophic status among the four seasons. The fuzzy water quality in- ilarity between the observation and the eutrophic status. There- dex signiﬁcantly varied between the four seasons in the bay fore, Chl-a, TP and Secchi may play an important role in (F = 23.07, p < 0.00). determining the eutrophic status and can be major driving factors The annual spatial pattern of trophic status in Daya Bay as for deteriorating water quality. In contrast, the similarity member- determined by the mean fuzzy water quality index is shown in ships of DO, BOD5 and DIN of the monitoring sites were close to 1 Fig. 9. In general, the water quality of Daya Bay belonged to the (Fig. 8), representing the similarity between the observation and second cluster in the standard of Chinese seawater quality. Chl-a, the oligotrophic status. Thus, DO, BOD5 and DIN may be important TP and Secchi may be major driving factors for deteriorating water in determining the oligotrophic status and can be major driving quality (Fig. 10). The fuzzy water quality index of monitoring sites factors for oligotrophic status. S3, S8, S10 and S11 was less than 60. The similarity memberships In general, the water quality of Daya Bay belonged to the second of Chl-a, TP and Secchi of these monitoring sites were close to 0 cluster in the standard of Chinese seawater quality in winter and (Fig. 10). Therefore, these variables (Chl-a, TP and Secchi) may be major driving factors for the deteriorating water quality. 54'

Annual Fuzzy Water Quality Index S3 53.64 4. Discussion 51' . S8 54.03 . 4.1. Temporal characteristics and key factors 48' S11 56.11 1 . S1 S10 57.12 Seasonality has an important influence on trophic status of 45' . S4 61.36 water. The values of trophic status parameters were found to de- 10 FWQI<60 S . pend on the season and weather conditions at the time of sam- S7 62.31 42' pling. Precipitation changes during the different seasons; there is . 8 2 S2 63.01 S 9 1 plenty of rain from May to October, and there is less from Novem- o S S . 22 N S5 63.13 ber to April (Fig. 11). The temporal trophic status in Daya Bay was 39.00' 7 S12. 63.3 identified by CA, PCA and the fuzzy logic approach. The trophic sta- S5 S 36' 6 . tus in marine water was not absolutely determined by the local cli- 4 S S9 63.64 S mate (spring, summer, autumn, and winter), because the trophic S3 2 . S S6 63.96 33' state in marine water was also related to pollution characteristics 1 . S S1 66.1 (such as hydrodynamic conditions, aquaculture, and discharge fre- FWQI>60 . 30' quency and type). A one-way ANOVA was used to compare these variables between groups. These variables significantly varied between groups in the bay (p < 0.00). 27' 27' 33' 114oE 45' 51' DO was highest in winter and lowest in summer (Fig. 3b). The 39.00' inverse relationship between temperature and DO is a natural process, because warm water easily becomes saturated with oxygen Fig. 9. The trophic status of water quality of the fuzzy water quality index in Daya and thus can hold less DO (Wu et al., 2009b). The DIN concentra- Bay. tion was the lowest in spring among the four seasons; it may be associated with the phytoplankton bloom. Nutrients are intro-

800 2001 2002 700 2003 2004 600 2005 2006

500

400

Rainfall mm 300

200

100

0 Jan Feb Mar Apr May Jan Jul Aug Sep Oct Nov Dec

Fig. 10. The similarity membership of six variables (Secchi, DO, BOD5, Chl-a, DIN Fig. 11. Monthly precipitation in Pingzhou, which is close to Daya Bay, as and TP) in a spatial pattern. determined by the Hong Kong Observatory (http://www.weather.gov.hk).

8 M.-L. Wu et al. / Marine Pollution Bulletin xxx (2010) xxx–xxx duced into the bay by rivers and sewage discharges in summer and farming in Daya Bay has increased from an annual production of can be released into the water under certain environmental condi- 100 tons (440 ha cage culture area) in 1988 to 58,573 tons tions (Xu, 1989). The mean concentration of BOD5 in autumn and (13,298 ha cage culture area) in 2005, a 586-fold increase in pro- summer was higher than in spring and winter, suggesting a high duction over 17 years (http://www.hdofa.gov.cn/; http:// load of dissolved organic matter added from land-based resources, www.lgtj.gov.cn/). PCA extracted the important information and such as domestic wastewater, agricultural-related activities and driving factors determining the difference between the clusters industrial effluents (Wu et al., 2009a). Therefore, the water quality DA and DB. Based on the fuzzy logic approach, Chl-a, TP and Secchi in summer was worse than that in the other seasons (Fig. 8). may be major driving factors for deteriorating trophic status of The results indicate that the CA technique is useful in offering a water quality (Fig. 8). reliable classification of the trophic state. However, the results of DIN may be a major driving factor for deteriorating water qual- CA cannot distinguish corresponding key factors that play an ity in Jiaozhou Bay (Sun et al., 2007). In most areas of Sanya Bay, important role in these groups. PCA can support more information the water quality was favorable and in agreement with the na- about the trophic status of water quality and corresponding fac- tional water quality criterion I (Che et al., 2009). Coastal water tors. The fuzzy logic approach can further identify the differences quality is especially vulnerable to the effects of frequent human among the trophic status of water quality in different sites and activity and land-based pollution. can distinguish the corresponding driving factor. 4.3. Characteristics of CA, PCA and the fuzzy logic approach 4.2. Spatial characteristics and key factors Chemometric techniques (CA, PCA and the fuzzy logic ap- The results of CA, PCA and the fuzzy logic approach show that proach) were used to differentiate the trophic status of water qual- two clusters can describe marine aquaculture and other human ity in the monitoring sites. In CA, each monitoring site belongs to activities that take place in S3, S8, S10 and S11 and the non-aqua- just one cluster. Results of CA can only supply the similarities be- culture area. tween monitoring sites (clusters), rather than obtain important The scores of S3, S8, S10 and S11 are principally due to nutrients factors playing a role in determining the difference between clus- and Chl-a. The clockwise Euler Residual Current in spring, summer ters. Principal component analysis can not only interpret the char- and autumn in Daya Bay (Xu, 1989) carried the nutrients from acteristics by clustering the samples, but can also describe their land-based water from the west and north in the bay through this different characteristics and help to find the relationship between area, while the tidal current carried the nutrients from the South different variables by the variable lines (loadings). In the fuzzy lo- China Sea through this area as well, which may be the cause of gic approach, each monitoring site has a degree of belonging to the higher concentration of Chl-a in this area (Qiu et al., 2005). clusters. Thus, points on the edge of a cluster may be in the cluster The cage culture in the north (Fanhe port) also has an important ef- to a lesser degree than points in the center of cluster. In addition, it fect on water quality (Huang et al., 1999). Human activities have a can supply major driving factors for deteriorating water quality. strong influence on the aquatic environment in the western and northern coastal parts of Daya Bay (Huang et al., 1999). The con- 1 5. Conclusion centrations of BOD5 in group DB and group DA were 1.73 mgL and 1.28 mgL1, respectively. The concentrations of the parame- In this study, the temporal and spatial patterns of trophic status ters related to anthropogenic pollution like BOD5 were higher in group DB than in group DA. of water quality were identified using CA, PCA and the fuzzy logic approach. CA and PCA obtained similar results regarding the tem- The PO4–P concentrations were higher in group DB than in group DA. Domestic wastewaters, particularly those containing poral and spatial patterns of trophic status of water quality. In tem- detergents, industrial effluents and fertilizer run-off, contribute poral characteristics, water quality grouped into four groups to elevated levels of phosphates in the water column. Phosphate (winter, spring, summer and autumn) and was related to local cli- concentrations can indicate the presence of predominantly anthro- mate. In spatial characteristics, water quality in Daya Bay was di- pogenic pollutants (Iscen et al., 2008). The DIN concentrations had vided into two groups by chemometrics. S3, S8, S10 and S11 spatial distributions that increased from the south and east to the were in Dapeng Cove, Aotou Harbor and northeast parts of Daya west and north of Daya Bay, due mainly to industrial effluents and Bay, which are areas of anthropogenic activity. The rest of the monitoring sites were in the south, central and eastern parts of Daya sewage (Wu et al., 2009a). The amount of NH4–N from industrial waste water was 34,000,000 kg year1, and the total nitrogen and Bay, which are areas that experience water exchanges from South China Sea. The fuzzy logic approach further revealed more useful NH4–N charge of domestic pollution was 807,600 and 646,800 kg year1 (Zheng et al., 1998) in Daya Bay, respectively. information about the temporal and spatial patterns of trophic sta- Because of the topography, the self-cleaning capacity is the worst tus of water quality. Chemometrics identified the natural gradients in Dapeng Cove, and it is almost equivalent in Fanhe Port, Yaling and variability intrinsic to coastal areas, as well as ongoing struc- Bay and the northern part in the bay, but the self-cleaning capacity tural and functional changes occurring due to human impacts. in these areas is still weaker than that in the southern and eastern These results may be valuable for achieving sustainable use of parts of the bay (Liu et al., 1999). coastal ecosystems in Daya Bay. This result indicates that contamination occurred mainly from municipal wastewater and fish-farming. The nutrients play an Acknowledgements important role in the water quality in the marine aquaculture area and anthropogenic sources of pollution (group DB). Concentrations This research was supported by the project of knowledge inno- of nutrients in the western and northern coastal parts of Daya Bay vation program of Chinese Academy of Sciences (No. KSCX2-SW- were higher than in other parts (Wang et al., 1996; Wu et al., 132 and No. KZCX2-YW-Q0Y-02), Young People’s Innovation Foun- 2009a). Nutrient levels were higher at S8 and S3 than elsewhere dation of the South China Sea Institute of Oceanology, the knowl- (Wang et al., 2006). edge innovation program of Chinese Academy of Sciences (No. In Daya Bay, increasing cage culture makes the trend of eutro- SQ200913), the Fund of Key Laboratory of Global Change and Mar- phication more and more distinct, and the phytoplankton biomass ine-Atmospheric Chemistry, SOA (No. GCMAC0906), the project of stays high in Dapeng Cove and Aotou Bay (Song et al., 2004). Fish knowledge innovation program of South China Sea Institute of

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