Water Quality Management Based on Division of Dry and Wet Seasons in Pearl River Delta, China

Clean – Soil, Air, Water 2012, 40 (4), 381–393 381

Xiaoyun Fan1 Research Article Baoshan Cui1 Kejiang Zhang2 Zhiming Zhang1 Water Quality Management Based on Division of Hongbo Shao3 Dry and Wet Seasons in Pearl River Delta, China

1School of Environment, Beijing Normal University, State Key Joint In the Pearl River Delta (PRD), river water quality deteriorates continually due to the Laboratory of Environmental population increase and ongoing industrialization and urbanization. In this study, a Simulation and Pollution Control, water quality management paradigm based on the seasonal variation is proposed. For Beijing, P. R. China better exploring the seasonal change of water quality, wavelet analysis was used to 2Department of Civil Engineering, University of Calgary, Alberta, analyze the division of dry and wet seasons in the PRD during 1952–2009. Then water Canada quality seasonal variation in 2008 and relevant impact factors were analyzed by multi- 3The CAS / Shandong Provincial Key variate statistic methods as a case to make some management measures. The results Laboratory of Coastal Environmental show that there are some differences of dry and wet seasons division among different Process, Yantai Institute of Costal years. Wet season mainly appear from April to September, which occupy the largest Zone Research, Chinese Academy of Sciences(CAS), Yantai 264003, China proportion among the 58 years (about 70%) and then followed by the wet season from May to October (about 13.8% of the total years). As to the water quality of 2008, significant differences exist between dry and wet seasons for 17 water quality 2þ 2þ parameters except TP, NO3 ,Fe , and Zn . Levels of parameters pH, EC, CODMn, þ 2 BOD5,NH4 ,SO4 , and Cl in dry season are much higher than those in wet season. In dry season the variations of river water quality are mainly influenced by domestic sewage, industrial effluents, and salt water intrusion. While in wet season, except the aforementioned pollution sources, drainages from cultivated land and livestock farm are also the main factors influencing water pollution. Thus, water quality management measures are proposed in dry and wet seasons, respectively. The results obtained from this study would further facilitate water quality protection and water resources management in the PRD.

Keywords: Multivariate statistic methods; Pollution; Water resources; Wavelet analysis Received: March 4, 2011; revised: June 1, 2011; accepted: June 6, 2011 DOI: 10.1002/clen.201100123

1 Introduction The Pearl River and its various tributaries are the main water resources of the PRD [6]. River system provides water for the major The Pearl River Delta (PRD) is situated in the southeast China. rural, agricultural, urban, and industrial activities in this region. Although the area of this region occupies less than 0.5% of all the The daily water use for human and ecosystem also depends on country’s territory, it has produced about 20% of the national GDP, surrounding river water quality. In the Guangdong province the absorbed about 30% of foreign capital, and the exports have reached 8 8 total discharges of sewage were 67.7 10 t in 2008 and 44.7 10 t to about 40% since the implementation of open-door policy and in 2000. About 50% was increased in the last eight years [7]. The economic reform [1, 2]. Many new cities have appeared on the number of factories in the PRD was close to about 80% of the total previous farm lands with the rapid economic development and number of the whole province [7]. Although regulations of sewage urbanization. Meanwhile, the environmental quality especially river treatment get more restricted in Guangdong province in recent water quality in this region is deteriorating due to the increased years, most sewage is still directly or indirectly discharged into rivers discharges of untreated domestic sewage, industrial wastewater, by various ways due to signiﬁcant economic activities, extensive and rural non-point pollution arising from the rapid economic development of economy, and lagging of facilities used for sewage growth [3–5]. treatment [8, 9]. A variety of water problems, such as eutrophication, algal blooms, oxygen depletion and contamination of water and loss of resources are also induced by increasing human pressures resulted from agricultural, industrial, and domestic use [10–12]. Correspondence: Professor B. Cui, School of Environment, Beijing The deterioration of river water quality poses great threat to the Normal University, State Key Joint Laboratory of Environmental safety of water supplies [13, 14]. In recent years, although water Simulation and Pollution Control, No. 19 Xinjiekouwai Street, Beijing 100875, P. R. China resource of this region is plentiful, many cities have appeared water E-mail: [email protected]; [email protected] shortages at different periods due to water pollution [15, 16]. The assessment and management of water quality is critical to control Abbreviations: CWT, continuous wavelet transform; PC, principle component; PCA, principle component analysis; PRD, Pearl River Delta; the water contamination and to improve the water quality effec- VF, varifactor tively in the PRD.

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For now, more concerns focus on the potential water quality rainfall and runoff as well as their relationship change over time. assessment in the rapidly urbanizing PRD [4, 5, 15, 17, 18]. The O¨zger et al. [38] also investigated the low frequency drought varia- changes of water quality are attributed to a number of natural or bility using the wavelet analysis. Beecham and Chowdhury [39] anthropogenic factors. Over recent years, accelerated anthropogenic identified the temporal variability in rainfall intensities and the nutrient discharges have exerted great pressure on the water quality proportion dry ratios by applying the wavelet power spectra. management in the PRD [19]. In Victoria Harbor and its vicinity of In this paper, the water quality management according to the this region, nutrient dynamics of water were driven by an inte- difference in dry and wet seasons was investigated. The main objec- gration of various factors, mainly including Pearl River Estuarine tives are: (1) To class the dry and wet seasons by using wavelet waters, local sewage effluent, coastal/shelf seawater, monsoon analysis of the daily rainfall records; (2) to identify measures of winds, and biological utilization [20]. Ho and Hui [21] presented that water quality management for dry and wet seasons by taking the in Dongjiang River contaminant sources were mainly associated water quality dataset of 2008 as an example. with human activities, such as domestic and industrial discharges, fertilizer and pesticide applications, and soil erosion due to 2 Material and methods deforestation. Chen et al. [22] combined composite pollution index with remote sense to evaluate water quality in the Pearl River 2.1 Study area Estuary. Chau and Jiang [3] simulated the transport of water quality The Pearl River is the largest river system in southeast China con- parameter COD in the Pearl River Estuary using a three-dimensional sisting of three major tributaries: West River, North River, and East numerical pollutant transport model combined with a synchronized River, which all gather into the Pearl River Delta (PRD) [4]. There are numerical hydrodynamic model. some big cities located here, such as Guangzhou, Foshan, Furthermore, river water quality is also influenced by stream flow Zhongshan, Zhuhai, Dongguan, and Shenzhen. The PRD (218400– [23], which is related to local rainfall level and seasonal variation [24]. 238N, 1128–1138200E) occupies 26% of the total land area of The reduction of stream flow might lead to an increase of contami- Guangdong Province [40], and belongs to the subtropical zone with nant concentration. Prathumratana et al. [23] presented that there a mean annual temperature ranging from 14 to 228C and a mean was positive correlations between hydrological parameters (such as annual precipitation from 1200 to 2200 mm [41]. It includes a variety precipitation, mean water level, stream flow) and some water quality þ of water channels, such as main rivers, streams, and ditches [15]. The parameters (such as CODMn, BOD5,NH ). In general, the concen- 4 water system in the PRD is one of the most intricate deltaic river trations of pollutants in surface waters are significantly higher network structures on the earth. The drainage density is about 0.68– during the dry season than that during the wet season, which is 1.07 km km2 [1, 42] in this region. The study area together with the due to the dilution by large amount of rainfall and stream flow from locations of water quality monitoring stations is illustrated in Fig. 1. upstream in the wet season [12, 24]. During the wet period, the rainfall intensity and runoff all influence the dilution effects of accumulated contaminants and their transportation to the receiv- 2.2 Method for the division of dry and wet seasons ing water [25, 26]. For example, the increased runoff and erosion or period induced by greater rainfall intensities could lead to an increase in phosphorus transport especially particulate phosphorus [27]. Sporre- Wavelet analysis is a mathematical tool for the process and synthesis Money et al. [28] and Shigaki et al. [29] also found that the concen- of signals and images with stationary and non-stationary datasets trations of the reactive and particulate phosphorus in runoff [34, 35]. It can be used to study the temporal variations in detail from increase with the rainfall intensity enhancement based on a rainfall both frequency and time domains by adjusting related signals. The simulation study. And there is also a link between phosphorus continuous wavelet transform (CWT) can be used to identify signifi- runoff from agricultural fields and freshwater eutrophication [29]. cant cycles and their occurrence time in a specified period [36]. In Most of the studies have been conducted on water quality evalu- this study, the dominant pattern of rainfall variation and its change ation and related pollution factors [18, 30–32]. Unfortunately, stud- over time were analyzed by Morlet CWT. ies on water quality improvement based on seasonal variations are According to the results obtained from the CWT analysis, when comparatively rare. The PRD shows a strong seasonal variation of the wavelet coefficients of monthly rainfall are less than zero, the temperature and precipitation. Much rainfall and stream flows corresponding duration is the dry season, while if they are greater appear in the wet season coincided with typhoons, and flooding than zero, the corresponding duration is the wet season (see Fig. 2). is common in this region. Many rivers consist of stormwater runoff Information associated with the duration of the dry and wet seasons during the wet season. Lu et al. [33] reported that significant season- is important for water quality management [28, 29]. The determi- ality of rainfall and river discharges were the main hydrologic nation of the high and low flow period can be done in the same factors influencing river nutrient fluxes in the PRD. River water manner (see Fig. 2). quality management combined with seasonal change will be of scientific and practical importance in safe water supply. 2.3 Data source Traditionally, wet season in the PRD is mainly from April to September. In order to better control water deterioration, it is 2.3.1 Rainfall data significant to analyze the dry and wet seasons distribution in every year for better controlling water deterioration. Wavelet analysis is Guangzhou is one of the important economic development centers an important tool for partitioning the variation of signals into scale in the PRD. All the regions in the PRD belong to the same climate and time location domains [34, 35], which has been widely used in zone, and the annual rainfall does not significantly vary among the climate and hydrology characterization [36]. Using the continuous different regions in the PRD. In this study, Guangzhou was chosen as wavelet analysis, Nakken [37] found that the temporal variability of an example to analyze the temporal rainfall variations in this region.

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Figure 1. Map of study area with the location of water quality monitoring station along with 1, Xiaolan; 2, Shawan; 3, Shimen; 4, Tianhe; 5, Xipaotai; 6, Rongqi; 7, Sanshui; 8, Makou; 9, Maan; 10, Guanchong; 11, Ganzhu; 12, Gaoyao; 13, Huangpu; 14, Haizhuqiao; 15, Daao; 16, Humen; 17, Hengmen; 18, Haiwei; 19, Xintang; 20, Zhangpeng; 21, Shilongnan; 22, Shilongbei; 23, Mayong; 24, Dasheng; 25, Boluo.

The daily rainfall records of Guangzhou during 1952–2009 were 2.4 Statistic analysis methods obtained from the China Meteorological Administration (www.cma. gov.cn). Thus the quality of rainfall data for Guangzhou can be One-way analysis of variance (ANOVA) and principle component guaranteed in this study. For analysis purposes, the anomaly of analysis/principle factor analysis (PCA/PFA) are used to extract infor- rainfall records was calculated and then used as the basis for the mation related to the patterns of water quality change in the PRD. In following analysis. order to measure the variation of water quality parameter among stations and between dry and wet seasons, ANOVA was carried out. 2.3.2 Water quality data The PCA is widely used to reduce dimensions of multivariate problems [43]. Before the implementation of the PCA, correlation analysis The values of water quality parameters used in this study were was used to find the relationships among the water quality obtained from the Hydrological Bureau of Pearl River Water parameters. PCA was then employed to find the principle com- Resources Commission, China Ministry of Water Resources. More ponents (PCs) which are linear combinations of different variables. than 50 water quality parameters are available. Seventeen PFA, which follows the PCA, was used to find the principle factors parameters were selected, which can provide continuous measure- called varifactor (VF). These principle factors can indicate unobserv- ments at all 25 selected water quality monitoring stations in the able, hypothetical, latent variables. Significant PCs were extracted to Pearl River and its tributaries. The details on units, abbreviation, and reduce the contribution of variables, and were further processed by measurement methods of these 17 parameters are shown in Tab. 1. varimax rotation which would generate the VFs. Thus a fewer factors obtained from PFA can better explain the information inherent in a large number of original dataset. All the mathematical and statistical computations were conducted using EXCEL 2003 (Microsoft Office1) and Matlab 7.0 software. Basic statistics of the dataset on water quality were sum- marized in Supplementary Materials.

0 3 Results

Wavelet coefficiency coefficiency Wavelet 3.1 Inter-annual changes of rainfall The anomaly of annual rainfall during 1952–2009 is illustrated in Fig. 3a. Rainfall displays high and low patterns among different year High-flow period Low-flow period T (years or months) periods. Compared with other years, the maximum rainfall or wet season or dry season appeared in 2002 followed by the rainfall in 1975; and the minimum Figure 2. Sketch map for division of dry and wet period or high-flow and rainfall appeared in 1956 and followed by the one in 1984. From the low-flow period. least square ﬁtting results illustrated in Fig. 3a, the annual rainfall

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Table 1. The abbreviation, unit, determination method of seventeen water quality parameters

Parameters Abbreviation Unit Method

Air temperature AT 8C Temperature probe Water temperature WT 8C Temperature probe Electrical conductivity EC mScm1 Conductometry pH pH – pH probe Dissolved oxygen DO mg L1 Electrochemical probe 1 Chemical oxygen demand CODMn mg L Potassium permanganate 1 5-Day biological oxygen demand BOD5 mg L 5-day incubation þ 1 Ammonium nitrogen NH4 mg L Ion chromatography Fluor F mg L1 Ion chromatography Total phosphorus TP mg L1 Spectrophotometry 2 1 Sulfate SO4 mg L Ion chromatography Chloride Cl mg L1 Ion chromatography 1 Nitrate NO3 mg L Ion chromatography Iron Fe2þ mg L1 Atomic absorption spectrophotometry Manganese Mn2 mg L1 Atomic absorption spectrophotometry Copper Cu2þ mg L1 Atomic absorption spectrophotometry Zinc Zn2þ mg L1 Atomic absorption spectrophotometry from 1952 to 2009 appears a gradually increase trend. The anomaly which show a signiﬁcant cycle vibration. The two-time scale values of annual rainfall in Guangzhou during 58 years were represents the inter-decadal and inter-annual variations of annual analyzed using CWT to detect the detailed temporal periodic rainfall. Moreover, rainfall changes at small scale show the detailed patterns of rainfall. From Fig. 3b, strong annual changes are variation at the large scale background. Variations of rainfall detected by CWT among 58 years. From Fig. 3b, one can see that at much smaller scale are inﬂuenced by various environmental there are common wavelet variance peaks at both scales 5a and 18a, factors.

Figure 3. Anomaly (a) and Morlet wavelet transform (b) (positive values represent increase in rainfall, negative value represent decrease in rainfall and the zero corresponds to break point of rainfall) of annual rainfall during Figure 4. Changes of wavelet transform coefficient of annual rainfall anom- 1952–2009. alous series at 18a (a) and 5a (b) time scales.

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From Figs. 3b and 4, rainfall mainly experienced 3 and 10 high and 3.2 Monthly changes of rainfall and the division low period at the 18a scale (Fig. 4a) and at the 5a scale (Fig. 4b), of dry season and wet season in different respectively. There are significant inter-annual variations during the rainfall period 58 years. As to 5a scale, years of high-flow period mainly include 1953–1955, 1959–1961, 1965–1967, 1971–1972, 1974–1977, 1981– In this study, monthly variations of rainfall were analyzed by apply- 1983, 1987–1989, 1993–1996, 2000–2002, and 2006–2008. While ing CWT based on the inter-annual changes and change periods of the years of low-flow period mainly include 1952, 1956–1958, rainfall. From Fig. 3, although there are ten rainfall change periods, 1962–1964, 1968–1970,1973, 1978–1980, 1984–1986, 1990–1992, every change period includes a high-flow period and a low-flow 1997–1999, 2003–2005, 2009. The low-flow period will continue from period. No matter in high-flow period or low-flow period, there 2009 (see Fig. 3). are significant dry and wet seasons in each year. Amongst all the

Figure 5. Morlet wavelet transform (positive values represent increase in rainfall, negative value represent decrease in rainfall and the zero corresponds to break point of rainfall) and wavelet coefficient of monthly rainfall in 1953–1955 (upper), 1984–1986 (middle) and 2006–2008 (lower).

ß 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.clean-journal.com 386 X. Fan et al. Clean – Soil, Air, Water 2012, 40 (4), 381–393 change periods of rainfall at 5a scale, three periods, i.e., 1953–1955 (high-flow period), 1984–1986 (low-flow period), and 2006–2008 (high- flow period) which respectively represent the early stage, middle stage, and recent period, were chosen to detect the trends of monthly rainfall in Guangzhou. At the high-flow period of 1953–1955, monthly rainfall appears significant dry and wet seasons alternate change at 9 month scale (see Fig. 5). Wet seasons mainly varied from May to October in 1953 and 1954. However, in 1955 wet season varied from April to September. Thus period of other months in a year is the dry season. As to the low-flow period of 1984–1986, significant dry and wet seasons vibration appeared at 10 month scale (see Fig. 5). The wet season of the three years all appeared from April to September. For other high-flow period of 2006–2008, it is also at 10 month scale where significant dry and wet seasons changes are observed (Fig. 5). Months from April to September are also wet season, and other Figure 6. Months of wet season in each year during 1952–2009. months in one year are dry season. Similar to the aforementioned results, dry and wet seasons in other rainfall change periods were also be detected. Figure 6 shows the months of wet season in each year varying from 1952 to 2009. wet season, significant positive correlations only existed between 2 þ There are some differences of dry and wet seasons division among SO4 and Cl . EC and CODMn, BOD5, NH4 , and F are also positively 2 different years. From Fig. 6, one can see that wet season mainly correlated. Moreover, the values of EC, SO4 , and Cl in dry season appear from April to September, which occupy the largest pro- were much higher than that in wet season. In dry season concen- 2 portion among the 58 years (about 70%) and then followed by the tration of EC, SO4 , and Cl are about 5.34, 3.50, and 11.87 times of wet season from May to October (about 13.8% of the total years). which in wet season, respectively. In dry season, significant negative

correlations existed between pH and CODMn, pH and BOD5, pH and þ NH4 , pH and TP, pH and F , DO and CODMn, DO and BOD5, DO and 3.3 The case for variations of water quality in dry þ NH4 , DO and F , DO and TP; significant positive correlations existed and wet seasons þ between CODMn and BOD5, CODMn and NH4 , CODMn and F , CODMn 2 þ Significant inter-annual variations and seasonal changes in the PRD and TP, CODMn and SO4 , BOD5 and NH4 , BOD5 and F , BOD5 and TP, þ þ were identified through the analysis of rainfall at different time NH4 and F ,NH4 and TP. series, i.e., high-flow and low-flow period appeared in different years. No matter in high-flow and low flow period, there is a wet season and 3.3.2 Factors influenced the variation of water quality a dry season in each year. In this study, data of water quality between dry and wet seasons parameters in 2008 were used as an example to assess water quality. Correlation matrix of water quality parameters in dry and wet The year of 2008 is in the high-flow period. So, in this year, wet season seasons is presented in Tab. 2. The number of principal component varies from April to September, and dry season from January to (PC) retained to explain the underlying data structure is determined March and from October to December. by the eigenvalues. The cumulative variance proportion and screen plot for eigenvalues obtained in this study show significant changes 3.3.1 Changes of water quality parameters between dry of slope after the fourth eigenvalues for dry and wet seasons, respec- and wet seasons tively (see Fig. 8). PCs with eigenvalues greater than unity are The range, mean values, and standard deviations of 17 water quality retained for further analysis. Thus four PCs are retained to account parameters were analyzed in this study. Significant differences for associated information of the origin dataset in dry season or wet 2þ season. Four PCs explained 88.37% of total variance in dry season, (p < 0.05) for 17 water quality parameters except TP, NO3 ,Fe , and Zn2þ between dry and wet seasons were identified. Water and 86.13% of that in wet season (Fig. 8). The results of PCA for dry temperature and AT are much higher in wet season than that in and wet seasons based on correlation matrix of the parameters are shown in Tab. 3. The rotation of the principal components was dry season. However, the concentrations of DO, CODMn, BOD5, þ 2 2þ 2þ executed by varimax method with Kaiser Normalization. Results NH4 ,F ,SO4 ,Cl ,Mn , and Cu in dry season are greater than that in wet season. The same trend also appears in EC and pH. of varifactor (VF) from varimax rotation for dry and wet seasons Figure 7 shows the examples of box-plots for some parameters such are presented in Tab. 4. The first four VFs explained 80.53 and 80.94% þ of the total variance of the origin dataset for dry and wet seasons as pH, EC, CODMn, BOD5,NH4 , TP, NO3 and Cl in dry and wet seasons. The line across the box represents the median, whereas the separately. bottom and top of the box show the location of the first and third quartiles (Q1 and Q3). The whiskers are the lines which extend from 4 Discussion the bottom and top of the box to the lowest and highest observations in the region defined by Q1 1.5 and Q3 þ 1.5. Individual points with 4.1 Inter-annual and monthly changes of rainfall values outside these limits are shown with asterisks. 2 Significant positive correlations between EC and Cl , EC and SO4 , The rainfall in Guangzhou shows a gradually increase trend 2 SO4 , and Cl in dry season are identified (see Tab. 2). However, in during 1952–2009 in the PRD. Xu et al. [44] also reported that

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þ Figure 7. Box Plots for parameters (a, pH; b, EC; c, CODMn; d, BOD5;e,NH4 ;f,NO3 ; g, TP; and h, Cl) of water quality in dry and wet seasons.

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Table 2. Correlation matrix of water quality for dry and wet seasons

þ 2 2þ 2þ 2þ 2þ AT WT EC pH DO CODMn BOD5 NH4 F TP SO4 Cl NO3 Fe Mn Cu Zn Dry season AT 1.000 WT 0.746 1.000 EC 0.029 0.251 1.000 pH 0.558 0.422 0.241 1.000 DO 0.862 0.719 0.097 0.703 1.000 CODMn 0.710 0.441 0.202 0.560 0.866 1.000 BOD5 0.769 0.509 0.090 0.621 0.885 0.937 1.000 þ NH4 0.648 0.378 0.067 0.543 0.747 0.823 0.881 1.000 F 0.601 0.169 0.311 0.494 0.650 0.769 0.867 0.893 1.000 TP 0.635 0.315 0.060 0.482 0.728 0.826 0.916 0.949 0.947 1.000 2 SO4 0.066 0.368 0.955 0.230 0.049 0.249 0.074 0.081 0.312 0.091 1.000 Cl 0.042 0.321 0.992 0.233 0.048 0.183 0.035 0.026 0.255 0.024 0.985 1.000 NO3 0.401 0.101 0.173 0.239 0.361 0.539 0.628 0.496 0.703 0.648 0.217 0.145 1.000 Fe2þ 0.019 0.229 0.259 0.312 0.156 0.312 0.242 0.353 0.407 0.416 0.404 0.318 0.269 1.000 Mn2þ 0.720 0.361 0.252 0.667 0.848 0.910 0.923 0.877 0.881 0.881 0.273 0.229 0.687 0.439 1.000 Cu2þ 0.607 0.130 0.441 0.537 0.531 0.642 0.668 0.611 0.783 0.664 0.417 0.383 0.742 0.373 0.801 1.000 Zn2þ 0.209 0.441 0.117 0.472 0.342 0.116 0.172 0.078 0.077 .004 0.176 0.131 0.247 0.036 0.127 0.096 1.000

Wet season AT 1.000 WT 0.699 1.000 EC 0.148 0.283 1.000 pH 0.322 0.383 0.190 1.000 DO 0.252 0.186 0.724 0.607 1.000 CODMn 0.274 0.022 0.626 0.555 0.908 1.000 BOD5 0.353 0.240 0.694 0.672 0.972 0.918 1.000 þ NH4 0.447 0.323 0.523 0.705 0.859 0.865 0.908 1.000 F 0.568 0.301 0.548 0.567 0.783 0.861 0.869 0.903 1.000 TP 0.655 0.396 0.443 0.548 0.692 0.783 0.734 0.850 0.861 1.000 2 SO4 0.039 0.428 0.372 0.003 0.337 0.550 0.280 0.207 0.390 0.371 1.000 Cl 0.223 0.539 0.174 0.035 0.174 0.349 0.108 0.034 0.133 0.075 0.894 1.000 NO3 0.563 0.261 0.007 0.057 0.025 0.047 0.005 0.026 0.170 0.349 0.313 0.217 1.000 Fe2þ 0.607 0.500 0.154 0.721 0.397 0.425 0.434 0.485 0.543 0.695 0.231 0.195 0.559 1.000 Mn2þ 0.629 0.383 0.453 0.632 0.736 0.788 0.768 0.830 0.891 0.956 0.419 0.189 0.414 0.809 1.000 Cu2þ 0.348 0.565 0.379 0.247 0.227 0.242 0.250 0.309 0.391 0.521 0.095 0.095 0.265 0.499 0.519 1.000 Zn2þ 0.270 0.092 0.265 0.386 0.475 0.365 0.461 0.337 0.224 0.054 0.039 0.027 0.488 0.131 0.038 0.157 1.000

the rainfall of Pearl River increased from 1951 to 2000. This trend 4.2 Water quality variation and correlation of water may be induced by long-term climatic changes and gradual changes quality parameters of land use arising from urbanization [37]. According to the results obtained from CWT analysis, the annual rainfall series The variations in water quality in the PRD are attributed to many appear different high-flow and low-flow alternate changes at factors. All these factors interact with each other. For parameters AT different time scales. At 5a scale, the rainfall experienced the and WT, significant difference was identified in dry and wet seasons, most prominent cycle vibration. For example, the period 2006– which were mainly caused by seasonal variation in this region. The 2008 was the high-flow period. In fact, flooding disaster happened similar trends were observed for other parameters such as EC, þ in this region in 2008 was caused by the continuous rainfall CODMn, BOD5 and NH4 , and significant lower concentrations of which coincided with the analysis results. In the future, low-flow them were also detected in wet season. Similar seasonal variation period will continue in this region. Li et al. [45] also observed in concentration of BOD5 was also observed in the Fuji river basin, the inter-annual variations of rainfall by using CWT to analyze Japan by Shrestha and Kazama [46]. This might be induced by exces- the rainfall in Hebei province. Rainfall intensity varies significantly sive dilution effect arising from plenty of rainfall and inflow from amongst 12 months in each year. Among the 58 years, months upstream in wet season [12, 24]. In this region, precipitation is from April to September in some years receive more rain than different in dry and wet seasons; there is a large amount of rainfall other months, which are defined as the wet season. And wet in wet season from April to September, but little from October to season in other years appeared from May to October, which is March [47]. The changes of river water discharge are mostly related to different from the general convention. Thus the dry and wet rainfall in this region [33]. During the wet season, the discharge of seasons patterns of rainfall for different years are different, the Pearl River accounts for 80% of the yearly total [30], and the which might influence the variation of water quality [28, 29]. discharges of major tributaries (West River, North River, and East The CWT of rainfall effectively identified significant temporal var- River) are much higher than those in dry season [48]. As the increase iability in the frequency scales and the dry and wet seasons in of rainfall and river flow from upstream, a large number of pollu- each year. tants from farmland and street were carried into rivers.

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While in wet season, the intensity of salt water intrusion decreased due to the large river inflow from upstream. The concentrations of 2 ions except SO4 and Cl then increased [49, 50] for a large number of pollutants carried by a lot of rainfall and runoff. However, the concentration of nitrate did not show distinct difference between wet season and dry season in this study. This result is not consistent with the results presented in Wong and Wong [4], which show that the concentration of nitrate is lower in summer and autumn but higher in winter and spring. Whatever in dry season or wet season, there is significant positive correlations between pH and DO, which are mainly due to the change of DO concentration. The DO concentration can influence carbonate balance in water, and hence influence pH value in river water. Significant negative correlations were identified between AT and DO, and WT and DO in dry season. Osode and Okoh [51] provided a similar result in the study about the physicochemical qualities of a receiving watershed for wastewater in Eastern Cape Province, South Africa. This is because temperature changes greatly in dry season and warmer water is more easily saturated with oxygen [46, 52, 53] and the production speed of oxygen is less than the consumption speed of oxygen with increasing temperature and decreasing discharge [12].

4.3 Parameters related to the variation of water quality between dry and wet seasons For each principal component, component loadings indicate the correlation between the original variables and the newly formed Figure 8. Screen plot of the eigenvalues of principal components in dry components, which can be used to determine the relative impor- season (a) and wet season (b). tance of a parameter compared to others in a PC rather than the importance of the component itself [54–56]. From Tab. 3, in dry In dry season, rainfall, runoff, and river inﬂow from upstream season, PC1 explains 50.90% of the total variance of origin dataset, have decreasing trends; and there is an increasing trend for salt and is highly positively contributed by organic pollution related water intrusion which mainly appears in the junction of rivers and parameters (CODMn and BOD5), nutrients-related parameters (NH4 oceans from October to the following March [31]. Thus in this period and TP), heavy metal pollution parameters (Mn2þ and Cu2þ) and 2 higher SO4 and Cl concentrations and EC value exist in this region. nonmetal pollution parameters (F ). Meanwhile, PC1 was highly

Table 3. Loadings of 17 water quality parameters on three significant principal components for dry and wet seasons

Parameters Dry season Wet season

PC1 PC2 PC3 PC4 PC1 PC2 PC3 PC4

AT 0.784 0.365 0.168 0.326 0.568 0.659 0.083 0.033 WT 0.427 S0.691 0.422 0.224 0.407 0.750 0.357 0.184 EC 0.275 0.843 0.383 0.201 0.622 0.266 0.086 0.632 pH 0.696 0.049 0.460 0.244 0.709 0.048 0.262 0.537 DO S0.868 0.307 0.274 0.071 S0.866 0.337 0.186 0.073 CODMn 0.922 0.058 0.016 0.027 0.885 0.404 0.022 0.017 BOD5 0.955 0.196 0.061 0.028 0.903 0.274 0.243 0.025 þ NH4 0.899 0.135 0.169 0.119 0.911 0.093 0.250 0.067 F 0.908 0.151 0.255 0.009 0.937 0.065 0.024 0.028 TP 0.911 0.091 0.310 0.113 0.921 0.163 0.106 0.003 2 SO4 0.267 0.912 0.266 0.054 0.381 0.487 0.750 0.115 Cl 0.237 0.887 0.373 0.126 0.167 0.558 0.742 0.119 NO3 0.654 0.214 0.510 0.144 0.278 0.506 0.654 0.056 Fe2þ 0.389 0.416 0.147 S0.708 0.709 0.421 0.277 S0.358 Mn2þ 0.980 0.027 0.045 0.064 0.961 0.134 0.171 0.072 Cu2þ 0.801 0.296 0.117 0.143 0.487 0.434 0.069 0.423 Zn2þ 0.128 0.392 S0.670 0.395 0.238 0.583 0.559 0.178 Eigenvalue 8.648 3.581 1.777 1.009 8.229 2.976 2.338 1.088 Total variance (%) 50.895 21.064 10.462 5.957 48.441 17.520 13.737 6.431 Cumulative variance (%) 50.895 71.959 82.421 88.378 48.441 65.961 79.698 86.129

ß 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.clean-journal.com 390 X. Fan et al. Clean – Soil, Air, Water 2012, 40 (4), 381–393

Table 4. Loadings of 17 water quality parameters on three principal factors for dry and wet seasons

Parameters Dry season Wet season

VF1 VF2 VF3 VF4 VF1 VF2 VF3 VF4

AT 0.572 0.028 0.671 0.139 0.248 0.711 0.258 0.065 WT 0.187 0.263 0.836 0.176 0.120 0.488 0.592 0.267 EC 0.112 0.985 0.023 0.059 0.694 0.032 0.145 0.017 pH 0.442 0.227 0.514 0.123 0.488 0.264 0.113 0.716 DO 0.622 0.051 S0.764 0.109 S0.943 0.068 0.069 0.220 CODMn 0.756 0.147 0.489 0.345 0.922 0.187 0.267 0.104 BOD5 0.868 0.003 0.449 0.109 0.963 0.101 0.004 0.237 þ NH4 0.921 0.033 0.231 0.069 0.894 0.313 0.128 0.115 F 0.975 0.192 0.041 0.026 0.843 0.439 0.064 0.042 TP 0.971 0.043 0.156 0.100 0.687 0.694 0.035 0.011 2 SO4 0.138 0.956 0.138 0.209 0.309 0.187 0.913 0.128 Cl 0.068 0.992 0.077 0.060 0.087 0.024 0.972 0.147 NO3 0.713 0.096 0.104 0.091 0.118 0.639 0.249 0.128 Fe2þ 0.392 0.237 0.213 0.441 0.223 0.766 0.114 0.596 Mn2þ 0.854 0.185 0.374 0.202 0.674 0.697 0.123 0.192 Cu2þ 0.732 0.349 0.113 0.031 0.180 0.581 0.086 0.041 Zn2þ 0.082 0.090 0.518 0.014 0.488 0.470 0.074 0.219 Total variance (%) 40.721 19.304 17.408 3.092 37.162 21.861 14.716 7.196 Cumulative variance (%) 40.721 60.025 77.433 80.525 37.162 59.023 73.739 80.935

negatively contributed to by parameters DO. Thus this component the water quality parameters related to metal and degree of acid mainly measures the water quality parameters related to chemical and alkaline. contamination, which is a synthesized indicator of the total var- 2 iance. This component also reveals that the parameters EC, SO4 , and Cl are less important in explaining the variation of water quality 4.4 Pollution source induced changes of water because the loading coefficients of these variables are lower. PC2 is quality between dry and wet seasons mainly comprised of water quality parameters including mineral- 2 þ related parameters (EC), nonmetal parameters (SO4 and Cl ), which From Tab. 4, in dry season, high loading of BOD5,NH4 , TP, and F all is negatively contributed by parameter WT. This component explains contribute to the VF1, which suggests the same source for the strong 21.06% of the total variance and mainly determines water quality correlation among them. This factor explains 40.72% of the total parameters associated with mineral parameters. PC3 accounts for variance and represents organic pollution mainly from domestic 10.46% of the total variance and consists of a negative loading of wastewater, industrial effluents, and discharges from wastewater 2þ parameter Zn and a positive loading of parameter NO3 . This treatment plants. This result is supported by the studies of Shrestha component is associated with metal parameters and nutrient and Kazama [46] and Zhang et al. [58]. VF2 explains 19.30% of the total 2 parameters. Nitrate is more related to the use of organic and inor- variance, and consists of parameters EC, SO4 , and Cl . This factor is ganic fertilizers [57]. Thus this component mainly explains related to nonmetal pollution. Thus, river water quality variation is parameters related to agricultural activities. PC4 represents 5.96% further affected by salt water intrusion and effluents from industrial of total variance in dataset and has a strong contribution from Fe2þ, plants such as chemical industry and chemical pharmaceutical thus this component is related to metal parameters of water quality. factory, etc. VF3 accounts for 17.41% of the total variance, has a In wet season, four components characterize river water quality in positive loading of parameter WT and a negative loading of the PRD. Most parameters such as organic pollution related parameter DO. This factor indicates that the variation of water þ parameters (CODMn and BOD5), nutrients-related parameters (NH4 quality also is affected by temperature which is related to the and TP), heavy metal pollution parameters (Fe2þ and Mn2þ) and changes of DO [12]. þ nonmetal pollution parameters (F ) are positively contribute to In wet season, water quality parameters CODMn, BOD5, and NH4 the PC1 which accounts for 48.44% of the total variance in dataset. are positively contributed to the VF1 which represents 37.16% of the While PC1 has a negative loading of parameter DO. Similar to the total variance. While DO negatively contributed to the VF1. This result obtained from the dry season, this component also seems to factor is mainly associated with organic pollution and nutrient measure water quality parameters associated with chemical con- pollution [11], which indicates the variation of river water quality tamination. PC2 explains 17.52% of the total variance in the dataset are mainly influenced by runoff from cultivated and livestock farms, with a strong contribution from physical parameters (AT and WT). domestic waste water, and industrial effluents discharged from Obviously, this component mainly is associated with temperature, factories such as textile mill, food, chemical industry, and manu- and is influenced by the temperature of effluents discharged into facture leather. Zhu et al. [18] presented that the industrial, agri- river. PC3 accounts for 13.74% of the total variance in dataset, which culture, mining processes, lumbering and domestic sewage are the 2 consists of a positive loading of nonmetal parameters (SO4 ,Cl and main pollutants sources, and among the sources, the most serious NO3 ). PC4 explains 6.43% of the total variance in dataset, which pollution is induced by domestic sewage and garbage in this region. includes mineral-related parameters (EC and pH) and metal The VF2 with a strong loading of AT, TP, Fe2þ, and Mn2þ explains parameters (Fe2þ and Cu2þ). This component mainly characterizes 21.86% of total variance. It indicates that the variation of river water

ß 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.clean-journal.com Clean – Soil, Air, Water 2012, 40 (4), 381–393 Water Quality Management Based on Division of Dry and Wet Seasons 391 quality is further affected by weathering variation and anthropo- different cities in the PRD to make sure the safety of freshwater genic activities such as applying fertilizer and pesticide, domestic resources, for example, implement West-to-East water transfer proj- sewage, and industrial effluents, etc. VF3 explains 14.72% of the total ect, construct some wetlands or reservoirs to store water in wet 2 variance, which consists of a high loading of parameters SO4 and season when freshwater is abundant, and also build some tide-block- Cl. It shows that the effluents from chemical plants also affect the ing flood gates to prevent the sea water intrusion; (4) in this period, variation of water quality. periodic cleaning the sewage and waste in the constructed channel is In dry season the variation of water quality are mainly influenced required because most of effluents are directly discharged into river by domestic sewage, industrial effluents, and salt water intrusion, channels with little fresh river water input. while in wet season, besides the aforementioned pollution sources, In wet season, winds are southerly in summer and typhoons drainages from cultivated land and livestock farm are also the main coincide with hot rainy season. Precipitation will lead to higher þ pollution sources. Whatever in dry season or wet season, BOD5,NH4 , and more frequent flooding in the PRD. Besides the effluents from TP, and Mn2þ are all the main factors influencing the river water factories and domestic sewage, intensive agricultural activities [64] quality of the PRD. The similar results also appeared in the water and livestock production usually provide the dominant inputs of quality of Beijing, a tributary of the Pearl River [56]. The pollutants, nutrient into the river water. Runoff from urban areas is another especially nutrients were mainly from domestic sewage, industrial important pollution sources to the receiving water [26, 65]. During effluents, agriculture fertilizer and marine culture [10]. Similar this period, amount of fertilizer, pesticides, and herbicides used in results were also reported in the Yangtze River Delta where most agriculture should be controlled strictly throughout the river system rivers are heavily contaminated by higher concentrations of N and P [66]. The discharge of effluents and the dumping of animal waste into related to chemical compounds, especially in the peri-urban areas the river should not be permitted. Split of wastewater and rainwater [24, 59]. In the Ebro Delta, Spain where river water received pollu- is needed in the future. Considering the numerous rivers and chan- tants mainly from industrial effluents, agricultural wastewater, and nels and complicated water system in this region, in the short term a large amount of pesticides annual input [60]. Water quality is more actions should be taken to intercept pollutant from upstream and 2 affected by SO4 and Cl in dry season compared to that in wet city area. In the long term, further study should be put on how to season. This indicates that except for wastewater from chemical connect pollution control with the self-purification capacity of plants, the variation of river water quality might be related to salt rivers, and make better use of the intricate deltaic river network. water intrusion induced by the decrease of rainfall and river flow Besides, city sanitation departments should further enlarge the from upstream in dry season. Early studies [61, 62] found that there trash collecting frequency and strengthen the supervision and were negative correlations (negative power-low and/or exponential inspection on the activities of collection, transportation and disposal relationship) between freshwater inflows and the length of salt of various wastes in dumps, especially the open ones. water intrusion in many estuaries. The PRD has higher salinity in dry season induced by salt water intrusion. Water deterioration is a serious problem in this region, which is induced by inadequate contaminant source control and a lack of wastewater treatments. 5 Conclusions In this study, measures are proposed to manage river water quality 4.5 Measures for water quality management in the PRD. The dry and wet seasons are divided based on the rainfall trends during 1952–2009. The water quality dataset of 2008 is used as According to the analysis of water quality parameters of 2008 in the an example. The seasonal variation of water quality and the impor- PRD, water quality shows obvious variation and is influenced by tant factors driving this variation in the PRD were investigated. different pollution factors in dry and wet seasons. As the major water Contaminant sources responsible for the water quality changes were resource for human and ecosystem, some actions have been pro- also identified. Significant differences in water quality were found posed and/or taken to protect river water quality and manage the in dry and wet seasons arising from different pollution sources. water resource in this region [63]. Through the analysis of contami- Variation of water quality was mainly related to the seasonal nant sources and pollutants factors, Zhu et al. [18] presented that it changes of rainfall and inflow from upstream, and salt water intru- was necessary to form official water resource management agencies sion. Some suggestions were then provided to effectively control to build a water pollution monitoring system and charge for water pollution sources and manage water resources in the PRD. Due to a use. More wastewater treatment plants are required to reduce the lack of sufficient data, some important pollutants such as POPs are concentrations of pollutants before they are discharged into this not considered in this paper. Further investigation is required. region. More strict regulations are also required to reduce the waste discharges from factories [4, 15]. Acknowledgments In this study, except the aforementioned measures, some suggestions for river water quality management are presented below. In dry This study is financially supported by the National Natural Science season, reduction of river flow and rainfall will lead to more exten- Foundation (U0833002; 50939001; 41071330). sive and serious sea water intrusion [23], which will result in higher content of mineral ions and will threat the safety of drinking water. The authors have declared no conflict of interest. Meanwhile, the dilution potential of wastewater from industries and domestic sewage will be reduced such that more pollutants will stay in the receiving water. Thus in this period, the following actions are References recommended: (1) To control the discharges of wastewater in this [1] Q. Zhang, C. Y. Xu, Y. Q. D. Chen, T. 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ß 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim www.clean-journal.com 392 X. Fan et al. Clean – Soil, Air, Water 2012, 40 (4), 381–393

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