Assessment of eutrophication and water quality in the estuarine area of Lake Wuli, Lake Taihu, China

Assessment of eutrophication and water quality in the estuarine area of Lake Wuli, Lake Taihu, China

Science of the Total Environment 650 (2019) 1392–1402 Contents lists available at ScienceDirect Science of the Total Environment journal homepage: w...

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Science of the Total Environment 650 (2019) 1392–1402

Contents lists available at ScienceDirect

Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Assessment of eutrophication and water quality in the estuarine area of Lake Wuli, Lake Taihu, China Junli Wang, Zishi Fu, Hongxia Qiao, Fuxing Liu ⁎ Eco-environmental Protection Research Institute, Shanghai Academy of Agricultural Sciences, Shanghai 201403, PR China Shanghai Engineering Research Centre of Low-carbon Agriculture (SERCLA), Shanghai 201415, PR China

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Eutrophication and status of water quality were assessed in the estuarial area of Lake Wuli. • Differences were observed between seasons and three different parts of Lake Wuli. • The wet season experienced worse eutrophication and water quality than the dry season. • East Wuli had worse eutrophication and water quality status than the other parts of the lake. • WQImin had stricter standards than WQI when analyzing water quality in this research.

a r t i c l e

i n f o

Article history: Received 14 June 2018 Received in revised form 24 August 2018 Accepted 10 September 2018 Available online 11 September 2018 Keywords: Water quality Eutrophication TLI WQI Lake Wuli Estuarine area

a b s t r a c t Our study assessed the actual water situation in the estuarine area of Lake Wuli, Meiliang Bay, Lake Taihu, China, based on eutrophication levels and status of water quality using the trophic level index (TLI) and water quality index (WQI) methods. In the wet (August 2017) and dry (March 2018) seasons, 22 estuarine areas were tested at 69 sampling sites, which included lake and rivers. Five parameters—chlorophyll a (Chl-a), total phosphorus (TP), total nitrogen (TN), Secchi disk (SD) and permanganate index (CODMn)—were measured to calculate the TLI, and 15 parameters—temperature (T), pH, electrical conductivity (EC), dissolved oxygen (DO), total dissolved solids (TDS), TN, TP, ammonium (NH4-N), nitrate (NO3-N), nitrite (NO2-N), CODMn, calcium (Ca2+), magnesium (Mg2+), chloride (Cl−) and phosphate (PO4-P)—were measured to calculate the WQI. The average TLI and WQI values in the wet season were 61.69 and 60.70, respectively, and the eutrophication level and water quality status were worse than that in the dry season (TLI: 57.40, WQI: 65.74). Significant differences were observed between three parts of Lake Wuli (West, Middle and East). Regardless of wet or dry season, East Wuli had worse eutrophication levels and water quality status than the other parts, whereas West Wuli showed less severe levels. DO, TN and CODMn used in the minimum WQI (WQImin) were the most effective parameters in our study. WQImin had stricter standards than WQI when analyzing water quality in the estuarine area of Wulihu. Factor analysis from principal component analysis (PCA) indicated that N might be the main factor affecting water quality of the most eastern sites in the wet season, and P may be the main factor in the dry season. Our results provide a valuable contribution to inform decision-making for the management of water environments by providing the actual water situation of the estuarine area of Lake Wuli. © 2018 Elsevier B.V. All rights reserved.

⁎ Corresponding author at: Eco-environmental Protection Research Institute, Shanghai Academy of Agricultural Sciences, Shanghai, 201403, PR China. E-mail addresses: [email protected] (J. Wang), [email protected] (H. Qiao), [email protected] (F. Liu).

https://doi.org/10.1016/j.scitotenv.2018.09.137 0048-9697/© 2018 Elsevier B.V. All rights reserved.

J. Wang et al. / Science of the Total Environment 650 (2019) 1392–1402

1. Introduction Anthropogenic development of the Lake Taihu Basin in China has led to considerable pollution of lake ecosystems. Lake Taihu, located in the center of the basin, provides the main source of drinking water for Wuxi and Suzhou, Jiangsu Province, and is the main water supply for Shanghai and East Zhejiang province, but it has experienced numerous ecological problems since the 1960s, particularly eutrophication and cyanobacterial blooms (Qin et al., 2007; Paerl et al., 2011). Water quality of Lake Taihu has improved to some degree due to water pollution control since the 1980s (Chen et al., 2003); however, rapid urbanization in surrounding areas has still resulted in polluted water discharge into the water network around the lake. Meiliang Bay, which is located in the north part of Lake Taihu, is one of the most seriously polluted areas (Yan et al., 2016). Thus, pollution control and regional management in Meiliang Bay present significant challenges for the Chinese government. Lake estuaries represent the transition area for water flow exchange between rivers and lakes, and the estuarine ecosystem is relatively fragile and sensitive to environmental factors, such as water dynamics and human activities. Estuaries usually accumulate large quantities of land-sourced pollutants through water flow and sediment deposition (Guan et al., 2009). Due to advantageous geographical positions, estuarine regions are also subject to socioeconomic development and may have to withstand high pressure due to anthropogenic disturbance (Barbier and Silliman, 2011). Because of the natural variability of estuarine ecosystems, there are some structural and functional problems limiting, some species are not capable of developing with habitat degradation (Elliott and Whitfield, 2011). A number of construction projects, such as sluices and dams have decreased the degree of connectivity between river and lake ecosystems and altered hydrodynamic conditions and corresponding ecosystem responses (Wan and Konyha, 2015). For example, the ecological environment of the estuaries of Lake Wuli, a part of Meiliang Bay in the urban area of Wuxi City, Jiangsu Province, has experienced an evidently deteriorating trend due to overexploitation. In order to establish a healthy water ecosystem and restore species diversity, it is important to identify the actual status of its estuaries. Based on the findings, it may be possible to develop targeted measures to restore these fragile water ecosystems. Understanding the trophic status of lakes provides an indication of an ecosystem's current structure and function, which facilitates the prediction of future trends in an ever-changing environment, and this information can be used to formulate appropriate mitigation strategies. Appropriate methodologies are essential to improve eutrophication. The Chinese National Environment Monitoring Center has recommended a method based on a trophic level index (TLI), which is designed to identify nutrition levels in lakes (China Environmental Monitoring Station, 2001). The TLI method has been employed widely by integrating the simplicity of univariate analysis with the accuracy of multivariate analysis (Liu et al., 2011). In general, the trophic levels of lakes are assessed using five main indicators of responses to nutrition: Chl-a (chlorophyll a), TP (total phosphorus), TN (total nitrogen), SD (Secchi disk) and CODMn (permanganate index) (Xiang et al., 2014). TLI was proposed based on Chl-a and the analysis of other indicators by means of desirable, correlation generated results. The index has been widely applied in research on eutrophication in lakes in New Zealand and China, such as Lake Hayes, Lake Okareka, Lake Oligotrophic, Lake Chaohu and Lake Dongting (Burns et al., 2000; Xiang et al., 2014; Trolle et al., 2014; Zhi et al., 2016). The purpose of TLI application is to predict the effects of potential management interventions leading to reduced algal blooms and increased water clarity. However, determining and measuring the five eutrophication indicators still leads to many uncertainties in estuarine areas, particularly estuaries close to cities. It should be possible to obtain more valuable information to assist decision makers in ecological management and for accurate measurement of water quality.

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Analysis of water quality is an important factor in elucidating how man-made activities affect the ecological integrity of an estuarine area. High nutrients and pollutants are transported by rivers and lakes and accumulate in estuaries, which receive land and water pollution loads from various sources (Gopal et al., 2018). In order to evaluate the baseline water quality of aquatic ecosystems, a water quality index (WQI) has been recommended, which is based on a simple expression of the general water quality by a single number, which summarizes large quantities of water quality data (Debels et al., 2005). WQI is a very useful and practical tool to classify surface waters or to assess pollution levels in a water body (Lermontov et al., 2011; Bakan et al., 2010). Moreover, WQI can identify the changing trends in water quality and it can facilitate comparisons between different sampling sites (Sun et al., 2016). Using the WQI method, Pesce and Wunderlin (2000) assessed the effect of urban discharge on the water quality of Suquya River in Argentina; Kannel et al. (2007) evaluated spatial and temporal changes of water quality in Bagmati River, Nepal; Sánchez et al. (2007) assessed the pollution levels along the Guadarrama and Manzanares rivers in Spain; Simoes et al. (2008) investigated the effects of aquaculture on Macuco and Queixada rivers, Brazil; Kocer and Sevgili (2014) and Sener et al. (2017) assessed the environmental impacts of land-based trout farms on Esen Stream and spatial variations in water quality of Aksu River in Turkey, and Wu et al. (2018) evaluated the spatial changes in river water quality in Lake Taihu Basin, China. The WQI method can convert multiple water quality parameters into a single value which reflects the actual status of the environment. Thus, application of the WQI method to evaluate estuarine water quality is an effective approach to providing integrated information for managers, instead of comparing a large number of results of various parameters. However, the evaluation of WQI generally requires the measurement of a considerable number of parameters, whereas minimum WQI (WQImin) generally uses a limited number of parameters (3 or 5), yet it shows high linear correlation with WQI (Akkoyunlu and Akiner, 2012; Sánchez et al., 2007). In this research, the key parameters representing water quality used for WQImin were also analyzed for simple and cost-effective water assessment in the estuarine area of Lake Wuli. Lake Wuli estuaries are located beside cities, whose hydrology and water quality are subjected to multiple impacts from regional geology and human activity. In this study, the actual water situation of the estuaries of Lake Wuli were determined by collecting 69 water samples from 22 estuarine areas in wet and dry seasons (August 2017, March 2018). The objectives of this study were (1) to assess the level of eutrophication using TLI, and (2) to classify the water quality status using WQI and to evaluate spatial and seasonal differences across the estuaries. The purpose of the study is to provide valuable information for governments and environmental managers by appropriately describing the variations in eutrophication levels and status of water quality of Lake Wuli estuaries.

2. Materials and methods 2.1. Study area Lake Wuli (31°30′07″–31°32′48″N, 120°15′11″–120°13′54″E) is located in Meiliang Bay, north of Lake Taihu (Fig. 1A) and it encompasses a surface area of 8.6 km2. It is about 6 km from east to west, 0.3–1.2 km from north to south and 21 km in circumference. The lake is part of Meiliang Bay extending into the urban area of Wuxi City, which is the second largest city in the Jiangsu Province of China. Wuxi, a highly populated and developed area, occupies an important position in the Lake Taihu Basin; the population is 4.86 million and gross domestic product (GDP) reached RMB 921 billion in 2016 (Wuxi Yearbook, 2017). As located in a subtropical monsoon climate zone, southeast wind mainly occurs in summer while northwest wind prevails in winter. Average annual temperature of Wuxi is 18.0 °C (12.2–20.1 °C), and average

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Fig. 1. Location of study area in Lake Taihu, China (A) and water sampling sites of Lake Wuli estuaries (B). Notes: No.#(No.) means estuarine number # (sampling numbers); No.-No. indicates water sampling sites.

annual precipitation is 843.6 mm. In Jiangsu Province, Wuxi is the only urban area around Lake Taihu; the rest are agricultural counties. In the study area, 22 lakeshore estuaries were investigated; the rivers all connect to Lake Wuli and they are distributed across Wuxi city (Fig. 1B). From the most northern estuary in Lake Wuli, 1 to 22 were numbered clockwise: Xiaoxuanhe (1#), Ludianqiaobang (2#), Huangjiabianbang (3#), Chendahe (4#), Xuxiangbang (5#), Hutianzhuangbang (6#), Lixihe (7#), Dongxuxiangbang (8#), Wangxiangbang (9#), Miaojingbang (10#), Maligang (11#), Lucunhe (12#), Weitianlihe (13#), Liangtanghe (14#), Dongguxiangbang (15#), Donghuwaihe (16#), Shangfengzuibang (17#), Weinisihenghe (18#), Zhangzhuangxianghe (19#), Xitiexiangbang (20#), Qitangbang (21#) and Chongshanhe (22#). Lake Wuli was divided into three parts: West Wuli (sampling locations of 1#, 2#, 3#, 22#), Middle Wuli (sampling locations of 4#, 5#, 6#, 7#, 8#, 17#, 18#, 19#, 20#, 21#) and East Wuli (sampling locations of 9#, 10#, 11#, 12#, 13#, 14#, 15#, 16#). In the estuarine area, most of the 22 rivers flowing into Lake Wuli are controlled by gates or dams; the gate or dam condition, water flow status and water sampling sites for each estuary are listed in the supplementary materials (Table S1). The land use varies along the rivers in the scope of the study area. For the lake side of each estuary, lands for sights dominated 22 rivers. For the river side of estuaries,

residential and commercial lands or other public lands were mainly observed. According to monitoring data in 2000, the water quality of Lake Wuli was classified as inferior V (GB 3838-2002, Ministry of Environmental Protection of China, 2002), and the concentration of total nitrogen (TN), total phosphorus (TP) and permanganate index (CODMn) reached up to 6.6, 0.2 and 8.1 mg L−1, respectively. Lake Wuli experienced the worst outbreaks of cyanobacteria bloom in 2000. As such, the State Council designated it as a priority lake for remediation (Wang and Wang, 2014). In 2003, an ecological restoration project was implemented and sediment dredging was performed in the whole lake to reduce internal nutrient loading (Chen et al., 2009). After nearly three years' effort, the water quality was evidently improved; by 2005, the concentrations of TN, TP and CODMn were 5.6, 0.14 and 5.6 mg L−1, respectively. In order to protect the improved water quality and to facilitate water diversion, gates or dams were built in the rivers connected to Lake Wuli. The lakeshore estuaries are special areas formed by the intersection of lake and river, which is affected by the dual influence of lake and river waters which, in turn, affects the water quality of both lake and river. But the construction of gates and dams has obstructed the hydrodynamic process, and this has affected the exchange process between the rivers and lakes.

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b TLI(∑) ≤ 70) and hyper eutropher (TLI(∑) N 70). The eutrophication status of each sampling site was calculated from the TLI value.

2.2. Sample collection and laboratory analysis Sixty-nine sampling sites covering 22 rivers were carefully selected to represent the whole estuarine area of Lake Wuli. Taking into account the variations of seasons, two sampling events were conducted in a wet (August 2017) and dry (March 2018) season. In order to minimize the effects of weather, efforts were made to collect the samples in sunny conditions. The sampling site of 10-4 was disrupted by construction in March 2018, so there was no sampling of this site in the dry season. Water parameters in situ, including pH, water temperature (T), dissolved oxygen (DO), total dissolved solids (TDS) and electrical conductivity (EC) were obtained using a multi-parameter water quality analyzer (HI9829, Hanna, Italy). Transparency was measured by a Secchi disc (SD), and chlorophyll a (Chl-a) was determined using a chlorophyll a fluorescence detector (FuloroQuik, AMI, USA). Then, mixed water samples from three replications in each sampling site were collected in 500 mL plastic bottles, which were rinsed with surface water before sampling. After sampling, all bottles were transferred to the laboratory, stored in a refrigerator and analyzed as soon as possible. TN, TP and CODMn were analyzed according to the standard Chinese method (Standard Method for the Examination of Water and Wastewater Editorial Board, 2002). The concentrations of ammonium (NH4-N), nitrate (NO3-N), nitrite (NO2-N) and phosphate (PO4-P) were measured by a continuous flow analytical system (AA3, Seal, Germany). The concentrations of calcium (Ca2+), magnesium (Mg2+) and chloride (Cl−) were measured using a 930 Compact ICFlex ion chromatograph (Metrohm, Switzerland).

2.4. Water quality index (WQI) calculations In order to evaluate the water quality status more objectively, the water quality of the estuarine area of Lake Wuli was assessed using water quality index (WQI) values. The WQI is used to summarize different water quality parameters converted from large quantities of data into a single number. Fifteen parameters of T, pH, EC, DO, TDS, TN, NH4-N, NO3-N, NO2-N, TP, PO4-P, CODMn, Ca2+, Mg2+ and Cl− were used to calculate the WQI; the equation used here was proposed by Pesce and Wunderlin (2000) as follows: WQI ¼

n X i¼1

n X

Pi

ð8Þ

i¼1

where n is the total number of parameters, Ci is the normalization value assigned to parameter i, and Pi is the relative weight assigned from 1 to 4 to parameter i (Table S2). The WQI values graded the water quality, ranging from 1 to 100: in range of N90 is excellent, (70–90] is good, (50–70] is medium, [25–50] is poor, and b25 is very poor. For each site, the WQI values were averaged to determine the spatial and seasonal WQI value. In order to assess the status of water quality of Lake Wuli estuarine areas in a more simple and cost-effective way, a WQImin method was considered in the study according to the equation below (Pesce and Wunderlin, 2000):

2.3. Trophic level index (TLI) calculations The trophic condition of the estuarine area of Lake Wuli was assessed using TLI values (China Environmental Monitoring Station, 2001). For both qualitative and quantitative aspects, TLI is a weighted sum based on the correlations between Chl-a and other substances. Chl-a, TP, TN, SD and CODMn were used to calculate the TLI, and formulas of each were established as follows:

C i Pi =

WQImin ¼

n X

C i =n

ð9Þ

i¼1

TLI ðChl−aÞ ¼ 10½2:5 þ 1:086lnðChl−aÞ

ð1Þ

where n is the total number of parameters, and Ci is the value after normalization (Table S2). The selection of parameters used in WQImin was based on the results of linear regression analysis. All 137 samplings collected in wet (69 samplings) and dry (68 samplings) seasons were used to calculate the selected water parameters and the relationship between WQI and WQImin.

TLI ðTPÞ ¼ 10½9:436 þ 1:624lnðTPÞ

ð2Þ

2.5. Data analysis

TLI ðTNÞ ¼ 10½5:453 þ 1:694lnðTNÞ

ð3Þ

TLI ðSDÞ ¼ 10½5:118−1:94 ln ðSDÞ

ð4Þ

TLI ðCODMn Þ ¼ 10½0:109 þ 2:661lnðCODMn Þ

ð5Þ

where the unit of Chl-a is mg m−3; the units of TP, TN and CODMn are mg L−1, and SD represents the Secchi disk, where the unit is m. The TLI equation was calculated as follows using the national standard: m X X W j  TLIð jÞ TLI ¼

ð6Þ

j¼1

W j ¼ r2ij =

m X

r 2ij

ð7Þ

j¼1

where TLI(j) is the composite index of j with the correlative weight Wj; rij is the correlation coefficients between the reference Chl-a and each parameter j (Chl-a, 1; TP, 0.84; TN, 0.82; SD, −0.83; CODMn, 0.83), and m is the number of indicators. The rij value was obtained from the 26 main lake survey data sets for China (Jin et al., 1995). The TLI ranges from 0 to 100, with high values representing high eutrophication levels. Trophic status is classified into five grades based on the TLI(∑) scores: oligotropher (TLI(∑) b 30), mesotropher (30 ≤ TLI (∑) ≤ 50), light eutropher (50 b TLI(∑) ≤ 60), middle eutropher (60

All statistical analyses were performed with SPSS statistical software (IBM SPSS Statistics Ver. 22.0). Significant differences between the mean values of water parameters at the spatial and seasonal scales were evaluated with Kruskal-Wallis tests. Significant differences due to lake locations and seasons on indexes were determined by one-way analysis of variance (ANOVA) with a general linear model, using the LSD test at 5% significance level. In order to test the response of main water parameters to WQI, stepwise multiple linear regression analyses were carried out to calculate WQImin, including 15 parameters used in WQI. Pearson correlation analysis was performed to detect relationships between WQI with TLI and WQImin. The TLI and WQI maps were constructed by ArcGIS (Ver. 10.2) software using the inverse distance weighting method. Factor analysis and drawing using a principal component analysis (PCA) method was performed using SIMCA software (Ver. 14.1). 3. Results and discussion 3.1. Water variables in the estuarine area of Lake Wuli Water variables can reflect changes in watersheds of rivers, and two of the most important factors affecting water variables are regional geology and human activity (Yang et al., 2012). A statistical summary of the water variables measured at all sampling sites for the estuaries of Lake Wuli in wet and dry seasons is provided in Table 1. It shows that

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Table 1 Water variables summarized at all sampling sites for the three parts of Lake Wuli in wet and dry seasons. Parameter

T (°C) pH EC (μS cm−1) DO (mg L−1) TDS (mg L−1) SD (m) Chl-a (mg m−3) TN (mg L−1) NH4-N (mg L−1) NO3-N (mg L−1) NO2-N (mg L−1) TP (mg L−1) PO4-P (mg L−1) CODMn (mg L−1) Ca2+ (mg L−1) Mg2+ (mg L−1) Cl− (mg L−1)

Wet season

Statistics

Dry season

Statistics

West Wuli (n = 13)

Middle Wuli (n = 28)

East Wuli (n = 28)

H

P

West Wuli (n = 13)

Middle Wuli (n = 28)

East Wuli (n = 27)

H

P

30.23 ± 0.62 7.40 ± 0.16 385.65 ± 41.96b 5.25 ± 0.73a 246.69 ± 41.39b 0.55 ± 0.13a 29.19 ± 7.51 1.24 ± 0.36b 0.46 ± 0.40b 0.17 ± 0.16b 0.10 ± 0.10b 0.08 ± 0.05b 0.03 ± 0.01 4.44 ± 0.99b 17.73 ± 2.53 25.28 ± 7.32 27.78 ± 2.27b

30.30 ± 0.51 7.35 ± 0.28 414.39 ± 48.31b 4.58 ± 1.50a 300.79 ± 93.11a 0.46 ± 0.13b 36.91 ± 20.09 1.71 ± 0.66b 0.60 ± 0.57b 0.32 ± 0.21b 0.22 ± 0.18a 0.27 ± 0.53ab 0.12 ± 0.28 4.78 ± 1.13b 20.28 ± 4.72 20.90 ± 6.30 35.50 ± 12.39a

30.28 ± 0.43 7.36 ± 0.15 443.86 ± 79.56a 3.45 ± 2.15b 288.46 ± 78.03ab 0.43 ± 0.10b 40.02 ± 22.24 5.23 ± 5.57a 4.09 ± 5.62a 0.58 ± 0.52a 0.15 ± 0.16ab 0.42 ± 0.44a 0.12 ± 0.13 8.33 ± 6.02a 20.18 ± 4.30 22.76 ± 9.17 38.18 ± 9.54a

0.064 1.017 6.541 8.856 2.999 8.383 1.873 27.708 25.353 8.153 7.360 19.742 19.315 10.945 2.468 2.996 16.564

0.968 0.601 0.038 0.012 0.223 0.015 0.392 b0.001 b0.001 0.017 0.025 b0.001 b0.001 0.004 0.291 0.224 b0.001

10.31 ± 0.51b 7.86 ± 0.23a 325.69 ± 58.38b 7.16 ± 2.21a 225.77 ± 40.97b 0.72 ± 0.15a 6.59 ± 4.33b 0.97 ± 0.61b 0.25 ± 0.17b 0.44 ± 0.38ab 0.15 ± 0.10 0.02 ± 0.01b 0.01 ± 0.01b 2.98 ± 0.31c 17.32 ± 3.84b 24.84 ± 5.78 31.44 ± 2.38b

10.74 ± 0.49a 7.61 ± 0.23b 356.39 ± 53.65b 4.21 ± 2.30b 252.04 ± 37.39a 0.53 ± 0.15b 12.72 ± 10.24ab 2.29 ± 2.15a 0.96 ± 1.26b 0.77 ± 0.94a 0.15 ± 0.14 0.12 ± 0.10ab 0.03 ± 0.02ab 4.02 ± 0.74b 20.98 ± 5.44a 21.75 ± 9.16 35.55 ± 7.66b

10.65 ± 0.51ab 7.63 ± 0.19b 390.11 ± 53.74a 4.34 ± 3.01b 268.56 ± 36.63a 0.49 ± 0.16b 16.08 ± 12.39a 2.81 ± 1.73a 1.97 ± 1.89a 0.41 ± 0.24b 0.18 ± 0.15 0.18 ± 0.23a 0.05 ± 0.06a 5.06 ± 1.75a 20.49 ± 4.63ab 23.70 ± 14.39 43.78 ± 10.46a

7.497 11.493 14.398 10.881 11.778 14.600 7.112 15.276 18.351 4.113 0.268 17.455 14.091 19.332 4.239 3.108 25.551

0.024 0.003 0.001 0.004 0.003 0.001 0.029 b0.001 b0.001 0.128 0.875 b0.001 0.001 b0.001 0.120 0.211 b0.001

Notes: West Wuli included sampling locations of 1#, 2#, 3#, 22#; Middle Wuli included sampling locations of 4#, 5#, 6#, 7#, 8#, 17#, 18#, 19#, 20#, 21#, and East Wuli included sampling locations of 9#, 10#, 11#, 12#, 13#, 14#, 15#, 16#. Mean values ± standard deviations with different letters (a, b and c) are significantly different (P b 0.05) between the three parts of Lake Wuli in the same season.

most water parameters of the estuarine area varied at a statistically significant level between the three parts of Lake Wuli in two seasons. EC is directly related to the concentration of dissolved solids in the water, and high EC values are caused by contaminants in surface waters. In our study, regardless of wet or dry season, the changes in EC values were always East Wuli N Middle Wuli N West Wuli. Biological changes by aerobic or anaerobic organisms were determined by the factor DO, where the value of 4 to 6 mg L−1 is proven to show healthy aquatic life in water (Avvannavar and Shrihari, 2008). SD indicates the clarity of the water, reflecting the concentrations of suspended matter. TDS usually has a good correspondence with the hardness and conductivity of water. In the wet season, the average DO (5.25 mg L−1) and SD (0.55 m) were highest in West Wuli, followed by Middle Wuli, and the lowest values were observed in East Wuli (DO, 3.45 mg L−1; SD, 0.43 m), while TDS in the dry season showed the opposite trend. In the dry season, the average DO (7.16 mg L−1) and SD (0.72 m) in West Wuli were also highest among the three parts of Lake Wuli, and the lowest DO (4.21 mg L−1) and SD (0.49 m) occurred in Middle and East Wuli, respectively. The Chl-a level in water reflects the biomass of phytoplankton. The mean Chl-a values in East Wuli in the wet and dry seasons were 40.02 and 16.08 mg m−3, respectively, which were higher than the other two parts of Lake Wuli. Chl-a in West Wuli was the lowest; the values were 29.19 and 6.59 mg m−3 in the wet and dry seasons, respectively. Excessive nitrogen, phosphorus and organic matter are some of the main causes of water pollution. The nutrient concentrations were relatively high in East Wuli in the two seasons. In the wet season, the mean TN, NH4-N and TP were 5.23, 4.09 and 0.42 mg L−1 in East Wuli, respectively. According to China's environmental quality standards for surface water (GB 3838-2002), the water quality was classified as inferior V level. At this level, the concentration of TN, NH4-N and TP should be below 2.00, 2.00 and 0.20 mg L−1, respectively. In the dry season, the values of TN, NH4-N and TP were 2.81, 1.97 and 0.18 mg L−1 in East Wuli, respectively, and only TN was classified as inferior V level. The mean concentration of TN, NH4-N and TP were lowest in West Wuli with values of 1.24, 0.46 and 0.08 mg L−1 in the wet season, and 0.97, 0.25 and 0.02 mg L−1 in the dry season, respectively. N and P are components of domestic sewage. East Wuli area is situated in the old district of Wuxi City with large population, and West Wuli area is located in natural parks with little human intervention. The discharge of domestic

sewage leaded to higher N and P concentrations in east part than in west part of Lake Wuli. Based on earlier study about the water environment in Lake Taihu (Zhu, 2008), the average concentrations of TN and TP in Lake Taihu were approximately 2 and 0.1 mg L−1, respectively. In the research, whether in the wet season (TN, 3.05 mg L−1; TP, 0.29 mg L−1) or in the dry season (TN, 2.24 mg L−1; TP, 0.12 mg L−1), the mean concentrations of TN and TP in our research area were always higher than that in the Lake Taihu. Due to the man-made activities and the dual affect by rivers and lakes, the pollutions accumulated in estuaries (Gopal et al., 2018), which might lead to the higher N and P concentrations. It was reported that in Meiliang Bay, N was the main source of pollution, and TN concentrations were significantly higher than TP concentrations (N7:1) (Chen et al., 2003). In our study, the TN:TP ratio reached 15.5 and 29.2 in the wet and dry seasons averaged across all sampling sites, which indicated that N continued to accumulate in the estuarine area of the lake. Nutrient concentration parameters showed the most significant variation (P b 0.05) among the three parts of Lake Wuli in two seasons, except for NO3-N and NO2-N in the dry season. CODMn usually reflects pollution by organic and inorganic oxidizable substances in water. In East Wuli, the mean concentrations of CODMn were 8.33 and 5.06 mg L−1 in the wet and dry season, respectively. The values were significantly higher than in the other two parts of Lake Wuli. In West Wuli, CODMn had the lowest values of 4.44 and 2.98 mg L−1 in the wet and dry seasons, respectively. Although East Wuli had the highest CODMn level, it appears that organic matters were not the main polluting substances in the estuarine area of Lake Wuli. Ca2+ and Mg2+ are usually the dominant cations in river waters (Chatterjee et al., 2010). The Ca2+ and Mg2+ concentrations did not differ significantly either in the wet or dry season between the three parts of Lake Wuli. The permissible limit of Mg2+ is 30 mg L−1 according to drinking water standards (World Health Organisation, 1996), and the average Mg2+ concentration in the estuarine area did not exceed this limit. Average Cl− concentration had locational trends of East N Middle N West of Lake Wuli in two seasons. It has been reported that the high values of Cl− in water may result from pollution by anthropogenic activity and domestic sewage waste (Chatterjee et al., 2010). Pearson linear correlation generated using 17 water parameters (T, pH, EC, DO, TDS, SD, Chl-a, TN, NH4-N, NO3-N, NO2-N, TP, PO4-P, CODMn, Ca2+, Mg2+, Cl−) are shown in Table S3.

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3.2. Eutrophication assessment based on the TLI Meiliang Bay used to be the most severely eutrophicated area of Lake Taihu, which had higher nutrient concentrations (e.g., inorganic and organic forms of N and P) than other areas of the lake (Cai et al., 2010). Although cyanobacterial blooms have not occurred in current years in Lake Wuli, eutrophication assessment is still necessary to prevent them in the future, especially in the anthropogenically influenced estuarine area. TLI was usually introduced as an index for describing the eutrophication status of water. In this study, the TLI (based on Chl-a, TP, TN, SD and CODMn) showed variations in values from 48.63 to 89.76 in the estuarine area of Lake Wuli in the wet season (Fig. 2A), and from 45.00 to 72.01 in the dry season (Fig. 2B). The final TLI map of the estuarine area was prepared using ArcGIS and it is presented in Fig. 3. The range in TLI between the two seasons represents a shift of four trophic levels, from hyper eutropher to mesotropher. Overall, the TLI of West Wuli samples was lower than that of Middle Wuli samples, which was lower than East Wuli samples. There were significant (P b 0.05) spatial (west, middle and east) and seasonal (wet and dry) effects on the TLI

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of Lake Wuli. The mean TLI in the wet season in West Wuli was 55.10, and almost all sampling sites reached light eutrophic levels except 1-1 (mesotropher), while in the dry season the average TLI decreased to 42.94, and all sampling sites were mesotrophic levels. In Middle Wuli, the mean TLI in the wet season was 59.75, and four trophic levels were recorded: 7.1% of sampling sites reached hyper eutrophic levels, 39.3% reached middle eutrophic levels, 50.0% reached light eutrophic levels, and only 3.9% were mesotropher. In the dry season, the average TLI in Middle Wuli was 52.98 and the number of trophic levels decreased to three; 14.3% reached middle eutrophic levels, 57.1% reached light eutrophic levels, and 28.6% were mesotropher. The mean TLI in the wet season in East Wuli reached 66.68, and hyper, middle and light eutrophic levels accounted for 28.6%, 50.0% and 21.4%, respectively. In the dry season, the average TLI decreased to 58.85, and hyper, middle, light eutropher, and mesotrophic levels accounted for 3.7%, 33.3%, 37.0% and 26.0%, respectively. Temperature is considered to be one of the primary factors determining the seasonal dynamics of eutrophication level (Herb and Stefan, 2003). It was reported that in the winter, with decreasing temperatures, the dominance of algae obviously decreased

Fig. 2. Spatial and seasonal variations of TLI, WQI and WQImin in the estuarine area of Lake Wuli. Notes: Different letters (a, b, and c) indicate significant difference (P b 0.05) between mean values.

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Fig. 3. The spatial distributions of TLI in estuarine area of Lake Wuli in wet (August 2017, A) and dry (March 2018, B) seasons.

Fig. 4. The spatial distributions of WQI (A and B) and WQImin (C and D) in the estuarine area of Lake Wuli in wet (August 2017) and dry (March 2018) seasons.

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(Jiang et al., 2018). As water temperature in the research changed from 30.28 °C in the wet season to 10.62 °C in the dry season, the eutrophication level decreased. The increase in the severity of eutrophication will damage the function of the water body and may even harm the entire ecological system (Paerl and Paul, 2012). However, regardless of wet or dry season, the eutrophication state was more serious in East Wuli, compared to Middle and West Wuli. Eutrophication remediation measures should be more focused in the east part of Lake Wuli. 3.3. Water quality assessment based on the WQI WQI was used in our study in order to evaluate the overall status of water quality. WQI assembles data from regular parameters of water quality and provides a value with an understandable explanation of water quality in a specific area and time (Hoseinzadeh et al., 2015). In our study, the WQI value was calculated by T, pH, EC, DO, TDS, TN, NH4-N, NO3-N, NO2-N, TP, PO4-P, CODMn, Ca2+, Mg2+ and Cl− for each sampling site and both seasons (wet and dry). The WQI values ranged from 40.00 to 78.08 in the wet season, and from 47.12 to 83.65 in the dry season (Fig. 2C and D). In the wet season, 6 of 13 sites in West Wuli were classified as good, and the others were all recorded as medium (Fig. 4A). Most of the sampling sites (85.71%) in Middle Wuli had a medium water quality status, and only 3 and 1 sampling sites had a good and poor status, respectively. In East Wuli, 67.86% of sampling sites were classified as medium, and others were all poor, which were focused in the most eastern locations. In the dry season, the status of water quality in the estuarine area was generally better than in the wet season. In West and Middle Wuli, 84.62% (11 of 13) and 30.77% (8 of 26) sampling sites were classified as good, and others were all medium (Fig. 4B). In East Wuli, 33.33%, 44.44% and 22.22% of sites were classified as good, medium and poor water quality, respectively. Additionally, no WQI value above 90 and below 25 were recorded, which means there were neither excellent or very poor sites observed in the estuarine area of Lake Wuli. Locations and seasons had significant effects on WQI values (P b 0.05). The seasonally variations of WQI were potentially influenced by the physical and chemical properties of water, such as temperature and DO (Avvannavar and Shrihari, 2008; Jiang et al., 2018). In this study, the worse water quality in the wet season could be attributed to rainfall, which could increase surface runoff to waters and bring more pollutions. Moreover, it was reported that an increase evaporation may also lead to higher pollution concentrations (Zhao et al., 2013). Pearson linear correlation showed that WQI had a significant negative relationship (R2 = 0.7387, P b 0.001) with TLI (Fig. 5). Overall, the WQI distribution map showed that, West Wuli had relatively good water quality within the estuarine area, which

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was consistent with its location in natural parks with little human intervention. Middle Wuli had relatively medium water quality within the estuarine area, which was consistent with artificial parks with considerable human intervention. East Wuli is a narrow area located in old urban areas, the accumulation of pollutants was more serious. Most of the eastern estuarine area is situated in residential quarters with considerable human activity, but the density of drainage pipe network is small; thus, the water quality deteriorated markedly. We presented that in the estuary area of Lake Wuli, most of the 22 rivers are controlled by gates or dams, and the presence of dams has a considerable effect on water quality characteristics (Kurunc et al., 2006). At several estuarine locations in our study, significant differences in river side and lake side water quality were observed, such as 1#, 2#, 3#, 5#, 11#, 12#, 13#, 19# and 20#. Due to the existence of gates or dams, no water exchange between the lake and the rivers on these estuaries (Table S1), while the river side water quality was worse than that in the lake side because of urban activities. Karakaya and Evrendilek (2010) found that even when no dam is present, differences in water quality were observed due to the emissions of a variety of pollutants from catchments areas. Despite the existence of enclosure in the location of 14#, water exchange between the lake and the river, significant water quality differences in two sides of the enclosure were observed due to the pollutants accumulation. The evaluation of WQI generally requires measurement of several parameters. The cost and time for analysis limits the acceptability of WQI. Sun et al. (2016) reported that correlated water variables in WQI explain high levels of variance but lack robustness; thus, it is possible to select the main variables which can explain most of the variance in the water quality. Pesce and Wunderlin (2000) proposed the WQImin model, which usually uses 3 or 5 parameters to calculate WQI. Stepwise linear regression was used in our study to screen the main water parameters (≤ 5), which significantly affect WQI (Table 2). It showed that TN made the largest contribution to WQI based on the data set (R2 = 0.689, P b 0.001). DO, CODMn, NH4-N and NO2-N were selected sequentially and considerably increased the R2 value of the models. Due to key roles for the aquatic life, DO is often used as one of the 3 or 5 parameters to calculate WQI (Kannel et al., 2007). TN concentration contains NH4-N and NO2-N, so only TN was selected in our WQImin, and it indicated nutrient pollution. CODMn can indicate organic pollution. Hence, TN, DO and CODMn were established as the critical parameters in the training of WQImin. Results of our WQImin did not show greater variations from WQI (Fig. 5B, R2 = 0.9409, P b 0.001). Although WQImin showed a similar spatial changing trend with WQI on the water quality in the estuarine area of our study, the values of WQImin were always lower than WQI regardless of wet or dry season (Fig. 4). Therefore, WQImin might

Fig. 5. Relationships between WQI with TLI and WQImin based on the testing data sets.

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Table 2 Stepwise linear regressions of WQI with 15 water parameters examined after normalization. Model Linear model

R2

P

1 2 3 4

0.689 0.902 0.945 0.966

b0.001 b0.001 b0.001 b0.001

5

54.513 + 5.192 (TN) 47.927 + 3.534 (TN) + 1.240 (DO) 39.919 + 2.907 (TN) + 1.115 (DO) + 1.465 (CODMn) 40.020 + 1.917 (TN) + 1.093 (DO) + 1.274 (CODMn) + 0.774 (NH4-N) 36.189 + 1.233 (TN) + 1.119 (DO) + 1.267 (CODMn) + 1.086 (NH4-N) + 0.962 (NO2-N)

0.987 b0.001

give stricter standards than WQI on the spatial and seasonal analysis of the water quality in the estuarine area of Lake Wuli and provides a simpler measurement procedure and a comparatively lower analytical cost. The water quality assessments using WQImin are shown in Figs. 2E, F, 4C and D. The level of WQImin in the dry season (13.33–76.67) was usually higher than the wet season (0.00–63.33). There were three and four levels of water quality based on WQImin in the wet and dry seasons, respectively, which showed very poor status in WQImin maps compared to WQI maps. In the wet season, 2 (8–3 and 9–4) of 28 sites in Middle Wuli were classified as very poor water quality status, and 35.71% of sampling sites were very poor in East Wuli. In the dry season, 2 (8–2 and 9–2) of 28 sites in Middle Wuli were classified as very poor water, and in East Wuli, the very poor ratios decreased to 29.63%. On the whole, it seems that the most eastern location (11#, 12#, 13# and 14#) of Lake Wuli had the worst water quality.

3.4. Special sites analysis Principal component analysis (PCA) method is a factor analysis which transforms a large number of correlated variables into a smaller number of underlying factors (principal component). A meaningful association with the variables of interest is given by the PCA method (Farnham et al., 2003). Therefore, PCA can achieve a significant reduction in the dimensionality of the original data set (Yang et al., 2010). In our study, two PCA factors were extracted using all 17 water parameters; the total percentages of variance were 44.6% and 55.2% in the wet and dry seasons, respectively (Fig. 6A and C). Distribution of the sampling sites over a bidimensional space using the two factors' score are presented in Fig. 6B and D. Based on the results, in the wet season, the determination from three sites found in 12# (12-2, 12-3 and 12-4) separated from the rest of the data and dispersed over the bidimensional space. The water quality of the three sites was all hyper eutropher based on TLI; poor levels were based on WQI, and very poor levels were based on WQImin at this time. From Fig. 6A and B, it seems that NH4-N and TN were the main factors affecting 12-3 and 12-4, and NO3-N and NO2-N were the main factors affecting 12-2. It has been proved that N transportation and transformation were quite different between the lake and rivers due to distinctive biochemical processes and hydrological regimes (Yu et al., 2018). The sites of 12-3 and 12-4 were in the upstream region of Lucunhe estuary, which was located in residential areas, subjected to the stress of major anthropogenic activities, and the N pollution may come mainly from domestic sewage discharge (Chen et al., 2014). In this area, a sewage treatment plant had an impact on the regional river, which can process 200,000 tons of water per day.

Fig. 6. Principal component analysis based on the water variables in wet (A and B) and dry (C and D) seasons. Note: Ellipses indicate 95% confidence limit.

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According to discharge standard of pollutants for municipal wastewater treatment plant (GB 18918-2002), first criterion A criterion is the basic requirement for cycle water discharged from treatment plant, and TN and NH4-N are applicable for 15 and 5 mg L−1, respectively. Thus, TN and NH4-N discharged from sewage were about 3000 and 1000 kg per day in the related area, respectively, and there were seven drain outlets in the region of Lucunhe, which increased water pollution. The site of 12-2 located near the lake may be due to local biological activities; NO3-N and NO2-N were the main effects in this sample. In the dry season, six sites (21-1, 21-2, 21-3, 12-4, 13-3 and 13-4) separated from the rest of the data and were dispersed over the bidimensional space. 21# (Qitangbang) located in Middle Wuli, was under construction at the time of sampling. It might be an accident that the three sites of 21# were outside of the 95% confidence limit. Based on TLI, the 12-4 site was hyper eutropher, and the sites of 13-3 and 13-4 were at middle eutrophic level. Based on WQI and WQImin, the three sites all had poor and very poor status, respectively. The arrows in Fig. 6C and D showed that among the nutrients pollution, PO4P and TP might the main factors affecting the three sites in dry season. Owing to the impact of anthropogenic activities at this location of Lake Wuli, the sampling sites had a similar factors effect. In addition, N may be the main factor affecting water quality of these special sites in the wet season, and P may be the main factor in the dry season. N pollution is severe in Lake Taihu Basin (Wang et al., 2007), and domestic sewagederived N contributed the major N load to the Lake Taihu (Yu et al., 2018). Using the PCA method, Zhao et al. (2011) also found that NH4N highlighted the critical roles in affecting the water quality in Lake Taihu, especially in the wet season, rainfall plays an important role in terms of N inputs to the lake (Luo et al., 2007). P was the limiting factor in terms of eutrophication control in winter in Lake Taihu (Xu et al., 2010, 2013), this phenomenon was also found in Lake Horowhenua, New Zealand (White et al., 1991). Thus, PCA found that P might be the main factor affecting water quality of these special sites in the dry season. The most eastern part of Lake Wuli had the worst water quality and this may be associated with more city wastewater discharge and larger population size than the other areas. Thus, local governments should prioritize further measures to manage and control pollution in this area to avoid wider-scale pollution.

4. Conclusions TLI and WQI methods were applied to assess the eutrophication level and water quality status of the estuarine area of Lake Wuli in this study. The results showed that the actual water situation showed distinct seasonal variation. The wet season was worse than the dry season for both eutrophication levels and water quality status, in all three parts of Lake Wuli. The average TLI and WQI values in the wet season were 61.69 and 60.70, and in the dry season they were 57.40 and 65.74, respectively. Also, the actual water situation presented distinct spatial variation, in both wet and dry seasons, East Wuli had the worst eutrophication levels and water quality status, followed by Middle Wuli and West Wuli. Due to anthropogenic and topographic influences, the most eastern part of Lake Wuli had the most serious water pollution. Factor analysis from PCA indicated that N might be the main factor affecting the water situation of the most eastern sites in the wet season, and P may be the main factor in the dry season. The WQImin in our study used three water parameters–DO, TN and CODMn–which had a significant relationship with WQI, with even stricter standards than WQI when analyzing water quality in the estuarine area of Lake Wuli. This will provide a more beneficial and low-cost method for the evaluation of the water quality of the study area in the future. Our results are beneficial to assist government and management to make evidence-based water ecological restoration decisions in the estuarine area of Lake Wuli.

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Acknowledgements The study was supported by Major Science and Technology Program for Water Pollution Control and Treatment (2017ZX07203-005). Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.scitotenv.2018.09.137.

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