A polymer grafted oxidomethoxidovanadium(V) complex of an ONO donor ligand mimicking peroxidase activity

Article abstract of DOI:10.1016/j.poly.2015.05.010

The reaction between [V^IVO(acac)₂] and the ONO donor tridentate ligand H₂hap-iah (I) [H₂hap-iah = Schiff base obtained by the condensation of equimolar amounts of o-hydroxyacetophenone (hap) and indole-3-acetic hydrazide (iah)] in an equimolar ratio under an oxygen atmosphere in refluxing methanol gives [V^VO(OMe)(hap-iah)] (1). Treatment of 1 in methanol with H₂O₂ in the presence of KOH results in the formation of K[V^VO(O₂)(hap-iah)] (2). Complex 1 has been grafted into chloromethylated polystyrene cross-linked with 5% divinylbenzene {now abbreviated as PS-[V^VO(OMe)(hap-iah)] (3)} via covalent bonding through the imino nitrogen atom of the indole group. The two complexes have been characterized by various spectroscopic techniques (IR, electronic, ¹H, ¹³C and ⁵¹V NMR, ESI-MS), analytical and thermal studies. The polymer-grafted complex 3 has also been analyzed by field-emission scanning electron micrographs (FE-SEM) as well as energy dispersive X-ray (EDAX) studies. The polymer-grafted complex 3 has been used as a catalyst for the peroxidase-like oxidation of pyrogallol to purpurogallin in pH 7 buffer solution. Its high peroxidase mimicking ability, easy separation from the reaction medium and the reusability, without any considerable decrease in activity, suggest the practical utility of the catalyst.

Full text of DOI:10.1016/j.poly.2015.05.010

Ecological Indicators 58 (2015) 113–121
Contents lists available at ScienceDirect
Ecological Indicators
journal homepage: www.elsevier.com/locate/ecolind
Relationship between phytoplankton and environmental factors in
landscape water supplemented with reclaimed water
Hai-jiang Zhaoa, Yi Wanga,∗, Liang-liang Yanga, Luo-wei Yuana,b, Dang-cong Penga
a School of Environmental and Municipal Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China
b Xi’an Qingyuan Wastewater Treatment and Reuse Co. Ltd, Xi’an 710086, China
a r t i c l e i n f o
a b s t r a c t
Article history:
The water quality of Feng-qing Lake, which is a landscape lake supplemented with reclaimed water, was
surveyed to investigate the relationship between phytoplankton and environmental variables. A total of
29 water samples were collected to analyze temporal variations of phytoplankton and environmental
factors from July 2013 to June 2014. Six phyla and 39 genera of phytoplankton were identified when the
lake was supplied with reclaimed water. Among these, Cyanophyta and Chlorophyta account for 38.46%
and 30.77% of phytoplankton, respectively. The dominant species in the lake are Pseudanabaena limnetica
and Chlorella vulgaris, which are present the entire year. Other leading species include Cosmarium sp.
and Raphidiopsis curvata. Principal component analysis (PCA) was conducted to analyze the relationship
among environmental factors. Canonical correspondence analysis (CCA) was performed to investigate
the relationship between environment factors and dominant species. The PCA result showed that tem-
perature (T), total phosphorus (TP), total nitrogen (TN), transparency, and dissolved oxygen are the main
factors that affect the eutrophication level of the lake. The CCA result revealed that TN, PO43−–P, chemical
oxygen demand (COD), T, and chlorophyll a exhibit a close relation with dominant species. In particular,
TN, salinity, and COD influence the growth of P. limnetica; T and COD influence the growth of R. curvata;
and T, PO43−–P, NH3–N, and pH influence the growth of C. vulgaris and Cosmarium sp.
Received 28 November 2014
Received in revised form 10 February 2015
Accepted 25 March 2015
Keywords:
Landscape lake
Reclaimed water
Phytoplankton
Environmental factor
PCA
CCA
© 2015 Published by Elsevier Ltd.
1. Introduction
south, leaving the northern and western China to experience per-
petual droughts. Xi’an, being a semi-arid city, has an estimated
Urban rivers and lakes play important roles in landscaping and
local weather modification. Xi’an, the capital of Shaanxi province,
located in northwestern China, has a population of 843 million
at the end of 2009. The increasing population and changing cli-
matic conditions have made the urban district of the city very often
extremely hot in recent years. Thus, the government of Xi’an has
released a 571028 Plan in 2012 that will make 28 artificial lakes
in the urban district to improve ecological conditions and alleviate
the heat island effect in the metropolitan area of Xi’an. To realize
its normal scenic environment function, landscape water requires
an adequate and reliable water supply.
Water scarcity in China is becoming increasingly severe (Jiang,
2009). China is among the 13 countries in the world with the
lowest water availability, and the per capita water availability of
China is about a quarter of the world average (Yi et al., 2011).
Moreover, a majority of the available water is concentrated in the
total water resource availability of 24.22 hundred million cubic
meters per year. Although Xi’an is facing severe water shortage,
the city is also being planned to be developed into the third inter-
national metropolis of China by 2020. By then, one of the dominant
social and economic problems in rapid urbanization of Xi’an will
be its population, which is estimated to be more than 1280 million.
When huge population and high population density is considered,
its average annual water availability per capita will decline greatly.
Therefore, Xi’an will not be able to meet the water demand in the
near future.
Wastewater reclamation and reuse have drawn increasing
attention as integral components of water resource management to
address the water resource crisis (Chu et al., 2004). Reclaimed water
is widely used nowadays, resulting in a trend in which landscape
lakes are supplemented with reclaimed water (Wei et al., 2011),
such as the main lake of SOF Park in Beijing, China. As early as 2002,
faced with water shortage, Xi’an has begun conveying and utilizing
reclaimed wastewater in industrial cooling systems. However, the
amount of reclaimed wastewater in industry is still very limited
compared with the production capacity of 17.5 × 104 m3/d in the
∗
Corresponding author. Tel.: +86 15809285181; fax: +86 2982202729.
E-mail address: wangyi1003@sina.com (Y. Wang).
http://dx.doi.org/10.1016/j.ecolind.2015.03.033
1470-160X/© 2015 Published by Elsevier Ltd.

114
H.-j. Zhao et al. / Ecological Indicators 58 (2015) 113–121
Table 1
Water quality standard for scenic environment reuse of urban wastewater and the quality of reclaimed water in this study.
Water quality items of reclaimed water
BOD5 (mg/L)
pH value
Residual chlorine (mg/L)
DO (mg/L)
TP (mg/L)
TN (mg/L)
NH3–N (mg/L)
Standard
6
6–9
0.05
2.0
0.5
15
5
Actual application
0.07–5.02
6.82–7.57
0.01–0.29
1.63–7.51
0.08–0.28
4.58–27.41
0.21–17.18
The People’s Republic of China State Administration of Quality Supervision Inspection and Quarantine (2002).
centralized location of Xi’an. To expand the market of reclaimed
wastewater, the potential users, ornamental lakes in public parks,
were selected for replenishment with recycled water according to
the Wastewater Reclamation and Reuse Plan during the Twelfth
Five-Year Period (2011–2015) of Xi’an. Given that the scenic envi-
ronment water are scattered across the entire city, the necessary
pipeline networks for reclaimed water distribution, approximately
55 km, have been finished. Our research site, Feng-qing Lake, is
one of the demonstration projects in Xi’an and is replenished with
reclaimed wastewater from Qingyuan Wastewater Treatment and
Reuse Ltd. Co. through a pipeline of 8 km.
water depth of 1.0 m. The lake is located in Feng-qing Park, Xi’an
City, China. This lake is an ideal site for recreation and sightsee-
ing. The volume of water in the lake is approximately 38,000 m3,
which is provided by a reclaimed water pipeline from a wastewater
reclamation treatment plant in Xi’an. The quality of the reclaimed
water is presented in Table 1, and the total volume of reclaimed
water provided to the lake was 41,643 m3 for one year during this
study.
2.2. Sampling methods
However, the safety of reclaimed water has become a source of
dispute since the beginning of wastewater reuse because various
toxic chemicals and nutrient elements remain in wastewater even
after traditional reclamation treatment processes. These chemi-
cals cannot be completely eliminated, and they can accumulate
and pose potential risk to human health and the ecological sys-
tem (Asano et al., 2007; Wang et al., 2014; Rizzo et al., 2013).
Furthermore, a high concentration of nutrients in reclaimed water
can cause eutrophication because most urban lakes are shallow
with low-velocity water flow. For example, previous researchers
found that landscape water supplemented with reclaimed water
faces a high risk of algal bloom during summer through simula-
tion experiments (Li et al., 2011). Some investigators also found
that the dominant alga phyla in eutrophicated lakes supplemented
with reclaimed water are Cyanophyta and Chlorophyta (Li et al.,
2005). Currently, knowledge on reclaimed water reuse in scenic
environment is still insufficient.
New technologies in multivariate environmental studies have
been reported in recent years. Environmental flow diagram (EFD)
is a user-friendly software that can be used in all industrial compa-
nies and civil and energy projects for gathering detailed knowledge
of pollution level of an area to solve environmental crises (Valipour
et al., 2012a,b). EFD is a necessary and useful multivariate statistical
technique for the evaluation and interpretation of data with a view
to obtain better information about the water quality (Valipour et al.,
2013). Principal component analysis (PCA), one of the multivari-
ate methods, is often used to obtain a few independent variables
that can explain most of the variance from the original variables
(Jagadamma et al., 2008; Qiu et al., 2010). Another analysis tech-
nique is canonical correspondence analysis (CCA), which is a direct
gradient analysis technique and always used in aquatic ecological
studies (C¸ elekli et al., 2014).
The samples were collected using a water sampler (1000 mL)
at a depth of 0.5 m around the lake. Four sites (Fig. 1) were cho-
sen for each sampling from July 2013 to June 2014, in accordance
with the Standard Methods for Observation and Analysis in Lake
Eutrophication (Jin and Tu, 1990). The samples were collected five
times a month in summer (July, August, and September), twice a
month in spring and autumn (October, November, April, May, and
June), and once a month in winter (December, January, February,
and March). All samples were collected at approximately 11:00 AM
to 11:30 AM.
The physicochemical characteristics of surface water, including
water temperature (T), dissolved oxygen (DO), pH value, and trans-
parency (Secchi disk clarity, SD), were measured in situ using a
DO meter (HI9146, HANNA in China, Italy), a portable pH meter
(PH-HJ90 MODELB, Aerospace Computer Company, China), and a
Secchi disk (diameter: 20 cm). Phytoplankton samples were imme-
diately preserved in Lugol’s iodine solution and concentrated to
approximately 50 mL. Then, the phytoplankton samples were kept
for further taxonomic analysis and counting under a microscope
(ECLIPSE 90i, Nikon, Japan) in the laboratory. Meanwhile, a water
sample of approximately 2 L was kept in a cool and dark environ-
ment was and then transferred to the laboratory to measure the
concentrations of nutrients and organic matter.
In the present study, an urban landscape lake supplemented
with reclaimed water was selected to investigate the relationship
between environmental factors and phytoplankton community
structure. Water quality and phytoplankton communities were
continuously monitored for a year. PCA and CCA were conducted
to analyze the relationships among environmental factors as well
as between environmental factors and dominant species, respec-
tively.
2. Materials and methods
2.1. Landscape water and reclaimed water
Fig. 1. Four sampling sites around Feng-qing Lake. Water sample was collected
simultaneously from four sites around Feng-qing Lake and mixed together as the
final sample for measurement.
The landscape water selected for this study was Feng-qing
Lake, an artificial lake with an area of 38,000 m2 and an average

H.-j. Zhao et al. / Ecological Indicators 58 (2015) 113–121
115
Fig. 2. Time course of physical and chemical indexes in water body of Feng-qing Lake. The concentration of nutrient substance showed a seasonal variation, and pH value,
as well as DO maintained a high level in Feng-qing Lake.
2.3. Analytical methods
concentration, COD and salinity, TN and NH3–N concentration, and
phosphorus concentration and pH value, respectively.
The samples were directly analyzed for total phosphorus (TP),
total nitrogen (TN), chroma, turbidity, heterotrophic plate counts
(HPC), chlorophyll a (Chla), and chemical oxygen demand (COD),
whereas the filtered samples were analyzed for orthophosphate
(PO43−–P), ammonium (NH4+–N), and salinity. All of the analytical
measurements were obtained in accordance with standard meth-
ods (EPAC, 2002).
The concentrated phytoplankton samples were directly counted
in a plankton counting chamber (0.1 mL) using a Nikon ECLIPSE
90i optical digital microscope (ECLIPSE 90i, Nikon, Japan) at an
eyepiece magnification of 10× and an objective magnification of
40×. The cell numbers of different phytoplankton species in 100
random fields were determined. Phytoplankton species were iden-
tified according to Freshwater Algae in China (Hu et al., 1980) and
Atlas of Common Freshwater Algae in China (Weng and Xu, 2010).
As shown in Fig. 2(a), T of Feng-qing Lake was 26.14 4.42 ◦C in
summer (July 2013), 20.04 1.93 ◦C in autumn (September 2013),
18.90 1.56 ◦C in spring (May 2014), and approximately 0.40 ◦C in
winter (December 2013), with a total annual average temperature
of 18.5 ◦C. A large variation of temperature occurred in the lake due
to the extreme shallowness of the lake, which is easily influenced by
atmospheric temperature. DO concentration varied from 7.80 mg/L
to 14.97 mg/L, with the highest value reaching 14.97 mg/L in late
March and the lowest value (7.80 mg/L) was recorded in July when
the highest temperature occurred. DO concentration in winter was
significantly higher than those in other seasons and frequently
exceeded the saturation value, with an annual average value of
10.95 mg/L. A high DO concentration in water may be attributed
to two aspects. The first reason lies in sample time, i.e., 11:00 AM
to 11:30 AM, which is the time of day during which the oxygen
produced by photosynthesis is higher than that consumed by res-
piration (Xue et al., 2014). The second reason is that the lake is
extremely shallow, and thus, atmospheric restoration is the main
approach to supplying oxygen in water.
The salinity of Feng-qing Lake significantly changed, with the
lowest value of 292 mg/L in January and the highest value of
506 106 mg/L in April. This phenomenon can be related to the
change in solubility caused by T and phytoplankton growth. Salin-
ity increased from January to April with the increasing temperature,
and reached a peak in April (Fig. 2(b)), when quantities of phyto-
plankton began to develop in the lake. The monthly average COD
concentration fluctuated considerably in April and August. The COD
concentration was relatively stable, with an annual average value of
58 12 mg/L, which is higher than the class V standard of the Sur-
face Water Environment Quality Standards (State Environmental
Protection Administration, 2002).
2.4. Statistical analysis
The relationship between phytoplankton community and envi-
ronmental factors was analyzed via CCA using Canoco for Windows
4.5. Meanwhile, the relationship among environmental factors was
analyzed via PCA. Processing and graphing of data, as well as
ANOVA, were performed using Origin 9.0. The Pearson correlations
among environmental factors were determined using SPSS 20.0
software. Finally, t-test was conducted to check the significance
of parameters between two factors. The levels of significance used
were 0.05 (significant) and 0.01 (highly significant).
3. Results and discussion
3.1. Variations of the environmental factors
The concentrations of TN and NH3–N significantly varied with
the seasons. The monthly mean concentrations of TN and NH3–N
reached up to 3.52 0.65 mg/L in autumn (September 2013) and
1.54 0.2 mg/L in spring (April 2014). The lowest concentrations of
3.1.1. Physical and chemical indices
The physical and chemical indices in Feng-qing Lake over time
are shown in Fig. 2. Fig. 2(a)–(d) shows the time course of T and DO

116
H.-j. Zhao et al. / Ecological Indicators 58 (2015) 113–121
Fig. 3. Time course of sensory and biological indexes in water body of Feng-qing Lake. Biological indexes changed greatly for a whole year and the concentration of Chla
reached the highest in September.
TN and NH3–N both occurred during winter, with concentrations of
1.45 mg/L and 0.49 mg/L, respectively, as shown in Fig. 2(c), which
were significantly lower than those in other seasons. These regular
changes of nitrogen concentration may be caused by many rea-
sons, but mainly by the supplement water source, which was from
reclaimed wastewater with higher TN compared with fresh water.
At the same time, the nitrobacterium conveyed from the upstream
wastewater treatment plant may also be brought inevitably into
Feng-qing Lake. In summer, the higher temperature will enhance
the activity of nitrobacterium, which degrades NH3–N in water bio-
logically. Of course, other reasons such as biological utilization and
evaporation can also influence nitrogen concentrations. In short,
the above results indicate that the concentrations of TN and NH3–N
in Feng-qing Lake exhibited seasonal variation.
influence on the landscape function and esthetics of the surface
water (Van Houtven et al., 2014). Some investigators have reported
that the quantity of algae mostly influences the chroma of the water
body (Kolada, 2014). The sensory and biological indices of Feng-
qing Lake during the study period are shown in Fig. 3, in which
Fig. 3(a) presents the time course of SD and chroma, and Fig. 3(b)
shows the change in HPC and Chla concentration.
Transparency strongly influences the light available for phy-
toplankton photosynthesis, and light was directly related to
phytoplankton growth. Thus, transparency is one of the factors
affecting phytoplankton growth. The transparency of water in
Feng-qing Lake reached up to 76.0 cm in winter (January 2014)
and as low as 31.4 2.2 cm in summer (August 2013), which was
respectively attributed to the limited algal growth in winter and
overgrowth in summer. The chroma value fluctuated at a large
range, with the highest value of 44 in summer (August 2013) and
the lowest value of 15 in winter (January 2014).
Biological indices changed significantly during the study period.
For example, HPC varied from 790 cfu/mL in December to
120,000 cfu/mL, when the peak value was gained in April. Mean-
while, Chla concentration varied from 17.59 ␮g/L to 120.56 ␮g/L;
the top value was observed in September (Fig. 3).
In summary, the water quality of Feng-qing Lake supplemented
with reclaimed water presented a significant seasonal variation.
The water quality of Feng-qing Lake was better in winter than
in other seasons, and its condition worsened from March to
September, particularly in August and September.
The concentration difference between TP and PO43−–P was sig-
nificant; however, the variation trends of both parameters were
similar. The monthly mean concentrations of TP and PO43−–P
reached 0.102 0.013 mg/L in autumn (September 2013) and
0.036 0.021 mg/L in summer (July 2013). However, the low-
est concentrations of TP and PO43−–P were 0.035 mg/L in winter
(December 2013) and 0.002 mg/L in spring (March 2014), respec-
tively. TP concentration increased from December to September
and then decreased, which may be related to the phosphorus sedi-
ment in Feng-qing Lake. Beginning in March, the aquatic organism
in the lake and sediment began to grow with the increase of tem-
perature. Then, the growth and proliferation of aquatic organism
consumed much oxygen (O2), which brought the sediment and
water bottom under anoxic condition in April. At this time, large
amounts of P were released quickly from the sediment to water,
which led to the increase of TP in water. When TN and TP con-
centrations of each measurement were combined, the N:P ratio in
Feng-qing Lake was always above 16, which indicated that Feng-
qing Lake is P-limited, according to Redfield’s (1958) quantification.
The pH value of Feng-qing Lake varied at a range of 8.30–9.18.
The highest pH value was recorded in summer (July 2013), and
the lowest pH value was observed in winter (December 2013), as
shown in Fig. 2(d). In general, the pH value in the lake maintained
a high level throughout the year, which may be attributed to two
aspects. First, the base saturation in soils of north China is much
higher. Second, the photosynthesis of aquatic plant absorbs CO2 in
the water, which leads to the higher pH value.
3.2. Variations of phytoplankton communities
3.2.1. Phytoplankton structure and variations
A total of 39 genera of phytoplankton belonging to 6 phyla
and 22 families were identified in the 29 samples collected from
Feng-qing Lake during the year. Among these genera, 15 belong
to Cyanophyta, 12 belong to Chlorophyta, and 7 belong to Bacil-
lariophyta, which represent approximately 38.46%, 30.77%, and
17.95% of the total genera, respectively. In addition, the samples
included two genera in Euglenophyta (5.13%), two genera in Dino-
phyta (5.13%), and one genus in Cryptophyta (2.56%). The monthly
variation of genera is shown in Fig. 4.
In terms of monthly average community composition,
Cyanophyta was dominant and Chlorophyta ranked as second,
which shows that Feng-qing Lake supplemented with reclaimed
water is a Cyanophyta–Chlorophyta type lake (Recknagel et al.,
2014). The total genera number was high in autumn, e.g., 23 genera
in September, 26 in October, and 25 in November. The total genera
number was low in December, with only 8 genera. The number
of genera belonging to Dinophyta and Euglenophyta remained
3.1.2. Sensory and biological indices
Sensory indices are frequently influenced by other factors, e.g.,
algae density and Chla concentration. Chroma, an important index
for evaluating the eutrophication of a lake, is also influenced by
phytoplankton algae and suspended solids. A high chroma indi-
cates that the water is seriously polluted, which has a negative

H.-j. Zhao et al. / Ecological Indicators 58 (2015) 113–121
117
eutrophicated lakes, particularly in a landscape lake supplemented
with reclaimed water. In addition, previous authors reported that
C. vulgaris frequently became the dominant species in landscape
water (Li et al., 2005; Fan et al., 2012). The present study found that
P. limnetica and C. vulgaris are the most dominant species in Feng-
qing Lake according to the result of monitoring for an entire year,
as shown in Fig. 5(a). Simultaneously, the competition between P.
limnetica and C. vulgaris was overt. According to the analysis of
the parameters of water quality and dominant species, the con-
centrations of DO and COD were low in May and June when the
dominant species was C. vulgaris. However, whether P. limnetica
and C. vulgaris compete for DO and COD remained unanswered.
Meanwhile, some results reported that P. limnetica rapidly grew
in a collaborative environment (Bano and Siddiqui, 2004), and this
species could initially outperform other microalgal species and con-
sequently dominate in culture (von Alvensleben et al., 2013).
Algal cell density and the relative richness of dominant species
are shown in Fig. 5(b). The figure shows that the highest algal
cell density occurred in April. In particular, the cell density
of P. limnetica rapidly increased from February to April (up to
2.61 × 108 cells/L), which is consistent with the results of Mounir
et al. (2013) and Li et al. (2013). However, this type of algae is mostly
abundant in August, September, and October. Meanwhile, the con-
centration of Chla also reached the highest in September, as shown
in Fig. 3(b). These results appeared to contradict some common
views that high algal cell density indicates a high concentration of
Chla.
In fact, Chla concentration, which is a composite indicator of
phytoplankton quantity, was influenced by several factors, such as
the cell size of algae and the concentration of different algae. On one
hand, although algal cell density was highest in April, the cell size of
P. limnetica in April was significantly smaller than that in September
(Fig. 6). On the other hand, the Chla concentration of Chlorophyta
was considerably more than that of Cyanophyta when Cyanophyta
was the most dominant algae (>90% of the total genera) in April,
as shown in Fig. 5. However, a large number of algae belonging to
Chlorophyta were observed in September, e.g., C. vulgaris, which
can explain why the cell density of algae was the highest in April.
Meanwhile, Chla concentration was lower during the same period.
The results also show that Chla concentration was more suitable
for evaluating the eutrophication of a lake than algal cell density.
In summary, P. limnetica, which belongs to Cyanophyta, and C.
vulgaris, which belongs to Chlorophyta, were the most dominant
algae species in Feng-qing Lake when the lake was supplemented
with reclaimed water. The richness of these two species was high
from August to October, and the highest density of the aforemen-
tioned alga cells occurred in April, which is similar to the result of
Chla concentration.
35
30
25
20
15
10
5
Chlorophyta
Pyrroptata
Euglenophyta
Cryptophyta
Cyanophyta
Bacillariophyta
0
Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun
time/month
Fig. 4. Time course of algae species in water body of Feng-qing Lake. Cyanophyta was
absolutely dominant and Chlorophyta ranked the second, showing that Feng-qing
Lake supplemented with reclaimed water belonged to Cyanophyta–Chlorophytatype
lake.
stable (i.e., 1–2 genera). Only one genus belonging to Cryptophyta
was initially found in July, August, and September, and then it
disappeared after October. However, the number of genera in
Bacillariophyta increased after October, which indicates that the
growth and reproduction of Cryptophyta are weak and may easily
be inhibited by Bacillariophyta. Moreover, phytoplankton that
belonged to Bacillariophyta existed all year, with the richest in
September, October, and November (Fig. 4), which is consistent
with the distribution rule of the total genera. These findings
suggest that Feng-qing Lake will become a eutrophic lake with
cyanobacterial blooms.
3.2.2. Dominant species and cell density
The dominant species of phytoplankton were determined based
on the Berger–Parker dominance index formula, as shown in Eq. (1):
ni
d =
,
(1)
N
where ni is the number of individuals of species i within a given
area in one month, and N is the total number of individuals of all
species within the given area in one month (Pan et al., 2014). Then,
the total number of individuals per month accounting for over 10%
of the species was determined as the dominant species, and that
over 40% was determined as the strong dominant species. The total
number of individuals per month between 1% and 10% was slated
for common species (Mai, 2003). The relative richness of dominant
species and algal cell density in Feng-qing Lake supplemented with
reclaimed water are shown in Fig. 5.
The most dominant specie in Feng-qing Lake was Pseudan-
abaena limnetica (Cyanophyta) for the entire year, except when
Chlorella vulgaris exhibited a relative richness of 50.00% and 58.88%
in May and June, respectively. Moreover, Raphidiopsis curvata
(Cyanophyta) and Cosmarium sp. (Chlorophyta) were the secondary
dominant species in July and September when the highest atmo-
spheric temperature occurred in Xi’an, which indicates that a high
temperature is suitable for the growth of a wide variety of algae,
as shown in Fig. 5(a). P. limnetica is frequently a dominant species
in freshwater lakes; it is a filamentous, non-heterocystous (Kolada,
2014), and non-toxic (Mischke, 2003) freshwater cyanobacterium
(Raphaël et al., 2006), with a certain degree of halotolerance (Stal,
2008). Lu et al. (2013) found that P. limnetica was the dominant
species in Dong-ping Lake with a low concentration of phospho-
rus. Meanwhile, Rojo and Alvarez Cobelas (1994) also showed that
the dominant algae species was P. limnetica in a Spanish lake dur-
ing an entire year. C. vulgaris is also the dominant species in several
3.3. Relation between dominant species and environmental
factors
3.3.1. PCA on environmental factors
PCA, which is one of the multivariate analysis methods, was used
to obtain several independent variables that can explain most of
the variances from the original variables (Liu et al., 2014a). The
method was also used to determine spatial and temporal changes
in physical and chemical conditions (Becker et al., 2009). PCA was
performed using Canoco for Windows 4.5.
T, DO, SD, salinity, TN, TP, Chla, chroma, turbidity, NH3–N,
PO43−–P, HPC, COD, pH value, and algal cell density were selected
as the independent variables for PCA. The PCA ordination triplot
is shown in Fig. 7, and the summary statistics for PCA is shown in
Table 2. In Fig. 7, the lines with arrows represent the environmental
factors, and the length of the arrow indicates the weight of the envi-
ronmental factors. The angles between arrows indicate the sign of

118
H.-j. Zhao et al. / Ecological Indicators 58 (2015) 113–121
Fig. 5. Time course of species richness and cells density of dominant algae in Feng-qing Lake. The mostly strongly dominant species in Feng-qing Lake was Pseudanabaena
limnetica (Cyanophyta) in a whole year except May and June, when Chlorella vulgaris had the relative richness of 50.00% and 58.88%, respectively. The highest algae cells
density occurred in April.
Fig. 6. Cell size of Pseudanabaena limnetica in water body of Feng-qing Lake. Pseudanabaena limnetica of Sep and Apr in Feng-qing Lake showed an obvious difference in size
even for the same algae.
correlation among environmental factors. The approximated cor-
relation was positive when the angle was sharp and negative when
the angle was larger than 90◦.
PCA using 15 environmental variables revealed two main axes
that can explain most of the variations (Fig. 7 and Table 2). The first
principal component (Axis 1) accounted for 51% of the variation and
increased to 69% (Table 2) when taken together with the second
principal component. The most important variables for Axis 1 ordi-
nation were T (−0.89), TP (−0.90), SD (0.91), Chla (−0.81), chroma
(−0.88), and turbidity (−0.87). Regarding Axis 2, TN (−0.66), NH3–N
(0.65), PO43−–P (0.79), and DO (0.50) were the most important vari-
ables for ordination (Fig. 7 and Table 2). The PCA results indicate
that the first principal component reflected the eutrophication lev-
els and the second principal component reflected the pollution
levels (Fig. 7). In general, the lengths of the environmental fac-
tors of T, TP, TN, SD, and DO were longer than those of the others,
which indicate that these five variables are the basic environmental
factors in landscape water supplemented with reclaimed water.
Table 2
Summary statistics for PCA analysis on the environmental variables.
Axis 1
Axis 2
Axis 3
Axis 4
Eigenvalues
T
COD
TN
TP
NH3–N
PO43−–P
pH
Salinity
SD
HPC
Chla
Algal density
Chroma
Turbidity
DO
0.51
−0.89
−0.51
−0.54
−0.90
−0.61
−0.44
−0.52
−0.84
0.91
−0.37
−0.81
−0.46
−0.88
−0.87
0.73
0.18
0.17
−0.31
−0.66
0.35
0.15
−0.31
−0.36
0.29
0.07
0.19
−0.64
−0.15
−0.01
−0.02
0.17
−0.10
0.65
0.79
0.39
−0.33
−0.30
0.30
−0.43
0.60
0.16
−0.12
−0.17
−0.15
−0.36
0.20
0.04
0.09
0.05
0.31
0.37
−0.13
0.74
−0.13
−0.15
−0.34
−0.41
−0.50
−0.18
0.83
−0.07
−0.10
0.38
Fig. 7. Ordination diagram of PCA exhibits environmental variables (arrows) in
water body of Feng-qing Lake. The first principal component reflected the eutro-
phication levels and the second principal component reflected the pollution levels.
T, TP, TN, SD, and DO were the basic environmental factors for estimating the eutro-
phication degree of a landscape lake supplemented with reclaimed water.

H.-j. Zhao et al. / Ecological Indicators 58 (2015) 113–121
119
Chla is the most important index for estimating the eutrophica-
tion degree of a lake. For example, the values of trophic level index
were determined annually from annual mean surface water con-
centrations of Chla, TN, TP, and SD (Özkundakci et al., 2014; Wang
et al., 2002). The PCA results of the environmental factors showed
that Chla concentration was closely related to TP (r = 0.583, p < 0.01),
TN (r = 0.449, p < 0.05), and COD (r = 0.415, p < 0.05) because of their
small angles, and thus, Chla concentration was negatively corre-
lated with SD (r = −0.567, p < 0.01) because of their obtuse angle
(Fig. 7). This result was consistent with the correlation mentioned
by Wang et al. (2002). In addition, Chla concentration was nega-
tively correlated with DO and positively correlated with salinity,
algal cell density, and pH value. However, the aforementioned cor-
relations were insignificant (p > 0.05).
With regard to sensory indices, SD was negatively corre-
lated with chroma (r = −0.904, p < 0.01), T (r = −0.639, p < 0.01), pH
(r = −0.481, p < 0.01), Chla (r = −0.567, p < 0.01), COD (r = −0.486,
p < 0.01), TN (r = −0.543, p < 0.01), and TP (r = −0.592, p < 0.01). In
particular, the correlation between SD and turbidity was highly sig-
nificant (r = −0.904, p < 0.01), which indicates that eutrophication
levels could be estimated approximately by sensory indices (Fig. 7).
Because the transparency is mostly governed by the amount of total
suspended solids, algal and organic detritus particles derived from
bloom decay together with co-occurring bacteria can cause further
increase in water turbidity (Cai et al., 1997).
Fig. 8. Ordination diagram of CCA exhibits dominant species and environmental
variables (arrows) in water body of Feng-qing Lake. T, PO43−–P, TN and COD are the
most important factors that influence dominant algae in the artificial lake supple-
mented with reclaimed water. P. limnetica and C. vulgaris are the most dominant
species throughout the year in landscape water supplied with reclaimed water.
Table 3
DO has a decisive significance in the growth and repro-
duction of various aquatic organisms. The PCA results show
that DO was negatively correlated with T (r = −0.744, p < 0.01),
TP (r = −0.744, p < 0.01), PO43−–P (r = −0.516, p < 0.01), salinity
(r = −0.432, p < 0.05), and chroma (r = −0.487, p < 0.01). Bacteri-
oplankton also exhibited a certain relationship with algal cell
density, and some researchers indicated that studies on bacterio-
plankton helped increase understanding on eutrophication levels,
which may provide the evaluation basis for overall water gov-
ernance (Silva et al., 2014). Bacterioplankton can be attached to
phytoplankton, which can significantly influence bacterioplankton
diversity. This finding indicates that bacterioplankton abundance
can reflect phytoplankton richness to a certain extent (Eiler and
Bertilsson, 2004). In the present study, HPC was positively related
to algal cell density (r = 0.613, p < 0.01), which is consistent with the
aforementioned literature (Fig. 7).
Summary statistics for the first two axes of CCA on the dominant species and envi-
ronmental variables.
Axis 1
Axis 2
Eigenvalues
0.454
0.999
0.120
0.995
Species–environment correlations
Cumulative percentage variance
of species data%
75.80
95.70
of species–environment relation%
Test of significance of first canonical axis
Test of significance of all canonical axes
76.10
F = 3.127
F = 23.420
96.10
P = 0.018
P = 0.002
was 0. These results show that the ordination result was credi-
ble because the linear combinations of the ordination axes and
the environmental factors provide a good representation of the
relationship between species and environmental factors (ter Braak,
1986; Jiang et al., 2014).
3.3.2. CCA on dominant species and environmental factors
Axis 1 was mostly correlated to T, PO43–P, and TN, with the
correlation coefficients of 0.609, 0.628, and −0.548, respectively,
whereas Chla and COD were mostly correlated to Axis 2, with the
correlation coefficients of 0.736 and 0.783, respectively, (Fig. 8),
which shows that these five environmental factors are closely
related to the dominant species. The development of Cyanophyta
populations is dependent upon the concentration of nutrients and
other ecological factors such as light, temperature, composition
and quantity of organic matter, and so on. There will be an out-
break of Cyanophyta bloom when eutrophic water bodies are
exposed to appropriate water temperature, air temperature, flow
rate, radiation, and other external conditions (Heisler et al., 2008).
In our research, environmental factors such as temperature (T)
and nutrient substrate were connected with the dominant species
(Cyanophyta and Chlorophyta) in the ordination diagram.
P. limnetica was closer to the center of CCA ordination, which
indicates that it has a wide tolerance to changes in the ecolog-
ical condition of landscape water supplemented with reclaimed
water. Moreover, P. limnetica was found to be closer to the arrows
of TN and salinity, which shows that TN and salinity have a signifi-
cant effect on the species. P. limnetica can grow well even under
low temperature and low phosphorus concentration. It has low
light requirement and can adapt to environmental changes even
in low-transparency (SD) environments. In addition, this species is
CCA was performed to analyze the relationship between phy-
toplankton and environmental variables, as well as to further
understand the driving factors that control phytoplankton commu-
nity structure (Li et al., 2013). The results of the CCA were visualized
in the form of ordination diagrams based on Axes 1 and 2 produced
using CanoDraw for Windows program. Quantitative environmen-
tal variables were represented by lines with arrows, and species
ˇ
scores were represented by symbols (Lepsˇ and Smilauer, 2003).
The directions of the arrows in the quadrant indicate the corre-
lations between environmental factors and the axis; the lengths of
the environmental variable arrows represent the relative explana-
tory power of phytoplankton species within the ordination (Li et al.,
2013). A total of 4 dominant species and 10 environmental factors
were chosen for CCA according to the frequencies and abundance
of phytoplankton. The CCA ordination triplot of Feng-qing Lake is
shown in Fig. 8, and the summary statistics for the first two axes of
CCA is shown in Table 3.
The eigenvalues of Axes 1 and 2 were 0.454 and 0.120, respec-
tively. Together, these axes explained 57.4% of the total variance
(Table 3). Species–environment correlations for species Axes 1 and
2 were 0.999 and 0.995, respectively (Table 3). The first two species
axes were approximately vertical, with a correlation of 0.004. More-
over, the correlation between the first two environmental axes

120
H.-j. Zhao et al. / Ecological Indicators 58 (2015) 113–121
frequently the dominant species in shallow lakes (Mischke, 2003;
Casé et al., 2008; Chomérat et al., 2007). P. limnetica has been found
in a wide range of salinities (from 180 mg/L to 2350 mg/L), but it
reached highest abundances (>108 cells/L) between 330 mg/L and
940 mg/L (median 650 mg/L) (Chomérat et al., 2007). R. curvata is
located in the first quadrant of the bioplot (Fig. 8) that is related to
T and COD and is suitable for survival in environments with high
temperature, which can explain why the second dominant alga is
R. curvata only in September. However, high COD has a negative
influence on the growth of R. curvata. C. vulgaris and Cosmarium sp.
both belong to Chlorophyta and are located in the first and fourth
quadrants, respectively. The two species are very close, which indi-
cates that their living environments are similar. T, TP, PO43−–P, SD,
NH3–N, and pH value have significant positive correlations with C.
vulgaris, which indicates that these environmental factors have the
strongest influence on C. vulgaris in Feng-qing Lake. Furthermore, C.
vulgaris can grow better in environments with high T, TP, PO43−–P,
SD, and NH3–N concentrations and pH value, but low salinity and
COD, which is consistent with the conclusion of Liu et al. (2014b).
C. vulgaris can remove NH3–N from wastewater. It can also be the
dominant species in water with high concentrations of NH3–N (Kim
et al., 2013; Lananan et al., 2014), which can explain why C. vul-
garis occurs alternately in Feng-qing Lake as the second dominant
species, as stated in Section 2.2. Cosmarium sp. and C. vulgaris can
survive in a similar environment, but Cosmarium sp. grows slowly
in water with a high pH value. Thus, the population of Cosmarium
sp. decreases with rising pH value.
PCA showed that T, TP, TN, SD, and DO were the main envi-
ronmental factors that influenced water quality. CCA showed that
TN, PO43−–P, COD, and T were the environmental factors with the
strongest influence on the dominant species in Feng-qing Lake. In
addition, TN and salinity had considerable influence on P. limnetica.
T and COD were the main environmental factors that influenced R.
curvata. Cosmarium sp. and C. vulgaris were mostly influenced by
T, TP, PO43−–P, SD, NH3–N, and pH. These findings imply that the
biomass of P. limnetica and Cosmarium sp. can be used as biological
quality indicators in landscape water supplemented with reclaimed
water.
Acknowledgements
This work was funded by Technology Bureau of Xi’an [Grant
number: SF1430] and State Key Laboratory of Pollution Control and
Resource Reuse Foundation (No. PCRRF14013). The research was
also supported by the innovative research team of Xi’an University
of Architecture and Technology. The authors express their gratitude
to the Qingyuan Wastewater Treatment and Reuse Ltd. Co. of Xi’an
for providing support in this research.
References
Asano, T., Burton, F.L., Leverenz, H.L., Tsuchihashi, R., Tchobanoglous, G., 2007. Water
Reuse-Issues, Technologies, and Applications. McGraw-Hill, New York, http://
dx.doi.org/10.1590/S1413-41522008000300001
Bano, A.P., Siddiqui, J.A., 2004. Characterization of five marine cyanobacterial species
with respect to their pH and salinity requirements. Pak. J. Bot. 36 (1), 133–143.
Becker, V., Huszar, V., Crossetti, L., 2009. Responses of phytoplankton functional
groups to the mixing regime in a deep subtropical reservoir. Hydrobiologia 628
(1), 137–151, http://dx.doi.org/10.1007/s10750-009-9751-7
In general, T, PO43−–P, TN, and COD are the most important
factors that influence dominant algae in the artificial lake supple-
mented with reclaimed water. Given that P. limnetica has a certain
degree of halotolerance (Stal, 2008) and less demand for T and
phosphorus, it can adapt to the environment of landscape water
supplemented with reclaimed water and frequently survive as the
strong dominant species. The other dominant species include C.
vulgaris, Cosmarium sp., and R. curvata. C. vulgaris, which is influ-
enced by T, TP, PO43−–P, NH3–N, and pH, frequently becomes the
second dominant species. P. limnetica and C. vulgaris are the most
dominant species throughout the year in landscape water supplied
with reclaimed water.
Cai, Q., Gao, X., Chen, Y., 1997. Dynamic variations of water quality in Taihu Lake and
multivariate analysis of its influential factors. J. Chin. Geogr. 7, 72–82, http://dx.
doi.org/10.1007/s11769-996-0058-6
Casé, M., Lec¸ a, E.E., Leitão, S.N., SantꢀAnna, E.E., Schwamborn, R., de Moraes Junior,
A.T., 2008. Plankton community as an indicator of water quality in tropical
shrimp culture ponds. Mar. Pollut. Bull. 56 (7), 1343–1352, http://dx.doi.org/
10.1016/j.marpolbul.2008.02.008
C¸ elekli, A., Öztürk, B., Kapı, M., 2014. Relationship between phytoplankton compo-
sition and environmental variables in an artificial pond. Algal Res. 5 (0), 37–41,
http://dx.doi.org/10.1016/j.algal.2014.05.002
Chomérat, N., Garnier, R., Bertrand, C., Cazaubon, A., 2007. Seasonal succession of
cyanoprokaryotes in a hypereutrophic oligo-mesohaline lagoon from the South
of France. Estuar. Coast. Shelf Sci. 72 (4), 591–602, http://dx.doi.org/10.1016/j.
ecss.2006.11.008
4. Conclusion
Chu, J.Y., Chen, J.N., Wang, C., Fu, P., 2004. Wastewater reuse potential analy-
sis: implications for China’s water resources management. Water Res. 38 (11),
2746–2756, http://dx.doi.org/10.1016/j.watres.2004.04.002
Eiler, A., Bertilsson, S., 2004. Composition of freshwater bacterial communities asso-
ciated with cyanobacterial blooms in four Swedish lakes. Environ. Microbiol. 6
(12), 1228–1243, http://dx.doi.org/10.1111/j.1462-2920.2006.01231.x
EPAC (Environmental Protection Agency of China), 2002. Standard Methods for the
Examination of Water and Wastewater, 4th ed. Chinese Environmental Science
Press, Beijing (in Chinese).
The water quality of Feng-qing Lake supplemented with
reclaimed water exhibited a significant seasonal variation. Accord-
ing to data for the entire year, the highest quality of the lake was
observed during winter. The water quality of the lake gradually
worsened from March to September, and the worst water quality
was recorded in summer.
The eutrophication of Feng-qing Lake supplied with reclaimed
water occurred rather quickly in summer and autumn. A total of 39
genera of phytoplankton, which belonged to 6 phyla and 22 fam-
ilies, were identified in Feng-qing Lake. Among these genera, 15
belong to Cyanophyta, which represents approximately 38.46% of
the total genera, 12 belong to Chlorophyta (30.77%), and 7 belong
to Bacillariophyta (17.95%) The number of genera was highest in
autumn (October) and lowest in winter (December) when algal cell
density was at the lowest. The dominant species were P. limnetica
and C. vulgaris, which belonged to Cyanophyta and Chlorophyta,
respectively. P. limnetica cell density was highest in April (up to
2.61 × 108 cells/L), whereas the cell density of C. vulgaris was high-
est in June (up to 8.15 × 107 cells/L). P. limnetica exhibited a strong
competitive advantage over other species, which resulted in its
dominance nearly throughout the year. C. vulgaris was the strong
dominant species in May and June, as well as the second dominant
species in other months.
Fan, J., Zhou, B.H., Zhang, H.T., Gao, L., 2012. Algae growth comparison in a landscape
pond supplied with reclaimed water. Res. Environ. Sci. 25 (2), 573–578, http://
dx.doi.org/10.13198/j.res.2012.05.95.fanj.001 (in Chinese).
Heisler, J., Glibert, P.M., Burkholder, J.M., Anderson, D.M., Cochlan, W., Dennison,
W.C., Dortch, Q., Gobler, C.J., Heil, C.A., Humphries, E., 2008. Eutrophication and
harmful algal blooms: a scientific consensus. Harmful Algae 8 (1), 3–13, http://
dx.doi.org/10.1016/j.hal.2008.08.006
Hu, H.J., Li, Y.Y., Wei, Y.X., Zhu, H.Z., Chen, J.Y., Shi, Z.X., 1980. Freshwater Algae in
China. Science Technology Press, Shanghai (in Chinese).
Jagadamma, S., Lal, R., Hoeft, R.G., Nafziger, E.D., Adee, E.A., 2008. Nitrogen fertiliza-
tion and cropping system impacts on soil properties and their relationship to
crop yield in the central Corn Belt, USA. Soil Tillage Res. 98 (2), 120–129, http://
dx.doi.org/10.1016/j.still.2007.10.008
Jiang, Y., 2009. China’s water scarcity. J. Environ. Manage. 90 (11), 3185–3196, http://
dx.doi.org/10.1016/j.jenvman.2009.04.016
Jiang, Y.J., He, W., Liu, W.X., Qin, N., Ouyang, H.L., Wang, Q.M., Kong, X.Z., He, Q.S.,
Yang, C., Yang, B., Xu, F.L., 2014. The seasonal and spatial variations of phyto-
plankton community and their correlation with environmental factors in a large
eutrophic Chinese lake (Lake Chaohu). Ecol. Indic. 40, 58–67, http://dx.doi.org/
10.1016/j.ecolind.2014.01.006
Jin, X.C., Tu, Q.Y., 1990. Standard Methods for Observation and Analysis in Lake
Eutrophication. China Environmental Science Press, Beijing (in Chinese).

H.-j. Zhao et al. / Ecological Indicators 58 (2015) 113–121
121
Kim, S., Lee, Y., Hwang, S.-J., 2013. Removal of nitrogen and phosphorus by Chlorella
sorokiniana cultured heterotrophically in ammonia and nitrate. Int. Biodeterior.
Biodegrad. 85, 511–516, http://dx.doi.org/10.1016/j.ibiod.2013.05.025
Kolada, A., 2014. The effect of lake morphology on aquatic vegetation development
and changes under the influence of eutrophication. Ecol. Indic. 38, 282–293,
http://dx.doi.org/10.1016/j.ecolind.2013.11.015
predictor variables by evolutionary computation. Environ. Model. Softw. 61 (0),
380–392, http://dx.doi.org/10.1016/j.envsoft.2014.03.014
Redfield, A.C., 1958. The biological control of chemical factors in the environment.
Am. Sci., 230A-221.
Rizzo, L., Manaia, C., Merlin, C., Schwartz, T., Dagot, C., Ploy, M.C., Michael, I., Fatta-
Kassinos, D., 2013. Urban wastewater treatment plants as hotspots for antibiotic
resistant bacteria and genes spread into the environment: a review. Sci. Total
Environ. 447, 345–360, http://dx.doi.org/10.1016/j.scitotenv.2013.01.032
Rojo, C., Alvarez Cobelas, M., 1994. Population dynamics of Limnothrix redekei,
Oscillatoria lanceaeformis, Planktothrix agardhii and Pseudanabaena limnetica
(cyanobacteria) in a shallow hypertrophic lake (Spain). Hydrobiologia 275–276
(1), 165–171, http://dx.doi.org/10.1007/BF00026708
Lananan, F., Abdul Hamid, S.H., Din, W.N.S., Ali, N., Khatoon, A., Jusoh, H., Endut, A.A.,
2014. Symbiotic bioremediation of aquaculture wastewater in reducing ammo-
nia and phosphorus utilizing Effective Microorganism (EM-1) and microalgae
(Chlorella sp.). Int. Biodeterior. Biodegrad. 95 (Part A), 127–134, http://dx.doi.
org/10.1016/j.ibiod.2014.06.013
ˇ
Lepsˇ, J., Smilauer, P., 2003. Multivariate Analysis of Ecological Data Using CANOCO.
Cambridge University Press, Cambridge, pp. 149–162.
Silva, L.H.S., Huszar, V.L.M., Marinho, M.M., Rangel, L.M., Brasil, J., Domingues, C.D.,
Branco, C.C., Roland, F., 2014. Drivers of phytoplankton, bacterioplankton, and
zooplankton carbon biomass in tropical hydroelectric reservoirs. Limnologica
48, 1–10, http://dx.doi.org/10.1016/j.limno.2014.04.004
Stal, L.J., 2008. Phenotypic and genetic diversification of Pseudanabaena spp.
(cyanobacteria). ISME J. 3 (1), 31–46, http://dx.doi.org/10.1038/ismej.2008.78
State Environmental Protection Administration, 2002. Surface Water Quality
Standard of the People’ s Republic of China. China Environment Science Press,
Beijing (in Chinese).
Li, C.L., Zhou, L., Jia, H.F., Zhang, X.J., Wang, Y.S., Gan, Y.P., Zhou, J., Shi, L., Gao,
J.H., 2005. Experimental research on the scenery safeguard system of reclaimed
water. Water Wastewater Eng. 31 (8), 6–9, http://dx.doi.org/10.3969/j.issn.
1002-8471.2005.08.003 (in Chinese).
Li, C., Qin, H.P., Zhang, Y.Y., Wang, W.W., 2011. Algae growth simulation of reclaimed
wastewater recycled to landscape water body in different seasons. Environ. Sci.
Technol. 34 (5), 47–51, http://dx.doi.org/10.3969/j.issn.1003-6504.2011.05.011
(in Chinese).
Li, Q.H., Chen, L.L., Chen, F.F., Gao, T.J., Li, X.F., Liu, S.P., Li, C.X., 2013. Maixi River
estuary to the Baihua Reservoir in the Maotiao River catchment: phytoplankton
community and environmental factors. Chin. J. Oceanol. Limnol. 31 (2), 290–299,
http://dx.doi.org/10.1007/s00343-013-2111-5
Liu, J., Wei, C.F., Xie, Q., Zhang, W.H., 2014a. Capacities of soil water reservoirs and
their better regression models by combining “merged groups PCA” in Chongqing,
China. Acta Ecol. Sin. 34 (1), 53–65, http://dx.doi.org/10.1016/j.chnaes.2013.11.
007
ter Braak, C.J.F., 1986. Canonical correspondence analysis: a new eigenvector tech-
nique for multivariate direct gradient analysis. Ecology 67 (5), 1167–1179.
The People’s Republic of China State Administration of Quality Supervision Inspec-
tion and Quarantine, 2002. The Reuse of Urban Recycling Water – Water Quality
Standard for Scenic Environment Use. Chinese Standard Press, Beijing (in Chi-
nese).
Valipour, M., Mousavi, S.M., Valipour, R., Rezaei, E., 2012a. Air, water, and soil pol-
lution study in industrial units using environmental flow diagram. J. Basic Appl.
Sci. Res. 2 (12), 12365–12372.
Liu, J.H., Yang, H.S., Wang, H., 2014b. Combined effects of temperature, salinity and
pH on the specific growth rate of chlorella. Acta Hydrobiol. Sin. 38 (3), 446–453,
http://dx.doi.org/10.7541/2014.63 (in Chinese).
Valipour, M., Mousavi, S.M., Valipour, R., Rezaei, E., 2012b.
A New Approach
for Environmental Crises and its Solutions by Computer Modeling. Interna-
tional Congress on Environmental Crises and its Solutions, Kish Island, Iran,
pp. 12.
Lu, X.T., Tian, C., Pei, H.Y., Hu, W.R., Xie, J., 2013. Environmental factors influencing
cyanobacteria community structure in Dongping Lake, China. J. Environ. Sci. 25
(11), 2196–2206, http://dx.doi.org/10.1016/S1001-0742(12)60297-6
Mai, X.W., 2003. Ecological Characteristics in Litopenaeus vannamei Desalination
Culture Ponds. Zhongshan University, Guangzhou (in Chinese).
Mischke, U., 2003. Cyanobacteria associations in shallow polytrophic lakes: influ-
ence of environmental factors. Acta Oecol. 24 (Suppl. 1), S11–S23, http://dx.doi.
org/10.1016/S1146-609X(03)00003-1
Mounir, B.B., Asma, H., Sana, B.I., Lotfi, M., Abderrahmen, B., Lotfi, A., 2013. What
factors drive seasonal variation of phytoplankton, protozoans and metazoans
on leaves of Posidonia oceanica and in the water column along the coast of the
Kerkennah Islands, Tunisia? Mar. Pollut. Bull. 71 (1–2), 286–298, http://dx.doi.
org/10.1016/j.marpolbul.2013.01.024
Valipour, M., Mousavi, S.M., Valipour, R., Rezaei, E., 2013. Deal with environmental
challenges in civil and energy engineering projects using a new technology. J.
Civil Environ. Eng. 3 (1), 127, http://dx.doi.org/10.4172/2165-784X.1000127
Van Houtven, G., Mansfield, C., Phaneuf, D.J., von Haefen, R., Milstead, B., Kenney,
M.A., Reckhow, K.H., 2014. Combining expert elicitation and stated preference
methods to value ecosystem services from improved lake water quality. Ecol.
Econ. 99, 40–52, http://dx.doi.org/10.1016/j.ecolecon.2013.12.018
von Alvensleben, N., Stookey, K., Magnusson, M., Heimann, K., 2013. Salinity toler-
ance of Picochlorum atomus and the use of salinity for contamination control
by the freshwater cyanobacterium Pseudanabaena limnetica. PLOS ONE 8 (5),
e63569, http://dx.doi.org/10.1371/journal.pone.0063569
Özkundakci, D., Hamilton, D.P., Kelly, D., Schallenberg, M., de Winton, M., Verburg, P.,
Trolle, D., 2014. Ecological integrity of deep lakes in New Zealand across anthro-
pogenic pressure gradients. Ecol. Indic. 37 (Part A), 45–57, http://dx.doi.org/10.
1016/j.ecolind.2013.10.005
Pan, X.J., Zhu, A.M., Zheng, Z.W., Qiao, H., Zhou, Q., Zhou, L.F., Zou, X., 2014. Struc-
tural characteristics and influencing factors of phytoplankton community in the
middle and lower reaches of Hanjiang River during spring season. Chin. J. Ecol.
33 (1), 33–40 (in Chinese).
Qiu, Y., Fu, B., Wang, J., Chen, L., Meng, Q., Zhang, Y., 2010. Spatial prediction of soil
moisture content using multiple-linear regressions in a gully catchment of the
Loess Plateau, China. J. Arid Environ. 74 (2), 208–220, http://dx.doi.org/10.1016/
j.jaridenv.2009.08.003
Raphaël, W., Christophe, B., Stana, G., Annick, W., Jirí, K., Lucien, H., 2006. Morpholog-
ical and molecular characterization of planktonic cyanobacteria from Belgium
and Luxembourg. J. Phycol. 42 (6), 1312–1332, http://dx.doi.org/10.1111/j.1529-
8817.2006.00284.x
Wang, F.H., Qiao, M., Lv, Z.E., Guo, G.X., Jia, Y., Su, Y.H., Zhu, Y.G., 2014. Impact of
reclaimed water irrigation on antibiotic resistance in public parks, Beijing, China.
Environ. Pollut. 184, 247–253, http://dx.doi.org/10.1016/j.envpol.2013.08.038
Wang, M.C., Liu, X.Q., Zhang, J.H., 2002. Evaluate method and classification standard
on lake eutrophication. Environ. Monit. China 18 (05), 47–49, http://dx.doi.org/
10.3969/j.issn.1002-6002.2002.05.018 (in Chinese).
Wei, D.B., Tan, Z.W., Du, Y.G., 2011. A biological safety evaluation on reclaimed
water reused as scenic water using a bioassay battery. J. Environ. Sci. 23 (10),
1611–1618, http://dx.doi.org/10.1016/S1001-0742(10)60631-6
Weng, J.Z., Xu, H.S., 2010. Atlas of Common Freshwater Algae in China. Science
Technology Press, Shanghai (in Chinese).
Xue, P.F., Chen, C.S., Qi, J.H., Beardsley, R.C., Tian, R.C., Zhao, L.Z., Lin, H.C., 2014.
Mechanism studies of seasonal variability of dissolved oxygen in Mass Bay: a
multi-scale FVCOM/UG-RCA application. J. Mar. Syst. 131, 102–119, http://dx.
doi.org/10.1016/j.jmarsys.2013.12.002
Yi, L., Jiao, W., Chen, X., Chen, W., 2011. An overview of reclaimed water reuse
in China. J. Environ. Sci. 23 (10), 1585–1593, http://dx.doi.org/10.1016/S1001-
0742(10)60627-4.
Recknagel, F., Ostrovsky, I., Cao, H., 2014. Model ensemble for the simulation of
plankton community dynamics of Lake Kinneret (Israel) induced from in situ