DSpace at VNU: Ecophysiological responses of young mangrove species Rhizophora apiculata (Blume) to different chromium contaminated environments

Science of the Total Environment 574 (2017) 369–380

Contents lists available at ScienceDirect

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

Ecophysiological responses of young mangrove species Rhizophora
apiculata (Blume) to different chromium contaminated environments
Kim Linh Nguyen a,b, Hoang Anh Nguyen b,c, Otto Richter c,⁎, Minh Thinh Pham a, Van Phuoc Nguyen b
a
b
c

Nong Lam University, Ho Chi Minh City, Vietnam
National University Ho Chi Minh City, Institute for Environment and Resources, Vietnam
Braunschweig University of Technology, Institute of Geoecology, Germany

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

• Young mangrove species Rhizophora
apiculata was cultivated in an artificial
wetland simulating tidal inundation.
• Response of R. apiculata to chromium
contamination strongly depends on
soil/water environments and fertilizer.
• The addition of nutrients amplifies the

toxic effect of chromium.
• The response is modeled by a nonlinear
multivariate growth model.

a r t i c l e

i n f o

Article history:
Received 24 June 2016
Received in revised form 8 September 2016
Accepted 8 September 2016
Available online 14 October 2016
Edited by: F.M. Tack
Keywords:
Mangrove
Rhizophora apiculata
Chromium
Nutrient
Microorganism
Plant growth modelling

⁎ Corresponding author.
E-mail address: (O. Richter).

/>0048-9697/© 2016 Elsevier B.V. All rights reserved.

a b s t r a c t
Many mangrove forests have suffered from the contaminated environments near industrial areas. This study addresses the question how these environments influence the renewal of mangrove forests. To this end ecophysiological responses of the young mangrove species Rhizophora apiculata (Blume) grown under combinations of
the factors heavy metals (here chromium), nutrition and soil/water environment were analyzed. We tested

the hypothesis that soil/water conditions and nutrient status of the soil strongly influence the toxic effect of chromium. Seedlings of R. apiculata were grown in three different soil/water environments (natural saline soil with
brackish water, salt-leached soil with fresh water and salt-leached-sterilized soil with fresh water) treated with different levels of chromium and NPK fertilizer. The system was inundated twice a day as similar to natural tidal condition in the mangrove wetland in the south of Vietnam. The experiments were carried out for 6 months. Growth
data of root, leaf and stem, root cell number and stomata number were recorded and analyzed. Results showed
that growth of R. apiculata is slower in natural saline soil/water condition. The effect of chromium and of nutrients
respectively depends on the soil/water condition. Under high concentrations of chromium, NPK fertilizer amplifies
the toxic effect of chromium. Stomata density increases under chromium stress and is largest under the combination
of chromium and salty soil/water condition. From the data a nonlinear multivariate regression model was derived
capturing the toxicity threshold of R. apiculata under different treatment combinations.
© 2016 Elsevier B.V. All rights reserved.


370

K.L. Nguyen et al. / Science of the Total Environment 574 (2017) 369–380

1. Introduction
Mangrove wetlands are highly efficient in adsorbing and absorbing
wastewater-borne pollutants. Mangrove trees have inherent physical,
chemical and biological properties for adsorption and/or utilization of
nutrients and heavy metals (Zhang et al., 2007; Ke and Tam, 2012;
Akhand et al., 2011; Usman et al., 2013; Naidoo et al., 2014). The ability
of heavy metal accumulation of mangroves is well established for example in Rhizophora sp. and Avicennia marina with the order
Cu N Zn N Cr N Ni N Cd N Pb (Akhand et al., 2011; Nazli and Hashim,
2010). Thus, it was proposed to use mangrove vegetation in wastewater
treatment systems (Sodré et al., 2013; Tansel et al., 2013; Ouyang and
Guo, 2016).
On the other hand, mangrove forests provide many ecosystem services such as flood control, shoreline stabilization and storm protection,
sediment and nutrient retention and are hot spots of biodiversity in
tropical and subtropical regions. Their ecological and socioeconomic significance has been recognized in many studies (Acharya, 2002; Lee et
al., 2014). Therefore, protection and conservation of mangrove ecosystems are important issues.

Thus, to balance between the needs to use mangrove vegetation for
filtering pollutants and the necessity to protect mangrove ecosystems,
the tolerance thresholds of mangrove vegetation to the concentrations
of pollutants need to be investigated. This motivated our investigation
on the physiological responses of mangroves under elevated contamination levels of pollutants.
Growth responses of some mangrove species under the effects of salinity (Takemura et al., 2000), nutrition and light intensity have been
discussed (Ye et al., 2005; Chen and Ye, 2014; Dangremond et al.,
2015; Alongi, 2011). The significance of trace metals on plant physiological processes, such as photosynthetic performance and salt secretion,
was stated (Naidoo et al., 2014; MacFarlane and Burchett, 2002) as
well as the studies on metal accumulation in roots, stems and leaves
of mangroves (Lewis et al., 2011; Keshavarz et al., 2012; Mahdavi et
al., 2012). It has been addressed that, the uptake by the roots of mangrove trees and the uptake by the benthos and microorganisms are
the main processes causing the retention and degradation of substances
(Hawkins et al., 1998; Yim and Tam, 1999), while the bioavailability of
substances depends on multiple factors, such as salinity (Yim and
Tam, 1999; Ye et al., 2005; Jayatissa et al., 2008), temperature, redox potential (Lewis et al., 2011) and nutrient supply (Alongi, 2011). However,
studies on the responses of mangroves to the combinations of stress factors and the toxicity thresholds of mangrove species to heavy metals
contaminated environments are still limited.
In our study, we investigated the hypothesis that the combined effects of soil type, plant nutrition and contamination by heavy metals
(here chromium) generate complex response patterns both of plant
growth and plant physiology. To this end, an experimental system in
an artificial mangrove wetland was constructed in the LongThanh mangrove forest (106.9358° N, 10.6475° E), located in the south of Vietnam.
LongThanh mangrove forest is situated in the vicinity of a large industrial area principally involved in plating, alloying, tanning, textile dyes and
other activities. The area is moderately polluted by organic substances
and heavy metals, especially chromium, which accumulated in the sediments and in the vegetation (Nguyen et al., 2014). The mangrove species Rhizophora apiculata Blume which is a mangrove tree species of
tropical coastal environments, propagates through viviparous propagules, is the dominant species in this forest. We used chromium in our
experiments. Although chromium is not an essential element, it can be
taken up by the plants along with essential elements like iron. It has
been documented that an excess level of chromium will exert strong adverse effects on plant growth leading to a decrease in productivity and
ultimately to death (Oliveira, 2012).

The paper focuses on the assessment of the chromium toxic tolerance of young R. apiculata through plant physiological responses. A
three factorial experiment with the factors chromium load, soil

nutrition and soil type was conducted. For each factor combination,
the response of the trees was observed during 6 months monitoring
the variables root length, root number, root cell density, tree diameter,
tree height, leaf number, leaf area and stomata density. Since the characteristics of soil and the associated diversity of microbial communities
are important components in the retention and transformation of substances in plants, the soil factor comprises natural saline soil, saltleached soil and salt-leached-sterilized soil. To quantify the toxic effects,
a mathematical growth model was then developed for simulating the
growth pattern of the young mangrove R. apiculata in dependence of
chromium and nutrients in different soil/water environments.
2. Materials and methods
2.1. Design of the artificial mangrove wetland
2.1.1. Experimental set-up
The experiments were carried out in a greenhouse containing ten
aquaria and ten reservoirs each with a volume of 750 L. Each pair of reservoir-aquarium was used for one treatment combination. Water was
pumped from the reservoir into each aquarium by a submersible
pump at a rate of 750 L/h lasting for 1 h. A valve at the bottom of each
aquarium allowed for a slow discharge of the water from the aquarium
back to the reservoir (1 h). Pots with young mangrove plants were
placed into aquaria which were kept flooded twice a day simulating
the conditions in a tidal zone. Water infiltrated the pots via the surface
and by holes. The experimental-setup simulated the semi-diurnal tidal
condition in the area. The spatial dimensions of the set-up are given in
Fig. 1.
2.1.2. Plant culture
Young mangrove species R. apiculata were collected from the
LongThanh mangrove forest with heights between 35 and 40 cm and
leaf numbers between 4 and 6. They were cultivated individually in
40 cm × 40 cm (diameter × depth) plastic pots with drainage holes,

each pot contained 6 kg of soil. Thirty pots were placed in each aquarium. All plants were grown under natural light in the air-conditioned
glasshouse at ~35 °C (cf. Fig. 1).
2.1.3. Experimental design
A three factorial trial was performed with the factors “Soil/Water”,
“Fertilizer” and “Chromium Load”.
The soil/water factor comprised
i) Brackish water + natural saline sediment (NN)
ii) Fresh water + salt-leached sediment (WN)
iii) Fresh water + salt-leached and sterilized sediment (AN)

Natural saline soil (NN) was taken from the LongThanh mangrove
forest.
For the production of salt-leached soil (WN), the natural saline soil
(NN) was immersed in fresh water, was stirred and replaced by new
fresh water every day until soil salinity was zero.
Basic soil characteristics were analyzed, at the beginning of the
study, to obtain some basic descriptive data of the soils in the forest.
Soil samples were homogenized in an agate mortar and air dried at
room temperature. A sample of 0.5 g of the oven dried homogenized
soil samples were directly digested with aqua regia according to DIN
EN 13346 (2000). The concentrations of elements in the aqua regia extracts were determined with an ICP OES. Total nitrogen was analyzed
using an elemental analyzer LECO TruSpec CHN Macro. The standard deviation, determined by multiple measurements for all elements b2%.
Basic soil properties and concentrations of some heavy metals in the
natural saline soil and salt-leached soil are shown in Table 1.


K.L. Nguyen et al. / Science of the Total Environment 574 (2017) 369–380

371

Fig. 1. The experimental system: twice a day the contaminated water was pumped from the reservoirs to the aquaria. The dimension unit of the system is in cm.

Salt-leached-sterilized soil (AN) was obtained from salt-leached soil
by sterilization in the oven at 121 °C, 2 atm for 10 h.
Brackish water used for the experiment with natural saline soil (NN)
was pumped from the DongKho river nearby, 50 m away from the
greenhouse. Fresh water was pumped from a groundwater well in the
area and was used for the experiment with salt-leached soil and saltleached-sterilized soil.
The fertilizer factor comprised two levels zero and 15 g. The fertilizer
consisted of ammonium, phosphorus and potassium with mass fractions 16, 16 and 8 respectively (NPK 16-16-8). Fertilizer level 15 g was
applied three times in three months, 5 g per pot each time, the first
time at day 10 of the experiment.
Chromium levels were 0 mg/L, 500 mg/L and 1000 mg/L of Cr (III)+.
Chromium was added to the water in the form of chromium sulphate
Cr2(SO4)3.
Each treatment was arranged as randomized complete design (RCD)
with 24 replications for non-destructive samples and three replications
for destructive samples. The experimental design is shown in Table 2.

Table 1
Soil characteristics at the beginning of the experiments.

Parameter

Unit

Natural
saline
Salt-leached soil
(NN)

soil (WN)

Pore water
salinity
Sand
Silt
Clay
Total nitrogen
Total
Phosphorous
Total Kali
Total Na
As
Pb
Cd
Cu
Zn
Cr

PSU

0

15.1

0.0761
0.0147

24.61
32.27

43.12
0.0536
0.00755

%
%
%
%
%

%
0.53
%
0.39
mg/kg
mg/kg
mg/kg
mg/kg
mg/kg
mg/kg 22.8

0.49
0.69
6.07
12.7
b0.0167
29.4
57.9
15.6

Threshold
values
Notes
Practical salinity
unit

12
120
5
70
200

Sediment quality
guidelines of
Vietnam (QCVN 03:
2008)

2.2. Morphometric measurements
Stem height, stem diameter and leaf number were measured
monthly for each plant. Plant mass, leaf area, root cell number, stomata
number, root length and root number were recorded after 3 months and
6 months respectively.
Plants were removed from their pots to ensure roots were intact and
salt-leached to remove soil and other debris. Stems, roots and leaves
were blotted dry and weighed fresh. Plant parts were then placed in labelled paper bags and dried at 60 °C for 7 days, after which they were
dry weighed. Biomass data of this study (not shown here) were used
to validate a comprehensive growth model in terms of a system of differential equations (Richter et al., 2016).
Root cell density were determined according to Glime and Wagner
(2013), the main roots were sliced at the tips and immersed in Javel
water (sodium hypochlorite NaOCl) for 15 min, then rinsed with distilled water. After that the samples were dipped into acetic acid for

5 min, rinsed again with distilled water and then dried by filter paper.
These samples were then colored and cut into 1-mm2-slices to count
the number of cells using a microscope.
For the determination of stomata number, the second pair of leaves
from each plant shoot was chosen. The abaxial epidermis of the leaf was
cleaned first using a degreased cotton ball, and then carefully smeared
with collodion for approximately 15 min. The thin film was peeled off
from the leaf surface. Numbers of stomata for each film unit (1 mm2)
were counted under a microscope.
2.3. Statistical analysis and regression model
The data were analyzed by a 4 factorial analysis of variance appropriate to the experimental design. The statistical model implies
the factors soil (S), nutrient (N) and chromium (Cr). In addition to
the three experimental factors, the factor time was taken into consideration as a fourth factor. The interactions of the factor time
with the other factors allow to draw conclusions on the time profiles
of the dependent variable, e.g., if there is a significant interaction between the factors chromium and time, then the shapes of the curves
are different.
yijklm ¼ μ þ Si þ Nu j þ Crk þ Tl þ ½S NuŠij þ ½S CrŠik þ ½S TŠil þ ½Nu CrŠjk þ ½Nu TŠjl þ ½Cr TŠkl þ
½Cr Nu SŠ þ ½Nu S TŠ þ ½Cr S TŠ þ ½Cr Nu TŠ þ ½CrNu S TŠ þ εijklm


372

K.L. Nguyen et al. / Science of the Total Environment 574 (2017) 369–380

Table 2
Experimental design.
Soil level

ID

Water and soil

1

AN0-0
AN15-0
AN0-500
AN15-500
AN0-1000
AN15-1000
WN0-0
WN15-0
WN0-500
WN15-500
WN0-1000
WN15-1000
NN0-0
NN15-0
NN0-500
NN15-500
NN0-1000
NN15-1000

Fresh water + wash-sterilized sediment

2

3

Nutrient level (NPK 16-16-8)

Chromium (Cr III+) level
1 (0 mg/L)

1 (0 g/pot)
2 (500 mg/L)
2 (15 g/pot)
3 (1000 mg/L)
Fresh water + washed sediment

1 (0 mg/L)
1 (0 g/pot)
2 (500 mg/L)
2 (15 g/pot)
3 (1000 mg/L)

Brackish water + natural sediment

i ¼ 1; 2; 3
j ¼ 1; 2
k ¼ 1; 2; 3
l ¼ 1; 2; 3; 4; 5; 6
m ¼ 1; 2; :::; n

Post hoc tests were performed according to the method of
Bonferroni, i.e. adjusting the significance level with respect to the number of multiple comparisons. Furthermore, multivariate nonlinear regression models for the growth of the variables height, diameter and
leaf number were derived. For the growth of the young trees an exponential growth law is appropriate, where the growth rate depends on
the fertilizer level Nu and chromium load Cr. The models were applied
to each soil type separately since soil type is a qualitative factor. The
nonlinear multivariate regression problem was solved by the

NonLinearFit procedure in Mathematica®. The detailed form of two
models and the model fits are presented in section 4. Model comparisons were performed based on the Akaike information criterion AIC
(Akaike, 1974).
3. Results I: statistical data analysis
Detailed ANOVA tables are given in the appendix A. An overview of
all ANOVA results is presented in Table 4.
3.1. Root growth
Table 3 shows mean values and standard deviation of the root length
and root number of young R. apiculata for each treatment combination.
The data shows the negative effect of the chromium treatment on root
elongation, this value is lower than that of treatments with no chromium input for all soil types. Among different soils, at chromium level 1
(no chromium) root length has the order AN N WN N NN. However,
when compare between the treatments with chromium, root length
has the order WN N NN N AN. With the addition of nutrient, root length
of soil WN at chromium level 2 (500 mg/L) is the highest among the
three soils, the lowest value happened with soil AN at the third month
but with soil NN at the sixth month.
Root number is highest at treatment WN in all nutrient levels and Cr
levels. The lowest values occurred with the treatment AN at month 3
and with treatment NN at month 6 for all nutrient levels.
These features are corroborated by the results of the ANOVA: The
table for the root length (cf. Table A.1, Appendix A) shows significant
main effects of the factors soil, chromium and time (p value b 10−3).
There is no significant nutrient effect. The interactions between the

1 (0 mg/L)
1 (0 g/pot)
2 (500 mg/L)
2 (15 g/pot)
3 (1000 mg/L)

factors chromium and soil (Cr S), soil and time (S T), nutrient and
time (Nu T) and chromium and time (Cr T) are significant (p
value b 0.05). The three-way interaction between the factors chromium,
nutrient and soil (Cr Nu S) is highly significant (p value b 10−3), showing that the combined effect of chromium and nutrients depends on the
soil type.
For the root numbers (cf. Table A.2, appendix A), we find significant
main effects of the factors soil, nutrient and time. The interactions between chromium and soil (Cr S), soil and time (S T), nutrient and time
(Nu T) are significant.
3.2. Stem growth
Fig. 2a–c shows the box plots of stem height for each soil type under
all combinations of the chromium and nutrient factors. Stem growth is
highest at treatment WN and lowest at treatment AN. The growth
curves show different forms under the influence of the chromium
loads. With no chromium, the height increases over the whole time period. The addition of nutrient enhances tree growth in the WN and NN
soils. Surprisingly, in soil AN (sterilized soil) the addition of nutrients
does not have positive effect and even leads to a diminished growth in
the case of high chromium levels. The data of tree diameter shows similar result.
The ANOVA tables for plant height and plant diameter (Tables A.3
and A.4, Appendix A) show highly significant main effects of all factors.
Second order interactions are also highly significant with the exception
of the soil-time interaction (S T) at the height table. For both variables,
diameter and height, the three-way interaction (Cr Nu S) is highly significant. This means that the combined effect of chromium and nutrient
is differing with respect to soil type as seen in Fig. 2.
3.3. Leaf growth
Fig. 3a–c shows the development of leaf number in each soil under
all combinations of chromium and nutrient. The highest value of leaf
number occurred at treatment WN and the lowest occurred at the treatment NN. At the beginning of the experiment, each plant had 6 leaves.
During the first 3 months, plants in all treatments formed from 2 to 3
new leaves per month. After 3 to 4 months the effect of chromium is noticeable. Leaves were severely damaged at the high chromium concentrations during the last two months of the experiment.

The results of the ANOVA given in Table A.5 in appendix A confirm
these complex patterns. All main effects and all second and third
order interactions are highly significant (p values b 0.05). The different
shapes of the time curves seen in Fig. 3 are reflected by the significant
interaction terms in the ANOVA table. Furthermore, the soil specific


K.L. Nguyen et al. / Science of the Total Environment 574 (2017) 369–380

373

Table 3
Mean values and standard deviation of physiological data of young R. apiculata for each treatment combination. AN: Fresh water + salt-leached and sterilized sediment, WN: Fresh water
+ salt-leached sediment, NN: Brackish water + natural saline sediment.
Root cell number
(cell/mm2)

Stomata number
(s/mm2)

Root length (cm)

Root number (per
plant)

Leaf area (cm2)

Soil
type

Nutrient
(mg)

Concentration of
Cr(III+) (mg/L)

Three
months

Six months

Three
months

Six
months

Three
months

Six
months

Three
months

Six
months

Three

months

Six months

AN

0

0

252.67 ±

363.33 ±

41.67 ±

42.33 ±

22.83 ±

39.93 ±

4±0

5.66 ±

474.33 ±

959.33 ±

AN

15

0

54.012
306.33 ±

17.926
411.33 ±

3.51
40.33 ±

3.21
47.33 ±

2.93
29.83 ±

4.81
39 ± 6

4.33 ±

1.53
7.33 ±

21.78

518.67 ±

213.03
1131.70 ±

AN

0

500

13.796
227.67 ±

14.978
320.33 ±

6.66
52.67 ±

4.16
59.33 ±

1.07
23 ± 5

34 ± 7

0.58
4±1

1.53
5.33 ±

107.36
293.1
436 ± 24.98 772 ±

AN

15

500

22.679
263.33 ±

18.903
347.33 ±

3.06
53.67 ±

3.79
59.67 ±

17.3 ±

18.33 ±

4±1

1.53
5.33 ±

484.67 ±

154.07
924.33 ±

AN

0

1000

36.226
182 ±

44.23
201 ±

4.04
62.33 ±

2.52
65.33 ±

2.07
11.53 ±

2.08
19.36 ±

2.66 ±

2.08
5±1

78.01
384.33 ±

148.18
409.67 ±

AN

15

1000

23.302
252.67 ±

31.796
5.13
178 ± 67.29 60.33 ±

3.51
66.00 ±

2.72
16.06 ±

2.05
26.36 ±

0.58
3±1

6±1

32.624
425 ± 63

41.02
326.33 ±

WN

0

0

43.247
262.33 ±

377.33 ±

2.52

45.33 ±

2
46.67 ±

2.18
21.46 ±

0.80
42.5 ±

5.33 ±

5±1

544.33 ±

93.18
1073.3 ±

WN

15

0

37.899
358 ±

55.194

489 ±

7.77
47.67 ±

5.51
50.33 ±

1.11
22.03 ±

4.44
30 ± 6

1.15
5.33 ±

4.33 ±

57.59
106.93
616 ± 83.21 1353.3 ±

WN

0

500

29.866

256 ±

18.358
336 ±

3.51
55.00 ±

3.51
55.67 ±

5.32
17.16 ±

24.33 ±

0.58
5.33 ±

1.15
5±1

175.1
486 ± 31.05 831.67 ±

WN

15

500

28.844
281.67 ±

33.779
373 ±

7.55
57.67 ±

2.52
58.00 ±

3.06
20.16 ±

2.52
30.16 ±

1.15
4.67 ±

8 ± 2.65 592.67 ±

77.84
977.67 ±

WN

0

1000

31.533
226.33 ±

39.661
267.33 ±

4.51
66.33 ±

4.58
68.33 ±

4.75
26 ± 2.65

8.69
34 ±

0.58
4.67 ±

5.67 ±

37.42
473.67 ±

65.59

521.67 ±

WN

15

1000

29.4
265.67 ±

55.537
261.67 ±

2.08
65.00 ±

3.51
66.67 ±

18.83 ±

7.69
24 ± 3.5

0.58
4.67 ±

1.15
7.67 ±

94.31
543.67 ±

80.63
517.33 ±

NN

0

0

43.097
221.33 ±

49.943
324.33 ±

4.36
53.67 ±

4.04
58.00 ±

6.25
18.83 ±

22.67 ±

0.58
3.67 ±

0.58
6.33 ±

54.23
421.33 ±

26.41
760 ±

NN

15

0

84.69
282.67 ±

30.567
385.33 ±

4.04
52.00 ±

4
54.67 ±

3.82
20.83 ±

3.01
28.5 ±

0.58
3.33 ±

0.58
7±1

23.86
477.67 ±

121.06
883.67 ±

NN

0

500

24.786
209.33 ±

44.602
291 ±

3.61
69.33 ±

4.51
75.00 ±

7.37
15.67 ±

3.77
21.5 ±

0.58
3.33 ±

5±1

51.25
45.63
394 ± 28.83 643.33 ±

NN

15

500

32.146
242 ±

38.974
330.33 ±

6.03
70.33 ±

3.61
72.67 ±

3.06
17.17 ±

3.5
21.67 ±

0.58
3.33 ±

5.67 ±

450.67 ±

86.22
794 ± 78.26

NN

0

1000

41.581
122.67 ±

27.538
188 ±

3.21
71.33 ±

5.03
72.00 ±

1.90
16.97 ±

3.51
20 ±

0.58
3±0

1.53
4.33 ±

41.86
352.67 ±

393.33 ±

1000

25.502
198 ±

21.794
146 ± 28

2.08
73.00 ± 2

2
76.33 ±

4.10
16.97 ±

2.18
14.83 ±

3.67 ±

1.15
4.67 ±

50.94
355.33 ±

40.27
274.33 ±

3.06

4.13

2.36

0.58

0.58

75.79

86.0

NN

15

28.618

effect of the nutrients is reflected by the significant two-way interaction
between the soil and nutrient factors (Nu S), the three-way interaction
(Cr Nu S) and the three-way interaction (Cr Nu T). The latter significant
interaction confirms that the shapes of the leaf number depend on the
combinations of Cr and nutrient.
For leaf area (cf. Table 3), a similar response pattern was observed,
the highest value occurred at the treatment WN and the lowest one occurred at the treatment NN. At chromium level 3, the decrease in leaf

area is amplified by the nutrient at the treatments AN and NN. The results of the ANOVA given in Table A.6 in Appendix A confirm these complex patterns. Almost all main effects and all second order interactions

are highly significant, except the interaction effects of nutrient and soil
(Nu S), chromium and soil (Cr S) and nutrient and time (Nu T). It
shows that these interaction effects are not significant for the development of leaf area. Similar to leaf number, the three-way interaction
chromium – nutrient – time (Cr Nu T) is significant.

Table 4
Summary of the results of the ANOVAs: **: p b 10−3, *: p b 0.05, –: p ≥ 0.05.
Factor

Height

Diameter

Number of leaves

Leaf area

Stomata density

Root cell density

Root length

Root number

Soil
Nutrient
Chromium
Time
Nutrient-soil interaction

Chromium-soil interaction
Soil-time interaction
Chromium-nutrient interaction
Nutrient-time interaction
Chromium-time interaction
Chromium-nutrient-soil interaction
Nutrient-soil-time interaction
Chromium-soil-time interaction
Chromium-nutrient-time interaction

**
**
**
**
**
**
–
**
**
**
**
–
–
–

**
**
**
**
**

**
**
**
**
**
**
–
–
**

**
**
**
**
**
**
**
**
**
**
*
*
**
**

**
**
**
**
–

–
*
*
–
**
–
–
–
*

**
–
**
**
–
**
–
–
–
–
–
–
–
–

**
**
**
**
–

–
–
*
–
**
–
–
–
*

**
–
**
**
–
**
*
–
*
*
**
–
–
–

**
*
–
**
–

*
*
–
*
–
–
–
**
–


374

K.L. Nguyen et al. / Science of the Total Environment 574 (2017) 369–380

Fig. 2. a–c. The development of stem height (in cm) of R. apiculata when exposed to different concentrations of Cr and nutrient during 6 months in three different soils (a: AN, b: WN, c:
NN). In all soils tree growth is inhibited by chromium. The addition of nutrient enhances the tree growth in the WN and NN soils but give no effect to AN soil.

3.4. Cell number and stomata number
The response pattern of cell number and stomata number shows interesting features. Root cell density (cf. Table 3) was increasing from
month 3 to month 6 in the presence of nutrient in almost all treatments

except in the experiments with highest Cr load (Cr level 3), where root
cell numbers decreased (in the treatment AN) or were retarded (treatments WN and NN) from month 3 to month 6. For all three soil types,
maximum values occurred in soil type WN (salt-leached soil) and minimum values occurred in soil type NN (natural saline soil). The results of


K.L. Nguyen et al. / Science of the Total Environment 574 (2017) 369–380

375

Fig. 3. a–c. The development in time of leaf number of young R. apiculata exposed to different concentrations of Cr and nutrient during 6 months in three different soils (a: AN, b: WN, c:
NN). In all three soils, leaf numbers decrease after about 3 months under the highest chromium level. In the sterilized soil (AN) and in the natural saline soil (NN), there is a strong
synergistic effect of the nutrient at the maximum chromium load.

the ANOVA (cf. Table A.7, Appendix A) show significant main effects for
all factors. In addition, the two-way interactions between chromium
and nutrient (Cr Nu), chromium and time (Cr T) and the tree-way interaction chromium-nutrient-time (Cr Nu T) are significant.
In contrast to the retarded trend of root cell number, stomata density increases with increasing chromium load (cf. Table 3 and Fig. 4). After three

months, the minimum value of stomata density occurred at the treatment
AN but after six months the lowest value occurred at the treatment WN.
The density of stomata is highest at the treatment NN in both 3 and
6 months periods. The effect of nutrient on stomata density is not clear.
The ANOVA table (Table A.8, Appendix A) for the stomata density
shows significant main effects of the factors soil, chromium and time.


376

K.L. Nguyen et al. / Science of the Total Environment 574 (2017) 369–380

Fig. 4. Stomata density at month 3 and at month 6 in dependence on chromium load and nutrient. Note that stomata density increases with increasing chromium load.

There are no significant interactions except a significant interaction between chromium and soil (Cr S). There are no significant effects of the
factor nutrient.
4. Result II: nonlinear regression model
Preliminary remark: the model development was motivated by the
exploratory data analysis and the results of the ANOVAs. Model selection was guided by the use of the Akaike information criterion
(Akaike, 1974). For the response variables plant height, plant diameter

and leaf number the ANOVAs show significant interactions between
the factors chromium and nutrient, between nutrient and time and between chromium and time. Since the factor levels of nutrient and chromium are quantitative, it is challenging to capture this response pattern
by nonlinear regression models. Here, we have to distinguish between
the variables plant height and diameter on the one hand and on leaf
number and leaf area on the other, since the former increases with
time whereas the latter may decrease with time after a period of increasing under high chromium concentrations. Therefore, two models
were developed, which are both based on an exponential growth law
(Eq. (1)). The models are specified by the form of the dependence of
the growth rate μ on the chromium (Cr) and nutrient (Nu) levels and
on time
f ðNu; Cr; t Þ ¼ a exp½μ ðNu; Cr; t Þ t Š

and 1000 mg/L and the variable Nu codes the amounts of nutrient 0
and 15 g per pot respectively. If all factor levels take the value of 1 (no
chromium, no fertilizer), the growth rate is equal to k0, the natural
growth rate. The parameters k1, k2 and k3 can take on negative or positive values according to the influence of the factor on the growth. The
first and the second term describe the effect of chromium and nutrient
respectively and the third term takes into account the interaction between chromium and nutrient. As we have seen in the foregoing section, the interaction might be positive or negative. This model fits
satisfactorily to the growth data as shown in Fig. 5 for the tree diameter.
To demonstrate the negative interaction between chromium and nutrient, we have chosen the dataset obtained for the soil AN. Under high
chromium level, the growth of the diameter in the fertilized plants is
lower than the growth without fertilizer (Fig. 6a). If no chromium is
given, tree growth is stimulated by the fertilizer as has to be expected
(Fig. 6b). Application of the model to the height data gives similar results.

ð1Þ

In the case of plant height and diameter, the growth rate μ(Nu,Cr) is
made dependent on nutrient level Nu and chromium level Cr by a multiple linear approach with an interaction between nutrient and chromium factor (Eq. (2))
μ ðNu; CrÞ ¼ k0 þ k1 ðNu−1Þ þ k2 ðCr−1Þ þ k3 ðCr−1ÞðNu−1Þ

ð2Þ

Note that there is no dependence on time of the growth rate. The
three levels of the variable Cr code chromium concentrations of 0, 500

Fig. 5. Increment of tree diameter at 3 different chromium levels for nutrient level 1 (no
nutrient added) and soil 1 (AN). The points are data; the response surface is generated
by the model defined by Eqs. (1) and (2) with the parameters given in Table B.1,
Appendix B.


K.L. Nguyen et al. / Science of the Total Environment 574 (2017) 369–380

Because of the complex response pattern of the leaf number, a more
complicated form of the growth rate had to be devised. The growth
curves observed under the different chromium levels (cf. Fig. 3) run parallel for a certain time and separate after about 4 months. Under the
highest chromium level, the mean leaf number decreases after attaining
a maximum value. This behavior cannot be described by a growth rate
independent of time, because the sign of the growth rate
μ(Nu, Cr)determines the growth behavior from the beginning. There is
either an increase or a decrease. Therefore, a time dependent growth
rate is introduced based on the assumption that the buildup of toxic
concentrations within the trees takes a certain time. This explains why
in the first three months the curves run almost parallel. This effect is
captured by the first factor of the third term of Eq. (3). Toxic effects develop only if a critical time tc is approached and surpassed. Furthermore,
a nonlinear response of the growth rate to chromium was introduced
taking into account a threshold effect, i.e. chromium toxicity is effective

377

only if a certain threshold level thr is surpassed (Eq. (3)). This effect is
captured by the second factor of the third term of Eq. (3).
"   #!
" 
 #
t 2
SðCr; NuÞ 2
ð1‐Exp −
μ ðNu; Cr; tÞ ¼ k0 þ k1 ðNu−1Þ− 1‐Exp −
tc
thr

ð3Þ
with
SðCr; NuÞ ¼ ðCr−1Þ þ k2 ðCr−1ÞðNu−1Þ

ð4Þ

To allow for an interaction between chromium and nutrient level,
the term k2 (Cr − 1) (Nu − 1) was added to the chromium level. Eqs.
(3) and (4) imply the following effects.

Fig. 6. (a) Under high chromium level, fertilizing has a negative effect on the growth. (b) If no chromium is given, tree growth is enhanced by adding fertilizer.


378

K.L. Nguyen et al. / Science of the Total Environment 574 (2017) 369–380

i) If chromium level equals 1, i.e. no chromium added to the water,
and nutrient level equals 1, i.e. no nutrient given, the growth rate
reduces to the natural saline soil specific growth rate k0, which
increases to k0 + k2(Nu − 1) if nutrient is added.
ii) If chromium is added to the water, plant growth is affected only if
the chromium level approaches a threshold value thr.
iii) Toxic effects slowly build up.
iv) Toxic effects are amplified if a nutrient is added.

Fig. 7 shows the response of leaf number under different combinations of Cr and nutrients on the three soils.

5. Discussion
Considering the diversity of the detailed analyses given above, it
might be helpful to summarize the results to get the overall picture.
We have therefore devised the following table, where the outcomes of
the ANOVA of each variable are put together.
5.1. Response of R. apiculata to different soil types
There is a highly significant effect of soil type (S) on all variables. Post
hoc tests (Bonferroni) show that soil WN (salt-leached soil) has the
highest rates and the soil NN (natural saline soil) has the lowest rates

Fig. 7. Time course of leaf numbers for 3 soil types. Blue data points: no fertilizer, red data points: addition of 15 g fertilizer per pot. The response surfaces are generated by the model
defined by Eqs. (1), (3) and (4). Note that leaf numbers decrease after 4 months under the highest chromium load (level 3). Fertilizing (red data points) amplifies the toxic effect of
chromium. Parameters for these models are given in Tables B.7–B.9 in Appendix B.


K.L. Nguyen et al. / Science of the Total Environment 574 (2017) 369–380

for the parameters diameter, height, number of leaves, leaf area, root
cell density, root length, root number. In contrast, stomata density is

highest in the soil NN (natural saline soil).
Clearly, salinity in the natural saline soil (NN) can be considered as
an additional toxic effect influencing uptake of nutrients and thus
plant growth. This might be due to competitive inhibition of ammonium
uptake by sodium, as discussed by Naidoo (1987). Reef et al. (2010)
discussed nutrient availability of mangroves by the redox state of the
soil, which may be influenced by salinity. This explains why we found
that growth of R. apiculata is fast in the soil salt-leached with fresh
water (WN) and low in the natural saline soil (NN).
A limitation of microorganism population in the soil (as in the treatment AN) is a disadvantage for the growth and chromium tolerance of R.
apiculata, since microorganisms stimulate the development of the root
system and are capable of rendering chromium species inactive in the
soil, many microbes were reported to reduce Cr under aerobic and anaerobic conditions (Jeyasingh and Philip, 2005; Shukla et al., 2009). In
our experiments, growth variables in the soil AN are smaller than
those in the soil WN since it lacked of a population of chromium retention microorganisms.
5.2. Response of R. apiculata to different levels of nutrient and chromium
The nutrient factor (Nu) has a highly significant effect on all variables except root length and stomata number. Chromium has a significant effect on all variables except the root number. Post hoc tests
reveal negative effects on root length, shoot cell number, leaf number,
leaf area, height and diameter. However, stomata density is highest
under the highest chromium load.
These results are in agreement with the findings of other authors.
Root length is more affected by chromium than by other heavy metals
(Shanker et al., 2005). Results from Panda and Patra (2000) showed
that at a chromium concentration of 1 μM, the root length of seedlings
of wheat (cv. Sonalika) cultivated on different N fertilizer levels increased. However, under higher chromium concentrations, root length
decreased regardless of the N fertilizer level. Reduction in root growth
caused by chromium toxicity could be due to the inhibition of root cell
division, root elongation or to the extension of cell cycle in the roots
(Shanker et al., 2005).
Shanker et al. (2005) showed that the reduction of plant height in

chromium stress conditions is mainly due to the reduction in root
growth leading to lesser nutrients and water transport to the above
parts of the plant. In addition, chromium transport to the aerial parts
of the plant can have a direct impact on the cellular metabolism of
shoots contributing to the retardation of plant height.
Following Ghani (2011), typical symptoms observed in chromium
intoxication are chlorosis, necrosis and red brownish discoloration of
the leaves. In our experiments, beside typical symptom reported,
wilting of leaves at the top occurred and ring spots appeared on leaf surfaces of R. apiculata. These symptoms are similar to those in iron deficiency. It could be that the more chromium plants accumulated the
less iron they uptake as these two elements compete in binding with
the same acceptor at the root cell membrane.
The increment of stomata density as response to heavy-metal toxic
stress has been reported in the literature for other plants. It has been
found that stomata density in tobacco leaves increased with elevated
chromium intoxication (Orcen et al., 2013). Stomata are by far the
most influential components in gas exchange. The regulation of wateruse efficiency in plants occurs, in part, by changes in stomatal density.
Stomatal density is regulated by regulators. Factors that increase stomatal density might be working through the positive regulators (basic
helix-loop-helix; bHLH SPCH, MUTE and FAMA). By contrast, those
that decrease the number of stomata might be working through the
negative ones (SDD1, TMM, ER-family, YDA and the mitogen-activated
protein kinase MAPK module). To cope with stress, plant produces
more enzymes to catalyze the process of phosphorylation, enhancing

379

photosynthesis to supply energy for plant. Thus the processes of gas exchange and evaporation increase. In this case, plant has to produce more
stomata.
5.3. Response of R. apiculata to interactions of environmental factors
The second and third order interactions of the factors Cr and nutrient
(Cr Nu), Cr and time (Cr T) and Cr, nutrient and time (Cr Nu T) are highly

significant for the variable root cell density. This means the effects of
chromium and nutrient are dependent on each other and also dependent on time. Cell number decreases with the presence of Cr. In the
treatments with Cr loads, cell number is higher when adding nutrient.
Root cell development at the tips of the main root (root tip meristem)
represents the elongation of the root. This value is also an indicator for
the growth of the whole plant, when the number of cells in this region
reduced, root growth is inhibited. If this situation lasts long, roots will
be weakened. This will affect the uptake of water and nutrient to the
plant.
For stem growth, at the highest chromium load of 1000 mg/L (Cr
level 3), growth stops after 3 to 4 months. With the exception of soil
AN, the addition of nutrients mitigates the chromium's growth inhibition which is manifested by the significant interaction between the nutrient and chromium factor (Nu Cr). The interactions between the
chromium factor and the time factor (Cr T) refer to the different shapes
of the growth curves in dependence of chromium level as shown in the
regression model (Eq. (2)) and in Fig. 5. The interaction between the nutrient and the time factor (Nu T) has the same interpretation.
For leaf growth, as observed from ANOVA table and the regression
model (Eq. (3)), the response pattern of the leaf number to the treatments is most complex and shows one surprising result: the amplification of the inhibitory effect of chromium by the addition of nutrient. The
most striking feature is that in the soils AN and NN the decrease in leaf
number under high chromium levels is enhanced by the nutrient,
whereas under no chromium, the development of leaf number is promoted by the nutrient. All these effects are shown in Fig. 7, where the
response surfaces for the two nutrient levels are superimposed. It is
clearly that the toxic effect of chromium on the leaf number is amplified
under the addition of nutrients (red data points). One also recognizes
the nonlinear effect of chromium. The growth curves do not differ
much under chromium levels 1 (no chromium) and 2 (500 mg/L). At
chromium level 3 (1000 mg/L) leaf numbers are drastically reduced
after about 4 months.
The effect of chromium on plant growth and the interaction between
chromium and nutrient uptake have been well documented in the literature. According to Oliveira (2012), chromium, being structurally similar to other essential elements, may affect plant mineral nutrition.
Uptake of both macro nutrient (N, P, K) and micro nutrient decreased

with increasing of chromium concentration in irrigation of paddy
(Sundaramoorthy et al., 2010). High concentration of chromium may
displace the nutrients from physiological binding sites and consequently decrease in uptake and translocation of essential elements (Oliveira,
2012). Turner and Rust (1971) found that nutrient solution with
9.6 μM Cr decreased the uptake of K, Mg, P, Fe and Mn in root of soybean.
Barcelo et al. (1985) described the inhibition of P, K, Zn, Cu, Fe translocation within the plant parts when bean plants were exposed to Cr. Excess chromium interfered with the uptake of Fe, Mo, P, and N and
effected the translocation of P, S, N, Zn and Cu from roots to tops
(Chatterjee and Chatterjee, 2000). The reduction in N, P, K and other elements could be due to the reduced root growth and impaired penetration of roots into the soil due to chromium toxicity (Shanker et al.,
2005). On the other hand, chromium stress may affect the photosynthesis in terms of CO2 fixation, electron transport, photophosphorylation
and enzyme activity, leading to a decrease in productivity and ultimately to death. Disorganization of the chloroplast ultrastructure and the inhibition of electron transport processes due to chromium and a
diversion of electrons from the electron-donating side of Photosystem


380

K.L. Nguyen et al. / Science of the Total Environment 574 (2017) 369–380

I (PSI) to chromium is a possible explanation for chromium-induced decrease in photosynthetic rate. Besides, the alteration of photosynthetic
pigments by chromium may be a further explanation of the inhibition
of photosynthesis under chromium toxic stress (Shanker et al., 2005).
6. Conclusion
The results of the ANOVA and of the regression model allowed us to
identify toxicity thresholds and growth responses of young mangrove
trees as a function of chromium, fertilizer and soil type. Growth response
patterns show that the toxic effect of chromium is augmented in the natural
soil. Most surprising is the fact that in the natural soil fertilization amplifies
the effect of chromium. Furthermore, adverse effects of chromium
contaminations become manifest only after a time delay of several months.
The deactivation of microorganisms in the soil proved to be a disadvantage for the growth and chromium tolerance of R. apiculata compared
to the growth on salt-leached soil and natural soil. The microorganism's

activities stimulated the development of the root system.
These findings are important for the planning of mangrove restoration and replantation in heavily contaminated environments. The nonlinear regression model derived from the experiments can be used to
assess the feasibility of such measures.
Based on the model results the following caveats apply for reforestation with young mangrove plants:
i) Chromium contamination has a long term effect on plant growth.
Initial success of replantation may be followed by a complete breakdown of the plant population.
ii) Replanting in heavily contaminated soils with high salinity is likely to fail. In this situation addition of fertilizer may have adverse effects.
Although we were able to capture the interaction between chromium stress and fertilizing in a comprehensive nonlinear regression
model, the physiological mechanism behind the amplification of the
chromium effect needs to be further studied.
Acknowledgements
This work is part of the VNUHCM-BMBF Project EWATEC-COAST,
supported by the Bundesministerium für Bildung und Forschung of Germany (BMBF) Grant No. O2WCL1217A and by the Vietnam National
University of Ho Chi Minh City (VNU-HCM) Grant No. NDT2012-2401/HD-KHCN.
Supplementary data
Supplementary data to this article can be found online at http://dx.
doi.org/10.1016/j.scitotenv.2016.09.063.
References
Acharya, G., 2002. Life at the margins: the social, economic and ecological importance of
mangroves. Madera y Bosques Número especial 53–60.
Akaike, H., 1974. A new look at the statistical model identification. IEEE Trans. Autom.
Control 19 (6), 16–723.
Akhand, A., Chanda, A., Dutta, S., Hazra, S., Sanyal, P., 2011. Comparative study of heavy
metals in selected mangroves of Sundarban ecosystem. J. Environ. Biol. 33,
1045–1049.
Alongi, D., 2011. Early growth responses of mangroves to different rates of nitrogen and
phosphorus supply. J. Exp. Mar. Biol. Ecol. 397, 85–93.
Barcelo, J., Poschenrieder, C., Gunse, B., 1985. Effect of chromium (VI) on mineral element
composition of bush bean. J. Plant Nutr. 8, 211–217.
Chatterjee, J., Chatterjee, C., 2000. Phytotoxicity of cobalt, chromium and copper in cauliflower. Environ. Pollut. 109, 69–74.

Chen, Y., Ye, Y., 2014. Effects of salinity and nutrient addition on mangrove Excoecaria
agallocha. PLoS One 9 (4).
Dangremond, E., Feller, I., Sousa, W., 2015. Environmental Tolerances of Rare and Common Mangroves along Light and Salinity Gradients. Oecologia.
Ghani, A., 2011. Effect of chromium toxicity on growth, chlorophyll and some mineral nutrients of Brassica juncea L. Sci. Technol. 4, 197–202.
Glime, J.M., Wagner, D.H., 2013. Laboratory techniques: slide preparation and stains. In:
Glime, J.M. (Ed.), Bryophyte Ecology. (Vol. 3). Ebook sponsored by Michigan Technological University and the International Association of Bryologists.

Hawkins, A., Smith, R., Tan, S., Yasin, Z., 1998. Suspension-feeding behaviour in tropical bivalve molluscs: Perna viridis, Crassostrea belcheri, Crassostrea iradelei, Saccostrea
cucculata and Pinctada margarifera. Mar. Ecol. Prog. Ser. 166, 173–185.
Jayatissa, L., Wickramasinghe, W., Dahdouh-Guebas, F., Huxham, M., 2008. Interspecific
variations in responses of mangrove seedlings to two contrasting salinities. Int. Rev.
Hydrobiol. 93 (6), 700–710.
Jeyasingh, J., Philip, L., 2005. Bioremediation of chromium contaminated soil: optimization of operating parameters under laboratory conditions. J. Hazard. Mater. B118,
113–120.
Ke, L., Tam, N.F., 2012. Phytoremeditaion Using Constructed Mangrove Wetlands: Mechanisms and Application Potential. Nova Science Publishers, Inc.
Keshavarz, M., Mohammadikia, D., Gharibpour, F., Dabbagh, A.-R., 2012. Accumulation of
heavy metals (Pb, Cd, V) in sediment, roots and leaves of mangrove species in Sirik
creek along the sea coasts of Oman. Iran. J. Appl. Sci. Environ. Manage. 16 (4),
323–326.
Lee, S., Primavera, J., Dahdouh-Guebas, F., McKee, K., Bosire, J., Cannicci, S., ... Record, S.,
2014. Ecological role and services of tropical mangrove ecosystems: a reassessment.
Global Ecology and Biogeography, (Global Ecol. Biogeogr.) 23, 726–743.
Lewis, M., Pryor, R., Wilking, L., 2011. Fate and effects of anthropogenic chemicals in mangrove ecosystems: a review. Environ. Pollut. 159, 2328–2346.
MacFarlane, G.R., Burchett, M.D., 2002. Toxicity, growth and accumulation relationships of
copper, lead and zinc in the grey mangrove Avicennia marina (Forsk.) Vierh. Mar. Environ. Res. 54, 65–84.
Mahdavi, E., Rahimi, A.E., Amini, H., 2012. Pb and Cd accumulation in Avicennia marina
from Qeshm Island, Persian Gulf. Iran. J. Fish. Sci. 11 (4), 867–875.
Naidoo, G., 1987. Effects of salinity and nitrogen on growth and water relations in the
mangrove. Avicennia marina (Forsk.) Vierh. New Phytol. 107, 317–325.
Naidoo, G., Hiralal, T., Naidoo, Y., 2014. Ecophysiological responses of the mangrove

Avicennia marina to trace metal contamination. Flora 209, 63–72.
Nazli, M., Hashim, N., 2010. Heavy metal concentrations in an important mangrove species, Sonneratia caseolaris, in Peninsular Malaysia. Environment Asia (Special Issue)
3, 50–55.
Nguyen, H.A., Richter, O., Huynh, D.H., Nguyen, K.L., Kolb, M., Nguyen, V.P., Tran, B.T.,
2014. Accumulation of Contaminants in Mangrove Species Rhizophora apiculata
Along ThiVai River in the South of Vietnam. EWATEC-COAST: Technologies for Environmental and Water Protection of Coastal Zones in Vietnam. Contributions to 4th
VNU – HCM International Conference for Environment and Natural Resources,
ICENR. Cuvillier, Göttingen, Germany (ISSN: 2363-7218. ISBN: 978-3-95404-852-6).
Oliveira, H., 2012. Chromium as an environmental pollutant: insights on induced plant
toxicity. Journal of Botany 375843 (8 pages).
Orcen, N., Nazarian, G., Gharibkhani, M., 2013. The responses of stomatal parameters and
SPAD value in Asia tobacco exposed to chromium. Pol. J. Environ. Stud. 22 (5),
1441–1447.
Ouyang, X., Guo, F., 2016. Paradigms of mangroves in treatment of anthropogenic wastewater pollution. Sci. Total Environ. 544, 971–979.
Panda, S., Patra, H., 2000. Nitrate and ammonium ions effect on the chromium toxicity in
developing wheat seedlings. Proceedings of the National Academy of Sciences India.
Section B, Biological Sciences 70 (1), 75–80 (ISSN 0369–8211).
Reef, R., Feller, I.C., Lovelock, C.E., 2010. Nutrition of mangroves. Tree Physiol. 30,
1148–1160. />Richter, O., Nguyen, H.A., Nguyen, K.L., Nguyen, V.P., Biester, H., Schmidt, P., 2016.
Phytoremediation by mangrove trees: experimental studies and model. Chem. Eng.
J. 294, 389–399.
Shanker, A., Cervantes, C., Loza-Tavera, H., Avudainayagam, S., 2005. Chromium toxicity in
plants. Environ. Int. 31, 739–753.
Shukla, O.P., Rai, U., Dubey, S., 2009. Involvement and interaction of microbial communities in the transformation and stabilization of chromium during the composting of
tannery effluent treated biomass of Vallisneria spiralis L. Bioresour. Technol. 100,
2198–2203.
Sodré, V., Caetano, V.S., Rocha, R.M., Carmo, F.L., Medici, L.O., Peixoto, R.S., ... Reinert, F.,
2013. Physiological aspects of mangrove (Laguncularia racemosa) grown in microcosms with oil-degrading bacteria and oil contaminated sediment. Environ. Pollut.
172, 243–249.
Sundaramoorthy, P., Chidambaram, A., Ganesh, K., Unnikannan, P., Baskaran, L., 2010.

Chromium stress in paddy: (i) nutrient status of paddy under chromium stress; (ii)
phytoremediation of chromium by aquatic and terrestrial weeds. Comptes Rendus Biologies 333, 597–607.
Takemura, T., Hanagata, N., Sugihara, K., Baba, S., Karube, I., Dubinsky, Z., 2000. Physiological and biochemical responses to salt stress in the mangrove, Bruguiera gymnorrhiza.
Aquat. Bot. 68, 15–28.
Tansel, B., Lee, M., Tansel, D.Z., 2013. Comparison of fate profiles of PAHs in soil, sediments and mangrove leaves after oil spills by QSAR and QSPR. Mar. Pollut. Bull.
73, 258–262.
Turner, M., Rust, R., 1971. Effect of chromium on growth and mineral nutrition of soybeans. Soil Sci. Soc. Am. Proc. 35, 55–758.
Usman, R.A., Alkredaa, R.S., Al-Wabel, M.I., 2013. Heavy metal contamination in sediments
and mangroves from the coast of Red Sea: Avicennia marina as potential metal
bioaccumulator. Ecotoxicol. Environ. Saf. 97, 263–270.
Ye, Y., Tam, N.F.-Y., Lu, C.-Y., Wong, Y.-S., 2005. Effects of salinity on germination, seedling
growth and physiology of three salt-secreting mangrove species. Aquat. Bot. 83,
193–205.
Yim, M., Tam, N., 1999. Effects of wastewater-borne heavy metals on mangrove plants
and soil microbial activities. Mar. Pollut. Bull. 39, 179–186.
Zhang, F.-Q., Wang, Y.-S., Lou, Z.-P., Dong, J.-D., 2007. Effect of heavy metal stress on antioxidative enzymes and lipid peroxidation in leaves and roots of two mangrove plant
seedlings (Kandelia candel and Bruguiera gymnorrhiza). Chemosphere 67, 44–50.