Home Journals Articles Updates About
CN
image
收藏切换
Model assessment of nutrient removal via planting Sesuvium portulacastrum in floating beds in eutrophic marine waters: the case of aquaculture areas of Dongshan Bay
收藏切换
PDF
Xuehai Liu1, 4, 5, Xinming Pu1, 3, 6, *, Donglian Luo2, Jing Lu1, 4, 5, Zili Liu1
Acta Oceanologica Sinica | 2019, 38(12) : 91 - 100
Less
收藏切换
Acta Oceanologica Sinica | 2019, 38(12): 91-100
• Marine Biology •
Model assessment of nutrient removal via planting Sesuvium portulacastrum in floating beds in eutrophic marine waters: the case of aquaculture areas of Dongshan Bay
Full
Xuehai Liu1, 4, 5, Xinming Pu1, 3, 6, *, Donglian Luo2, Jing Lu1, 4, 5, Zili Liu1
Affiliations
  • 1 First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China
  • 2 Fujian Fishery Research Institute, Xiamen 361012, China
  • 3 Laboratory of Marine Ecology and Environmental Science, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao 266237, China
  • 4 Laboratory for Regional Oceanography and Numerical Modeling, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao 266237, China
  • 5 Key Laboratory of Marine Science and Numerical Modeling, Ministry of Natural Resources, Qingdao 266061, China
  • 6 Key Laboratory of Science and Engineering for the Marine Ecological Environment, Ministry of Natural Resources, Qingdao 266061, China
Published: 2019-12-25 doi: 10.1007/s13131-019-1492-5
Outline
收藏切换

Many coastal seas are severely eutrophic and required to reduce nutrient concentrations to meet a certain water quality standard. We proposed a method for nutrient removal by planting Sesuvium portulacastrum at the water surface using the floating beds in the aquaculture area of the Dongshan Bay as an example, which is an important net-cage culture base in China and where dissolved inorganic nitrogen (DIN) and dissolved inorganic phosphate (DIP) reach 0.75 mg/L and 0.097 mg/L, respectively far exceeding China’s Grade IV water quality standards. Numerical simulations were taken using the ecological model, field observations and field plantation experimental results to assess the environmental restoration effects of planting S. portulacastrum at some certain spatial scales. Our field experiments suggested that the herbs can absorb 377 g/m2 nitrogen and 22.9 g/m2 phosphorus in eight months with an inserting density of ~60 shoot/m2. The numerical experiments show that the greater the plantation area is, the more nutrient removal. Plantation in ~12% of the study area could lower nutrients to the required Grade II standards, i.e., 0.2 mg/L<DIN≤0.3 mg/L and 0.015 mg/L<DIP≤0.03 mg/L. Here the phytoremediation method and results provide helpful references for environmental restoration in other eutrophic seas.

phytoremediaton  /  Sesuvium portulacastrum  /  ecological model  /  nutrient removal
Xuehai Liu, Xinming Pu, Donglian Luo, Jing Lu, Zili Liu. Model assessment of nutrient removal via planting Sesuvium portulacastrum in floating beds in eutrophic marine waters: the case of aquaculture areas of Dongshan Bay[J]. Acta Oceanologica Sinica, 2019 , 38 (12) : 91 -100 . DOI: 10.1007/s13131-019-1492-5
Full Text
Less
1 Introduction
Less
Irrational exploitation such as sea reclamation, pollution discharge, and excessive aquaculture has significantly deteriorated the ecological environment of many seas (Duarte et al., 2015) and resulted in the fact that some of environment conditions are no longer meeting the requirements of their functions in water quality (Islam and Tanaka, 2004; Li et al., 2015b). For seriously polluted marine areas involving some aquaculture bays, pollutant removal to restore and improve water conditions is an important issue that needs to be urgently resolved for sustainable utilization of marine resources (Wenzel, 2016).
The combination of fish or shellfish net-cage culturing and macroalgae raft farming is often an effective way to restore the aquaculture water (Neori et al., 2004; Li et al., 2015a). The cultured animals can devour excessive phytoplankton and other organic matters while the macroalgae such as Gracilaria lamaneiformis, Laminaria japonica, Porphyra tenera and the like can absorb excessive nutrients (Dumbauld et al., 2009; Forrest et al., 2009; Troost, 2010; Mineur et al., 2015). This kind of mixed cultivation has been well applied in multiple bays in China such as the Sanggou Bay and Xiangshan Bay (Wang et al., 2014; Wu et al., 2015).
In the intensive aquaculture area of the Dongshan Bay (Fig. 1) as the study area in this paper, which is an important aquaculture base in China, the dissolved inorganic nitrogen (DIN) and dissolved inorganic phosphate (DIP, i.e., PO4-P) have seriously exceeded the water quality standards (MEP, 2004) in recent years due to excessive aquaculture activities and poor water exchange conditions, leading to frequent mass deaths of cultured organisms and enormous fishery losses (Tang et al., 2005). This area is named as Bachimen and once was the China’s largest net-cage culture base with ~140 000 fish cages (Tang et al., 2005). According to China’s standard of seawater quality (Table 1), aquaculture-function waters require to meet Grade II standards (MEP, 2004). For DIN and DIP, the required concentration ranges are 0.2 mg/L<DIN≤0.3 mg/L and 0.015 mg/L<DIP≤0.03 mg/L. However, our observations in this area show that the concentrations of DIN and DIP are far over Grade IV standards. Therefore, it is urgent to find an effective method for regulation and control so as to decrease the severe eutrophication and restore the ecological environment. Nevertheless, low water transparency (<1 m, our observation) in this intensive aquaculture area makes it unsuitable for planting macroalgae in water.
On the basis of multiple plant selections and plantation experiments for phytoremediation, we chose to plant Sesuvium portulacastrum at the water surface using the floating beds to restore the environment. Sesuvium portulacastrum is a perennial, herbaceous facultative halophyte naturally growing in the tropical and subtropical coastal areas (Lonard and Judd, 1997; Fan et al., 2010; Slam et al., 2015). Researchers (Dou et al., 2011; Wu et al., 2011; Huang et al., 2013; Boxman et al., 2017) through their experimental studies have shown that the herb could efficiently absorb nitrogen and phosphate. For an example, Wu et al. (2011) reported that its nutrient absorption rate is 4.28–11.11 μmol/(g·d) for nitrogen and 0.14–0.74 μmol/(g·d) for phosphorus under a certain conditions. In addition, rich in calcium, iron and potassium, this herb is a wild vegetable with potential health care function and a good animal feed and has a great medicinal value (Kanth et al., 2009; Fan et al., 2010; Lokhande et al., 2013).
When choosing S. portulacastrum used for phytoremediation in eutrophic water, the essential concern is to explore a rational plantation scale so as to make nutrient reduced to a reasonable level. In the past, laboratory and field experiments have proved that this herb can absorb DIN and DIP and improve the environment (Huang et al., 2013; Boxman et al., 2017). However, no information in the literature is available about its practical application in large-scale plantation and its environmental restoration capacity. Without identification of the reasonable plantation scale in theory, it is not only expensive but also difficult to attain the goal if planting in an unjustified scale.
Numerical models can simulate 3D biochemical cycling across scales providing a quantitative hypothesis of process dynamics and rates among planktons, nutrients, discharged chemical matters and cultured organisms (Fulford et al., 2010; Wild-Allen et al., 2013; Jiang et al., 2015; Xia and Jiang, 2016). Using models, Fulford et al. (2010) evaluated the ecosystem response to oyster restoration and nutrient load reduction, and Ibarra et al. (2014) studied environmental impacts and carrying capacity of bivalve culture. So far, no study was reported about model assessment in phytoremediation of S. portulacastrum in eutrophic waters.
In this study, we used a 3D ecological model to quantitatively evaluate the ability of S. portulacastrum in reducing eutrophication and explore the rational plantation scale for phytoremediation, taking the intensive aquaculture area of the Dongshan Bay for example. To this end, based on interdisciplinary field observations and field plantation experimental results we built the ecological model for the aquaculture area, simulated the variations in key biochemical elements by planting S. portulacastrum in the restoration area at some certain scales, assessed the phytoremediation effect, and finally determined the reasonable plantation scale.
2 Data and methods
Less
2.1 Observation data
2.1.1 Environmental elements
The background concentrations of the biological and chemical elements used in this study were the average results of twice observations performed on May 17 and 26, 2013. Figure 1 shows the ten investigation sites, seven of which are distributed in the environmental restoration area (i.e., Bachimen), and three in the vicinity of the open boundary of the ecological model. The main environmental elements include temperature, salinity, DIN, DIP, chlorophyll a (Chl a) and particulate organic carbon (POC). The values of detritus were obtained by deducing Chl a from POC.
The hydrodynamic model validation was based on the data (water depths, velocities and directions of water currents) observed from 9:00 am on 6 September to 10:00 am on 7 September 2013 at Site A in Bachimen and Site B in the vicinity of the ecological model boundary (Fig. 1). Current velocities were measured using HXH03-1S ultrasonic Doppler flow meters. The water depths and currents were measured every hour.
2.1.2 Detritus release from aquaculture
According to our investigations in 2003 conducted by Fujian Fisheries Research Institute, there were a total of ~10 000 fish cages in the Dongshan Bay, among which ~6 000 were located in Bachimen area. In the bay, the culture produced 3 166.8 t residual baits (containing 46.9 t organic nitrogen and 41.8 t phosphate) and 933.5 t fish manure (containing 8.5 t organic nitrogen and 5.6 t phosphate) per year. Based on the proportion of the cage quantity in Bachimen to the total quantity in the bay, 60% of these organic detritus were allocated to Bachimen water.
2.2 Field experiments of planting S. portulacastrum
The plantation experiments were carried out in the environmental restoration area by Fujian Fisheries Research Institute in 2014–2015. About 3 000 terrestrial S. portulacastrumi plants were transplanted from the seashore saline-alkaline land to the floating beds. Figure 2 shows the photos of S. portulacastrumi planted in the area. The plants with ~15 cm length stems were inserted into the floating beds in an initial density of ~60 shoot/m2 (Fig. 2a) and reached the fast-growth period six months later (Fig. 2b).
According to our experiments, after eight months of growth from August 2014 to April 2015, the density of S. portulacastrumi was up to 1 488 plant/m2, the average length of stem and root was 128 cm and 52 cm, respectively, the average weight of stem and leaf was 63.3 g/plant, the average root weight was 39.2 g/plant, and the average fresh biomass was up to 152.5 kg/m2. In the period, the estimated nitrogen and phosphorus from water in the biomass were 377.0 g/m2 and 22.9 g/m2, respectively. Based on the experiments under laboratory conditions, Wu et al. (2011) reported that the nutrient absorption rate is 4.28–11.11 μmol/(g·d) for nitrogen and 0.14–0.74 μmol/(g·d) for phosphorus. According to their results, we estimated the total absorption in eight months is 225.0–580.9 g/m2 for nitrogen and 16.3–85.9 g/m2 for phosphorus. Our values from field plantation experiments are within their limits.
2.3 Model setup and numerical experiments
2.3.1 The ecological model
The ecological model was established based on the hydrodynamic model coupled with the biochemical process with consideration of the effects of aquaculture and phytoremediation. The principle for selecting model variables is that important biochemical processes should be reflected but not too complicated. Here the conversions among nutrients and organic matters are required to be simulated. In the case of a lack of zooplankton (Z) observation, we used the NPD type model with variables of nutrients (N), phytoplankton (P) and detritus (D), with consideration of the detritus release from aquaculture and the nutrient absorption by S. portulacastrum (Sp). In the model, nutrients include DIN and DIP with units of mg/L, phytoplankton is represented using the Chl a concentration with a unit of mg/m3, and detritus is expressed in mg/L (calculated by carbon). All mass conversions for each variable were computed as a source or sink in the hydrodynamic model to achieve simulations of biochemical processes. Figure 3 shows the conceptual sketch of the ecological model, and the process equations of the model are defined as
$\begin{split}& \frac{{\rm d}}{{{\rm d}t}}P = Grow\_P - Rsp\_P - Exc\_P - Mort\_P\;,\\& \frac{{\rm d}}{{{\rm d}t}}N = - Grow\_P + Rsp\_P + Rm\_D - Grow\_Sp\;,\\& \frac{{\rm d}}{{{\rm d}t}}D = Mort\_P - Rm\_D + Exc\_P + Exc\_Aq - Sk\_D\;.\end{split}$
where $\dfrac{{\rm d}}{{{\rm d}t}} = \dfrac{\partial }{{\partial t}} + u\dfrac{\partial }{{\partial x}} + v\dfrac{\partial }{{\partial y}} + w\dfrac{\partial }{{\partial z}} - \dfrac{\partial }{{\partial x}}\left( {{A_{\rm{h}}}\dfrac{\partial }{{\partial x}}} \right) - \dfrac{\partial }{{\partial y}} \left( {{A_{\rm{h}}}\dfrac{\partial }{{\partial y}}} \right) - $$ \dfrac{\partial }{{\partial z}}\left( {{A_{\rm{v}}}\dfrac{\partial }{{\partial z}}} \right) $. u, v and w are water current velocities. Ah and Av are the horizontal and vertical turbulent diffusivities. Through literature references (Hu et al., 2004, 2016; Liu et al., 2010, 2015) and model calibration by numerical experiments, the functions of biogeochemical processes are expressed as follows:
The algal growth $Grow\_P = {g_{\rm{P}}}{{\rm{e}}^{{\mu _{\rm{P}}}T}} \min \left[ \min \left( {\dfrac{{DIN}}{{DIN + {K_{\rm{N}}}}},{\rm{}}\dfrac{{DIP}}{{DIP + {K_{\rm{P}}}}}} \right) ,\right.$$\left.\dfrac{I}{{{I_{{\rm{opt}}}}}}{{\rm{e}}^{\left( {1 - \frac{I}{{{I_{{\rm{opt}}}}}}} \right)}} \right]P $, where the maximum algal growth rate at 0°C is gP=0.062 h–1, and the exponent coefficient related to water temperature T is μP=0.05°C–1 (Liu et al., 2015). Half-saturation constants of nutrients are KN=2.0 μmol/L and KP=0.125 μmol/L. I=I0 e–kz is light intensity, Iopt=66 W/m2 is the optimum radiation, I0 is the solar radiation with effective photosynthesis at sea-surface, and the reduction coefficient of light is given by k=k0+k1P+k2P2/3 (Radach and Moll, 1993) with k0=0.65 m–1, k1=0.008 8 m2/mg and k2=0.054 m/mg2/3.
The metabolism of phytoplankton is expressed as $Rsp\_P = $${m_{\rm{P}}}{{\rm{e}}^{{\gamma _{\rm{P}}}T}}P$, where mP=0.001 h–1 is the maximum respiration rate at 0°C and γP=0.063 3°C–1 is the exponent coefficient related to temperature (Liu et al., 2010).
The algal death is $Mort\_P = {d_{\rm{P}}}{{\rm e}^{{\gamma _{\rm{d}}}T}}P$(Hu et al., 2016), where the natural mortality rate at 0°C is given by dP=0.009 5 h–1 and γd=0.065°C–1 is the exponent coefficient.
The algal excretion Exc_P=EPGrow_P, where EP=0.25 is the excretive coefficient.
The detritus remineralization $Rm\_D= em \cdot {{\rm{e}}^{{\gamma _{\rm{D}}}T}}D$ (Hu et al., 2016), where em=0.002 1 is the remineralization rate at 0°C and γD=0.06°C–1 is the exponent coefficient.
The sinking of detritus $Sk\_D = {W_{\rm{S}}}\dfrac{{\partial D}}{{\partial z}}$, where WS=5×10–6 m/s is the sinking velocity (Hu et al., 2004).
The solar radiation I0=ISPa(1–Ra)F, where the reflective coefficient at sea surface Ra=0.04 (Liu et al., 2010), the penetration ratio through cloud Pa=0.75, and the light energy rate for photosynthesis F=0.47. The radiation above cloud is given by ${I_{\rm{S}}} = I'$(sind(ψ)sind(n)+cosd(ψ)cosd(n)cosd(λ)), when h1<h<h2 (else IS=0), where $I'$=I10(1.006+0.334 3 cosd(φ)+0.001 sind(φ)), ψ=arcsin (sind(23.5)sin(2π(ID–80)/365)), φ=2π(ID–2.84)/365, λ=–15h–E+292.5, ${h_1} = \left( {292.5 - E - \lambda '} \right)/15$, ${h_2} = \left( {292.5 - E + \lambda '} \right)/$15, I10=1 367 W/m2, $\lambda '$=arccosd(-tand(n)tand(φ)), ID is the day’s sequence number in a year, and n, E and h are latitude, longitude and Beijing standard hour, respectively (Hu et al., 2016).
The specific ratio of Chl a to nitrogen is 1.68 g/mol, the ratio of carbon to nitrogen is 75.6 g/mol in detritus, and the mole ratio of nitrogen to phosphorus is 16 for phytoplankton and detritus (Liu et al., 2010, 2015).
Exc_Aq is the detritus release from aquaculture, and Grow_Sp is the absorption of nutrients by S. portulacastrum. Their values are given from our field experimental results shown in Section 2.2 and Section 2.3.3.
2.3.2 Model configuration
The biochemical processes were simulated using a physical-biogeochemical coupled plankton ecosystem model (PECOM) (Wan et al., 2001) which has been well used in some seas in China and abroad (e.g., Hu et al., 2004, 2016; Liu et al., 2010, 2015). Compared to other ecological models with more complex biochemical processes (e.g., Anderson et al., 2010; Jiang et al., 2015; Xia et al., 2016), our model is a type of NPZD mainly focusing on the conversation process among these biochemical variables and satisfies the demand of this study. Besides, Princeton Ocean Model (POM) (Mellor, 2004) was used to reproduce the physical process.
In the study bay, tidal currents are the prominent physical process (Zheng et al., 2013), so wind-circulation and baroclinicity related to salinity were not considered in the hydrodynamic model. In the model, a water-level forcing is adopted at the open boundary,
$H = {h_0} + {\sum A _i}\cos ({w_i}t - {\theta _i} + {\phi _i}),$
where h0, Ai, θi and ϕi represent the average depth, amplitude, phase lag and initial phase of tide constituents, respectively. Four component tides are M2, S2, O1 and K1. The modeled region covers the whole of the Dongshan Bay (Fig. 1), and the open boundary is at the bay mouth. The harmonic constants of these tides in Eq. (2) were obtained from interpolating harmonic tidal results of an operational high-resolution wave-tide-circulation coupled ocean model for the China seas (Wang et al., 2016; Zhao et al., 2018). The grid distances in horizontal directions of east-west and south-north are both ~200 m. In vertical, five layers were equally split. The calculation step is 2 s for the outer mode and 10 s for the internal mode.
The investigated results of biochemical variables, salinity and temperature were used for initial values at each grid point by performing spatial inverse-distance interpolation. An upstream advection scheme (Mellor, 2004) was adopted at the open boundary where the values were given by averaging observations at three outmost stations. With same grids as the physical model, the ecological model area covers the west of the long dash line (shown in Fig. 2). The circumstances in May of 2013 were simulated with a calculation step of 0.5 h. In order to make the biochemical variables do not change following the spring and neap tides, the model simulated the moderate tide situation. Because the model was verified using the background concentration in May of 2013 for evaluating phytoremediation, the simulation was conducted under the light intensity condition of this month.
2.3.3 Numerical experiments for the assessment
After verification of the background ecological model, the phytoremediation effects of planting S. portulacastrum was then numerically assessed by involving its absorption of nutrients into the environmental restoration area as shown in Fig. 1. The absorption rates were obtained by equally dividing the values of 377.0 g/m2 (N) and 22.9 g/m2 (P) by the growing period of 240 d into each hour time in the model.
Some area proportions (from 5% to 20% with an interval of 1%) of the whole restoration area used for planting S. portulacastrum were taken in numerical experiments for the assessment. To compare the phytoremediation effects, the numerical experiments were taken within a same time period as that of the background model.
3 Results and discussion
Less
3.1 The observed background concentrations
Figure 4 shows the observed depth-averaged background concentrations of DIN, DIP, Chl a and detritus. It can be seen that the nutrient concentration is relatively high in Bachimen, where the average DIN and DIP is 0.75 mg/L and 0.097 mg/L, respectively, much higher than Grade IV standards, i.e., 0.4 mg/L<DIN≤0.5 mg/L and 0.03 mg/L<DIP≤0.045 mg/L (MEP, 2004), and also the mean concentrations in the Dongshan Bay where DIN was 0.30–0.40 mg/L and DIP was 0.04–0.06 mg/L (Chen et al., 2014). The water in Bachimen is of severe pollution and eutrophication. The concentrations of Chl a and organic detritus at the Bachimen mouth are higher than those in the water enclosed by Bachimen and the outer area. Their average concentrations in Bachimen are 4.56 mg/m3 and 3.14 mg/L, respectively. Higher Chl a and detritus concentrations, especially Chl a, are conducive to food intake of aquatic organisms. As the main function of Bachimen is aquaculture, the water should meet Grade II quality standards. The concentrations of nutrients have exceeded environmental carrying capacities and need to be reduced to the required ones.
3.2 Model validation
3.2.1 Water levels and currents
Figure 5 shows the simulated and observed water levels, surface current velocities and surface current directions at the two observation sites. It can be seen that the simulated water levels and velocities are consistent with observations, and the simulated curves are basically coincided with the observed. Overall, the model objectively reproduces the tidal process in the study area. The maximum tidal range of the two observation sites is ~3.5 m and the maximum current velocity is ~0.46 m/s in the inner of Bachimen, and 0.7 m/s (relatively high) in the outer. Moreover, the model and observations indicate that the tidal process is the dominant hydrodynamic process in the Dongshan Bay, which tallies with the study by Zheng et al. (2013).
The observed and modeled results indicate that the tide in the study area is semidiurnal and shows a characteristic of strong reciprocating flows. The observations by Zheng et al. (2009) also show these features. The main flow directions at the two measurement sites are from northeast to southwest. The northeastward is the flood direction and the southwestward is the ebb direction. Besides, the maximum velocity at the flooding period is smaller than that at the ebbing. The overall tidal current flows along the coastline with velocities which are higher at the bay mouth than those in the inner bay and higher in the middle deep area than those of the near-shore shallow water. These regimes also have been observed by Zheng et al. (2009, 2013).
3.2.2 Nutrients and organic matters
Figure 6 shows the modeled distributions of the depth-averaged background DIN, DIP, Chl a and detritus, and Fig. 7 compares the modeled concentrations of them and the observed at seven investigation sites which are marked from west to east in the rectangle in Fig. 1.
Figure 6 indicates that the simulated DIN and DIP are the highest in the inner of Bachimen, where the average DIN is ~0.82 mg/L (nearly equal to the observed value of 0.75 mg/L) and the average DIP reaches 0.10 mg/L (nearly equal to the observed value of 0.097 mg/L), which are far higher than Grade IV standards. With the longer distance from Bachimen and the water increasing depth, the nutrient concentrations gradually decrease. Chl a and detritus are relatively high at the mouth of Bachimen. In Bachimen, the average concentrations of Chl a and detritus are 4.8 mg/m3 and 3.0 mg/L, respectively, which are very close to the observed values of 4.56 mg/m3 and 3.14 mg/L. Due to the poor water exchange, nutrient concentrations are high in Bachimen. Moreover, due to high transparency, appropriate depth and favorable nutrient composition to the algal growth, Chl a is the highest at the mouth of Bachimen.
It can be seen from Figs 4 and 6 that the simulated values and distributions of DIN, DIP, Chl a and detritus are in good agreement with observations. Their average concentrations are close to the observed, which can be also seen from Fig. 7 by comparing the simulations and the observations at the seven sites. The modeled values for the four variables are around the observations. The average deviation between the simulations and observations of these sites is 13% for DIN and 15% for DIP. In general, the model effectively describes the background concentration of key ecological elements in the study area and can be used to assess phytoremediation.
3.3 Nutrient removal by S. portulacastrum
The laboratory and field experiments have proved that S. portulacastrum can absorb N and P and improve marine environment (Huang et al., 2013; Boxman et al., 2017). However, no information in literature was available about its practical application in large-scale plantation and environmental restoration capacity in seawater. To evaluate the eutrophic decease in the restoration area beforehand, the numerical simulations were taken for some plantation scales (given in Section 2.3.3). It is proved that S. portulacastrum can absorb excessive nutrients to efficiently restore the environment. Figures 8a and b show the simulated changing processes of DIN and DIP concentrations under circumstances of plantation within 70 d from 1 May 2013, and Fig. 8c shows the nutrient concentrations after phytoremediation.
The numerical experiments show that if the plantation scale is 5%–7% of the restoration area, the concentration of DIN is still higher than the Grade IV standard and the water is still of severe pollution. If the plantation scale increases to 9%, DIN is reduced to 0.41 mg/L, in line with Grade IV, indicating that the water is moderately polluted. If the scale increases to 10%–12%, DIN is reduced to the Grade III standard (0.3 mg/L<DIN≤0.4 mg/L) and the water is mildly polluted. If the scale increases to 13%–16%, the concentration is reduced to the required for Grade II, and the water becomes relative clean. If the scale increases to 17%, the concentration is reduced to ≤0.2 mg/L, in line with Grade I and the water is clean.
For the DIP, if the plantation scale is 9%, the average concentration is simulated to be 0.046 mg/L and the water is severely polluted. If the plantation scale increases to 10% and 11%, the concentration is reduced to 0.041 mg/L and 0.035 mg/L, respectively, in line with the Grade IV standard. If the scale increases to 12%–17%, DIP is reduced to meet the Grade III standard and the required Grade II standard. If the scale increases to 18%, the concentration is reduced to 0.015 mg/L, in line with the Grade I standard (≤0.015 mg/L), and the water becomes clean.
Overall, if the plantation scale is less than 9%, the restoration area is still severely polluted. If the plantation scale is 11%, the overall water quality in the area is of Grade IV and the water is moderately polluted. If the scale is 12%, the nutrient level meets the Grade III standard. If the plantation scale reaches 13%, DIN and DIP concentrations are reduced to 0.28 mg/L and 0.027 mg/L, respectively, meeting the required standard of Grade II. If the scale reaches 18%, the water quality is Grade I.
Numerical assessments indicate that planting S. portulacastrum in 13% of the restoration area can reduce nutrient concentrations to the levels meeting Grade II standards suitable for aquaculture.
3.4 The effects of planting S. portulacastrum on organic matters
As the main function of the study area is fish culture and the fish need to intake organic matters in water, especially phytoplankton, a higher organic content can reduce the dependence on feed and the brought pollution. Planting S. portulacastrum could decrease nutrients and bring about their resultant restrictions to algal growth, thereby reducing the biomass of phytoplankton and the primary productivity. Therefore, when decreasing the levels of nutrients, it is better not to significantly reduce the concentrations of organic matters.
Our model shows the simulated Chl a and detritus concentrations after lowering the nutrient level via planting S. portulacastrum (Table 2). When the plantation scale is 10%, 12%, 13%, 14% and 15%, Chl a is reduced to 3.4, 2.8, 2.4, 1.8 and 1.4 mg/m3, respectively, and the average detritus is 2.0, 1.4, 1.1, 0.85 and 0.74 mg/L, respectively. Obviously, a direct result of planting S. portulacastrum is that the algal growth is limited, resulting in a decrease of the concentrations of organic matters. The larger the plantation scale is, the lower the concentrations. The laboratory tank experiments (Lin et al., 2011) and field campaign (Huang et al., 2013) also proved that S. portulacastrum could remove organic matters in water. In order to facilitate fish-culturing, the optimal acreage for plantation should be defined to decease the nutrient concentration to the required level, while not significantly decrease organic matters. When the plantation scale is 13%, Chl a is decreased to about half of the initial concentration. Although the plantation at this scale has a significant impact on phytoplankton, its concentration is not reduced too much. Besides, fish culture is mainly dependent on feed rather than phytoplankton in water. Therefore, 13% acreage for planting S. portulacastrum is a reasonable scale.
3.5 Combination of planting S. portulacastrum and controlling the aquaculture scale
To facilitate the plantation, we proposed to reduce a certain scale of cage-culturing and plant S. portulacastrum on the top of these cages. The ecological model was used to simulate the restorative effects of reducing the aquaculture scale by 10%, 11%, 12% and 13%, respectively (meanwhile planting the herbs on these idle cages and the floating beds, the total plantation area is equivalent to this proportion of the restoration area). In the model, the reduction of aquaculture means decreasing the flux of organic detritus by the corresponding proportion. The simulation results show that under this method, when the plantation scale is 10%, 11%, 12% and 13%, the average concentrations of DIN is reduced to 0.35, 0.33, 0.3 and 0.26 mg/L, respectively, and DIP is reduced to 0.032, 0.028, 0.025 and 0.021 mg/L, respectively (Table 3). The water quality after phytoremediation at the latter two scales, rather than the former two, meets the Grade II standard.
Therefore, we proposed to cut the scale of fish cage culturing by 12% and use these culturing facilities and the floating beds to plant S. portulacastrum in 12% of the restoration area. By doing so, we can restore the nutrient-polluted water to meet the Grade II standard. In this circumstance, the concentrations of Chl a and detritus are 3.0 mg/m3 and 1.3 mg/L, respectively, higher than those of planting S. portulacastrum at 13% of the area without reducing aquaculture.
4 Conclusions
Less
In this study, the phytoremediation for the severe eutrophic water via planting S. portulacastrum in the floating beds was evaluated using the ecological model based on field surveys and field plantation experimental results. The field experiments show that S. portulacastrum can absorb 377 g/m2 nitrogen and 22.9 g/m2 phosphorus in eight months with an initial inserting density of ~60 shoot/m2. The model assessment suggests that for the aquaculture area of the Dongshan Bay where the nutrient level with 0.75 mg/L nitrogen and 0.097 mg/L phosphorus far exceeded China’s Grade IV water quality standards, if S. portulacastrum is planted at 13% of the area in the above inserting density, or alternatively at 12% combining with reduction of aquaculture by this percent, DIN and DIP concentrations will be reduced to the Grade II standards (0.2 mg/L<DIN≤0.3 mg/L, 0.015 mg/L<DIP≤0.03 mg/L, suitable for aquaculture). These assessment results provide the basis for the future plantation for environmental restoration.
Taking the case of the intensive aquaculture area of the Dongshan Bay, our study indicates that S. portulacastrum has a strong ability of nutrient removal. The study shows a new method to restore marine environment for eutrophic waters by planting this herb, and further provides a helpful reference for determining the reasonable plantation scale for other eutrophic waters.
Funding
Less
  • The Public Science and Technology Research Funds Projects of Ocean under contract Nos 201305043 and 201205009; the National Basic Research Program (973 Program) of China under contract No. 2015CB755904; the National Natural Science Foundation of China under contract No. 41276036.
References
Less
Anderson T R, Gentleman W C, Sinha B. 2010. Influence of grazing formulations on the emergent properties of a complex ecosystem model in a global ocean general circulation model. Progress in Oceanography, 87(1–4): 201–213, doi: 10.1016/j.pocean.2010.06.003
Boxman S E, Nystrom M, Capodice J C, et al. 2017. Effect of support medium, hydraulic loading rate and plant density on water quality and growth of halophytes in marine aquaponic systems. Aquaculture Research, 48(5): 2463–2477, doi: 10.1111/are.13083
Chen Baohong, Ji Weidong, Zhou Kaiwen, et al. 2014. Nutrient and eutrophication characteristics of the Dongshan Bay, South China. Chinese Journal of Oceanology and Limnology, 32(4): 886–898, doi: 10.1007/s00343-014-3214-3
Dou Bixia, Huang Jianrong, Li Lianchun, et al. 2011. Research on effects of nutrient and phosphate removal from marine aquaculture system by Sesuvium portulacastrum. Journal of Hydroecology (in Chinese), 32(5): 94–99
Duarte C M, Borja A, Carstensen J, et al. 2015. Paradigms in the recovery of estuarine and coastal ecosystems. Estuaries and Coasts, 38(4): 1202–1212, doi: 10.1007/s12237-013-9750-9
Dumbauld B R, Ruesink J L, Rumrill S S. 2009. The ecological role of bivalve shellfish aquaculture in the estuarine environment: a review with application to oyster and clam culture in West Coast (USA) estuaries. Aquaculture, 290(3–4): 196–223, doi: 10.1016/j.aquaculture.2009.02.033
Fan Wei, Li Wenjing, Fu Gui, et al. 2010. Sesuvium portulacastrum L., a promising halophyte in research and application. Journal of Tropical and Subtropical Botany (in Chinese), 18(6): 689–695
Forrest B M, Keeley N B, Hopkins G A, et al. 2009. Bivalve aquaculture in estuaries: Review and synthesis of oyster cultivation effects. Aquaculture, 298(1–2): 1–15, doi: 10.1016/j.aquaculture.2009.09.032
Fulford R S, Breitburg D L, Luckenbach M, et al. 2010. Evaluating ecosystem response to oyster restoration and nutrient load reduction with a multispecies bioenergetics model. Ecological Applications, 20(4): 915–934, doi: 10.1890/08-1796.1
Hu Haoguo, Wan Zenwen, Yuan Yeli. 2004. Simulation of seasonal variation of phytoplankton in the Southern Huanghai (Yellow) Sea and analysis on its influential factors. Haiyang Xuebao (in Chinese), 26(6): 74–88
Hu Haoguo, Wang Jia, Liu Hui, et al. 2016. Simulation of phytoplankton distribution and variation in the Bering-Chukchi Sea using a 3-D physical-biological model. Journal of Geophysical Research, 121(6): 4041–4055
Huang Lingfeng, Zhuo Jianfu, Guo Weidong, et al. 2013. Tracing organic matter removal in polluted coastal waters via floating bed phytoremediation. Marine Pollution Bulletin, 71(1–2): 74–82, doi: 10.1016/j.marpolbul.2013.03.032
Ibarra D A, Fennel K, Cullen J J. 2014. Coupling 3-D Eulerian bio-physics (ROMS) with individual-based shellfish ecophysiology (SHELL-E): a hybrid model for carrying capacity and environmental impacts of bivalve aquaculture. Ecological Modelling, 273: 63–78, doi: 10.1016/j.ecolmodel.2013.10.024
Islam S, Tanaka M. 2004. Impacts of pollution on coastal and marine ecosystems including coastal and marine fisheries and approach for management: a review and synthesis. Marine Pollution Bulletin, 48(7–8): 624–649, doi: 10.1016/j.marpolbul.2003.12.004
Jiang Long, Xia Meng, Ludsin S A, et al. 2015. Biophysical modeling assessment of the drivers for plankton dynamics in dreissenid-colonized western Lake Erie. Ecological Modelling, 308: 18–33, doi: 10.1016/j.ecolmodel.2015.04.004
Kanth S V, Preethi S, Keerthi B, et al. 2009. Studies on the use of Sesuvium portulacastrum—Part II: Preservation of skins. Journal of the American Leather Chemists Association, 104(1): 25–32
Li Chunqiang, Yu Xiaoling, Peng Ming. 2015a. The roles of polyculture with Eucheuma gelatinae and Gafrarium tumidum in purification of eutrophic seawater and control of algae bloom. Marine Pollution Bulletin, 101(2): 750–757, doi: 10.1016/j.marpolbul.2015.10.001
Li Keqiang, Zhang Li, Li Yan, et al. 2015b. A three-dimensional water quality model to evaluate the environmental capacity of nitrogen and phosphorus in Jiaozhou Bay, China. Marine Pollution Bulletin, 91(1): 306–316, doi: 10.1016/j.marpolbul.2014.11.020
Lin Yongqing, Wu Jiaxin, Zheng Xinqing, et al. 2011. Removal of suspended particulate matter in seawater by Sesuvium portulacastrum L. planted in floating-bed. Journal of Xiamen University (Natural Science) (in Chinese), 50(5): 909–914
Liu Xuehai, Li Ruixiang, Yuan Yeli. 2010. Numerical experiments of HAB inducement in Qingdao adjacent coastal area. Acta Oceanologica Sinica, 29(5): 109–120, doi: 10.1007/s13131-010-0069-0
Liu Xuehai, Wang Zongling, Zhang Mingliang, et al. 2015. Carrying capacity of manila clam Ruditapes philippinarum in Jiaozhou Bay estimated by an ecosystem model. Fisheries Science (in Chinese), 34(12): 733–740
Lokhande V H, Gor B K, Desai N S, et al. 2013. Sesuvium portulacastrum, a plant for drought, salt stress, sand fixation, food and phytoremediation. A review. Agronomy for Sustainable Development, 33(2): 329–348, doi: 10.1007/s13593-012-0113-x
Lonard R I, Judd F W. 1997. The biological flora of coastal dunes and wetlands. Sesuvium portulacastrum (L.) L. Journal of Coastal Research, 13(1): 96–104
Mellor G L. 2004. User’s guide for a three-dimensional, primitive equation, numerical ocean model. Technical report, Program in Atmospheric and Oceanic Sciences. Princeton: Princeton University, 56
MEP (Ministry of Environmental Protection of the People’s Republic of China). 2004. GB 3097–1997 Marine water quality standard (in Chinese). Beijing: Standard Press of China, 15–16
Mineur F, Arenas F, Assis J, et al. 2015. European seaweeds under pressure: consequences for communities and ecosystem functioning. Journal of Sea Research, 98: 91–108, doi: 10.1016/j.seares.2014.11.004
Neori A, Chopin T, Troell M, et al. 2004. Integrated aquaculture: rationale, evolution and state of the art emphasizing seaweed biofiltration in modern mariculture. Aquaculture, 231(1–4): 361–391, doi: 10.1016/j.aquaculture.2003.11.015
Radach G, Moll A. 1993. Estimation of the variability of production by simulating annual cycles of phytoplankton in the central North Sea. Progress in Oceanography, 31(4): 339–419, doi: 10.1016/0079-6611(93)90001-T
Slama I, M’Rabet R, Ksouri R, et al. 2015. Water deficit stress applied only or combined with salinity affects physiological parameters and antioxidant capacity in Sesuvium portulacastrum. Flora- Morphology, Distribution, Functional Ecology of Plants, 213: 69–76, doi: 10.1016/j.flora.2015.04.004
Tang Kunxian, Jiao Nianzhi, You Xiuping, et al. 2005. Bioremediation of Gracilaria lichenoides in fish cage-farming areas. Journal of Fishery Sciences of China (in Chinese), 12(2): 156–161
Troost K. 2010. Causes and effects of a highly successful marine invasion: case-study of the introduced Pacific oyster Crassostrea gigas in continental NW European estuaries. Journal of Sea Research, 64(3): 145–165, doi: 10.1016/j.seares.2010.02.004
Wan Zenwen, Yuan Yeli, Wang Jia. 2001. PECOM Manual: A Physical-Biogeochemical Coupled Plankton Ecosystem Model. Technical report. Qingdao: First Institute of Oceanography, SOA
Wang Xilong, Du Jinzhou, Ji Tao, et al. 2014. An estimation of nutrient fluxes via submarine groundwater discharge into the Sanggou Bay-a typical multi-species culture ecosystem in China. Marine Chemistry, 167: 113–122, doi: 10.1016/j.marchem.2014.07.002
Wang Guansuo, Zhao Chang, Xu Jiangling, et al. 2016. Verification of an operational ocean circulation-surface wave coupled forecasting system for the China’s seas. Acta Oceanologica Sinica, 35(2): 19–28, doi: 10.1007/s13131-016-0810-4
Wenzel B. 2016. Organizing coordination in fisheries and marine environmental management: Patterns of organizational change in Europe. Ocean & Coastal Management, 134: 194–206
Wild-Allen K, Skerratt J H, Whitehead J, et al. 2013. Mechanisms driving estuarine water quality: a 3D biogeochemical model for informed management. Estuarine, Coastal and Shelf Science, 135: 33–45, doi: 10.1016/j.ecss.2013.04.009
Wu Hailong, Huo Yuanzi, Han Fang, et al. 2015. Bioremediation using Gracilaria chouae co-cultured with Sparus macrocephalus to manage the nitrogen and phosphorous balance in an IMTA system in Xiangshan Bay, China. Marine Pollution Bulletin, 91(1): 272–279, doi: 10.1016/j.marpolbul.2014.11.032
Wu Xiaojie, Shen Yuchun, Ye Ning, et al. 2011. The preliminary study of Sesuvium portulacastrum on the nitrogen and phosphorus absorption rate. Chinese Agricultural Science Bulletin (in Chinese), 27(20): 92–96
Xia Meng, Jiang Long. 2016. Application of an unstructured grid-based water quality model to Chesapeake Bay and its adjacent coastal ocean. Journal of Marine Science and Engineering, 4(3): 52, doi: 10.3390/jmse4030052
Zhao Chang, Yin Liping, Wang Guansuo, et al. 2018. The Modelling of Ulva prolifera transport in the Yellow Sea and its application. Oceanologia et Limnologia Sinica (in Chinese), 49(5): 1075–1083
Zheng Binxin, Li Jiufa, Liao Kangming, et al. 2013. Study on the characteristics of tidal and residual currents in the Dongshan Bay of Fujian. Advances in Marine Science (in Chinese), 31(2): 188–195
Zheng Binxin, Liao Kangming, Zeng Zhi, et al. 2009. Study on the characteristics of tidal current dynamic in Dongshan Bay. Journal of Oceanography in Taiwan Strait (in Chinese), 28(4): 546–552
Appendix
Less
Year 2019 volume 38 Issue 12
PDF
51
29
Cite this Article
BibTeX
Article Info
doi: 10.1007/s13131-019-1492-5
  • Receive Date：2018-12-21
  • Online Date：2026-04-01
  • Published：2019-12-25
Article Data
Affiliations
History
  • Received：2018-12-21
  • Accepted：2019-05-30
Funding
The Public Science and Technology Research Funds Projects of Ocean under contract Nos 201305043 and 201205009; the National Basic Research Program (973 Program) of China under contract No. 2015CB755904; the National Natural Science Foundation of China under contract No. 41276036.
Affiliations
    1 First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China
    2 Fujian Fishery Research Institute, Xiamen 361012, China
    3 Laboratory of Marine Ecology and Environmental Science, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao 266237, China
    4 Laboratory for Regional Oceanography and Numerical Modeling, Pilot National Laboratory for Marine Science and Technology (Qingdao), Qingdao 266237, China
    5 Key Laboratory of Marine Science and Numerical Modeling, Ministry of Natural Resources, Qingdao 266061, China
    6 Key Laboratory of Science and Engineering for the Marine Ecological Environment, Ministry of Natural Resources, Qingdao 266061, China

Corresponding:

References
Share
https://castjournals.cast.org.cn/joweb/aos/EN/10.1007/s13131-019-1492-5
Share to
QR

Scan QR to access full text

Cite this article
BibTeX
Citations
表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
  • BibTeX
  • EndNote
  • RefWorks
  • TxT
Links
Articles
Latest Articles
Most Read
Collections
Updates
Events
News
Multimedia
About
About Us
Contact
No. 86 Xueyuan South Road, Haidian District, Beijing
100081
010-62199257
qkjq@cast.org.cn

Copyright © 2025 China Association for Science and Technology. All rights reserved. For all open access content, the relevant licensing terms apply.
Sponsored by the Office of the Leading Group for Cybersecurity and Informatization of CAST, and supported by Science and Technology Review Publishing House