Bingxin Huang, Yue Chu, Rongjuan Wang, Yixiao Wang, Lanping Ding. Effects of main ecological factors on the growth of marine green alga Caulerpa sertularioides using the response surface methodology[J]. Acta Oceanologica Sinica, 2023, 42(11): 90-97. doi: 10.1007/s13131-023-2171-0
Citation: Xing Huang, Xueping Wang, Xiuzhen Li, Zhongzheng Yan, Yongguang Sun. Occurrence and transfer of heavy metals in sediments and plants of Aegiceras corniculatum community in the Qinzhou Bay, southwestern China[J]. Acta Oceanologica Sinica, 2020, 39(2): 79-88. doi: 10.1007/s13131-020-1555-7

Occurrence and transfer of heavy metals in sediments and plants of Aegiceras corniculatum community in the Qinzhou Bay, southwestern China

doi: 10.1007/s13131-020-1555-7
Funds:  The National Basic Research Program (973 Program) of China under contract No. 2010CB951203; the National Natural Science Foundation of China under contract Nos 41201179, 41201525, 41907320 and 41901226; the foundation of the State Key Laboratory of Estuarine and Coastal Research under contract No. SKLEC-KF-201309; the foundations of Guangxi Key Laboratory of Marine Disaster in the Beibu Gulf, Beibu Gulf University under contract Nos 2018TS01 and 2018TS04.
More Information
  • Corresponding author: E-mail: xzli@sklec.ecnu.edu.cn
  • Received Date: 2019-03-12
  • Accepted Date: 2019-05-06
  • Available Online: 2020-04-21
  • Publish Date: 2020-02-25
  • Mangrove wetlands can reduce heavy metal pollution by trapping heavy metals. In this study, the concentration, transport and bioaccumulation of Cr, Cd, Cu, Zn and Pb in the sediments and different parts of Aegiceras corniculatum at four different sites in the Qinzhou Bay in southwestern China were investigated. The results showed that although the potential ecological risk of all five heavy metals was slight, the concentration of Cr was at a moderate pollution level due to the emissions of industries and aquaculture waste water. Core sediment records indicated that the concentrations of heavy metals at the depth of 0–20 cm were relatively high, showing an increasing trend of heavy metals over the past 20–30 years. Cr, Cu, Pb and Cd accumulated mainly in the roots of A. corniculatum, while Zn accumulated mainly in the stems. Aegiceras corniculatum showed the strongest transport capacity for Zn and Cu and the strongest bioaccumulation ability for Cd. Compared with other mangrove communities, A. corniculatum can be chosen as a restoration species in tropical and subtropical coastal zones polluted by Zn, Cu and Cd.
  • †These authors contributed equally to this work.
  • Caulerpa is a genus of marine green algae distributed mainly in tropical and subtropical waters and has more than 90 species. The thalli contain caulerpin, an alkaloid extracted from Caulerpa. The compound has anti-inflammatory activity, is rich in minerals and vitamins (Jiang et al., 2011), and has great value in application in the industries of food (de Gaillande et al., 2017), medicine (Gao, 2014; Chaves Filho et al., 2022), high-grade fertilizer (Wang, 2010), energy (Huang, 2012), and bioremediation of marine waters (Landi et al., 2022). Physiological and ecological studies have mostly focused on some environmental factors such as temperature (Li et al., 2022; Shi et al., 2022), salinity (Guo et al., 2015; Cai, 2021), irradiances (Stuthmann et al., 2021; Zhong et al., 2021), nutrient (Liu et al., 2016; Zhang et al., 2020) and heavy metal salt stress (Pang et al., 2021) for cultivated Caulerpa lentillifera in Southeast Asian countries. However, due to invasive nature, some species caused ecological problems in the Mediterranean, America, Australia, and other waters in the world (Davis et al., 1997; Meinesz et al., 2001; Verlaque et al., 2004; Anderson, 2005; Lapointe et al., 2005; Klein and Verlaque, 2008; Kang et al., 2021). In a eutrophic sea where some species boom excessively, vast and thick algal mats would form on the seafloor, which hamper sunlight transmission and seawater exchange, posing a large threat to local mariculture activities or coral reef growth (Williams and Schroeder, 2004). In environments with poor water quality, these blooming species have a strong ability to absorb nutrients from water and sediments (Williams, 1984; Kolar and Lodge, 2001). Therefore, they have the potential for sewage treatment (Landi et al., 2022).

    Some of the Caulerpa species are highly invasive such as Caulerpa taxifolia and Caulerpa racemosa (Anderson, 2005; Fernández and Cortés, 2005; Liu et al., 2019). Caulerpa sertularioides (Anderson, 2005; Fernández and Cortés, 2005), also known as green feather alga, is similar in appearance and has the same mode of reproduction as C. taxifolia, including well-developed stolons and rhizoids for easy attachment to substrates (Fig. 1). It is highly invasive because its fragment of stolons can grow continuously from its apices (Smith and Walters, 1999), and spread quickly in the tropical northeast Pacific (Withgott, 2002). Its bloom damaged the coral reefs seriously (Smith et al., 2010). Some scientists believed that the high invasion ability might be related to a favorite temperature and the quick proliferation of thallus fragments (Fernández and Cortés, 2005). Some affected countries (such as Spain and France) are very concerned about the invasion and have tried to eradicate or control it by banning the trade of aquatic species (Klein and Verlaque, 2008).

    Figure  1.  The external morphology of Caulerpa sertularioides.

    In China, C. sertularioides was distributed mainly along the coast of the South China Sea in Taiwan, Hainan, and other islands (Ding et al., 2015; Liu et al., 2019). During the growing season, its blooms and the biomass abounds along the coast of the South China Sea (Fig. 2). However, few previous studies focused on its antimicrobial activities (Kumar et al., 2011), and polysaccharides and sterols (Shevchenko et al., 2009; Chaves Filho et al., 2022). More studies are required to elucidate its invasion characteristics and potential.

    Figure  2.  The field population of Caulerpa sertularioides in the intertidal zone of Yinggehai, Hainan.

    The response surface methodology (RSM) is a new statistical method for solving multivariable problems and exploring optimal process parameters by analyzing the regression equation (Mee, 2009; Mäkelä, 2017). It has been widely used in the research of life science in recent years because of its advantage of direct display selection of optimal operating conditions in experiment design (Kim et al., 2019; Srinivas et al., 2019; Faramarzi et al., 2019; da Silva et al., 2019; Vishwakarma and Banerjee, 2019; Ebadi et al., 2019; Nur et al., 2019). In China, the RSM method has recently been used to analyze the algal polysaccharides of Caulerpa lentillifera, demonstrating its advantages in the analysis of the active ingredients of algae (Tong et al., 2022).

    In this study, effects of irradiance, temperature, and salinity on the growth of C. sertularioides are studied by using RSM. By determining and analyzing the optimum conditions of multiple ecological factors, it will provide reasonable data for cultivation, prevention and control of biological invasion of C. sertularioides in the natural waters.

    The samples of C. sertularioides were bought from Tianjin’s aquariums and pre-cultured for one week in a seawater tank at room temperature, the thalli were washed and cleaned with disinfected seawater to remove debris, and then cultured under aeration for 30 days under the conditions of irradiance (37.5 ± 6.25) μmol/(m2·s), temperature (25 ± 1)℃, and salinity (30 ± 1) to obtain adequate biomass for the following experiment.

    The thalli with long stolons were picked out and cut with sterilized scissors into at least 3 cm-long fragments carefully, making sure each fragment containing some blades and rhizoids (Smith and Walters, 1999), and placed in the air for 15 s for better wound healing. The fresh weight of each fragment was between 0.31–0.38 g (the mean standard deviation was (0.35 ± 0.02) g).

    Referring to the cultivation experience and data, three ecological single-factor experiments, in which three main factors, e.g., irradiance, salinity, and temperature are tested and each is assigned a variable value, while the other two are constants. The conditions were set as: (1) irradiance at 18.75 μmol/(m2·s), 25.00 μmol/(m2·s), 31.25 μmol/(m2·s), 37.50 μmol/(m2·s), 43.75 μmol/(m2·s) and 50.00 μmol/(m2·s), salinity at 30 and temperature at 25℃; (2) temperature at 19℃, 22℃, 25℃, 28℃, 31℃ and 34℃, irradiance at 37.5 μmol/(m2·s) and salinity at 30; and (3) salinity at 15, 20, 25, 30, 35 and 40, irradiance at 37.5 μmol/(m2·s) and temperature at 25℃.

    Each sample (including three fragments) was placed in a 250 mL conical flask containing 200 mL seawater that was renewed daily and cultured in the thermostatic illumination incubator (Jiangnan Instrument Co., Ltd., Ningbo, type GXZ) referring to the different treatments above. The total cultivation cycles last a week. Each treatment group was replicated three times. All samples were taken out and weighted wet at the end of the week, and then the SGR (specific growth rate) was calculated.

    A central combination design experiment and the verification experiment on the results of the RSM of the three factors were performed using multi-factor experimental method. The results of the single-factor experiment were used to confirm the horizontal range of the ecological factor. The Box-Behnken central combination design, or the Box-Behnken design for simplicity—an RSM method was used to determine the SGR of the thalli fragments, and the optimum conditions of irradiance, temperature, and salinity.

    The experimental data were statistically analyzed using Design-Expert 10.

    SGR can be expressed as:

    $$ {\rm{ SGR}} =\frac{W_t-W_0}{t} \times 100 \%, $$ (1)

    where W0 is the fresh mass (g) of fragments at the initial stage; Wt is the fresh mass (g) of fragments at the end of the experiment; and t is the number of days of the experiment.

    SGR of the fragments increased at first and then decreased with the increase of the variable factor. Peaks appeared at irradiance 37.5 μmol/(m2·s), temperature 25℃, and salinity 30, under which the SGR was 4.35%, 4.22%, and 4.33%, respectively (Figs 35).

    Figure  3.  The SGR of Caulerpa sertularioides in different irradiances.
    Figure  4.  The SGR of Caulerpa sertularioides in different temperatures.
    Figure  5.  The SGR of Caulerpa sertularioides in different salinities.

    Irradiance, temperature, and salinity are considered the three key factors affecting the growth of the species, and SGR is the response value. According to the results of the single-factor experiments, the horizontal ranges of the three factors were set and analyzed in the Box-Behnken design (Table 1), in which 17 three-factor combinations were determined and the SGRs of thalli fragments of the 17 combinations were measured (Table 2).

    Table  1.  Levels of variables in the Box-Behnken experimental design
    FactorIrradiance /(μmol·m−2·s−1)Temperature/℃Salinity
    Low31.252225
    High43.752835
     | Show Table
    DownLoad: CSV
    Table  2.  Combinations of the Box-Behnken experimental design
    Serial numberIrradiance/
    (μmol·m−2·s−1)
    Temperature/℃SalinitySGR/%
    137.522352.84
    237.525304.62
    337.528353.06
    437.525304.52
    537.522252.46
    643.7528303.25
    731.2522301.93
    837.525304.52
    943.7522303.20
    1031.2528302.65
    1143.7525253.41
    1237.525304.70
    1331.2525352.63
    1437.528253.26
    1531.2525252.38
    1643.7525353.38
    1737.525304.62
     | Show Table
    DownLoad: CSV

    Design-Expert 10 was used for the quadratic multiple regression of the data shown in Table 2. The regression equation was established as below:

    $$ \begin{aligned}{\rm{SGR}}(\%)= &\;4.60+0.46A+0.22B+0.049C-0.17AB-0.074AC-\\&0.14BC-0.90A^2-0.94B^2-0.75C^2, \end{aligned} $$ (2)

    where A is irradiance, B is temperature, and C is salinity. The influences of factors A and B were highly significant (P < 0.000 1), and those of factors AB and BC were extremely significant (P < 0.01).

    In addition, as ANOVA results show, the model of regression equation was highly significant (P < 0.000 1), while the lack-of-fit was not significant (P > 0.05), thus the mode could better describe the real relationship between various factors and the response value (SGR) (Table 3). In our response surface analysis (Figs 611), the factors involved in multiple interactions were irradiance (in μmol/(m2·s)), temperature (℃), and SGR (%). The results show that within the set range, the SGR of the fragments increased first and then decreased with the increases in irradiance, temperature, and salinity, and the interactions of irradiance-temperature and temperature-salinity were extremely significant. According to the analysis of the model, the best combination condition for the growth of fragments was irradiance 39.03 μmol/(m2·s), temperature 25.29℃, and salinity 30.06, under which the SGR was the best, reaching 4.66%.

    Table  3.  Analysis of variance
    SourceSum of squaresdfMean squareF valueP valueSignificance
    Model12.8991.43304.92<0.000 1Significant
    A−irradiance1.6711.67355.29<0.000 1
    B−temperature0.410.485.16<0.000 1
    C−salinity0.0210.0194.080.083
    AB0.1110.1123.760.001 8
    AC0.0210.0224.640.068
    BC0.0810.08317.670.004
    A23.413.4724.44<0.000 1
    B23.7413.74796.58<0.000 1
    C22.3412.34498.31<0.000 1
    Residual0.03374.70 × 10−3
    Lack of fit9.07 × 10−333.02 × 10−30.510.80Insignificant
    Pure error0.02445.95 × 10−3
    Cor total12.9316
    Note: df: Degrees of Freedom. Cor total: This row shows the amount of variation around the mean of the observations. The model explains part of it, the residual explains the rest.
     | Show Table
    DownLoad: CSV
    Figure  6.  SGR of Caulerpa sertularioides affected by irradiance.
    Figure  7.  SGR of Caulerpa sertularioides affected by temperature.
    Figure  8.  SGR of Caulerpa sertularioides affected by salinity.

    To verify the optimum combination conditions, another experiment was conducted under the above-stated optimum condition. Within the scope of the precision of experimental instruments, the SGR of the thalli fragments was measured to be 4.66%, which is consistent overall with the predicted results of response surface method.

    Some species of genus Caulerpa are common invasive green algae. Their growth and development are affected by local coastal environmental conditions. Taking C. racemosa as an example, its growth varied in temperature in terms of season, region, or water depth. As reported previously, its blade length reached 6 cm in October at 0–3 m depth in Leghorn, Italy (Piazzi and Cinelli, 1999), and at a deeper depth of 17 m in Marseille, France; its blade height in summer was on average 2 cm only and no such a summer peak was observed (Ruitton et al., 2005). Moreover, C. racemosa had fewer blades in winter at a depth of 2 m in coastal waters of northern Italy (Piazzi et al., 2001). In Japanese waters, Caulerpa species begin to develop in spring and become mature in summer (Wang, 2015). In salinity and irradiance, C. racemosa grew fastest in salinity 30–40 and light intensity 20–60 μE/(m2·s) as reported in the intertidal zone/subtidal reef of southwestern coastal Australia (Carruthers et al., 1993). In China, a study showed that the optimum conditions for C. sertularioides growth were 26℃, salinity 27.5, and irradiance 25 μmol/(m2·s) (Zhong et al., 2021). Similarly, C. racemosa on Taiwan Island in China grew best in seawater temperatures ranging from 24–28℃, while the biomass reduced dramatically below 22℃ or above 31℃ (Shi, 2008).

    All these data provide references for the monitoring and control of the invasion of Caulerpa species. However, the above-mentioned cases are complicated and imprecise among the three parameters. Therefore, RSM was introduced and applied to this study.

    RSM is a commonly used method for experimental design, which is applicable for multi-factor and multi-level experimental designs and is convenient, and has good predictability (Stensrud et al., 2000; Nazzal et al., 2002; Kramar et al., 2003; Hadiyat et al., 2022). Currently, it is widely used for biological enzyme medium configuration and in food processing (Zhao et al., 2013; Gong et al., 2022; Pinheiro et al., 2022). In this study, we first determined preliminarily growth conditions of C. sertularioides fragments in a single-factor manner: 25℃ in temperature, 30 in salinity, and 37.5 μmol/(m2·s) in irradiance, under which the SGR was the best. Subsequently, the interactions among irradiance, temperature, and salinity, and an optimum ecological multi-factor combination condition were established and analyzed in RSM.

    The results of RSM show that the interactions between irradiance and temperature, and temperature and salinity were extremely significant. Temperature regulates algal growth by affecting enzyme activity (Wang et al., 2014; Feng et al., 2021). Salinity regulates ion exchange by affecting osmotic pressure (Flexas et al., 2004). The enzymes require the activation of specific ions (Wells and Di Cera, 1992); too high or too low salinity could affect the activity of enzymes or their carriers (Okur et al., 2002). The irradiance mainly affects the photosynthesis of algae (Dennison, 1987), in which certain enzymes are involved (Bischof et al., 2000). The interactions between temperature and salinity, and between temperature and irradiance have been observed to be significant in other algae Prorocentrum donghaiense (Xu et al., 2010) and Skeletonema costatum (Yu, 2005).

    The optimum combination condition indicated by RSM was: irradiance 39.03 μmol/(m2·s), temperature 25.29℃, and salinity 30.06. The R2Adj (adjusted coefficient of determination) of the multiple correlation coefficient R after the analysis of variance was 0.99, indicating that 99% of the change in the response value is derived from the selected variable, which means that the error of this experiment is very small. By analyzing the response surface of the interaction terms in the regression equation (Figs 911), we found that the interaction among temperature, salinity, and irradiance is significant in the selected range, which is consistent with the result of the model analysis (Table 3), indicating that the model could be used to optimize the growth conditions of the fragments of C. sertularioides, and to predict its SGR. The SGR value determined by the verification experiment was higher than the maximum SGR of the single-factor experiment, and also higher than the SGR of C. sertularioides measured under 16 of the total 17 combined conditions determined in the Box-Behnken design (see Table 2), but slightly lower than one of the conditions, which is speculated that it was caused by an experimental error. The result indicates that the optimum combination conditions of ecological factors for the growth of C. sertularioides optimized by RSM (irradiance 39.03 μmol/(m2·s), temperature 25.29℃, and salinity 30.06) are suitable for the growth of C. sertularioides. In addition, it also indicates that the optimum combination conditions (the irradiance, temperature, and salinity) of ecological factors for the growth of C. sertularioides optimized in this study can be taken as the center and be appropriately extended to combine in the range of appropriate growth conditions, which provided new theoretical data and solutions for the cultivation, invasion prediction, and monitoring of Caulerpa species in China and around the world, and offer some new scientific data for future in-depth researches in this regard. Based on the literatures and our result, with further research and data mining, we predict that the RSM method will be better applied in the following aspects of macroalgae: (1) species or taxa (new cultivars) that have not been studied because an optimal set of culture conditions need to be obtained; (2) germplasms that require intensive orientation cultured, where changes in the microenvironment often cause them to undergo qualitative changes, such as the transition from the growth to the reproductive stage; (3) the analysis of environmental hazards of cultivated species in the field, which facilitates the acquisition of new insights.

    Figure  9.  SGR of Caulerpa sertularioides affected by irradiance and temperature.
    Figure  10.  SGR of Caulerpa sertularioides affected by irradiance and salinity.
    Figure  11.  SGR of Caulerpa sertularioides affected by temperature and salinity.

    As known from the current works of literature, those blooming macroalgae generally adapt to their environment very quickly through multiple pathways, which means they are extremely viable. C. sertularioides is also extremely adaptable to its environment which is similar to other Caulerpa species. In addition to sexual reproduction (which had few been seen in the literature), it can grow and spread on the seafloor through its stolons and fragments or branches. In them, the rate of speed by the fragments is much faster. In the previous research and cultivations of Caulerpa species, asexual materials were generally used, mainly fragments or branches. They are much more economical and conveniently available than sexual ones. Therefore, our experiment was implemented using fragments rather than whole individuals.

    The effects of ecological factors on growth of C. sertularioides, an invasive potential blooming green alga, were studied. Its optimum conditions of irradiance, temperature and salinity for the growth of its fragments were determined in the response surface methodology (RSM). Using the Box-Behnken design, the conditions were optimized and verified to be irradiance 39.03 μmol/(m2·s), temperature 25.29℃, and salinity 30.06, under which the SGR reached 4.66%. As the research progresses and the data are fully explored, the RSM method may have great potential application in the environmental adaptation characteristics of new macroalgal cultivars, intensive orientation cultured germplasms, and environmental hazard analysis of cultivated species in the field.

    We thank Roger Z. YU, a Canadian english editor for help in modifying the language.

  • [1]
    Agoramoorthy G, Chen Fnan, Hsu M J. 2008. Threat of heavy metal pollution in halophytic and mangrove plants of Tamil Nadu, India. Environmental Pollution, 155(2): 320–326. doi: 10.1016/j.envpol.2007.11.011
    [2]
    Alongi D M, Clough B F, Dixon P, et al. 2003. Nutrient partitioning and storage in arid-zone forests of the mangroves Rhizophora stylosa and Avicennia marina. Trees, 17(1): 51–60. doi: 10.1007/s00468-002-0206-2
    [3]
    Analuddin K, Sharma S, Jamili, et al. 2017. Heavy metal bioaccumulation in mangrove ecosystem at the coral triangle ecoregion, Southeast Sulawesi, Indonesia. Marine Pollution Bulletin, 125(1–2): 472–480. doi: 10.1016/j.marpolbul.2017.07.065
    [4]
    Bayen S. 2012. Occurrence, bioavailability and toxic effects of trace metals and organic contaminants in mangrove ecosystems: a review. Environment International, 48: 84–101. doi: 10.1016/j.envint.2012.07.008
    [5]
    Che R G O. 1999. Concentration of 7 heavy metals in sediments and mangrove root samples from Mai Po, Hong Kong. Marine Pollution Bulletin, 39(1–12): 269–279. doi: 10.1016/S0025-326X(99)00056-9
    [6]
    Chen Xiaoyong, Tsang E P K, Chan A L W. 2003. Heavy metals contents in sediments, mangroves and bivalves from Ting Kok, Hong Kong. China Environmental Science (in Chinese), 23(5): 480–484
    [7]
    Cheng Han, Liu Yong, Tam N F Y, et al. 2010. The role of radial oxygen loss and root anatomy on zinc uptake and tolerance in mangrove seedlings. Environmental Pollution, 158(5): 1189–1196. doi: 10.1016/j.envpol.2010.01.025
    [8]
    Cuong D T, Bayen S, Wurl O, et al. 2005. Heavy metal contamination in mangrove habitats of Singapore. Marine Pollution Bulletin, 50(12): 1732–1738. doi: 10.1016/j.marpolbul.2005.09.008
    [9]
    Defew L H, Mair J M, Guzman H M. 2005. An assessment of metal contamination in mangrove sediments and leaves from Punta Mala Bay, Pacific Panama. Marine Pollution Bulletin, 50(5): 547–552. doi: 10.1016/j.marpolbul.2004.11.047
    [10]
    Gan Huayang, Liang Kai, Lin Jinqing, et al. 2013. Distribution and accumulation of arsenic, cadmium and mercury in coastal wetland sediment of northern Beibu Gulf. Marine Geology & Quaternary Geology (in Chinese), 33(3): 15–28
    [11]
    Hakanson L. 1980. An ecological risk index for aquatic pollution control. a sedimentological approach. Water Research, 14(8): 975–1001. doi: 10.1016/0043-1354(80)90143-8
    [12]
    He Qinfei, Jiang Yi, Liu Xiu, et al. 2011. An analysis of soil properties of different types of mangroves in Qinzhou bay. Wetland Science & Management (in Chinese), 7(3): 45–48
    [13]
    Kehrig H A, Pinto F N, Moreira I, et al. 2003. Heavy metals and methylmercury in a tropical coastal estuary and a mangrove in Brazil. Organic Geochemistry, 34(5): 661–669. doi: 10.1016/S0146-6380(03)00021-4
    [14]
    Lau S S S. 2000. The significance of temporal variability in sediment quality for contamination assessment in a coastal wetland. Water Research, 34(2): 387–394. doi: 10.1016/S0043-1354(99)00344-9
    [15]
    Li Chungan, Dai Huabing. 2015. Mechanism analysis of temporal dynamics in mangrove spatial distribution in Guangxi, China: 1960–2010. Acta Ecologica Sinica (in Chinese), 35(18): 5992–6006
    [16]
    Li Liuqiang, Ding Zhenhua, Liu Jinling, et al. 2008. Distribution of heavy metals in surficial sediments from main mangrove wetlands of China and their influence factors. Haiyang Xuebao (in Chinese), 30(5): 159–164
    [17]
    Li Lilin, Mo Liping, Lu Yuan. 2014. Heavy metal content studies in sediments of Qinzhou port mangrove wetlands. Western China Communication Science & Technology (in Chinese), (5): 76–80
    [18]
    Liang Yan, Wong Minghung. 2003. Spatial and temporal organic and heavy metal pollution at Mai Po Marshes Nature Reserve, Hong Kong. Chemosphere, 52(9): 1647–1658. doi: 10.1016/S0045-6535(03)00505-8
    [19]
    Liu Baolin, Hu Ke, Jiang Zhenglong, et al. 2011. Distribution and enrichment of heavy metals in a sediment core from the Pearl River Estuary. Environmental Earth Sciences, 62(2): 265–275. doi: 10.1007/s12665-010-0520-8
    [20]
    Liu Yong, Tam N F Y, Yang Jingxian, et al. 2009. Mixed heavy metals tolerance and radial oxygen loss in mangrove seedlings. Marine Pollution Bulletin, 58(12): 1843–1849. doi: 10.1016/j.marpolbul.2009.07.023
    [21]
    Liu Jinling, Wu Hao, Feng Jianxiang, et al. 2014. Heavy metal contamination and ecological risk assessments in the sediments and zoobenthos of selected mangrove ecosystems, South China. CATENA, 119: 136–142. doi: 10.1016/j.catena.2014.02.009
    [22]
    MacFarlane G R, Burchett M D. 1999. Zinc distribution and excretion in the leaves of the grey mangrove, Avicennia marina (Forsk.) Vierh. Environmental and Experimental Botany, 41(2): 167–175. doi: 10.1016/S0098-8472(99)00002-7
    [23]
    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. Marine Environmental Research, 54(1): 65–84. doi: 10.1016/S0141-1136(02)00095-8
    [24]
    MacFarlane G R, Koller C E, Blomberg S P. 2007. Accumulation and partitioning of heavy metals in mangroves: a synthesis of field-based studies. Chemosphere, 69(9): 1454–1464. doi: 10.1016/j.chemosphere.2007.04.059
    [25]
    MacFarlane G R, Pulkownik A, Burchett M D. 2003. Accumulation and distribution of heavy metals in the grey mangrove, Avicennia marina (Forsk.) Vierh.: biological indication potential. Environmental Pollution, 123(1): 139–151. doi: 10.1016/S0269-7491(02)00342-1
    [26]
    Machado W, Silva-Filho E V, Oliveira R R, et al. 2002. Trace metal retention in mangrove ecosystems in Guanabara Bay, SE Brazil. Marine Pollution Bulletin, 44(11): 1277–1280. doi: 10.1016/S0025-326X(02)00232-1
    [27]
    Meng Xianwei, Xia Peng, Li Zhen, et al. 2016. Mangrove degradation and response to anthropogenic disturbance in the Maowei Sea (SW China) since 1926 AD: mangrove-derived OM and pollen. Organic Geochemistry, 98: 166–175. doi: 10.1016/j.orggeochem.2016.06.001
    [28]
    Mokrzycki E, Uliasz-Bocheńczyk A, Sarna M. 2003. Use of alternative fuels in the Polish cement industry. Applied Energy, 74(1–2): 101–111. doi: 10.1016/S0306-2619(02)00136-8
    [29]
    Nagelkerken I, Blaber S J M, Bouillon S, et al. 2008. The habitat function of mangroves for terrestrial and marine fauna: a review. Aquatic Botany, 89(2): 155–185. doi: 10.1016/j.aquabot.2007.12.007
    [30]
    Pan Ke, Wang Wenxiong. 2012. Trace metal contamination in estuarine and coastal environments in China. Science of the Total Environment, 421–422: 3–16. doi: 10.1016/j.scitotenv.2011.03.013
    [31]
    Percival J B, Outridge P M. 2013. A test of the stability of Cd, Cu, Hg, Pb and Zn profiles over two decades in lake sediments near the Flin Flon Smelter, Manitoba, Canada. Science of the Total Environment, 454–455: 307–318. doi: 10.1016/j.scitotenv.2013.03.011
    [32]
    Perdomo L, Ensminger I, Fernanda Espinosa L, et al. 1999. The mangrove ecosystem of the Ciénaga Grande de Santa Marta (Colombia): observations on regeneration and trace metals in sediment. Marine Pollution Bulletin, 37(8–12): 393–403. doi: 10.1016/S0025-326X(99)00075-2
    [33]
    Preda M, Cox M E. 2002. Trace metal occurrence and distribution in sediments and mangroves, Pumicestone region, southeast Queensland, Australia. Environment International, 28(5): 433–449. doi: 10.1016/S0160-4120(02)00074-0
    [34]
    Qin Guangqiu, Yan Chongling, Lu Haoliang. 2007. Influence of heavy metals on the carbohydrate and phenolics in mangrove, Aegiceras corniculatum L., seedlings. Bulletin of Environmental Contamination and Toxicology, 78(6): 440–444. doi: 10.1007/s00128-007-9204-9
    [35]
    Qiu Yaowen, Yu Kefu, Zhang Gan, et al. 2011. Accumulation and partitioning of seven trace metals in mangroves and sediment cores from three estuarine wetlands of Hainan Island, China. Journal of Hazardous Materials, 190(1–3): 631–638. doi: 10.1016/j.jhazmat.2011.03.091
    [36]
    Ramanathan A L, Subramanian V, Ramesh R, et al. 1999. Environmental geochemistry of the Pichavaram mangrove ecosystem (tropical), southeast coast of India. Environmental Geology, 37(3): 223–233. doi: 10.1007/s002540050380
    [37]
    Ramos e Silva C A, da Silva A P, de Oliveira S R, et al. 2006. Concentration, stock and transport rate of heavy metals in a tropical red mangrove, Natal, Brazil. Marine Chemistry, 99(1–4): 2–11. doi: 10.1016/j.marchem.2005.09.010
    [38]
    Ray A K, Tripathy S C, Patra S, et al. 2006. Assessment of Godavari estuarine mangrove ecosystem through trace metal studies. Environment International, 32(2): 219–223. doi: 10.1016/j.envint.2005.08.014
    [39]
    Saenger P, McConchie D, Clark M W. 1991. Mangrove forests as a buffer zone between anthropogenically polluted areas and the sea. In: Proceedings of 1990 Workshop on Coastal Zone Management. Yeppoon, Qld: Geo-Processors Pty Ltd, 280–299
    [40]
    Senthilkumar B, Purvaja R, Ramesh R. 2013. Vertical profile distribution and accumulation of heavy metals in mangrove sediments (Pichavaram), southeast coast of India. Journal of Applied Geochemistry, 15(3): 318–355
    [41]
    Shriadah M M A. 1999. Heavy metals in mangrove sediments of the United Arab Emirates shoreline (Arabian Gulf). Water, Air, and Soil Pollution, 116(3–4): 523–534
    [42]
    Tam N F Y, Wong Y S. 1995. Spatial and temporal variations of heavy metal contamination in sediments of a mangrove swamp in Hong Kong. Marine Pollution Bulletin, 31(4–12): 254–261. doi: 10.1016/0025-326X(95)00141-9
    [43]
    Tam N F Y, Wong Y S. 1996. Retention and distribution of heavy metals in mangrove soils receiving wastewater. Environmental Pollution, 94(3): 283–291. doi: 10.1016/S0269-7491(96)00115-7
    [44]
    Tam N F Y, Wong Y S. 1997. Accumulation and distribution of heavy metals in a simulated mangrove system treated with sewage. Hydrobiologia, 352(1–3): 67–75
    [45]
    Tam N F Y, Wong Y S. 2000. Spatial variation of heavy metals in surface sediments of Hong Kong mangrove swamps. Environmental Pollution, 110(2): 195–205. doi: 10.1016/S0269-7491(99)00310-3
    [46]
    Tam N F Y, Wong Y S, Lan C Y, et al. 1995. Community structure and standing crop biomass of a mangrove forest in Futian Nature Reserve, Shenzhen, China. Hydrobiologia, 295(1–3): 193–201. doi: 10.1007/BF00029126
    [47]
    Tam N F Y, Wong Y S, Wong M H. 2009. Novel technology in pollutant removal at source and bioremediation. Ocean & Coastal Management, 52(7): 368–373
    [48]
    Thomas G, Fernandez T V. 1997. Incidence of heavy metals in the mangrove flora and sediments in Kerala, India. Hydrobiologia, 352(1–3): 77–87
    [49]
    Usman A 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. Ecotoxicology and Environmental Safety, 97: 263–270. doi: 10.1016/j.ecoenv.2013.08.009
    [50]
    Usman A R A, Lee S S, Awad Y M, et al. 2012. Soil pollution assessment and identification of hyperaccumulating plants in chromated copper arsenate (CCA) contaminated sites, Korea. Chemosphere, 87(8): 872–878. doi: 10.1016/j.chemosphere.2012.01.028
    [51]
    Wang Beihong, Ma Zhihong, Fu Weili. 2008. Determination of heavy metal in soil by high pressure sealed vessels assisted digestion-atomic absorption spectrometry. Transactions of the Chinese Society of Agricultural Engineering (in Chinese), 24(S2): 255–259
    [52]
    Wang Qiang, Mei Degang, Chen Jingyan, et al. 2019. Sequestration of heavy metal by glomalin-related soil protein: implication for water quality improvement in mangrove wetlands. Water Research, 148: 142–152. doi: 10.1016/j.watres.2018.10.043
    [53]
    Wang Xiaojuan, Wang Wenbin, Yang Long, et al. 2015. Transport pathways of cadmium (Cd) and its regulatory mechanisms in plant. Acta Ecologica Sinica (in Chinese), 35(23): 7921–929
    [54]
    Weis J S, Weis P. 2004. Metal uptake, transport and release by wetland plants: implications for phytoremediation and restoration. Environment International, 30(5): 685–700. doi: 10.1016/j.envint.2003.11.002
    [55]
    Wu Qihang, Tam N F Y, Leung J Y S, et al. 2014. Ecological risk and pollution history of heavy metals in Nansha mangrove, South China. Ecotoxicology and Environmental Safety, 104: 143–151. doi: 10.1016/j.ecoenv.2014.02.017
    [56]
    Xia Peng, Meng Xianwei, Li Zhen, et al. 2016. Sedimentary records of mangrove evolution during the past one hundred years based on stable carbon isotope and pollen evidences in Maowei, SW China. Journal of Ocean University of China, 15(3): 447–455. doi: 10.1007/s11802-016-2687-4
    [57]
    Xu Youhou, Liao Riquan, Su Jia, et al. 2017. The content and pollution evaluation of six heavy metals in surface water and plankton in the eastern area of Qinzhou Bay. Oceanologia et Limnologia Sinica (in Chinese), 48(5): 960–969
    [58]
    Yan Xinxing, Liu Guoting. 2006. Study on deposition characteristics and channel siltation in offshore zone of Qinzhou Bay. Journal of Waterway and Harbor (in Chinese), 27(2): 79–83
    [59]
    Yan Zhongzheng, Tam N F Y. 2013. Differences in lead tolerance between Kandelia obovata and Acanthus ilicifolius seedlings under varying treatment times. Aquatic Toxicology, 126: 154–162. doi: 10.1016/j.aquatox.2012.10.011
    [60]
    Zhang Dan, Li Darong, Chen Jianhua, et al. 2014a. Distribution and pollution characteristics of heavy metals in marine and riverine sediments of Qinzhou Bay, China. Safety and Environmental Engineering (in Chinese), 21(5): 11–15
    [61]
    Zhang Shaofeng, Lin Mingyu, Wei Chunlei, et al. 2010. Pollution assessment and potential ecological risk evolution for heavy metals in the sediments of Qinzhou Bay. Marine Science Bulletin (in Chinese), 29(4): 450–454
    [62]
    Zhang Zaiwang, Xu Xiangrong, Sun Yuxin, et al. 2014b. Heavy metal and organic contaminants in mangrove ecosystems of China: a review. Environmental Science and Pollution Research, 21(20): 11938–11950. doi: 10.1007/s11356-014-3100-8
    [63]
    Zhou Yanwu, Peng Yisheng, Li Xulin, et al. 2011. Accumulation and partitioning of heavy metals in mangrove rhizosphere sediments. Environmental Earth Sciences, 64(3): 799–807. doi: 10.1007/s12665-011-0904-4
    [64]
    Zuo Ping, Wang Yaping, Min Fengyang, et al. 2009. Distribution characteristics of heavy metals in surface sediments and core sediments of the Shenzhen Bay in Guangdong Province, China. Acta Oceanologica Sinica, 28(6): 53–60
  • Relative Articles

  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(4)  / Tables(3)

    Article Metrics

    Article views (457) PDF downloads(101) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return