Trans. Nonferrous Met. Soc. China 25(2015) 3436-3441

Comprehensive ecological risk assessment for heavy metal pollutions in three phases in rivers

Ying ZHANG¹, Jun ZHOU^1,2, Feng-jie GAO¹, Bao-jie ZHANG², Biao MA², Li-qing LI³

1. College of Resources and Environment, Northeast Agricultural University, Harbin 150030, China;

2. Heilongjiang Research Academy of Environmental Science, Harbin 150056, China;

3. School of Energy Science and Engineering, Central South University, Changsha 410083, China

Received 27 November 2014; accepted 10 March 2015

Abstract: Literature lacked in providing a comprehensive research on heavy metal detection in aquatic, biological and sedimentary states of rivers. The present study was imparted with all these three components of the river. Heavy metal toxicity or pollution index was used as a tool for ecological risk assessment by considering the single state studies conducted by many researchers. An intensive ecological risk assessment model was constructed and heavy metals were indicated as a serious threat to the environment. The model was applied to determining five toxic heavy metals in three states of the Songhua River. According to the ecological risk index, heavy metal pollution in three phases was categorized as aquatic>biological>sedimentary, while the overall descending order of heavy metal ecological risk index was as Cd>Hg>As>Pb>Cr. Cd and Hg were selected as the priority pollutants of Songhua River.

Key words: comprehensive ecological risk assessment; priority pollutants selection; heavy metal; Songhua River

1 Introduction

An increase in industrial production and urbanization was believed responsible for increasing concentration of heavy metals into the natural environment, not only posing serious threat to individuals and species but also causing a negative effect on the ecological system. Excessive amount of heavy metals resulted in severe impacts on human body, such as acute and chronic intoxication, cancer, teratogenesis, and mutations [1]. Currently, heavy metal contamination got great attention by researchers due to its potential hazard to environmental pollution locally and globally [2]. Different methods were adopted and implemented to calculate an ecological risk assessment of heavy metals in Xiawan Port sediments like potential ecological risk index (RI), risk assessment code (RAC) by modifying an index, modified potential ecological risk index (MRI) [3]. Four different methods, named mineralogical analysis, three-stage BCR sequential extraction procedure, dynamic leaching test and Hakanson potential ecological risk index method were used to evaluate the zinc residual leaching and its potential ecological risks on environment [4]. Ten fish species were collected from Bangshi River at Savar in Bangladesh in two different seasons to measure eight heavy metals (Pb, Cd, Ni, Cr, Cu, Zn, Mn, and As) [5]. The water quality index was implemented for heavy metals risk assessment and water quality characterization of River Soan, Pakistan [6]. However, most of the researchers detected heavy metals in single medium only. No intensive research on heavy metal pollution in all aquatic, biological and sedimentary phases of the river was reported. Although many studies investigated screening methods for priority pollutants in aqueous environments, most of them focused on heavy metal contamination of organic matters causing great threat to human body [7]. This was a problem to formulate a scientific evaluation model for determination of heavy metals as priority pollutants for intensive research on target rivers due to the demand of high standard heavy metal detection devices and operators. By considering heavy metal pollution assessment models [8-10] and priority pollutant screening methods [11-13] adopted and observed in different researches, the present study opted relevant indexes to construct a comprehensive ecological risk assessment model to detect heavy metals in aqueous, biological and sedimentary phases. Heavy metals with higher ecological risks were selected as the priority pollutants to control heavy metal pollution in rivers.

2 Migration and transmission of heavy metals among three phases in rivers

Heavy metals after entering in any environmental component were easily transmitted and accumulated in food chains [14], same in the case of water bodies, heavy metals eventually entered in human bodies by edible marine food like fish [15]. This process not only poisoned the fish, but also caused the alarming situation towards humans. Moreover, heavy metals in water bodies showed a tendency to react with organic polymers, forming a complex or chelate by adhering to surface of clay minerals. These pollutants were ultimately settled and accumulated in sediments and acted as secondary pollutants in water bodies by undergoing a series of physical, chemical and biological processes [16]. The migration and transmission process of heavy metals in river ecosystem is shown in Fig. 1.

3 Construction of risk evaluation model for heavy metals in three phases in rivers

3.1 Construction of index system

Table 1 shows the major assessment models for heavy metal pollutions locally and globally. Heavy metals pollution in rivers were mainly influenced by biological toxicity, absorption and contamination extent. By keeping in view the model construction concepts in literature, in the current study, toxicity coefficient, pollution index, and detection rate were selected as the key factors to construct the risk assessment model to detect heavy metals in rivers.

Fig. 1 Simulation of migration and transmission process of heavy metals in river ecosystem

3.1.1 Toxicity coefficient of heavy metals (T_rⁱ)

Toxicity coefficient T_rⁱ represented the toxicity level and the biological sensitivity for heavy metals. Toxicity coefficients commonly used are shown in Table 2.

3.1.2 Pollution index of heavy metals (C_fⁱ)

Pollution index C_fⁱ represented the richness and pollution degree of a single heavy metal, which was denoted in Eq. (1):

                               (1)

where Cⁱ is the detected value of a single heavy metal (mg/kg); C_nⁱ is the background value (mg/kg).

3.1.3 Detection rate of heavy metals (F_sⁱ)

Detection rate F_sⁱ symbolized the pollution scale and detection rate of a single heavy metal, which was shown in Eq. (2):

                                (2)

where Sⁱ is the number of sections that detected a single heavy metal; S_tⁱ indicates the total number of detection sections.

Table 1 Main models of heavy metal pollution assessment

Table 2 Toxicity coefficients of different heavy metals

3.2 Weight of indexes

3.2.1 Weight determination

The weight determination method of heavy metals (ζ, η, θ) in the three phases was as follows: 1) Heavy metals weight determination coefficients in aquatic, biological and sedimentary phases were x, y and z, respectively; 2) Dividing water environment according to water qualities into I-V categories, and weight coefficients for these categories were α, β, γ, δ and ε, respectively; 3) Heavy metal weight coefficients x, y and z were multiplied with α, β, γ, δ and ε to obtain the weight coefficient of each heavy metal in every selected phase of water bodies, α_i, β_i, γ_i, δ_i and ε_i; 4) Determination of the number of different water quality categories A, B, C, D and E; 5) Calculation of the mean arithmetic weight of each heavy metal in various water components a, b and c to measure the heavy metal weight in aquatic, biological and sedimentary phases ζ, η, θ, respectively (Eq. (3)). Table 3 shows the heavy metals weights in three phases in rivers.

         (3)

where

3.2.2 Weight value determination

The analytic hierarchy process was adopted to determine the weight coefficient. This section referred to the concentration comparison method for a single heavy metal in different water environments to construct a verification matrix as described in Surface Water Quality Assessment Standard GB3838—2002. Six experts were invited to construct the verification matrix for I-V water quality in aquatic, biological and sedimentary phases. The common conclusion of their researches was adopted to construct the weight coefficient matrix, as shown in Table 4.

Table 3 Weight distribution of heavy metals in three phases in rivers

Table 4 Weight coefficient of different heavy metals in rivers

3.3 Comprehensive ecological risk index in three- phases in rivers

By considering the evaluation indexes in literature, the ecological risk indexes were established to detect heavy metals in aquatic, biological, and sedimentary phases (represented by W, F and S, Eq. (4)). Heavy metal impact on human was varied depending upon their accumulation in different phases, consequently, different risk weights were assigned to the heavy metals in different phases. Comprehensive ecological risk index R was obtained in Eq. (5) and heavy metals with the higher R values were selected as the priority pollutants in rivers.

                    (4)

                          (5)

4 Model application and verification

4.1 Model application

4.1.1 Research region selection and data collection

The model was applied to the third largest river in China named Songhua River. Songhua River was connected to Nen River on the north and the Second Songhua River on the south. The two branches were merged into the mainstream of Songhua River at Sancha River and joined Heilongjiang River at Tongjiang City, having great impacts on the water quality of Heilongjiang River. Five main control pollutants Hg, Cd, Cr, As and Pb were selected as research objects and the samples were collected from the Second Songhua River and trunk stream of Songhua River from May 2011 to May 2012.

The water samples were collected from 10 sections for 8 times. Overall 88 fish samples were collected from 5 sections for once, including catfish (sarcophagous), carp and crucian (omnivory), chub (herbivore). One sediment sample was collected from each of 8 selected sections. The background information of fish samples was assembled from Ref. [19], 16 water samples and 23 sediment samples were collected from six rivers in May 2011 (Fig. 2). All samples were detected by ICP-MS.

Fig. 2 Spatial distribution of sampling sections

4.1.2 Model calculation and results analysis

The target river was segmented according to water quality classification standards prescribed in Jilin Province Surface Water Function Regions DB22/388—2004 and Heilongjiang Province Surface Water Environment Regionalization and Water Quality Supplementary Standards DB23/485—1998. Shaokou to Tongjiang section was selected as the research target and it was divided into 11 water sampling areas, whereas, 1 section was followed under category II, 7 in category III and 3 in category IV. The calculation process of weight values was as follows, such as Hg.

The results of ecological risk assessment of heavy metals in Songhua River are show in Table 5 and Fig. 3. Table 5 and Fig. 3 explained that: 1) The biological toxicity of the five types of heavy metals in Songhua River were different with toxicity coefficient in a descending order of Hg>Cd>As>Pb>Cr, C_fⁱ showed that the richness of heavy metals in the three phases was as aquatic>biological>sedimentary, F_sⁱ represented a higher detection rate of heavy metals; 2) The detection rate of Hg, Cr, and Pb in aquatic phase was less than 1, but the pollution index was marked up to 1.40, 5.11 and 3.37, respectively, this phenomenon indicated the uneven spatial distribution of these three kinds of heavy metals, mainly higher values were detected in aquatic phase, the pollution index of Hg and Pb in sediments was smaller than 1, but the detection rate was 100%, this was due to their high background values; 3) The ecological risk index of Hg in biological phase was the highest (52), greater than the sum of risk indexes in water and solid phases (49), the ecological risk of Cd was similar in three phases, while the other 3 heavy metals in an descending order was water>biological>solid; 4) The comprehensive ecological risk index (R) descending order of five heavy metals was Cd(37.64)>Hg(28.48)> As(19.14)>Pb(7.41)> Cr(5.67), and Cd and Hg were selected as priority pollutants in Songhua River.

Table 5 Comprehensive ecological risk assessment of heavy metals in Songhua River

Fig. 3 Ecological risk index of heavy metals in three phases in Songhua River

4.2 Model verification

ZHU and WANG [20] reviewed about 51 papers consisted of 34478 samples from seven major Chinese water systems at different time. According to the heavy metal characteristics analysis, Songhua River was ranked third on ecological risk, following Pearl River and Haihe River. The ecological risk of Hg was higher than that of Cd. SUN et al [21] reported higher concentrations of Cd in fish of the Second Songhua River than that of the trunk stream Songhua River, while Hg concentration in fish was found higher in the trunk stream of the Songhua River. LU et al [22] investigated the higher ecological risk index of Hg and Cd in sediment samples collected from middle and lower stream of the Second Songhua River. The research evidenced the Hg and Cd as the greatest ecological risk to water, fish and sediments of the Songhua River. These researches were consistent with the results of the present study, which demonstrated the significance of this model.

4.3 Model discussion

Toxicity coefficient and heavy metal concentration were the most important indexes for most models (Table 1). By analyzing the model construction and selection methods of priority pollutants in foreign studies, the comprehensive ecological risk assessment model was constructed. The results illustrated that heavy metals with higher toxicity coefficient have higher comprehensive ecological risk index (R and T_r have significant correlation), but lacked in direct correlation due to the influence of pollutant concentration, background value and detection rate. For example, T_r(Cd)r(Hg), but R(Cd)>R(Hg). The low detection rate of Hg in aquatic phase also decreased the ecological risk index of Hg in biological and sedimentary phases, while an ecological risk index of the Cd was calculated higher. Therefore, the detection rate was considered as an important influencing factor for comprehensive effects. This model was designed to screen out the priority heavy metal pollutants from rivers by adopting comprehensive ecological risk indexes. It was not only time and cost efficient, but also helpful for actual heavy metal assessment and transformation in the rivers. In addition, this work probed the integrated ecological risk of five types of toxic heavy metals, and was only applied in the case study of Songhua River, but further researches on more heavy metals and rivers were needed to explain the specifications of the application.

5 Conclusions and prospects

1) Heavy metal toxicity coefficient, pollution index and detection rate were selected as the key factors to calculate the ecological risk index in aquatic, biological and sedimentary phases. Different parameters such as sampling numbers, water quality and pollutant concentrations were selected for absolute study of three phases. Finally, the comprehensive ecological risk assessment model was constructed and heavy metals of higher risk indexes were concluded as priority pollutants.

2) The model was applied to assessing five types of toxic heavy metals in the Songhua River. The results concluded higher detection rate of heavy metals and descending order of their biological toxicity was as Hg>Cd>As>Pb>Cr, while the richness in phases was categorized as aquatic> biological> sedimentary.

3) The ecological risk index of five heavy metals in the single medium was as aquatic>biological> sedimentary, while the comprehensive ecological risk index in three phases was Cd>Hg>As>Pb>Cr. Therefore, Cd and Hg were selected as the priority pollutants of the Songhua River.

4) The team decided further study to mitigate the model limits as this was only applied for heavy metal detection in the Songhua River. Parameter selection and weight determination still needed intensive studies. Moreover, the research on the risk hierarchy was acting as a milestone in future studies.

References

[1] WANG Bin, ZHANG Zheng. The features and potential ecological risk assessme of soil heavy metals in Tianjin suburban farmland [J]. Environmental Monitoring in China, 2012, 28(3): 23-26. (in Chinese)

[2] FORSTNER V. Metal pollution in the aquatic environment [M]. Berlin: Springer, Verleg, 1978: 110-192.

[3] ZHU Hui-na, YUAN Xing-zhong, ZENG Guang-ming, JIANG Min, LIANG Jie, ZHANG Chang, YIN Juan, HUANG Hua-jun, LIU Zhi-feng, JIANG Hong-wei. Ecological risk assessment of heavy metals in sediments of Xiawan port based on modified potential ecological risk index [J]. Transactions of Nonferrous Metals Society of China, 2012, 22(6): 1470-1477.

[4] MIN Xiao-bo,XIE Xian-de, CHAILi-yuan, LIANG Yan-jie, LI Mi, KE Yong. Environmental availability and ecological risk assessment of heavy metals in zinc leaching residue [J]. Transactions of Nonferrous Metals Society of China, 2013, 23(1): 208-218.

[5] RAHMAN M S, MOLLA A H, SAHA N, RAHMAN A. Study on heavy metals levels and its risk assessment in some edible fishes from Bangshi River, Savar, Dhaka, Bangladesh [J]. Food Chemistry, 2012, 134(4): 1847-1854.

[6] NAZEER S, HASHMI M Z, MALIK R N. Heavy metals distribution, risk assessment and water quality characterization by water quality index of the River Soan, Pakistan [J]. Ecological Indicators, 2014, 43: 262-270.

[7] XU Qiu-jin, LI Li, LIANG Cun-zhen, CHEN Xiao-ying. Screening of priority control pollutants from the rural drinking water sources in Huai’an city [J]. China Environmental Science, 2013, 33(4): 631-638. (in Chinese)

[8] G. Index of geoaccumulation in sediments of the Rhine River [J]. Geojournal, 1969, 2: 108-118.

[9] HAKANSON L．An ecological risk index fox aquatic pollution control: A sediment ological approach [J]. Water Res, 1980, 14(8): 975-1001.

[10] HILTON J, DAVISON W, OCHSENBEIN U. A mathematical model for analysis of sediment coke data: Implications for enrichment factor calculation and trace transport mechanisms [J]. Chemical Geology, 1985, 48: 281-291.

[11] PEI Shu-wei, ZHOU Jun-li, LIU Zheng-tao. Research progress on screening of environment priority pollutants [J]. Journal of Environmental Engineering Technology, 2013, 3(4): 363-368. (in Chinese)

[12] ATSDR. Substance priority list (SPL) resource page [EB/OL] [2013-01-28]. http://www. atsdr. Cdc. Gov/SPL/resources/index. html.

[13] EC. Priority substances supporting information and documentation [EB/OL] [2013-01-28]. http://ec. europa. eu/environment/water/ water-dangersub/lib_pri_substances. html.

[14] ZHUANG Ping, ZOU Hui-ling, SHU Wen-sheng. Biotransfer of heavy metals along a soil-plant-insect-chicken food chain: Field study [J]. Journal of Environmental Science, 2009, 21(6): 849-853.

[15] JIA H J, REN H F, SATOH S C. Comparison of pretreatment condition of cadmium in fish sample and diet by microwave digestion method for ICP AES [J]. Journal of the Tokyo University of Marine Science and Technology, 2005, 1: 41-46.

[16] SUTHAR S, NEMA A K, CHABUKDHARA M, GUPTA S K. Assessment of metals in water and sediment of Hindon River, India: Impact of industrial and urban discharges [J]. Journal of Hazardous Materials, 2009, 171(1-3): 1088-1095.

[17] ANGULO E. The Tomlinson pollution load index applied to heavy metal, ‘mussel-watch’ data: A useful index to assess coastal pollution [J]. Science of the Total Environment, 1996, 187(1): 19-56.

[18] WANG Guo-ping, LIU Jing-shuang, TANG Jie. Assessment of heavy metal pollution of wetlands at downstream of an inland river [J]. Rural Eco-Environment, 2004, 20(2): 50-54. (in Chinese)

[19] Editorial Board of Chinese Academy of Sciences Changchun Branch. Research on environmental problems of Songhua River [M]. Beijing: Science Press Ltd, 1992. (in Chinese)

[20] ZHU Qing-qing, WANG Zhong-liang. Distribution characteristics and source analysis of heavy metals in sediments of the main river systems in China [J]. Earth and Environment, 2012, 40(3): 305-313. (in Chinese)

[21] SUN Jing-wen, YU Hong-bing, LI Yun-fei. Monitoring and pollution assessment of heavy metals in fishes of Songhua River [J]. South-to-North Water Transfers and Water Science ＆ Technology, 2013, 11(5): 29-33. (in Chinese)

[22] LU Ji-long, HAO Li-bo, ZHAO Yu-bo, BAI Rong-jie, SUN Su-mei, PANG Chen-chen. Contents and potential ecological risk of heavy metals in middle and lower reaches of Second Songhua River [J]. Environmental Science & Technology, 2009, 32(5): 168-172. (in Chinese).

河流三相空间重金属污染综合生态风险评价

张 颖¹，周 军^1,2，高凤杰¹，张宝杰²，马 彪²，李立清³

1. 东北农业大学 资源与环境学院，哈尔滨 150030；2. 黑龙江省环境科学研究院，哈尔滨 150056；

3. 中南大学 能源科学与工程学院，长沙 410083

摘  要：当前，有关重金属在河流水相、生物相和固相三相空间的综合污染效应研究尚未见报道，因此主要围绕河流三相空间重金属风险评估展开研究。大量学者在开展单一介质空间生态风险评估时，主要考虑重金属毒性系数和污染指数。本文构建的综合生态风险评价模型表明重金属对环境安全具有严重威胁。该模型应用于松花江5种有毒重金属污染综合效应评价，结果表明，5种有毒重金属在单一介质中生态风险指数均表现为水相>生物相>固相，三相空间综合生态风险指数由高到低排序为Cd>Hg>As>Pb>Cr。在此基础上，将Cd和Hg筛选为松花江重金属优控污染物。

关键词：综合生态风险评价；优控污染物筛选；重金属；松花江

(Edited by Yun-bin HE)

Foundation item: Project (2010467038) supported by the Special Fund for Environmental Research in the Public Interest, China

Corresponding authors: Bao-jie ZHANG; Tel: +86-451-51970502; E-mail: 13633636380@126.com;

Jun ZHOU; Tel: +86-451-5197516; E-mail: zhoujunhky@126.com

DOI: 10.1016/S1003-6326(15)63979-6