An unconstrained ordination- and GIS-based approach for identifying anthropogenic sources of heavy metal pollution in marine sediments

Marine Pollution Bulletin 146 (2019) 100–105

Contents lists available at ScienceDirect

Marine Pollution Bulletin journal homepage: www.elsevier.com/locate/marpolbul

An unconstrained ordination- and GIS-based approach for identifying anthropogenic sources of heavy metal pollution in marine sediments

T

⁎

Yang-Guang Gua,d,e, , Yan-Peng Gaob,c a

South China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Guangzhou 510300, China Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou 510006, China c School of Environmental Science and Engineering, Guangdong University of Technology, Guangzhou 510006, China d Key Laboratory of Fishery Ecology and Environment, Guangdong Province, Guangzhou 510300, China e Key Laboratory of Open-Sea Fishery Development, Ministry of Agriculture and Rural Affairs, Guangzhou 510300, China b

A R T I C LE I N FO

A B S T R A C T

Keywords: Heavy metal Sediments Nonmetric multidimensional scaling Geographic information system (GIS) Pollution

A new method consisting of enrichment factor (EF) determination, nonmetric multidimensional scaling (NMS), and the geographic information system (GIS) technique was firstly developed to identify anthropogenic heavy metal sources in marine sediments of Hong Kong. Firstly, the EF was determined to differentiate between heavy metals originating from human and natural sources. Subsequently, NMS was applied to identify various source patterns of heavy metals, and the NMS score was calculated and spatially interpolated using GIS technology to evaluate the spatial influences of anthropogenic impacts in different areas. The concentrations of heavy metals in sediments of Hong Kong substantially exceeded their background values, demonstrating anthropogenic pollution. Two different types of human sources could be identified via NMS, one representing the industrial pollution discharges in the period from the 1960s to the 1980s before pollution control was introduced and one representing sewage discharge before the Tolo Harbour Action Plan in the mid-1980s.

1. Introduction Sediments are considered to be major heavy metal reservoirs in aquatic environments, but may also act as pollution sources and release heavy metals back into the water column (Gu et al., 2012; Dunlea et al., 2017; Samanta and Dalai, 2018). Heavy metals in aquatic environments are a global concern because of their persistence and subsequent bioaccumulation in aquatic habitats, consequently entering the food chain and negatively affecting human health (Koenig, 2000; Fu et al., 2014; Lu et al., 2015). Identifying the possible heavy metal sources in sediments is crucial to better understand and control the heavy metal pollution in aquatic ecosystem. For this purpose, several methods have been developed, of which three models, namely positive matrix factorization (PMF), absolute principal components analysis - multiple linear regression (APCA-MLR), and factor analysis (FA), combined with GIS technology, have been most widely used in source identification studies (Zhou et al., 2007a; Gu et al., 2012; Pekey and Doğan, 2013; Huang et al., 2018). However, these models have several constraints and may result in the loss of useful information in dimensionality reduction of datasets when the used data are not well transformed to meet the requirements of data

⁎

normality and/or equal variance prior to analysis (Roweis and Saul, 2000; Hinton and Salakhutdinov, 2006; Hui et al., 2015); consequently, these techniques may not be used appropriately. Nonmetric multidimensional scaling (NMS) is a typical unconstrained ordination method, which makes no assumptions about the distribution of the underlying data (Jiang et al., 2010; Hui et al., 2015) and is extensively used in community ecology (Gerig et al., 2016; Coutinho et al., 2017; Ulloa Ulloa et al., 2017). The enrichment factor (EF) is a useful method to evaluate anthropogenic influences of heavy metals in sediments (Zhang et al., 2007). The spatial distribution of metal pollution is useful to assess the possible sources of enrichment and to identify “hot spot” areas (Gu et al., 2012). In this context, a new method, based on the combination of EF, NMS, and GIS, was developed to identify anthropogenic sources of heavy metals in sediments. Namely, EF was applied to determine the degree of pollution by heavy metals from natural and human sources in Hong Kong. Subsequently, NMS, coupled with GIS, was undertaken to produce continuous spatial patterns at regional scales and to explore anthropogenic activities and heavy metal origins.

Corresponding author at: South China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Guangzhou 510300, China. E-mail address: [email protected] (Y.-G. Gu).

https://doi.org/10.1016/j.marpolbul.2019.06.008 Received 22 February 2019; Received in revised form 3 June 2019; Accepted 3 June 2019 0025-326X/ © 2019 Elsevier Ltd. All rights reserved.

Marine Pollution Bulletin 146 (2019) 100–105

Y.-G. Gu and Y.-P. Gao

2. Material and methods

studies of in Hong Kong's marine sediments (Zhou et al., 2007a; Zhou et al., 2007b).

2.1. Study setting and data collection 2.4. Data pretreatment and statistical analysis Hong Kong, covering an area of 2754 km2 (1104 km2 of land and 1651 km2 of marine waters), is located in the east of the Pearl River Estuary (PRE) north of the South China Sea (22°90′–22°37′N, 113°52′–114°30′E). As one of the most densely populated places in the world, its marine environment has been heavily impacted by anthropogenic activities (e.g., land reclamation, mariculture, transportation, and effluent disposal) (Zhou et al., 2007a; HKG, 2015). Since the mid1950s, growing manufacturing industries and urbanization have been contributing to substantial heavy metal pollution, severely contaminating the marine sediments of Hong Kong (Zhou et al., 2007b; Tang et al., 2008). In recent years, a sewage disposal strategy has been implemented by the Government, and 16 sewerage master plans have been launched (HK, 2019). The Hong Kong Environmental Protection Department (HKEPD) is the waste disposal authority responsible for the planning and development of waste treatment and disposal facilities and has established an integral marine environmental quality monitoring program in 1986 (HK, 2019). Within this program, sediment samples are obtained at 59 sites twice a year and analyzed for over 40 physical and chemical parameters. For this study, sedimentary heavy metal and fine-grain-size data (< 63 μm) were downloaded from the open database for Marine Water Quality Monitoring Data at the HKEPD website (https://www.epd.gov. hk/epd/epic/english/data_marine.html). The selected data comprise seven heavy metals (Cd, Pb, Cr, Ni, Cu, Zn and Hg), monitored twice a year at 59 stations (Fig. 1) from 2012 to 2016. Based on the relatively recent data, heavy metal sources can be identified and further heavy metal pollution can be managed.

To reduce dilution of contaminated fine-grained (< 63 μm) sediment by coarse particles (> 63 μm), concentrations of heavy metals in a bulk sediment sample should be normalized by particle size distribution (Mudroch and Azcue, 1995; Loring and Rantala, 1992). Based on the normalization procedure developed by Loring and Rantala (1992), concentrations of heavy metals are plotted against the percentage of the < 63 μm in the bulk sediments to establish the relationship between grain sizes. Subsequently, unary linear regression is conducted to compute heavy metal concentrations in fine-grained sediments (< 63 μm). The above detail is described in the Supplementary material. Accordingly, heavy metal concentrations in bulk sediments were normalized to fine-grained sediments. For any heavy metal with a concentration below the reporting limit (RL), 1/2 RL was applied to calculate the actual concentration. The one-sample t-test is used to determine whether the mean of a single variable is statistically different from a specified constant (IBMKC, 2019). In this study, the mean concentrations of Cd, Pb, Cr, Ni, Cu, Zn, and Hg in the sampled sediments were compared with their corresponding background values based on one-sample t-tests, using the software package SPSS 19.0 for Windows. According to Jiang et al. (2010), NMS analysis using the Bray-Curtis distance measure and 30 random initial starting configurations were adopted to avoid a local stress minimum. The NMS analysis was carried out using the vegan package in R software version 3.4.1. The vegan package is an ordination method for analysis of diversity and other functions (Oksanen et al., 2019). Inverse distance weighting (IDW) interpolation, which assumes that things that are close to one another are more alike than those that are farther apart, is widely used to elucidate spatial variation and distribution of pollutants including metals (Gu et al., 2012; Mesnard, 2013). Considering the distribution of metals has resemble traits to IDW's assumption, IDW method with weighting power of 2.0 was implemented to illuminate spatial variations of NMS scores in this study produced by NMS analysis. A good agreement between measured line and predicted line with the acceptable mean error (< 0.05) and RootMean-Square (< 0.05) were obtained using ArcGIS 10.0, indicating that IDW interpolation was effectively applied in this study.

2.2. An unconstrained ordination- and GIS-based method An unconstrained ordination- and GIS-based method (UOGM) is a new scheme that consists of three sequential steps. The first step, considering the enrichment factor (EF), is conducted to distinguish individual metal from anthropogenic and natural sources (Section 3.2). In the second step, NMS is used to identify source patterns of metals (Section 3.3), and in the third step, GIS is applied to evaluate influences of human impacts, based on NMS scores (Section 3.3). The new method requires the number of sampling sites no < 15, so that the NMS analysis can be performed using the vegan package in R software. In addition, the sampling sites should not be distributed close to a straight line in order to facilitate the interpolation of the data in the GIS.

3. Results and discussion 3.1. Heavy metal concentration

2.3. Enrichment factor model

The basic descriptive statistics of Cd, Pb, Cr, Ni, Cu, Zn, and Hg in the marine sediments of Hong Kong are presented in Table 1. Among the selected heavy metals, the most abundant metal was Zn, reaching a mean concentration of 146.0 mg/kg. The second and third most abundant metals were Cu and Pb, respectively, with average concentrations of 88.7 and 47.9 mg/kg, respectively. The mean concentration of Cr was 39.3 mg/kg, approximately twice as high as that of Ni (20.4 mg/kg). Mean Cd and Hg levels were 0.4 and 0.2 mg/kg, respectively. The heavy metal concentrations displayed a high degree of variability, indicated by the large coefficients of variation (CV) from 54.5% for Pb to 300.1% for Cu. Previous studies showed that the elevated CV may be a result of anthropogenic sources (Luo et al., 2007; Li et al., 2013; Lv, 2019). The three highest CV values were found for Cu, Cd, and Hg, suggesting that these three metals may be more impacted by anthropogenic activities than other heavy metals. The mean concentrations of Cd, Pb, Cr, Ni, Cu, Zn, and Hg in the sampled sediments were notably higher than their corresponding background values (Table 1), indicating significant enrichment via anthropogenic activities. For comparison, the heavy metal

The EF was determined to evaluate the level of contamination and the possible anthropogenic impact on sediments. According to Gu et al. (2016a), the EF model is calculated as follows:

EF =

(Metal/Fe)sample (Metal/Fe) background

where (Metal/Fe)sample is the heavy metal to Fe ratio in a sample; (Metal/Fe)background is the natural background value of the metal to Fe ratio. The background values of Cd, Pb, Cr, Ni, Cu, Zn and Hg are listed in Table 1. Generally, metals such as Fe, Al, and Mn are applied as normalizers (Elmaleh et al., 2012; Duodu et al., 2016; Gu et al., 2016a). Previous studies have demonstrated that Fe as a normalizer can be used in Hong Kong's marine sediments (Zhou et al., 2007a; Zhou et al., 2007b), which is why we used it in this study. However, a background value of Fe in the study area is not available, and therefore, we used the background value of the earth crust, which is 30,890 mg/kg (Wedepohl, 1995), which has als been implemented in heavy metal contamination 101

Marine Pollution Bulletin 146 (2019) 100–105

Y.-G. Gu and Y.-P. Gao

Fig. 1. Map of the study area and sampled sites in Hong Kong's sea area and locations of WCZs illustrated in Figs. S1–S10, redrawn and derived from https://cd.epic. epd.gov.hk/EPICRIVER/marine/history/sediment/; WCZ: Water Control Zone.

3.3. Anthropogenic impacts of heavy metals

concentrations of other bays/estuaries around the world are shown in Table 1. The levels in the sediments of Hong Kong are higher than those in Bohai Bay (China) (Gao et al., 2014), the Yangtze River Estuary (China) (Wang et al., 2015), Qinzhou Bay (China) (Gu et al., 2015), and Florida Bay (USA) (Caccia et al., 2003), but lower than those at Daya Bay (China) (Gu et al., 2016b), the Pearl River Estuary (China) (Ip et al., 2007), and the Brisbane River Estuary (Australia) (Duodu et al., 2017).

We conducted NMS to identify the two groups of heavy metals for all sediment samples, which represented 96.1% of the total information. Moreover, stress was used to examine the validity of the NMS before interpreting the results; the stress value was 0.0357, indicating a good dimensionality reduction (Lattin et al., 2003). The studied heavy metals were clearly separated into two groups by NMS ordination (Fig. 3). The NMS1 was associated with Cd, Cu, and Hg (NMS1, 91.1% of the total variance), representing human sources according to Fig. 2; NMS2 was associated with Pb, Cr, Ni, and Zn (NMS2, 5.0% of the total variance), likely representing other sources of anthropogenic pollution. The spatial distributions of the two different types of anthropogenic impacts (NMS1 and NMS2) are given in Fig. 4. The regions influenced by the first type of human pollution sources (NMS1) were centered in the areas around Victoria Harbor (Fig. 4A). According to the marine water quality in Hong Kong from 2012 to 2016, heavy metals in the sediments of Victoria Harbor are derived from human sources associated with industrial pollution discharges in the period from the 1960s to the 1980, prior to the introduction of pollution control (HKEPD, 2013, 2014, 2015, 2016, 2017). The regions impacted by the second type of human impacts (NMS2) were found in inner Tolo Harbor (TS2, TS3 and TS7) (Fig. 4B). Sampling points TS2, TS3, and TS7 were located in estuaries of the Lam Tsuen River (Tai Po New Town) and the Shing Mun River (Sha Tin New Town). The two rivers carried large amounts of various pollutants into

3.2. Differentiating natural and anthropogenic impacts The heavy metal concentrations in sediments never reflect the degree of contamination from anthropogenic influences because of grainsize distribution and mineralogy (Tam and Yao, 1998). Therefore, EF is thought as a convenient approach to assess anthropogenic influences of heavy metals in sediments (Liaghati et al., 2004; Gu et al., 2016a). Furthermore, this approach can be used to differentiate heavy metals from natural and anthropogenic sources (Gu et al., 2016a). According to the EF criteria of our previous study (Gu et al., 2016a), if the EF value is below 0.5, the metal is mainly from natural sources, while EF values between 0.5 and 1.5 indicate that the metal is entirely from either natural or anthropogenic sources. An EF value > 1.5 indicates that the metal is mainly a result of anthropogenic pollution. At most sites, the EF values for Cd, Pb, Cr, Ni, Cu, and Zn were higher than 1.5 from 2012 to 2016, suggesting that theses metals mainly originated from anthropogenic sources (Fig. 2). 102

Marine Pollution Bulletin 146 (2019) 100–105

Y.-G. Gu and Y.-P. Gao

Table 1 Basic statistics of heavy metal concentrations in Hong Kong's marine sediments (n = 539) compared with the average heavy metal concentration in sediments of other bays/harbors in the world (mg/kg, dry weight).

This study

BVa BHBb YREc DYBd PRE

e

QZB

f

FBg BREh

Mean, SD Median Range CV% Mean, Range Mean Range Mean, Range Mean, Range Mean, Range Mean Range Range

SD

SD SD SD

Cd

Pb

Cr

Ni

Cu

Zn

Hg

0.4⁎ ± 0.8 0.1 < 0.1–13.3 212.4 0.05 0.24 ± 0.17 0.04–0.84 0.19 0.07–0.71 0.08 ± 0.04 0.02–0.13 n.a n.a 0.16 ± 0.07 0.08–0.28 n.a n.a 0.6–0.9

47.9⁎ ± 28.7 40.0 15.0–400.0 59.8 19 21.2 ± 18.3 5.9–97.0 23.8 14.18–45.09 51.3 ± 13.69 27.24–66.89 16.0 ± 96.3 47.9–13.7 46.56 ± 19.84 11.45–70.32 8.4 3–15.7 25–126

39.3⁎ ± 37.8 32.0 13.0–510.0 96.1 7 n.a n.a 79.1 50.35–123.1 91.30 ± 35.12 35.19–135.42 87.6 ± 22.0 33.8–135 50.18 ± 29.47 6.61–84.91 162 60–347 82–332

20.4⁎ ± 11.1 20.0 5–160.0 54.5 10 n.a n.a 31.9 19.92–42.19 n.a n.a 34.8 ± 10.1 10.6–54.1 27.03 ± 9.86 9.30–41.07 21 5–54 20–34

88.7⁎ ± 266.3 29.0 6.0–4300.0 300.1 7 28.1 ± 8.1 7.2–44.0 24.7 9.67–49.21 29.63 ± 16.83 9.05–62.61 46.8 ± 17.0 6.2–100 27.07 ± 13.03 1.89–43.45 15 7–32 20–110

146.0⁎ ± 94.2 110.0 42.0–9 80.0 64.6 40 102.5 ± 33.5 56.3–308.5 82.9 46.52–126.74 143.42 ± 28.54 92.25–172.06 140 ± 42 55.1–268 73.60 ± 29.29 28.01–121.00 31 10–48 142–257

0.2⁎ ± 0.3 0.1 < 0.05–2.7 148.3 0.07 0.05 ± 0.03 0.01–0.18 n.a n.a 0.03 ± 0.01 0.02–0.05 n.a n.a n.a n.a n.a n.a 1–2

Note: ⁎ p < 0.01 significance level. a Background value (BV) of marine sediments in Hong Kong (Lau et al., 1993); n.a: not available. b BHB: Bohai Bay (China) (Gao et al., 2014). c YRE: Yangtze River Estuary (China) (Wang et al., 2015). d DYB: Daya Bay (China) (Gu et al., 2016a, 2016b). e PRE: Pearl River Estuary (China) (Ip et al., 2007). f QZB: Qinzhou Bay (China) (Gu et al., 2015). g FB: Florida Bay (USA) (Caccia et al., 2003). h BRE: Brisbane River Estuary (Australia) (Duodu et al., 2017). 0.4

Stress=0.0357 0.3 0.2

Cd Zn

NMS2 (5.0%)

0.1

Hg 0.0

Cu

Pb

-0.1

Cr

-0.2

Ni -0.3 -0.4 -0.4

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

NMS1 (91.1%)

Fig. 2. Enrichment factors (EFs) of heavy metals in Hong Kong's marine sediments. Note: Values were normalized using Fe and local background levels in Hong Kong (Lau et al., 1993) and the earth crust average as references (Wedepohl, 1995). Error bar represents standard deviation.

Fig. 3. Nonmetric multidimensional scaling (NMS) ordination of heavy metals in Hong Kong's marine sediments, using Bray-Curtis similarity matrix.

Hong Kong, NMS, coupled with GIS, was undertaken to explore the spatial distribution patterns of heavy metals in marine sediments and to determine the anthropogenic impacts. Based on our results, the concentrations of heavy metals were significantly higher than their corresponding background values, indicating considerable anthropogenic enrichment. Enrichment factors indicated that Cd, Pb, Cr, Ni, Cu, Zn, and Hg were primarily derived from human activities. The NMS analysis further subdivides anthropogenic impacts and their influenced areas; the two groups explained 91.1 and 5% of the total variances. The mainly anthropogenic sources in the two areas were (1) discharge of industrial waste in the period from the 1960 to the 1980s and (2) sewage discharge before the Tolo Harbour Action Plan in the mid1980s. In addition, GIS-based analysis was implemented to locate these

Tolo Harbor, while sewage affected inner Tolo Harbour before the Tolo Harbour Action Plan in the mid-1980s (HKEPD, 2013, 2014, 2015, 2016, 2017). Nevertheless, discharges from these two rivers have been stopped long ago. However, heavy metals are still accumulated in the sediments of the estuaries of the two rivers because of the poor tidal exchange with outer Tolo Harbour and Mirs Bay (Xu et al., 2011; Archana et al., 2016; Li et al., 2017; Liu et al., 2019). Therefore, the hotspot of NMS2 in inner Tolo Harbor is probably the result of riverine delivery, combined with poor tidal exchange. 4. Conclusions Based on the HKEPD's heavy metal data in marine sediments of 103

Marine Pollution Bulletin 146 (2019) 100–105

Y.-G. Gu and Y.-P. Gao

Fig. 4. Spatial distribution of scores of NMS1 (A) and NMS2 (B), using the inverse distance weighting (IDW) technique in GIS.

two sources.

interest Scientific Institution Basal Research Fund, the South China Sea Fisheries Research Institute, CAFS (2018ZD01), and the Key Laboratory of Fishery Ecology and Environment of Guangdong Province (FEEL2017-14). We gratefully acknowledge the Hong Kong Environmental Protection Department for providing the raw data of marine sediment quality. We are also grateful to the anonymous reviewers for helpful

Acknowledgements This study was funded by the Guangzhou Science and Technology Program key projects, China (201707010368), the Central Public104

Marine Pollution Bulletin 146 (2019) 100–105

Y.-G. Gu and Y.-P. Gao

comments on the manuscript.

Hazard. Mater. 354, 161–169. Hui, F.K.C., Taskinen, S., Pledger, S., Foster, S.D., Warton, D.I., 2015. Model-based approaches to unconstrained ordination. Methods Ecol. Evol. 6, 399–411. IBMKC (IBM Knowledge Center), 2019. One-sample T test. https://www.ibm.com/ support/knowledgecenter/en/SSLVMB_sub/statistics_mainhelp_ddita/spss/base/idh_ ttss.html. Ip, C.C.M., Li, X.D., Zhang, G., Wai, O.W.H., Li, Y.-S., 2007. Trace metal distribution in sediments of the Pearl River Estuary and the surrounding coastal area, South China. Environ. Pollut. 147, 311–323. Jiang, S.J., Bralower, T.J., Patzkowsky, M.E., Kump, L.R., Schueth, J.D., 2010. Geographic controls on nannoplankton extinction across the Cretaceous/Palaeogene boundary. Nat. Geosci. 3, 280. Koenig, R., 2000. Wildlife deaths are a grim wake-up call in eastern Europe. Science 287, 1737–1738. Lattin, J.M., Carroll, J.D., Green, P.E., 2003. Analyzing Multivariate Data. China Machine Press. Lau, M.M.-m., Rootham, R.C., Bradley, G.C., 1993. A strategy for the management of contaminated dredged sediment in Hong Kong. J. Environ. Manag. 38, 99–114. Li, X.Y., Liu, L.J., Wang, Y.G., Luo, G.P., Chen, X., Yang, X.L., Hall, M.H.P., Guo, R., Wang, H., Cui, J.H., He, X.Y., 2013. Heavy metal contamination of urban soil in an old industrial city (Shenyang) in Northeast China. Geoderma 192, 50–58. Li, X.L., Luo, J.H., Guo, G., Mackey, H.R., Hao, T.W., Chen, G.H., 2017. Seawater-based wastewater accelerates development of aerobic granular sludge: a laboratory proofof-concept. Water Res. 115, 210–219. Liaghati, T., Preda, M., Cox, M., 2004. Heavy metal distribution and controlling factors within coastal plain sediments, Bells Creek catchment, southeast Queensland, Australia. Environ. Int. 29, 935–948. Liu, Y., Not, C., Jiao, J.J., Liang, W.Z., Lu, M.Q., 2019. Tidal induced dynamics and geochemical reactions of trace metals (Fe, Mn, and Sr) in the salinity transition zone of an intertidal aquifer. Sci. Total Environ. 664, 1133–1149. Loring, D.H., Rantala, R.T.T., 1992. Manual for the geochemical analyses of marine sediments and suspended particulate matter. Earth Sci. Rev. 32, 235–283. Lu, Y.L., Jenkins, A., Ferrier, R.C., Bailey, M., Gordon, I.J., Song, S., Huang, J.K., Jia, S.F., Zhang, F.S., Liu, X.J., Feng, Z.Z., Zhang, Z.B., 2015. Addressing China's grand challenge of achieving food security while ensuring environmental sustainability. Sci. Adv. 1, e1400039. Luo, W., Wang, T.Y., Lu, Y.L., Giesy, J.P., Shi, Y.J., Zheng, Y.M., Xing, Y., Wu, G.H., 2007. Landscape ecology of the Guanting Reservoir, Beijing, China: multivariate and geostatistical analyses of metals in soils. Environ. Pollut. 146, 567–576. Lv, J.S., 2019. Multivariate receptor models and robust geostatistics to estimate source apportionment of heavy metals in soils. Environ. Pollut. 244, 72–83. Mesnard, L., 2013. Pollution models and inverse distance weighting: some critical remarks. Comput. Geosci. 52, 459–469. Mudroch, A., Azcue, J.M., 1995. Manual of Aquatic Sediment Sampling. CRC Press. Oksanen, J., Blanchet, F.G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., Minchin, P.R., O'Hara, R.B., Simpson, G.L., Solymos, P., Stevens, M.H.H., Szoecs, E., Wagner, H., 2019. Package ‘vegan’. https://cran.r-project.org/web/packages/vegan/vegan. pdf. Pekey, H., Doğan, G., 2013. Application of positive matrix factorisation for the source apportionment of heavy metals in sediments: a comparison with a previous factor analysis study. Microchem. J. 106, 233–237. Roweis, S.T., Saul, L.K., 2000. Nonlinear dimensionality reduction by locallylinear embedding. Science 290, 2323–2326. Samanta, S., Dalai, T.K., 2018. Massive production of heavy metals in the Ganga (Hooghly) River estuary, India: global importance of solute-particle interaction and enhanced metal fluxes to the oceans. Geochim. Cosmochim. Acta 228, 243–258. Tam, N.F.Y., Yao, M.W.Y., 1998. Normalisation and heavy metal contamination in mangrove sediments. Sci. Total Environ. 216, 33–39. Tang, C.W.Y., Ip, C.C.M., Zhang, G., Shin, P.K.S., Qian, P.Y., Li, X.D., 2008. The spatial and temporal distribution of heavy metals in sediments of Victoria Harbour, Hong Kong. Mar. Pollut. Bull. 57, 816–825. Ulloa Ulloa, C., Acevedo-Rodríguez, P., Beck, S., Belgrano, M.J., Bernal, R., Berry, P.E., Brako, L., Celis, M., Davidse, G., Forzza, R.C., Gradstein, S.R., Hokche, O., León, B., León-Yánez, S., Magill, R.E., Neill, D.A., Nee, M., Raven, P.H., Stimmel, H., Strong, M.T., Villaseñor, J.L., Zarucchi, J.L., Zuloaga, F.O., Jørgensen, P.M., 2017. An integrated assessment of the vascular plant species of the Americas. Science 358, 1614–1617. Wang, H.T., Wang, J.W., Liu, R.M., Yu, W.W., Shen, Z.Y., 2015. Spatial variation, environmental risk and biological hazard assessment of heavy metals in surface sediments of the Yangtze River estuary. Mar. Pollut. Bull. 93, 250–258. Wedepohl, H.K., 1995. The composition of the continental crust. Geochim. Cosmochim. Acta 59, 1217–1232. Xu, J., Lee, J.H.W., Yin, K.D., Liu, H.B., Harrison, P.J., 2011. Environmental response to sewage treatment strategies: Hong Kong's experience in long term water quality monitoring. Mar. Pollut. Bull. 62, 2275–2287. Zhang, L.P., Ye, X., Feng, H., Jing, Y.H., Ouyang, T., Yu, X.T., Liang, R.Y., Gao, C.T., Chen, W.Q., 2007. Heavy metal contamination in western Xiamen Bay sediments and its vicinity, China. Mar. Pollut. Bull. 54, 974–982. Zhou, F., Guo, H., Liu, L., 2007a. Quantitative identification and source apportionment of anthropogenic heavy metals in marine sediment of Hong Kong. Environ. Geol. 53, 295–305. Zhou, F., Guo, H.C., Hao, Z.J., 2007b. Spatial distribution of heavy metals in Hong Kong's marine sediments and their human impacts: a GIS-based chemometric approach. Mar. Pollut. Bull. 54, 1372–1384.

Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.marpolbul.2019.06.008. References Archana, A., Li, L., Shuh-Ji, K., Thibodeau, B., Baker, D.M., 2016. Variations in nitrate isotope composition of wastewater effluents by treatment type in Hong Kong. Mar. Pollut. Bull. 111, 143–152. Caccia, V.G., Millero, F.J., Palanques, A., 2003. The distribution of trace metals in Florida bay sediments. Mar. Pollut. Bull. 46, 1420–1433. Coutinho, F.H., Silveira, C.B., Gregoracci, G.B., Thompson, C.C., Edwards, R.A., Brussaard, C.P.D., Dutilh, B.E., Thompson, F.L., 2017. Marine viruses discovered via metagenomics shed light on viral strategies throughout the oceans. Nat. Commun. 8, 15955. Dunlea, A.G., Murray, R.W., Santiago Ramos, D.P., Higgins, J.A., 2017. Cenozoic global cooling and increased seawater Mg/Ca via reduced reverse weathering. Nat. Commun. 8, 844. Duodu, G.O., Goonetilleke, A., Ayoko, G.A., 2016. Comparison of pollution indices for the assessment of heavy metal in Brisbane River sediment. Environ. Pollut. 219, 1077–1091. Duodu, G.O., Goonetilleke, A., Ayoko, G.A., 2017. Potential bioavailability assessment, source apportionment and ecological risk of heavy metals in the sediment of Brisbane River estuary, Australia. Mar. Pollut. Bull. 117, 523–531. Elmaleh, A., Galy, A., Allard, T., Dairon, R., Day, J.A., Michel, F., Marriner, N., Morhange, C., Couffignal, F., 2012. Anthropogenic accumulation of metals and metalloids in carbonate-rich sediments: insights from the ancient harbor setting of Tyre (Lebanon). Geochim. Cosmochim. Acta 82, 23–38. Fu, J., Zhao, C.P., Luo, Y.P., Liu, C.S., Kyzas, G.Z., Luo, Y., Zhao, D.Y., An, S.Q., Zhu, H.L., 2014. Heavy metals in surface sediments of the Jialu River, China: their relations to environmental factors. J. Hazard. Mater. 270, 102–109. Gao, X.L., Zhou, F.X., Chen, C.-T.A., 2014. Pollution status of the Bohai Sea: an overview of the environmental quality assessment related trace metals. Environ. Int. 62, 12–30. Gerig, B.S., Chaloner, D.T., Janetski, D.J., Rediske, R.R., O'Keefe, J.P., Moerke, A.H., Lamberti, G.A., 2016. Congener patterns of persistent organic pollutants establish the extent of contaminant biotransport by Pacific Salmon in the Great Lakes. Environ. Sci. Technol. 50, 554–563. Gu, Y.G., Wang, Z.H., Lu, S.H., Jiang, S.J., Mu, D.H., Shu, Y.H., 2012. Multivariate statistical and GIS-based approach to identify source of anthropogenic impacts on metallic elements in sediments from the mid Guangdong coasts, China. Environ. Pollut. 163, 248–255. Gu, Y.G., Lin, Q., Yu, Z.L., Wang, X.N., Ke, C.L., Ning, J.J., 2015. Speciation and risk of heavy metals in sediments and human health implications of heavy metals in edible nekton in Beibu Gulf, China: a case study of Qinzhou Bay. Mar. Pollut. Bull. 101, 852–859. Gu, Y.G., Gao, Y.P., Qin, L., 2016a. Contamination, bioaccessibility and human health risk of heavy metals in exposed-lawn soils from 28 urban parks in southern China's largest city, Guangzhou. Appl. Geochem. 67, 52–58. Gu, Y.G., Wang, X.N., Lin, Q., Du, F.Y., Ning, J.J., Wang, L.G., Li, Y.F., 2016b. Fuzzy comprehensive assessment of heavy metals and Pb isotopic signature in surface sediments from a bay under serious anthropogenic influences: Daya Bay, China. Ecotoxicol. Environ. Saf. 126, 38–44. Hinton, G.E., Salakhutdinov, R.R., 2006. Reducing the dimensionality of data with neural networks. Science 313, 504–507. HK (Hong Kong), 2019. Hong Kong: The Facts Environmental Protection. https://www. gov.hk/en/about/abouthk/factsheets/docs/environmental_protection.pdf. HKEPD (Hong Kong Environment Protection Department), 2013. Marine water quality in Hong Kong in 2012. https://www.epd.gov.hk/epd/sites/default/files/epd/english/ environmentinhk/water/hkwqrc/files/waterquality/annual-report/ marinereport2012.pdf. HKEPD (Hong Kong Environment Protection Department), 2014. Marine water quality in Hong Kong in 2013. https://www.epd.gov.hk/epd/sites/default/files/epd/english/ environmentinhk/water/hkwqrc/files/waterquality/annual-report/ marinereport2013.pdf. HKEPD (Hong Kong Environment Protection Department), 2015. Marine water quality in Hong Kong in 2014. https://www.epd.gov.hk/epd/sites/default/files/epd/english/ environmentinhk/water/hkwqrc/files/waterquality/annual-report/ marinereport2014.pdf. HKEPD (Hong Kong Environment Protection Department), 2016. Marine water quality in Hong Kong in 2015. https://www.epd.gov.hk/epd/sites/default/files/epd/english/ environmentinhk/water/hkwqrc/files/waterquality/annual-report/ marinereport2015.pdf. HKEPD (Hong Kong Environment Protection Department), 2017. Marine water quality in Hong Kong in 2016. https://www.epd.gov.hk/epd/sites/default/files/epd/english/ environmentinhk/water/hkwqrc/files/waterquality/annual-report/ marinereport2016.pdf. HKG (Hong Kong Goverment), 2015. Hong Kong: the facts. https://www.gov.hk/. Huang, Y., Deng, M.H., Wu, S.F., Japenga, J., Li, T.Q., Yang, X.E., He, Z.L., 2018. A modified receptor model for source apportionment of heavy metal pollution in soil. J.

105