Marine debris and pollution indexes on the beaches of Santa Catarina State, Brazil

Regional Studies in Marine Science 31 (2019) 100771

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Marine debris and pollution indexes on the beaches of Santa Catarina State, Brazil ∗

Camila Burigo Marin a , Henrique Niero a , , Isadora Zinnke a , Maria Amélia Pellizzetti b , Paulo Henrique Santos c , Adriana Cestari Rudolf a , Mayara Beltrão a , Daniela de Souza Waltrick a , Marcus Polette a a

Escola do Mar, Ciência e Tecnologia, Universidade do Vale do Itajaí, Uruguai St. 458, Itajaí, Santa Catarina, Brazil Instituto Federal de Educação, Ciência e Tecnologia Catarinense, Joaquim Garcia St., Camboriú, Santa Catarina, Brazil c Colégio São José, Silva St. 365, Itajaí, Santa Catarina, Brazil b

highlights • • • •

Plastic items were the most common collected debris (80% of total sampled). 17 beaches (68%) were classified as extremely dirty considering the General Index. General Index was appropriate to assess beach cleanness and microtrash threat. Dissipative beaches located near ports had high concentrations of plastic pellets.

article

info

Article history: Received 3 August 2018 Received in revised form 25 July 2019 Accepted 25 July 2019 Available online 31 July 2019 Keywords: Coastal management Pellets Plastic Urban beaches Pollution index

a b s t r a c t Marine debris is defined as any persistent solid material disposed into the marine and/or coastal environment. The impact of these pieces of debris, especially plastic, have been reported around the world as causing environmental degradation, disease dissemination, transport of chemical toxins and public health issues. The extent of the effects of marine debris and beach cleanliness can be assessed using indexes such as General Index (GI), Clean-Coast Index (CCI) and Pellet Pollution Index (PPI). Thus, this study analyzed all debris collected from 25 beaches located in 11 counties in the State of Santa Catarina, Brazil. The quali-quantitative analysis was used for individual beaches according to the above indexes. Although plastic was the overall most common debris category, granulated polystyrene was the most common debris in nine of the beaches in this study. From the three indexes employed in this study, GI appears to be the most appropriate as it considers all debris sizes, while CCI underestimates the pollution level of the beaches as it only takes into consideration plastic debris over 2 cm. Similarly, PPI ranked all sites as having low pollution levels, despite the high threats that pellets may pose to marine biota. © 2019 Elsevier B.V. All rights reserved.

1. Introduction Marine debris consists of any solid material directly discarded into the marine and coastal environments or carried by wind, river or pluvial systems to this environment (UNEP, 2009). Data from the International Coastal Cleanup Day shows that the most retrieved debris includes cigarette butts, plastic bottles, plastic bottle caps, food wrappers, plastic bags, plastic lids, plastic straws and stirrers, glass beverage bottles and foam takeaway containers (Bergmann et al., 2015; Ocean Conservancy, 2017). The quantity and composition of marine debris differ in different parts of the world due to the local activities generating waste and natural ∗ Corresponding author. E-mail address: [email protected] (H. Niero). https://doi.org/10.1016/j.rsma.2019.100771 2352-4855/© 2019 Elsevier B.V. All rights reserved.

characteristics intrinsic to each location, such as river systems, coastal morphodynamics and ocean dynamics (Bergmann et al., 2015). Although any type of solid or semi-solid waste in the marine environment is a cause for concern, plastic is by far the most studied (Hatje et al., 2013) and the most problematic due to its persistence and inherent problems that it creates in the environment. It has been estimated that the amount of plastic and microplastic floating in oceans is approximately 5.25 trillion pieces, equivalent to 269,000 tons (Eriksen et al., 2014). Plastic is commonly used for the manufacture of almost everything we use in our daily lives, such as flasks and bottles, clothes, food wrappers, packaging, construction materials, vehicles, bags, home appliances, resulting in high volumes of discarded plastic (Rios et al., 2007).

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The Joint Group of Experts on the Scientific Aspects of Marine Environmental Protection (GESAMP) defines microplastics as any plastic material of 5 mm in size or smaller (GESAMP, 2015). Microplastics found in the environment can be either primary or secondary microplastics. Primary microplastics are plastics manufactured to be of small size, which has applications as surface cleaners, cosmetic and industrial plastic powders, plastic nanoparticles and raw material (plastic pellets). Plastic pellets are an industrial granular raw material up to 5 mm in size used in the manufacturing of ‘‘user plastics’’. Plastic pellets are accidentally released into the environment during manufacturing and transport and consequently found in large quantities in the marine environment (Derraik, 2002; Manzano, 2009; Mato et al., 2001). Secondary microplastics can derive directly from larger plastic items. These items break and fragment into smaller parts when exposed to UV light and other weathering agents and remain as microplastics in the environment (Minchin, 1996). Inadequate disposal of urban solid residues may result in a negative impact on public health and to the environment. The impacts of marine debris have been widely reported worldwide, indicating problems such as visual pollution, changes to original landscapes, disease dissemination, transport of chemical toxins, dispersal of invasive species and loss of biodiversity (Araújo and Costa, 2004; Carneiro, 2014; Carvalho-Souza and Tinoco, 2011; Derraik, 2002; Ivar Do Sul and Costa, 2014; Laist, 1997; Murphy et al., 2017; Santos et al., 2001; Yukie et al., 2001). Therefore, indexes were created to help manage marine debris pollution in coastal environments to classify beaches, such as the General Index (GI), the Clean-Coast Index (CCI) (Alkalay et al., 2007) and the Pellet Pollution Index (PPI) (Fernandino et al., 2015). These tools allow beaches to be classified according to their degree of debris pollution. The problem associated to the dispersal of marine debris goes far beyond being an environmental nuisance, it is also a social and public health problem (Smith et al., 2018; Carbery et al., 2018). Therefore, the aim of this study was to investigate the types and quantities of marine debris on the central coast of Santa Catarina State, Brazil, and assess their environmental impacts by employing the above cited indexes and selecting the best method to monitor marine debris pollution. In this way, beaches were classified according to their level of debris content and a comparison between the indexes was made. Also, an effort was made to investigate if the distance to ports and beach morphodynamic would influence the results of the determined indices and residues found on the beaches. 2. Materials and methods 2.1. Study area The presence of marine debris along the coastal zone of Santa Catarina State, Brazil, was investigated in 25 beaches from 11 municipalities from the central-north to central-south zones (Fig. 1). The main aspects considered while selecting each municipality for this study were geographical distribution, beach state, main economic activity (e.g. fishing, tourism, civil construction, agriculture) and distance to main ports: Imbituba, and Itajaí-Açú river Port Complex (Table 1).

reach) along the beach. Two observers collected (by visual observation) all debris up to one-meter distance through the length of the transect covering a total area of 100 m2 . Sampling was performed in low tide. Non-organic debris measuring 10 cm or less were collected for this study. All collected material was then transported to the laboratory where they were classified (Fig. 2) into 16 categories of debris type: pellets (A), food wrapper (B), cigarette butts (C), fishing tackle (D), metal (E), plastic line (F), cotton string and thread (G), paper (H), plastic smaller than 5 mm (I), plastic larger than 5 mm (J), plastic handle (e.g. from cotton bud and lollipop) (K), straw wrapper (L), drinking straw (M), polystyrene foam — smaller than 5 mm (N), polystyrene foam — larger than 5 mm (O). A caliper rule was used for the separation of debris smaller than 5 mm. All debris not fitting the above criteria, such as residues from civil construction (bricks and concrete fragments) and any material with undetermined composition, were classified as ‘‘other’’ (P). In this study, we considered plastic the categories A, B, C, D, F, G, I, J, K, L and M. The general definition that microplastic consists of any plastic material of 5 mm or less was adopted in this study (GESAMP, 2015). Each category was quantified and the results were used to rank each beach according to different indexes. The Clean Coastal Index (CCI) was developed for assessing the cleanliness level of the coast (Alkalay et al., 2007). It is employed as a tool for evaluation of the ‘‘Clean Coastal’’ program that takes place in Israel. According to Alkalay et al. (2007), this index is calculated using the formula below, with coefficient K (value = 20) inserted into the equation for statistical and convenience reasons. The index only takes plastic items into account. The result corresponds to the beach CCI rank as: ‘‘very clean’’, 0–2; ‘‘clean’’, 2–5; ‘‘moderate’’, 5–10; ‘‘dirty’’, 10–20; or ‘‘extremely dirty’’, 20 and higher.

( CCI =

Total number of plastic parts Sampled area

) ×K

(1)

It is important to notice that this method only regards plastic items with a size greater than 2 cm. However, for this study, all items below 5 mm, between 5 mm and 10 cm and pellets were considered. Four variations of the index were calculated: CCI including all sampled plastic items; CCI without pellets; CCI without plastic items smaller than 5 mm; and CCI without pellets and plastic items smaller than 5 mm. In this way, we could compare these results to the literature as well as observe changes in the beach classification incurred by the category of plastic used at the CCI calculations. The General Index (GI) employed in this study is the same as proposed for the Clean Coastal Index but considering all types of debris instead of just plastic items. Pellet Pollution Index (PPI) was calculated following Eq. (2) proposed by Fernandino et al. (2015), where n is the number of collected pellets, a is the sampled area in m2 and p is the correction coefficient (p = 0.02). The degree of pellet pollution is given in a scale of 0 to 3, where 0–0.5 is ‘‘very low’’, 0.5–1.0 ‘‘low’’, 1.0–2.0 ‘‘moderate’’, 2.0–3.0 ‘‘high’’, and higher than 3.0 ‘‘very high’’.

[ PPI =

n(items) a(m2 )

] ×p

(2)

2.3. Statistical analyses 2.2. Sampling and analysis Sampling was undertaken between March 16th and April 1st, 2017 using a transect. As proposed by Carneiro (2014), a 50 m long measuring tape was randomly (without looking for spots with a high or low concentration of debris) placed parallel to the coastline at the swash mark (marked by the tide at its highest

Permutational Multivariate Analysis of Variance (PERMANOVA) was performed with PRIMER 6 & PERMANOVA+ Software (Version 6.1.11 and 1.0.1, respectively, PRIMER-e Ltd). This test was first used to assess if there were any significant differences between indexes, and in a second moment to assess difference to indexes and item types found on beaches in affect

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Fig. 1. Study area highlighting the 25 beaches where samples were collected.

Table 1 Municipalities of study sites, their location, population, beach state (Silveira et al., 2011) and main economic activities. n.i.: not informed. Municipality Beach

Population (2010)

Population (2016)

Morphodynamic state

Economic activities

Source

Balneário Piçarras

1. Centro

17.078

21.253 (up to 5x in summer)

Reflective

Tourism and fishery

IBGE (2017) and Balneário Piçarras (2017)

Penha

2. Alegre, 3. Vermelha

25.141

30.262

Reflective, intermediate

Tourism, fishery, aquaculture

Navegantes

4. Gravatá

60.556

74.964

Dissipative

Tourism, trade, service provision (port and airport related), artisanal and industrial fisheries, agriculture

IBGE (2017), Litoral De Santa Catarina (2017) and Prefeitura Municipal De Navegantes (2017)

Itajaí

5. Atalaia, 6. Praia Brava

183.373

208.958

Dissipative, intermediate

Activities related to the port complex, fishery and tourism

IBGE (2017) and Prefeitura Municipal De Itajaí (2017)

Balneário Camboriú

7. Praia Central, 8. Taquarinhas, 9. Taquaras, 10. Estaleiro, 11. Estaleirinho

108.089

131.727

Dissipative, reflective, reflective, reflective, reflective

IBGE (2017) and Prefeitura Tourism, trade, service Municipal De Balneário Camboriú provision and civil construction (2017)

Itapema

12. Meia Praia

45.797

59.147

n.i.

Fishery, tourism and civil construction

IBGE (2017) and Prefeitura Municipal De Itapema (2017)

Porto Belo

13. Perequê, 14. Araçá, 15. Central

16.083

19.744

n.i.

Fishery and tourism

IBGE (2017) and Prefeitura Municipal De Porto Belo (2017)

Bombinhas

16. Canto Grande, 17. Zimbros

14.293

18.052

n.i.

Fishery and tourism

IBGE (2017) and Prefeitura Municipal De Bombinhas (2017)

Palhoça

18. Pinheira, 19. Guarda do Embaú

137.334

161.395

Dissipative, intermediate

Industry, trade, fishing and tourism

IBGE (2017) and Prefeitura Municipal De Palhoça (2017)

Garopaba

20. Silveira, 21. Ferrugem

18.138

Intermediate, intermediate

Tourism, civil construction, artisanal fishery, public services, agriculture, trade, livestock and clothing industries

IBGE (2017) and Prefeitura Municipal De Garopaba (2017)

Imbituba

22. 23. 24. 25.

Port related activities, fishing and tourism

IBGE (2017) and Prefeitura Municipal De Imbituba (2017)

Rosa, Ibiraquera, Porto, Vila

40.170

21.573 (up to 140.000 tourists Dec–Feb)

43.624

Intermediate, dissipative, dissipative, intermediate

IBGE (2017) and Prefeitura Municipal De Penha (2017)

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Fig. 2. General process of debris collection, sorting and classification. (A) Debris collected on Estaleirinho beach; (B) Sorting and classification of debris in the laboratory; (C) Classified debris: lines, pellets and plastic fragments; Images by Paulo Henrique Santos.

with the distance to port categories (0 to 11 km distance, 11 to 22 km, 22 to 33 km, 33 to 44 km, 44 to 55 km) and beach morphodynamic states (reflective, intermediate and dissipative). Similarity matrices were generated on Log (x+1) transformed indexes and items quantities values using the S17 Bray Curtis similarity method. PERMANOVA Main Test and PairWise Test were conducted using standard parameters (Sums of squares type: Type III (partial); Fixed effects sum to zero for mixed terms; Permutation method: Unrestricted permutation of raw data; Number of permutations: 999; Monte Carlo Test active). To analyze differences relative to the distance to ports, a design was created with beach distance to port categories as factors. Beach distance to the closest port (Itajaí Port and Navegantes Port, both located in the Port Complex of Itajaí-Açú river, or Imbituba Port. Fig. 1) was measured using Google Earth V.7.3. To analyze differences regarding the morphodynamic beach state, a design was created employing beach states reflective, intermediate and dissipative as factors. The level of significance was set at p < 0.05. Cluster analyses were conducted using similarity matrices generated on square root transformed data using the S17 Bray–Curtis similarity method (Clarke, 1993; Clarke and Gorley, 2006). Indexes values and debris types found on beaches were also tested by Analysis of Variance (ANOVA) or Kruskal–Wallis’s Test, according to the influence of beach morphodynamic state (reflective, intermediate and dissipative) and distance to ports categories (0 to 11 km distance, 11 to 22 km, 22 to 33 km, 33 to 44 km, 44 to 55 km). Data normality assumption was tested using the Shapiro–Wilk’s test, homogeneity of variances was checked by Bartlett’s Test and mathematical transformation (log 10 or square root) applied if necessary. ANOVA was used for parametric data, whereas Kruskal–Wallis’s Test was used for nonparametric data. Post-hoc comparison (Tukey’s Test for parametric and Dunn’s Test for nonparametric) was conducted to multiple comparisons between distance to port categories and beach morphodynamic states. The level of significance was set at p < 0.05. Such analyses were performed with the statistical R-software (R. development Core Team, 2018). To investigate if beaches with similar levels of pellets concentration were also similar in distance to ports Cluster analysis was performed using PRIMER 6 & PERMANOVA+ Software (Transformation: square root; Resemblance: S17 Bray Curtis Similarity; 65% similarity level) (Version 6.1.11 and 1.0.1, respectively, PRIMER-e Ltd).

3. Results and discussion 3.1. Abundance and density 6953 items belonging to 16 categories of debris types were collected from 25 sampled beaches as follows (Table 2): (A) pellets 9%; (B) food wrappers 0.9%; (C) cigarette butts 8.2%; (D) fishing tackle 0.9%; (E) metal 0.3%; (F) plastic lines 5.2%; (G) cotton string and thread 1.1%; (H) paper 1.6%; (I) plastic < 5 mm 24.1%; (J) plastic > 5 mm 28.8%; (K) plastic stick 1.1%; (L) plastic straw wrapper 0.8%; (M) drinking straw 0.4%; (N) polystyrene foam < 5 mm 10.9%; (O) polystyrene foam > 5 mm 3.0%; and (P) others 3.7%. Plastic items corresponded to the larger part (69%) of the collected debris (Fig. 3). On most beaches, plastic items corresponded to over 80% of the total items collected. Many studies (e.g. Brazil; European coast, Galgani et al., 2000; Australia, Whiting, 1998; and Alaska, Hess et al., 1999) report plastic as the major debris collected from coastal zones. In Brazil, over 85% (in the number of items) of all debris retrieved from Salvador, Bahia, were reported to be plastic (Andrade Neto, 2014). In Bombinhas, plastic represented the second most common item collected from both the beach and underwater, corresponding to up to 27%, whereas cigarette butts (34% in high season) and civil construction residues (29% in low season) were the first most common debris types (Carneiro, 2014). The percentage of polystyrene foam materials surpassed those of plastic in some beaches (i.e. Zimbros, Canto Grande, Meia Praia, Praia Central de Porto Belo, Estaleiro, Estaleirinho, Taquarinhas e Praia do Rosa). Large amounts of polystyrene foam have previously been reported in this area (Carneiro, 2014), as well as overseas (Washington and Alaska - USA, Davis and Murphy, 2015; British Columbia - Canada, Williams et al., 2011), which could be related to fishing activities. Another important source of styrofoam includes civil construction and tourism, both important economic activities in six of nine beaches where a great number of polystyrene foam items were collected (Table 1). The negative impact of styrofoam has been demonstrated in the literature, where it has been considered lethal to marine life (Lee et al., 2015; Murphy et al., 2017; Steer et al., 2017; Sussarellu et al., 2016).

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Table 2 Items collected and sorted into the 16 categories of debris. Beach

Centro Alegre Vermelha Gravatá Norte Atalaia Praia Brava Central BC Taquarinhas Taquaras Estaleiro Estaleirinho Meia Praia Perequê Araçá Central PB Canto Grande Zimbros Pinheira Guarda do Embaú Silveira Ferrugem Rosa Ibiraquera Porto Vila Total

Number of items found by category of debris A

B

C

D

E

F

G

H

I

J

K

L

M

N

O

P

0 1 179 68 108 48 3 0 0 0 0 35 0 1 0 3 2 23 1 0 0 0 0 131 24 627

5 3 0 0 1 0 17 0 0 0 2 4 0 0 2 0 0 5 1 1 0 10 0 3 10 64

38 34 8 2 13 7 37 7 5 13 18 12 26 4 5 3 18 14 23 2 5 47 7 89 136 573

0 0 0 3 0 0 0 0 0 0 0 45 0 0 0 0 0 3 0 0 0 0 0 10 2 63

4 3 0 0 0 0 4 1 0 0 0 4 1 2 0 0 0 0 1 0 0 0 0 1 0 21

17 5 4 23 43 4 21 5 37 3 1 7 3 3 1 44 17 33 13 0 0 3 3 45 25 360

1 0 0 0 2 0 5 0 0 0 0 55 0 0 0 1 2 3 6 1 0 1 0 0 0 77

11 4 0 0 0 1 23 0 2 1 0 0 0 0 1 0 0 8 6 1 0 30 2 5 13 108

2 7 36 905 94 22 10 4 4 0 0 54 8 4 6 1 9 151 42 0 1 5 22 187 100 1674

35 40 130 554 163 57 90 10 30 4 26 71 25 17 25 14 53 151 56 4 7 14 0 317 107 2000

2 1 10 5 4 7 4 0 1 0 0 3 0 0 0 0 0 5 2 1 0 1 2 22 5 75

3 1 0 0 0 1 13 0 0 0 1 4 0 0 0 0 2 5 6 0 0 2 1 0 15 54

4 0 0 1 1 4 0 1 2 0 0 0 0 0 1 0 0 2 2 0 0 0 0 3 8 29

2 2 12 3 7 2 71 0 4 1 5 403 2 0 2 133 37 7 0 0 0 0 0 39 29 761

1 0 11 4 12 0 24 0 3 2 0 27 0 0 2 43 56 4 0 0 1 1 0 9 10 210

1 7 8 8 11 3 7 21 17 17 50 22 6 5 0 2 2 13 7 0 2 0 1 19 28 257

Fig. 3. Quantity of plastic and non-plastic debris collected at each site. The total number of items and their relative proportions are also displayed. Ports location is indicated.

3.2. Indexes comparison A direct relation between the number of beaches deemed ‘‘extremely dirty’’ and the number of categories of debris employed in the index calculation was observed (Fig. 4). However, for some beaches the classification remained the same as more categories of debris were employed in the calculation, showing that such additional types of debris may not have much impact on these beaches. 68% (17) of all sampled beaches were classified as ‘‘extremely dirty’’ when all debris collected in the beach was considered in GI index, whereas only 28% (7) of beaches are in the same category when CCI index is calculated excluding plastics smaller than 5 mm and pellets. This indicates that not considering

all sizes of debris can underestimate the degree of pollution on beaches. When GI results were compared to the less inclusive category of CCI (excluding plastics <5 mm and pellets) a shift was observed for all classes as more beaches were ranked in cleaner categories: ‘‘dirty’’ changed from 4% to 16%, ‘‘moderate’’ from 20% to 12% and ‘‘clean’’ or ‘‘very clean’’ increased from 4% to 16%. A clear example of this is seen in Praia Alegre, which was ranked as ‘‘extremely dirty’’ when employing the GI index; ‘‘dirty’’ using CCI index including all plastics and CCI index excluding pellets; and ‘‘moderate’’ using CCI index excluding plastic smaller than 5 mm and CCI excluding plastic smaller than 5 mm and pellets (Table 3). Out of the 25 beaches sampled, 9 (Central - Porto Belo, Central - Balneário Camboriú, Atalaia, Gravatá Norte, Vermelha, Vila,

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C.B. Marin, H. Niero, I. Zinnke et al. / Regional Studies in Marine Science 31 (2019) 100771 Table 3 Calculated beach pollution indexes, including all adapted CCI methods. GI: General Index; CCI: Clean-Coast Index; PPI: Pellet Pollution Index. Classification groups are ED: Extremely Dirty; D: Dirty; M: Moderate; C: Clean; VR: Very Clean; VL: Very Low. Ports location is indicated.

Fig. 4. Rank distribution (%) for sampled beaches employing General Index (GI), Clean-Coast Index (CCI) and CCI variations. Categories are ED: Extremely Dirty; D: Dirty; M: Moderate; C: Clean; VC: Very Clean. Numbers next to bars represent the number of beaches ranked in each category.

Porto, Silveira and Pinheira) displayed the same classification under both GI and CCI indexes. This shows that there was no difference between the index considering only plastic and the index also considering non-plastic items in these locations (Table 3). Other locations, however, displayed a higher degree of pollution only when using the GI index (Zimbros, Canto Grande, Perequê, Araçá, Estaleiro, Estaleirinho, Taquaras, Taquarinhas, Centro, Rosa and Ferrugem). Although these beaches displayed a lower level of pollution when using CCI, it remained the same within all variations of this index, implying that there was no difference in the use of different categories of plastic among these locations. Different indexes have also resulted in different beaches classification when plastics smaller than 5 mm were excluded from the analysis (Guarda do Embaú and Ibiraquera). Praia Brava, on the

other hand, displayed a different classification only when pellets were excluded, due to the considerable amount of this material. Balneário Camboriú has beaches in all possible categories of GI and CCI: very clean (Estaleiro), clean (Taquarinhas), moderate (Estaleirinho), dirty (Taquaras) and extremely dirty (Praia central). This is most likely a result of the diverse use and occupation of this locality. It is well-known that there is a strong influence of tourism and beach uses on the plastic type and quantity found on coastal areas (GESAMP, 2015). The CCI ranks of less popular and better-preserved beaches were considerably smaller, except for Taquaras, in which the high amount of plastic lines collected may be associated with the local fishing activity. Praia Central, the main beach in Balneário Camboriú, displayed the worst condition of all beaches sampled from this municipality, which is probably explained by the intense tourism activity and level of urbanization. All beaches were classified as ‘‘very low’’ degree of pollution by the PPI index. Similar results were observed in other parts of the country. For instance, most beaches in the coast of Salvador, Bahia, were ranked as ‘‘very low’’, except for some locations, such as Pituba beach, where the concentration of pellets per square meter was ‘‘high’’ (Fernandino et al., 2015). On the other hand, pellets pollution in Guanabara Bay reached about 795 pellets per square meter during the summer, and so ranked as ‘‘very high’’ with PPI (De Carvalho and Neto, 2016). The importance of determining the presence and quantity of pellets in the environment is linked to the risk it offers to wildlife through ingestion and the risk of contamination from adsorbed heavy metals (Gall and Thompson, 2015; Holmes et al., 2012; Massos and Turner, 2017) and chlorinated compounds (Endo et al., 2005) and its biomagnification through the food chain. It is important to remark that the presence of pellets may often be underestimated due to the adopted methodology. In general, samples are collected from the swash mark or high-water line, however, these items may accumulate elsewhere on the beach profile. This has been observed at Ibiraquera beach, where

C.B. Marin, H. Niero, I. Zinnke et al. / Regional Studies in Marine Science 31 (2019) 100771

the wind creates an accumulation zone behind the foredunes (authors personal observation). In contrast to the high levels of pollution evidenced in some beaches, the amount of plastic collected from Santa Catarina beaches was smaller than the numbers reported in other regions (Table 4). For instance, beaches with the highest observed number of total plastics (Gravatá Norte), pellets (Praia Vermelha) and microplastics (Gravatá Norte) had smaller or similar values to the lower concentrations of debris found in the literature. This suggests that the beaches of Santa Catarina display a comparatively low degree of pollution. Although a method for the assessment of beach pollution has been established by the Clean-Coast Index, there is still controversy, where authors opt to consider specific materials to the detriment of others. Given the myriad of methodologies employed by different studies, a standard methodology for the sampling of plastics needs to be defined to identify and classify debris (Hidalgo-Ruz et al., 2012). Results are highly dependent on the methodology employed in the sampling and processing of samples, making it difficult to compare them (Filella, 2015). A wellestablished index leads to better decision-making concerning the allocation of funds for environmental protection. 3.3. Statistical analyses No significant difference was observed between normalized indexes values (p-value = 0.074). However, because of the proximity to the alpha value (α = 0.05), post-hoc test was used. Even so, none of the pairs of indexes presented a significant difference (p-value > 0.05). On the other hand, it is possible to observe that the GI index is the one that is most different from the others, reaching a p-value of 0.061 between GI and CCI calculated with plastics, except pellets and microplastics (CCI-PEPMp). Significant differences were observed between indices values concerning the distance of ports and the beach morphodynamic state both in ANOVA and PERMANOVA tests (p-value < 0.05). Multiple comparisons indicate differences between distances of 22–33 and 33–44 km, and between 0–11 and 11–22 km in PERMANOVA analysis, and between distances of 0–11 and 11–22, 0–11 and 22–33, 11–22 and 33–44, and 22–33 and 33–44 km in ANOVA analysis. Post hoc tests identified significant differences between dissipative and reflective beaches. CCI with all plastics (CCI-AP), CCI with plastics except pellets (CCI-PEP) and PPI showed significant differences concerning the distance to ports in the PERMANOVA test, whereas only CCI-AP and CCI-PEP showed a significant difference in the ANOVA test. The comparison between distances of 0–11 and 11–22 km was significant in both tests. This may be related to the fact that most beaches with a distance between 11–22 km have a reflective beach state, which reduces the deposition of the detritus in the swash mark. On the other hand, PERMANOVA analysis indicates the influence of proximity to ports with the occurrence of pellets between distances from 0–11 and 44–55 km. Only CCI-PEP values showed a significant difference about the beach morphodynamic state (p-value = 0.041) in the ANOVA test. However, p-values of CCI-AP, CCI with plastics except for microplastics (CCI-PEMp), CCI-PEPMp and PPI came close to the stipulated alpha value. The number of debris types found on the beaches was significantly different in relation to the distance to ports and the beach morphodynamic state, both in PERMANOVA and ANOVA tests (pvalue < 0.05). Multiple comparisons of the PERMANOVA analysis indicate a significant difference between the distances of 0–11 and 22–33 km, and 0–11 and 11–22 km, and between reflective and dissipative beaches. The ANOVA analysis indicates significant differences between distances of 0–11 and 11–22, 0–11 and 22– 33, and 11–22 and 33–44 km for the number of residues collected

7

on beaches. A significant difference was observed for plastics (p-value = 0.04), pellets (p-value = 0.02) and microplastics (pvalue = 0.02) regarding the distance to ports in the analysis of the PERMANOVA, but in the ANOVA analysis only microplastics distribution along the coast can be related to the distance to ports (p-value < 0.05). Multiple comparison analysis suggests that the distances between 0–11 and 11–22 km were the ones that caused the most significant difference between the number of debris types. The comparison between distances 11–22 and 44–55 km also suggests the influence of ports on the distribution of pellets on the beaches (p-value = 0,025). Microplastic also showed significantly different distribution between 0–11 and 22– 33 km, and 0–11 and 44–55 km. In the ANOVA test, the number of pellets and microplastics found on beaches showed a significant difference in relation to the beach morphodynamic state (p-value < 0.05). Dissipative and reflective beaches were the ones that had the most significant difference between the number of residues found as suggested by multiple comparison analysis. The categories 0–11 and 44–55 km, expected to have the highest contrast in pellets concentration, showed no significant difference. This confirms that other factors are influencing the pellet distribution along the coast of Santa Catarina, besides the dispersions of pellets by ports. Most beaches between 11–22 km have a reflective morphodynamic state, which causes the minor deposition of debris carried by ocean currents in these beaches. Also, beaches like Pinheira, with a high-level of debris concentration, despite being far from a port, has excellent characteristics for deposition of residues, such as being protected from strong swells and having a dissipative state. Since pellets concentration on beaches is frequently associated to a close distance to ports (GESAMP, 2015), and PPI p-value between beaches located at 0–11 and 44–55 km of ports was equal to the stipulated alpha of 0.05, a cluster analysis was employed to show, in the form of groups, which beaches were influenced by the distance to port. Cluster analysis identified 3 groups (A, B and C) based on the similarity value of 65 (Fig. 5). Group A consisted of beaches where no pellets were found. Beaches containing between 1 and 3 pellets per sampled area, were clustered in group B. Group C consisted of all beaches with a concentration of pellets superior to 23. It is interesting to note that five of eight beaches in group C are located near ports (0 – 11 km). This can show that ports are contributing to pellet concentration on near beaches. However, analyzing group A, some beaches near ports (like Ibiraquera and Rosa) did not show pellets accumulation in the swash mark. This can be attributed to the accumulation of pellets in the foredunes, away from the beach shore (as observed by the authors during the sampling). Also, beaches like Ferrugem, Silveira, Taquaras, Taquarinhas and Estaleirinho, besides located near ports (11–22 km), did not show high levels of pellets concentration, probably due to their reflective morphodynamic state. Itajaí-Açú River Port Complex and Imbituba Port have one distinct characteristic. The Itajaí-Açú Port Complex lies on the Itajaí-Açú River, which has an average flow rate of 420 m3 s−1 with peaks that can reach more than 3000 m3 s−1 (Schettini et al., 2005). This makes the river a debris dispersal vector, carrying debris along the coastline and specially to beaches on the north since the river plume spreads to north and northeast (Schettini, 2002). On the other hand, the Port of Imbituba is not located near a river, thus this type of debris dispersion does not occur, and debris generated in the port (such as pellets) tend to accumulate at very close beaches, like Porto beach. This statement can be seen comparing Gravatá Norte and Ibiraquera. These two beaches are located at the same distance to a port. The former is located at 9 km to the north from Itajaí-Açú Port Complex (and so from Itajaí-Açú River), whereas the second one is located at 7 km to

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Table 4 Total number of plastics, pellets and microplastics collected per square meter, CCI for all plastics and PPI rank. CCI: Clean Coastal Index; PPI: Pellet Pollution Index; ED: Extremely Dirty; D: Dirty; M: Moderate; VC: Very Clean. Reference

Location

Sampling sites

Plastics (m2 ) (total)

Pellets (m2 )

Microplastics (m2 )

CCI all plastics rank

PPI rank

Fernandino et al. (2015)

Salvador Bahia, Brazil

Salvador Coast Pituba Beach

– –

23 132

– –

– –

Very low High

Southeast Brazil

Guanabara Bay

–

795

12 a 1300

ED

Very high

De Carvalho and Neto (2016) Louro and Widmer (2017)

Santa Catarina, Brazil

Campeche Beach

–

1.16

–

–

Very low

Widmer and Hennemann (2010)

Santa Catarina, Brazil

Florianópolis

0.9

–

–

D

–

Moreira et al. (2016)

São Paulo, Brazil

Itaguaré Beach Boracéia Beach

– –

130 26

– –

– –

High Low

Suciu et al. (2017)

Rio de Janeiro, Brazil

Praia Grande Beach urbanized sector Non-urbanized sector

5.25

–

–

ED

–

0.5

–

–

D

–

Cargo Beach Green Sands Beach

42,341 210

5079 11

– –

ED ED

Very high Very low

Aksa, Versova, Juhu and Dadar

68.83

5.5

32.56

ED

Very low

McDermid and McMullen (2004)

Hawaiian archipelago

Jayasiri et al. (2013)

Mumbai, India

Hidalgo-Ruz and Thiel (2013)

Chile

Aysén Easter Island

– –

– –

169 805

ED ED

– –

Turner and Holmes (2011)

Malta Island

Ghajin Tuffieha White Tower Bay

– –

167 1.6

– –

– –

Very high Very low

Santa Catarina, Brazil

Praia Vermelha Gravatá Norte Guarda do Embaú Ibiraquera Estaleiro

3.59 15.59 1.23 0.28 0.07

1.79 0.68 0.01 0 0

0.36 9.05 0.42 0.22 0

ED ED ED M VC

Very Very Very Very Very

Present study (examples)

low low low low low

Fig. 5. Cluster analysis showing groups A, B and C, consisting of beaches grouped by means of similarity in pellets concentration.

the north from Imbituba Port. In Gravatá Norte 15.6 plastic items m−2 and 0.7 pellets m−2 were found, whereas in Ibiraquera only 0.28 plastic items m−2 and none pellets were found in the swash mark. 4. Conclusions Corroborating with world standards, plastic items were the most common debris collected on the beaches (over 80%). However, this result was not a rule. In 36% of the sampled sites, the debris category with the highest occurrence was particularly granular polystyrene foam. This observation is probably associated with the strong civil construction sector and the fishing activity, which are the main economic activities in these locations.

Reflective beaches tend to have lower debris concentration in the tide swash mark, whereas beaches with the dissipative morphodynamic state tend to accumulate higher amounts of debris, mainly microplastics and plastic pellets. Beaches located near ports also have shown to accumulate more pellets than beaches far away of ports, but the morphodynamic state of the beach can strongly change this assumption, as pellets were not found in reflective beaches near ports. In Santa Catarina, 72% of the beaches were classified as dirty according to the GI index. The percentage of sites classified as dirty and extremely dirty drops to 28% using the CCI index (without plastic under 5 mm and pellets). This demonstrates that the CCI index, although calculated with types of debris seen by most beach users (larger than microplastic), may not reflect the

C.B. Marin, H. Niero, I. Zinnke et al. / Regional Studies in Marine Science 31 (2019) 100771

reality of these sites. Comparing the application of the GI and CCI indexes, it is suggested that the GI guarantees greater environmental protection since it encompasses all size fractions and all categories of debris commonly found in coastal environments. Therefore, we recommend its use by environmental managers and decision-makers to create laws and programs to ensure beach quality and environmental health. Declaration of competing interest None. Acknowledgments The authors would like to thank all those who helped with sample collecting and beach cleaning. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. References Alkalay, R., Pasternak, G., Zask, A., 2007. Clean-coast index, a new approach for beach cleanliness assessment. Ocean Coast Manag. 50, 352–362. http: //dx.doi.org/10.1016/j.ocecoaman.2006.10.002. Andrade Neto, G.F., 2014. Ocorrência, Distribuição e Grau de Poluição Por Pellets, Lixo de Praia e Lixo Bentônico Nas Praias Do Município de Salvador, Bahia, Brasil (Master’s thesis). Universidade Federal da Bahia, Brazil, Retrieved from https://repositorio.ufba.br/ri/bitstream/ri/21545/1/dissertacao_ gerson_Fern{and}ino_Neto_2014pdf. Araújo, M.C.B., Costa, M.F., 2004. Quali-quantitative analysis of the solid wastes at Tamandare bay, pernambuco, Brazil. Trop. Oceanogr. 32, 159–170. Balneário Piçarras, 2017. Sobre Piç. http://www.balneariopicarras.com.br/ picarras/sobrepicarras.htm (accessed 26.04.17). Bergmann, M., Gutow, L., Klages, M. (Eds.), 2015. Marine Anthropogenic Litter. Springer. Carbery, M., O’Connor, W., Palanisami, T., 2018. Trophic transfer of microplastics and mixed contaminants in the marine food web and implications for human health. Environ. Int. 115, 400–409. http://dx.doi.org/10.1016/j.envint.2018.03. 007. Carneiro, D.A.T., 2014. Lixo Marinho E O Potencial Energético - O Caso Da Praia Do Embrulho Em Bombinhas (SC). In: Monograph, Universidade do Vale do Itajaí, Brazil, Retrieved from http://siaibib01univali.br/pdf/Diulie%20Tavares. pdf. Carvalho-Souza, G.F., Tinoco, M.S., 2011. Avaliação do Lixo Marinho em Costões Rochosos na Baía de Todos os Santos, Bahia, Brasil. J. Int. Coast. Zone. Manag. 11, 135–143. http://dx.doi.org/10.5894/rgci231. Clarke, K.R., 1993. Non-parametric multivariate analyses of changes in community structure. Aust. J. Ecol. 18, 117–143. http://dx.doi.org/10.1111/j.14429993.1993.tb00438.x. Clarke, K.R., Gorley, R.N., 2006. PRIMER V6: User Manual/Tutorial. PRIMER-E. Plymouth Marine Laboratory, The United Kingdom, p. 192. Davis, W., Murphy, A.G., 2015. Plastic in surface waters of the inside passage and beaches of the Salish Sea in Washington State. Mar. Pollut. Bull. 97, 169–177. http://dx.doi.org/10.1016/j.marpolbul.2015.06.019. De Carvalho, D.G., Neto, J.A.B., 2016. Microplastic pollution of the beaches of Guanabara Bay, Southeast Brazil. Ocean Coast Manag 128, 10–17. http: //dx.doi.org/10.1016/j.ocecoaman.2016.04.009. Derraik, J.G.B., 2002. The pollution of the marine environment by plastic debris: a review. Mar. Pollut. Bull. 44, http://dx.doi.org/10.1016/S0025-326X(02) 00220-5. Endo, S., Takizawa, R., Okuda, K., Takada, H., Chiba, K., Kanehiro, H., Ogi, H., Yamashita, R., Date, T., 2005. Concentration of polychlorinated biphenyls (PCBs) in beached resin pellets: Variability among individual particles and regional differences. Mar. Pollut. Bull. 50, 1103–1114. http://dx.doi.org/10. 1016/j.marpolbul.2005.04.030. Eriksen, M., Lebreton, L.C.M., Carson, H.S., Thiel, M., Moore, C.J., Borerro, J.C., Galgani, F., Ryan, P.G., Reisser, J., 2014. Plastic pollution in the world’s oceans: More than 5 trillion plastic pieces weighing over 250, 000 tons afloat at sea. PLoS One 12, http://dx.doi.org/10.1371/journal.pone.0111913. Fernandino, G., Elliff, C.I., Silva, I.R., Bittencourt, A.C., 2015. How many pellets are too many? The pellet pollution index as a tool to assess beach pollution by plastic resin pellets in Salvador, Bahia, Brazil. J. Int. Coast Zone Manag. 15, http://dx.doi.org/10.5894/rgci566. Filella, M., 2015. Questions of size and numbers in environmental research on microplastics: methodological and conceptual aspects. Environ. Chem. 12, 527–538. http://dx.doi.org/10.1071/EN15012.

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