Monitoring marine debris in two sandy beaches at ...

UNIVERSITY OF THE AZORES

Monitoring marine debris in two sandy beaches at Faial Island - Azores

Department of Oceanography and Fisheries

Catharina Diogo Pieper Master in Integrated Studies of the Ocean

Supervisors Professora Doutora Maria da Anunciação Mateus Ventura Professora Doutora Regina Tristão da Cunha

HORTA, October 2013

Thesis submitted for the Degree of Master of Science

Catharina Pieper © All Rights Reserved


Dedication I dedicate every single minute of effort, devotion, enthusiasm, self-conflicts, battles, laughing, tears and thoughts that this work took of me, to a person I miss every single day. I hope I make you proud, wherever you might be.

To my grandfather.

Acknowledgments My deepest gratitude goes to my ever supporting family. You always encouraged my dreams and goals, and give me strength whenever I started to doubt of myself. There are not enough words that resume what you mean to me. Thank you for believing in me. I would like to thank both of my supervisors, São and Regina, for encouraging me to realize this research, since day one. Distance was not always an easy issue, although, we figured out how to make things work. I would like to express my gratitude for being so patient with me, for abdicating of your precious time, and for believing in this work. Dr. Ana Martins is also a key-person of this work. Thank you for answering to loads of research questions and existential doubts. You always encouraged me to keep going on. Due to you, I was able to establish contact with Mister Jeffrey Royle, which gave me many interesting ideas of how to work with my data. I would like to acknowledge to Dr. Gui Menezes. You spent half an hour of your time looking to my data, and simply added the most important “+1” in my life. After that, I was able to smile to my statistics. Special thanks go towards Pipa, we shared the most stressful, awkward and funny moments during these last weeks. Thank you to Ruca. You were always there helped me out whenever I needed. Thank you to Ricardo. You’d always had a solution when I panicked the first days looking to my data. In addition, thank you to all of my colleagues of the University, some of you followed my efforts since the very beginning, providing me with encouragement, advices and useful ideas. To Mónica and all of my friends back home, thank you for being part of my life, despite the distance that separates us. To Aika, my dog. You remind me to keep a clear mind, at least twice a day while our walkouts. Finally, I want to specially thank to João. You are the most patient and balanced human being that I know. You’ve always listened to all of my problems, even if the subject was constantly the same. You surely are a crucial part of my self-development during this last year. You know what you mean to me. Your presence in my life is a blessing. Catharina Pieper

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“I just want to say one word to you – just one word.” “Yes sir.” “Are you listening?” “Yes sir, I am.” “Plastics.” “Exactly how do you mean?” “There’s a great future in plastics…” Mr. McGuire to Ben The graduate, 1969

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Abstract Marine debris are part of a global environmental issue that causes effects not only in the world’s oceans, but also in coastal areas. Therefore, it is possible to visualize waste even at the most remoteness islands, as in the case of the Azores. In order to evaluate data that allows access to loads of debris and their possible fluctuations along time and location, a total of 30 transects at two different beaches (Conceição and Porto Pim) was sampled during seven months (November to May). Items within 2 to 30 cm, were organized in 7 different categories, showing that densities of debris varied from 0 to 1.940 items/m2, and that plastic was the most predominant type of beached waste (96% at Porto Pim and 82% at Conceição). During February, prevailing wind and swell conditions from WSW and W, respectively, seem to have provided ideal conditions to achieve the highest abundance of debris in general at both beaches (MConceição = 1.973 ± 0.103; MPorto Pim = 2.726 ± 0.103). It was possible to validate that location and time of the year, while monitoring, influenced the presence of certain categories of debris, subjecting a relation between physical and environmental factors in their abundance. This pioneer study will continue to supply the type of data that is crucial for further understanding of debris dynamics and fluctuations in the North Atlantic Ocean, enabling comparisons to similar researches at different sites. Obtained results also suggest that there is a huge need to raise awareness and consciousness to local citizens and population in general, due to unthoughtful habits that are still practiced in everyday life.

Resumo Os resíduos marinhos constituem parte de um problema ambiental global, causador de impactes não apenas nos oceanos, mas também nas áreas costeiras. Deste modo, torna-se possível visualizar resíduos mesmo em ilhas mais remotas, como é o caso dos Açores. De forma a avaliar dados que permitam um acesso rápido à quantidade de resíduos e às suas flutuações ao longo do tempo e do local, um total de 30 transectos foram amostrados em duas praias distintas (Conceição e Porto Pim), num período de sete meses (Novembro a Maio). Itens entre 2 a 30 cm foram organizados em 7 categorias distintas, mostrando que a sua densidade variou desde 0 a 1.940 itens/m2, e que o plástico representou o tipo de resíduos arrojados com maior predominância (96% em Porto Pim e 82% na Conceição). No mês de Fevereiro, as condições prevalecentes de vento e ondulação dos quadrantes WSW e W, respectivamente, parecem ter providenciado as condições ideais para atingir a maior abundância de resíduos em geral, em ambas as praias (MConceição = 1.973 ± 0.103; MPorto Pim = 2.726 ± 0.103). Foi possível validar que o local e a altura do ano, durante a monitorização, influenciaram a presença de determinadas categorias de resíduos, fazendo sugerir Catharina Pieper

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uma possível relação entre factores físicos e ambientais na abundância dos mesmos. Este estudo pioneiro poderá continuar a contribuir na obtenção de dados com informação crucial para aumentar o conhecimento acerca da dinâmica e flutuações de resíduos no Norte do Oceano Atlântico, o que permitirá comparações com estudos semelhantes, realizados em locais distintos. Os resultados obtidos também sugerem que existe uma grande necessidade de sensibilizar e consciencializar os cidadãos locais, assim como a população em geral, de modo a alterar a práticas e hábitos enraizados na vida quotidiana.

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General Index 1. 1.1 1.2 2. 2.1 2.2 2.3 2.4 2.5 2.6 2.7 3. 3.1 3.2 3.3 3.4 3.5 4. 4.1 4.2 4.3 4.3.1 4.3.2 4.3.3 4.3.4 4.3.5 4.3.6 4.3.7 4.4 4.4.1 4.4.2 4.4.3 4.4.4 4.4.5 5. 5.1 5.2 5.3 5.4 5.5 5.7 5.8 6. 7. 8.

INTRODUCTION ................................................................................................................................ 9 ORGANIZATION OF CONTENTS ............................................................................................................. 9 OBJECTIVES ..................................................................................................................................... 9 MARINE DEBRIS.............................................................................................................................. 11 DEFINITION ................................................................................................................................... 11 SOURCES ...................................................................................................................................... 12 THE FAITH OF PLASTICS .................................................................................................................... 14 IMPACTS OF MARINE DEBRIS ............................................................................................................. 17 PLASTIC SIZES & SYMBOL CLASSIFICATION ............................................................................................ 21 WHAT IS THE EU DOING? THE MARINE DIRECTIVE................................................................................ 22 DEBRIS REPORTING SYSTEMS ............................................................................................................. 23 METHODS ..................................................................................................................................... 25 STUDY SITES .................................................................................................................................. 25 SURVEY DESIGN ............................................................................................................................. 27 STATISTICAL ANALYSIS...................................................................................................................... 30 DENSITY OF DEBRIS ......................................................................................................................... 30 ABUNDANCE OF DEBRIS ................................................................................................................... 30 RESULTS ....................................................................................................................................... 32 TEMPORAL AND SPATIAL DENSITIES OF BEACH LITTER ............................................................................. 32 GENERAL DEBRIS ABUNDANCE ........................................................................................................... 35 ANALYSIS BY CATEGORY OF DEBRIS ..................................................................................................... 37 PLASTICS....................................................................................................................................... 37 CLOTHS/FABRIC ............................................................................................................................. 40 GLASS .......................................................................................................................................... 40 METALS ........................................................................................................................................ 42 RUBBER ........................................................................................................................................ 43 PROCESSED LUMBER ....................................................................................................................... 45 OTHERS ........................................................................................................................................ 46 PLASTICS ITEMS .............................................................................................................................. 49 FRAGMENTS .................................................................................................................................. 49 CAPS ............................................................................................................................................ 50 POLYSTYRENE ................................................................................................................................ 51 CIGARETTE TIPS/FILTERS................................................................................................................... 52 ROPES/SMALL NET PIECES ................................................................................................................ 54 DISCUSSION................................................................................................................................... 56 SPATIAL DISTRIBUTION OF DEBRIS ON THE DUNAR SYSTEMS ..................................................................... 56 POTENTIAL INPUTS AND SOURCES ...................................................................................................... 56 POLLUTION STATE OF BEACHES .......................................................................................................... 58 POLLUTION SOURCES ....................................................................................................................... 59 SIGNIFICANCE OF FACTORS BEACH AND MONTH IN ABUNDANCES ............................................................. 60 INFLUENCE OF METEOROLOGICAL AND OCEANIC CONDITIONS THAT AFFECT DEBRIS ABUNDANCE ..................... 61 RISKS AND HAZARDS TO PUBLIC HEALTH............................................................................................... 65 CONCLUSIONS................................................................................................................................ 67 BIBLIOGRAPHIC REFERENCES ............................................................................................................. 72 APPENDICES .................................................................................................................................. 82

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Index of tables TABLE I - SPECIES GROUP WITH THE GREATEST NUMBER OF INDIVIDUALS AND RESPECTIVE PERCENTAGE ............................ 19 TABLE II - MONITORING DETAILS OF THE SHORELINE SITE......................................................................................... 27 TABLE III - MEAN GENERAL DEBRIS DENSITIES. ...................................................................................................... 34 TABLE IV - MEAN GENERAL DEBRIS ABUNDANCE ................................................................................................... 37 TABLE V - STATISTICAL PARAMETERS OF THE TWO-WAY ANOVA, APPLIED TO THE GENERAL DEBRIS ABUNDANCE. .............. 35 TABLE VI - MEAN PLASTICS DEBRIS ABUNDANCE .................................................................................................... 39 TABLE VII - STATISTICAL PARAMETERS OF THE TWO-WAY ANOVA, APPLIED TO THE PLASTICS DEBRIS ABUNDANCE. ............ 39 TABLE VIII - MEAN CLOTHS/FABRIC DEBRIS ABUNDANCE ........................................................................................ 40 TABLE IX - STATISTICAL PARAMETERS OF THE TWO-WAY ANOVA, APPLIED TO THE CLOTHS/FABRIC DEBRIS ABUNDANCE ..... 40 TABLE X - MEAN GLASS DEBRIS ABUNDANCE......................................................................................................... 42 TABLE XI - STATISTICAL PARAMETERS OF THE TWO-WAY ANOVA, APPLIED TO THE GLASS DEBRIS ABUNDANCE. ................. 42 TABLE XII - MEAN METALS DEBRIS ABUNDANCE .................................................................................................... 43 TABLE XIII - STATISTICAL PARAMETERS OF THE TWO-WAY ANOVA, APPLIED TO THE METALS DEBRIS ABUNDANCE ............. 44 TABLE XIV - MEAN RUBBER DEBRIS ABUNDANCE ................................................................................................... 45 TABLE XV - STATISTICAL PARAMETERS OF THE TWO-WAY ANOVA, APPLIED TO THE RUBBER DEBRIS ABUNDANCE .............. 45 TABLE XVI - MEAN PROCESSED LUMBER DEBRIS ABUNDANCE .................................................................................. 46 TABLE XVII - STATISTICAL PARAMETERS OF THE TWO-WAY ANOVA, APPLIED TO THE PROCESSED LUMBER DEBRIS ABUNDANCE.......................................................................................................................................... 46

TABLE XVIII - MEAN PROCESSED OTHERS DEBRIS ABUNDANCE ................................................................................ 48 TABLE XIX - STATISTICAL PARAMETERS OF THE TWO-WAY ANOVA, APPLIED TO CATEGORY OTHERS DEBRIS ABUNDANCE .... 48 TABLE XX - MEAN PLASTIC FRAGMENTS DEBRIS ABUNDANCE ................................................................................... 49 TABLE XXI - STATISTICAL PARAMETERS OF THE TWO-WAY ANOVA, APPLIED TO THE PLASTIC FRAGMENTS DEBRIS ABUNDANCE.......................................................................................................................................... 50

TABLE XXII - MEAN PLASTIC CAPS DEBRIS ABUNDANCE ........................................................................................... 51 TABLE XXIII - STATISTICAL PARAMETERS OF THE TWO-WAY ANOVA, APPLIED TO THE PLASTIC CAPS DEBRIS ABUNDANCE. ... 51 TABLE XXIV - MEAN POLYSTYRENE DEBRIS ABUNDANCE. ........................................................................................ 52 TABLE XXV - STATISTICAL PARAMETERS OF THE TWO-WAY ANOVA, APPLIED TO THE POLYSTYRENE DEBRIS ABUNDANCE. ... 53 TABLE XXVI - MEAN CIGARETTE TIPS/FILTERS DEBRIS ABUNDANCE ........................................................................... 54 TABLE XXVII - STATISTICAL PARAMETERS OF THE TWO-WAY ANOVA, APPLIED TO THE CIGARETTE TIPS/FILTERS DEBRIS ABUNDANCE.......................................................................................................................................... 54

TABLE XXVIII - MEAN ROPES/SMALL NET PIECES DEBRIS ABUNDANCE. ...................................................................... 55 TABLE XXIX - STATISTICAL PARAMETERS OF THE TWO-WAY ANOVA, APPLIED TO THE ROPES/SMALL NET PIECES DEBRIS ABUNDANCE.......................................................................................................................................... 56

TABLE XXX - COMPOSITION OF BEACHED DEBRIS IN DIFFERENT REGIONS .................................................................... 60 TABLE XXXI - INDICATOR ITEMS BY SOURCE CATEGORY ........................................................................................... 61

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I NDEX

OF FIGURES

FIGURE 1 - EXAMPLE OF MARINE DEBRIS WASHED ASHORE ...................................................................................... 11 FIGURE 2 - WORLD PLASTICS PRODUCTION BETWEEN 1950-2011. .......................................................................... 14 FIGURE 3 - REPRESENTATION OF THE MAIN SOURCES AND MOVEMENT PATHWAYS FOR PLASTICS IN THE MARINE ENVIRONMENT ...................................................................................................................................... 15

FIGURE 4- A MODEL SIMULATION OF THE PROJECTED DISTRIBUTION OF MARINE LITTER IN THE OCEAN IN TEN YEARS............ 17 FIGURE 5 - AVERAGE PLASTIC CONCENTRATION AS A FUNCTION OF LATITUDE .............................................................. 17 FIGURE 6 – LARGE DIMENSION MARINE DEBRIS AT ONE OF THE STUDY SITES. ............................................................... 17 FIGURE 7 - NUMBER OF ENTANGLEMENT AND INGESTION RELATED ENCOUNTERS PER DECADE. ....................................... 18 FIGURE 8 - LOCATION OF THE AZORES ARCHIPELAGO, THE CITY OF HORTA AND THE STUDY SITES .................................... 27 FIGURE 9 – EXAMPLES OF DIFFERENT ITEMS CLASSIFIED WITHIN THE CATEGORIES OF THE SURVEY DESIGN.......................... 28 FIGURE 10 - SPECIFIC DEBRIS ITEMS FROM EACH CATEGORY TYPE. ............................................................................. 29 FIGURE 11 - SCHEMATIC OVERVIEW OF BEACH SURVEY DESIGN................................................................................. 29 FIGURE 12 - PERCENTAGE OF THE SAMPLED CATEGORIES OF CONCEIÇÃO.................................................................... 32 FIGURE 13 - HIGH-TIDE LINE WITH REED AND FRAGMENTS OF PLASTICS AT CONCEIÇÃO. ................................................ 33 FIGURE 14 - PERCENTAGE OF SAMPLED CATEGORIES OF PORTO PIM. ........................................................................ 34 FIGURE 15 - DENSITY OF PLASTICS AND GLASS AT BOTH STUDY SITES ......................................................................... 34 FIGURE 16 - DENSITY OF DEBRIS AT BOTH STUDY SITES (CLOTHS, METAL, RUBBER, LUMBER, OTHER AND LARGE ITEMS). .... 35 FIGURE 17 - HIGH-TIDE LINES ............................................................................................................................ 35 FIGURE 18 - REPRESENTATION OF THE INTERACTION EFFECT OF GENERAL DEBRIS ABUNDANCE. ....................................... 37 FIGURE 19 - REPRESENTATION OF THE ABSENCE OF THE INTERACTION EFFECT OF PLASTICS ABUNDANCE ........................... 38 FIGURE 20 - REPRESENTATION OF THE INTERACTION EFFECT OF CLOTHS/FABRIC ABUNDANCE. ........................................ 40 FIGURE 21 - REPRESENTATION OF THE INTERACTION EFFECT OF GLASS ABUNDANCE ...................................................... 41 FIGURE 22 – REPRESENTATION OF THE ABSENCE OF THE INTERACTION EFFECT OF METALS ABUNDANCE ............................ 43 FIGURE 23 - REPRESENTATION OF THE ABSENCE OF THE INTERACTION EFFECT OF RUBBER ABUNDANCE ............................. 44 FIGURE 24 - REPRESENTATION OF THE INTERACTION EFFECT OF PROCESSED LUMBER ABUNDANCE. .................................. 46 FIGURE 25 - REPRESENTATION OF THE INTERACTION EFFECT OF OTHERS ABUNDANCE ................................................... 47 FIGURE 26 - REPRESENTATION OF THE ABSENCE OF INTERACTION EFFECT OF PLASTIC FRAGMENTS ................................... 49 FIGURE 27 - REPRESENTATION OF THE ABSENCE OF THE INTERACTION EFFECT OF PLASTIC CAPS. ...................................... 50 FIGURE 28 - - REPRESENTATION OF THE INTERACTION EFFECT OF POLYSTYRENE ........................................................... 52 FIGURE 29 - REPRESENTATION OF THE INTERACTION EFFECT OF CIGARETTE TIPS/FILTERS ............................................... 53 FIGURE 30 - REPRESENTATION OF THE ABSENCE OF INTERACTION EFFECT OF ROPES/SMALL NET PIECES ............................ 55 FIGURE 31 - EVIDENCE OF INCRUSTATIONS OF DEBRIS WASHED ASHORE. .................................................................... 57 FIGURE 32 – TOTAL NUMBER OF DAYS OF PREVAILING WIND AND SWELL DIRECTIONS DURING MONITORING ..................... 62

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I NDEX

OF APPENDICES

TABLE A - LOST FISHING GEAR AROUND THE WORLD ............................................................................................... 82 TABLE B - TYPES OF THERMOPLASTIC PLASTICS, MOST COMMON APPLICATIONS AND SYMBOLS ....................................... 82 TABLE C - SAMPLING AREA AND SHORELINE CHARACTERISTICS DATA SHEET OF PORTO PIM BEACH ................................... 83 TABLE D - DETAILED SURVEY DATA SHEET OF THE TWO SURVEY SITES, INCLUDING: SAMPLING TIME, COORDINATES, TIDE HEIGHTS AND WEATHER CONDITIONS. ........................................................................................................ 84

TABLE E - COMPARISON OF MEAN DEBRIS DENSITIES .............................................................................................. 85 TABLE F - DIAGRAMS OF WIND AND SWELL CONDITIONS .......................................................................................... 85

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1. Introduction The monitoring of marine debris in two sandy beaches at Faial Island - Azores, is a master project that took place at the Department of Oceanography and Fisheries (Horta), during the academic year of 2012-2013, and is submitted for the title of Master of Science in Integrated Studies of the Ocean. This is a pioneering study, capable to fill out a gap of information about local coastal debris, their abundance and seasonal fluctuations. In addition, further research might link this knowledge to possible major input sources in large and meso-scale circulation, along with its behavior and durability in the water column. These understandings allow the possibility to create more and better defined laws of waste management in order to diminish and prevent drastic impacts of pollution, in a local and overall context.

1.1 Organization of contents The arrangement of contents is outlined in 8 chapters, where meticulous information about the state of the art, sampling and statistical methodologies, along with their further discussion and conclusions are disclosed in more detail. The obtained results of this field investigation, make available a high amount of information, which is capable to enhance knowledge about marine pollution in general, plus, possible major sources that contribute to this state, in addition to the fact that there is a huge need to raise awareness of the deleterious effects that this unthoughtful production and disposal of waste causes.

1.2 Objectives The main objectives of this study consist in monitoring the quantity and types of debris that have been washed up on the shoreline of two beaches (Porto Pim (PP) and Conceição (PC)) with different exposure to the sea’s actions, localized in Faial Island (Azores). These findings will enable the development of a standing-stock data base, with the total quantity of debris, as well as its density, in a 7 month period. These results will serve as a possible long-term basis, allowing analysis of the balance among input and output taxes of debris, and thus, recognizing the impacts of this sort of anthropogenic pollution. Catharina Pieper

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Other pertinent issues that may also be discussed based in our results are:  Does the density and types of items vary between beaches? Is there any change along the sampling months?  Is it possible to diagnose the key-sources (e.g. domestic waste, fishing fleet) of marine debris?  Does the surrounding environment affect the presence of debris?

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2. Marine debris

2.1 Definition It is common knowledge that marine habitats throughout the world are contaminated with man-made items and solid waste (SCBD, 2012). Being a consequence of human activities, this has contributed for a major decline of the world’s biological diversity, and the problem has become so critical that combined human impacts could have accelerated extinction rates to 1000–10,000 times the natural rate (Lovejoy, 1997). In the oceans, there are many sources of threats to marine life that are introduced in a variety of forms, such as: overexploitation, harvesting, dumping of waste, pollution, alien species, land reclamation, dredging and global climate change (Derraik, 2002). Probably the biggest impact in the marine environment is based on an action that every human being contributes to, everyday – marine debris. According to Galgani et al. (2010), this definition includes any form of manufactured or processed material discarded, disposed of or abandoned in the marine environment (Figure 1). It consists of items made or used by humans that enter the sea, whether deliberately or unintentionally, including transport of these materials to the ocean by rivers, drainage, sewage systems or by wind. Because of the ubiquity of these polluting objects, it is possible to identify marine debris from the poles to the equator and from shorelines, estuaries and the sea surface, to ocean floor. Although types (e.g. glass, metal, processed lumber, rubber, clothes, etc.) (Cheshire et al., 2009) and absolute quantities vary, it is clear that plastic materials represent the major constituents of these debris (Barnes et al., 2009; Ryan et al., 2009; Browne et al., 2011), which proportion consistently varies between 60 to 80% of the total marine waste (Gregory & Ryan, 1997).

Figure 1 - Example of marine debris washed ashore. Catharina Pieper

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2.2 Sources To determinate all the inputs that promote the entrance of marine waste in the environment is a very complex challenge (Derraik, 2002). Due to several pathways, including materials that are accidentally lost, carelessly handled or left behind by beachgoers (Pruter, 1987; Wilber, 1987), there are also other ways that allow debris to reach the ocean, such as: transport by rivers and municipal wastewater systems, dumping from fishing fleets and merchant ships (Pruter, 1987; Cawthorn, 1989; Feldkamp et al., 1989; Williams & Simmons, 1997). Furthermore, there are also other major sources that influence waste quantities in a substantial way. Allsopp et al. (2006) described that around 80% of marine debris are from land-based sources while the remaining 20% are from oceanbased sources. The first ones are typically blown, washed or discharged into the sea (Sheavly, 2005), which means that these type of inputs represent numerous categories: 

Storm water discharges: storm drains that collect water and transport waste, directly discharge wastewater into nearby streams, rivers or the ocean (U. S. EPA, 2002c);



Combined sewer overflows: during heavy rains the handling capacity of the wastewater treatment system may be exceeded and the sewage plus storm water is then not treated, which could imply directly discharged effluents to nearby rivers or oceans (U. S. EPA, 2002c; Sheavly, 2005);



Littering: is the result of leaving debris in a carelessly way at the coast. This involves many different sectors, such as: common citizens, fishermen, forestry workers, construction and mining operations (U. S. EPA, 2002c; Sheavly, 2005), in addition to demolition wastes;



Solid waste disposal and landfills: run-off from landfills that are located in coastal areas or near to rivers may find its way into the marine environment, as well as waste that may be lost during its collection or

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transportation, and illegal dumping of domestic or industrial waste (U. S. EPA, 2002c; Sheavly, 2005); 

Industrial activities: industrial products may become marine debris if they are improperly disposed of on land, or if they are lost during transport or loading/unloading, at port facilities (U. S. EPA, 2002c).

Ocean-based sources can be originated from accidental loss, indiscriminate littering or illegal disposal, and it can also be the result of waste management disposal practices that were carried out in the past (Sheavly, 2005). These kinds of inputs include: 

Commercial fishing: marine debris are generated when there is a fail to retrieve fishing gear, when occurs the discard of fishing gear or other waste overboard (U.S. EPA, 1992c; Sheavly, 2005). Derraik (2002) mentioned that in 1975, the world´s fishing fleet dumped into the sea approximately 135.400 tons of plastic fishing gear and 23.600 tons of synthetic packaging material (Cawthorn, 1989; DOC, 1990);



Recreational boaters: may deposit waste overboard such as bags, food packaging and fishing gear (Sheavly, 2005). According to UNESCO (1994), these boaters dispose approximately 52% off all waste in the United States of America;



Merchant, military and research vessels: waste may be accidentally released or blown into the water, or may be deliberately thrown overboard (U. S. EPA, 1992c; Sheavly, 2005);



Offshore oil and gas platforms and exploration: may generate items which are deliberately or accidentally released into the marine environment (U. S. EPA, 2002c; Sheavly, 2005).

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2.3

The faith of Plastics In the last 30 to 40 years, the

nature of waste ending up in the marine environment has suffered a big change because of the increase in use of plastic and synthetics (Allsopp et al., 2006). It is also common sense that debris may be found near the source of input and at densely populated places, being also observed in the most remote places, far away from any evident source (e.g. islands in the middle of oceans and polar

Figure 2 - World Plastics production between 1950-2011. Extracted from: PlasticsEurope (2012).

regions) (UNEP, 2005). The majority of features that plastics possess allow it to be versatile and to have an amplified productiveness, from year to year (Derraik, 2002). As a result of the enormous expansion in the 1950s, the worldwide plastics production rose up to 280 million tons in 2011 (PlasticsEurope, 2012) (Figure 2). Beyond that, plastic resources consists of synthetic organic polymers, which are lightweight, buoyant and durable, having a slow degradation rate (Laist, 1987; Moore et al., 2001), and combined properties that result in a serious hazard to the environment (Pruter, 1987). There have been many attempts to quantify plastic on beaches, sea floor, water column and on the sea surface. A large amount of these results have showed that marine debris are ubiquitous in the world’s oceans and shorelines, and that higher quantities are found in the tropics and in the mid-latitudes, comparing to areas towards the poles (Thompson et al., 2009). The prevalent amounts of these human-made items are located in shipping lanes, around fishing areas and in oceanic convergence zones (Allsopp et al., 2006). Once they reach the ocean, plastic debris start to float and are capable to travel on currents through innumerous miles, being therefore dispersed to all the neighbor oceans (Derraik, 2002; Sheavly, 2005) (Figure 3). Other categories of debris like glass, metal, rubber and additional types of plastic (e.g. PVC and nylon) tend

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to sink (U. S. EPA, 2002), and are subjected to a very slow biodegradation process (due to wave action, oxidation and ultraviolet light) which breaks some items into smaller fragments over time. Together with this mechanical procedure, there is also chemical degradation that changes, in a significantly way, the average molecular weight of polymers, causing an inevitably weakening (Andrady, 2011). Small degraded fragments turn out to be brittle enough to settle on the sediments and might persist for centuries (Derraik, 2002). In addition to these mechanisms, there are many other that Andrady (2011) mentions, with particular interest to the fact that degradation initiated by solar UV radiation turns to be very proficient in plastics exposed in air or lying on a beach surface. Nonetheless, this author also describes that the same material, when exposed to sunlight, at the same location, and floating in seawater, presents a severely retarded degradation rate.

Figure 3 - Representation of the main sources and movement pathways for plastics in the marine environment, with sinks occurring (a) on beaches, (b) in coastal waters and their sediments and (c) in the open ocean. Extracted from: Ryan et al. (2009).

Accordingly to these facts, there has been the concern to verify if in the point out sites (e.g. convergence zones) there is, in fact, buoyant marine debris. Through the studies of sea surveys of the United States National Oceanic and Atmospheric Administration (NOAA) in 1988, and the observations of the scientist Captain Charles Moore in 1997, emerged the first reveal and visual confirmation that definitely there are massive amounts of debris that act like a “soup” at the surface of the water column, forming a garbage patch in the Pacific Ocean (Moore et al., 2001, 2002; Catharina Pieper

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Moore, 2008). Since this revelation, research has shown that there are potentially five garbage patches (gyres) scattered globally, located in the North and South Pacific Ocean, North and South Atlantic Ocean and Indian Ocean (Sesini, 2011) (Figures 4 and 5). Due to the durability and persistence of some marine debris, once they enter in a gyre system, they can remain for long periods of time, meaning that the concentration of debris within these systems can be considerably greater than in other areas of the ocean (STAP, 2012). Moore et al. (2001), for instance, found densities of plastic of 334,271 pieces per km2, or 5114 g per km2, within the North Pacific Subtropical Gyre — the largest recording of debris in the Pacific Ocean. In the North Atlantic Ocean little is known about the distribution of plastics, particularly at the east side of the Gulf Stream. Additionally, according to Morét-Ferguson et al. (2010), there is even a lesser amount of information about physical properties of the debris, mainly: individual size, shape, density and elemental composition. This was also one of the first studies providing evidences for understanding potential pollution sources and ecological implications of pelagic plastic debris. Model estimations compiled from a combination of mathematical and physical data concluded that the total amount of plastics in the five garbage patches is estimated to be around 36,950 ton. Additionally, the same author (Sesini, 2011) accomplished that 9,064 tons of garbage plastic do exist in the North Atlantic, while in the North Pacific there subsist around 20,240 tons of waste. It seems that these values tend to increase in an exponential way because of the massive global production of plastic materials every year. Data from 2009 reveals that this manufacture produced roughly around 230 million tons (PlasticsEurope, 2012) of which 29,8 million tons were used and discarded in the United States of America (U.S.A.) (Sesini, 2011).

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Figure 4 - A model simulation of the projected distribution of marine litter in the ocean in ten years. Extracted from: Sesini (2011).

Figure 5- Average plastic concentration as a function of latitude (bars, units of pieces per km 2), and modeled concentration (color shading). The highest plastic concentrations were observed in subtropical latitudes (22-38°N). Extracted from: STAP (2011).

2.4

Impacts of marine debris Although the issue of marine waste has been ignored for a long time (Derraik,

2002), plastic debris have become increasingly recognized as a global ocean-wide problem due to its ubiquity and recalcitrance, allowing particles to persist for estimated years to millennia (Andrady, 2003; Barnes et al., 2009). Another fact is that in the marine environment, the apparent abundance of marine life and the vastness of the oceans have led to the dismissal of the proliferation of plastic debris as a potential hazard (Laist, 1987). Due to an increasing divulgation of the media and a relatively concern of the public in general, the aesthetical issues that plastic waste entails, are slowly ceasing to be ignored (Derraik, 2002; Murray, 2009). The expanding knowledge about the deleterious impacts on marine biota, has led to the conclusion that there are innumerous threats that endanger marine life. Aesthetics meeting economical concern Discarded and/or accidentally lost plastic and other manufactured materials are no longer an exception in waste aggregations

along

shorelines

and

beaches (Figure 6). Since this is a very visual question, there are also strongly emotive issues associated with local

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Figure 6 – Large dimension marine debris at one of the study sites (Porto Pim - February, 2013).

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beach users and tourist perceptions. Thereat, there has been an economic concern over the visitor numbers that maybe a significant factor for local economics (Murray, 2009). Technology facilities nowadays, allow people to react immediately and report a situation that reflects personal observations of harrowing images of seabirds, marine mammals, or even fish that has been entangled in abandoned beach-cast or lost netting (Murray, 2009). Such cases can be published to the media or the internet, causing economic losses, health issues and harm to local biota, as well as, long-term deterioration in beach aesthetic values (Gabrielides, 1995).

Entanglement and “ghost fishing” One of the most serious threats to marine animals is undoubtedly the entanglement in plastic debris, with emphasis of discarded fishing gear (Derraik, 2002). If this happens, an entanglement could mean drowning to marine mammals, reptiles and/or birds, loss of the ability to catch food or avoid predators, incurable wounds due to abrasive or cutting action of attached debris (Laist, 1987; Jones, 1995; Laist, 1997) and infections (Table I). Also the reduced fitness to that the animal is subjected, leads to a significant increase in energetic costs of travel (Feldkamp et al., 1989). Another anthropogenic danger is lost or abandoned fishing nets, which poses a particular great risk (Jones, 1995). This type of “ghost fishing”, leads to a never ending catch of fish and other animals that sink or are lost on the seabed (Laist, 1987). On the other hand, there are correspondingly caused detrimental impacts on fish stocks, plus on endangered species and benthic environments (Macfadyen et al., 2009) (Figure 7) (Table A).

A

B

Figure 7 - Number of entanglement and ingestion related encounters per decade for A) individuals and B) species reported (mean number per year averaged across decade ±SE). A decade is defined as e.g. 1960-1969. Adapted from: SCBD (2012). Catharina Pieper

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Ingestion of marine debris by animals There have been plenty of documented situations where animals suffered a painful death due to the intake of debris, mostly plastics, for instance seabirds and sea turtles. The injuries caused, mainly due to traps and gillnets (Table A) effect a significantly high number of marine mammals and turtles that were entangled, include internal and external wounds; suppurating skin lesions; ulcerating sores; blockage of digestive tract followed by satiation; starvation; general debilitation often leading to death; reduction in quality of life and reproductive capacity; drowning and limited predator avoidance; impairment of feeding capacity; and the possibility that plastic resin pellets may absorb and concentrate, potentially damaging toxic compounds from sea water (Gregory, 1978, 1991; Oehlmann et al., 2009). Other harmful effects that result from this ingestion include: blockage of gastric enzyme secretion, diminished feeding stimulus, lowered steroid hormone levels, delayed ovulation and reproductive failure (Derraik, 2002) (Table I).

Table I - Species group with the greatest number of individuals and respective percentage (between brackets) reporting entanglement and ingestion records of debris. Extracted from: SCBD (2012).

Number of species with entanglement records Species

Total number of

group

known species

Number of specie s with ingestion records

Laist (1997)

SCBD (2012)

Laist (1997)

SCBD (2012)

115

32 (28%)

52 (45%)

26 (23%)

30 (26%)

Fish

16,754

34 (0.20%)

66 (0.39%)

33 (0.20%)

41 (0.24%)

Seabirds

321

51 (16%)

67 (21%)

111 (36%)

119 (38%)

Sea Turtles

7

6 (86%)

7 (100%)

6 (86%)

6 (86%)

Marine Mammals

Smothering As mentioned previously, vertical transport of plastics in the water column, is very complex and requires an understanding of the multiple biophysical and chemical processes that contributes to the plastic breakdown, and its buoyancy (Ye & Andrady, 1991). Roughly, half of all plastics are neutrally to positively buoyant and float on the sea surface (U. S. EPA, 1992). Gradually with time, organism and sediment fouling, adds weight to the particles, causing the plastics to sink and, eventually, reach the Catharina Pieper

19


seabed (Barnes et al., 2009). The other 50% of plastics are negatively buoyant, which means that they sink within the water column until neutral buoyancy or the sea floor is reached (U. S. EPA, 2011). In accordance with Moore et al. (2005), there are many reports that confirm plastic accumulation, starting from the first water column layers, down to a depth of roughly 30 meters. Several studies have shown that land-sourced materials are frequent on canyon floors (Gregory, 2009). These objects can be tracked from the coast in their progressive passage to abyssal depths, as well as at considerable distance offshore (Galgani et al., 2000). During their research, Acha et al. (2003) revealed that bottom salinity fronts in estuarine environments may act as debris-accumulating barriers, comparable to those associated with surface waters next to convergence zones, oceanic fronts and eddies (Gregory, 1999b). Moreover, the increase of density may possibly be affected by rapid and heavy fouling of floating objects that could sink even quicker to the sea floor. However, grazing organisms may episodically clean fouled surfaces leading to yo-yoing periods of submergence and resurfacing until permanent settlement to the sea ﬂoor occurs (Ye & Andrady, 1991). Other impacts caused from these blanketing effects of plastic sheeting on the sea bottom, could lead to anoxia and hypoxia induced by inhibition of gas exchange among pore water and sea water (Gregory, 2009), besides the creation of artificial hard grounds (Harms, 1990).

Alien species Because of the floatability of debris, there is a wide range of sessile and motile marine organisms that are attracted to them, causing a trans-oceanic dispersal of marine and terrestrial individuals (Gregory, 2009). The hard surface of pelagic plastic provides an alternative substrate for a number of opportunistic colonizers. With the quantities of these synthetic and non-biodegradable materials in marine debris increasing manifold over the last ﬁve decades, dispersal will be accelerated and prospects for invasions by alien and possibly aggressive invasive species could be enhanced (Gregory, 2009). This synthetic habitat is able to offer short-term conditions, since it does not provide ideal conditions that are able to constitute durable food chains or trophic webs that suit larval and juvenile stages of many marine organisms,

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and to serve as protection to predators that aggregate under floating devices (Winston et al., 1997).

Social, economic and safety impacts There are many other impacts that are less conspicuous than those referred before. These impacts include (Cheshire et al., 2009): Social impacts:  Loss of visual amenity ;  Loss of indigenous values;  Perceived or actual risks to marine organisms and humans health and safety; Economic impacts:  Cost to tourism (loss of visual amenity and obstruction to beach use);  Cost to vessel operators (downtime and damage due to entanglements);  Losses to fishery and aquaculture operations due to damage or entanglements;  Cost for cleanup, animal rescue operations, recovery and disposal; Public safety impacts:  Navigational hazards (loss of power or steerage at sea potentially life threatening);  Hazards to swimmers and divers (entanglements);  Cuts, abrasions and stick (puncture) injuries;  Leaching of poisonous chemicals;  Explosive risk (e.g. gas cylinders that are washed ashore).

2.5 Plastic sizes & symbol classification There are several different ways of describing plastic fragments in the marine environment. For instance, the terms “microplastics” and “microlitter” have been defined with dissimilar meanings, that is, Gregory & Andrady (2003) considered microlitter as “the barely visible particles that pass through a 500 μm sieve but retained by a 67 μm sieve (~0.06-005 mm in diameter)”; other authors (e.g. Moore, Catharina Pieper

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2008), defined the microparticles as being in the size range 100 mm). It is also possible to label plastics in line with their type, most common use and symbol (1 to 7). This information is summarized in Table B.

2.6

What is the EU doing? The Marine Directive In 2008, the European Commission published documents (The Integrated

Maritime Policy and the Marine Strategy Framework Directive - Marine Directive) with an overview of relevant legislation, polices and strategies that have the goal to reach the “Good Environmental Status” (GES) – “(…) status of marine waters where these provide ecologically diverse and dynamic oceans and seas which are clean, healthy and productive” – of all marine waters of the European Union by 2020. The decision under Article 9(3) of the Marine Directive, on criteria of good environmental status, addresses marine litter in descriptor 10, and aims at achieving that "Properties and quantities of marine litter do not cause harm to the coastal and marine environment". Currently, there are some projects running that support the EU Member States to achieve the GES status, such as: MARELITT; Marlisco and CleanSea Project (European Commission, 2013). Concerning solid and semi-solid waste that resulted from human individual or industrial activities, Portaria Nº 209/2004 of March 13 refers to the Waste European List. In this List, different types of waste represent a six-digit code, while a two and a four-digit account the numbers of chapters and subchapters (Ambiente Portugal, 2013).

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2.7 Debris reporting systems Due to all the sources, impacts and magnitude that debris have, there is a need to develop and effectively manage this issue, and thus build up a good understanding of the problem. In addition, it is also essential to comprehend the principle types and inputs of litter in the marine environment. To achieve this aim it is crucial to ensure that relevant quality data is available, allowing a comprehensive analysis of the nature and sources of litter, how these are changing through time and how do they response to management interventions (Cheshire et al., 2009). There are many types of methodologies that are able to investigate marine debris and their sources. The United Nations Environment Programme (UNEP) report from 2009, defends that there are three basic types of studies/surveys:  Beach debris surveys;  Benthic debris surveys;  Observations made by divers, submersibles or camera tows;  Collection of debris via benthic trawls;  Floating debris surveys, which include:  Observations made from ship or aerial based platforms;  Collection of litter via surface trawls.

Beach surveys have long been the primary tool for measuring the load of marine debris in coastal and marine systems (Cheshire et al., 2009). Besides of having relatively low cost logistics to be applied, they also provide an invaluable mechanism for education and building community, understanding and awareness. This kind of research has been the most used method for estimating loads of debris accumulation in the sea (e.g. Ribic et al., 1992; ANZECC, 1996a; Rees & Pond, 1998; Kiessling, 2003; Stuart, 2003). In order to establish values that estimate quantities, there is the need to use a common and consistent method in sampling protocols, so that results can be compared and understood through long-term, broad scale and comparative studies (Cheshire et al., 2009). Moreover, and in accordance with the same investigators,

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standardization in the definition of objectives is also crucial to compare results among reports. Therefore, these primary objectives include (Cheshire et al., 2009):

 Quantification and characterization of marine debris for the purposes of developing and evaluating the effectiveness of management, control, enforcement and/or mitigation strategies in particular integration with solid waste management;  Understanding the level of threat posed by marine debris to biota and ecosystems;  Providing comparable datasets to support national, regional and global assessments of marine debris.

NOAA’s report of 2012 from Opfer et al., states that there are two types of shoreline surveys: accumulation and standing-stock surveys. The first one (accumulation), provides information on the rate of deposition (flux) on debris onto the shoreline, and is suited to areas that have beach cleanups, as debris is removed from the entire length of shoreline, during each site visit. Despite of being a more labor-intensive method, this type of survey is generally used to determinate the rate of debris deposition (e.g. number of items per unit of area or per unit of time), and to provide information about debris type and weight. However, it is impracticable to obtain values of density of debris for the reason that its removal causes biases on the amount of debris present during the subsequent surveys (Opfer et al., 2012). The second kind of surveys is standing-stock studies, which supplies information on the amount and types of debris on the shoreline. The amount of items are tallied within discrete transects at the shoreline, allowing a quick assessment of the total load of debris, which determines the density (number of items per unit of area) of debris present. This density reflects the long-term balance between debris input and removal, which delivers the understanding of the overall impact of debris (Opfer et al., 2012).

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3. Methods

3.1 Study sites Two beach debris surveys where realized at two different sites located in the Island of Faial, one of nine islands that comprises the Azores Archipelago positioned in the mid northeast Atlantic (36-40°N, 24-32°W). Samplings started in November, the 19th (2012) and finished in May, the 20th (2013). Porto Pim The sandy beach of Porto Pim (38°31’29’’N 28°37’32’’W) () is a south-west semi enclosed facing bay, that has approximately 292 meters in length, and a maximum tidal range of 1 m (Nash & Santos, 1998). North and south coast areas are rocky, being the upper border limited by sandy dunes with vegetation and rocky cliffs that act like a protection barrier to the beach. With a carrying capacity (number of users) over 500 (Regional Secretariat of the Environment and the Sea, 2011), Porto Pim can be easily accessed by pedestrians all along the year, allowing some beachgoer activity dynamics even during winter time. In addition, this beach is located in the city of Horta, the most important urban area of the island, so that there are several houses and cafe places close by. There are no rivers or rivulets that surround the study location and flow directly into the sea water, however it is possible to find few pipes or drain inputs in the area. Regular cleanups are daily performed during the bathing season, whereas in the remaining months only large debris items (> 30 cm) (Opfer et al., 2012) are hand removed after stormy weather conditions. Meticulous information regarding coordinates, surrounding environments, aspect, substratum type, types of back of shoreline and tidal distances were recorded on data sheets for each study site (Table C). Meteorological conditions (e.g. wind and swell direction; precipitation; tide heights) were logged on data sheets at each monitoring day (Table D).

Conceição The second study site, is also a sand constituted beach known as Conceição (38°32’35’’N 28°37’08’’W) (Figure 8). It is a south-east facing shoreline positioned close to the harbour of Horta city, with about 162 meters of extension and a maximum tidal Catharina Pieper

25


range of 0.75 m (Tempera, 2009). There are fixed pebble areas on the left side of the beach, as well as, constantly moving pebble areas, close to the water’s edge. The upper border of the shore is limited by a stone wall and a narrow road that leads to a park and the municipal pools. The exposure to the Faial-Pico Channel means a bigger dynamic of the proportions of sand and gravel, in a more or less continuously mode. In close proximity, the Conceição rivulet drains to the sea during rainy days, being limited by a small breakwater, as well as the nearby harbor. According to SRAM (2011), this shoreline has a carrying capacity of 500 users, which turns this site into an attractive place for beachgoers during the bathing season. Similar to Porto Pim, Conceição can be easily accessed by pedestrians and vehicles, and due to its sea agitation and swell, surfers and fishermen are regular attenders. Regular cleanups are only performed during bathing season; however, large debris items and reeds are occasionally hand removed after strong rainy days or storms. Likewise to PP, exhaustive information about the surrounding environment, along with single transect details and weather conditions, were taken at PC (Table D and E).

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A

B

Figure 8 - Location of the Azores Archipelago, the city of Horta and the study sites: A represents Porto Pim beach and B represents Conceição beach. Maps were adapted from ImagDOP.

3.2 Survey Design In order to follow a commonly used method that estimates loads of debris in the sea plus standing-stocks, the guidelines proposed by Opfer et al. (2012) where adapted in this study (Table II). Such adjustments included: 10 m beach segments, a target size of 2-30 cm, a higher sampled area in Porto Pim (200 m), the presence of a breakwater at Conceição, the sporadic occurrence of cleanup activities at both study sites, as well as, the adding of other specific items to the categories list, that were not mentioned by the followed methodology, but were considered to be important to add due to its abundance. Table II - Monitoring details of the shoreline site according to Opfer et al. (2012).

List of survey procedures Shoreline site characteristics

Survey Design

Data sheets Catharina Pieper

Preference to sandy beaches or pebble shoreline Clear, direct and year-round access At least 100 meters in length, parallel to the water; No breakwaters or jetties No regular cleanup activities The 100 meter shoreline site has to be divided into 5 meter segments Each segment (transect) is numbered from the right to the left of the beach, starting in the back of the shoreline (where the substrate changes, e.g. from sand to gravel or at the first barrier, e.g. vegetation line) to the water’s edge; Four transects are randomly selected for each survey day along with a random number table; On a daily basis, 20% of the monitoring area is covered and analyzed during low tide time; GPS coordinates and total length of the segment have to be taken for all transects at both borders; The number of debris items within 2.5-30 centimeters are counted, classified and recorded in data sheets. Objects above this size are registered in the large items section; With the purpose of comparison between results, data sheets with

27


information about the shoreline characterization and debris density required to be filled out likewise in each survey day.

Macro-debris (>20mm) (Barnes et al., 2009) within the size of 2 to 30 cm were counted at both sites (Porto Pim and Conceição beaches). Items were registered and classified into eight different main categories (Figure 9 and 10): plastic, glass, metal, cloths/fabric, processed lumber, rubber, others and large debris items). Additionally, the density of debris of the equivalent categories mentioned beyond, was calculated for each beach during the survey months. Six survey days took place in the mid of each sampling month (November until May), where 10 transects per site where randomly chosen, according to a random number table. The borders of the sampling area were limited to the zone of stretching from the low tide mark, to the point where permanent beach vegetation or gravel first appeared (Figure 11). A total of 10 transects, with a 10 m wide limitation, and with different tidal distances, were realized at PC, while at PP, 20 transects were conducted due to the large extension of the shoreline site. Virtue to that fact, in one month, 10 of the 20 transects were randomly chosen to survey, whereas in the next month, the other remaining transects, that were not sampled, were conducted. Evidences of clean-up work or changes to the surrounding environment were annotated when applied. Detailed debris types information of each category was accessed to enhance facts about potential debris sources (Figure 11).

Figure 9 – Examples of different items classified within the categories of the survey design.

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Figure 10 - Specific debris items from each category type. Adapted from: Opfer et al. (2012).

A

10 Meters

Figure 11 - Schematic overview of beach survey design using 10 m wide transects: A – represents width of beach. Adapted from: Topçu et al. (2013).

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3.3 Statistical analysis With the aim of analyzing the information about the amount and types of debris sampled at the shorelines, a series of statistical tests was applied to the data. These tests were used to estimate the relational strength between the independent variables: Beach (with two levels: PC and PP) and Month (with six levels: November, December, January, February, March and April), and the dependent variable (Debris Abundance). Both independent variables were assumed to be fixed factors, since data has been gathered from all the levels of the factors that were considered of interest. Samplings did not occur at Porto Pim during the month of May, due to municipal works with a backhoe that dug the areal to lay submarine cables. Thus, this month was only included for spatial and temporal analysis of densities, being left out of the more detailed analysis. It was considered that the 10 transects (N = 10) sampled at each month and at each site, represented the independent replicates for the combination of factors of this study. The interpretation of the data analysis was done based on the works of Maroco (2007) and Zar (2010). The statistical results were stated in accordance with the American Psychological Association (APA, 2001). The software SPSS (v. 20.0, Armonk, NY, IBM Corp.) was used to run the analysis of abundances classified in general items categories and five most abundant plastic items.

3.4 Density of Debris Besides the quantification of the abundance (A) of debris, the density (D) of items was also considered in terms of number of items per m 2 (D = N/A) (Topçu et al., 2013). Therefore, the width of each sampled transect was measured at each shoreline. The resultant data was organized in eight categories to allow quick assessment of the total load of debris, in comparison to the total sampled area. This specific analysis was accomplished using the Microsoft Excel (v. 2013) software.

3.5 Abundance of debris To investigate the influence of time (factor: Month) and location (factor: Beach), on debris abundance, a two-way ANOVA was performed (Widmer &

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Hennemann, 2010). In order to verify the assumptions of normality and variance homogeneity of the mentioned test, the Kolmogorov-Smnirnov test with the Lilliefors Significance Correction, and the Levene test were applied (Maroco, 2007). It was considered that p-values > 0.05 indicated a normal distributed variable of the two samples, as well as the homogeneity of the sample’s variances. A corrective logarithmic transformation of the data was done, if normality and equal variances assumptions were not met (Ribic et al., 2010). In case this transformation was not able to remove heterogeneity of variances, the ANOVA tests were performed with untransformed data, since Analysis of Variance is a robust test for such condition (Underwood, 1997). Following to this, a multiple comparisons, post-hoc HSD Tukey’s test was conducted, due to its robustness to deviations to normality and homogeneity of variances (Maroco, 2007), with the aim of evaluating which groups, defined by the interaction of factors, differ significantly, whenever H0 was rejected. The previously mentioned statistical treatments were applied on general debris abundance, categories (plastics, cloths/fabric, glass, metal, rubber, processed lumber and others), and the five most abundant plastic items (fragments, caps, polystyrene, cigarette tips/filters and ropes/small net pieces), where the following hypotheses were tested:

H0a: Debris Abundance is the same between beaches. H1a: Debris Abundance varies between beaches.

H0b: Debris Abundance does not differ along months. H1b: Debris Abundance varies along months.

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4. Results

4.1 Temporal and spatial densities of beach debris At Conceição beach, the mean total density of debris was 0.049 items/m2 and values ranged from minimum of 0 items/m2 to maximum of 0.502 items/m2. The categories with the highest percentages that were present during this monitoring, were plastics (82%) and glass (13%), while the remaining categories (processed lumber, metal, rubber and cloths/fabric) represented only 1% of the total, each (Figure 12). Regarding the temporal densities (Table III), the highest values were reached in the months of February (plastics density: 0.502 items/m2), January (plastics density: 0.400 items/m2) and May (plastics density: 0.339 items/m2). The peak value of density for glass was reached in November with 0.097 items/m2 (Figure 15). Particularly interesting observations made at the sampling location, showed that the majority of the target class of items (2-30 cm) was found close to the breakwater and at the first transects of the beach (PC1, PC2 and PC3). On the other hand, a substantial part of plastic items (e.g. fragments, polystyrene, caps and cigarette tips/filters) and big accumulations of reeds were found every time high-tide lines were visually distinct in the sand (Figure 13). Furthermore, other particular observations notified that, for instance, glass items were more present close to the water’s edge.

Figure 12 - Percentage of the sampled categories of Conceição.

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Table III - Mean general debris densities with corresponding standard error (M±SE), recorded for both beaches, during seven months at PC and six months at PP. This month was not considered for further statistical analysis.

Beach

Month

M

SE

PC

Nov

0.052

0,029

Dec

0.043

0,025

PP

Jan

0.067

0,047

Feb

0.082

0,059

Mar

0.048

0,033

Apr

0.043

0,034

May*

0.053

0,040

Nov

0.066

0,048

Dec

0.131

0,105

Jan

0.212

0,170

Feb

0.283

0,231

Mar

0.154

0,121

Apr

0.166

0,131

Figure 13 - High-tide line with reed and fragments of plastics at Conceição.

Comparatively to the first study site, the mean total density of debris of Porto Pim was much higher (0.148 items/m2), with values varying from minimum of 0 items/m2 to a maximum of 1.940 items/m2). Here, plastics denoted the most predominant percentage of the site’s categories, representing 96% of all quantified waste. Glass (3%) and rubber (1%) were the second and the third classes that prevailed at this location (Figure 14). During the six sampling months, February was also the month where debris had the peak value (plastics density: 1.940 items/m2), followed by January (plastics density: 1.433 items/m2) and April (plastics density: 1.103 items/m2). Maximum values for glass were reached in April (0.046 items/m2), while rubber had major density in February (0.017 items/m2) (Figures 15 and 16; Table I). At this Catharina Pieper

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shoreline, loads of small sized plastic items were recognized close to the dunar systems of the beach (transects PP14 to PP18), mainly, at the first 10 meters of these transects. When high-tide mark lines were present, a noteworthy quantity of diverse plastic fragments was very outstanding in the sand (Figure 17).

Figure 14 - Percentage of sampled categories of Porto Pim.

Figure 15 - Density of Plastics and Glass at both study sites

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Figure 16 - Density of debris at both study sites (Cloths, Metal, Rubber, Lumber, Other and Large items).

Figure 17 - High-tide lines (left) with detailed contents (right) at Porto Pim.

4.2 General debris abundance The analysis of the general debris abundance resulted in non-normal distributed data, as well as heterogeneity among variances, for both of the independent factors (Beach and Month). Subsequently, and to validate the assumptions of ANOVA for α = 0.05, a logarithmic transformation of data (Log (x+1)) was performed, in accordance to Zar (2010). The results of the two-way ANOVA analysis revealed that the Beach had a significantly effect on the general abundance of debris (F(1,108) = 113.721; P = 0.000). Substantial higher quantities of items were found at Porto Pim when compared to Conceição (Table III). Likewise, the Month also presented statistically significant effects on the general abundance of debris that where washed ashore (F(5,108) = 4.187; P = Catharina Pieper

35


0.002). February was the month where we recorded the highest amount of items in both beaches (Table IV). Nevertheless, there was not a statistically significant interaction between the factors (F(5,108) = 1.688; P = 0.144) (Table V) (Figure 18). After the multiple comparisons (post-hoc) test, it was possible to recognize that only February differed significantly from the months of April and November (Table IV).

Table IV - Mean general debris abundance with corresponding standard error (M±SE), recorded for both beaches, during six sampling months (two-way ANOVA, α = 0.05). Means followed by different letters report significantly different results between months, within the same beach.

Beach

Month

M

SE

PC

Novb

80,500

16,136

Dec

70,200

8,171

Jan

105,700

28,458

Feba

111,000

22,394

Mar

67,900

17,575

Aprb

60,300

11,317

Novb

127,600

23,189

Dec

351,200

39,060

Jan

459,200

40,652

Feba

626,200

46,100

Mar

336,800

47,919

Aprb

347,700

52,673

PP

Table V - Statistical parameters of the two-way ANOVA, applied to the general debris abundance in two sandy beaches, along a six months sampling period (α = 0.05).

Catharina Pieper

Sources of variation:

MS

df

F

p

Beach

11.816

1

113.721

0.000

Month

0.435

5

4.187

0.002

Beach * Month

0.175

5

1.688

0.144

36


Figure 18 - Representation of the interaction effect on the abundance of General debris, for Beach*Month.

4.3 Analysis by category of debris 4.3.1 Plastics The data analysis of plastics abundance was confirmed to be non-normally distributed and not homogeneous among variances, for both of the independent factors (Beach and Month). Thereafter, and to validate the assumptions of ANOVA for α = 0.05, a logarithmic transformation of data (Log (x+1)) was performed, likewise stated in Zar (2010). The results of the two-way ANOVA analysis revealed that the Beach significantly affected the abundance of plastics present (F(1,108) = 121.267; P = 0.000). So, large amounts of plastics were found at Porto Pim in comparison to Conceição (Table VI). Equally, the Month also presented statistically significant effects in the abundance of plastics that were washed ashore (F(5,108) = 5.183 P = 0.000). February was the month where we recorded the highest amount of plastics on both beaches (Table VI). Though, there was not a statistically significant interaction between the factors (F(5,108) = 0.767; P = 0.575) for this category (Table VII) (Figure 19). After the multiple comparisons (post-hoc) test, it was possible to recognize that February differed significantly from the months of November and March, and November differed from January (Table VI).

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Table VI - Mean plastics debris abundance with corresponding standard error (M±SE), recorded for both beaches, during six sampling months (two-way ANOVA, α = 0.05). Means followed by different letters report significantly different results between months, within the same beach.

Beach

Month

M

SE

PC

Novb

54,500

15,360

Dec

49,100

6,754

Janc

49,100

27,264

Feba

97,000

19,692

Marc

56,600

16,474

Apr

56,600

11,299

Nov b

113,700

20,703

Dec

337,500

37,166

Janc

442,200

110,475

Feb a

613,000

99,015

Mar c

319,700

55,459

Apr

330,700

112,735

PP

Table VII - Statistical parameters of the two-way ANOVA, applied to the plastics debris abundance in two sandy beaches, along a six months sampling period (α= 0.05).


MS

df

F

p

Beach

15.420

1

121.267

0.000

Month

0.659

5

5.183

0.000

Beach * Month

0.098

5

0.767

0.575

Figure 19 - Representation of the interaction effect on the abundance of category Plastics, for Beach*Month.

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4.3.2 Cloths/Fabric The data analysis of cloths/fabric abundance revealed to be non-normal and non-homogeneous among variances, for both of the independent factors (Beach and Month). In accordance with Zar (2010), the assumptions of ANOVA for α = 0.05, where validated by applying a logarithmic transformation of data (Log (x+1)). The results of the two-way ANOVA analysis revealed that the Beach did not have a significantly effect on the abundance of cloths/fabric (F(1,108) = 0.165; P = 0.686 (Table VIII). Equally, the Month also did not present significantly effects in the abundance of cloths/fabric that were washed ashore (F(5,108) = 2.121; P = 0.068) (Table VIII), nor was there a statistically significant interaction between the factors (F(5,108) = 0.574; P = 0.720) for this category (Table IX) (Figure 20). Table VIII - Mean cloths/fabric debris abundance with corresponding standard error (M±SE), recorded for both beaches, during six sampling months (two-way ANOVA, α = 0.05). Means followed by different letters report significantly different results between months, within the same beach.

Beach

Month

M

SE

PC

Nov

1,700

0,651

Dec

0,500

0,167

Jan

0,700

0,423

Feb

0,300

0,153

Mar

0,500

11,189

Apr

0,200

0,133

Nov

1,200

0,593

Dec

1,000

0,394

Jan

0,700

0,396

Feb

0,700

0,473

Mar

0,300

0,153

Apr

0,600

0,267

PP

Table IX - Statistical parameters of the two-way ANOVA, applied to the cloths/fabric debris abundance in two sandy beaches, along a six months sampling period (α= 0.05).

Catharina Pieper


MS

df

F

p

Beach

0.008

1

0.165

0.686

Month

0.105

5

2.121

0.068

Beach * Month

0.028

5

0.574

0.720

39


Figure 20 - Representation of the interaction effect on the abundance of category Cloths/fabric , for Beach*Month.

4.3.3 Glass The abundance of glass, relatively to factors Beach and Month was not normal distributed and variances where not homogenous. Thereafter, and to validate the assumptions of ANOVA for α = 0.05, a logarithmic transformation of data (Log (x+1)) was performed, as stated in Zar (2010). The results of the two-way ANOVA revealed that the Beach did not have a significantly effect on the abundance of glass (F(1,108) = 0.284; P = 0.595) (Table X). Likewise, the Month also did not significantly affect the abundance of glass that was washed ashore (F(5,108) = 0.567; P = 0.725) (Table X). However, there was a statistically significant interaction between the factors (F(5,108) = 3.765; P = 0.004) for this category (Table XI) (Figure21).

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Table X - Mean glass debris abundance with corresponding standard error (M±SE), recorded for both beaches, during six sampling months (two-way ANOVA, α = 0.05). Means followed by different letters report significantly different results between months, within the same beach.

Beach

Month

M

SE

PC

Nov

21,300

9,527

Dec

19,000

8,663

Jan

13,500

4,277

Feb

9,300

2,454

Mar

5,700

1,146

Apr

1,700

0,684

Nov

8,500

2,029

Dec

10,400

3,528

Jan

12,900

7,165

Feb

6,400

2,437

Mar

13,500

5,334

Apr

13,800

1,825

PP

Table XI - Statistical parameters of the two-way ANOVA, applied to the glass debris abundance in two sandy beaches, along a six months sampling period (α= 0.05).


MS

df

F

p

Beach

0.065

1

0.284

0.595

Month

0.129

5

0.567

0.725

Beach * Month

0.858

5

3.765

0.004

Figure 21 - Representation of the interaction effect on the abundance of category Glass, for Beach*Month.

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41


4.3.4 Metals After testing the assumptions of the Analysis of Variance, the results revealed that, with a 5% error probability, the distribution of both of the independent variables was considered to be non-normal and heterogenic. A logarithmic transformation (Log (x+1)) (Zar, 2010) was performed, although this adjustment did not reach normal distribution, nor homogeneity of variances (P =0.00). The analysis performed on the untransformed data (two-way ANOVA) revealed that the Beach had a significant effect on the abundance of metal (F(1,108) = 14.412; P = 0.000) (Table XII). Substantial higher quantities of items were found at Conceição when compared to Porto Pim (Table XII). Contrariwise, the Month did not have a significant effect in the abundance of metals that were washed ashore (F(5,108) = 2.129; P = 0.067). Besides this, there was not a statistically significant interaction between the factors (F(5,108) = 1.621; P = 0.161) for this category (Table XIII) (Figure 22).

Table XII - Mean metals debris abundance with corresponding standard error (M±SE), recorded for both beaches, during six sampling months (two-way ANOVA, α = 0.05). Means followed by different letters report significantly different results between months, within the same beach.

Beach

Month

M

SE

PC

Nov

1,400

0,521

Dec

0,200

0,200

Jan

0,300

0,153

Feb

1,000

0,471

Mar

1,300

0,335

Apr

0,600

0,221

Nov

0,300

0,153

Dec

0,200

0,133

Jan

0,100

0,100

Feb

0,200

0,133

Mar

0,200

0,133

Apr

0,300

0,213

PP

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42


Table XIII

-

Statistical parameters of the two-way ANOVA, applied to the metals debris abundance in two sandy

beaches, along a six months sampling period (α= 0.05).


MS

df

F

p

Beach

10.208

1

14.412

0.000

Month

1.508

5

2.129

0.067

Beach * Month

1.148

5

1.621

0.161

Figure 22 – Representation of the interaction effect on the abundance of category Metals, for Beach*Month.

4.3.5 Rubber The data analysis of rubber abundance was not normally distributed and nonhomogeneous for variances, for both of the independent factors (Beach and Month). In accordance to Zar (2010), we accomplished the assumptions of ANOVA for α = 0.05, by applying the logarithmic transformation of data (Log (x+1)). The results of the two-way ANOVA analysis revealed that the Beach had a significantly effect on the abundance of rubber (F(1,108) = 37.556; P = 0.000). Accordingly, higher quantities of rubber were found at Porto Pim in comparison to Conceição (Table XIV). Parallel to this, the Month also had significantly effects in the abundance of rubber that was washed ashore (F(5,108) = 3.751; P = 0.004). February was the month where we recorded the highest amount of rubber on both beaches (Table XIV). Still, there was not a statistically significant interaction between the factors (F(5,108) = 0.311; P = 0.906 for this category (Table XV) (Figure 23). After the multiple comparisons (post-hoc) test, it was possible to realize that only February differed significantly from the months of December, March and April (Table XIV).

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Table XIV - Mean rubber debris abundance with corresponding standard error (M±SE), recorded for both beaches, during six sampling months (two-way ANOVA, α = 0.05). Means followed by different letters report significantly different results between months, within the same beach.

Beach

Month

M

SE

PC

Nov

1,100

0,547

Dec b

0,500

0,342

Jan

0,400

0,221

Feb a

1,800

0,533

Mar b

0,400

0,221

Apr

0,300

0,213

Nov

3,200

0,879

Dec b

1,500

0,428

Jan

3,300

0,920

Feb a

5,500

1,416

Mar b

2,400

0,806

Apr b

1,800

0,646

PP

Table XV - Statistical parameters of the two-way ANOVA, applied to the rubber debris abundance in two sandy beaches, along a six months sampling period (α= 0.05).


MS

df

F

p

Beach

2.934

1

37.556

0.000

Month

0.293

5

3.751

0.004

Beach * Month

0.024

5

0.311

0.906

Figure 23 - Representation of the interaction effect on the abundance of category Rubber, for Beach*Month.

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4.3.6 Processed Lumber The data for processed lumber concerning the already mentioned factors, did not meet the assumptions of the ANOVA, i.e., p-values testing for normality and homogeneity were below 0.05. A logarithmic transformation (Log (x+1)) (Zar, 2010) was applied to the data, and the assumptions of ANOVA for α = 0.05 where validated. The results of the two-way ANOVA analysis revealed that nor the Beach (F(1,108) = 0.397; P = 0.530) nor the Month (F(5,108) = 2.163; P = 0.064) (Table XVI), presented any significant effect on the abundance of processed lumber found. However, there was a statistically significant interaction between the factors (F(5,108) = 2.921; P = 0.016) for this category (Table XVII) (Figure 24). Table XVI - Mean processed lumber debris abundance with corresponding standard error (M±SE), recorded for both beaches, during six sampling months (two-way ANOVA, α = 0.05). Means followed by different letters report significantly different results between months, within the same beach.

Beach

Month

M

SE

PC

Nov

0,100

0,100

Dec

0,700

0,213

Jan

0,100

0,100

Feb

1,300

0,496

Mar

0,500

0,307

Apr

0,500

0,165

Nov

0,300

0,153

Dec

0,100

0,100

Jan

0,300

0,153

Feb

0,300

0,153

Mar

1,000

0,298

Apr

0,400

0,163

PP

Table XVII - Statistical parameters of the two-way ANOVA, applied to the processed lumber debris abundance in two sandy beaches, along a six months sampling period (α= 0.05).

Catharina Pieper


MS

df

F

p

Beach

0.012

1

0.397

0.530

Month

0.063

5

2.163

0.064

Beach * Month

0.085

5

2.921

0.016

45


Figure 24 - Representation of the interaction effect on the abundance of category Processed lumber, for Beach*Month.

4.3.7 Others After testing the assumptions of the Analysis of Variance, the results revealed that, with a 5% error probability, the distribution of both of the independent variables was considered to be non-normal and heterogenic. A logarithmic transformation (Log (x+1)) proposed by Zar (2010) for this type of data was performed, but still did not reach normality, nor homogeneity of variances (P = 0.05). The non-transformed data submitted to the 2way-ANOVA revealed that the Beach had not a significantly effect on the abundance of category Others (F(1,108) = 3.629; P = 0.059) (Table XVIII), unlike the Month that presented a significantly effect in the abundance of Others that were washed ashore (F(5,108) = 2.750; P = 0.022). March recorded the highest amount of category Others in Conceição, while in Porto Pim, December was the prevailing month that stood out (Table XVIII). Simultaneously, there was a statistically significant interaction between the factors (F(5,108) = 2.478; P = 0.036) (Table XIX) (Figure 25). After the multiple comparisons (post-hoc) test, it was possible to recognize that only March differed significantly from the months January and February (Table XVIII).

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46


Table XVIII - Mean processed Others debris abundance with corresponding standard error (M±SE), recorded for both beaches, during six sampling months (two-way ANOVA, α = 0.05). Means followed by different letters report significantly different results between months, within the same beach.

Beach

Month

M

SE

PC

Nov

0,400

0,267

Dec

0,200

0,133

Jan b

0,300

0,213

Feb b

0,300

0,153

Mar a

2,900

1,472

Apr

0,400

0,400

Nov

0,400

0,163

Dec

0,500

0,269

b

0,000

0,000

Feb b

0,100

0,100

Mar a

0,300

0,213

Apr

0,100

0,100

PP

Jan

Table XIX - Statistical parameters of the two-way ANOVA, applied to category Others debris abundance in two sandy beaches, along a six months sampling period (α= 0.05).


MS

df

F

p

Beach

6.068

1

3.629

0.059

Month

5.468

5

2.750

0.022

Beach * Month

2.206

5

2.478

0.036

Figure 25 - Representation of the interaction effect of the abundance of category Others, for Beach*Month.

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4.4 Plastics items 4.4.1 Fragments To the non-normal and heterogeneous existent data, a logarithmic transformation (Log (x+1)) was applied (Zar, 2010), and the assumptions for testing ANOVA were accomplished. Results obtained from the Analysis of Variance, showed that the Beach had a significantly effect on the abundance of plastic fragments (F(1,108) = 117.205; P = 0.000), with a greatest abundance of fragments at Porto Pim (Table XX). Parallel to this, the Month also presented significantly effects in the abundance of plastic fragments that were washed ashore (F(5,108) = 4.901; P = 0.000). February was the month where we recorded the highest amount of plastics on both beaches (Table XX), and that is probably why it differed significantly from the months of November and March, when we performed the multiple comparisons (post-hoc) test. November also differed significantly with the month of January. Simultaneously, there was no statistically significant interaction between the factors (F(5,108) = 1.041; P = 0.397) (Table XXI) (Figure 26).

Table XX - Mean plastic fragments debris abundance with corresponding standard error (M±SE), recorded for both beaches, during six sampling months (two-way ANOVA, α = 0.05). Means followed by different letters report significantly different results between months, within the same beach.

Beach

Month

M

SE

PC

Nov b

26,400

7,901

Dec

26,900

5,964

Jan c

64,900

22,103

Feb a

55,400

15,191

c

28,100

9,665

Apr

34,000

9,645

Nov b

64,700

14,377

Dec

224,200

29,398

Jan c

328,700

93,874

Feb a

471,200

75,661

Mar c

235,900

45,383

Apr

250,700

92,922

Mar

PP

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48


Table XXI - Statistical parameters of the two-way ANOVA, applied to the plastic fragments debris abundance in two sandy beaches, along a six months sampling period (α= 0.05).


MS

df

F

p

Beach

2.934

1

117.205

0.000

Month

0.293

5

4.901

0.000

Beach * Month

0.024

5

1.041

0.397

Figure 26 - Representation of the interaction effect on the abundance of category Plastic fragments, for Beach*Month.

4.4.2 Caps To the non-normal and heterogeneous data a logarithmic transformation (Log (x+1)) was applied (Zar, 2010), which made it possible to meet the ANOVA assumptions. The factorial analysis demonstrated that the Beach had a significantly effect on the abundance of plastic caps (F(1,108) = 70.261; P = 0.000). A higher number of caps was recorded at Porto Pim when compared to Conceição (Table XXII).The Month also had a significant effect in the abundance of plastic caps that were washed ashore (F(5,108) = 2.466; P = 0.037). February was the month where we recorded the highest amount of plastics on both beaches (Table XXII). Simultaneously, there was no significantly interaction between the factors (F(5,108) = 2.205; P = 0.059 (Table XXIII) (Figure 27). After the multiple comparisons (post-hoc) test, it was possible to recognize that only February differed significantly from November (Table XXII).

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49


Table XXII - Mean plastic caps debris abundance with corresponding standard error (M±SE), recorded for both beaches, during six sampling months (two-way ANOVA, α = 0.05). Means followed by different letters report significantly different results between months, within the same beach.

Beach

Month

M

SE

PC

Nov b

6,300

2,017

Dec

2,300

0,616

Jan

6,200

1,965

Feb a

8,200

1,705

Mar

4,200

1,389

Apr

2,700

0,667

Nov b

7,700

2,161

Dec

35,000

4,553

Jan

33,900

7,712

Feb a

42,000

8,640

Mar

26,500

6,330

Apr

22,100

7,457

PP

Table XXIII - Statistical parameters of the two-way ANOVA, applied to the plastic caps debris abundance in two sandy beaches, along a six months sampling period (α= 0.05).


MS

df

F

p

Beach

12.401

1

70.261

0,000

Month

0.435

5

2.466

0,037

Beach * Month

0.389

5

2.205

0,059

Figure 27 - Representation of the interaction effect on the abundance of category Plastic caps, for Beach*Month.

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50


4.4.3 Polystyrene After testing the assumptions of the Analysis of Variance (p < 0.05), the results revealed that the distribution of the variables was not normal and the variances were heterogenic. A logarithmic transformation (Log (x+1)) was again performed (Zar, 2010), however, this adjustment did not reach normality nor homocedasticity of variances (p = 0.000). The untransformed data subjected to 2way-ANOVA revealed that the Beach had a significantly effect on the abundance of polystyrene (F(1,108) = 12.163; P = 0.001), with the higher amounts found at Conceição beach (Table XXIV). Similarly, the analysis showed that the Month also had a significant effect on the abundance of polystyrene that was washed ashore (F(5,108) = 8.091; P = 0.000) (Table XXIV). March exhibited the highest quantity of polystyrene debris in Conceição, while in Porto Pim, November stood out (Table XXIV). Simultaneously, there was a statistically significant interaction between the factors (F(5,108) = 6.876; P = 0.000 (Table XXV) (Figure 28). After the multiple comparisons (post-hoc) test, it was possible to recognize that only March differed significantly from November, December, January, February and April (Table XXIV). Table XXIV - Mean polystyrene debris abundance with corresponding standard error (M±SE), recorded for both beaches, during six sampling months (two-way ANOVA, α = 0.05). Means followed by different letters report significantly different results between months, within the same beach.

Beach

Month

M

SE

PC

Nov b

2,300

1,001

Dec b

0,400

0,163

Jan b

3,100

0,875

Feb b

2,300

0,790

Mar a

12,500

3,478

Apr b

0,700

0,367

Nov b

1,600

0,476

Dec b

0,800

0,291

b

1,200

0,712

Feb b

1,100

0,504

Mar a

1,500

0,582

Apr b

1,000

0,537

PP

Jan

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51


Table XXV - Statistical parameters of the two-way ANOVA, applied to the polystyrene debris abundance in two sandy beaches, along a six months sampling period (α= 0.05).


MS

df

F

p

Beach

165.675

1

12.163

0.001

Month

110.215

5

8.091

0000

Beach * Month

93.655

5

6.876

0.000

Figure 28 - - Representation of the interaction effect on the abundance of category Polystyrene, for Beach*Month.

4.4.4 Cigarette tips/filters To the non-normal and heterogeneous data of factors Beach and Month (p