Microzooplankton as an indicator of environmental quality at an industrial complex in the Brazilian Amazon

Ecological Indicators 66 (2016) 220–229

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Microzooplankton as an indicator of environmental quality at an industrial complex in the Brazilian Amazon Brenda Natasha Souza Costa a,b,∗ , Samara Cristina Campelo Pinheiro b , Marcelo de Oliveira Lima b , Lílian Lund Amado a a b

Postgraduate Program in Aquatic Ecology and Fisheries, Federal University of Pará, Brazil Environment Section Evandro Chagas Institute, Brazil

a r t i c l e

i n f o

Article history: Received 8 October 2015 Received in revised form 17 January 2016 Accepted 18 January 2016 Keywords: Zooplankton Pollution Freshwaters Rotifera

a b s t r a c t The large volume of water in the Pará River, together with governmental incentives, has attracted many industries to the city of Barcarena, Brazil. This industrial activity has the potential to cause changes to aquatic environments. Zooplankton species are considered good indicators of environmental changes. We assessed the association between changes in community composition and proximity to an industrial-port complex, and identified potential bioindicator species in these environments. Five quarterly sampling points were selected along the Pará River (P1–P5) in 2012. The zooplankton community in this region is composed of 64 species. The highest total densities were recorded in February and November, which are both during the rainy season. Zooplankton density was greatest at P3, which was near an industrialport complex, suggesting that industrial activity affected zooplankton density. An IndVal test showed the rotifer Filinia opoliensis (r = 0.86, p = 0.02) to be a possible bioindicator of environmental quality in the study area. This paper contributes to the discussion of the impacts of installing industrial plants and large ports in the Amazon. © 2016 Elsevier Ltd. All rights reserved.

1. Introduction The Amazon River is one of the largest in the world with about 6990 km in length. Its headwaters are located in southern Peru and flows into the Atlantic Ocean in northern Brazil, between Amapá and Pará States, and in this area is formed the immense and important Amazon estuary. The dynamics of this estuary and tidal range has a strongly influence on regional rivers (Barthem and Goulding, 1997; Lima et al., 2001). The Para river has about 300 km in length and 20 km wide. Its headwaters are located in the Pará State next to Breves Strait and flows into the Atlantic Ocean. In its natural flow, receives the contribution of other larger rivers, such as Tocatins, Guama and Moju rivers, and forms several bays on the southern portion of the Marajo Island. As main features, Pará river has turbid and fresh water. However, it is also recorded the occurence of brackish water near its mouth at certain seasonal periods (Barthem and Goulding, 1997; Crist et al., 2012; Gregório and Mendes, 2009).

∗ Corresponding author at: Postgraduate Program in Aquatic Ecology and Fisheries, Federal University of Pará, Brazil. E-mail address: [email protected] (B.N.S. Costa). http://dx.doi.org/10.1016/j.ecolind.2016.01.033 1470-160X/© 2016 Elsevier Ltd. All rights reserved.

Among the main cities on the banks of the Pará River are Abaetetuba and Barcarena, both in Pará State. Factors, such as the great availability of water from the rivers, the construction of the Vila do Conde harbor, and tax waivers, have attracted companies to Barcarena over the past decades. These companies have implemented industrial processes for the production of fertilizers, pig iron, alumina, aluminum ingots, aluminum cables, manganese, and processed kaolin. The products from this industrial complex, as well as rude ores and agricultural products (grains and oxen), are exported through Vila do Conde and other private ports in the region (Lima et al., 2011). Most of these industrial and port activities discharge effluent into the aquatic environment and it is possible to trace the origin of such effluent to characterize potential sources of pollution. These effluents may contain both inorganic and organic contaminants that undergo changes in its concentration (dilution) through bioprocessing, resulting in the increase, decrease, or inactivation of contaminant toxicity (Zagatto and Bertoletti, 2008). In addition to the continuous release of port and industrial effluents, the area of Vila do Conde has a history of environmental accidents deriving from failures of industrial and port process control. Such events have culminated, over the last years, in the discharge of large amounts of liquid and solid materials containing toxic substances. Among the most prominent cases are the large red mud spills in 2003 and 2009 that were due to the breach of

B.N.S. Costa et al. / Ecological Indicators 66 (2016) 220–229

ALUNORTE’s tailing ponds. These spills affected the Murucupi River and reached Furo do Arrozal, a large water drainage between the Pará and the São Francisco rivers (Lima et al., 2009). Another important case is the sequence of spills (both solid and liquid) between 2003 and 2014 from kaolin tailing ponds into the Curuperê and Dendê streams and reaching the Pará River. In both cases, there were great environmental impacts (physical, chemical, and biological) that affected the biotic and abiotic aquatic environments, in addition to social damage due to the effects of these spills on the quality of water for human consumption and increased risks to human health caused by environmental exposure to contaminants (Carneiro et al., 2007). The environmental damage caused by environmental accidents near Vila do Conde is similar to that caused by a tailing-pond breach in the city of Ajka (Hungary) in October 2010. The tailing pond contained red mud, which contaminated the soil and waters in the region. Torna Creek and Marcal River were affected and the flood that resulted from the incident reached rural settlements and agricultural areas. Studies in the area have shown that the sudden discharge of a large amount of red mud affected various trophic levels. Some organisms are able to quickly re-establish and adapt after such accidents, whereas others may not be able to recover at all and become locally extinct (Gelencsér et al., 2011; Ruyters et al., 2011). Anthropogenic activities contribute to the enrichment of natural waters and to the eutrophication of aquatic environments by increasing nutrient content, organic matter, and turbidity, and decreasing the oxygen dissolved in surface waters (Uriarte and Villate, 2004). Changes in the physical and chemical features of these environments may lead to shifts in the base of the food chain and result in trophic interactions that affect all organisms at all trophic levels. Environmental changes may cause changes to the life cycles, ecological niches, or trophic levels of organisms in affected aquatic environments (Zagatto and Bertoletti, 2008; Dutto et al., 2012). The base of the food chain is mainly comprised of the planktonic community, which is sensitive to environmental changes. Planktonic communities live in water columns, are subject to currents, and have limited locomotion (Sipaúba-Tavares and Rocha, 2003). Plankton is classified into phytoplankton, zooplankton, icthyoplankton, and bacterioplankton. Zooplankton are consumers that are the link for energy transfer between phytoplankton and higher trophic levels (Dussart, 1964; Sipaúba-Tavares and Rocha, 2003). Most plankton are considered excellent bioindicators of environmental impacts because they have short life cycles and respond quickly to changes in the environment (Costa et al., 2004). Zooplankton includes individuals of several taxonomic categories that have different trophic levels, functions, and distinctive features. Zooplankton communities are therefore diversified and complex biocenoses (Garrison, 2010; Esteves, 2011). Zooplankton may respond to environmental changes in different ways. These organisms sometimes undergo changes ranging from cell modifications that result from mutations to modifications at the community level, including changes in species composition, diversity, and density. Environmental changes may lead to the disappearance of some species or the permanence and adaptation of opportunistic species (Mclusky, 1989; Uriarte and Villate, 2004). Changes in zooplankton communities have been used as an important tool for assessing the effects of anthropogenic activities and, consequently, pollution in aquatic systems (Moraitou-Apostolopoulou and Ignatiades, 1980; Marneffe et al., 1996; Uriarte and Villate, 2004; Jiang et al., 2010). In freshwater ecosystems, zooplankton communities are predominantly comprised of Rotifera, Cladocera, Copepoda (Cyclopoida and Calanoida), and Protista (Dantas et al., 2009). In

221

impacted environments, there is an increase in the abundance of Cladocera, Rotifera, and Cyclopoida, whereas Calanoida is adapted to oligotrophic environments, and may disappear from waters undergoing eutrophication (Perbiche-Neves et al., 2013). In this study, the zooplankton community was characterized at points distributed along the Pará River at different distances from the industrial-port complex, in order to evaluate variations in the community along a probable contamination gradient. Our objectives were as follows: (1) to identify whether there is an association between the presence of specific bioindicators and proximity to the industrial and port area of Vila do Conde, Barcarena, Brazil, and (2) if there are species in the area that may be quality bioindicators of this environment. 2. Materials and methods 2.1. Study area The study was developed in Pará river in the stretch between the Abaetetuba and Barcarena cities. Despite the daily strong influence of the tide changes, in this stretch there is fresh water as predominance. The tidal cycle occurs every 6 h and the high tide is up to 4 m. However, the sampling was always at ebbing tide (from the source to the mouth) (Souza and Lisboa, 2005). The mean annual rainfall ranges from 2300 to 2800 mm. The annual variation in rainfall is characterized by a rainy season and a less rainy season. Rainfall tends to increase during the rainy season, which lasts from November to April, and peak rainfall occurs in March and April. Rainfall decreases during the less rainy season, which lasts from May to October, and reaches its minimum in September and October (INMET, 2014; Moraes et al., 1998). Monthly rainfall data for the study area were obtained from the National Institute of Meteorology (Instituto Nacional de Metereologia, INMET) database. It is noteworthy that in the less rainy period the water volume of the Pará river decreases and there is a greater influence of the Atlantic Ocean. In these periods may be variations in parameters such as turbidity, transparency, salinity and concentration of cations and anions, depending on evaluated stretch (Gregório and Mendes, 2009). As can be variations in the rainfall data, the monthly precipitation data in the study area were obtained from the National Institute of Meteorology database (INMET). 2.2. Sampling Sampling was carried out in February (rainy period), May (less rainy period), August (less rainy period), and November (rainy period) 2012. Sampling occurred during the spring ebbing tide (full moon). Five sampling stations were defined, and distributed upstream (P1 and P2), in front (P3), and downstream (P4 and P5) of the industrial and port complex in the district of Vila do Conde (Fig. 1). Water sampling was performed at a depth of 0.3 m using previously washed 500 and 1000 mL polypropylene flasks. Samples for chlorophyll-a determination were obtained through direct collection of sub-surface water with sterilized 250 mL polypropylene flasks that were, stored, and transported in isothermal boxes. Samples for the qualitative study of the zooplankton community were obtained by horizontal trawling at the water column subsurface, with a 64-␮m mesh size plankton net. Samples for the quantitative study were obtained by filtering 200 L of water through a 10 L graduated stainless steel bucket. Samples were fixed in a 6% formaldehyde solution (Bicudo and Bicudo, 2006) and stored in insulated boxes.

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B.N.S. Costa et al. / Ecological Indicators 66 (2016) 220–229 2008

2009

2010

2011

2012

Total Precipitation (mm)

800 IB

II

IA

700 600 500 400 300 200 100

December

November

October

September

August

July

June

May

April

March

February

January

0

Period Fig. 1. Study area in the Pará River, near Barcarena and Abaetetuba cities, Pará, Brazil.

Fig. 2. Rainfall from 2008 to 2012. IA and IB: Increase in Rainfall; and II: Decrease in Rainfall. Source: INMET, 2014.

2.3. Physicochemical analyses

2.5. Statistics

The following variables were measured in situ: temperature (T), hydrogenionic potential (pH), electrical conductivity (EC), total dissolved solids (TDS), salinity (SAL), and dissolved oxygen (DO), using a portable, HI9828 multi-parameter meter (Hanna Instruments, USA) that had been previously calibrated. Water transparency was estimated using a 30-cm diameter Secchi disk. Turbidity (TRB), apparent color (COLOR), total suspended solids (TSS), and chemical oxygen demand (COD) were determined by UV–vis spectrophotometry. The five-day incubation technique was used to determine the biochemical oxygen demand (BOD). nitriteN (N-NO2 − ), nitrate-N (N-NO3 − ), ammoniacal nitrogen (N-NH4 ), ammonia (NH3 ), phosphate (PO4 3− ), sulfate (SO4 2− ), hardness, alkalinity, fluoride (F− ), and chloride (Cl− ) were determined using an ICS Dual 2000 ion chromatograph system (Dionex Corporation, Sunnyvale, CA, USA). The methods followed APHA et al. (2012).

We used principal component analysis (PCA) as implemented in the Minitab 17 program to examine differences in the limnological features of the surface water between the collection points and seasonal periods (Legendre and Legendre, 2012). In order to verify the presence of outliers in the data set was carried Grubbs test (Grubbs, 1950) using Minitab 17. The significance levels were 95% (p < 0.05). In order to assess the similarity in community composition and density between study points and periods, we used the bifactor analysis of similarity ANOSIM (Clarke and Warwick, 2011). Indicator species analysis (IndVal) was carried out to identify the typical species at each sampling point. This analysis combined density and frequency of occurrence for each species (Dufrêne and Legendre, 1997). These analyses were conducted using the R Project program available at http://www.r-project.org.

2.4. Zooplankton and chlorophyll-a

3. Results

Qualitative analyses of the zooplankton community were carried out under an inverted optical microscope (Axiovert 40C, Carl Zeiss) coupled to an image capture system (AxiocamMRc). Taxonomic identification of the organisms was performed to the lowest possible level. The density of the zooplankton community (org m−3 ), was estimated using the subsample sedimentation method described by Utermöhl (1958). This methodology is currently used in high sedimentation environments, such as the Amazon rivers (Aberle et al., 2012; Areas et al., 2006; Burkill et al., 1993; Fileman and Burkill, 2001; Gaul and Antia, 2001; Henjes et al., 2007; Kim et al., 2007; Moscatello et al., 2011; Stelfox-Widdicombe et al., 2004; Stoecker et al., 2014, 2008). Subsamples were counted using an inverted optical microscope (Axiovert 40C, Carl Zeiss) with 200 times magnification described by Garzio and Steinberg (2013). Zooplankton taxa were classified as: very frequent (≥70%), frequent (<70% and ≥30%), infrequent (<30% and ≥10%), or sporadic (<10%) (Mateucci and Colma, 1982). Chlorophyll-a samples were analyzed using a spectrophotometric method and absorbance was measured at 630, 645, 665, and 750 nm (Parsons and Strickland, 1963). Due to technical problems during the sampling and transportation steps, it was not possible to obtain samples from all of the collection points. Therefore, the data are presented as averages for each season.

3.1. Limnology According to rainfall values from 2008 to 2012, two seasonal periods are evident for the study region; an intense rainy period, with increasing rainfall from November through March (IA and IB, Fig. 2), and a less rainy period, when rain decreases from April to August and remains relatively low in September and October (II, Fig. 2). Because of these temporal variations in the rainfall cycle, we compared results between seasonal periods (Fig. 2). Chlorophyll-a concentrations differed between seasons. In the rainy period, we found average concentrations of 5.3 mg L−1 (3.8–7.7), whereas in the less rainy period the average concentration was only 3.3 mg L−1 (2.6–4.1). The physicochemical values of the surface water are shown in Table 1 (Appendix I). PCA of physicochemical factors resulted in distinct groups within the studied months (Fig. 3). This emphasizes the presence of distinctive abiotic features that are associated with sampling points and periods. Three groups were formed (A, B, C). Sampling point P1 in February and points P4 and P5 in November, were characterized as outliers. In Fig. 3, groups A, B, and C represent samples collected in February, May and August combined, and November, respectively. PC1 (31.9%) separated groups A and C (quadrants IV and II, respectively) from group B (quadrant I and III). PC2 (17.8%) only resulted in separation between groups A and C.

Table 1 Seasonal variation of physicochemical variables in the Pará River in 2012. Physicochemical variables

August

November

P1

P2

P3

P4

P5

P1

P2

P3

P4

P5

P1

P2

P3

P4

P5

P1

P2

P3

P4

P5

29.52 7.16 41.00 21.00 0.02 7.26 40.0 11.25 10.00 17.00 37.00 20.35 0.03 0.85 0.38 0.09 1.92 0.05 11.43 16.00 2.01 0.31

29.66 7.46 46.00 23.00 0.02 9.19 50.0 8.25 9.00 10.00 15.00 8.25 0.02 0.81 0.32 0.21 3.46 0.06 12.38 16.00 2.38 0.27

29.59 6.89 44.00 22.00 0.02 7.18 50.0 9.25 3.00 5.00 12.00 6.60 0.02 0.94 0.34 0.20 2.59 0.05 12.75 16.00 2.10 0.28

29.55 7.26 52.00 26.00 0.02 6.54 70.0 8.00 21.00 11.00 17.00 9.35 0.02 0.89 0.12 0.05 5.16 0.06 14.07 16.00 2.89 0.10

29.64 7.42 51.00 25.00 0.02 5.26 60.0 12.25 12.00 11.00 17.00 9.35 0.02 0.83 0.24 0.08 4.91 0.06 12.85 17.00 2.71 0.20

29.85 7.31 32.00 16.00 0.01 5.58 50.0 6.00 22.50 5.50 12.00 7.80 0.02 0.36 0.02 0.11 1.02 0.03 5.37 15.00 1.62 0.01

30.11 7.04 18.00 9.00 0.02 7.00 80.0 4.50 21.00 3.50 14.00 9.10 0.01 0.35 0.00 0.08 1.21 0.07 5.91 20.00 1.96 0.00

30.46 7.17 34.00 17.00 0.01 8.26 80.0 4.50 59.50 5.50 19.00 12.35 0.02 0.37 0.02 0.11 0.81 0.03 3.99 20.00 1.84 0.01

30.02 7.20 36.00 18.00 0.02 5.84 100.0 4.50 25.50 5.50 10.00 6.50 0.03 1.76 0.10 0.04 3.23 0.03 8.63 10.00 2.06 0.08

30.08 7.06 34.00 17.00 0.01 6.26 90.0 4.50 30.50 2.50 12.00 7.80 0.03 0.72 0.09 0.06 1.78 0.03 7.43 20.00 1.47 0.07

30.10 7.43 46.00 23.00 0.02 7.08 80.0 6.50 11.00 13.00 13.00 5.00 0.03 0.95 0.20 0.12 1.51 0.03 5.72 20.00 1.24 0.16

29.90 7.42 55.00 27.00 0.02 9.78 90.0 4.50 3.00 7.50 9.00 5.00 0.01 1.93 0.16 0.18 1.53 0.03 5.68 20.00 2.60 0.13

29.80 7.75 52.00 26.00 0.02 8.94 100.0 3.50 15.00 4.00 9.00 4.00 0.02 1.62 0.18 0.12 1.51 0.03 5.77 20.00 2.12 0.15

29.80 7.12 56.00 28.00 0.02 5.71 100.0 4.00 8.00 5.00 12.00 7.00 0.02 2.53 0.11 0.15 5.11 0.03 5.92 18.00 1.84 0.09

30.20 6.49 51.00 25.00 0.02 6.65 80.0 5.50 7.50 9.50 9.00 4.00 0.02 1.65 0.17 0.17 2.57 0.03 6.07 20.00 1.76 0.14

28.99 7.43 67.00 33.00 0.03 7.81 60.0 16.00 11.00 10.00 14.00 6.00 0.03 0.94 0.28 0.13 2.46 0.08 6.33 30.00 5.23 0.23

29.22 7.98 53.00 26.00 0.02 7.29 70.0 12.00 19.00 2.00 13.00 8.00 0.01 0.83 0.23 0.20 2.50 0.08 3.37 15.00 5.56 0.19

28.96 7.68 60.00 30.00 0.03 6.67 50.0 15.00 16.00 8.00 16.00 10.00 0.02 0.88 0.26 0.14 2.52 0.07 4.59 25.00 4.65 0.21

29.54 7.93 81.00 41.00 0.04 5.29 60.0 12.00 31.00 1.00 13.00 5.00 0.02 0.67 0.16 0.17 3.53 0.03 6.37 20.00 12.02 0.13

29.05 7.69 109.00 55.00 0.05 5.98 60.0 19.00 21.00 9.00 14.00 13.00 0.03 0.72 0.24 0.18 4.51 0.03 7.32 17.00 22.05 0.20

B.N.S. Costa et al. / Ecological Indicators 66 (2016) 220–229

T (◦ C) pH EC (␮S cm−1 ) TDS (mg L−1 ) SAL (mg L−1 ) DO (mg L−1 ) Transparency (cm) TRB (UNT) COLOR (UC) TSS (mg L−1 ) COD (mg L−1 ) BOD (mg L−1 ) N-NO2 − (mg L−1 ) N-NO3 − (mg L−1 ) N-NH4 (mg L−1 ) P04 3− (mg L−1 ) SO4 2− (mg L−1 ) F− (mg L−1 ) Hardness (mg L−1 ) Alkalinity (mg L−1 ) Cl− (mg L−1 ) NH3 (mg L−1 )

May

February

223

B.N.S. Costa et al. / Ecological Indicators 66 (2016) 220–229

4

P4 I

PC2 (17.8%)

2 P5 0

P4

P3 P2

P1

Month August February May Nov ember

P5

B P3

P4 P5 P2 P1

C P2

II P3 P1

P4 P3

-2

P5 P2 A

-4

450000

Total Density (org.m-3 )

224

360000

270000

180000

90000 III P1

IV

-5,0

-2,5

0,0 2,5 PC1 (31 .9%)

0

5,0

P1 P2 P3 P4 P5 P1 P2 P3 P4 P5 P1 P2 P3 P4 P5 P1 P2 P3 P4 P5

-6

7,5

February

May

August

November

Samples 0,3 0,2

Nitrate-N COLOR

0,1

PC2 (17.8%)

CE Chloride TDS pH SAL Phosphate

Transparency

Alkalinity

Sulfate TRB

T

0,0

Fig. 4. Total density of zooplankton in the Pará River during 2012.

DO Nitrite-N

-0,1 Ammonia Fluoride Nitrog en Amonniacal Hardness TSS BOD

-0,2 -0,3

COD

-0,4 -0,4

-0,3

-0,2

-0,1

0,0 0,1 PC1 (31.9%)

0,2

0,3

0,4

Fig. 3. PCA for physicochemical variables in the Pará River in 2012. (A) Score plot for the first 2 components; (B) Loading plot for the first 2 components.

3.2. Zooplankton community The zooplankton community was composed of 64 taxa, distributed among twenty genera, sixteen families, nine orders, nine classes, and seven phyla. The most representative family was Brachionidae (8 identified species), followed by Trichocercidae (5 species) and Trochosphaeridae, Lecanidae, and Bosminidae (4 species each). Table 2 (in Annex I) shows the information regarding all recorded species/groups. Of the observed taxa, 20% were very frequent, 22% were frequent, 43% were infrequent, and 15% were sporadic. Taxa with greater than 70% frequency were: Brachionus mirus (70%), Filinia terminalis (85%), Calanoida copepodites (95%), Cyclopoida sp1 (95%), Bdelloidea sp5 (95%), Codonella cratera (95%), Cyclopoida copepodites (100%), Keratella americana (100%), Keratella cochlearis (100%), nauplii (100%), Bdelloidea sp2 (100%), and Tintinnnina sp2 (100%). The highest zooplankton densities were recorded during the rainy season in February (962,200 org m−3 ) and November (888,600 org m−3 ). Among sampling points, the highest densities were recorded at P3, situated in front of the industrial- port complex (Fig. 4).

3.3. Bioindicators The IndVal test showed that Filinia opoliensis (IndVal = 0.86, p = 0.02) has elevated fidelity and specificity to the sampling point in front of the industrial complex, and may be a bioindicator of environmental quality.

The same test also highlighted Moina minuta (IndVal = 0.97, p = 0.005), Filinia longiseta (IndVal = 0.94, p = 0.005), Brachionus caudatus (IndVal = 0.82, p = 0.025), and Bosminopsis deitersi (IndVal = 0.81, p = 0.05). All of these species had specificity and fidelity to February and November, which corresponds to the period with highest rainfall. 4. Discussion Increased rainfall during the rainy period results in a favorable environment for microorganisms. It also results in a relatively high rate of primary production because nutrients and other substances leach from the soaked soils of the region into the rivers. During the less rainy period, the nutrient content in the water that results from the leaching process decreases and, simultaneously, the water volume in the rivers decreases. This may result in the concentration of substances associated with either anthropogenic or natural processes. However, the water volume in the rivers also decreases, which may result in the concentration of substances associated with either anthropogenic or natural substances (Costa et al., 2016; Melão, 1999; Navarro and Modenutti, 2012).Group A in our PCA was comprised of all sampling points except P1 in February. This group was characterized by high hardness (12.70 ± 0.95 mg L−1 ) and fluoride (0.06 ± 0.01 mg L−1 ) values. These values differed from those obtained for any other sample collected. This high degree of hardness and high fluoride content coincides with the high rainfall observed in February. During periods of high rainfall, nutrient leaching was found to intensify, resulting in an increase of calcium and magnesium concentrations in the drainages. This explains the high mean level of hardness in February. The high concentration of fluoride we observed in the waters may also be associated with leaching. Fluoride concentrations are likely to be high in the soil of the region because, as has been well described in the literature, industries that produce aluminum ingots through electrolytic processes release fluoride-rich vapors that precipitate through rain. In the industrial and port area of Vila do Conde, there is a company that uses this method of production (Gomes, 2007). P1 during February 2012 (outliers) was associated with highest measured levels of BOD (20.35 mg L−1 ), COD (37.00 mg L−1 ), TSS (17.00 mg L−1 ), and ammoniacal nitrogen (0.38 mg L−1 ). In Grubbs test these results of BOD, COD, TSS and ammonia nitrogen were identified as outliers and all showed p = 0.000. These high values indicate that there was probably competition for oxygen among organisms at this point. The lowest water transparency (40 cm) was also observed at this time. The reduction of the euphotic layer leads to an increase in the competition for oxygen among

Table 2 Classification and frequency of occurrence of zooplankton organisms in the Pará River in 2012. Taxa

February P1

P2

X

X X x

P3

P4

P5

x

X

P1

August P2

P3

P4

P5

P1

x

November P2

P3

x

x

x

x x x x

x x x x

x

P4

P5

P1

P2

FR (%) P3

P4

P5

x x

x x x

x x

x

x

x

x x

x

x

x

x

x x

x

x

x x x

x x x

x x x

x x

x

x x

x x

x x

x x

x x

x x

x x x

x x

x x x

x

x x

x x x

x x

x x x

x x x

x x x x

x x

x x

x x

x

x

x

x x

x x

x x

x x

x x x

x x

x x x

x x x

x x

x

x x

x

x x

x x

x

x x

x x

Frequent Infrequent

25 50 40 85

Infrequent Frequent Frequent Very Frequent

10

Infrequent

x

x

x

x

x

x

x x

x

x

x x

x x

x

x

x

x x x

x

x

x x x x

x x x x

Frequent Very Frequent Infrequent

100 100 30

Very Frequent Very Frequent Frequent

15 10 40

Infrequent Infrequent Frequent

x

x

x

x

35

Frequent

x x

5 5 10 20 40

Sporadic Sporadic Infrequent Infrequent Frequent

x x x

40 70 20

x

x x

Sporadic

x

x

x x x

30 15

5 x x

Classification

x

x x

x x x

x

x

x

x

x

x

x

x

x

x x

x

x

x

x

x

x

x

x

x x

x

x

x

x

x

15 100 25 95 25 5

B.N.S. Costa et al. / Ecological Indicators 66 (2016) 220–229

Phylum: Rotifera Class: Eurotatoria Order: Flosculariaceae Family: Hexarthridae Genera: Hexarthra Hexarthra sp. Schmarda, 1854 Hexarthra sp1 Schmarda, 1854 Family: Trochosphaeridae Genera: Filinia Filinia camasecla Myers, 1938 Filinia longiseta Ehrenberg, 1834 Filinia opoliensis Zacharias, 1898 Filinia terminalis Plate, 1886 Order: Ploima Family: Asplanchnidae Genera: Asplanchna Asplanchna sp1 Gosse, 1850 Family: Brachionidae Genera: Anuraeopsis Anuraeopsis sp1 Lauterborn, 1990 Genera: Brachionus Brachionus caudatus Barrois & Daday, 1984 Brachionus mirus Daday, 1905 Brachionus zahniseri gessneri Hauler, 1956 Genera: Keratella Keratella americana Carlin, 1943 Keratella cochlearis Gosse, 1851 Keratella lenzi Hauer, 1937 Keratella sp1 Boy de St. Vicent, 1822 Family: Lecanidae Genera: Lecane Lecane bulla Gosse, 1851 Lecane lunaris Ehnerberg, 1832 Lecane papuana Murray, 1913 Genera: Monostyla Monostyla elachis Harring & Myers, 1926 Family: Trichocercidae Genera: Trichocerca Trichocerca capucina Wierzejski & Zacharias, 1893 Trichocerca gracilis Tessin, 1890 Trichocerca similis Wierzejski, 1893 Trichocerca sp1 Lamarck, 1801 Trichocerca jenningsi Voigt, 1957 Order: Bdelloidea Bdelloidea sp1 Bdelloidea sp2 Bdelloidea sp3 Bdelloidea sp5 Bdelloidea sp8 Bdelloidea sp.

May

Infrequent Very Frequent Infrequent Very Frequent Infrequent Sporadic 225

226

Table 2 (Continued) Taxa

February P1

P2

P3

P4

P5

P1

August P2

P3

P4

P5

P1

November P2

P3

P4

P5

P1

x x

x

x

P2

FR (%) P3

P4

P5

10 10

Infrequent Infrequent

x

25

Infrequent

x x

20 10 25

Infrequent Infrequent Infrequent

x

x

x

x

x

x

x

x

x x

x

x

x

x x

5 10

x

x

5

x

x

x

x

x x

x x

x

x

x

x

x

x

x

x

x

x

x

x

x x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x

x x

x

x

x

x

x

x

x x x x

x

x

x

x

x

x

x

x

x

Classification

Infrequent

Sporadic

100 15

Very Frequent Infrequent

95

Very Frequent

x

25

Infrequent

x

x

20 20 5

Infrequent Infrequent Sporadic

x

x

45

Frequent

30

Frequent

x

x

Sporadic

B.N.S. Costa et al. / Ecological Indicators 66 (2016) 220–229

Phylum: Lobosa Class: Testacealobosa Order: Arcellinida Family: Arcellidae Genera: Arcella Arcella sp. Ehrenberg, 1832 Arcella vulgaris Ehrenberg, 1830 Family: Centropyxidae Genera: Centropyxis Centropyxis aculeata Ehrenberg, 1838 Family: Difflugiida Genera: Difflugia Difflugia elegans Penard, 1890 Difflugia pyriformes Perty, 1849 Difflugia sp. Leclerc, 1815 Family: Lesquereusiidae Genera: Lesquereusia Lesquereusia sp. Schlumberger,1845 Genera: Netzelia Netzelia wailesi Ogden, 1980 Phylum: Cercozoa Class: Imbricatea Order: Euglyphida Family: Euglyphidae Genera: Euglypha Euglypha acanthophora Ehrenberg, 1841 Phylum: Ciliophora Class: Polihymenophorea Order: Oligotrichida Tintinnina sp1 Tintinnina sp4 Family: Codonellidae Genera: Codonella Codonella cratera Leidy, 1877 Phylum: Arthropoda Class: Branchiopoda Order: Diplostraca Neonate of cladocera Family: Bosminidae Genera: Bosmina Bosmina hagmanni Stingelin, 1904 Bosmina longirostris Müller, 1785 Bosmina sp. Baird, 1845 Genera: Bosminopsis Bosminopsis deitersi Richard, 1895 Family: Sididae Genera: Diaphanosoma Diaphanosoma birgei Korinek, 1981 Family: Daphniidae Genera: Ceriodaphinia

May

Infrequent 10

Frequent

Infrequent 20

30

x

x x

x

x x x x

x

x

x

x

x

x

x

x

x

x x x x x

x

x

x

x

x

x

x

x

x

x

x

x x x

x

x x

x

x

x x x

x

Very Frequent Very Frequent Infrequent Sporadic Frequent Infrequent Very Frequent 100 95 10 5 55 10 95 x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x

x x

Very Frequent 100 x x x x x x x x x x x x x x x x x

x

x x x x x

Ceriodaphinia cornuata Sars, 1885 Family: Moinidae Genera: Moina Moina minuta Hansen, 1899 Class: Maxillopoda Nauplii Order: Cyclopoida Copepodite of Cyclopoida Cyclopoida sp1 Cyclopoida sp2 Order: Calanoida Calanoida sp1 Calanoida sp2 Copepodite of Calanoida Phylum: Mollusca Class: Gastropoda Larva of Gastropoda Class: Bivalvia Larva of Bivalve Phylum: Annelida Class: Polychaeta Polichaeta

P4 P3 P2

x

x x x

x

x

P4 P3 P1

P2 May

P1

P5 February Taxa

Table 2 (Continued)

x

Frequent 65 x x

P2 P1 P1 P5

August

P2

P3

P4

P5

November

P3

x

P4

x

P5

5

FR (%)

Sporadic

Classification

B.N.S. Costa et al. / Ecological Indicators 66 (2016) 220–229

227

microorganisms in the water column, mainly due to the low production of oxygen (Bezerra-Neto and Pinto-Coelho, 2001). Group C was formed by P1–P3 during November 2012 and is related to high pH (7.74 ± 0.22), phosphate concentration (0.16 ± 0.03 mg L−1 ), sulfate concentration (3.1 ± 0.91 mg L−1 ), and turbidity (14.8 ± 2.95 UNT). The increase of these variables was coincident with the initial increase in rainfall. At the beginning of the rainy season, nutrients and a large amount of particulate material are leached from soils, which increase turbidity. As turbidity and pH increase, the aquatic environment tends to become more alkaline, and the phosphate and sulfate contents carried from rocks as a result of the leaching tend to become more stable. However, the observed increase in sulfate levels may be associated with the intensification of effluent discharge containing red mud used in the alumina production. These effluents are highly alkaline and neutralized with sulfuric acid (H2 SO4 ) before final discharge. As rainfall increases, tailing ponds increase in volume. Discharge from these ponds then intensifies in order to relieve pressure in containment reservoirs. During periods of high rainfall intensity effluent accumulates in the tailing ponds. Historically, environmental accidents have resulted in the discharge of such accumulated effluent directly into the Pará river, near sampling points P3 and P4 (Santos et al., 2003). However, measurements at points P4 and P5 in November were outliers. Point P5 differed from other points due to high conductivity (109 ␮S cm−1 ), salinity (0.05 mg L−1 ), TDS (55.00 mg L−1 ), and chloride (22.05 mg L−1 ), whereas point P4 was characterized by low DO (5.29 mg L−1 ). In Grubbs test these results were also identified as outliers (p < 0.05). Because November was the beginning of the rainy period, these points were the closest to the river mouth, and these points were located downstream of the industrial area, particulate matter likely accumulated to a greater degree at points P4 and P5 in November than at other sampling points or times. The high salinity observed at point P5 may have been associated with ocean intrusion into the estuary during this period and by the proximity of the sampling site to the river mouth (Gregório and Mendes, 2009). Group B was related to high temperature (30.03 ± 0.21 ◦ C) and transparency (85.00 ± 15.09 cm). As rainfall decreases, so does leaching from soils. The associated decline in particulate materials in the water column allowed light penetration, thus increasing transparency (Navarro and Modenutti, 2012). A similarity test (ANOSIM) indicated that the composition of the zooplankton community differed significantly between studied months (r = 0.529; p = 0.001). Such differences might be explained by the seasonal periods; the wettest period provides the input of nutrients through leaching, increasing the microalgae biomass, as observed by the higher concentrations of chlorophyll-a. With greater availability of food, consequently also increases zooplanktom density in the envi˜ and Monroy-González, 2014; Pinheiro ronment (Aranguren Riano et al., 2013). Zooplankton densities from the rainy season were not correlated with N-NO3 − (r = 0.75; p = 0.01). N-NO3 − is easily carried by rain and benefits phytoplankton growth and, consequently, zooplankton. The lowest zooplankton densities were recorded in May and August. These low densities were correlated with COLOR (r = 0.67; p = 0.04), COD (r = 0.62; p = 0.06), and BOD (r = 0.65; p = 0.04). This indicates that anthropogenic substances may have accumulated in the water at these times. Turbidity may have been high due to a decline in the volume of river waters during this period (Wetzel, 1993). The point in front of the port (P3) was associated with relatively high levels of phosphate and nitrate. These are important nutrients for zooplankton growth because they cause primary production to intensify. Point P3 may have received these nutrients from the port

228

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and industrial activity. Samples from this site also contained relatively high concentrations of sulfate and fluoride ions, suggesting that it may have been influenced by ALBRAS (Alumínio Brasileiro S.A.) (one of the products of electrolytic plants is an increase in the fluoride levels of water) and ALUNORTE (Alumínio Brasileiro S.A.) industrial plants (the highly alkaline red mud effluent is neutralized daily with sulfuric acid (H2 SO4 ) before it is discharged onto the Pará river) (Pinto-Coelho et al., 2005). Anthropogenic discharges increase turbidity and lower DO and transparency, which are variables that directly affect the zooplankton community. At Point 3, there was a negative correlation between zooplankton density and DO (r = −0.98; p = 0.02). We can infer that high zooplankton density results in a high rate of oxygen consumption, which is correlated to transparency (r = −0.99; p = 0.01) and turbidity (r = 0.88; p = 0.12) and this might be clearly explained by the large concentration of organisms in the environment, and the total suspended solids (Fantin-Cruz et al., 2011). However, our results indicate that these effects of substances originating at the industrial-port complex on the zooplankton community were more significant near the industrial-port complex, as the zooplankton composition and density were similar at upstream and downstream sites. This pattern indicates that the Pará River has self- cleansing capacity, even though it continuously receives contaminated effluents. Little is known about the physiology of Filinia opoliensis; however, some studies highlight its easy development and adaptation to eutrophic environments (Baião and Boavida, 2005; Lucinda et al., 2004; Vitorio, 2006). The leaching process of nutrients intensifies during the period of highest rainfall, when the environment becomes appropriate for development of Moina minuta, Filinia longiseta, Brachionus caudatus, and Bosminopsis deitersi. These species are usually observed in environments rich in suspended material and organic matter (Costa et al., 2004; Lucinda et al., 2004; Mahar et al., 2000). Research in freshwater environments is of extreme importance because industrial areas and ports have been increasingly installed on the banks of large-volume rivers, such as those in the Amazon region, China, and India (Malik et al., 2013; Li et al., 2014; Yu et al., 2014). The dilution and dispersion of pollutants is lower in these environments than in marine environments and the effects of pollutants on the biotic environment are relatively immediate.

5. Conclusion This study shows that the zooplankton in the Pará River is influenced by the outflow of residues from activities at an industrial-port complex. Proximity to the industrial-area changed the composition and density of the zooplankton. The intensification of primary production at point P3 indicates that these activities influence zooplankton community density and composition. There was also a good association between zooplankton density and the specific regional seasonality, since the density increased in high rainfall periods. The rotifer Filinia opoliensis is a potential bioindicator of environmental quality and its presence in front of the urban complex is an indication that anthropogenic activities influence the zooplankton community structure and the Pará River may already be undergoing eutrophication. Supplementary studies must be conducted to assess the presence of this species throughout the Pará River and in the river basins of the Tocantins and Amazonas. This information is important for determining whether the species is a useful bioindicator of environmental quality in Amazon rivers. Public policies are needed to improve monitoring in the beginning stages and after the implementation of port and industrial activities in Amazon region. These activities are pollutant and even

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