Ciencias Nucleares
 
Assessment of heavy metal pollution in sediments from the mampostón sub-watershed (mayabeque, cuba) using X-ray fluorescence analysis
Estudio por fluorescencia de rayos x de la polucion por metales pesados en sedimentos de la subcuenca Mamposton (Mayabeque, Cuba)
 

María Aurora Mesa Pérez1Universidad Agraria de La Habana (UNAH), San José, Mayabeque, Cuba.

Rayner Hernández Pérez2Instituto Superior de Tecnologías y Ciencias Aplicadas de la Universidad de La Habana (InSTEC-UH), Avenida Salvador Allende y Luaces, La Habana, Cuba.

Oscar Díaz Rizo2Instituto Superior de Tecnologías y Ciencias Aplicadas de la Universidad de La Habana (InSTEC-UH), Avenida Salvador Allende y Luaces, La Habana, Cuba.*✉:odrizo@instec.cu

 

1Universidad Agraria de La Habana (UNAH), San José, Mayabeque, Cuba.

2Instituto Superior de Tecnologías y Ciencias Aplicadas de la Universidad de La Habana (InSTEC-UH), Avenida Salvador Allende y Luaces, La Habana, Cuba.

 

* odrizo@instec.cu

 

Abstract

Concentrations of nickel, copper, zinc and lead in surface sediments from 16 stations located in Mampostón sub-watershed (Mayabeque, Cuba) were estimated by X-ray fluorescence analysis. The Cu, Zn and Pb contents in sediments shows a different level of contamination across the studied stations. The application of modified degree of contamination (mCd) allowed to classify the metal pollution in Ganuza and Mampostón rivers and in Pedroso reservoir. The comparison with Sediment Quality Guidelines and toxicity mean quotients shows that 100% of the sediments are associated with the occasional presence of possible adverse effects to human health.

Key words: 
heavy metals; Cuba; X-ray fluorescence analysis; sediments; pollution; agriculture; aquaculture; sedimentary basins.
 
Resumen

Se determinan, por Fluorescencia de Rayos X, las concentraciones de Níquel, Cobre, Zinc y Plomo en los sedimentos superficiales de 16 estaciones en la subcuenca Mampostón (Mayabeque, Cuba).Los contenidos de Cu, Zn y Pb en los sedimentos muestran diferentes niveles de contaminación en las estaciones estudiadas. La aplicación del grado de contaminación modificado (mCd) permitió clasificar como alta o baja la contaminación de los ríos Ganuza y Mampostón, así como del embalse Pedroso. La comparación con las Guías de Calidad de Sedimentos y del cociente promedio de toxicidad, mostró que el 100% de los sedimentos estudiados están asociados a la presencia ocasional de posibles efectos adversos a la salud humana.

Palabras claves: 
metales pesados; cuba; análisis por fluorescencia de rayos X; sedimentos; contaminación; agricultura; acuacultura; cuencas sedimentarias.
 
 
 
Introduction

Mampostón sub-watershed is the smallest hydrological and hydrographic system of Mayabeque River basin in the center of Mayabeque province, Cuba. This hydrographic unit was remodeled in 1970 with the construction of two artificial reservoirs: Pedroso (with a capacity of 4 Mm3) and Mampostón (150 Mm3) (see figure 1). Both Mampostón and its tributary Ganuza rivers flow their waters (as well as their potential contaminant charge) into Pedroso reservoir. This water is pumped toward the Mampostón reservoir, which has no direct contaminant source, and it is subsequently sent to irrigate the crops in the South-West region of the most important agricultural zone of the western Cuban provinces, via the artificial channel Mampostón-Güira de Melena. Furthermore, the Pedroso reservoir has an overflow channel into the Mayabeque River and its waters are used to irrigate the sugar cane fields located on the eastside of the Mayabeque province. Additionally, an important aquaculture center is located on the Mampostón reservoir, focused in tilapia (Oreochromis spp), tench (Tinca tinca) and catfish (Clarias gariepinus) farming.

The presence of numerous and potential contamination sources in the area (for example, dairy, rum, paint and asphalt factories), whose residual waters are put into the basin tributaries, indicates a real possibility to contaminate the different pluvial ecological systems of the watershed. It is well know that sediments are the main repository of the pollutants in aquatic ecosystems.

 
Figure 1.  Location of the studied stations in the Ganuza (G) and Mampostón rivers (M) and Pedroso (P) and Mampostón (MP) reservoirs.
 

In this sense, the main goal of the present research is to study the heavy metal content in sediments of the Mampostón-Pedroso hydrographic system, in order to evaluated its ecological status.

Materials and Methods

Sediment samples were collected at the beginning of the rainy season in 16 stations, and are located near the previously considered potential contamination sources (see figure 1): four stations in Ganuza river (G1 - 100 m north of a dairy factory, G2 - 50 m south of dairy factory , G3 - 100 m south of rum factory and G4 - 50 m south of paint factory); five stations in Mampostón river (M1 - 100 m south of aluminum factory and 50 m north of asphalt factory, M2 - 50 m south of asphalt factory, M3 - in the national highway bridge, M4 - free zone and M5 - 100 m south of a research center), four stations in Pedroso reservoir (P1 - Americano River flow into reservoir, P2 - Mampostón River flow into reservoir, P3 - south border of reservoir and P4 - overflow channel of Pedroso reservoir), and additionally, in three stations in Mampostón reservoir (PM1 - PM3). In all cases, sediment samples were collected in the middle of the rivers or five meters offshore in the reservoirs.

All samples were dried at 60oC. Large rock debris, mollusk skeletons and organic debris were removed before sieving. The fine fraction (< 63 mm) was extracted by sieving and newly dried at 60oC until obtaining a constant weight.

Metal concentrations were determined by external standard method of X-ray fluorescence analysis (XRF), using the Certified Reference Materials (CRM) IAEA-356, IAEA-Soil-5, IAEA Soil-7, BCR-2 and BCSS-1 “Marine sediment” as standards. All samples and CRM were mixed with cellulose (analytical quality) in proportion 4:1 and pressed at 15 tons into 5 grams pellets of 25 mm in diameter and 4-5 mm in height. Pellets were studied using Canberra Si(Li) detector (150 eV energy resolution at 5.9 keV, Be window thickness = 12.0 mm) coupled to a multichannel analyzer. A 238Pu (1.1 GBq) excitation source with ring geometry was used. All spectra were processed with WinAxil code [1[1] WinAxil code. Version 4.5.2. WinAxil [software]. CANBERRA: MiTAC, 2005.]. Detection Limits were determined according to Padilla et al. [2[2] PADILLA R, MARKOWICZ A, WEGRZYNEK D, et. al. Quality management and method validation in EDXRF analysis. X-Ray Spectrom. 2007; 36(1): 27-34.] (in concentration units) as LD = 3 σ/mt, where m is the sensibility in counts.seg-1 per concentration unit, σ is the standard deviation of the area of the background windows (peak window at 1.17 times the FWHM) and t is the measuring time (4 hours).

The accuracy was evaluated using the SR criterion, proposed by McFarrell, et. al. [3[3] QUEVAUVILLER PH, MARRIER E. Quality assurance and quality control for environmental monitoring. Weinheim: VCH, 1995.]:

 
SR=ǀCexp-Crepǀ+2σCrep100%  
 

where: Cexp - experimental value, Crep - reported certified value and σ is the standard deviation of Cexp. Based on this criterion, the similarity between the certified value and the analytical data obtained by proposed methods is divided into three categories: SR ≤ 25% = excellent; 25 < SR ≤ 50% = acceptable, SR > 50% = unacceptable. The analysis of five replica of the CRM IAEA-SL-1 is presented in table 1. All heavy metals determined by XRF analysis are “excellent” (SR ≤ 25%) and the obtained results show a very good correlation (r2 = 0.9996) between certified and measured values.

 
Table 1.  XRF analysis of CRM IAEA-SL-1 (mean ± SD, n = 5, in mg.kg-1), SR values and Detection Limits.
MetalMeasured valuesCertified valueSR(%)DL (mg.kg-1)
Ni44.5 ± 2.344.91910
Cu32 ± 2301114
Zn220 ± 11223185
Pb36.2 ± 1.737.7113
 

The level of contamination was expressed using the modified degree of contamination (mCd), defined as [4[4] ABRAHIM G, PARKER R. Assessment of heavy metal enrichment factors and the degree of contamination in marine sediments from Tamaki Estuary, Auckland, New Zealand. Environ Monit Assess. 2008; 136(1-3): 227-238.]:

 
mCd=i=1nCfin  
 

where: n is the number of analyzed elements, i the element and Cf the contamination factor, determined as

 
Cfi=CXiCbi  
 

where: CX and Cb are the metal content in studied sample and baseline, respectively. The classification of the sediments according to the modified degree of contamination (mCd) is the following: mCd < 1.5 - very low degree of contamination; 1.5 < mCd < 2 - low degree of contamination; 2 < mCd < 4 - moderate degree of contamination; 4 < mCd < 8 - high degree of contamination; 8 < mCd < 16 - very high degree of contamination; 16 < mCd < 32 - extremely high degree of contamination; mCd ≥ 32 - ultra-high degree of contamination. Independently that this method was developed for estuarine sediments, its use for marine and freshwater ecosystems is expanded, due the possibility to evaluate the impact of the contaminants to the biota and its possible risk to the human health.

In order to assess the possible risk to human health, numerical Sediment Quality Guidelines (SQGs) were used.

SQGs have been developed for many potentially toxic substances, i.e., trace elements, chlorinated organic, and polynuclear aromatic hydrocarbons [5[5] MACDONALD DD, INGERSOLL CG, BERGER TA. Development and evaluation of consensus-based sediment quality guidelines for freshwater ecosystems. Arch Environ Contam Toxicol. 2000; 39(1): 20-31.]. Sediments are thus classified as non-contaminated, moderately contaminated and heavily contaminated, based on the SQG of USEPA with the threshold effect level (TEL) and probable effect level (PEL) values [6[6] LONG ER, MACDONALD DD, SMITH SL, CALDER FD. Incidence of adverse biological effects within ranges of chemical concentrations in marine and estuarine sediments. Environ Manag. 1995, 19(1): 81-97., 7[7] United States Environmental Protection Agency. Office of Science and Technology. National Sediment Quality Survey EPA-823-R-04-007. Second ed. Washington DC, 2004.] In order to obtain a more realistic measure of the sediments toxicity, mean quotients were introduced according to the following equation:

 
PEL-Q=i=1nCi/PELin  
 

where: Ci is the concentration of element i in sediments, PELi the guideline value for the element i and n the number of metals. PEL-Q is the probable effect level quotient. These mean quotients can be used in case of existence of multiple contaminants in the sediment, where the adverse effects caused by each chemical are additive and not antagonistic [8[8] VIOLINTZIS C, ARDITSOGLOU A, VOUTSA D. Elemental composition of suspended particulate matter and sediments in the coastal environment of Thermaikos Bay, Greece: delineating the impact of inland waters and wastewaters. J Haz Mat. 2009; 166(2-3): 1250-1260.]. The classification of sediments according to PEL-Q is as follows: PEL-Q values of < 0.1, 0.11 - 1.5, 1.51 - 2.3 and > 2.3 coincide with 10, 25, 50 and 76 %, of toxicity, respectively [9[9] McCREADY S, BIRCH GF, LONG ER. Metallic and organic contaminants in sediments of Sydney Harbour, Australia and vicinity-a chemical dataset for evaluating sediment quality guidelines. Environ Inter. 2006; 32(4): 455-465. ]. Consequently, four relative levels of contamination have been created (low, medium low, medium high and high).

Results and Discussion

Average concentrations and standard deviations of the studied heavy metal contents in sediments from the rivers and reservoirs from Mampostón sub-watershed are presented in table 2. In general, the metal content in Mampostón river, when compared to contents reported for other Cuban rivers, shows two patterns: it is lower than content reported for the contaminated urban rivers from Havana and Camaguey cities, and similar to those reported for rural rivers from Pinar del Río and Santiago de Cuba. The exception are the Cu and Zn contents in Ganuza river. On the other hand, the heavy metal content in Mampostón and Pedroso reservoirs are in the same order than those reported for other Cuban reservoirs.

 
Table 2.  Heavy metal content (Mean σ SD, in mg.kg-1) determined in sediments from Mampostón sub-watershed and its comparison with contents reported for other Cuban rivers and reservoirs.
ProvinceNiCuZnPbReferences
Rivers
GanuzaMayabeque76 ± 13211 ± 219114 ± 10235 ± 26-
MampostónMayabeque26 ± 1059 ± 1744 ± 616 ± 2-
San DiegoPinar del Río62 ± 852 ± 272 ± 428 ± 2[10[10] DIAZ RIZO O, ELEN RUDNIKAS A, D´AKESSANDRO RODRIGUEZ K, et al. Assessment of historical heavy metal content in healing muds from San Diego river (Cuba) using nuclear analytical techniques. Nucleus. 2013; (53): 19-23.]
AlmendaresHavananr176 ± 132305 ± 183111 ± 47[11[11] OLIVARES RIEUMONT S, LIMA L, DE LA ROSA D, et al. Water hyacinths (Eichhornia crassipes) as indicators of heavy metal impact of a large land fill on the Almendares River near Havana, Cuba. Bull Environ Contam Toxicol. 2007; 79: 583-587.]
San FranciscoHavananr312,8398,7116,7[11[11] OLIVARES RIEUMONT S, LIMA L, DE LA ROSA D, et al. Water hyacinths (Eichhornia crassipes) as indicators of heavy metal impact of a large land fill on the Almendares River near Havana, Cuba. Bull Environ Contam Toxicol. 2007; 79: 583-587.]
TínimaCamagüey171 ± 101105 ± 73149 ± 37201 ± 192[12[12] MARQUEZ PEÑAMARÍA MG, RAMOS SANCHEZ LB, FERNANDEZ TERRA Z, et. al. Contaminación de metales pesados en los sedimentos de los Ríos Tínima y Hatibonico. AXIOMA. 2012; 9(2): 7-13.]
HatibonicoCamagüey701 ± 566219 ± 131147 ± 2470 ± 56[12[12] MARQUEZ PEÑAMARÍA MG, RAMOS SANCHEZ LB, FERNANDEZ TERRA Z, et. al. Contaminación de metales pesados en los sedimentos de los Ríos Tínima y Hatibonico. AXIOMA. 2012; 9(2): 7-13.]
San JuanSantiago de Cubanr33-5957-839 -18[13[13] ARGOTA PEREZ J. Evaluación ecotoxicológica por disponibilidad a metales pesados durante periodo de lluvia en el ecosistema San Juan, Santiago de Cuba - Cuba. Campus 2016; 21: 25-36.]
Reservoirs
PedrosoMayabeque55 ± 3669 ± 1457 ± 1816 ± 4-
MampostónMayabeque33 ± 1163 ± 440 ± 412 ± 1-
PalmaritoHavananr35 ± 659 ± 79 ± 2[14[14] PIS RAMIREZ MA. Impacto de los metales contaminantes en la calidad de la tilapia (Oreocromis aureus) cultivada en Cuba [tesis de Maestría en Ciencia Tecnología de Alimentos]. Instituto de Farmacia y Alimentos: Universidad de La Habana, 1999.]
Niña BonitaHavananr35 ± 1380 ± 3438 ± 12[14[14] PIS RAMIREZ MA. Impacto de los metales contaminantes en la calidad de la tilapia (Oreocromis aureus) cultivada en Cuba [tesis de Maestría en Ciencia Tecnología de Alimentos]. Instituto de Farmacia y Alimentos: Universidad de La Habana, 1999.]
HanabanillaVilla Claranr34 ± 2887 ± 4423 ± 5[15[15] PIS RAMIREZ MA, LEZCANO LEON MM, SERRANO PIÑEIRO P. Metales pesados en trucha (Micropterus salmoides floridanus) de la presa Hanabanilla, Cuba. Revista AquaTIC. 2008; 29: 1-9.]
nr - not reported
 

The behavior of the Ni content in the studied stations (figure 2) show an irregular pattern, oscillating the Ni-PEL value. It is has been shown that a high Ni-content in Cuban soils and sediments is common. For example, in un-urbanized soils from Havana city was reported a Ni-content of 58 ± 13 mg.kg-1 [16[16] DIAZ RIZO O, ECHEVARRIA CASTILLO F, ARADO LOPEZ JO, et al. Assessment of heavy metal pollution in urban soils from Havana City, Cuba. Bull Environ Contam Toxicol . 2011; 87: 414-419.], and the average natural Ni-content in Cuban agricultural soils was fixed in 294.2 mg.kg-1 [17[17] RODRIGUEZ ALFARO M, MONTERO A, MUÑIZ UGARYE O, et al. Background concentrations and reference values for heavy metals in soils of Cuba. Environ Monit Assess . 2015; 187: 4198.]. For this reason, the origin of the Ni content determined in sediments from the Mampostón sub-watershed must be considered as natural.