American Journal of Water Science and Engineering
Volume 2, Issue 5, September 2016, Pages: 29-42

Groundwater Quality Assessment in Central Argentine Provinces

Alfonsina Ester Andreatta1, 2 *, Susana Providencia Garnero1, Jorge Antonio Garnero1

1Regional Faculty of San Francisco, National Technology University, Cordoba, Argentine

2Research and Development in Chemical Technology, Faculty of Exact, Physical and Natural Science, University of Cordoba, Cordoba, Argentine

Email address:

(A. E. Andreatta)

*Corresponding author

To cite this article:

Alfonsina Ester Andreatta, Susana Providencia Garnero, Jorge Antonio Garnero. Groundwater Quality Assessment in Central Argentine Provinces. American Journal of Water Science and Engineering. Vol. 2, No. 5, 2016, pp. 29-42. doi: 10.11648/j.ajwse.20160205.11

Received: October 13, 2016; Accepted: November 14, 2016; Published: December 29, 2016


Abstract: In order to assess groundwater quality in the Northeast of Córdoba and Northwest of Santa Fe, both of them Argentine provinces, representative samples of groundwater used for animal consumption, irrigation and, to a lesser extent, human consumption were taken at various locations and depths, and identified with their GPS coordinates. The knowledge of the groundwater quality is of vital importance for the people who use it. In all, 50 samples were analyzed in duplicate for color, turbidity, hydrogen potential, conductivity, hardness, total alkalinity, chloride, sulfate and total dissolved solids. Nitrates, nitrites, ammonium, arsenic, iron and fluoride concentrations were also determined according standard references. The chemical oxygen demand assay was performed on 50% of the samples. The results were subjected to a statistical analysis in order to establish the concentration of certain components in water and the influence of the geographic location. A strong positive relationship was found between hardness, chloride and sulfate, and no dependence was found between the total alkalinity and the remaining parameters. Different kind of positive relationship has been found between the research parameters: strong, between nitrites, fluoride and ammonium; moderately between arsenic and COD; and finally soft for nitrates with nitrites. In addition, no relationship nitrates and iron has been found. It was determined that none of the samples, taken between May and November 2013, complied with the Argentine Food Code requirements for drinking water and therefore, to animal and human feed consumption, their acceptability is excluded.

Keywords: Groundwater, Potability, Argentine Food Code


1. Introduction

Presently, in some areas of the Argentinian provinces of Córdoba and Santa Fe, residents drill boreholes in order to obtain groundwater, which they use for animal consumption, irrigation and, to a lesser extent, human consumption. There are also locations where there is no tap water and residents use groundwater for all household needs. The area under study is representative of the Northeast (NE) of the province of Córdoba and Northwest (NW) of the province of Santa Fe, a dairy region with numerous milking yard farms and intense agricultural and livestock activity due to the favorable local soil and climate conditions. Critical groundwater components include nitrites, nitrates, ammonium, arsenic, iron and fluoride. A short review of these parameters as obtained from Di.P.A.S. [1] can be found below.

In natural waters, nitrogen is present in different forms, including organic nitrogen (vegetable and animal protein and manure), ammoniacal nitrogen (metabolic, agriculture and industrial processes), and nitrate and nitrite compounds. Decomposition by microorganisms transforms the organic nitrogen material into ammoniacal nitrogen. In nature, in the presence of oxygen, ammoniacal nitrogen turns into nitrites, and then nitrates. Ammonia in water indicates possible contamination with bacteria, sewage, or animal manure. The natural nitrate and nitrite concentrations have been gradually increasing due to fertilizers, sewage, and industrial liquid waste produced by livestock activities, combustion and aerosols. The most important effects of nitrates on the environment are the pollution of water bodies with nitrogen compounds (and microorganisms), leading to eutrophication and urban air pollution. The presence of ammonia in drinking water does not have an immediate effect on health; however, ammonia can reduce disinfection efficiency, cause the formation of nitrites in distribution systems, obstruct manganese elimination by filtration, and cause organoleptic problems [2].

Arsenic can be found in water naturally, and sometimes in very high concentrations, since it is present in the crust of the earth. It is formed by erosion or volcanic processes, but it can also be caused by industrial discharges. In the environment, inorganic arsenic is found as metallic arsenic, trivalent arsenic (III) like arsenic trioxide (As3O5), and pentavalent arsenic (V) like arsenic pentoxide (As2O5). It appears in high concentrations in soft waters rich in sodium bicarbonate (alkaline). On the other hand, in waters rich in calcium and magnesium salts, arsenic either does not appear or is present in low concentrations. Due to the accumulation of arsenic in the human body and its toxicity and carcinogenic action, this parameter must be monitored in the supply of water.

Fluoride, as an element, can be found in volcanic gases and in sedimentary or igneous rocks. Fluoride compounds are found in groundwater in larger quantities than in surface water. Intake of certain concentrations of fluoride ions in drinking water prevents tooth decay. It is also known that fluoride causes dental fluorosis, which causes white spots to appear on teeth when the fluoride content of consumption water exceeds an acceptable proportion.

Iron in high concentrations can cause stains in fabrics and sanitary devices, impart color and turbidity to water, and confer a characteristic metallic taste on it. In water deposits or in areas with low water circulation, ferruginous and manganous waters can promote the development of iron and manganese bacteria, with the development of color and fetid odor.

In Argentine, previous studies have been performed on the quality of groundwater and surface water. For example, nitrate pollution of aquifers in rural areas was investigated in the area near Balcarce city, in the province of Buenos Aires [3]. Galindo et al. [4], analyzed the quality of surface water and groundwater in the Northeast of the province of Buenos Aires. In addition, Nicolli el at. [5], and Raychowdhury, et al. [6] analyzed the arsenic content and trace elements in groundwater in the Chaco Pampeana region. The researches of Smedley et al. [7]; Borzi et al. [8] and Zabala et al. [9] in La Pampa province; Pampean region and Pampeano aquifer in the Del Azul Creek basin respectively, focused on the hydrogeochemistry of arsenic, fluoride, nitrates and other inorganic components in groundwater.

The parameters herein investigated were separated into characterization parameters, including color, turbidity, hydrogen potential, conductivity, hardness, total alkalinity, chloride, sulfate, total dissolved solids (TDS), and research parameters, including nitrates, nitrites, ammonium, arsenic, iron and fluoride, and chemistry oxygen demand (COD). With this aim, 50 groundwater samples were analyzed in duplicate for all the aforementioned parameters and the chemical oxygen demand assay was performed on 50% of the samples. Table 1 shows the maximum allowable values for water potability according to the Argentine Food Code (AFC) [10] relevant to the characterization and research parameters studied in this work.

Table 1. Maximum values allowed for the characterization and research parameters for potable water, according to the AFC.

  Parameter (unit of measurement) Maximum value allowed
Characterization Color 3 NTU
  Turbidity 5, Pt-Co scale
  pH (upH) 6.5-8.5
  Conductivity (dS/m) Not mentioned
  Hardness (mg/L) 400
  Total alkalinity (mg/L) Not mentioned
  Chloride (mg/L) 350
  Sulfate (mg/L) 400
  TDS (mg/L) 1500
Research Arsenic (mg/L) < 0.05
  Nitrites (mg/L) 0.1
  Nitrates (mg/L) 45
  Ammonium (mg/L) 0.20
  Iron (mg/L) 0.30
  Fluoride (mg/L) 0.7 to 1.2 at T* = 17.7°C
  COD (mg/L) Not mentioned

*Annual temperature average

2. Materials and Methods

Samples of groundwater were taken in clean 1-L bottles, after allowing for a 3-min recirculation of water. Color, turbidity, hydrogen potential, conductivity, hardness, total alkalinity, chloride, sulfate and TDS were determined as characterization parameters, and nitrates, nitrites, ammonium, arsenic, iron and fluoride and COD were determined as research parameters.

Table 2 presents the analytical method, the standard reference, the reagents and the equipments used in the different analytical techniques, according to Clesceri (1992) [11].

Table 2. Analytical method, standard reference, reagent and equipment according to [11].

Analysis Analytical method Standard Reference Reagents Equipment
Color Visual 2120 B - Glassware
Turbidity Nephelometric 2130 B - Spectrophotometer
Alkalinity Titration 2320 B HCl 0.1N, Phenolphthalein 0.1%, Helianthine 0.1% Glassware
Hardness Titration 2140 C EDTA 0.1 M, Eriochrome Black T Glassware
Conductivity Conductimetric 2510 B KCl 0.1 N Conductivity meter
Chloride Argentometric Cl(-) B AgNO3 0.1 N, K2CrO4 5% Glassware
pH Electrometric 4500 H(+) B Buffer pH 7, Buffer pH 4 pH meter
TDS Gravimetric 2540 C - Drying oven
Ammonium Nesslerization 4500 NH3C Nessler reagent, HACH no. 21194-49. HACH no. 23766-26 reagent. HACH no. 23765-26 APV Spectrophotometer
Nitrates Cadmiun reduction 4500 NO3(-) F NitraVer5 reagent, HACH no. 14034-99 Spectrophotometer
Nitrites Colorimetric 4500NO2(-) B NitriVer3 reagent, HACH no. 21071-69 Spectrophotometer
Sulfate Turbidimetric 4500SO4(2-) E SulfaVer4 reagent, HACH no. 12065-99 Spectrophotometer
COD Colorimetric 5220 D COD reagent at 150 mg/L, HACH no. 212580-25 Thermoreactor
Arsenic Colorimetric 3500 AsC Arsen 50 Quantofix reagent, no. 332706, Macherey-Nagel Kit of materials
Iron Colorimetric 3500 Fe D FerroVer Reagent, HACH no. 21057-69 Spectrophotometer
Fluoride Colorimetric 4500 F D Spadns reagent, HACH no. 444-49 Spectrophotometer

The following are the equipments with their models: Comboi HI 98130 conductivity meter, Hach DR2800 spectrophotometer, Altronix TPX-I pH meter, VelpScientífica ECO25 thermoreactor. Also, a drying oven model Dalvo SB464, a METTLER gravimetric scale model P1000N (0.001 g), and a Denver analytical scale model APX-200 (0.0001 g) were used.

Standard deviation (SD) and standard error (SE) were used to evaluate the differences between the samples as per the following equations:

(1)

(2)

where Qi are the different parameters studied, N is the number of experimental data, exp indicates experimental data, and average is the mean value obtained from the data.

3. Results and Discussion

Tables A1-A2, and Table A3 available in Appendix A and B respectively, summarizes all the information on the 50 groundwater samples with their decimal GPS (geographic positioning system) coordinates, sexagesimal GPS coordinates, vector GPS coordinates, water well depth, stratified depth criterion, presence of sediments, presence of odor, color, turbidity, hydrogen potential, conductivity, hardness, total alkalinity, chloride, sulfate, TDS, nitrates, nitrites, ammonium, arsenic, iron and fluoride, and COD. The samples were obtained in the Northeast (NE) of the province of Córdoba, and Northwest (NW) of the province of Santa Fe.

From this study, it can be deduced that 26% of the samples present sediments, while only one sample presents odor; 20% of the analyzed samples exceed the maximum allowed value for color, while 14% exceed the turbidity allowed value for potable water according to the AFC. The samples tested can be classified depending on the depth at which they were obtained: 10 m (2%), 12 m (2%), 15 m (6%), 18 m (8%), 20 m (34%), 25 m (12%), 50 m (18%), 80 m (12%), 110 m (4%), and 130 m (2%). The depths were stratified using the following criterion: shallow depths (10 m, 12 m, and 15 m), corresponding to 10% of the samples; medium depths (18 m, 20 m, 25 m, and 50 m) with 72% of the samples; and great depths (80 m, 110 m, and 130 m) with 18% of the samples.

Figures 1-2 show the influence of well depth on the characterization parameters (hardness, total alkalinity, chloride, sulfate, TDS) and on the research parameters (nitrates, nitrites, ammonium, arsenic, fluoride, iron and COD), respectively. The segment of each bar is the standard deviation obtained from the different samples at each well depth. The horizontal lines represent the applicable maximum allowable values for the range [10] showed in Table 1. The sample taken at a 12 m well depth was not included in Figure 2, due to its low representativeness.

As can be seen in Figure 1(A), the hardness content for the samples of 12, 18, 80, 110 and 130 m are within the maximum allowable values as per AFC. The remaining parameters, chloride, Fig. 1(C); sulfate, Fig. 1(D); and TDS, Fig. 1(E) exceed the allowed values for most of the samples. From Table A2, available in the Appendix A section, it can be seen that 32% of the samples exceed the maximum allowable values for hardness, 62% for chloride and sulfate, and 82% for TDS according to the AFC.

In Figure 1(B), total alkalinity is shown to decrease with well depth, while the content of fluoride, sulfate and TDS do not depend on this parameter. As shown in Figure 1(C-E), the dependence of fluoride, sulfate and TDS concentrations with well depth can be observed to be similar between each other.

Figure 1. Concentrations representing (A) hardness, (B) total alkalinity, (C) chloride, (D) sulfate and (E) TDS parameters for different depths. The horizontal lines represent the allowable limit according to the AFC as shown in Table 1.

Figure 2(A) shows a logarithmic trend in nitrate concentration versus well depth; that means that lower concentrations of nitrates can be found for greater well depths. Nitrite concentration shows a lower dependence on well depth, while ammonium, arsenic, iron and fluoride concentrations do not show dependence with this parameter, as per Figure 2(B-E). The geological origin of arsenic, iron and fluoride explains the different concentration values for the different depths.

Of the total of samples, 52% exceeded the maximum allowable values according to the AFC in nitrates, 46% in nitrites, 86% in ammonium, 68% in arsenic, 60% in fluoride and 38% in iron. These values, which can be obtained from Table A3, are available in the Appendix B section and can be observed in Figure 2.

Figure 2. Concentration representing (A) nitrates, (B) nitrites, (C) ammonium, (D) arsenic, (E) iron and fluoride, (F) COD (mg/L) according to the depth of the groundwater analyzed. The horizontal lines represent the allowable limit according to the AFC as shown in Table 1.

Table 3 shows the average, SD, maximum value (max), and median values for the research and characterization parameters obtained for the groundwater samples analyzed at all the different well depths. Furthermore, Table 4 shows the statistical analysis in average, SD, SE, minimum (min) and maximum for the characterization and research parameters according to the stratified depth criterion. From Table 4, it can be deduced that there is no dependence of chloride, sulfate, TDS, iron and COD with well depth, while it the concentration of nitrates, nitrites, ammonium, arsenic and fluoride can be found to decrease with well depth. Table 4 also includes the ANOVA letters for the different parameters investigated, following the stratified depth criterion. From this analysis, of the characterization parameters, total alkalinity and hardness present a strong and moderate dependence with the stratified depth respectively. However, chloride, sulfate and TDS do not present dependence with well depth. Regarding the research parameters ammonium, arsenic, fluoride, iron and COD, there is no significant dependence with well depth, while nitrate and nitrite variables vary significantly with this parameter. Nitrate and nitrite concentrations decrease as depth increases, with greater influence for nitrates than for nitrites. This is consistent, since nitrites are derived from the biological reduction of nitrates.

 

Table 3. Statistical values obtained for the characterization and research parameters measured on the water samples.

Parameter (unit) Average SD Max Median
Characterization
pH (upH) 7.5716 0.5223 9.280 7.435
Conductivity (dS/m) 4.3928 2.3171 10.38 4.120
Hardness (mg/L) 337.72 253.88 1600 260.0
Total Alkalinity (mg/L) 850.42 310.29 1708 876.5
Chloride (mg/L) 686.06 598.82 2592 488.0
Sulfate (mg/L) 805.85 837.18 4814 539.5
TDS (mg/L) 2881.0 1635.6 7058 2737
Research        
Nitrates (mg/L) 66.374 56.610 230 45.84
Nitrites (mg/L) 4.6069 10.332 60.0 0.081
Ammonium (mg/L) 0.6615 0.6573 3.86 0.480
Arsenic (mg/L) 0.1495 0.2336 1.00 0.050
Fluoride (mg/L) 1.3911 0.7353 4.56 1.250
Iron (mg/L) 0.5081 1.0722 7.56 0.160
COD (mg/L) 23.550 22.123 69.5 25.60

Table 4. Statistical analysis of the parameters according to the stratified depth criterion. Letters for the ANOVA analysis based on a Fisher’s LSD (least significant difference) of (p< 0.05) for the parameters*.

Variable Stratified depth n Average (mg/L) ANOVA letters SD (mg/L) SE (mg/L) Min (mg/L) Max (mg/L)
  Characterization parameters
Hardness Low 10 314 AB 152.26 48.148 200.0 600.0
  Medium 72 380.17 A 275.64 32.485 60.00 1600
  High 18 181.11 B 108.89 25.667 70.00 440.0
Total alkalinity Low 10 1012.8 A 161.42 51.045 755.0 1220
  Medium 72 867.93 A 282.17 33.254 244.0 1708
  High 18 690.17 B 412.72 97.280 220.0 1446
Chloride Low 10 857.60 A 786.54 248.73 266.0 2343
  Medium 72 640.51 A 550.51 64.879 73.00 2592
  High 18 773.00 A 678.89 160.02 71.00 2236
Sulfate Low 10 941.20 A 1115.6 352.80 38.00 3100
  Medium 72 781.08 A 846.25 99.732 74.00 4814
  High 18 829.78 A 646.26 152.32 85.00 2100
TDS Low 10 3445.9 A 1783.9 564.11 1863 6610
  Medium 72 2730.1 A 1574.1 185.51 33.00 7058
  High 18 3171.0 A 1781.1 419.81 258.0 6059
  Research parameters
Nitrates Low 10 139.79 A 59.037 18.669 71.76 230.0
  Medium 72 69.679 B 50.040 5.8973 7.970 201.0
  High 18 12.368 C 7.4039 1.7451 0.880 22.15
Nitrites Low 10 10.944 A 15.707 4.9671 0.040 42.90
  Medium 72 4.6091 AB 10.371 1.2223 0.017 60.00
  High 18 1.0780 B 2.2241 0.5242 0 7.000
Ammonium Low 10 0.6331 A 0.2952 0.0934 0.169 1.080
  Medium 72 0.7075 A 0.7281 0.0858 0 3.860
  High 18 0.4933 A 0.4655 0.1097 0 1.430
Arsenic Low 10 0.2360 A 0.4035 0.1276 0.005 1.000
  Medium 72 0.1283 A 0.1637 0.0193 0 0.500
  High 18 0.1861 A 0.3346 0.0789 0 1.000
Fluoride Low 10 1.5110 A 0.7087 0.2241 0.4600 2.160
  Medium 72 1.3981 A 0.7509 0.0885 0 4.560
  High 18 1.2967 A 0.7144 0.1684 0.7 3.200
Iron Low 10 0.3070 A 0.4179 0.1321 0.040 1.110
  Medium 72 0.5779 A 1.2066 0.1422 0.030 7.560
  High 18 0.3406 A 0.6685 0.1576 0.010 2.180
COD Low 6 11.850 A 18.453 5.8352 0 38.50
  Medium 38 25.861 A 22.721 2.6777 0 69.50
  High 6 20.617 A 20.508 4.8338 0 48.60

*For a given parameter, averages with the same letter do not present significant differences (p < 0.05)

In order to assess the variability of the characterization and research parameters with the geographical positions, a multivariate analysis was used on the principal components (PC) using Infostat, a statistical software [12].

Gabriel, K.R. [13-14] proposed scatter diagrams, called biplots, where the observations and variables are on the same plane in order to obtain joint relations between the different parameters. In this case, these biplots were used to show the geographic coordinates and the different values for the characterization and research parameters.

GPS coordinates for each sample, given in the sexagesimal system, were converted into a single vector (GPS vector coordinates) obtained as the square root of the sum of the squares of the West longitude and South latitude coordinates, respectively. This vector was also multiplied by a factor of 10 for a better identification of the different samples on the biplot. The GPS vector coordinates for each groundwater sample is available in Table A1 of the Appendix A section.

Figure 3 represents the biplot of the geographic locations identified with points, using hardness, total alkalinity, chloride, sulfate and TDS as characterization variables. Two reduced dimensions were used, representing 74.5% of the samples. The cophenetic correlation coefficient was 0.956, an acceptable value for the reduction degree achieved. The PC1 and PC2 described are 56.2% and 18.3%, respectively; 56.2% of the variability of the samples (PC1) was defined for hardness, chloride and sulfate, with a high projection on the positive PC1 semiaxis. The weights of these variables were similar, suggesting similar contribution of each variable to sample variability. On the other hand, 18.3% of sample variability was represented by total alkalinity and TDS variables, with a greater influence for total alkalinity than for TDS on the positive PC2 semiaxis.

Figure 3. Multivariate analysis of characterization parameters and GPS vector coordinates.

For PC1, the following sites were located: 691.86, 690.66, 692.73, 694.68, 692.22, 692.08, 695.24, 691.95, 695.23, 692.84, 697.73, 695.76, 695.49, 693.89, and 693.58. The negative PC1 semiaxis was not been defined for the mayority proyection of any parameteres. From the data dispersion, it can be seen that the composition of all the samples located on the positive PC1 semiaxis is similar, but different from the composition of those located on the PC1 negative semiaxis. However, it is not possible to infer which samples cause this difference.

On the other hand, the following sites were located on the positive PC2 semiaxis: 691.95, 691.86, 692.08, 690.66, 659.23, 692.73, 694.68, 692.22, 694.96, 695.21, 692.81, 690.78, 691.77, 695.10, 696.31, 692.02, 690.86, 692.98, 691.85, 691.86, 692.71, and 693.79. On the other hand, the negative PC2 semiaxis contains only sulfate with a low contribution. From Figure 3, it can be deduced there is a strong positive relationship between hardness, chloride and sulfate, and no dependence at all between total alkalinity and the remaining parameters.

Figure 4 represents the biplot of the geographic locations, identified with points, and nitrates, nitrites, ammonium, arsenic, fluoride, iron and COD as research parameters. The PC1 and PC2 allow for an explanation of 60% of the total variability. The cophenetic correlation coefficient, as a measurement of the degree of dimensional reduction achieved, was 0.915. PC1 and PC2 were 40.3% and 19.4%, respectively: 40.3% of sample variability was explained by nitrites, ammonium, arsenic, fluoride and COD, because they were the variables with greatest projection on the positive PC1 semiaxis. The weights of the variables were similar, suggesting similar contributions of each variable to sample variability. On the other hand, 19.4% of their variability was explained by nitrates and iron, with more weight on the PC2 axis and more contribution of nitrates than iron.

Figure 4. Multivariate analysis of research parameters and GPS vector coordinates.

The following geographic coordinates were located on the positive PC1 region: 695.24, 692.08, 690.57, 691.95, 691.86, 695.23, and 692.71. The negative PC1 axis was described for iron with a low vector weight, including the following sites: 695.59, 696.09, 696.04, 694.80, 693.89, 696.60, 691.96, 690.78, 692.84, 697.73, 695.21, 696.31, 691.97, and 695.10. The same as in Figure 3, the composition of all the samples located on the positive PC1 semiaxis is similar, but different from the composition of those located on the PC1 negative semiaxis. However, it is not possible to infer which samples cause this difference.

The PC2 was defined by nitrates along the positive PC2 semiaxis. In this region, the following geographic vector coordinates were located: 695.24, 695.08, 697.73, 691.97, 690.78, 695.10, 696.60, 696.31, 695.21, 692.84, and 691.96. On the other hand, in the negative PC2 semiaxis, only iron was found for the coordinates 691.86, 691.95, 695.23, 692.71, 696.04, 696.09, 694.80, 693.89, and 695.49.

Strong positive relationships between nitrites, fluoride and ammonium were found, as well as moderately positive relationships between arsenic and COD, and slightly positive relationships between nitrates and nitrites. In addition, no relationship was found between nitrates and iron. Besides, no relationship between iron and the remaining parameters was found, and its presence does not seem to be related to the geographical position: it is dispersed in the areas analyzed. Furthermore, the geographical positions 691.86 and 672.81 are similar in terms of COD and arsenic.

4. Conclusion

A total of 50 groundwater samples, taken between May and November 2013, were analyzed for color, turbidity, hydrogen potential, conductivity, hardness, total alkalinity, chloride, sulfate, TDS, nitrates, nitrites, ammonium, arsenic, iron and fluoride, and COD. The groundwater samples, identified with their GPS coordinates, are representative of the Northeast (NE) and Northwest (NW) of the Argentinian provinces of Córdoba and Santa Fe, respectively. The results were statistically analyzed in order to determine the influence of the geographic location on the different parameters.

The presence of arsenic, iron and fluoride is due to a geological process, and their values are different. From the ANOVA study, a strong dependence can be deduced between groundwater depth and the total alkalinity and nitrate concentrations, while the relationship with the hardness and nitrite concentrations is only moderate.

The multivariate analysis performed on the principal components has made it possible to discriminate the dependence of the different parameters with their corresponding geographical positions. A strong positive relationship was found between hardness, chloride and sulfate, and no dependence was found between the total alkalinity and the remaining parameters. Different kind of positive relationship has been found between the research parameters: strong, between nitrites, fluoride and ammonium; moderately between arsenic and COD; and finally soft for nitrates with nitrites. In addition, no relationship nitrates and iron has been found. Finally, from the 50 samples analyzed of groundwater, none of them is included in the potable water term, according to the AFC for drinking water. Therefore, to animal and human feed consumption, their acceptability is excluded, while is necessary to investigate other parameters before watering.

Acknowledgments

The authors wish to thank Universidad Tecnológica Nacional (PID 1826) and A.E. Andreatta wish to thank Consejo Nacional de Investigaciones Científicas y Técnicas, and Universidad Nacional de Córdoba, all of them from Argentina, for the financial support. The authors also thank F. Francescato, A. Arposio, R. Marlatto, E. Yafar, F. Luengo, M. Rovero, E. Carrillo, V. Caporalli, and N. Ferrero for groundwater sampling and characterization.

Appendix A

Characterization of 50 groundwater samples from Northeast (NE) of Córdoba, and northwest (NW) of Santa Fe provinces, Argentine.

Table A1. Characterization Parameters: Date sample, town, decimal GPS, depth, stratified depth and sediment of the groundwater samples analyzed

Sample Data Sample Town Decimal GPS Depth (m) Stratified depth Sediment
1 07-Apr-13 Colonia Tacurales, Santa Fe -30.80283 20 Medium no
1     -61.78967 20 Medium no
2 07-Apr-13 Colonia Tacurales, Santa Fe -30.80909 20 Medium no
2     -61.80388 20 Medium no
3 07-Apr-13 Morteros, Córdoba -30.56867 18 Medium yes
3     -61.08200 18 Medium yes
4 14-Apr-13 Morteros, Córdoba -30.74131 15 Low yes
4     -62.00596 15 Low yes
5 14-Apr-13 Morteros, Córdoba -30.72799 20 Medium no
5     -62.08520 20 Medium no
6 19-Apr-13 Brinkmann, Córdoba -30.86910 20 Medium no
6     -62.02750 20 Medium no
7 19-Apr-13 Colonia Vignaud, Córdoba -30.83160 20 Medium no
7     -61.95430 20 Medium no
8 19-Apr-13 Brinkmann, Córdoba -30.86910 20 Medium no
8     -62.02700 20 Medium no
9 08-May-13 San Francisco, Córdoba -31.42943 20 Medium no
9     -62.08498 20 Medium no
10 14-May-13 Sastre, Santa Fe -31.77221 12 Low no
10     -61.82398 12 Low no
11 17-May-13 Morteros, Córdoba -30.71373 15 Low no
11     -61.88282 15 Low no
12 17-May-13 Morteros, Córdoba -30.72362 18 Medium no
12     -61.86919 18 Medium no
13 17-May-13 Morteros, Córdoba -30.70257 20 Medium yes
13     -61.86673 20 Medium yes
14 17-May-13 Morteros, Córdoba -30.70623 50 Medium yes
14     -62.01003 50 Medium yes
15 17-May-13 Brinkman, Córdoba -30.86938 25 Medium no
15     -62.04218 25 Medium no
16 07-Jun-13 Morteros, Córdoba -30.61788 80 High yes
16     -62.05190 80 High yes
17 07-Jun-13 Brinkmann, Córdoba -30.85731 80 High yes
17     -62.02967 80 High yes
18 25-Jun-13 Morteros, Córdoba -30.71437 15 Low no
18     -62.00696 15 Low no
19 28-Jun-13 Morteros, Córdoba -30.68093 80 High yes
19     -62.00968 80 High yes
20 28-Jun-13 Colonia 10 de Julio, Córdoba -30.51911 20 Medium no
20     -62.18560 20 Medium no
21 29-Jun-13 Freyre, Córdoba -31.18889 20 Medium no
21     -62.10472 20 Medium no
22 17-Jul-13 Freyre, Córdoba -31.20806 18 Medium no
22     -62.11028 18 Medium no
23 17-Jul-13 Freyre, Córdoba -31.22667 50 Medium no
23     -62.11306 50 Medium no
24 26-Jul-13 Freyre, Córdoba -31.21472 25 Medium no
24     -62.12278 25 Medium no
25 29-Jul-13 Altos de Chipión, Córdoba -31.00000 80 High no
25     -62.32500 80 High no
26 12-ag-13 Freyre, Córdoba -31.26111 110 High no
26     -62.12750 110 High no
27 13-Aug-13 Morteros, Córdoba -30.63528 80 High no
27     -62.05833 80 High no
28 13-Aug-13 Morteros, Córdoba -30.66083 25 Medium no
28     -62.02056 25 Medium no
29 13-Aug-13 Colonia 10 de Julio, Córdoba -30.58056 50 Medium yes
29     -62.05111 50 Medium yes
30 16-Aug-13 Porteña, Córdoba -30.99417 50 Medium no
30     -62.11000 50 Medium no
31 16-Aug-13 Porteña, Córdoba -31.07444 20 Medium yes
31     -62.00694 20 Medium yes
32 21-Aug-13 Altos de Chipión, Córdoba -30.99222 50 Medium no
32     -62.32389 50 Medium no
33 21-Aug-13 Altos de Chipión, Córdoba -30.99972 50 Medium no
33     -62.32556 50 Medium no
34 13-Set-13 Freyre, Córdoba -31.23528 50 Medium no
34     -62.11139 50 Medium no
35 23-Set-13 Colonia Vignaud, Córdoba -30.84325 80 High no
35     -61.95335 80 High no
36 02-Nov-13 Colonia Valtelina, Córdoba -31.06861 25 Medium no
36     -62.19200 25 Medium no
37 02-Nov-13 Colonia Vignaud, Córdoba -30.81250 20 Medium no
37     -61.98611 20 Medium no
38 19-Nov-13 Colonia Castelar, Santa Fe -31.60588 20 Medium no
38     -62.04460 20 Medium no
39 19-Nov-13 Frontera, Santa Fe -31.43917 130 High no
39     -62.06752 130 High no
40 20-Nov-13 Zenon Pereyra, Santa Fe -31.56192 18 Medium no
40     -61.89731 18 Medium no
41 19-Nov-13 Esmeralda, Santa Fe -31.61645 20 Medium no
41     -61.93303 20 Medium no
42 23-Nov-13 Freyre, Córdoba -31.14930 50 Medium yes
42     -62.43370 50 Medium yes
43 24-Nov-13 Freyre, Córdoba -31.10450 20 Medium yes
43     -62.13810 20 Medium yes
44 24-Nov-13 Freyre, Córdoba -31.18470 20 Medium yes
44     -62.28970 20 Medium yes
45 24-Nov-13 Sarmiento, Santa Fe -31.11640 25 Medium yes
45     -61.14540 25 Medium yes
46 25-Nov-13 Porteña, Córdoba -62.06194 25 Medium no
46     -31.01167 25 Medium no
47 04-Dec-13 Altos de Chipión, Córdoba -30.95000 50 Medium no
47     -62.35000 50 Medium no
48 04-Dec-13 Altos de Chipión, Córdoba -30.95000 20 Medium no
48     -62.35000 20 Medium no
49 04-Dec-13 La Paquita, Córdoba -30.90772 10 Low no
49     -62.21396 10 Low no
50 18-Dec-13 Porteña, Córdoba -31.07274 110 High no
50     -62.04333 110 High no

Table A2. Characterization Parameters: Olor, color, turbidity, pH, conductivity, hardness, Total alkalinity, chloride, sulfate and TDS of the groundwater samples analyzed

Sample Olor Color Turbidity pH (upH) Conductivity (dS/m) Hardness (mg/L) Total alkalinity (mg/L) Chloride (mg/L) Sulfate (mg/L) TDS (mg/L)
1 no 5 3 7.25 4.75 560 805 993 700 3230
1 no 5 3 7.21 4.68 562 781 995 900 3182
2 no 5 3 7.44 10.21 1340 475 2556 2500 6963
2 no 5 3 7.46 10.38 1600 477 2592 2700 7058
3 no > 5 > 3 7.38 4.84 280 817 886 600 3291
3 no > 5 > 3 7.38 4.88 320 781 889 400 3318
4 no > 5 > 3 8.79 9.72 560 780 2308 2900 6610
4 no > 5 > 3 8.76 9.68 400 755 2343 3100 6582
5 no > 5 3 9.22 5.64 400 1045 1491 500 3849
5 no > 5 3 9.19 5.68 320 826 1420 700 3849
6 no 5 3 7.15 7.66 440 634 1917 1000 5209
6 no 5 3 7.18 7.64 440 878 1882 980 5195
7 no 5 3 7.94 2.85 200 1000 230 340 1938
7 no 5 3 7.8 2.74 240 1049 284 360 1863
8 no >5 >3 7.26 1.58 480 634 74 74 1074
8 no >5 >3 7.27 1.56 460 708 73 75 1061
9 no 5 3 7.43 3.01 190 976 355 800 2047
9 no 5 3 7.48 2.96 200 970 373 860 2013
10 no 5 3 7.19 3.40 260 1177 479 320 2312
10 no 5 3 7.2 3.28 260 1183 408 400 2230
11 no 5 3 7.87 4.1 200 1098 604 38 2788
11 no 5 3 7.9 3.96 240 1025 606 40 2693
12 no 5 3 7.45 7.4 120 1098 320 520 5032
12 no 5 3 7.5 7.42 200 1074 322 524 5046
13 no > 5 >3 7.12 7.58 600 903 817 920 5154
13 no > 5 >3 7.13 7.24 640 903 817 1090 4858
14 no > 5 > 3 7.66 2.2 200 1025 107 260 1496
14 no > 5 > 3 7.74 2.2 120 1135 81 300 1496
15 no 5 3 7.53 2.8 220 1196 213 260 1904
15 no 5 3 7.56 2.82 160 1267 391 280 1917
16 yes > 5 > 3 8.86 8.73 440 517 2236 1600 5936
16 yes > 5 > 3 8.98 8.91 440 523 2201 2100 6059
17 no > 5 3 8.28 8.4 120 828 142 118 5712
17 no > 5 3 8.35 8.39 200 804 177 118 5705
18 no 5 3 7.25 2.89 210 957 284 520 1911
18 no 5 3 7.23 2.74 210 957 266 560 1863
19 no > 5 3 7.88 2.97 80 1147 355 340 2079
19 no > 5 3 7.99 2.99 100 1147 284 320 1972
20 no 5 3 7.72 2.33 150 1464 213 255 1584
20 no 5 3 7.62 2.4 170 1708 284 268 1632
21 no 5 3 7.8 4.85 570 1098 568 680 3395
21 no 5 3 7.88 4.78 540 976 710 781 3250
22 no 5 3 7.83 3.5 300 976 284 340 2380
22 no 5 3 7.87 3.56 360 1220 426 511 2420
23 no 5 3 7.03 3.64 820 830 462 554 2475
23 no 5 3 7.09 3.68 860 854 604 725 2502
24 no 5 3 7.05 6.15 410 610 1207 380 4182
24 no 5 3 7.04 6.1 390 604 1047 340 4148
25 no 5 3 7.26 2.5 80 1446 178 250 1700
25 no 5 3 7.34 2.59 120 1446 142 250 1761
26 no 5 3 7.07 4.34 190 396 816 980 2591
26 no 5 3 7.01 4.33 200 396 816 980 2944
27 no 5 3 7.43 4.14 230 970 852 1022 2815
27 no 5 3 7.38 4.22 230 976 852 1022 2869
28 no 5 3 7.19 7.74 410 1067 1544 1852 5263
28 no 5 3 7.3 7.78 370 1073 1437 1724 5290
29 no 5 3 7.45 4.42 170 1213 781 937 3005
29 no 5 3 7.47 4.23 340 1098 745 894 2876
30 no > 5 > 3 9.24 1.71 70 943 177 213 1163
30 no > 5 > 3 9.28 1.81 60 949 142 170 1231
31 no 5 3 7.38 6.96 710 647 1065 4733 177
31 no 5 3 7.35 7.08 680 634 1065 4814 188
32 no 5 3 7.26 1.8 120 1183 213 1224 40
32 no 5 3 7.24 1.8 110 1104 178 1224 33
33 no 5 3 7.4 0.77 170 573 107 128 523
33 no 5 3 7.41 0.73 150 567 107 128 523
34 no 5 3 7.33 6.05 340 787 1136 1363 4114
34 no 5 3 7.34 6.03 330 793 1029 1235 4100
35 no 5 3 6.63 0.39 80 220 71 85 265
35 no 5 3 6.6 0.38 70 226 71 85 258
36 no 5 3 7.37 4.09 150 1342 532.5 639 2781.2
36 no 5 3 7.39 4.29 140 1281 532.5 639 2917.2
37 no 5 3 7.46 6.06 370 890.6 852 1022.4 4120.8
37 no 5 3 7.51 6.11 360 890.6 887.5 1065 4154.8
38 no 5 3 7.65 3.11 220 1068 284 340 2170
38 no 5 3 7.65 3.11 220 1068 284 340 2170
39 no 5 3 7.45 5.32 220 342 1065 1278 3618
39 no 5 3 7.51 5.25 180 329 1030 1236 3570
40 no 5 3 7.9 2.46 110 864 248 297 1722
40 no 5 3 7.87 2.43 100 875 213 255 1700
41 no 5 3 7.59 3.99 370 830 426 511 2793
41 no 5 3 7.57 4.15 210 811 426 525 2900
42 no 5 3 7.2 5.1 700 580 710 852 3570
42 no 5 3 7.3 5.16 760 640 852 1022 3612
43 no 5 3 7.41 2.25 280 305 355 420 1570
43 no 5 3 7.47 2.35 380 366 213 256 1650
44 no 5 3 7.44 2.5 380 244 355 426 1750
44 no 5 3 7.48 3.5 420 610 497 1108 2450
45 no 5 3 7.28 2.9 440 366 284 340 2030
45 no 5 3 7.32 2.92 500 427 426 510 2040
46 no 5 3 7.31 1.95 110 1128 178 213 1326
46 no 5 3 7.28 1.9 140 1098 142 170 1292
47 no 5 3 7.33 5 760 555 639 767 3500
47 no 5 3 7.37 5.2 800 580 781 937 3640
48 no 5 3 7.64 1.58 260 610 142 170 1068
48 no 5 3 7.68 1.62 300 634 248 297 1102
49 no 5 3 7.67 5.47 200 976 568 682 3720
49 no 5 3 7.71 5.52 600 1220 710 852 3750
50 no 5 3 7.38 5.14 138 353 1311 1574 3610
50 no 5 3 7.42 5.18 142 357 1315 1578 3614

Appendix B

Research parameters of 50 groundwater samples from Northeast (NE) of Córdoba, and northwest (NW) of Santa Fe provinces, Argentine.

Table A3. Research Parameters: Nitrate, Nitrite, ammonium, arsenic, fluoride, iron and COD of the groundwater samples analyzed

Sample Nitrate (mg/L) Nitrite (mg/L) Ammonium (mg/L) Arsenic (mg/L) Fluoride (mg/L) Iron (mg/L) COD (mgO2/L)
1 94.80 0.080 0.273 0.025 1.54 0.40  
1 137.30 0.050 0.286 0.025 1.61 0.30  
2 80.60 0.050 3.146 0.050 3.00 0.35 69.5
2 38.10 0.030 2.639 0.050 2.88 0.35 46.9
3 63.80 0.165 0.312 0.050 0.00 7.56  
3 54.50 0.162 0.324 0.100 0.00 6.68  
4 202.40 42.900 0.793 0.050 2.05 0.05 38.5
4 177.20 36.300 0.858 0.100 1.93 0.08 32.6
5 18.60 6.000 1.360 0.300 0.67 0.10  
5 17.27 10.000 1.380 0.300 1.11 0.09  
6 63.35 0.043 0.350 0.050 0.70 0.05 0
6 57.15 0.026 0.290 0.050 0.70 0.08 0
7 49.00 4.000 0.350 0.050 0.75 0.19  
7 39.00 7.000 0.350 0.050 0.75 0.14  
8 82.00 7.000 0.820 0.025 0.80 2.09 0
8 93.00 11.000 0.820 0.025 0.84 1.94 0
9 8.86 0.020 0.390 0.050 0.81 0.03  
9 7.97 0.017 0.260 0.050 0.87 0.03  
10 71.76 0.040 0.169 0.005 0.46 0.08 0
10 81.50 0.053 0.221 0.005 0.48 0.11 0
11 110.80 6.000 0.440 1.000 1.98 0.24  
11 103.20 4.000 0.470 1.000 1.96 0.21  
12 100.60 0.069 0.220 0.050 1.34 0.35 0
12 96.70 0.033 0.240 0.050 1.68 0.42 0
13 129.80 6.000 0.650 0.050 1.89 1.37  
13 139.10 8.000 0.620 0.050 1.92 1.62  
14 34.60 0.056 0.650 0.050 0.98 0.44 0
14 27.00 0.066 0.600 0.050 0.78 0.46 0
15 33.70 0.060 0.260 0.100 1.40 0.06  
15 35.20 0.036 0.140 0.100 1.43 0.04  
16 13.10 0.050 1.430 0.000 0.70 0.12 41.5
16 9.10 0.040 1.430 0.000 0.76 0.13 48.6
17 22.15 0.043 0.260 0.100 1.70 0.11  
17 19.50 0.041 0.470 0.100 1.80 0.13  
18 105.00 0.056 0.820 0.050 0.74 0.08 0
18 104.00 0.086 1.080 0.050 1.23 0.04 0
19 21.30 4.000 0.890 1.000 3.20 0.18  
19 20.80 6.000 0.920 1.000 2.60 0.12  
20 30.50 0.030 0.700 0.500 1.98 0.68 48.0
20 31.90 0.040 0.730 0.500 2.04 0.45 52.0
21 158.00 0.082 0.400 0.050 1.35 0.16  
21 150.00 0.092 0.320 0.050 1.45 0.18  
22 184.00 0.530 0.000 0.050 1.30 0.03 39.4
22 201.00 0.640 0.000 0.050 1.36 0.07 34.8
23 26.60 1.000 0.012 0.000 1.40 0.12  
23 27.50 2.300 0.017 0.000 1.46 0.16  
24 138.00 2.000 1.404 0.000 1.80 0.09 32.5
24 151.00 2.200 1.989 0.000 2.01 0.07 45.6
25 19.00 0.036 0.200 0.500 0.85 0.04  
25 18.00 0.049 0.170 0.500 0.95 0.06  
26 6.20 0.023 0.290 0.000 0.70 2.18 15.3
26 5.30 0.013 0.300 0.000 0.78 2.14 18.3
27 19.00 7.000 0.390 0.050 1.00 0.27  
27 18.60 2.000 0.410 0.050 0.90 0.38  
28 62.90 7.000 0.730 0.500 1.35 0.97 43.5
28 66.40 9.000 0.690 0.500 1.30 0.98 55.2
29 21.30 0.040 0.520 0.100 1.39 0.56  
29 19.90 0.060 0.600 0.100 1.45 0.53  
30 51.00 11.000 2.560 0.500 4.44 0.18 55.8
30 51.00 21.000 3.860 0.500 4.56 0.17 64.3
31 177.00 50.000 2.180 0.050 2.33 0.50  
31 188.00 60.000 2.200 0.050 2.11 0.64  
32 39.90 0.116 0.220 0.100 0.89 0.21 29.8
32 32.80 0.073 0.290 0.100 1.10 0.17 32.7
33 31.00 0.040 0.440 0.300 0.90 0.20  
33 30.00 0.043 0.580 0.300 1.10 0.15  
34 34.00 0.106 0.260 0.500 1.20 0.10 43.5
34 31.00 0.129 0.280 0.500 1.32 0.10 52.8
35 8.86 0.020 0.000 0.000 1.77 0.02  
35 7.53 0.023 0.000 0.000 1.75 0.03  
36 51.39 0.059 0.420 0.100 1.05 0.04 0
36 45.19 0.049 0.420 0.100 1.09 0.07 0
37 140.00 21.000 0.810 0.100 0.80 0.09  
37 153.00 24.000 0.770 0.100 0.90 0.08  
38 132.00 0.040 0.400 0.100 1.10 0.48 0
38 132.00 0.040 0.400 0.100 1.60 0.54 0
39 7.09 0.031 0.810 0.000 0.70 0.01  
39 5.32 0.035 0.910 0.000 0.74 0.01  
40 55.00 0.040 0.310 0.500 0.84 0.04 0
40 42.90 0.059 0.270 0.500 0.88 0.03 0
41 106.70 16.500 0.680 0.100 1.00 0.06  
41 121.80 13.200 0.770 0.100 0.96 0.07  
42 68.20 0.320 0.820 0.010 1.06 0.38 43.8
42 71.70 0.300 0.820 0.010 1.10 0.38 43.4
43 35.00 0.040 0.470 0.025 1.20 0.09  
43 38.00 0.060 0.500 0.025 1.26 0.13  
44 31.00 0.050 0.520 0.025 1.23 0.16 32.0
44 49.00 0.070 0.550 0.025 1.27 0.20 32.0
45 53.00 10.000 0.580 0.010 1.54 0.15  
45 49.00 18.000 0.640 0.010 1.50 0.15  
46 43.00 0.110 0.490 0.100 1.20 0.70 15.5
46 33.00 0.120 0.490 0.100 1.10 0.50 18.5
47 44.70 0.090 0.790 0.000 2.02 0.82  
47 46.50 0.110 0.840 0.000 2.06 0.86  
48 18.20 0.049 0.210 0.025 1.91 1.47 23.4
48 19.90 0.063 0.260 0.025 1.95 1.51 27.8
49 212.00 8.000 0.710 0.050 2.12 1.07  
49 230.00 12.000 0.770 0.050 2.16 1.11  
50 0.88 0.000 0.000 0.023 1.20 0.08 0
50 0.90 0.000 0.000 0.027 1.24 0.12 0

References

  1. Di.P.A.S. Provincial standards of control and quality of water to beverage. Res. 698/03. Argentine, 2003.
  2. WHO. World Health Organization, Ammonia in drinking-water. (WHO/SDE/WSH/03.04/1)2003; Disponible en:
    http://www.who.int/water_sanitation_health/dwq/ammonia.pdf.
  3. Costa, J.L.; Massone, H.; Martı́nez, D.; Suero, E.E.; Vidal, C.M.; Bedmar, F. Nitrate contamination of a rural aquifer and accumulation in the unsaturated zone. Agricultural Water Management, 57 (2002) 33-47.
  4. Galindo, G.; Sainato, C.; Dapeña, C.; Fernández-Turiel, J.L.; Gimeno, D.; Pomposiello, M.C.; Panarello, H.O. Surface and groundwater quality in the northeastern region of Buenos Aires Province, Argentina. Journal of South American Earth Sciences, 23 (2007) 336-345.
  5. Nicolli, H.B.; Bundschuh, J.; Blanco, M.d.C.; Tujchneider, O.C.; Panarello, H.O.; Dapeña, C.; Rusansky, J.E. Arsenic and associated trace-elements in groundwater from the Chaco-Pampean plain, Argentina: Results from 100 years of research. Science of The Total Environment, 429 (2012) 36-56.
  6. Raychowdhury, N.; Mukherjee, A.; Bhattacharya, P.; Johannesson, K.; Bundschuh, J.; Sifuentes, G.B.; Nordberg, E.; Martin, R.A.; Storniolo, A.d.R. Provenance and fate of arsenic and other solutes in the Chaco-Pampean Plain of the Andean foreland, Argentina: From perspectives of hydrogeochemical modeling and regional tectonic setting. Journal of Hydrology, 518, Part C (2014) 300-316.
  7. Smedley, P.L.; Nicolli, H.B.; Macdonald, D.M.J.; Barros, A.J.; Tullio, J.O. Hydrogeochemistry of arsenic and other inorganic constituents in groundwaters from La Pampa, Argentina. Applied Geochemistry, 17 (2002) 259-284.
  8. Borzi, G.E.; García, L.; Carol, E.S. Geochemical processes regulating F−, as and NO3− content in the groundwater of a sector of the Pampean Region, Argentina. Science of The Total Environment, 530–531 (2015) 154-162.
  9. Zabala, M.E.; Manzano, M.; Vives, L. Assessment of processes controlling the regional distribution of fluoride and arsenic in groundwater of the Pampeano Aquifer in the Del Azul Creek basin (Argentina). Journal of Hydrology, 541, Part B (2016) 1067-1087.
  10. AFC. Argentine Food Code. Chapter XII: water drinks, water and carbonated water. http://www.anmat.gov.ar. Argentine, 2012.
  11. Clesceri, L. Métodos normalizados para el análisis de aguas potables y residuales (APHA-AWWA-WPCF).Madrid, 1992.
  12. Di Rienzo, J.A.; Casanove, F.; Balzarini, M.; González, L.; Tablada, M.; Robledo, C. InfoStat, FCA, Universidad Nacional de Córdoba. Argentine. v.2015; Disponible en: http://www.infostat.com.ar.
  13. Gabriel, K.R. The biplot graphic display of matrices with application to principal component analysis Biometrika, 58 (1971) 453-467.
  14. Gabriel, K.R. Biplot display of multivariate matrices for inspection of data and diagnosis. In V. Barnett(Ed.) Interpreting Multivariate Data. London, 1981.

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