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
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
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.  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 .
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 . Galindo et al. , analyzed the quality of surface water and groundwater in the Northeast of the province of Buenos Aires. In addition, Nicolli el at. , and Raychowdhury, et al.  analyzed the arsenic content and trace elements in groundwater in the Chaco Pampeana region. The researches of Smedley et al. ; Borzi et al.  and Zabala et al.  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)  relevant to the characterization and research parameters studied in this work.
|Parameter (unit of measurement)||Maximum value allowed|
|Turbidity||5, Pt-Co scale|
|Conductivity (dS/m)||Not mentioned|
|Total alkalinity (mg/L)||Not mentioned|
|Research||Arsenic (mg/L)||< 0.05|
|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) .
|Analysis||Analytical method||Standard Reference||Reagents||Equipment|
|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:
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  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 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.
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.
|Total Alkalinity (mg/L)||850.42||310.29||1708||876.5|
|Variable||Stratified depth||n||Average (mg/L)||ANOVA letters||SD (mg/L)||SE (mg/L)||Min (mg/L)||Max (mg/L)|
*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 .
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.
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.
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.
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.
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.
Characterization of 50 groundwater samples from Northeast (NE) of Córdoba, and northwest (NW) of Santa Fe provinces, Argentine.
|Sample||Data Sample||Town||Decimal GPS||Depth (m)||Stratified depth||Sediment|
|1||07-Apr-13||Colonia Tacurales, Santa Fe||-30.80283||20||Medium||no|
|2||07-Apr-13||Colonia Tacurales, Santa Fe||-30.80909||20||Medium||no|
|7||19-Apr-13||Colonia Vignaud, Córdoba||-30.83160||20||Medium||no|
|9||08-May-13||San Francisco, Córdoba||-31.42943||20||Medium||no|
|10||14-May-13||Sastre, Santa Fe||-31.77221||12||Low||no|
|20||28-Jun-13||Colonia 10 de Julio, Córdoba||-30.51911||20||Medium||no|
|25||29-Jul-13||Altos de Chipión, Córdoba||-31.00000||80||High||no|
|29||13-Aug-13||Colonia 10 de Julio, Córdoba||-30.58056||50||Medium||yes|
|32||21-Aug-13||Altos de Chipión, Córdoba||-30.99222||50||Medium||no|
|33||21-Aug-13||Altos de Chipión, Córdoba||-30.99972||50||Medium||no|
|35||23-Set-13||Colonia Vignaud, Córdoba||-30.84325||80||High||no|
|36||02-Nov-13||Colonia Valtelina, Córdoba||-31.06861||25||Medium||no|
|37||02-Nov-13||Colonia Vignaud, Córdoba||-30.81250||20||Medium||no|
|38||19-Nov-13||Colonia Castelar, Santa Fe||-31.60588||20||Medium||no|
|39||19-Nov-13||Frontera, Santa Fe||-31.43917||130||High||no|
|40||20-Nov-13||Zenon Pereyra, Santa Fe||-31.56192||18||Medium||no|
|41||19-Nov-13||Esmeralda, Santa Fe||-31.61645||20||Medium||no|
|45||24-Nov-13||Sarmiento, Santa Fe||-31.11640||25||Medium||yes|
|47||04-Dec-13||Altos de Chipión, Córdoba||-30.95000||50||Medium||no|
|48||04-Dec-13||Altos de Chipión, Córdoba||-30.95000||20||Medium||no|
|49||04-Dec-13||La Paquita, Córdoba||-30.90772||10||Low||no|
|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)|
|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|
|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|
|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|
|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|
Research parameters of 50 groundwater samples from Northeast (NE) of Córdoba, and northwest (NW) of Santa Fe provinces, Argentine.
|Sample||Nitrate (mg/L)||Nitrite (mg/L)||Ammonium (mg/L)||Arsenic (mg/L)||Fluoride (mg/L)||Iron (mg/L)||COD (mgO2/L)|