Next Article in Journal
Analysis of Water Supply Capacity of a Sand Dam
Previous Article in Journal
Development Trends and Research Frontiers of Preferential Flow in Soil Based on CiteSpace
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Water
Volume 14
Issue 19
10.3390/w14193040
water-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Pollution Evaluation of the El Pueblito River in Queretaro, Mexico, Using the Water Quality Index

by
Enrique Rodriguez-Nuñez
1,
Christian Hernandez-Mendoza
2,
Victor Perez-Moreno
1 and
Arely Cardenas
3,*
1
Faculty of Chemistry, Autonomous University of Queretaro, Cerro de las Campanas s/n, Queretaro 76010, Mexico
2
CONACYT—Laboratory of Environmental Geotechnics, Facultad de Ingeniería, Autonomous University of Queretaro, Circuito Universitario S/N, Queretaro 76010, Mexico
3
National Council of Science and Technology, CONACYT, Faculty of Chemistry, Autonomous University of Queretaro, Cerro de las Campanas s/n, Queretaro 76010, Mexico
*
Author to whom correspondence should be addressed.
Water 2022, 14(19), 3040; https://doi.org/10.3390/w14193040
Submission received: 23 August 2022 / Revised: 19 September 2022 / Accepted: 20 September 2022 / Published: 27 September 2022
(This article belongs to the Section Water Quality and Contamination)
Browse Figures
Review Reports Versions Notes

Abstract

:
This study evaluated the water pollution of the El Pueblito River by employing physicochemical parameters and biological indicators. Monitoring was conducted weekly during three months at seven sampling sites. To ease the representation of the results of this analysis, the water quality index (WQI) was calculated. The results obtained by the physicochemical analysis show the same trends as those of the analyses performed with bioassays using Daphnia magna and the WQI. The obtained values showed a deterioration of water quality from upstream to downstream due to the discharges into the river that occurred as it went through the city of Corregidora. The WQI values shifted from good water quality (83.00 ± 7.40) upstream to poor water quality (27.00 ± 0.85) downstream. This study also shows the utility of the WQI and how a Daphnia magna toxicity test can be an affordable and fast tool used to indicate high levels of pollution, such as in the case of El Pueblito River.

1. Introduction

Water is a vital liquid for living organisms and it is used in different human activities; the increasing population and consequent economic activities are leading to water quality deteriorations and water quantity reductions. These changes generate negative impacts on living organisms, ecosystems, and their interactions [1]. Pollution issues have been observed in river water, mainly in the least developed countries. Scarcity problems and social inequalities worsen the situations, and authorities are trying to preserve the natural water quality. For example, the rapid growth of urban areas and the associated economic activities in some regions of Latin America and Asia have increased so much that governments are no longer able to develop water treatment facilities or even keep the existing ones operating.
One of the main problems involves discharges in superficial eater bodies [2,3]. Discharge sources are factors that influence the water quality composition, making it susceptible to pollution, and, therefore, reducing water’s capacity to attenuate or degrade waste and contaminants, changing the composition of water components. The natural origin of the discharge shows that water is affected by weather, minerals present in the soil, and atmospheric depositions of compounds, such as dust, wind, etc. [1]. Human origins of the discharges are mainly due to human discharges that contain chemicals used in agricultural households and industrial activities, such as pesticides, herbicides, fertilizers, organic solvents, and degreasing/disinfecting compounds. These human-originated discharges have altered water quality worldwide, leading to quality depletion of shallow water [1,4].
The influence of human activities and natural effects impact water quality in surface bodies and the quality measurements are not static values. Due to their systemic nature, water quality changes with time and space; therefore, constant monitoring activity is essential. Water quality depends on physical, chemical, and biological properties; thus, it is crucial to monitor such changes, enabling the detection of temporal and spatial variabilities [1,5,6]. Water quality can be associated with changes in different parameters, such as nutrient concentrations, pH values, sediment loads, dissolved oxygen levels, and the addition of compounds, such as oil, grease, mercury, trace metals, pesticides, and nonmetallic toxins that harm humans and wildlife, which depend on these resources. The water quality index (WQI), is a powerful parameter that is helpful in the evaluation [1,5,7].
Information about water quality can be evaluated with different parameters, such as nutrient concentrations, pH values, sediment loads, dissolved oxygen levels, and the presence of to toxic compounds. Even when most of the microorganisms present in the environment are favorable for the environment, microbial pollution mainly originates from urban contamination and agriculture. The presence of these organisms induces diseases or premature death in humans and livestock. In most downstream communities, people near these polluted areas, whether due to housing or recreational or work activities, are exposed to microbial pathogens [8]. In these cases of microbial contamination, and due to variations in time or in different sites within the same water body, some parameters are employed and analyzed using different methodologies to test the water quality. The most common method is to detect fecal contaminants; coliforms are used as the primary indicators of pathogenic microorganisms [1,9,10].
It is essential to monitor water quality to estimate the impact of activities and discharges on water bodies, or to determine their possible usages. Nevertheless, common values of the individual parameters are not sufficient or easy to understand and provide information about the global water quality [11,12,13]. There are tools that can be used to determine water quality trends and values, but some are dynamic modeling methods or implement multivariate statistics. Cluster analyses are used to order variables or data in similar groups, and factor analyses are practical [14].
Authorities and researchers have recommended the use of an expression that is able to provide information about the global quality of water; this parameter is called the WQI [11,15]. The WQI is a powerful parameter, proposed by Horton in 1965, which represents water quality with a single value. It englobes a large number of physicochemical and biological parameters, ranging from 3 to 20, and it eases their interpretations as they are converted into a simple value (formed by weights assigned to the employed variables) [8,11,16]. The WQI value is basically a number that allows contrasting the values in general; therefore, it is commonly used to classify, assign, and determine water pollution [14,17,18], and is adapted and improved according to the application or the region [13]. When the parameters are selected adequately, they can help to prevent repetitive and unnecessary information, making it easier to estimate and use WQI [13,19].
However, toxicity information is not included in WQI studies because water-containing substances that are harmful to aquatic life, animals, and humans are excluded for this measurement [8]. Some researchers have addressed the problem of the evaluation of water quality by using ecological indicators or the use of bioindicators, based on the fact that physical and chemical properties do not always offer entire perspectives of the water quality [9,11,20,21,22]. Water quality, which is also known or related to ecological health, can also be represented, such as the responses of different types of aquatic organisms. Special attention is given to those that demonstrate more sensitivity to the conditions in the environment, such as the most employed diatoms, zooplankton, and macroinvertebrates [23]. Bioindicators are sometimes used by sampling the population of the species from the analyzed place; for example, coral [24], diatoms [25], Allocapnia, Glossosoma, and Hesperoperla [21], or by taking water samples ex situ for further evaluation with sensitive organisms.
Bioassays are widely employed to evaluate surface water and even discharges. They are used as complex or multiparametric assays because they comprise a series of parameters, and because they are affordable, easier to perform, and take little time [26]. One of the most common organisms employed for evaluating toxicity is Daphnia magna [27], due to its sensitivity to toxic substances, short reproduction span, adaptability, and small space required for maintenance and lab tests [26]. Several studies have evaluated the responses of bioindicators and their responses to factors. For example, Daphnia’s death or immobility has been tested for pH [28], conductivity, salinity [27], and potassium dichromate [29]. However, the combination of factors also influences the ecological impacts of biological indicators, mainly in complex waters; for example, in surface water where there are discharges that make a complex mixture of compounds, such as in seawater, lakes, or rivers.
This study analyzed and characterized pollution in the El Pueblito River, which is located in Corregidora, Queretaro, Mexico. In 2008, a hydrological and environmental diagnostic was performed, and a program focused on recovering the river was created (with the diagnostic results as the basis). One of the strategies to recover the space and avoid pollution was to deliver environmental education to the population that inhabited areas near the river. In 2011, a project that focused on planning and mitigating pollution on El Pueblito River was implemented. Several important milestones were achieved: the installation of biofilters, the placing of some structural works, such as rock traps for stabilization, restoration of the sinuosity of the river, and the use of deflectors [30].
In this sense, the main objective of this work was to compare the water quality obtained by physicochemical parameters with bioassays. The monitoring of water quality employing physicochemical parameters of El Pueblito River will be translated to WQI. Bioassays, such as the Daphnia magna survival rate and Sorghum bicolor seed germination rates, were carried out. Finally, an analysis of the data was performed to establish the interrelations between physicochemical parameters, the relationship between physicochemical parameters and bioindicators, and the relationship between WQI and bioindicators throughout the river.

2. Materials and Methods

2.1. Study Area

El Pueblito River is located southwest of the Queretaro state in Mexico and it is part of hydrological region number 12; El Pueblito River’s water comes from the Huimilpan River. In the rainy season, it feeds from the following rivers: Bravo, Hondo, Apapataro, El Zapote, and El Plata. El Pueblito River starts at the Batan dam, and it flows for 16.5 km until it reaches the river junction with the Queretaro River. El Pueblito is an ecological space; it is the habitat of important local species, such as turtles (tortuga casquito), crabs, and an endemic shrimp-like crustacean (known locally as acocil) [30].
There is a little information available about the El Pueblito River. What can be found consists of some reforestation campaigns and other population awareness programs focused on pollution-associated health problems and the importance of their conservation due to the species in the zone [30].

2.2. Sampled Sites

Seven locations were selected; the sites were chosen in places where changes in water quality were perceived to result from discharges. Moreover, the last two sampling sites were selected because they were in the most accessible area, closest to the end of the riverbed. The site details and coordinates are presented in Table 1; Figure 1 presents their locations. Sampling sites were named after their locations and are as follows: Batan (BAT), Manantial (MAN), and Santa Barbara (STA); there are two river water contribution points near the Piramides (PIR) and Rastro (RAS) sites. Two other sites follow—Puente (PUE) and Barrera (BAR). Each sample consisted of 2 L of water, collected by hand in clean and sterile plastic bottles. All bottles were previously rinsed with the river water of the corresponding sampling site; after taking the sample, it was transported at 4 °C.

2.3. Physicochemical Parameters

Physicochemical parameters, such as pH, conductivity (µS/cm), total dissolved solid (TDS in mg/L) content, and temperature (°C) were measured in situ with an Apera PC60 instrument, Shanghai Sanxin Instrumentation. Samples were stored at 4 °C in the dark.
The following parameters were determined according to the corresponding official Mexican standard. Total phosphorus (PT) content was measured according to NMX-AA-029-SCFI-2001 [31]. Biological oxygen demand (BOD5) was determined after five days of incubation at 20 °C in the dark; we measured the difference in the dissolved oxygen according to the Mexican normative [32]. Chemical oxygen demand (COD) was determined using the open reflux method. Total solids (TS) were determined by drying the sample at 105 °C, after which the volatile suspended solids (VSS) were estimated, followed by calcination at 525 °C [33,34,35]. Total coliform organisms were determined with multiple tube tests employing the most likely number method [36].

2.4. Biological Indicators

2.4.1. Daphnia Magna Assays

Organisms were cultured in a 5 L container with an air pump, and they were fed three times a week. From this culture, Daphnia neonates were collected. Five organisms were exposed to each sample in triplicate. Samples from RAS, PUE, BAR, and PIR were analyzed, employing dilutions with distilled water at 10, 25, 50, and 75%; the original sample was evaluated too. The rest of the samples were assessed without further treatment. As a positive control, 0.5 mg/L potassium dichromate was employed, and in the case of the negative control, water from the initial culture was used. All samples were kept under static conditions for 48 h.
Mobility was determined at 48 h using an Olympus® sz4045 stereoscope (Tokyo, Japan). Mortality was considered with the absence of any appendicular movement and the lack of a heartbeat. Immobility occurred when the organism could not swim, but appendicular movement was still present [37]. LC50 was estimated by a graphical method by interpolating the points of a plot of the observed mortality versus the base 10 logarithm of the percent effluent concentration [38] employing an LC50 calculator [39].

2.4.2. Bioassay with Sorghum Seed Base

Sorghum seeds were submerged for 24 h in distilled water. After that, a wet chamber was prepared with 5 mL of each sample and approximately 1 g of cotton with dimensions of 7 cm × 3 cm; moreover, a blank with 5 mL of distilled water was prepared. Fifteen seeds were sown in each cotton containing media. After 72 h of incubation in the dark, the root length (R) and epicotyl length (E) were measured. The average was compared against the blank length (R0 and E0), and growth is expressed according to the formula of the root length index Ri = Ri/R0 × 100 and epicotyl length Ei = Ei/E0 × 100 [40].

2.5. Water Quality Index

The WQI was estimated following the formula by the National Sanitation Foundation (NSF)
WQI = Σni=1 CiPi.
where,
  • n = the total number of parameters.
  • Ci = value normalized assigned to each parameter i, according to the quality.
  • Pi = relative weight assigned to each parameter (from 1 to 4) [16].
  • For the estimation of WQI, the values of Ci and Pi are presented in Table 2.

3. Results and Discussion

3.1. Physicochemical Parameters

Temperature is a complex parameter that comprises, for example, dissolved oxygen concentration, human impact due to economic activities, and the structure of the waterbody, with an important impact in the metabolism of aquatic organisms [39]. We found temperatures ranging from 21.1 to 24.8 °C. Since there were no historical temperature records found to compare with, we can only note that the data obtained showed minimal variations between the sampled sites (and in time). There was no evidence of thermal pollution. Our results agreed with those of Shirin and collaborators, regarding a range of temperatures from 16.0 to 27.5 [40]. Additionally, the pH was measured in situ and values ranged from 7.24 to 8.39; these data do not show significant variation. Moreover, these pH values were within the limits that support aquatic life [15,42]. Therefore, these tests reveal that this parameter was not useful in identifying contamination by itself, the same occurred with temperature. A summary of the results is presented in Table 3, and complete information is presented in the supplementary material (Table S1 and Figures S1–S8).
In the same way, conductivity was evaluated, obtaining a mean of 1111 ± 573 µS/cm; along the river, the values ranged between 119 and 2230 µS/cm, with the lowest values presented upstream at sites STA, MAN, and BAT. Conductivity values increased with the existence of mineral acids, salts, or contaminants that could be discharged [41]. This is consistent with the discharges in the El Pueblito River and the increasing values downstream. Similar results in conductivity were presented in Santiago River with values from 555 to 2430 µS/cm [9]. Another parameter associated with conductivity is TDS; the measured mean was 786 ± 386 ng/L, and the data obtained at the different sample points presented considerable changes. For example, the sites of RAS, PIR, PUE, and BAR reported values between 925 and 1570 ng/L, while in SAN, MAN, and BAT, the values were between 306 and 576 ng/L. Higher TDS values are associated with discharge points, increasing conductivity values downstream.
The measured total solid content had a mean value of 722.35 ± 410.06 mg/L; higher values up to 1575.00 mg/L were seen at RAS and the lowest value was seen at BAT at 5 mg/L. To determine the organic matter presented in these solids, volatile suspended solids (VSS) were calculated. Higher values were observed at RAS (885 mg/L), and lower values were observed at BAT (5 mg/L) with a high variation between sites; the mean was 279.35 ± 228.63 mg/L. The presence of higher concentrations of TS and organic matter in these places (due to discharges) indicates that the most significant contribution of organic matter is presented at the RAS site, and in the subsequent downstream places.
Another critical parameter, BOD5, represents the amount of oxygen consumed to degrade the compounds contained in a sample. The mean value was 107.7 ± 122.7 mg O2/L, and data obtained at the different stations presented variations. For example, the maximum value was obtained at the RAS site, and minimal values > 1 mg O2/L were reported at more than one time at STA, MAN, and BAT sites. The BOD5 always increased downstream after the discharge locations of RAS and PIR, similar to VSS, TS, and TDS results. The values approved for discharge by the Mexican government are 30 mg O2/L, while observing higher values and pollution in PIR and downstream sites [43].
Similarly, and to complete the analysis of COD, the mean COD was 360.98 ± 380.96 mgO2/L. Similar to BOD5, conductivity, and TDS, there were significant variations in COD data at the different points. The lowest values were found at SAN, MAN, and BAT, from not detectable to 111.67 mgO2/L, while the values at the rest of the sampling points ranged from 97.66 to 1289 mgO2/L. Similar to other parameters, the COD values also increased downstream due to discharges into the river flow through inhabited territory. Higher values of these parameters indicated lower concentrations of dissolved oxygen and, therefore, a negative impact in aquatic life. With this in mind, biodegradability was calculated, showing that in the most polluted sites, the mean biodegradability ratios of BOD5/COD were 0.19 in PUE, 0.27 in RAS, 0.44 in BAR, and 0.57 in PIR, showing that the pollutants present in the water were not mainly biodegradable in a period of five days.
Regarding PT, the mean was 12.06 ± 12.63 mg/L. The highest concentrations were found at RAS, BAR, PUE, and PIR, with concentrations ranging from 7.08 to 39.93 mg/L. The lowest concentrations, with values one order of magnitude lower, were found at MAN, STA, and BAT, with concentrations ranging from 1.94 to 0.14 mg/L. The concentration of PT in the water is essential because high concentrations lead to environmental damage due to the lack of oxygen and the promotion of growth of algae and plants [44]. This parameter presents the same increasing trend as other pollutants, e.g., COD, BOD, conductivity, and total solids, contributing to the evidence with the chemical parameters of highly polluted sites.

3.2. Biological Parameters and Bioassays

In this section, the analysis of biological factors is presented. Coliform microorganisms were determined using the most probable number (MPN). The lowest values obtained were presented upstream in BAT, MAN, and STA, showing the same trend as physicochemical parameters, with mean values < 0.3 MPN/100 mL. In contrast, sites PIR, RAS, BAR, and PUE reported values < 2400.0 MPN/100 mL, showing the presence of biological contamination by coliform microorganism. This contamination with coliforms was associated with discharges containing fecal pollution, and the limit according to EPA for recreational water was 200 fecal coliform per 100 mL [45].
It was also important to analyze other ecological factors, one of them was toxicology. It was determined with Daphnia organisms, using them to estimate the LC50 because of their sensitivity to several pollutants [46]. Results show that at STA, MAN, and BAT, there were no observed effect concentrations (NOEC) in the Daphnia organisms; meaning that the functions of this organisms were not affected by the quality of the water from these places in the analyzed conditions. Even when our toxicity results are negative, chronic toxicity should not be excluded, and further studies are required to exclude chronic toxicity [38]. Moreover, the results showed a clear difference of affectation compared to the sites where the LC50 concentrations ranged from 15 to 90%, being PUE, BAR, RAS, and PIR sites, indicating that these presented a higher toxicity for Daphnia organisms. Furthermore, in the diluted samples of these four highly polluted sites, 50% of the Daphnia population was not able to survive. This finding is in agreement with COD and BOD5 results, where one possible cause of the mortality is the decreasing amount of O2 available due to higher values of the physicochemical values [28].
The sorghum seeds assay, which consists of germination and the posterior measurements of roots and epicotyl lengths, showed growth inhibition in epicotyl and root lengths compared to the blank (Figures S11 and S12). However, the inhibition of root growth was more significant when the water samples were from the RAS and BAR sites, with significant growth inhibition rates of 82.74 and 63.21%, respectively, and compared to the blank. In the case of epicotyl length, the average presented more inhibition (71.1 ± 19.8%) compared to that of the roots (99.1 ± 44.0%). These results show that the water conditions from these sites have characteristics that inhibit the growth and development of the plant, as it was previously reported that water quality negatively affects barley seed germination [47]. An unexpected result was the enhanced growth in the samples taken from MAN and PIR (124.27 ± 67.96% for the epicotyl and 114.14 ± 39.73% for the roots), these results could have been a response to the presence of organic compounds, which can be used as nutrients for the plant. However, more studies are needed in order to establish the factors in water with greater impact on the germination process. Please find the complete results in the supplementary information are presented in (Figures S9 and S10).

3.3. Parameters Correlation

Besides the individual analysis of biological and physicochemical parameters, the correlation between the different parameters was also analyzed (see Table 4). Higher correlation values, superior to 0.9, were presented between PT and total coliforms = COD > BOD > TDS. Likewise, VSS with COD > TDS; TDS with conductivity > VSS > total coliforms > COD; COD with VSS > LC50 = fecal coliforms > BOD5 > TDS and fecal coliforms with PT > TDS = COD. High correlations between PT, conductivity, and COD were also observed in other studies [48] and could be associated with the presence of soluble compounds in water and their contributions to COD and BOD5. Moreover, VSS correlates well with conductivity, and fecal coliforms are positively associated with BOD. The same strong association between parameters, such as COD and BOD, was presented in the discharge of the Ganga River [49].
The correlation of COD and LC50 is 0.92, indicating that when COD increases, the same occurs with the toxicity of the effluent. In the case of BOD, the correlation with LC50 is 0.84, showing a lower correlation with those values, similar to that reported by Al-Rosyid and collaborators [46]. A higher correlation with COD is attributed to the non-biodegradable compounds associated with COD; on average, the biodegradability of the effluent is 15.64%.

3.4. Water Quality Index

According to the results presented above, individual evaluations of the parameters and their correlations provide a description of the state of the water of El Pueblito River. Nevertheless, it is challenging to interpret or assess the global water quality from these results. For that reason, we calculated the WQI value of the method presented by the National Sanitation Foundation (see ratings in Table 5). The calculation was performed using multiple parameters and applying the normalization factor and weight shown in Table 2; the results are presented in Table 6.
The WQI average follows the trend of RAS < BAR < PIR < PUE < STA < BAT < MAN. The MAN site (83.00 ± 7.40) is a place near a contribution point, at which the water presents good quality. Upstream, at sites BAT and STA, the water has good quality; however, after receiving contributions from PIR and RAS, the water quality decreases (32.25 ± 2.65 and 27.00 ± 0.85, respectively), along with the water quality index, as presented in Figure 2.
At the downstream site, PUE, the WQI was 39.22 ± 1.92, and at BAR, the WQI was 31.13 ± 1.14, showing that after passing through the urban area, the water became very polluted and the assimilative capacity of the river was no longer able to buffer the increased amount of pollution discharged in it. Therefore, pollution is associated with discharges that change the WQI from values above 78 in STA to WQI under 40 in downstream sites.

4. Comparison of WQI and Biological Indicators

It is widely known that physicochemical parameters do not provide enough information about water quality [20] because the components in water can interact, changing the final toxicity of the effluent (thus, the relevance of including biologically-based toxicity tests). In fact, several research studies have been conducted using biological indicators as references or tools to deduce physicochemical parameters [20,50]. This way, in cases where it is not possible to monitor all individual parameters associated with pollution, biological toxicity tests represent viable alternatives to obtaining information. Our study also presents the correlation between WQI and bioindicators.

5. Conclusions

The results of this study indicate that the upstream analyzed sites (MAN, BAT, and STA) in El Pueblito River presented good water quality, but downstream of RAS, PIR, BAR, and PUE, the water quality decreased slightly. The results of the analysis at the last point downstream (PUE) showed a slight natural attenuation of pollution, but it did not affect water quality. Due to the high pollution levels, even with attenuation, the water quality remained poor. The physicochemical parameters at different sites of the El Pueblito River showed evidence of higher levels of contamination downstream after the discharge points of water into the river (at PIR and RAS). Parameters, such as COD, BOD, PT, TS, and TDS, are evidence of this pollution. The same behavior was observed with the results of LC50 with Daphnia magna and the global parameter WQI, with both methods confirming the results obtained by individual factors. Furthermore, sorghum germination did not correlate with the rest of the values, perhaps due to the presence of PT and other compounds that were employed as substrates to the germination and growth of seeds (promoting the growth). Thus, in this case, it was not a good bioindicator to correlate with the pollution of the river. There is a correlation between individual parameters, e.g., COD, BOD, VSS, PT, showing consistency, and the same tendency between them and biological indicators, such as LC50 and total coliforms. We also determined that in the El Pueblito River, for WQI with very bad quality, LC50 values were under 18% dilution. With this evidence, it is important to communicate that LC50 is a good, fast, and affordable indicator that can be associated with higher or lower pollution in El Pueblito River. Otherwise, in the conditions of our experiments, there was no good numerical correlation index to associate WQI values with LC50; perhaps a clear correlation could be performed by increasing the number of dilutions at the LC50 assays, allowing to obtain more information. One of the limitations of this work is that the sampling was performed in the rainy season; for future research, it will be important to consider the whole year.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/w14193040/s1, Table S1. Coordinates of sampled sites, Figure S1: Variations of pH at different sampled sites in El Pueblito river, Figure S2: Variation of conductivity at different sampled sites in El Pueblito river, Figure S3: Variation of TDS at the different sampled sites in El Pueblito river, Figure S4: Variation of total solids in different sampled sites of El Pueblito river, Figure S5: Variation of volatile suspended solids in different sampled sites of El Pueblito river, Figure S6: Variation of BOD5 at the different sampled sites in El Pueblito river, Figure S7: Variation of COD per week at the different sampled sites in El Pueblito river, Figure S8: Variation of total phosphorous per week at the different sampled sites in El Pueblito river, Figure S9: Variation of total coliforms expressed as MPN per week in the different sampled sites in El Pueblito river, Figure S10: Variation of LC50 Daphnia magna, the samples’ concentration in % volume/volume per week at different sampled sites in El Pueblito river, Figure S11: Germination bioassay with sorghum, percentage of rooth growth inhibition against blank per week at different sites in El Pueblito river, Figure S12: Germination bioassay with sorghum, percentage of epicotyl growth inhibition against blank per week at different sites in El Pueblito river.

Author Contributions

Conceptualization, project administration, and supervision: A.C.; methodology: V.P.-M. and C.H.-M.; experimental performance and data analysis; E.R.-N. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was approved by the Ethics Committee of Chemistry Faculty of Autonomous University of Queretaro with the number CBQ20/018.

Data Availability Statement

Not Applicable.

Acknowledgments

We acknowledge D. Carpio-Vazquez for contributing to the sampling process.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Carr, G.M.; Neary, J.P. Water Quality for Ecosystem and Human Health, 2nd ed.; United Nations Environment Programme, Global Environment Monitoring System (GEMS)/Water Programme: Ontario, ON, Canada, 2008. [Google Scholar]
  2. Peña-Guzmán, C.; Ulloa-Sánchez, S.; Mora, K.; Helena-Bustos, R.; Lopez-Barrera, E.; Alvarez, J.; Rodriguez-Pinzón, M. Emerging Pollutants in the Urban Water Cycle in Latin America: A Review of the Current Literature. J. Environ. Manag. 2019, 237, 408–423. [Google Scholar] [CrossRef] [PubMed]
  3. Zhou, Q.; Yang, N.; Li, Y.; Ren, B.; Ding, X.; Bian, H.; Yao, X. Total Concentrations and Sources of Heavy Metal Pollution in Global River and Lake Water Bodies from 1972 to 2017. Glob. Ecol. Conserv. 2020, 22, e00925. [Google Scholar] [CrossRef]
  4. Nathaniel, S.P.; Nwulu, N.; Bekun, F. Natural Resource, Globalization, Urbanization, Human Capital, and Environmental Degradation in Latin American and Caribbean Countries. Environ. Sci. Pollut. Res. 2021, 28, 6207–6221. [Google Scholar] [CrossRef] [PubMed]
  5. Thakre, G.; Shrivastava, N.; Mishra, P.K. Analytical Studies on Water Quality Index of River Tapti. Int. J. Chem. Sci. 2011, 9, 1401–1409. [Google Scholar]
  6. Ober, J.; Karwot, J.; Rusakov, S. Tap Water Quality and Habits of Its Use: A Comparative Analysis in Poland and Ukraine. Energies 2022, 15, 981. [Google Scholar] [CrossRef]
  7. Zhang, H.; Xu, L.; Huang, T.; Yan, M.; Liu, K.; Miao, Y.; He, H.; Li, S.; Sekar, R. Combined Effects of Seasonality and Stagnation on Tap Water Quality: Changes in Chemical Parameters, Metabolic Activity and Co-Existence in Bacterial Community. J. Hazard. Mater. 2021, 403, 124018. [Google Scholar] [CrossRef]
  8. Kachroud, M.; Trolard, F.; Kefi, M.; Jebari, S.; Bourrié, G. Water Quality Indices: Challenges and Application Limits in the Literature. Water 2019, 11, 361. [Google Scholar] [CrossRef]
  9. Neachell, E. Book Review—Environmental Flows: Saving Rivers in the Thrid Millennium. River Res. Appl. 2014, 30, 132–133. [Google Scholar] [CrossRef]
  10. Ji, Y.; Wu, J.; Wang, Y.; Elumalai, V.; Subramani, T. Seasonal Variation of Drinking Water Quality and Human Health Risk Assessment in Hancheng City of Guanzhong Plain, China. Expo. Health 2020, 12, 469–485. [Google Scholar] [CrossRef]
  11. Koçer, M.A.T.; Sevgili, H. Parameters Selection for Water Quality Index in the Assessment of the Environmental Impacts of Land-Based Trout Farms. Ecol. Indic. 2014, 36, 672–681. [Google Scholar] [CrossRef]
  12. Mukate, S.; Wagh, V.; Panaskar, D.; Jacobs, J.A.; Sawant, A. Development of New Integrated Water Quality Index (IWQI) Model to Evaluate the Drinking Suitability of Water. Ecol. Indic. 2019, 101, 348–354. [Google Scholar] [CrossRef]
  13. Nong, X.; Shao, D.; Zhong, H.; Liang, J. Evaluation of Water Quality in the South-to-North Water Diversion Project of China Using the Water Quality Index (WQI) Method. Water Res. 2020, 178, 115781. [Google Scholar] [CrossRef]
  14. Boyacioglu, H.; Boyacioglu, H. Surface Water Quality Assessment by Environmetric Methods. Environ. Monit. Assess. 2007, 131, 371–376. [Google Scholar] [CrossRef]
  15. Debels, P.; Figueroa, R.; Urrutia, R.; Barra, R.; Niell, X. Evaluation of Water Quality in the Chillán River (Central Chile) Using Physicochemical Parameters and a Modified Water Quality Index. Environ. Monit. Assess. 2005, 110, 301–322. [Google Scholar] [CrossRef]
  16. Tyagi, S.; Sharma, B.; Singh, P.; Dobjal, R. Water Quality Assessment in Terms of Water Quality Index. Am. J. Water Resour. 2014, 1, 34–38. [Google Scholar] [CrossRef]
  17. Kannel, P.R.; Lee, S.; Lee, Y.S.; Kanel, S.R.; Khan, S.P. Application of Water Quality Indices and Dissolved Oxygen as Indicators for River Water Classification and Urban Impact Assessment. Environ. Monit. Assess. 2007, 132, 93–110. [Google Scholar] [CrossRef]
  18. Cude, C.G. Oregon Water Quality Index a Tool for Evaluating Water Quality Management Effectiveness. J. Am. Water Resour. Assoc. 2001, 37, 125–137. [Google Scholar] [CrossRef]
  19. Alexakis, D.E. Meta-Evaluation of Water Quality Indices. Application into Groundwater Resources. Water 2020, 12, 1890. [Google Scholar] [CrossRef]
  20. Džeroski, S.; Demšar, D.; Grbović, J. Predicting Chemical Parameters of River Water Quality from Bioindicator Data. Appl. Intell. 2000, 13, 7–17. [Google Scholar] [CrossRef]
  21. Sharifinia, M.; Mahmoudifard, A.; Imanpour Namin, J.; Ramezanpour, Z.; Yap, C.K. Pollution Evaluation in the Shahrood River: Do Physico-Chemical and Macroinvertebrate-Based Indices Indicate Same Responses to Anthropogenic Activities? Chemosphere 2016, 159, 584–594. [Google Scholar] [CrossRef]
  22. Delfanti, R.L.; Piccioni, D.E.; Handwerker, J.; Bahrami, N.; Krishnan, A.P.; Karunamuni, R.; Hattangadi-Gluth, J.A.; Seibert, T.M.; Srikant, A.; Jones, K.A.; et al. The Structure of Health Factors among Community-dwelling Elderly People. N. Engl. J. Med. 2018, 372, 2499–2508. [Google Scholar]
  23. Pham, T.L. Comparison between Water Quality Index (WQI) and Biological Indices, Based on Planktonic Diatom for Water Quality Assessment in the Dong Nai River, Vietnam. Pollution 2017, 3, 311–323. [Google Scholar] [CrossRef]
  24. Fabricius, K.E.; Cooper, T.F.; Humphrey, C.; Uthicke, S.; De’ath, G.; Davidson, J.; LeGrand, H.; Thompson, A.; Schaffelke, B. A Bioindicator System for Water Quality on Inshore Coral Reefs of the Great Barrier Reef. Mar. Pollut. Bull. 2012, 65, 320–332. [Google Scholar] [CrossRef]
  25. Wu, J.T. A Generic Index of Diatom Assemblages as Bioindicator of Pollution in the Keelung River of Taiwan. Hydrobiologia 1999, 397, 79–87. [Google Scholar] [CrossRef]
  26. Tyagi, V.; Chopra, A.; Durgapal, N.; Kumar, A. Evaluation of Daphnia Magna as an Indicator of Toxicity and Treatment Efficacy of Municipal Sewage Treatment Plant. J. Appl. Sci. Environ. Manag. 2009, 11, 46835. [Google Scholar] [CrossRef]
  27. Schuytema, G.S.; Nebeker, A.V.; Stutzman, T.W. Salinity Tolerance of Daphnia Magna and Potential Use for Estuarine Sediment Toxicity Tests. Arch. Environ. Contam. Toxicol. 1997, 33, 194–198. [Google Scholar] [CrossRef]
  28. El-Deeb Ghazy, M.M.; Habashy, M.M.; Mohammady, E.Y. Effects of PH on Survival, Growth and Reproduction Rates of the Crustacean, Daphnia Magna. Aust. J. Basic Appl. Sci. 2011, 5, 1–10. [Google Scholar]
  29. Gopi, R.A.; Ayyappan, S.; Chandrasehar, G.; Varma, K.K.; Goparaju, A. Effect of Potassium Dichromate on the Survival and Reproduction of Daphnia Magna. Bull. Environ. Pharmacol. Life Sci. 2012, 1, 89–94. [Google Scholar]
  30. Ciencia Tecnología e Innovación En Querétaro. Casos Exitosos: Saneamiento de Las Aguas Río El Pueblito. Available online: http://www.concyteq.edu.mx/concyteq//uploads/publicacionArchivo/2017-06-742.pdf (accessed on 10 September 2020).
  31. NMX-AA-029-SCFI-2001. Determinación De Fósforo Total En Aguas Naturales, Residuales Y Residuales Tratadas. Available online: https://www.gob.mx/cms/uploads/attachment/file/166773/NMX-AA-029-SCFI-2001.pdf (accessed on 10 September 2020).
  32. NMX-AA-028-SCFI-2001. Análisis De Agua—Determinación de La Demanda Bioquímica de Oxígeno En Aguas Naturales, Residuales (DBO5) y Residuales Tratadas. Available online: http://www.economia-nmx.gob.mx/normas/nmx/2001/nmx-aa-028-scfi-2001.pdf (accessed on 10 September 2020).
  33. NMX-AA-030/2-SCFI-2011. Determinación de La Demanda Químia de Oxígeno En Aguas Naturales, Residuales y Residuales Tratadas. Available online: https://www.gob.mx/cms/uploads/attachment/file/166775/NMX-AA-030-2-SCFI-2011.pdf (accessed on 10 September 2020).
  34. NMX-AA-034-SCFI-2015. Análisis de Agua- Medición de Sólidos y Sales Disueltas En Aguas Naturales, Residuales y Tratadas. Available online: https://www.gob.mx/cms/uploads/attachment/file/166146/nmx-aa-034-scfi-2015.pdf (accessed on 10 September 2020).
  35. Volkmar, E.C.; Henson, S.S.; Dahlgren, R.A.; O’Geen, A.T.; Van Nieuwenhuyse, E.E. Diel Patterns of Algae and Water Quality Constituents in the San Joaquin River, California, USA. Chem. Geol. 2011, 283, 56–67. [Google Scholar] [CrossRef]
  36. NMX-AA-042-SCFI-2015. Análisis de Agua - Enumeración de Organismos Coliformes Totales, Organismos Coliformes Fecales (Termotolerantes) y Escherichia Coli-Método Del Número Más Probable En Tubos Múltiples. D. Of. la Fed. 1–25. 2015. Available online: https://www.gob.mx/cms/uploads/attachment/file/166147/nmx-aa-042-scfi-2015.pdf (accessed on 10 September 2020).
  37. NMX-AA-087-SCFI-2010. Análisis de Agua- Evaluación de Toxicidad Aguada Con Daphnia Magna, Straus (Crustacea—Cladocera). Available online: https://www.gob.mx/cms/uploads/attachment/file/166797/NMX-AA-087-SCFI-2010.pdf (accessed on 10 September 2020).
  38. United States Enviromental Protection Agency. Methods for Measuring the Acute Toxicity of Effluents and Receiving Waters to Freshwater and Marine Organisms, 5th ed.; Environmental Protection Agency Office of Water: Washington, DC, USA, 2002; ISBN EPA-821-R-02-012.
  39. Inc, A.B. Quest Grtaph TM. Available online: https://www.aatbio.com/tools/lc50-calculator (accessed on 1 January 2022).
  40. Mitelut, A.A.C.; Popa, M.E. Seed Germination Bioassay for Toxicity Evaluation of Different Composting Biodegradable Materials. Rom. Biotechnol. Lett. 2011, 16, 121–129. [Google Scholar]
  41. Pesce, S.F.; Wunderlin, D.A. Use of Water Quality Indices To Verify the Córdoba City (Argentina) on Suquía River. Wat. Res. 2000, 34, 2915–2926. [Google Scholar] [CrossRef]
  42. Nagels, J.W.; Davies-Colley, R.J.; Smith, D.G. A Water Quality Index for Contact Recreation in New Zealand. Water Sci. Technol. 2001, 43, 285–292. [Google Scholar] [CrossRef] [PubMed]
  43. NOM-003-SEMARNAT-1997; SEMARNAT Normas Oficiales Mexicanas Normas Oficiales Mexicanas. Comisión Nacional del Agua: Mexico City, Mexico, 2013; pp. 1–65.
  44. Enad, H.Y.; Jaeel, A.J. Water Quality Index of Tigris River on Waist Governorate for Aquatic Life. IOP Conf. Ser. Mater. Sci. Eng. 2019, 584, 12029. [Google Scholar] [CrossRef]
  45. EPA. EPA Recreational Water Quality Criteria; U.S. Environmental Protection Agency: Washington, DC, USA, 2012; pp. 1–69.
  46. Al-Rosyid, L.M.; Titah, H.S.; Santoso, I.B.; Mangkoedihardjo, S. Review on BOD/COD Ratio Toxicity to Daphnia Magna, Artemia Salina and Brachydanio Rerio. Nat. Environ. Pollut. Technol. 2021, 20, 1741–1748. [Google Scholar] [CrossRef]
  47. Guiga, W.; Boivin, P.; Ouarnier, N.; Fournier, F.; Fick, M. Quantification of the Inhibitory Effect of Steep Effluents on Barley Germination. Process Biochem. 2008, 43, 311–319. [Google Scholar] [CrossRef]
  48. Kowalkowski, T.; Zbytniewski, R.; Szpejna, J.; Buszewski, B. Application of Chemometrics in River Water Classification. Water Res. 2006, 40, 744–752. [Google Scholar] [CrossRef]
  49. Shirin, S.; Yadav, A. Physico Chemical Analysis of Municipal Wastewater Discharge in Ganga River, Haridwar District of Uttarakhand, India. Curr. World Environ. 2014, 9, 536–543. [Google Scholar] [CrossRef]
  50. Teodorović, I.; Bečelić, M.; Planojević, I.; Ivančev-Tumbas, I.; Dalmacija, B. The Relationship between Whole Effluent Toxicity (WET) and Chemical-Based Effluent Quality Assessment in Vojvodina (Serbia). Environ. Monit. Assess. 2009, 158, 381–392. [Google Scholar] [CrossRef]
Water 14 03040 g001 550
Figure 1. Map of the study area. The blue line represents El Pueblito river, located in Corregidora, Queretaro, Mexico; the sampled sites are indicated with red squares. (a) location of: Mexico in America continent, Queretaro state in central Mexico and Corregidora city; (b) El Pueblito river study area. Note: Maps throughout this article were created using ArcGIS® software by Esri (Redlands, CA, USA).
Figure 1. Map of the study area. The blue line represents El Pueblito river, located in Corregidora, Queretaro, Mexico; the sampled sites are indicated with red squares. (a) location of: Mexico in America continent, Queretaro state in central Mexico and Corregidora city; (b) El Pueblito river study area. Note: Maps throughout this article were created using ArcGIS® software by Esri (Redlands, CA, USA).
Water 14 03040 g001
Water 14 03040 g002 550
Figure 2. WQI rates of El Pueblito river (blue line) indicating pollution levels in the sampled sites, according to Table 5 ranges.
Figure 2. WQI rates of El Pueblito river (blue line) indicating pollution levels in the sampled sites, according to Table 5 ranges.
Water 14 03040 g002
Table
Table 1. Coordinates of the sampled sites.
Table 1. Coordinates of the sampled sites.
PlaceCoordinatesAltitude, ft
NorthWest
1BAT20°30.845′100°25.881′6053
2MAN20°30.776′100°26.065′6047
3STA20°31.573′100°26.774′6023
4PIR20°32.951′100°27.300′6096
5RAS20°32.986′100°27.336′6096
6BAR20°30.827′100°25.952′6095
7PUE20°33582′100°28.006′6097
Note: Water samples were sampled from June 2019 to August 2019.
Table
Table 2. Water quality rating weights and normalization parameters.
Table 2. Water quality rating weights and normalization parameters.
ParameterWeight PiNormalization Factor Ci
1009080706050403020100
BOD5 ac30.523456810121518
COD ac3510203040506080100150210
Conductivity c17501000125015002000250030005000800012,00016,000
Phosphates ad10.161.63.26.49.616326496160240
Ph d177.588.599.51011121314
TDS a215025035040050090011001300160019002500
Total coliforms c45050010002000300040005000700010,00014,00019,000
Total solids a1100171249338441564712905117515261954
Note: Adapted from: b Cude, 2001; c Koçer and Sevgili, 2014 and d Pesce and Wunderlin, 2000 [11,18,41,42]. a Values in mg/L, conductivity in μS/cm, total coliforms MPN/100 mL.
Table
Table 3. Summary results of the obtained parameters of the water from El Pueblito river.
Table 3. Summary results of the obtained parameters of the water from El Pueblito river.
Parameter AverageMaximumMinimum
Temperature (°C)23.12 ± 1.4024.8021.10
pH7.82 ± 0.328.397.24
TDS (ng/L)786.27 ± 386.691570.00360.00
VSS (mg/L)279.54 ± 228.64885.005.00
TS (mg/L)722.35 ± 410.061575.005.00
Conductivity (S/cm)1.11 ± 0.582.230.12
CDO (mg/L)337.33 ± 395.53 *1289.00 *ND
BOD (mg/L)352.97 ± 397.75 *1248.00 *ND
Fecal coliforms (MPN/100 mL)1081.00 ± 1164.60 *2400.00 *0.3
Notes: Abbreviations: ND, not detectable. Quantification limits: COD 4 mg/L. * Values corresponding to polluted water according to Mexican regulations.
Table
Table 4. Spearman’s rank correlation coefficient for physicochemical parameters and bioassays in the El Pueblito River sample sites.
Table 4. Spearman’s rank correlation coefficient for physicochemical parameters and bioassays in the El Pueblito River sample sites.
Parameter ConductivityCODBODVSSSorghumLC50TDSPTTSTotal Coliforms
Conductivity1.00
COD0.691.00
BOD0.550.651.00
VSS 0.770.730.511.00
Sorghum−0.19−0.32−0.35−0.221.00
LC500.370.680.360.38−0.191.00
TDS0.980.800.620.71−0.310.501.00
PT0.810.830.710.66−0.210.510.871.00
TS0.360.300.230.620.180.040.360.371.00
Total coliforms0.740.670.690.82−0.210.460.760.810.191.00
Notes: p < 0.01.
Table
Table 5. Water quality rating according to National Sanitation Foundation Water Quality Index methods.
Table 5. Water quality rating according to National Sanitation Foundation Water Quality Index methods.
WQI Value RangeRating of Water Quality
91–100Excellent
71–90Good
51–70Medium
26–50Bad
0–25Very bad
Notes: values from Source: Tyagi et al., 2014 [26].
Table
Table 6. WQI for different sampled places.
Table 6. WQI for different sampled places.
Places/Week RASPIRPUESTAMANBATBAR
127.1236.5342.6578.9076.6579.3031.29
226.8029.9538.4789.6590.4590.9329.34
326.2932.6242.1289.4689.6273.3630.59
428.7835.2938.9061.1282.8689.6232.77
527.0131.5238.8068.0271.2967.8931.57
627.5830.5138.6378.8682.9073.0430.81
725.3928.6134.9583.6587.2081.8631.52
Average27.0032.1539.2278.5283.0079.4331.13
SD0.852.651.9211.427.409.441.14
Notes: Abbreviations: SD, standard deviation.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Rodriguez-Nuñez, E.; Hernandez-Mendoza, C.; Perez-Moreno, V.; Cardenas, A. Pollution Evaluation of the El Pueblito River in Queretaro, Mexico, Using the Water Quality Index. Water 2022, 14, 3040. https://doi.org/10.3390/w14193040

AMA Style

Rodriguez-Nuñez E, Hernandez-Mendoza C, Perez-Moreno V, Cardenas A. Pollution Evaluation of the El Pueblito River in Queretaro, Mexico, Using the Water Quality Index. Water. 2022; 14(19):3040. https://doi.org/10.3390/w14193040

Chicago/Turabian Style

Rodriguez-Nuñez, Enrique, Christian Hernandez-Mendoza, Victor Perez-Moreno, and Arely Cardenas. 2022. "Pollution Evaluation of the El Pueblito River in Queretaro, Mexico, Using the Water Quality Index" Water 14, no. 19: 3040. https://doi.org/10.3390/w14193040

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop