Emerging Water Quality Issues along Rio de la Sabana , Mexico

The basin of Rio de la Sabana is the largest tributary of the Tres Palos coastal lagoon in Southwest Mexico, east of Acapulco. This lagoon and its upstream basin areas have become a high priority area for the preservation of coastal and marine environments. To obtain information about water quality as affected by urban expansion since 2002, fourteen physicochemical parameters (temperature, pH, electrical conductivity, dissolved oxygen, ammonium, nitrate, nitrite, sulphate, phosphate), biochemical (biological and chemical oxygen demand, methylene blue active substances) and bacteriological parameters (total and fecal coliforms) were determined. This sampling was done for dry and rainy season conditions at seven locations (S1, S2, S3, ..., S7) along the river, spaced 3 to 6 km apart to a total of 30.4 km. The results were grouped into four zones: (Z1) reference, (Z2) transition, (Z3) polluted, (Z4) recovery. The Alborada (S5) and Tunzingo (S6) sites, adjacent to dense high-class residential areas (Z3), had the greatest pollution charges in both seasons, while the La Poza (S7) site near the Tres Palos lagoon (Z4) showed a decrease in pollution. All parameters correlated with increasing headto down-river sampling distance by following linear (pH, DO) or curvilinear patterns (all other parameters). Using sampling location and dry versus rainy sampling season as multivariate regression (predictor) variables led to least-squares capturing: 1) 66% to 95% of the T( ̊C), pH, DO, and 4 PO − variations, and 2) 57% to 96% of the log-linear variations of the other parameters. Among the parameters, How to cite this paper: Pineda-Mora, D., Toribio-Jiménez, J., Ma, T.L.-A., Juárez-López, A.L., González-González, J., RuvalcabaLedezma, J.C., Batista-García, R.A. and Arp, P.A. (2018) Emerging Water Quality Issues along Rio de la Sabana, Mexico. Journal of Water Resource and Protection, 10, 621-636. https://doi.org/10.4236/jwarp.2018.107035 Received: April 20, 2018 Accepted: July 1, 2018 Published: July 4, 2018 Copyright © 2018 by authors and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY 4.0). http://creativecommons.org/licenses/by/4.0/ Open Access DOI: 10.4236/jwarp.2018.107035 Jul. 4, 2018 621 Journal of Water Resource and Protection D. Pineda-Mora et al. T( ̊C), DO, and 4 PO − were not significantly affected by sampling season, while pH became so after deleting two higher than usual pH values at the S5 and S6 locations during the dry season.


Introduction
Accelerated urbanization processes are causing water-quality disruptions within rivers and streams across watersheds and regions.This is in part due to insufficient city planning towards environmental sustainability [1] which can lead to serious deteriorations in watershed-and river-based environmental and ecological services.Apart from noticing enhanced levels of water pollution and reduced biological viability in natural waterways, surficial water quality evaluations are needed to provide quantitative information for sustainable water-resource management [2] [3] [4].Since these evaluations need to address a host of sampled water quality indicators, it is necessary to determine how these indicators are patterned and vary across space and time in a predictive manner as advocated by, e.g., [5]- [10].The results so obtained can then be transformed into useful decision-supporting tools to restore and protect water quality locally to regionally [3] [9] [11].This article focuses on assessing water quality changes along a 30.4 km stretch of Rio de la Sabana as it flows east of Acapulco into the coastal Tres Palos lagoon (Figure 1).During the last two decades, the mid-low part of this sub-basin has experienced an accelerated population growth, which has led: 1) to serious water pollution and environmental degradation issues towards the river and to the coastal lagoons at Tres Palos and Puerto Marques [12] [13]; 2) to increased vulnerability to health risks across the new and flood-prone de la Sabana Valley settlements [13].
For these reasons, the Mexican government with support from the Spanish government initiated a potable water supply and wastewater sanitation project in 2012 to improve the quality of life, promote social equity and environmental sustainability across the de la Sabana Valley [12].As such, the Rio de la Sabana River-Tres Palos Lagoon area is now part of the hydrological priority region for preserving the biological importance of the coastal and marine Mitla-Chautengo Lagoon System [14].To understand and quantify how water quality varies along Riode la Sabana from its headwaters to the coastal lagoon, dry and rainy season water samples were taken from seven locations, all spaced 3 to 6.5 km apart.
These locations vary from low to densely populated areas.The samples were analyzed to inform about 14 physicochemical and biological parameters (temperature, pH, electrical conductivity, dissolved oxygen, biochemical and chemical oxygen demand, ammonium, nitrate, nitrite, sulphate, phosphate, methylene blue active substances, total coliforms, fecal coliforms).

Study area.
Rio de la Sabana begins north of Acapulco, with its headwater channels reaching up to about 2000 meters above sea level.Its main flow channel is approximately 57 kilometers long at its confluence with the Tres Palos coastal lagoon [12].The sub-basin area amounts to 466.3 km 2 with combined permanent flow channel lengths of 727 km, thus leading to a drainage density of 1.55 km −1 [15].The upper part of the sub-basin is comprised of steep sparsely populated terrain with many short flow channels fed by short-duration runoff.In contrast, the lower part widens into a broad floodplain, of which the eastern part is now densely populated [12].
Sampling strategy.
Water quality sampling was conducted in 2017 during the dry season (January) and rainy season (July) in 7 sites (Table 1) from the headwater to the mouth of Rio de la Sabana at the Tres Palos lagoon (Figure 1).These locations were selected based on Google Earths images and mapped inland-use, vegetation cover,  3).Within the impacted zone, there are numerous pollution sources due to solid waste dumpsites and industrial and household wastewater discharge [12] [13].
Parameter monitoring and analytical methods.
Fourteen physicochemical, biochemical and bacteriological water quality parameters (Table 2) were measured in water samples in each of the study sites, following established protocols [17].Temperature, pH and electrical conductivity were measured in situ.The other parameters were analyzed at a laboratory accredited by the Mexican Organization of Accreditation.Temperature, pH, electrical conductivity, ammonium, nitrates, nitrites, methylene blue active substances, sulfates and phosphates parameters were all measured in triplicate.
Dissolved oxygen, biochemical oxygen demand , chemical oxygen demand, and total and fecal coliforms were determined once using certified testing procedures.The results were organized in a data matrix (sites x parameters), where the rows refer to the sampling locations and the columns refer to the analyzed parameters (Table 2).These results were subsequently plotted by sampling location and sampling season.The order between the sample concentrations, locations, and season was examined by way of cluster analysis (CA).A correlation matrix was established to determine how the parameters relate to each other, to sampling location, and to season by way of principle component analysis.This was followed by nonlinear and linear least-squares multiple regression analyses to determine how each parameter varied quantitatively by sampling location and season.The non-linear analysis was based on the following equation: and the linear multiple regression analysis used the following equation:

Results and Discussion
The data for the 14 water quality parameters are listed in Table 3 and are plotted in Figure 2 by sampling location and season, with best-fitted Equation (1) results overlaid.As tabulated and as shown, most parameters except for pH and DO increased from S1 (representing the least populated area) towards the populated areas represented by S5 and S6, and dropped again at S7.In addition, most parameters increased from the rainy to the dry season due to lack of dilution, except 1) DO decreased slightly (due to higher flow rates and better aeration), but not significantly so likely due to low sampling density, and 2) water temperature remained more or less the same.The parameters that increased the most at S5  2)) for each of the 14 parameters listed in Table 3, by sampling location (S1, S2, … S7 along x-axis) and by pollution zone, colour-differentiated for each parameter from left to right by pollution level: nearly pristine (Z1), transitional (Z2), most polluted (Z3), and somewhat diluted (Z4).NO − , TC and FC.There were two notable outliers for pH during the dry sampling season at S4 (pH = 8.86) and S6 (pH = 9.4), somewhat parallel to high effluent concentrations pertaining to NH 3 , 3 NO − , MBAS, TC and TF.Also notable is the steep decline of DO at S5 and S6 for both the dry and rainy sampling season.This undoubtedly relates to elevated BOD and COD discharge at these locations, as also reported in [12] [13].
The best-fitted Equation ( 1) results for the parameters with non-linear S1 to S7 trends are listed in Table 4.The similarities among these trends are such that the c NL and d NL coefficients could be kept in common without significant R 2 and RMSE differences.In detail: The results in Table 4 are used to estimate maximum and minimum water pollution levels along the river transect for the dry and rainy season (Table 5).In turn, the ratios of these numbers can be interpreted as spatial and seasonal pollution indicators.For example, transitions from rainy to dry season led to a min/min and max/max fecal count multiplication factors of about 7600 at the headwater region and 11,500 at the maximum pollution locations.By rainy to dry season, the down-river fecal pollution count increased by a max/min  multiplication factor of 60 and 90, respectively.For the other parameters, down-river changes in pollution were most severe for NH 3 ,  NO − and NH 3 as the river flow rate drops from the rainy to the dry season.In this regard, it can be determined from Table 3 that: 1) the total N concentrations within the river water ( NO − , NH 3 combined) decreased on average from the dry to the rainy season by a factor of 3.5, likely due to dilution; 2) at S4, S5, S6, and S7, the combined 2 NO − and NH 3 concentrations amounted to 60% of total N during the dry season, and about 40% during the wet season(mostly NH 3 only); 3) the least amount of water-dissolved N in the form of 2 NO − and NH 3 occurred at S1, S2, and S3 during the dry season (<10%), and increased to about 20% during the wet season.
Cluster analysis.The CA-generated dendrograms are displayed by dry and rainy sampling season in Figure 3 also presenting the normalized parameter values in Table 2.These dendrograms show that locations S1, S2, S3 and S4 are grouped within Cluster A, thereby representing the upper part of the Rio de la Sabana watershed where industrial to residential discharge rates into the river are still low.Within this cluster, pollution levels increased in the order  -0.9 -0.7 -0.5 1.0 -1.0 -0.9 -0.8 -0.4 0.9 -0.9 -0.9 -0.During the dry season, the increases from S1 to S2 and S3 remained similar to each other, but the increases for S4 (located in an area with a greater presence of rural towns) were more similar to the elevated results for the Cluster B locations.Within Cluster B, the dendrogram positions switched from S7 < S5 < S6 during the dry to S7 < S6 < S5 during the rainy season.This suggests that overall water quality: 1) recovered somewhat for both seasons after passing through the S5 and S6 locations, presumably due to the influx of less contaminated floodplain water from the eastern less inhabited part of the Rio de la Sabana watershed; 2) was worst at S5 during the rainy season where pollutant inputs are likely highest due industrial and residential surface run-off; 3) was worst at S6 during the dry season mainly due to accumulating up-river sewage discharge during low river flow rates.
Based on location similarities, Clusters A and B were divided into four zones: 1) The Reference zone (S1, S2): located at the sub-basin's upper part, meeting national and international standards established for aquatic life protection [18]- [22].
2) The Transition zone (S3, S4): a peri-urban zone located between the sub-basin's upper and mid-low parts had all parameters except pH and DO rising from S3 to S4 above their values at S1 and S2.
3) The Pollution zone (S5, S6): all parameters except pH and DO were well above their average values and above national and international standards for aquatic life protection [18]- [22].
4) The Recovery zone (S7).Correlation matrix.All parameters other than DO and pH were highly positively correlated to one another during the rainy and the dry season, as shown in Table 6.Excluding the high pH values at S4 (pH = 8.86) and S7 (pH = (9.40)yielded a positive correlation between pH and DO for both season, with both remaining negatively related to all the other parameters.The generally gradual pH decline from S1 to S7 may be due to a greater presence of dissolved organic acids in response to a transition from upper-reach groundwater seepage to lower-reach surface run-off.The concurrent decrease in DO would be due to a stimulated chemical and biological oxygen demand due to enhanced oxygen-consuming fecal-matter containing effluents arriving at the S4 -S7 sampling locations [7] [12] [23] [24].This would also include the discharge of surfactants (represented by MBAS) and detergents [17] (NMX-AA-039-SCFI-2001).Consequently, effluent-associated parameters such as EC, BOD 5, COD, NH 3 , PO − were all highly correlated to one another for both seasons [13] [25].Their corresponding dry-to-rainy season reductions as plotted in Figure 2, are undoubtedly due to increased water flow dilution.
Analyzing the parameter correlation matrix with sampling location and season as two additional variables produced the Factor 2 versus Factor 1 plot in Figure 4 by way of principle component analysis (PCA).Here, Factor 1 refers to the S1, S2, S3, S4, S7, S5, S6 sampling sequence, while Factor 2 refers to the rainy (coded 0) versus the dry (coded 1) season.In Figure 4, the parameters entering   PO − and T(˚C) parameters are shown to be closely and positively related to sampling location but not to sampling season.
Multiple regression analysis.The best-fitted and most significant intercepts (a L ) and regression coefficients (b L , c L ) for Equation ( 2) are listed in Table 7 for all 14 parameters (p < 0.1).Also entered are the corresponding R 2 and RMSE values, to indicate the extent of variation capture and associated regression error for each parameter.As shown, all parameters were significantly related to sampling location.Only three parameters, namely T(˚C), PO − and DO, were not significantly affected by season.With DO, the increase from the dry to the rainy season was not significant at p < 0.1, even after excluding the low DO results at S4 and S5 (dry season) and S5 (rainy season).With pH, season sampling became significant after excluding the high dry-season pH values at the S4 and S6 (pH = 8.86 and 9.4) from the analysis.
Altogether, the results in Table 7 confirm that the location-specific and season-specific effects on water pollution are highly significant and can now in part D. Pineda-Mora et al.
be interpolated for any location along Rio de la Sabana.This can be done by way of the best-fitted Equation (1) and Equation (2) regression results, and by ranking specific locations using existing conditions at S1, S2, …, S7 as a guide.To this effect, locations along the Tres Palos lagoon may eventually experience pollution levels similar to the S5 and S6 locations.However, improved pollution mitigation may lower the pollutant levels at S4 to S7 locations through, e.g., biological denitrification, sulphate and phosphate removal by way of chemical and biological means, and effluent sterilization.
While DO is negatively related to all the other water quality parameters as well as location, its overall variations are best captured by way of the following multivariate regression equation, and as plotted in

Figure 1 .
Figure 1.Locator map centered on the Rio La Sabana-Tres Palos Lagoon sub-basin north to east within the Acapulco Municipality in southwest Mexico also showing the seven sampling locations along the river, with a development index from low to high, inversely related to the normalized difference vegetation index (NDVI).
with y representing any of the 14 parameters, x representing the samplinglocations S1, S2, S3, …., S7, a L and a NL refer to the intercepts, and b L , c L , b NL , c NL , d NL are regression coefficients.The resulting best-fitted extent of the parameter variations, indicated by the coefficient of variation (R 2 ) and the root mean square of the residuals, was improved for some of the parameters through log transformation.Equation (1) was applied to generate the best-fitted lines for each parameter by season, with sampling locations coded 1, 2, 3, 4, 5, 6, 7 according to their original order.Equation (2) was used to test the statistical significance of each parameter by location and season.
-Mora et al. and S6 from the rainy to the dry season were EC, BOD 5 , COD, 3 NO − , 2 1) a NL refers to the headwater values for each water parameter by season; 2) b NL quantifies the pollution extent for each water parameter by season; 3) c NL = 5.58 indicates that the maximum levels are associated with the S5 and S6 locations; 4) d NL = 2.60 quantifies the spatial pollution extent across the S1 to S7sampling locations.

3 NO − and 3 4 PO 2 NO 2 NO
with max/min pollution effects stronger for − during the rainy season, and stronger for MBAS, NH 3 and − and NH 3 differences were likely related to the increasing extent to which added 3 NO − is converted to

Figure 3 .
Figure 3. Zoning the physicochemical and bacteriological sampling results of Rio de la Sabana by way of hierarchical cluster dendrograms.Also shown: normalized parameter scores overlaid on value-coded background (from blue to yellow to dark red) by sampling season Top: dry season; bottom: rainy season.Note the seasonal reversal of the S5 and S6 locations.

Factor 1 (
S1 < S2 < S3 < S4 < S7 < S5 < S6) D. Pineda-Mora et al.towards the right are most positively related to sampling location while the parameters entering towards the top are most positively related to season (Factor 2).Both DO and pH were negatively influenced by sampling location (Factor 1), with pH positively related to the transition from the rainy to the dry season, while DO was not much influenced by this transition.The

Figure 5 .
Figure 5. Plotting actual versus best-fitted S1 to S7 DO values generated with Equation (3).Differences by season are marked by dot outline: none for rainy season; black for dry season.

Table 1 .
Location of study sites for water-quality characterization.

Table 2 .
Physicochemical and bacteriological parameters, units and methods used.

Table 3 .
Sampling results, by sampling location and season.
Figure 2. Scatter plots and best-fitted lines (Equation (

Table 4 .
Best-fitted non-linear regression analysis results (Equation (1)); pH and DO not included.

Table 5 .
Determining the minimum and maximum pollution levels and the corresponding ratios for the 12 parameters listed in Table4, by location and by season (pH and DO not included).
a min: a NL for T(˚C) and 10 aNL for all other parameters.b max: a NL + b NL for T(˚C) and 10 aNL+bNL for all other parameters.

Table 6 .
Correlation matrix for the 14 parameters in Table 2. T(°C) pH log 10 EC DO log 10 BOD 5 log 10 COD log 10 NH 3

Table 7 .
Best-fitted Equation (2) regression results for each parameter listed in Table3, with sampling location, season and Zone 4 (S7) as independent regression variables.