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 head- to 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 PO 3-4 variations, and 2) 57% to 96% of the log-linear variations of the other parameters. Among the parameters, T(°C), DO, and PO 3-4 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.
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) to serious water pollution and environmental degradation issues towards the river and to the coastal lagoons at Tres Palos and Puerto Marques [
2) to increased vulnerability to health risks across the new and flood-prone de la Sabana Valley settlements [
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 [
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 [
Sampling strategy.
Water quality sampling was conducted in 2017 during the dry season (January) and rainy season (July) in 7 sites (
Reach | Sampling location | Location name | Latitude | Longitude | Elevation m |
---|---|---|---|---|---|
Upper | S1 | km 39 | 17°2'47.28" N | 99°46'57.24" W | 400 |
S2 | km 34 | 17°1'27.84" N | 99° 47'38. 41" W | 300 | |
S3 | Puente Texca | 16°58'55.62" N | 99°49'6.51" W | 100 | |
Middle | S4 | Colonia La Venta | 16°55'25.26" N | 99°48'27.28" W | 40 |
S5 | Puente Alborada | 16°52'48.13" N | 99°49'6.00"W | 20 | |
S6 | Puente Tunzingo | 16°51'1.09" N | 99°47'37.38" W | 20 | |
Lower | S7 | Colonia La Poza | 16°47'33.09"N | 99°46'56.16"W | 3 |
hydrology and population density [
Parameter monitoring and analytical methods.
Fourteen physicochemical, biochemical and bacteriological water quality parameters (
y = a N L + b N L exp [ − ( ( x − c N L ) / d N L ) 2 ] (1)
Parameter | Unit | Analytical Method | ||
---|---|---|---|---|
1 | Temperature | T | ˚C | Mercury thermometer |
2 | pH | pH | No unit | Potentiometric |
3 | Electrical Conductivity | EC | µs/cm | Potentiometric |
4 | Dissolved Oxygen | DO | mg/L | Sodium Azide (Winkler) |
5 | Biochemical Oxygen Demand | BOD5 | mg/L | 5-day Oxygen Consumption |
6 | Chemical Oxygen Demand | COD | mg/L | Potassium Dichromate Organic Matter Oxidation |
7 | Ammonium Nitrogen | NH3 | mg/L | Kjeldahl Distillation |
8 | Nitrates | NO 3 − | mg/L | Brucine Sulfate (UV spectrometric) |
9 | Nitrites | NO 2 − | mg/L | Sulphanilamide |
11 | Sulfates | SO 4 2 − | mg/L | Barium Chloride (UV spectrometric) |
12 | Phosphates | PO 4 3 − | mg/L | Vanadomolymphophosphoric Acid |
10 | Methylene Blue Active Substances | MBAS | mg/L | Methylene Blue |
13 | Total Coliforms | TC | NMP/mL | Multiple-Tube Fermentation Technique |
14 | Fecal Coliforms | FC | NMP/mL | Multiple-Tube Fermentation Technique |
and the linear multiple regression analysis used the following equation:
y = a L + b L x + c L season (2)
with y representing any of the 14 parameters, x representing the samplinglocations S1, S2, S3, …., S7, aL and aNL refer to the intercepts, and bL, cL, bNL, cNL, dNL are regression coefficients. The resulting best-fitted extent of the parameter variations, indicated by the coefficient of variation (R2) 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.
The data for the 14 water quality parameters are listed in
Season | Location | Description | T (°C) | pH | EC | BOD5 | COD | DO | NH3 | NO 3 − | NO 2 − | SO 4 2 − | PO 4 3 − | MBAS | TC | FC |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Dry | S1 | km 39 | 22.6 | 8.44 | 160.5 | 39.2 | 60.2 | 5.42 | 0.2 | 9.2 | 0.08 | 50.2 | 0.02 | 0.15 | 9300 | 9300 |
S2 | km 34 | 26.9 | 8.23 | 190.6 | 39.3 | 65.5 | 5.84 | 0.2 | 20.1 | 0.14 | 49.8 | 0.04 | 0.14 | 9300 | 9300 | |
S3 | Pte. Texca | 27.7 | 8.26 | 329.0 | 47.1 | 76.0 | 5.35 | 0.6 | 28.6 | 0.21 | 75.2 | 0.08 | 0.32 | 150,000 | 150,000 | |
S4 | Col. La venta | 34.9 | 8.86 | 498.0 | 60.3 | 100.5 | 5.05 | 9.5 | 30.0 | 4.12 | 95.2 | 0.11 | 10.1 | 210,000 | 210,000 | |
S5 | Pte. Alborada | 32.6 | 7.96 | 1020.0 | 156.4 | 284.3 | 0.89 | 10.3 | 32.0 | 17.3 | 195.6 | 0.15 | 22.4 | 210,000 | 210,000 | |
S6 | Pte. Tunzingo | 35.3 | 9.40 | 1022.0 | 165.4 | 295.4 | 1.80 | 11.1 | 36.1 | 11.1 | 215.4 | 0.16 | 23.8 | 930,000 | 930,000 | |
S7 | Col. La poza | 33.4 | 7.67 | 887.0 | 137.3 | 280.2 | 2.05 | 9.4 | 28.6 | 6.94 | 208.3 | 0.14 | 18.6 | 150,000 | 150,000 | |
Rainy | S1 | km 39 | 24.0 | 7.51 | 73.7 | 5.3 | 11.2 | 6.23 | 0.26 | 4.27 | 0.01 | 15.28 | 0.01 | 0.10 | 1 | 1 |
S2 | km 34 | 26.9 | 7.69 | 94.4 | 7.5 | 15.3 | 6.15 | 0.30 | 4.54 | 0.01 | 14.52 | 0.01 | 0.10 | 2.7 | 2.0 | |
S3 | Pte. Texca | 28.2 | 7.49 | 123.8 | 10.5 | 20.4 | 5.87 | 0.31 | 6.63 | 0.02 | 21.04 | 0.02 | 0.15 | 26.7 | 15.0 | |
S4 | Col. La venta | 32.8 | 7.29 | 201.0 | 13.7 | 31.7 | 5.27 | 0.57 | 7.89 | 0.16 | 22.42 | 0.14 | 0.65 | 13.7 | 6.0 | |
S5 | Pte. Alborada | 32.6 | 7.23 | 569.1 | 36.8 | 92.7 | 1.86 | 5.13 | 25.24 | 0.27 | 90.00 | 0.19 | 9.25 | 79.0 | 68.7 | |
S6 | Pte. Tunzingo | 32.9 | 7.16 | 451.1 | 31.7 | 73.4 | 2.65 | 2.95 | 17.58 | 0.29 | 37.83 | 0.16 | 7.96 | 70.7 | 56.7 | |
S7 | Col. La poza | 33.1 | 7.24 | 473.9 | 33.5 | 79.7 | 2.38 | 3.22 | 19.01 | 0.15 | 78.27 | 0.15 | 7.90 | 24.7 | 14.0 |
and S6 from the rainy to the dry season were EC, BOD5, COD, NO 3 − , NO 2 − , 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 NH3, NO 3 − , 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 [
The best-fitted Equation (1) results for the parameters with non-linear S1 to S7 trends are listed in
1) aNL refers to the headwater values for each water parameter by season;
2) bNL quantifies the pollution extent for each water parameter by season;
3) cNL = 5.58 indicates that the maximum levels are associated with the S5 and S6 locations;
4) dNL = 2.60 quantifies the spatial pollution extent across the S1 to S7sampling locations.
The results in
Variable | Dry season | Rainy season | R2 | RMSE | ||||||
---|---|---|---|---|---|---|---|---|---|---|
aNL | bNL | aNL | bNL | |||||||
coeff. | st. error | coeff. | st. error | coeff. | st. error | coeff. | st. error | |||
T(°C) | 23.20 | 1.40 | 13.40 | 2.00 | 24.40 | 1.30 | 9.70 | 1.90 | 0.885 | 2.81 |
log10EC | 2.18 | 0.05 | 0.88 | 0.07 | 1.82 | 0.04 | 0.93 | 0.07 | 0.979 | 0.004 |
log10BOD5 | 1.49 | 0.06 | 0.72 | 0.10 | 0.71 | 0.06 | 0.86 | 0.07 | 0.971 | 0.0083 |
log10COD | 1.68 | 0.07 | 0.80 | 0.11 | 1.01 | 0.07 | 0.85 | 0.11 | 0.847 | 0.01 |
log10NH3 | −0.93 | 0.17 | 2.20 | 0.25 | −0.82 | 0.17 | 1.38 | 0.25 | 0.915 | 0.057 |
log10 NO 3 − | 1.14 | 0.09 | 0.49 | 0.15 | 0.45 | 0.12 | 0.85 | 0.16 | 0.902 | 0.015 |
log10 NO 2 − | −1.23 | 0.02 | 2.55 | 0.02 | −2.13 | 0.17 | 1.71 | 0.21 | 0.980 | 0.027 |
log10MBAS | −0.95 | 0.26 | 2.60 | 0.31 | −1.23 | 0.24 | 2.29 | 0.30 | 0.950 | 0.079 |
log10 SO 4 2 − | 1.66 | 0.10 | 0.71 | 0.15 | 1.12 | 0.10 | 0.70 | 0.15 | 0.902 | 0.21 |
log10 PO 4 3 − | −1.61 | 0.11 | 0.89 | 0.16 | −2.10 | 0.12 | 1.58 | 0.17 | 0.937 | 0.0207 |
log10FC | 4.02 | 0.25 | 1.74 | 0.37 | 0.10 | 0.25 | 1.60 | 0.37 | 0.980 | 0.124 |
log10TC | 4.02 | 0.12 | 1.74 | 0.37 | 0.24 | 0.12 | 1.67 | 0.37 | 0.982 | 0.0987 |
cNL = 5.58 ± 0.23; dNL = 2.60 ± 0.49.
Parameter | Dry Season | Rainy Season | Dry/Rainy Season | Location max/min | ||||
---|---|---|---|---|---|---|---|---|
mina | maxb | mina | maxb | min/min | max/max | dry | rainy | |
T(°C) | 23.2 | 36.6 | 24.4 | 34.1 | 0.95 | 1.1 | 1.6 | 1.4 |
EC | 151.4 | 1148.2 | 66.1 | 562.3 | 2.3 | 2.0 | 7.6 | 8.5 |
BOD5 | 30.9 | 162.2 | 5.1 | 37.2 | 6.0 | 4.4 | 5.2 | 7.2 |
COD | 47.9 | 302.0 | 10.2 | 72.4 | 4.7 | 4.2 | 6.3 | 7.1 |
NH3 | 0.12 | 18.62 | 0.15 | 3.63 | 0.78 | 5.1 | 158 | 24.0 |
NO 3 − | 13.8 | 42.7 | 2.8 | 20.0 | 4.9 | 2.1 | 3.1 | 7.1 |
NO 2 − | 0.059 | 20.9 | 0.007 | 0.380 | 7.9 | 55.0 | 355 | 51.3 |
SO 4 2 − | 45.7 | 234.4 | 13.2 | 66.1 | 3.5 | 3.5 | 5.1 | 5.0 |
PO 4 3 − | 0.025 | 0.191 | 0.008 | 0.302 | 3.1 | 0.6 | 7.8 | 38.0 |
MBAS | 0.11 | 44.7 | 0.06 | 11.5 | 1.9 | 3.9 | 398 | 195 |
FC | 6,456 | 588,843 | 0.9 | 51.3 | 7586 | 11482 | 91.2 | 60.3 |
TC | 6,456 | 354,813 | 1.7 | 81.3 | 3715 | 4365 | 55.0 | 46.8 |
amin: aNL for T(˚C) and 10aNL for all other parameters. bmax: aNL + bNL for T(˚C) and 10aNL+bNL for all other parameters.
multiplication factor of 60 and 90, respectively. For the other parameters, down-river changes in pollution were most severe for NH3, NO 3 − , NO 2 − , PO 4 3 − and MBAS, with max/min pollution effects stronger for NO 3 − and PO 4 3 − during the rainy season, and stronger for MBAS, NH3 and NO 2 − during the dry season.
The seasonal NO 3 − to NO 2 − and NH3 differences were likely related to the increasing extent to which added NO 3 − is converted to NO 2 − and NH3 as the river flow rate drops from the rainy to the dry season. In this regard, it can be determined from
1) the total N concentrations within the river water ( NO 3 − , NO 2 − , NH3 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 NO 2 − and NH3 concentrations amounted to 60% of total N during the dry season, and about 40% during the wet season(mostly NH3 only);
3) the least amount of water-dissolved N in the form of NO 2 − and NH3 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
S1 < S2 < S3
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 [
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 [
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
Analyzing the parameter correlation matrix with sampling location and season as two additional variables produced the Factor 2 versus Factor 1 plot in
T(°C) | pH | log10EC | DO | log10BOD5 | log10COD | log10NH3 | NO 3 − | log10 NO 2 − | log10 SO 4 2 − | PO 4 3 − | log10MBAS | log10TC | log10FC | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
T(°C) | 1.000 | 0.270 | 0.891 | −0.659 | 0.783 | 0.783 | 0.959 | 0.900 | 0.922 | 0.833 | 0.918 | 0.927 | 0.866 | 0.866 |
pH | −0.856 | 1.000 | 0.058 | 0.088 | 0.035 | −0.018 | 0.150 | 0.237 | 0.103 | 0.015 | 0.122 | 0.133 | 0.371 | 0.371 |
log10EC | 0.906 | −0.891 | 1.000 | −0.910 | 0.962 | 0.959 | 0.954 | 0.869 | 0.970 | 0.986 | 0.996 | 0.963 | 0.884 | 0.884 |
DO | −0.800 | 0.844 | −0.978 | 1.000 | −0.977 | −0.973 | −0.785 | −0.641 | −0.877 | −0.944 | −0.876 | −0.851 | −0.671 | −0.671 |
log10BOD5 | 0.910 | −0.872 | 0.996 | −0.968 | 1.000 | 0.997 | 0.867 | 0.744 | 0.929 | 0.986 | 0.943 | 0.917 | 0.776 | 0.776 |
log10COD | 0.909 | −0.884 | 0.999 | −0.975 | 0.998 | 1.000 | 0.863 | 0.737 | 0.924 | 0.986 | 0.938 | 0.913 | 0.754 | 0.754 |
log10NH3 | 0.816 | −0.848 | 0.981 | −0.998 | 0.970 | 0.978 | 1.000 | 0.850 | 0.977 | 0.913 | 0.958 | 0.988 | 0.876 | 0.876 |
NO 3 − | 0.760 | −0.801 | 0.958 | −0.985 | 0.947 | 0.954 | 0.983 | 1.000 | 0.819 | 0.799 | 0.908 | 0.796 | 0.930 | 0.930 |
log10 NO 2 − | 0.952 | −0.954 | 0.939 | −0.867 | 0.924 | 0.934 | 0.883 | 0.837 | 1.000 | 0.942 | 0.965 | 0.991 | 0.823 | 0.823 |
log10 SO 4 2 − | 0.765 | −0.780 | 0.940 | −0.959 | 0.931 | 0.938 | 0.951 | 0.969 | 0.802 | 1.000 | 0.972 | 0.941 | 0.837 | 0.837 |
PO 4 3 − | 0.928 | −0.928 | 0.949 | −0.897 | 0.925 | 0.942 | 0.915 | 0.879 | 0.986 | 0.850 | 1.000 | 0.957 | 0.915 | 0.915 |
log10MBAS | 0.869 | −0.909 | 0.990 | −0.985 | 0.979 | 0.987 | 0.988 | 0.952 | 0.930 | 0.927 | 0.944 | 1.000 | 0.836 | 0.836 |
log10TC | 0.848 | −0.779 | 0.863 | −0.789 | 0.894 | 0.873 | 0.788 | 0.797 | 0.837 | 0.760 | 0.784 | 0.806 | 1.000 | 1.000 |
log10FC | 0.790 | −0.775 | 0.873 | −0.831 | 0.899 | 0.881 | 0.828 | 0.847 | 0.819 | 0.784 | 0.779 | 0.829 | 0.984 | 1.000 |
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 PO 4 3 − 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 (aL) and regression coefficients (bL, cL) for Equation (2) are listed in
Altogether, the results in
Parameter | Intercept: aL | Regression coefficients | R2 | RMSE | ||||
---|---|---|---|---|---|---|---|---|
Location: bLa | Season: cLb | |||||||
Est. | St. Error | Est. | St. Error | Est. | St. Error | |||
T(˚C) | 23.4 | 1.6 | 1.7 | 0.3 | NS | 0.865 | 1.73 | |
pH | 7.6 | 0.3 | −0.62 | 0.06 | NS | 0.663 | 0.43 | |
pHc | 7.7 | 0.1 | −0.87 | 0.02 | −0.87 | 0.08 | 0.923 | 0.14 |
log10EC | 1.70 | 0.06 | 0.16 | 0.01 | 0.34 | 0.05 | 0.648 | 0.09 |
DO | 7.8 | 0.5 | −0.87 | 0.11 | NS | 0.861 | 0.80 | |
log10BOD5 | 0.63 | 0.07 | 0.14 | 0.01 | 0.69 | 0.06 | 0.570 | 0.11 |
log10COD | 0.92 | 0.07 | 0.15 | 0.02 | 0.58 | 0.06 | 0.948 | 0.11 |
log10NH3 | −1.2 | 0.2 | 0.30 | 0.04 | 0.36 | 0.18 | 0.824 | 0.33 |
log10 NO 3 − | 0.6 | 0.1 | 1.10 | 0.02 | 0.40 | 0.07 | 0.859 | 0.13 |
log10 NO 2 − | −2.6 | 0.2 | 0.36 | 0.05 | 1.4 | 0.2 | 0.901 | 0.37 |
log10 SO 4 2 − | 0.96 | 0.07 | 0.13 | 0.01 | 0.54 | 0.06 | 0.946 | 0.10 |
log10 PO 4 3 − | −2.1 | 0.2 | 0.20 | 0.03 | NS | 0.783 | 0.24 | |
log10MBAS | −1.8 | 0.4 | 0.43 | 0.05 | 0.44 | 0.21 | 0.862 | 0.40 |
log10TC | 0.1 | 0.3 | 0.27 | 0.06 | 3.8 | 0.2 | 0.962 | 0.00 |
log10FC | 0.1 | 0.3 | 0.27 | 0.06 | 4.0 | 0.2 | 0.965 | 0.44 |
aLocation: S1, S2, S3, S4, S7, S5, S6 coded 1, 2, 3, 4, 5, 6, 7 (note the change in order for S5, S6 and S7). bSeasons coded 0 (rainy), 1 (dry); NS: not siginificant. cpH outliers excluded.
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
DO = (0.84 ± 0.03) pH - (0.0059 ± 0.0005) EC; R2 = 0.923; RMSE = 0.59. (3)
In principle, this equation reflects the association of higher DO and pH and lower EC values in the upper river reach compared to the lower river reach. The lack of an inverse relationship between DO, BOD5 and COD is likely due to other DO influencing factors such as the diurnal oxygen release from photosynthesizing plants and algae within the river [
During the last decades, increased developments on the east and west sides of the Rio de la Sabana floodplain have led to extensive water pollution, with higher pollution levels registered for the dry and rainy seasons. This study marks the S5 and S6 locations as currently the most polluted locations. Upriver, the S4 location may also become increasingly vulnerable to pollution. Downriver, there was a slight reduction in water pollution, likely due to dilution caused by water seepage and run-off from the yet fairly undeveloped low-lying areas on the east and northern side of the river basin.
The analysis of this sampling effort revealed that all 14 water quality parameters were significantly related to sampling locations where current settlement densities and consequently effluent discharge rates would be highest. By induction, the above approach could prove useful: 1) in application at other settlement-affected locations, especially those along stream and effluent discharges towards the coastal Tres Palos and Puerto Marques lagoons, and 2) in encouraging initiatives towards intensifying wastewater treatment along Rio de la Sabana developments and elsewhere.
The work was supported by the Consejo Nacional de Cienciay Tecnología (CONACYT), (National Scholarships for Quality Postgraduate Programs), with field sampling and laboratory analyses done at Ingeniería en los Sistemas de Tratamientos de Aguas S.A. de C.V., and Protección Civil del Municipio de Acapulco.
The authors have declared that no conflict of interests exists.
Pineda-Mora, D., Toribio-Jiménez, J., Ma, T.L.-A., Juárez-López, A.L., González-González, J., Ruvalcaba-Ledezma, 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