Developing a water quality index in a tropical reservoir using a measure of multiparameters

Rangel‐Peraza

Sanhouse-García

et al. 2021

IJERPH

Agricultural activities are highly related to the reduction of the availability of water resources due to the consumption of freshwater for crop irrigation, the use of fertilizers and pesticides. In this study, the water quality of the Adolfo López Mateos (ALM) reservoir was evaluated. This is one of the most important reservoirs in Mexico since the water stored is used mainly for crop irrigation in the most productive agricultural region. A comprehensive evaluation of water quality was carried out by analyzing the behavior of 23 parameters at four sampling points in the period of 2012-2019. The analysis of the spatial behavior of the water quality parameters was studied by spatial distribution graphs using the Inverse Distance Weighting interpolation. Pearson correlation was performed to better describe the behavior of all water quality parameters. This analysis revealed that many of these parameters were significantly correlated. The Principal Components Analysis (PCA) was carried out and showed the importance of water quality parameters. Ten principal components were obtained, which explained almost 90% of the total variation of the data. Additionally, the comprehensive pollution index showed a slight water quality variation in the ALM reservoir. This study also demonstrated that the main source of contamination in this reservoir occurs near sampling point one. Finally, the results obtained indicated that a contamination risk in the waterbody and further severe ecosystem degradations may occur if appropriate management is not taken.

Section: Resultsmentioning

confidence: 99%

“…A previous study in the Adolfo Lopez Mateos (ALM) reservoir is reported by Quevedo-Castro et al [ 30 ] who implemented a water quality index (WQI). Using multiparametric statistical tools, they analyzed 26 water quality parameters obtained from the year 2012-2017.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Water Quality in A Tropical Reservoir in Mexico: Seasonal, Spatial and Multivariable Analysis

Rangel‐Peraza

Sanhouse-García

et al. 2021

IJERPH

“…The predominant vegetation is tropical deciduous forest, with small areas of pine-oak and pine forests toward the northwestern part [26]. Since high productivity is observed in the basin, a large proportion of the land is intended for agricultural and livestock activities [31].…”

Section: Methodsmentioning

confidence: 99%

“…The ALM reservoir (Culiacan, Mexico) is in the Humaya River basin (25°05′25″, 25°20′15″ North, 107°33′00″, 107°15′00″ West) at 186.5 m above the sea level (m.a.s.l.). The ALM dam is 105.5 m high, and 765 m long, considered one of the main sources of water for agricultural irrigation, power generation, fishing, and tourist activities [26][27][28], covering 11,354 ha and ranked tenth in Mexico according to its storage capacity [29] (Figure 1). The Humaya River basin is characterized by a warm humid climate toward the center and south, with summer rains.…”

Section: Study Areamentioning

confidence: 99%

Surface Water Quality Assessment through Remote Sensing Based on the Box–Cox Transformation and Linear Regression

Rangel‐Peraza

Monjardín-Armenta

et al. 2023

Water

A methodology to estimate surface water quality using remote sensing is presented based on Landsat satellite imagery and in situ measurements taken every six months at four separate sampling locations in a tropical reservoir from 2015 to 2019. The remote sensing methodology uses the Box–Cox transformation model to normalize data on three water quality parameters: total organic carbon (TOC), total dissolved solids (TDS), and chlorophyll a (Chl-a). After the Box–Cox transformation, a mathematical model was generated for every parameter using multiple linear regression to correlate normalized data and spectral reflectance from Landsat 8 imagery. Then, significant testing was conducted to discard spectral bands that did not show a statistically significant response (α = 0.05) from the different water quality models. The r2 values achieved for TOC, TDS, and Chl-a water quality models after the band discrimination process were found 0.926, 0.875, and 0.810, respectively, achieving a fair fitting to real water quality data measurements. Finally, a comparison between estimated and measured water quality values not previously used for model development was carried out to validate these models. In this validation process, a good fit of 98% and 93% was obtained for TDS and TOC, respectively, whereas an acceptable fit of 81% was obtained for Chl-a. This study proposes an interesting alternative for ordered and standardized steps applied to generate mathematical models for the estimation of TOC, TDS, and Chl-a based on water quality parameters measured in the field and using satellite images.

“…This Pearson correlation analysis was used to separately describe the impact of each water quality parameter and to study the relationship (or correlation) between the quantitative variables (Quevedo-Castro et al 2018; 2019). The purpose of the principal component analysis was to obtain a reduced number of linear combinations of the water quality parameters which easily explain the variability of the data(Liu et al 2021;Quevedo-Castro et al 2018;Wisitthammasri et al 2020…”

mentioning

confidence: 99%

Geostatistical analysis of the water quality of an aquifer in tropical and agricultural zone

Bustos-Terrones

García

et al. 2022

Preprint

Self Cite

The objective of this study was to identify water pollution problems and obtain useful information for the sustainable extraction of water from the Culiacan River aquifer. Data from 6 sampling sites were used in the period from 2012 to 2019. A spatial geostatistical analysis was carried out to describe the water quality of the aquifer by using isoconcentration contours. Likewise, a hydrogeochemical evaluation was carried out using seven water quality indices for irrigation purposes (SAR, CSR, SSP, KR, % Na, MH, PI). Finally, a multivariate statistical analysis was obtained through the analysis of principal components, Pearson correlation and cluster analysis identified the most important water quality variables in the Culiacan River aquifer based on their variability. The water quality index showed that the aquifer water quality was good in five sampling sites and excellent in one sampling site. Based on average concentrations, the ion content was Na2+ >HCO3- >Ca2+ >Mg2+ >Cl- >SO42- >K+, which did not present any ecological or public health risk. Finally, Sample Point 1 (SP1) was the location most affected by anthropogenic contamination. This site is within an urban area and is the shallowest sampling well.