Abstract
Groundwater quality and availability are essential for human consumption and social and economic activities in arid and semiarid regions. Many developing countries use wastewater for irrigation, which has in most cases led to groundwater pollution. The Mezquital Valley, a semiarid region in central Mexico, is the largest agricultural irrigation region in the world, and it has relied on wastewater from Mexico City for over 100 years. Limited research has been conducted on the impact of irrigation practices on groundwater quality on the Mezquital Valley. In this study, 31 drinking water wells were sampled. Groundwater quality was determined using the water quality index (WQI) for drinking purposes. The hydrogeochemical process and the spatial variability of groundwater quality were analyzed using principal component analysis (PCA) and K-means clustering multivariate geostatistical tools. This study highlights the value of combining various approaches, such as multivariate geostatistical methods and WQI, for the identification of hydrogeochemical processes in the evolution of groundwater in a wastewater irrigated region. The PCA results revealed that salinization and pollution (wastewater irrigation and fertilizers) followed by geogenic sources (dissolution of carbonates) have a significant effect on groundwater quality. Groundwater quality evolution was grouped into cluster 1 and cluster 2, which were classified as unsuitable (low quality) and suitable (acceptable quality) for drinking purposes, respectively. Cluster 1 is located in wastewater irrigated zones, urban areas, and the surroundings of the Tula River. Cluster 2 locations are found in recharge zones, rural settlements, and seasonal agricultural fields. The results of this study strongly suggest that water management strategies that include a groundwater monitoring plan, as well as research-based wastewater irrigation regulations, in the Mezquital Valley are warranted.
Highlights
Water scarcity is a growing challenge in the arid and semiarid regions of the world [1,2,3,4]
A total of 31 groundwater samples were collected from wells at urban settlements within the study area during the rainy season, in a sampling period from 15 July to 20 July 2014 (Figure 1)
According to [79], the principal component (PC) is expressed by the following equation: Zij = ai1 x1 j + ai2 x2 j + ai3 x3 j + . . . + air xrj where Z is known as the component score, a is the component loading, x is the estimated value of the variable, i is the component number, j is the sample number, and r is the total number of variables
Summary
Water scarcity is a growing challenge in the arid and semiarid regions of the world [1,2,3,4]. It is a challenge to evaluate the complex, highly dimensional groundwater hydrogeochemical datasets Multivariate statistical approaches, such as principal component analysis (PCA) and K-means clustering are robust tools for groundwater resources management [17,18,19,20,21]. They have been successfully used to define and understand the hydrogeochemical processes that dominate groundwater quality and identify pollution sources [22,23]. WQI was used to assess groundwater quality for drinking purposes and its spatial variations in a wastewater irrigation district in the Mezquital Valley, México
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