Assessment of urban groundwater quality using Piper trilinear and multivariate techniques: a case study in the Abuja, North-central, Nigeria

Abstract

Background: Groundwater pollution ensuing from ion exchange, weathering, agricultural and anthropogenic activities is on the rise in Nigeria. Since groundwater is used for domestic purposes, there is need for routine investigation. Findings on hydrochemistry of the groundwater components is essential for efficient and viable management. As a result, 25 Abuja water samples were collected for microbial and chemical analyses using standard methods. The cations, anions, soluble ions, trace elements, and heavy metal were obtained and compared with WHO standards. Results: The Discriminant analysis results shown that the parameters such as hydrogen ion concentration (pH), temperature (T), alkalinity (Alk), dissolve silica (SiO 2), and cations such as calcium (Ca 2+), potassium (K +), as well as anions such as carbonates (CO 3 2−), fluoride (F −), nitrates (NO 3 −), and heavy metal (Mn) were within the WHO guideline values for drinking water in all the samples for both seasons. Na + , SO 4 2− , EC, Mg 2+ , TDS, Fe 2+ , HCO 3 − , F − , TH and Cl − contents exhibited the most violation of drinking water standards with percent violations of 100, 76, 64, 56, 56, 44, 40, 40, 36 and 24%, respectively. The highest level of significant correlation was found to exist between K + and EC (r = 0.77, α = 0.05). Four hydro chemical clusters were identified from Hierarchical cluster analysis (HCA) with clearly partitioned water quality. Series and time series plot reveals TDS concentration value between 1200 and 2100 mg/l, has the highest with the mean and SD are 1433.76 and 459.38, respectively. Further analysis revealed that 16, 36 and 48% of the samples were the Ca-Cl, Na-Cl, and Mixed types, respectively. Conclusion: Groundwater in the Abuja district is mainly hard to very hard, slightly acidic in nature, polluted by ion exchange, agricultural activities, anthropogenic activities, and weathering. Therefore, there is also need for routine monitoring of groundwater in Abuja.

Assessment of groundwater quality, toxicity and health risk in an industrial area using multivariate statistical methods

“…The results obtained in this study were compared with the studies by Igibah and Tanko (2019) where results revealed that the water quality parameters showed wide spatial variations in the order Na + > SO 4 2 − > EC > Mg 2+ > TDS > Fe 2+ > HCO 3− > F − > TH > Cl − , ens uin g g rou ndw ater c ont ami nat ion fr om weat her ing , a g ric ult ure an d anthropogeni c a cti vities…”

“…One such approach would be hydrochemical investigations of groundwater frameworks which have set overwhelming attention on variations in the physical and chemical qualities of groundwater in time and space. Similar research by Igibah and Tanko (2019) has been studied carried out in assessment of urban groundwater quality using piper trilinear and multivariate techniques, where agriculture is the most significant commercial activity affecting the changes in groundwater quality by anthropogenic activity.…”

Spatio-Temporal Variation Characteristics of Water Quality and Its Response to Climate: A Case Study in Yihe River Basin

“…CA is an unsupervised pattern recognition technology, which can quantitatively determine the kinship relationship between a batch of samples without prior assumptions (Vega et al, 1998;Varol et al, 2012). Hierarchical cluster analysis (HCA) is one of the most general CA to classify water quality indicators into cluster groups according to their similarity or nearness (Igibah and Tanko, 2019). HCA has two forms, there are Q-type clustering (i.e., classifying samples) and R-type clustering (i.e., classifying observed variables of the research object).…”

“…Previously, a number of efforts about water quality in river basins have been made in many countries and regions, through which the spatiotemporal similarities and differences of varies water quality indicators were analyzed at different sampling points over multiple periods (Vitousek et al, 1997;Ravindra et al, 2003;Bellos and Sawidis, 2005;Singh et al, 2005;Kannel et al, 2007;Sickman et al, 2007;Varol et al, 2012;Wang et al, 2013;Putro et al, 2016;Rigi et al, 2019). Multivariate statistical analysis methods are reliable tools for water quality assessment and water pollution problem treatment (Singh et al, 2004(Singh et al, , 2005Igibah and Tanko, 2019). Due to their excellent performance in reducing the data dimension, extracting potential information, and verifying the spatial and temporal changes in water quality, multivariate statistical tools such as principal component analysis (PCA), factor analysis (FA), cluster analysis (CA), discriminant analysis (DA) and one-way analysis of variance (ANOVA) have been widely used to handle the massive and complex water quality data that generated through the water environment monitoring projects.…”

Modeling the Semivariogram of Climatic Scenario around Rivers by Using Stream Network Mapping and Hydrological Indicator

“…There is no doubt that climate changeability has given rise to harsh meteorological circumstances and has uninterruptedly disturbed the flow pattern of seas, rivers, and oceans together with the quality of surface water, particularly rivers and streams [1,2]. With the initiation of more flooding or overflowing and famines, warmer air is produced and excessive water content is held, which in turn makes precipitation styles extreme [3,4]. Streams, rivers, and lakes, which are fundamental resources for farming or agribusiness, industry, and companies and also a source of drinking and household water for people and animals, have been disparagingly influenced by climatic-changeability dynamics [5,6].…”