case study on groundwater pollution in india

Groundwater governance in India – A case study by World Bank

case study on groundwater pollution in india

It examines the impediments to better governance of groundwater, and explores opportunities for using groundwater to help developing countries adapt to climate change. It attempts to understand the practical issues that arise in establishing robust national governance frameworks for groundwater and in implementing these frameworks at the aquifer level.

The case study focused on the national, state and local levels. At the national and state levels, it analyzed the policy, legal, and institutional arrangements to identify the demand and supply management and incentive structures that have been established for groundwater management. At the local level, it assessed the operations, successes, and constraints facing local institutions in the governance of a number of aquifers within peninsula India, on the coast and on the plain of the Ganges river valley.

The report is divided into eight chapters following which a list of references used in the paper is used is provided. The first chapter in the beginning provides a brief background to the study and defines “groundwater governance”. In this report it refers to “refers to those political, social, economic, and administrative systems that are explicitly aimed at developing and managing water resources and water services at different levels of society that rely solely or largely on groundwater resources”. Following this the methodology used to carry out the study is elaborated where emphasis on pragmatic approaches, which can bring is incremental improvements with the given institutional framework is highlighted. The study is based on:

the findings and recommendations of “Deep Wells and Prudence: Towards Pragmatic Action for Addressing Groundwater Overexploitation in India” ,which focused mainly on aquifer intensive abstraction groundwater issues (World Bank 2010).
number of Groundwater Management Advisory Team (GW-MATE) case profile and strategic overview series publications, which addressed in more detail the local level in seven rural and urban aquifers; and
reports on groundwater quality-related aspects prepared by two local consultants aimed at addressing the technical/managerial and legal/institutional dimensions of aquifer protection in the country.

Chapter 2 is on “Resource Setting: Overexploitation and Groundwater Pollution”. It begins by highlighting the need to understand both physical and socio-economic environment to determine the availability of groundwater and its sustainability issues. It then goes on to elaborate the remarkable use of ground water for various purposes that has led to over exploitation of the resource. The chapter provides statistical data state wise on various issues related to ground water. Further the chapter sheds light on the ground water pollution.

Chapter 3 is on “The Governance Framework”. With a brief over view of key aspects related to ground water and its lacunas in the national water policy of 1987 and 2002 the report points at ground water in the Indian legal system and policy framework. Following which the institutions that govern the development and management of ground water is elaborated. This section covers the following issues: quality protection and pollution of ground water, its monitoring and surveillance the institutional capacity of institutions and financial issues.

Chapter 4 is on “Case Study Aquifers/Pilot Projects”. To cover the diverse rural and urban environments with different socioeconomic features seven cases of aquifers had been selected for this study. The chapter discusses in detail about these cases.

Chapter 5 is on “Findings and Lessons Learned”. It states that technical, legal, and institutional provisions are in a more or less acceptable. As far as the implementation of actions proposed by GWMATE is also uncertain as the institutional capacity is weak. The chapter then lays down a list of lessons learned about intensive groundwater use in hardrock peninsular India and alluvial Indo Gangetic Plain. It also highlights on the issue of coping with groundwater pollution issues.

Chapter 6 is on “Groundwater Governance and Climate Change Adaptation”. It gives a brief description (conjunctive use and recharge enhancement) of the World Bank’s study on ground water and climate change in cases where GW-MATE has been involved.

Chapter 7 is on “Recommendations”. A summary of: recommended implementation actions for managing intensive groundwater abstraction and actions required for protecting ground water pollution is given in this chapter. Further it also highlights at the actions required to strengthen state groundwater development and management agencies.

Chapter 8 provides list annexes of the report.

Click below to download the report.

Science News by AGU

Widespread Contamination Found in Northwest India’s Groundwater

Pathways for Contamination

The team also used isotope geochemistry to separate geogenic contaminants that are naturally occurring in the aquifer rocks, such as uranium and fluoride, and those that stem from human-made pollution, such as the high nitrate concentrations that come from untreated sewage and fertilizers leaking into shallow groundwater systems.

“Establishing the pathways of contamination for the various compounds is important for water treatment strategies,” says Richard Wanty , a geochemist with the U.S. Geological Survey in Lakewood, Colo., who was not involved in the new study.

“Natural contamination can be treated at point of use, but it’s generally not possible to fix something like uranium contamination at the source—it’s too pervasive,” he says.

Anthropogenic pollution, on the other hand, can be treated at the source. One such example might be stopping a wastewater plume from getting into groundwater supplies.

More of these kinds of geochemical studies will be needed for India to make progress in providing adequate sanitation and safe drinking water for its growing population of 1.3 billion people, Wanty says.

The study’s methods for vetting groundwater can be applied anywhere in the world that people rely on groundwater, he says.

“This is a very detailed study of a specific geographic area, but one could easily take the geochemical protocols and apply them to other parts of the world, both developed and underdeveloped,” Wanty says. “I hope people don’t refrain from reading this study because it says Rajasthan, India, in the title. This approach can be used anywhere people are using groundwater for drinking water.”

—Mary Caperton Morton ( @theblondecoyote ), Science Writer

Morton, M. C. (2019), Widespread contamination found in northwest India’s groundwater, Eos, 100 , https://doi.org/10.1029/2019EO130161 . Published on 05 August 2019.

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Coca-Cola Charged With Groundwater Depletion and Pollution in India

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An ongoing drought has threatened groundwater supplies across India, and many villagers in rural areas are blaming Coca-Cola for aggravating the problem.

Coca-Cola operates 58 water-intensive bottling plants in India . In the southern Indian village of Plachimada in Kerala state, for example, persistent droughts have dried up groundwater and local wells, forcing many residents to rely on water supplies trucked in daily by the government.

Groundwater Problem Began Several Years Ago

Some there link the lack of groundwater to the arrival of a Coca-Cola bottling plant in the area three years ago. Following several large protests, the local government revoked Coca-Cola’s license to operate last year and ordered the company to shut down its $25-million plant.

Similar groundwater problems have plagued the company in the rural Indian state of Uttar Pradesh, where farming is the primary industry. Several thousand residents took part in a 10-day march in 2004 between two Coca-Cola bottling plants thought to be depleting groundwater.

“Drinking Coke is like drinking farmer’s blood in India,” said protest organizer Nandlal Master. “Coca-Cola is creating thirst in India, and is directly responsible for the loss of livelihood and even hunger for thousands of people across India,” added Master, who represents the India Resource Center in the campaign against Coca-Cola.

Indeed, one report, in the daily newspaper Mathrubhumi , described local women having to travel five kilometers (three miles) to obtain drinkable water, during which time soft drinks would come out of the Coca-Cola plant by the truckload.

Coca-Cola Offers Sludge "Fertilizer" and Beverages with Pesticides

Groundwater isn’t the only issue. The Central Pollution Control Board of India found in 2003 that sludge from Coca-Cola’s Uttar Pradesh factory was contaminated with high levels of cadmium, lead, and chromium.

To make matters worse, Coca-Cola was offloading cadmium-laden waste sludge as “free fertilizer” to tribal farmers who live near the plant, prompting questions as to why they would do that but not provide clean water to local residents whose underground supplies were being “stolen.”

Another Indian nonprofit group, the Centre for Science and Environment (CSE), says it tested 57 carbonated beverages made by Coca-Cola and Pepsi at 25 bottling plants and found a “cocktail of between three to five different pesticides in all samples.”

CSE Director Sunita Narain, the winner of the 2005 Stockholm Water Prize, described the group’s findings as “a grave public health scandal.”

Coca-Cola Responds to Charges of Pollution and Groundwater Depletion

For its part, Coca-Cola says that “a small number of politically motivated groups” are going after the company “for the furtherance of their own anti-multinational agenda.” It denies that its actions in India have contributed to depleting local aquifers, and calls allegations “without any scientific basis.”

Citing excessive groundwater pumping, in 2014, Indian government officials ordered closed the Mehdiganj plant in the state of Uttar Pradesh. Since that time, Coca-Cola has undertaken a water replacement program, but unusually dry monsoons highlight the reality that water depletion continues to be a serious issue.

The History of Coca-Cola
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Photos of Coca Cola Company Brands
The Countries Where Coca-Cola Is Not Sold
Introduction to Green Technology
The Troubled History of Soda Pop and Carbonated Beverages
History of India's Caste System
Surname Meaning and Family History: Gupta
Ingredients in Coke and Their Function
Sarojini Naidu
10 Cities With the Highest Population Densities
Overview of the Rajput People of India
The History of Pepsi Cola
Recreate Coca Cola's Logo With These Spencerian Script Fonts
East India Company
Drought Causes, Stages, and Problems

General Studies

All Programmes

Study Material

Status of Groundwater in India

What’s in today’s article?

Why in news, availability of groundwater in india, groundwater extraction in india, groundwater contamination in india, groundwater crisis in india:, news summary: jal shakti ministry plans network of groundwater sensors, key highlights, benefits of network of groundwater sensors.

The Jal Shakti Ministry is working on an ambitious plan to deploy a vast network of groundwater sensors.
These sensors will continuously relay information on groundwater levels as well as the degree of contamination down to the taluk level.

Groundwater (GW) is the water that seeps through rocks and soil and is stored below the ground. The rocks in which GW is stored are called aquifers.
Hard-rock aquifers of peninsular India: These represent around 65% of India’s overall aquifer surface area, mostly found in central peninsular India .
Alluvial aquifers of the Indo-Gangetic plains: Found in the Gangetic and Indus plains in Northern India, these have significant storage spaces.
Out of the 1,123 BCM (Billion Cubic Meter) /year usable water resources of the country, the share of GW is 433 BCM/year and setting aside 35 BCM for natural discharge, the net annual GW availability for the entire country is 398 BCM.
In the latest Ground Water Resource Assessment-2022 , the total annual groundwater recharge in the country has been assessed as 437.60 billion cubic metres (BCM).
The annual extractable groundwater resource has been assessed as 398.08 bcm, with actual extraction of 239.16 bcm.
Anything above 70% is considered critical.
There are regions in Punjab, Haryana, Delhi and Rajasthan with groundwater blocks with over 100% extraction.
As per the Central Ground Water Board (CGWB), Groundwater contamination is mostly “geogenic” (natural) and has not significantly changed over the years.
However, nitrate contamination – a result of the use of nitrogenous fertilisers—has been observed.
Sections of nearly 409 districts have been confirmed with fluoride contamination and parts of 209 districts have noted arsenic contamination.
Given the interdependence of water, the environment and socioeconomic well-being, the challenges in Groundwater resource management are complex and multifaceted.
These include - Unregulated extraction; Excessive irrigation; Poor knowledge of Groundwater management system; GW pollution; Climate change.
As per the 2021 CAG report, Groundwater extraction in India increased from 58% to 63%, between 2004-17, exceeding the Groundwater recharge rate.
Over extraction at the current rate can threaten nearly 80% of drinking water over next two decades.
The Jal Shakti Ministry is planning to deploy a vast network of groundwater sensors that will continuously relay information related to groundwater levels and degree of contamination.
Currently, such information is only measured a handful of times a year and communicated via reports of the Central Groundwater Board.
The Central Groundwater Board currently relies on a network of about 26 thousand groundwater observation wells that require technicians to manually measure the state of groundwater in a region.
Piezometers measure groundwater levels, the recorders will transmit the information digitally.
Implemented by the Central Ground Water Board (CGWB), NAQUIM was launched in 2012 to map and assess the country's groundwater resources.
It aims to create a comprehensive database of aquifers in India and provide information on their location, extent, and characteristics.
So far, an area of 25.15 lakh square km has been covered under the NAQUIM studies.
The proposed network will continuously measure groundwater level and quality.
It will feed these data into a centralised network such as that of the National Water Informatics Centre (NWIC).
Such monitoring would make groundwater visible much the same way as air quality, air pressure, moisture, precipitation etc.
Groundwater forecasts to farmers would be useful for sowing and will help in faming activities.
Those regions and States that are known to have groundwater contamination, for instance, coastal salinity or excessive depletion, will be monitored more intensely for action by States.

Q1) What is National Water Informatics Centre (NWIC)?

The National Water Informatics Centre (NWIC) is a central agency under the Ministry of Jal Shakti, Government of India, established in 2018 to facilitate the effective management of water resources in the country. Its primary objective is to provide a centralized platform for collecting, collating, and analyzing water resources data and information from various sources and stakeholders.

Q2) What is Central Ground Water Board (CGWB)?

The Central Ground Water Board (CGWB) is a government agency in India that is responsible for the scientific and technical management of the country's groundwater resources. It was established in 1970 as a subordinate office of the Ministry of Jal Shakti, Government of India. The CGWB's primary objective is to assess, develop, and manage the country's groundwater resources in a sustainable manner. It conducts hydrogeological studies, monitors groundwater levels and quality, promotes artificial recharge of groundwater, and develops policies and guidelines for groundwater management.

Source: Jal Shakti Ministry plans network of groundwater sensors to monitor quality, contamination levels | Indian Express | WRI India

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Published: 28 May 2024

Using isometric log-ratio in compositional data analysis for developing a groundwater pollution index

Junseop Oh 1 ,
Kyoung-Ho Kim 2 ,
Ho-Rim Kim 3 ,
Sunhwa Park 4 &
Seong-Taek Yun 1

Scientific Reports volume 14 , Article number: 12196 ( 2024 ) Cite this article

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Environmental chemistry
Environmental impact

This study introduces a novel groundwater pollution index (GPI) formulated through compositional data analysis (CoDa) and robust principal component analysis (RPCA) to enhance groundwater quality assessment. Using groundwater quality monitoring data from sites impacted by the 2010–2011 foot-and-mouth disease outbreak in South Korea, CoDa uncovers critical hydrochemical differences between leachate-influenced and background groundwater. The GPI was developed by selecting key subcompositional parts (NH 4 + -N, Cl - , and NO 3 - - N) using RPCA, performing the isometric log-ratio (ILR) transformation, and normalizing the results to environmental standards, thereby providing a more precise and accurate assessment of pollution. Validated against government criteria, the GPI has shown its potential as an alternative assessment tool, with its reliability confirmed by receiver operating characteristic curve analysis. This study highlights the essential role of CoDa, especially the ILR -transformation, in overcoming the limitations of traditional statistical methods that often neglect the relative nature of hydrochemical data. Our results emphasize the utility of the GPI in significantly advancing groundwater quality monitoring and management by addressing a methodological gap in the quantitative assessment of groundwater pollution.

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Introduction.

Groundwater monitoring is an essential requirement for sustainable water resource management especially in areas where aquifers are under anthropogenic pressures such as overexploitation and pollution 1 , 2 . Groundwater quality monitoring, especially around hazardous waste storage and disposal facilities, is typically conducted in compliance with environmental regulations (e.g., EPA, 1986) to detect and prevent contamination that threatens human health and the environment 3 . Environmental criteria such as drinking water standards are used to ascertain the impacts and/or risks of groundwater contamination from the groundwater quality monitoring. However, such criteria often serve limited statutory and legal purposes, and may not provide a comprehensive understanding of the present status and temporal trend of groundwater quality for an early warning of groundwater contamination 4 . Therefore, it is needed to measure the relative concentrations of hazardous substances in groundwater compared to their environmental standards and/or background levels 5 , 6 , 7 .

In this context, the development of water quality index (WQI), such as the groundwater pollution index (GPI), serves as a surveillance tool to enhance both surface and groundwater quality assessment 8 , 9 , 10 , 11 . WQI and/or GPI are comprehensive metrics that aggregate various hydrochemical and biological parameters into a single, simplified score, providing a concise representation of overall water quality. Globally, a number of WQI models have been proposed, proving helpful for policy development and decision-making in water resource management 12 , 13 . Among them, multivariate statistical methods, especially dimensionality reduction techniques such as principal component analysis (PCA) and factor analysis (FA), have become prevalent in WQI development 14 , 15 , 16 , 17 . For instance, PCA provides an objective method for WQI development, typically using weighted linear combination of multiple parameters to calculate a minimal number of independent indices (scores), preserving the multivariate covariance structure dataset 18 , 19 , 20 . Moreover, even if given groundwater quality data may contain outliers, PCA can involve a robust procedure (e.g. robust PCA) 21 , 22 . However, traditional statistical approaches fail to adequately account for the compositional nature of groundwater quality datasets, which include multiple hydrochemical parameters. This oversight can lead to inaccurate conclusions, primarily due to two significant limitations: outliers and data closure issues 23 , 24 . Consequently, to address these limitations and facilitate the robust statistical development of an optimal WQI, the implementation of compositional data analysis (CoDA) is essential.

However, many statistical approaches have failed to consider the relative nature of groundwater quality data with multiple hydrochemical parameters, potentially leading to somewhat erroneous conclusions due to the relative variation of hydrochemical parameters necessitating the use of compositional data analysis (CoDa) for the statistical development of WQI.

Most environmental data involving groundwater quality data are compositional since they are usually reported in a relative unit (e.g., mg/L, mole/L, meq/L, etc.) 25 , 26 , 27 . Compositional data is typically defined as vectors with strictly positive summing up to a constant (i.e., closed number systems) 28 , and recently referred to as being parts of a whole which carry relative information 25 , 29 . Standard statistical approaches of compositional data are not straightforward due to the fact that they follow the Aitchison geometry (not the Euclidean geometry), pertaining to a sample space called the simplex 28 , 30 , 31 . Consequently, CoDA involves complex data transformation from the simplex to the Euclidean space before performing statistical analyses; three types logratio transformations has been suggested: the additive logratio (ALR) and the centered logratio (CLR) transformation 28 , and more recently, the isometric log-ratio (ILR) transformation 32 .

Recently, the increasing use of multivariate CoDa in interpreting groundwater quality data has led to the recommendation of the ILR-transformation before analysis 21 , 22 . This approach avoids the singularity issue inherent in the CLR transformation and maintains isometry between the simplex and real space 32 . The ILR transformation forms an orthonormal axis system (known as ILR-coordinates or balances) by dividing multivariate variables (compositioanl parts) into non-overlapping subsets through a sequential binary partition (SBP) 30 . Understanding the underlying processes in the data is beneficial for selecting appropriate SBP and forming ILR-coordinates 33 . This approach ensures subcompositional coherence, meaning that each subset of parts (subcomposition) preserves the information of the entire composition 28 , 34 . Consequently, ILR-coordinates of a selected subcomposition, which explain significant compositional changes in groundwater quality data, enhance the effectiveness of data exploration and interpretation 25 , 35 , 36 . Although CoDa, particularly the ILR transformation, is essential for statistical analysis of groundwater quality data, its application in indexing groundwater pollution has not yet been utilized.

This study aims to bridge this gap by investigating the applicability of the CoDa approach in developing a Groundwater Pollution Index (GPI). We propose a straightforward GPI, derived from the ILR-coordinate of a critical subcomposition, to effectively indicate groundwater pollution. Utilizing groundwater quality monitoring data from a study on the impact of livestock mortalities burial on groundwater 37 , we applied CoDa and robust PCA to assess multivariate hydrochemical data. This approach minimized the impact of outliers and accurately represented important subcompositional parts (parameters). From this analysis, we suggested a simple GPI as the ILR-coordinate of a selected important subcomposition, effectively revealing leachate pollution in groundwater. Our research addresses the current methodological gap by focusing on the indexing of groundwater pollution through compositional data analysis, marking a crucial step towards more accurate and reliable groundwater quality assessment.

Materials and methods

Groundwater quality monitoring data.

The burial pit sites (n = 30), from which our groundwater quality monitoring data were sourced, are regionally distributed across Korea. They were strategically selected to cover a broad geographic area and to provide a comprehensive dataset, reflecting the diverse hydrogeological conditions of the region. This study was based on the groundwater quality monitoring data collected from a monitoring program conducted to investigate leachate leakages from livestock carcass burial pits formed during the 2010–2011 foot-and-mouth disease (FMD) outbreak in South Korea. This monitoring program was carried out by the National Institute of Environmental Research (NIER) of South Korea on a quarterly basis throughout 2012 for 270 burial pit sites. As the monitoring result, the leachate leakages were confirmed in 29.6% of the burial pits according to the government (Ministry of Environment: ME) guideline for environmental management of carcass burial pits that involves the environmental criterion for three water quality parameters of electric conductivity (EC > 800 mS/cm), NH 4 + -N (> 10 mg/L), and Cl − (> 100 mg/L) 37 , 38 . Details about the burial sites established following the FMD epidemic and the subsequent monitoring programs are described in previous studies 37 , 39 .

For our analysis, we used the groundwater quality monitoring data only collected from 30 burial pit sites involving multiple parameters: 3 in-situ measurements (pH, EC, ORP), 10 hydrochemical ions (DO, BOD, COD, Total N, NH 4 + -N, Cl - , Ca 2 + , Na + , TP and PO 4 3 ), and 2 microbial parameters (TB and TC). This monitoring data includes a total of 100 analytical results (samples) comprising two types of groundwater samples representing livestock carcass leachate and nearby groundwater compositions, respectively. The leachate samples were obtained from the perforated drainpipes (as leachate wells: LW) installed at the top of burial pits, while the groundwater samples were collected from monitoring wells (MW) installed 10 m downgradient from burial pits. The nonparametric Mann–Whitney U test was used to evaluate the differences of groundwater pollution indices as well as water quality parameters between the two groups (LW and MW). The water quality analyses and measurements for the parameters were conducted at NIER following the standard methods for drinking water in South Korea 40 ; a detailed description of the methods (including QA/QC) can be found the original publication of NIER (2012) 41 .

Note that the groundwater quality monitoring data was selected because it clearly demonstrates the relative compositional changes between leachate-influenced and background groundwater. This distinction is crucial for validating the applicability of the ILR in developing a robust GPI within the framework of multivariate CoDA. To quantify groundwater contamination (i.e., leachate leakage from burial pits) using the proposed GPI, an assessment was conducted across the entire dataset. This involved evaluating the differences in contamination indices between two groundwater groups (Leachate Wells, LW, and Groundwater Wells, GW) and examining the discriminatory ability of the contamination index.

Compositional data analysis (CoDA)

Log-ratio transformation.

The groundwater quality monitoring data covers the compositional variables (i.e., hydrochemcial parameters) except for some physicochemical parameters and total coliforms. As mentioned above, compositional data is defined as vectors of positive real numbers in which the components (or parts) carry only relative information of some whole data 28 . Since compositional data are constrained to a sample space, called simplex, the standard statistical analyses relying on the Euclidean geometry may obtain spurious results 28 , 30 , 31 . Consequently, compositional data analysis (CoDA) involves complex data transformation from the simplex to the Euclidean space before performing statistical analyses; three types log-ratio transformations has been suggested: the Additive Log-Ratio (ALR) and the Centered Log-Ratio (CLR) transformation 28 , and more recently, the Isometric Log-Ratio (ILR) transformation 32 .

The ILR-transformation maps the D-parts (D-dimensional variables) of the composition in the simplex (S D ) into D-1 ILR-coordinates (called balances) in the Euclidean space (R D-1 ), allowing for standard statistical analysis techniques to be applied and preserving the relative information between parts 32 , 34 . The ILR-coordinates are defined using an orthonormal basis, which is created through a process called sequential binary partition (SBP) 30 , 42 . This procedure divides the parts of a full composition (or subcomposition) into binary non-overlapping groups in a sequential and hierarchical manner until all of the groups have only a single part. Given a composition of D parts, the ILR (z i ) between two non-overlapping groups can be defined for each of the SBP steps (D-1) as follows:

where g(c + ) represents the geometric mean of the r variables of the numerator of the balance, and g(c - ) represents the geometric mean of the s variables of the denominator. The ILR-coordinates, especially in high dimensions, are not readily interpretable due to the lack of a direct one-to-one relationship between raw and transformed parts. This necessitates expert-knowledge (e.g., geochemical stoichiometry to construct informative balances 36 . On the other hand, the CLR-transformation preserve the D-parts of the composition and useful for examining the relative variation of each part with respect to the whole compositional data. The CLR from S D to R D is defined as

where g(x) is the geometric mean of the composition x. However, the CLR-transformed coordinates are sub-compositionally incoherent since it depends on which parts are included in the geometric means as a common divisor. Additionally, their covariance and correlation matrices are singular due to the inherent constraint of all coordinates summing to zero 28 , 32 . Thus, ILR-transformation is highly recommended prior to the multivariate compositional data analysis 21 , 22 . Here, we employed the CLR-coordinates to represent the results (loadings and scores) from robust PCA (RPCA) built on ILR-transformed data, back-transformed to CLR space. This facilitates the development of more informative ILR balances as a groundwater pollution index for delineating leachate-induced groundwater pollution.

Robust principal component analysis (RPCA)

Principal Component Analysis (PCA) has been widely used to assess groundwater quality and to identify underlying processes such as groundwater contamination 43 , 44 , 45 , 46 . Moreover, it serves as an objective method for calculating water quality indices (WQI) through weighted linear combinations by selecting and weighting important groundwater quality parameters 8 . Robust principal component analysis (RPCA) is an approach robust to outlying samples unlike classical PCA which is sensitive to outliers 47 . However, PCA is sensitive to outliers in groundwater quality data, which typically exhibit a skewed covariance structure (i.e., a non-multivariate normal distribution) 48 . RPCA is an approach that is robust to outlying samples by identifying and mitigating the influence of outliers, thus providing a more reliable interpretation of the underlying processes in compositional data compared to traditional PCA 21 , 47 .

RPCA uses the minimum covariance determinant (MCD) estimator to calculate the sample arithmetic mean vector and the sample covariance matrix used for performing PCA. The MCD is designed to search a subset of at least h observations (> half of the total sample size n) with the smallest determinant of their sample covariance matrix resistant to outliers (for details, see Rousseeuw and Driessen, 1999) 49 . Therefore, RPCA based on MCD determines the location (mean) and scatter (covariance matrix) of data with a multivariate normal distribution, and the associated eigenvectors and eigenvalues provide the PC loadings and scores robust to outliers.

RPCA has been effectively applied in compositional data analysis 50 , 51 , 52 . The ILR transformation is essential for RPCA preferred over the CLR, due to the singularity of the CLR's covariance matrix, which results from the constant sum constraint of its components 52 . In this study, RPCA was conducted to establish the optimal SBP for a comprehensive explanation of groundwater quality monitoring data. This involved the identification of a specific subcomposition aimed at evaluating the influence of leachate from livestock carcasses on groundwater, for which the relevant ILR-coordinates were proposed. The ILR transformed data based on an arbitrary orthonormal basis were used in RPCA. For enhanced interpretability, the results (loadings and scores) of RPCA were back-transformed to the CLR and visually depicted via a biplot.

ILR-based Groundwater pollution index (GPI)

This study introduces a novel approach for constructing a GPI through the ILR transformation of multivariate hydrochemical parameters in groundwater quality monitoring data. This method involves a series of statistical procedures, incorporating PCA and CoDa. The framework for developing the ILR-based GPI consists of three major steps: (i) selecting key subcompositional parts using PCA (robust PCA in this study), (ii) performing the ILR transformation via Sequential Binary Partitioning (SBP), and (iii) carrying out normalization according to existing environmental standards or government guidelines. Detailed explanations of these steps are as follows.

PCA aids in selecting a subcomposition of key parameters (parts) that delineate the relative compositional change indicative of groundwater pollution (i.e., the leachate impact on background groundwater quality). These selected subcompositional parts are then transformed into ILR-coordinates (balances) via the SBP. Subsequently, the first ILR-coordinate with the highest variance is chosen to be used as a univariate GPI, effectively contrasting leachate-influenced parameters against those prevalent in the background groundwater. For instance, when dealing with a subcomposition of two parts, the ILR-coordinate Z is defined as follows, according to Eq. ( 1 ):

where C P and C B represent the concentrations in mg/L of parameters indicative of pollution and background respectively.

We normalized the ILR-coordinate Z proposed as a univariate GPI using the environmental criteria mentioned in Sect. 2.1. This procedure also validates the ILR-based GPI for practical purposes by comparing it with the government guidelines. The validation compares outcomes categorized into binary groups (polluted and non-polluted), determined by different cutoff values along the ILR-coordinate, with those classified according to the environmental criteria. We calculate diagnostic measures such as sensitivity (true positive rate) and specificity (true negative rate) to assess classification performance at varying cutoff points. From these results, an optimal cutoff of the ILR-based GPI, which yields the most similar classification result to the environmental criteria, can be derived. The optimal cutoff is identified using a receiver operating characteristic (ROC) curve, which plots sensitivity against 1-specificity at various cutoff points. It is chosen at the point on the ROC curve where both sensitivity and specificity reach their highest values.

Finally, the ILR-coordinate Z can be scaled to a centered value Z' by subtracting the cutoff and normalized to a range of 0 to 1 using the maximum and minimum values as follows:

This results in a normalized GPI, ranging from 0 to 1, which probabilistically assesses the impact of leachate on groundwater. Here, a normalized GPI value exceeding 0.5 indicates leachate pollution in accordance with the environmental criteria.

Our approach in alignment with the principle of subcompositional coherence in CoDa 28 . This principle ensures that an analysis conducted on a subcomposition is consistent with the analysis of the entire composition. This method provides a statistically reliable GPI that carries the relative information in groundwater quality monitoring data, transforming it from simplex to Euclidean space via the the log-ratio transformation.

All statistical procedures (i.e., CoDa and RPCA) of this study was carried out using the robCompositions 53 package in R software 54 .

Results and discussion

Characteristics of groundwater quality monitoring data: absolute versus relative concentrations.

Table 1 presents that the median values of most groundwater quality parameters except for redox potential (ORP) have significantly higher ( p < 0.05) concentrations in the leachate (LW) than in the nearby groundwater (MW). Previous studies have shown that such livestock mortality leachate contains high concentrations of inorganic and organic compounds (e.g., ammonium, alkalinity, chloride, sulfate, BOD, and COD) as a result of carcass decomposition 37 , 55 , 56 . The lower ORP in the leachate (median = -66 mv) compared to the surrounding groundwater (median = 115 mv) is due to anaerobic conditions prevailing in the burial pits 44 , 57 , 58 . The carcass leachate leakage from burial pits thus induces the subsequent increases of ionic concentrations in groundwater, exhibiting positive correlations with EC and TDS concentrations but negative correlations with ORP.

Given the elevated ionic concentrations within the leachate wells (LW) relative to the adjacent groundwater monitoring wells (MW), the log-scaled concentrations of Cl- and NH 4 + -N ions have strong positive Spearman correlations with EC values (ρ = 0.65 and 0.61), respectively in the total dataset (combined LW and MW) (Fig. 1 ). This indicates that the influence of leachate infiltration on proximate groundwater can be quantitatively diagnosed by measuring the correlation coefficients among hydrochemical parameters in the groundwater quality monitoring data. Nevertheless, as mentioned above (Sect. 2.2), the hydrochemical parameters are inherently compositional parts that carry relative information. Therefore, the correlations computed between any pair of log-transformed variables can be spurious and the log-ratio transformations such as centered log-ratio (CLR) and isometric log-ratio (ILR) are necessary for hydrochemical parameters 28 , 32 .

Bivariate relationships between log-transformed concentrations of Cl - and NH 4 + -N ions and log-transformed electrical conductivity (EC) (left), and the comparison of these two log-scaled concentrations between monitoring wells (MW) and leachate wells (LW) (right).

Figure 2 shows the bivariate relationships with correlation coefficients between the CLR-transformed values (i.e., relative to the geometric mean of all components) of Cl - and NH 4 + -N and log-transformed EC. A positive correlation is observed for NH 4 + -N (ρ = 0.56), consistent with the log-transformed data. Conversely, Cl - reveals a negative correlation (ρ = − 0.11) despite a positive association in its log-transformed data. This is attributed to the relatively high Cl - concentration in the groundwater from monitoring wells (MW) compared to that in the leachate (LW) (right in Fig. 2 ). The elevated Cl - levels in MW result from the influence of agricultural practices, such as the use of livestock manures and fertilizers, which affect the background levels of the groundwater near the burial pits, unlike NH 4 + -N, which is primarily originated from carcass leachate. These results demonstrate that the correlation structure in the total dataset can change when considering the relative compositions of individual hydrochemical parameters.

Bivariate relationships between clr-transformed concentrations of Cl - and NH 4 + -N ions and log-transformed electrical conductivity (EC) (left), and the comparison of these two clr values between Monitoring Wells (MW) and Leachate Wells (LW) (right).

Assessing the influence of leachate on groundwater quality using multivariate CoDa and RPCA

Given the multivariate compositional nature of hydrochemical data, it is necessary to employ a correlation matrix derived from log-ratio transformations to examine the interrelationships among various compositional parameters. Figure 3 shows the significant differences between between the correlation matrices of log-transformed and CLR-transformed variables (excluding EC, ORP, total bacteria, and total coliform) in the total dataset. This comparison demonstrates that the type of data transformation significantly influences the outcome of correlation analysis, as previously shown (Figs. 2 and 3 ). The result of log-transformed data (lower section of Fig. 3 ) reveals that the nine hydrochemical parameters (Cl - , Ca 2+ , Na + , BOD, COD, Total N, NH 4 + -N, Total P, PO 4 3- ), predominantly concentrated in the leachate (LW), exhibit positive correlations with each other. In contrast, these parameters display negative correlations with pH and redox-sensitive parameters (DO and NO 3 - -N), which typically decrease under anaerobic conditions.

Correlation matrix of twelve hydrochemical parameters in the total dataset, showing Spearman correlation coefficients for log-transformed data (upper triangle) and for clr-transformed data (lower triangle).

On the other hand, the correlation matrix for CLR-transformed data (upper section of Fig. 3 ) explains the relative compositional relationships based on their source attributions. For instance, parameters primarily originating from leachate (BOD, COD, Total N, NH 4 + -N) show inverse relationships with those dominant in background groundwater (Cl - , Ca 2+ , Na + ) as well as with redox-sensitive ions (DO and NO 3 - -N). It is noteworthy that the relative compositions of hydrochemical data are inherently influenced by the proportional contributions from various solute sources, such as carcass leachate and agricultural practices (e.g., livestock manures and fertilizers). Therefore, the application of CoDa (i.e., log-ratio transformations) can be more useful and relevant than using absolute concentrations (such as raw or log-transformed data) for a statistical and practical assessment of the impact of leachate leakage on groundwater quality.

In the context of multivariate CoDa, RPCA provides a more comprehensive explanation of the relative compositional changes in hydrochemical data. In this study, RPCA was applied to the ILR-transformed data, and then the loadings and scores of RPCA were back-transformed to the CLR-coordinates. From the result, the first two principal components (PC1 and PC2), accounting for 34.0 and 29.9% of the total variance, are extracted from the ILR-transformed data (Table 2 and Fig. 4 ). The loadings exhibit a correlation (or covariance) structure among the twelve hydrochemical parameters. Notably, PC1 has positive correlations with NH 4 + , BOD, and COD, which are predominantly enriched in carcass leachate, while it shows negative correlations with redox-sensitive parameters such as DO and NO 3 - -N. Ions such as Cl - , Na + , and Ca 2+ , despite their high absolute concentrations in leachate, show only weak correlations with PC1. This is attributed to their relative abundance in the background groundwater. On the other hand, PC2 has correlations with total P and PO 4 3- . However, both variables are redundant in the interpretation since they are immobile in groundwater due to their adsorption on soils and sediments 59 . Therefore, PC1 delineates the impact of leachate on groundwater quality, showing the relative increase in ionic concentrations compared to background levels and the formation of anaerobic conditions. These results are identical to the outcomes obtained from the correlation analysis on the CLR-transformed data.

Biplots of loadings and scores from robust principal component analysis (RPCA) of hydrochemical Parameters using isometric log-ratio (ilr). The illustrated loadings and scores have been back-transformed into centered log-ratio (clr) values for interpretation.

Accordingly, the robust scores along the first PC1 distinctly differentiate between leachate (LW) and groundwater samples (MW) in the total dataset, while also being robust to outliers (Fig. 4 B). The application of RPCA identified 110 outliers, constituting 22.9% of the total samples, which predominantly include leachate samples (LW). This suggests that the score, computed as a weighted linear combination of multivariate hydrochemical parameters, serves as an effective groundwater pollution index for assessing the impact of leachate on groundwater quality. Nevertheless, it is important to note that the eigenvectors, representing the loadings as weights of hydrochemical parameters, obtained from RPCA can be variable depending on the specific monitoring data used. This result significantly demonstrates that RPCA effectively reduces the dimensionality of compositional data and elucidates the impact of leachate contamination of groundwater by estimating a covariance structure that is robust to outlier samples. Additionally, the computation of scores involves complex transformations of the observed concentrations of multiple parameters into log-ratio values. Thus, we aim to identify critical subcompositioal parts that reflect the variability in RPCA scores and introduces their ILR-coordinate as a singular groundwater pollution index (GPI). This index serves as a versatile purpose tool for assessing the impact of leachate on groundwater quality.

Development of ILR-based groundwater pollution index (GPI)

In the context of multivariate CoDa, although the RPCA provides useful scores for evaluating the influence of leachate on groundwater quality, this study has adopted ILR transformation to develop a more straightforward method for formulating a univariate GPI. As explained above (in Sect. 2.2.), the ILR transformation results in D-1 Cartesian coordinates, known as balances, based on an orthonormal basis established through the Sequential Binary Partition (SBP) of D selected components. Here, we construct the SBP based on the PC loadings expressed with CLR (Fig. 4 ), which informs about the important subcompositional parts and their relationships showing the leachate pollution in groundwater quality. Figure 5 illustrates the SBP for the case of a D = 12 subcomposition partitioning the full set of hydrochemical parameters in accordance with the results of RPCA. From this partitioning, the eleven (D-1) independent isometric log-ratio (ILR) coordinates have been derived, according to Eq. ( 1 ).

Diagram of sequential binary partition (SBP) for D = 12 compositional parts, used in partitioning the full set of hydrochemical parameters for transformation into 11 balances (ilr-coordinates). The figure shows values of 1 and − 1, representing the compositional parts assigned as the numerator and denominator, respectively, for each balance.

Based on the SBP, we identified the second balance (labeled as ILR2 in Fig. 5 and Z1 in Table 3 ), which represents a binary partition excluding total P and PO 4 3- , as a critical ILR-coordinate for evaluating the impact of leachate on groundwater. The selected ILR-coordinate (Z1) uses BOD, COD, and NH 4 + -N ions, which is mainly produced from carcass decomposition, as the numerator; meanwhile the denominator involves Na + , Ca 2+ , H + , NO 3 - , DO, Cl - and NO 3 - -N ions, which are relatively dominant in the background groundwater affected by agricultural activities and oxic conditions. This log-ratio effectively retains the relative information of the data as shown in the results from RPCA exhibiting a significant correlation (ρ = 0.56) with the first principal component score (PC1) (Table 3 ). Additionally, it shows a positive correlation (ρ = 0.56) with electrical conductivity (EC) and a negative correlation (ρ = 0.56) with redox potential (ORP). Consequently, this ILR-coordinate is considered a reliable GPI in terms of ratio for assessing the effects of leachate on groundwater quality.

We further examined different ILR-coordinates derived from subcompositions with a reduced number of parts (specifically, D = 7, 5, and 3 parameters), using the same procedure to develop more simplified versions of the GPI. These ILR-coordinates not only correlate well with the PC1 but also effectively account for the variations in EC and ORP (Table 3 ). This result suggests that the ILR-coordinates sufficiently explain the relative information relevant to the hydrochemical processes by focusing on key parameters, rather than incorporating all measured parameters. This is due to the fact that the ILR transformation ensures the principle of subcompositional coherence of compositional data 60 .

The ternary diagram in Fig. 6 shows the distribution of three subcompositional parts (NH 4 + -N, Cl - and NO 3 - -N) characterized by two ILR-coordinates (ILR[NH 4 + -N |Cl - , NO 3 - -N] and ILR[Cl - | NO 3 - -N]) in the Euclidean space. The first of these coordinates corresponds to the Z3 in Table 3 explaining 90.1% of the total variance in the distribution. This ratio reflects the increase in NH 4 + -N relative to Cl - and NO 3 - -N, and differs shows a significant difference ( p < 0.05) between leachate (MW) and groundwater (GW) (Fig. 7 B). Consequently, the ILR-coordinates of three specifically selected parts (NH 4 + -N, Cl - and NO 3 - -N) provide the most simplified and practical form of GPI while optimally maintaining the essential information of groundwater quality monitoring data. We propose a univariate GPI to quantify the impact of leachate on groundwater, using the following ILR equation:

Ternary diagram illustrating the relative compositional changes among the subcompositions of NH 4 + -N, NO 3 - -N, and Cl - , with principal component 1 (PC1) highlighting the impact of leachate on groundwater (Left), and comparative analysis of the isometric log-ratio values for these subcompositions between Monitoring Wells (MW) and Leachate Wells (LW) (right).

( A ) Receiver operating characteristic (ROC) curve illustrating the classification performance of the ilr-based groundwater pollution index (GPI) in terms of sensitivity and 1-specificity, compared against environmental criteria (ME, 2011), and ( B ) histogram depicting the distribution of normalized ilr-based GPI values for the entire dataset of groundwater samples (n = 420), highlighting that 37% of the samples were identified as leachate-impacted using the optimal cutoff value of 0.5 (Z3 = − 0.87).

The ILR-coordinate (Z3), proposed as a GPI, was compared with the assessment results of leachate impact on groundwater, as outlined by the government's environmental criteria (mentioned in Sect. 2.1.). For this, data samples were categorized into binary groups based on varying ILR values, and these classifications were then juxtaposed with those designated as contaminated or uncontaminated according to the environmental criteria, measuring sensitivity and specificity. Such a comparison not only validates the GPI's potential as a viable alternative to the environmental criteria but also suggests an appropriate GPI cutoff that aligns with the criteria.

We determined the optimal cutoff for the GPI using a receiver operating characteristic (ROC) curve with an area under curve (AUC) of 0.78, which graphically represents sensitivity versus 1-specificity (recall) across various cutoff points. The optimal cutoff, determined at the point where sensitivity is maximized and 1-specificity is minimized, was identified as -0.87 (as shown in Fig. 7 A). At this point, the sensitivity was 0.67, correctly identifying 67% of samples as contaminated according to the Environmental Criteria, while the specificity was 0.88, accurately classifying 88% of uncontaminated samples (Table 3 ). These results validate the effectiveness of GPI in differentiating between contaminated and uncontaminated groundwater, confirming its reliability as a tool for environmental pollution assessment. Finally, the ILR-based GPI was adjusted to center around the cutoff and normalized between 0 and 1, utilizing the maximum and minimum values according to Eq. ( 4 ). Within this normalized scale, a GPI value exceeding 0.5 is established as the threshold for identifying leachate contamination, in accordance with the government's environmental criteria. Notably, this normalized GPI revealed that more than 80% of the entire monitoring dataset exceeded this 0.5 threshold, suggesting significant contamination.

This study utilized groundwater monitoring data from areas affected by the 2010–2011 foot-and-mouth disease outbreak in South Korea to highlight the effectiveness of CoDa in distinguishing between leachate contaminated and uncontaminated groundwater. The GPI, developed using CoDa and RPCA, significantly improves the accuracy and reliability of assessments by considering the relative nature of hydrochemical data, which is often overlooked by traditional statistical methods. The proposed GPI was validated against government environmental standards, demonstrating high sensitivity and specificity in distinguishing between contaminated and uncontaminated groundwater. These results not only validate the reliability of the GPI as an environmental pollution assessment tool but also suggest that it can play a crucial role in complementing existing environmental standards to enhance groundwater resource monitoring and management. Specifically, the CoDa approach proposed in this study overcomes the limitations of traditional methods by considering the relative nature of hydrochemical data, thereby providing a more accurate and reliable assessment tool. This is vital for policy making and environmental management, contributing to the protection and sustainable management of groundwater resources. Furthermore, the methodology and results of this study offer essential groundwork for future research and policy development.

Summary and conclusion

This research introduces an innovative Groundwater Pollution Index (GPI) that employs compositional data analysis (CoDa) and robust principal component analysis (RPCA) to advance the assessment of groundwater quality. Utilizing data collected from the groundwater monitoring of sites affected by the 2010–2011 foot-and-mouth disease outbreak in South Korea, this study highlights the effectiveness of CoDa in distinguishing significant hydrochemical differences between leachate-influenced groundwater and unaffected background samples.

The GPI is meticulously developed through a process that involves selecting essential subcompositional parts, specifically NH 4 + -N, Cl - and NO 3 - -N, using RPCA, conducting isometric log-ratio (ILR) transformation to address the compositional nature of hydrochemical data, and normalizing these results in accordance with environmental standards. The validation of the GPI against established government criteria, supported by receiver operating characteristic (ROC) curve analysis with an area under curve (AUC) of 0.78 underscores its potential as a robust alternative tool for groundwater pollution assessment. With a sensitivity of 0.67 and specificity of 0.88, the GPI effectively distinguished between contaminated and uncontaminated groundwater samples.

A significant contribution of this study is the emphasis on the importance of CoDa, particularly the ILR transformation, in overcoming the methodological limitations (i.e., outlier and data closure) of traditional statistical methods that often overlook the relative nature of hydrochemical data. This approach significantly enhances the accuracy and reliability of groundwater quality assessments. The proposed GPI aligns with existing environmental standards while serving as a more precise and reliable assessment tool, providing a robust framework for effective monitoring and management of groundwater resources. This is crucial for policy decision-making and environmental management, contributing to the protection and sustainable management of groundwater resources. Furthermore, the methodology and results of this study provide essential groundwork for future research and policy development. Researchers can build upon this work to conduct new studies and further refine the GPI, advancing the field of groundwater quality assessment.

Data availability

The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.

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Acknowledgements

This study was financially supported by the 2012 project (Title: A Study on Groundwater Quality Management Measures around Livestock Burial Sites (I); NEIR-2021-04-02-058) funded by the National Institute of Environmental Research (NEIR) and the Ministry of Environment of South Korea. The completion of this work was supported by the Korea University Grant and the Research Project (Development of integrated decision support model for environmental impact assessment project: 2022-003R) of Korea Environment Institute (KEI). Partial support was also given by the Basic Research Project (GP2021-007) of the Korea Institute of Geoscience and Mineral Resources (KIGAM).

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Junseop Oh & Seong-Taek Yun

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National Institute of Environmental Research, Incheon, 22689, South Korea

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Conceptualization was led by K-H.K.; Methodology was developed by J.O., K-H.K., and H-R.K.; The original draft was prepared by J.O., and H-R.K.; Review and editing were performed by K-H.K., S.-T.Y., and S.P.

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Oh, J., Kim, KH., Kim, HR. et al. Using isometric log-ratio in compositional data analysis for developing a groundwater pollution index. Sci Rep 14 , 12196 (2024). https://doi.org/10.1038/s41598-024-63178-6

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Received : 07 February 2024

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DOI : https://doi.org/10.1038/s41598-024-63178-6

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Groundwater pollution index (GPI)
Compositional data analysis (CoDa)
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Heavy metals pollution, distribution and associated human health risks in groundwater and surface water: a case of Kampala and Mbarara districts, Uganda

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Published: 23 May 2024
Volume 4 , article number 27 , ( 2024 )

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Idris O. Sanusi 1 ,
Godwin O. Olutona 1 , 2 ,
Ibrahim G. Wawata 3 , 4 , 5 &
Hope Onohuean 6 , 7

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Groundwater and surface water quality is of great significance for humanity as they serve as the primary drinking water sources globally. Due to population growth and the need to provide people with necessities that depend on water as an essential resource, these bodies of water are becoming more polluted. The present study involved the collection of groundwater and surface water samples from Kampala and Mbarara districts in Uganda during the dry and wet seasons. Also, concentrations of lead (Pb), manganese (Mn), cadmium (Cd), copper (Cu), and iron (Fe) were analyzed in order to evaluate the toxicity of metals, identify potential sources, and determine the health risk associated with their presence in water. Results showed that metals were observed with higher concentration during the wet season than the dry season. The concentrations of Fe (8.646 ± 0.00 mg/L), Mn (2.691 ± 0.01 mg/L) and Cd (0.090 ± 0.41 mg/L) measured in groundwater were significantly higher than those measured in surface water. However, only Cu was observed with higher concentration (0.322 ± 0.06 mg/L) in surface water during the wet season. Furthermore, the degree of contamination (C d ) and the heavy metal pollution index (HPI) were evaluated for both the wet and dry seasons. Results showed that few samples were found in the category of portable drinking water while majority are within the “poor” and “very poor” classes which require proper treatment before consumption. The oral hazard index (HI oral ) results showed that none of the samples are suitable for consumption; therefore, cause potential non-carcinogenic health issues to the consumer (HI oral > 1). Moreover, children are more at risk than adults in the study districts (higher HI values in both seasons). This study recommends frequent monitoring of quality of water and also effluent waste treatment by the major source of pollution.

Assessment of heavy metals contamination and associated risks in shallow groundwater sources from three different residential areas within Ibadan metropolis, southwest Nigeria

Investigation of heavy metal contamination and associated health risks in groundwater sources of southwestern Punjab, India

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1 Introduction

The United Nations convention regards the right to access clean drinking water as a universal human right [ 1 , 2 ]. However, for many developing nations in Asia, South America, and Africa, this human right is still merely an aspiration [ 3 ]. A number of human activities are related to the pollution of soil, groundwater, and surface water. These activities include mining, agriculture, landfilling, industrialization, urbanization and technological advancement. After a period of heavy rainfall, the pollutants are transported by runoff water into water reservoirs, where they are later consumed by people and animals [ 4 , 5 , 6 ]. Heavy metals have a high geochemical activity and are readily migrated through sediments, water, and suspended particles [ 7 ]. Thus, one of the global environmental problems that is more prevalent is the pollution of soils, sediments, groundwater, and surface water by different heavy metals [ 8 , 9 , 10 ]. In addition, most heavy metals have considerable biological activity and are resistant to biodegradation, which means they may accumulate in the food cycle and enter the human body [ 11 ]. Heavy metals can cause harmful effects such as carcinogenicity, teratogenicity, and mutagenicity when their amount in the human body surpass the regulatory limit [ 12 ]. For instance, elevated Pb concentrations have been linked to human cognitive deterioration [ 13 ], while high Cd concentrations have been linked to liver and skeletal problems as well as a higher chance of lung cancer [ 14 ]. In addition, long-term exposure to these hazardous metals may result in fatal conditions including cancer, Parkinson’s, Alzheimer’s, and multiple sclerosis [ 15 ]. Moreover, short-term exposure to these metals can induce physical, muscular, and neurological issues [ 16 , 17 ]. According to previous studies, fast economic growth has led to an increase in the amount of sewage released by chemical companies and the use of pesticides and fertilizers in agriculture, both of which have raised the level of heavy metals in aquatic ecosystems [ 18 ].

Uganda is a low-income nation where a large number of people lack access to clean water supplied by National Water and Sewerage Cooperation (NWSC) [ 19 ]. According to data from the Uganda NWSC, 34% of the nation's villages and 26% of Uganda's urban areas lack access to safe drinking water [ 20 ]. Thus, this population is dependent on groundwater and surface water, both of which are often contaminated. These sources usually include toxic heavy metals like lead, cadmium, chromium, zinc, and mercury along with dissolved organic and inorganic substances like trichloroethylene, volatile organic compounds, chlorides, fluorides, sulphates, carbonates, sodium, potassium, calcium, and magnesium [ 21 ]. Individuals who consume such types of water might experience further health issues [ 22 ]. Several studies have been conducted on heavy metals in surface and groundwater in various regions of Uganda. These include assessment of heavy metals in Pager River [ 23 ], drinking water [ 24 ], copper mine and tailing sites [ 25 ], groundwater [ 26 ], wastewater channel [ 27 ] and River Rwizi [ 28 ]. Bakyayita and co-workers [ 29 ] reported the toxicity of heavy metals in selected groundwater and surface water sources in Kampala city; however, the seasonal variation of the heavy metals from each location were not discussed. More studies on heavy metal regarding regional water quality is necessary to estimate the level of contamination and develop potential mitigation strategies. This is due to the fact that there is a growing need for water in the districts of Kampala and Mbarara to meet the demands of industry, agriculture, and household water supply.

This study presents the results of an extensive investigation to determine the concentration of heavy metals (Cu, Pb, Fe, Cd, and Mn) in surface and groundwater from the districts of Kampala and Mbarara. The two primary objectives of this study were: (1) Examine the presence of heavy metals in the water sources as well as the seasonal and spatial distribution. (2) Assess the pollution indices and the human health risks that these heavy metals contamination poses to the consumers.

2 Materials and methods

2.1 study area.

The study focused on Kampala and Mbarara districts which are located in the central and western parts of Uganda respectively with sampling points on groundwater (Fig. 1 ) and surface water (Fig. 2 ). The coordinates of the sampling sites mapped in the Figs. 1 and 2 are shown in Table 1 . In Kampala district (the nation’s most industrialized commercial district), water samples were collected from 2 protected springs (S1 and S2), 5 shallow wells (S3, S4, S5, S6 and S7) and 2 open springs (S8 and S9). In Mbarara district, wastewater samples were collected from 1 receiving pond (S10), 1 upstream pond (S11) and 1 downstream pond (S12). Moreover, the open ponds were sampled in the Katete sewage pond located in the Mbarara district (Fig. 2 ). The sample locations were selected based on criteria such as population density, the degree of human activity in the region, and the population's dependency on surface water and groundwater. To understand more fully the seasonal variations in occurrence and distribution of the heavy metals, the study covered three months in a dry season (February, March, and April 2023) as well as three months in a wet season (June, July, and August 2023).

Location of Kampala district and positions of sampling points

Location of Mbarara district and positions of sampling points

2.2 Sample collection

Water sampling, handling, storage, and chemical analysis were all performed according to international standard methods [ 30 ]. One-liter (1 L) polythene containers were used to collect the samples. Sampling containers were thoroughly washed with a 5% solution (v/v) of nitric acid and then rinsed with deionized water 24 h before collecting the samples. Moreover, all the containers were rinsed three times with the sample water before collection at each site. The collected samples were filtered, acidified with nitric acid [ 31 ] and stored at 4 °C before analysis.

2.3 Digestion and analysis of heavy metals

Exactly 50 ml aliquot of water sample was transferred into a Teflon vessel and 5 mL of concentrated HNO 3 was added. A thermostatic hot plate was then used to gently boil the mixture for about thirty minutes. The digested samples were transferred into a 50 mL standard flask and diluted to volume. An aliquot was taken from the resulting solution and sent for Atomic Absorption Spectroscopy (AAS) analysis. The concentrations of heavy metals in the samples were measured using the calibration curve results of each standard. For the quantitative determination of the heavy metals (Fe, Cd, Pb, Mn andCu), a Thermo Electron Atomic Absorption Spectrometer, model S SERIES system was used. The equipment was calibrated using reference standards of the metals and also blank. All samples were analyzed in the laboratory in triplicate at the Central Instrumentation Research Facility, Covenant University, Otta, Ogun State, Nigeria. The relative standard deviation of the method obtained are: Pb (2.70%), Cd (3.33%), Fe (1.50%), Cu (2.00%), and Mn (1.37%).

2.4 Heavy metal pollution index

Heavy metal pollution index (HPI) was calculated to determine the overall impact of individual metals on water quality. The rating and weight of each metal are used to calculate HPI. According to Rajmohan et al., the rate is an undefined number ranging from 0 to 1, and weight is inversely related to the drinking water regulatory limit of various heavy metal [ 32 ]. The maximum admissible concentration of Cu, Fe, Mn, Pb and Cd are 2.00, 1.00, 0.40, 0.01 and 0.005 mg/L respectively [ 33 ]. According to Basahi et al., the HPI is classified as: excellent with values ranging from 0 to 25, good (26–50), poor (51–75), very poor (76–100) and unsuitable when HPI value is greater than 100 [ 34 ].

2.5 Degree of contamination

The impact of heavy metals on a water body is measured by the degree of contamination (C d ) [ 34 , 35 ]. The categories of heavy metal pollution based on C d are as follows: C d < 1 indicates low pollution, C d from 1 to 3 indicates moderate pollution, and C d > 3 indicates high pollution in the water due to trace metals. C d is calculated using Eqs. ( 1 ) and ( 2 ).

where Mi and MACi are the measured level and the maximum acceptable concentration (MAC) of the i th metal as recommended by the [ 33 ], respectively, and Cf i is the contamination factor.

2.6 Statistical analysis

The average, standard deviation, and Pearson's correlation coefficient were obtained using Minitab ® statistical software version 21.4.1. The variance in the trace metals was examined using the ANOVA test. Additionally, Varimax rotation factor analysis was carried out to determine the best fit and explain the correlation among the measured heavy metals.

2.7 Human health risk

Oral injection of contaminated drinking water is the primary means which heavy metals pollution poses health risks to both adults and children. Alternative routes such as cutaneous contacts and aspiration are minor when compared to oral ingestion. The hazard quotient (HQ) and chronic daily intake (CDI, mg/kg/day) were determined in the present study using procedures recommended by the United States Environmental Protection Agency [ 36 ]. The approach reported by Rajmohan et al. [ 37 ] was also employed to determine the non–carcinogenic health risks (which depend on the hazard index) of each metal. The sum of the HQo ral values of each heavy metal was used to evaluate the oral hazard index (HI oral ) for individual sample. While samples with values greater than 1 may provide non-carcinogenic health concerns to the consumer, those with HI oral < 1, CDI oral < 1, and HQ oral < 1 are safe to drink.

3 Results and discussion

3.1 heavy metals distribution and assessment of drinking water quality.

Table 2 shows the heavy metal concentrations in each water source. During the dry season, site 6 had the highest mean Pb concentration (0.214 mg/L), with concentrations varying from 0.113 to 0.315 mg/L, while site 9 had the lowest average level (0.096 mg/L), with values varying from 0.069 to 0.121 mg/L. Additionally, there is a change in the spatial variations during the wet season, site 10 had the highest average concentration (0.197 mg/L), whereas site 9 was found to still have the lowest mean concentration in the wet season (0.112 mg/L). Results showed that almost all the sampling locations recorded Pb values which exceed the World Health Organization (WHO) maximum allowable concentration of drinking water (0.01 mg/L) [ 33 ]. Drinking water contaminated with lead (Pb) may lead to several health issues, including chronic bronchitis, gastrointestinal discomfort, lung cancer, neurological abnormalities, and impaired lung function [ 38 ]. Pb concentrations varied significantly (p < 0.05) across the sample locations; however, no significant variations (p > 0.05) were observed in the seasonal patterns of Pb distribution (Table 3 ). The average levels of Pb recorded in this study for dry and wet seasons (0.152 and 0.166 mg/L respectively) were substantially lower than mean concentrations (7.09 and 7.32 mg/L) of Pb in surface water, Bangladesh [ 39 ] but higher than the mean concentrations (0.006 and 0.003 mg/L) of Pb in Aiba Reservoir, Iwo, Nigeria [ 40 ].

Site 6 had the highest average concentration of Mn in the dry season (1.225 mg/L), with values varying from 1.115 to 1.335 mg/L. On the other hand, Site 3 had the lowest mean concentration of Mn (0.014 mg/L), with values ranging from below the detection limit (ND) to 0.067 mg/L. Additionally, site 4 recorded the highest average Mn concentration (2.691 mg/L), with concentrations varying from 2.688 to 3.101 mg/L during the wet season. However, Mn concentration was below the detection limit (ND) in site 3 throughout the wet season. The high concentrations in Mn detected in the shallow wells could be related to the increase in the release of manganese (Mn) from soil into groundwater through runoff from landfills or compost (inorganic matter). Results showed that 25% of samples had a mean concentration of Mn which surpassed the WHO allowable limit for drinking water (0.40 mg/L) while 75% were below the limit and are good for consumption. The seasonal patterns in the Mn distribution were significantly different (p < 0.05), although the Mn concentrations across the sample sites did not change significantly (p > 0.05). The average concentrations of Mn in this study for dry and wet season (0.431 and 0.668 mg/L) were higher than the average values (0.22 and 0.13 mg/L) of Mn in surface water, Niger Delta, Nigeria [ 41 ] and also average concentration (0.03 mg/L) of Mn in Gomti River, Bangladesh [ 42 ].

Moreover, the highest average level of Cd was observed at site 11 in dry season (0.019 mg/L) with values varying from 0.018–0.021 mg/L whereas the lowest average level was detected at site 2 (0.011 mg/L) with values varying from 0.008–0.016 mg/L. During the wet season, there were changes in the spatial distribution of Cd concentrations. At site 2, the mean concentration of Cd was the lowest (0.082 mg/L) and at site 4, the highest (0.090 mg/L). The high concentrations of Cd recorded in almost all the sampling locations could be attributed to run off of phosphate fertilizers into the soil (phosphate contains Cd as contamination) and also combustion emissions. Significant differences in Cd concentrations were observed for both seasonal and spatial variability across the locations (p < 0.05). The average Cd concentrations in this study (0.016 and 0.085 mg/L) were less than those in the Awash River (0.06 and 0.13 mg/L), Ethiopia [ 43 ], and dam water (3.76 and 5.12 mg/L) in Nairobi, Kenya [ 44 ].

During the dry season, site 10 had the highest mean concentration of Cu (0.146 mg/L), while site 12 had the lowest (0.034 mg/L), with values ranging from 0.003–0.086 mg/L. In addition, site 10 was found to have the highest mean level of Cu (0.322 mg/L) during wet season, with concentrations varying from 0.312 to 0.327 mg/L, while site 4 had the lowest mean concentration (0.235 mg/L). Results showed that all the samples had Cu concentrations below 0.400 mg/L and also are within the recommended limit (2.00 mg/L). Also, Cu concentrations varied significantly (p < 0.05) across the sites for both seasonal and spatial variations. The average concentrations of Cu (0.122 and 0.262 mg/L) observed in this study were lower than that of Cu (0.422 and 1.078 mg/L) in groundwater from OkeOdo, Iwo, Nigeria [ 45 ], but substantially higher than the concentration of Cu (7.3 µg/L) in groundwater, Hyderabad City, India [ 46 ].

The mean Fe concentration ranged from 0.081–4.45 mg/L from site 7 to site 10, with the latter having the highest average value (2.632 mg/L) in dry season. Furthermore, during the wet season, there was variation in the mean value across the study sites. At site 4, the highest mean value (8.646 mg/L) was recorded, with concentrations varying from 5.237 to 11.770 mg/L, while at site 2, the lowest concentration (0.081 mg/L) was recorded, with concentrations varying from 0.068 to 0.089 mg/L. Results indicated that 25% of samples surpassed the WHO allowable limit for drinking water while the remaining 75% of samples were within the limit (1.0 mg/L). The high value of Fe observed at site 4 during wet season could be related to corrosion of iron or the steel pipes used for the shallow well. The Fe concentrations at each sample location varied significantly (p < 0.05). On the other hand, there was no significant changes (p > 0.05) in the seasonal trends of the Fe distribution (Table 3 ). The average concentrations (0.865 and 1.223 mg/L) of Fe measured in this study for dry and wet season were significantly lower than the average concentrations (2.574 and 6.09 mg/L) in groundwater [ 45 ]. However, Omopariola and Adeniyi [ 47 ] reported mean concentrations of Fe in groundwater in Ayedaade, Oyo State, Nigeria, with lower concentration (0.75 mg/L) in dry season and higher (1.95 mg/L) concentration in wet season.

Results showed the following order of the mean metal concentrations (high to low): During the dry season, Fe > Mn > Pb > Cu > Cd. However, during wet season, the following decreasing sequence of heavy metal concentrations was observed: Fe > Mn > Cu > Pb > Cd. The high concentrations of Fe observed in wet season could be related to substantial runoff which had washed the iron-bearing soil particles into water sources. It could also be attributed to corrosion of iron or steel used for the shallow wells.

3.2 Comprehensive evaluation of trace metal pollution

3.2.1 heavy metal pollution index.

Table 4 shows that during the dry season, HPI varied from 14.14 to 220.48 with an average of 89.75, while in wet season, it varied from 9.46 to 638.13 with an average of 127.90. Figure 3 a and 4 a represent the grouping of the samples during both seasons. In dry season, results showed that 8% of samples fall into “excellent”, 25% fall into “good” while the “unsuitable” class had the highest (34%). The other 33% of samples are categorized into “poor” (8%) and “very poor” (25%). In addition, results also showed that 42% of samples fall into the “poor” category in wet season, however; there was a significant increase in samples with “excellent” category (16%). It could be said that few samples fall in the category of portable drinking water while majority are within the “poor” and “very poor” classes which require proper treatment and management.

Heavy metals pollution index, degree of contamination and HQ oral for dry season

Heavy metals pollution index, degree of contamination and HQ oral for wet season

3.2.2 Degree of contamination (C d )

The contamination index (C d ), similar to the HPI, is an essential indicator for evaluating the level of pollution in water sources. It was computed using Eqs. ( 1 ) and ( 2 ), and the statistical information is shown in Table 4 . The Cd concentration in the sample locations varied from -2.45 to 1.59 with an average value of -1.00 during the dry season, whereas it ranged from -2.56 to 12.49 with a mean value of -0.11 during the wet season. Results indicated that in the rainy season, 8% of the samples were classified as high (C d > 3); however, no sample was recorded with C d > 3 in dry season (Fig. 3 b and 4 b). In this study, site 4 (shallow well) was observed to have the highest C d value (12.49) and this occurred in the wet season. The C d correlates with the high value of HPI (638.13) observed. This could be due to the high concentration of Fe and Mn measured in wet season at the sampling site.

3.3 Multivariate statistical analysis

3.3.1 pearson correlation coefficient.

In order to investigate the relationships between the variables and to identify the source of the metals found in the particulate matter, the Pearson correlation coefficient is employed [ 48 ]. Strong correlation was defined as having a Pearson’s correlation coefficient between 0.9 and 1, while moderate correlation was defined as having a correlation coefficient between 0.9 and 0.5 [ 49 , 50 ]. This classification was also included in the analysis of this study in order to have a general understanding of the performance of each of the assessed water quality indicators. There was no relationship between the variables Pb, Mn, Cd, Cu, and Fe during the dry season. For each relationship, the correlation coefficient was less than 0.5, and no statistically significant association was identified (Table 5 ). However, during wet season, Cu had significant positive correlation with Pb ( r = 0.579), Fe had significant positive correlation with Cd ( r = 0.589) and also strong positive correlation with Mn ( r = 0.881). Other relationships found in the correlation matrix were not statistically significant. Moreover, the absence of no significant relationship between variables in the dry season indicates that sources of contamination of water bodies might be from different factors which are not common to all. Therefore, contamination sources could be from sewage discharge, wastewater treatment or surface runoff of agro-chemicals from agricultural practices. The results of this study showed that during the wet season, Fe and Mn exhibited a significant relationship. This could have occurred concurrently due to partial overlaps in the redox processes during reduction of Fe and Mn oxides, and it may be related to the significant concentrations observed at the sources [ 51 ].

3.3.2 Factor analysis

The factor analysis result obtained for dry season showed strong positive loading for only Pb in factor 1, negative strong loading in factor 2 and factor 3 for only Cu and Cd respectively (Table 6 ). It could be said that an increase in concentration of any of the variables will not significantly affect others (no significant correlation between the variables). Moreover, it could be said that the sources for each metal contamination in the water bodies are not similar; therefore, different factors could have contributed to the level of contamination. The statistical analysis indicated significant positive loadings of Mn and Fe in the wet season (Table 7 ), with factor 1 showing the largest variance (0.38%). The presence of positive loadings may result from a range of geogenic processes and anthropogenic activities including landfill leachate and sewage discharge. Furthermore, factor 2 indicated significant positive loadings for Pb and Cu. The strong correlation means that an increase in concentration of Pb leads to increase in concentration of Cu and this could be related to anthropogenic activities such as industrial processes, runoff of agrochemicals into surface water and over-pumping in shallow wells. However, only Cd showed a strong positive loading in factor 3 with no significant correlation with other variables. This means that an increase in level of Cd does not have any significant change in other variables. A possible explanation for this might be pollution from sources like sewage sludge and landfills in nearby areas of the study locations.

3.3.3 Human health risk assessment

In an effort to quantify the possible health risk associated with adults and children in the study districts consuming metal-polluted surface and groundwater orally, the human health risk assessment (HRA) was conducted. The present study estimated the hazard index (HI), hazard quotient (HQ oral ), and chronic daily intake (CDI, mg/kg/day) for each metal. Figures 3 c and 4 c show the proportion of samples that fall below the accepted limit (HQ < 1) for each metal. During dry season, Mn and Fe had HQ < 1 for both adults and children (100% of samples). However, only Fe had HQ value which was within the recommended limit in wet season while Mn had HQ > 1 (children) for 8% of the samples. In addition, Cu and Pb had HQ > 1 (more than 90% of samples) while Cd had 8% of samples with HQ > 1 for children in dry season. Moreover, Cu, Pb and Cd had HQ > 1 (adults and children) for more than 90% of the samples in wet season. The metals exhibit the following decreasing pattern, based on the proportion of samples that surpassed the permissible level: Pb > Cd = Mn = Fe = Cu (adults) and Pb > Cu > Cd > Mn = Fe (children) for dry season. Similarly, in wet season, the metals follow the decreasing trend for wet season: Cu = Cd > Pb > Mn = Fe (adults) and Pb = Cu = Cd > Mn > Fe (children). It could be said that HQ oral values of metals which exceeded the recommended limit were mostly recorded during wet season with Pb having the highest percentage.

During the dry season, HI oral calculated for adults in the study districts varied from 2.03 to 3.26 with an average of 2.61 and 3.43 to 5.51 with an average of 4.41 for children. Moreover, there was fluctuation in the HI oral computed in wet season, it varied from 5.11 to 5.66 with a mean value of 5.84 and 8.64 to 11.24 with a mean value of 7.86 for adults and children respectively (Table 4 ). Results showed that the HI oral values computed for all the sites were more than 1, this suggests that consumers could be exposed to potential non-carcinogenic health risks. Moreover, in the study locations children are significantly more susceptible to non-carcinogenic risks than adults. Similarly, the HI oral results reported in Owan River, Nigeria [ 52 ] and Ossiomo River, Nigeria [ 53 ] showed that children in the receptor population were predisposed to non-carcinogenic risk (HI oral > 1). Hence, the groundwater and surface water should be managed well by consumers and also treated (when necessary) before consumption.

4 Conclusion

Significant seasonal and spatial variation is observed in the majority of heavy metals assessed. In addition, the quality of water in the study sites with “poor” and “very poor” classes could be improved by proper treatment of wastewater and sewage before discharging to the environment, regulation of extensive application of agrochemicals and routine monitoring of water by protection agencies. According to the HI results, none of the research sites are appropriate for drinking and may provide non-carcinogenic health concerns to consumers (HI > 1). Hence, a mitigation plan should be implemented by the environmental protection agency to ensure that the community at large have access to quality drinking water.

Data availability

All data generated and analysed during this study are included in this article.

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Department of Pharmaceutical Chemistry and Analysis, School of Pharmacy, Kampala International University, Western Campus, P O BOX 71, Ishaka-Bushenyi, Uganda

Idris O. Sanusi & Godwin O. Olutona

Industrial Chemistry Programme, College of Agriculture Engineering and Science, Bowen University, Iwo, Nigeria

Godwin O. Olutona

Department of Pharmaceutics and Pharmaceutical Technology, School of Pharmacy, Kampala International University, Western Campus, Ishaka-Bushenyi, Uganda

Ibrahim G. Wawata

Institute of Biomedical Research (IBR), Kampala International University, Western Campus, Ishaka-Bushenyi, Uganda

Department of Pure and Industrial Chemistry, Kebbi State University of Science and Technology, Aliero, PMB +243 1144, Birnin Kebbi, Nigeria

Biomolecules, Metagenomics, Endocrine and Tropical Disease Research Group (BMETDREG), Kampala International University, Western Campus, Ishaka-Bushenyi, Uganda

Hope Onohuean

Biopharmaceutics Unit, Department of Pharmacology and Toxicology, School of Pharmacy, Kampala International University, Western Campus, Ishaka-Bushenyi, Uganda

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Conceptualization, sample preparation, data collecting and analysis, and paper preparation were done by IOS. Conceptualization, research design, supervision, and paper writing were done by GOO. IGW participated in the project design, supervision, execution, and editing. HO also assisted with the design, execution, and editing of the project. All authors read and approved the final draft of the manuscript.

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Sanusi, I.O., Olutona, G.O., Wawata, I.G. et al. Heavy metals pollution, distribution and associated human health risks in groundwater and surface water: a case of Kampala and Mbarara districts, Uganda. Discov Water 4 , 27 (2024). https://doi.org/10.1007/s43832-024-00087-9

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Heavy metals pollution, distribution and associated human health risks in groundwater and surface water: a case of Kampala and Mbarara districts, Uganda

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Assessment of heavy metals contamination and associated risks in shallow groundwater sources from three different residential areas within Ibadan metropolis, southwest Nigeria

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1 Introduction

2 Materials and methods

2.2 Sample collection

2.3 Digestion and analysis of heavy metals

2.4 Heavy metal pollution index

2.5 Degree of contamination

2.6 Statistical analysis

2.7 Human health risk

3 Results and discussion

3.2 Comprehensive evaluation of trace metal pollution

3.2.2 Degree of contamination (C d )

3.3 Multivariate statistical analysis

3.3.2 Factor analysis

3.3.3 Human health risk assessment

4 Conclusion

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