Heavy Metals and Polycyclic Aromatic Hydrocarbons Levels in Selected Occupational and Environmental Settings in Liberia: A Systematic Review

Abstract

Environmental degradation in Liberia has reached critical levels due to years of unregulated artisanal mining, poor waste management, and rapid urbanization have severely compromised the quality of its water and soil resources. This review critically evaluates the extent, sources, and implications of physicochemical and heavy metal contamination in Liberia’s aquatic and terrestrial environments. From an initial pool of 38 studies, 9 met the inclusion criteria based on relevance from 2020, quantitative data quality, and geographic coverage. The methodology combined descriptive statistics, correlation analysis, and multivariate statistics to trace pollution patterns and their geochemical drivers. Findings reveal severe contamination: water turbidity reached 320.00 NTU (mean: 42.85 NTU) against the WHO guideline of 5 NTU, while electrical conductivity peaked at 3000.00 µS/cm. Total dissolved solids averaged 267.92 mg/L, exceeding permissible levels near estuarine zones. Heavy metal concentrations surpassed WHO limits in numerous cases—Fe (up to 23.69 mg/L), Mn (9.12 mg/L), and Cd (0.40 mg/L) in water, and Fe (115,781.00 mg/kg), Ni, and Cd in soil. Strong correlations (eg, EC-TDS: r = .997; NO₃⁻-PO₄³⁻: r = .923; Zn-Mn: r = .90) suggest shared contamination pathways linked to mining runoff and agrochemical inputs. This review provides critical baseline data, identifies high-risk hotspots, and highlights the geochemical mechanisms driving pollution. It establishes the novelty of synthesizing Liberia-specific contamination trends with quantitative rigor, which had not been comprehensively undertaken. The study highlights the urgent need for environmental policy reform, site-specific remediation, and public health interventions to align national development with SDGs 3, 6, and 15.

Keywords

ecotoxicity geo and hydrochemical pollution mining-induced pollution review

Introduction

The presence, exceedance, and persistence of heavy metals (HMs) and polycyclic aromatic hydrocarbons (PAHs) in environmental and occupational settings have emerged as critical global concerns due to their severe impacts on human health and ecosystems.¹ These pollutants originate primarily from anthropogenic activities, including industrial processes, vehicular emissions, fossil fuel combustion, mining, and improper waste disposal.^2,3 In regions experiencing rapid industrialization, such as Liberia, contamination levels in soil, air, and water are rising at an alarming rate, posing significant health risks to local populations and threatening biodiversity.^4,5 This increasing environmental pollution not only deteriorates the quality of natural resources but also increases public health challenges, particularly in communities with limited access to healthcare and environmental remediation measures.

HMs and PAHs are particularly concerning due to their persistence in the environment and their ability to bioaccumulate. Unlike many organic pollutants that can undergo biodegradation, HMs remain stable over time, accumulating in soil, water, and living organisms. Their bioavailability increases through multiple exposure pathways, including dermal absorption, inhalation, and ingestion.^2,6 Prolonged exposure to toxic metals such as cadmium, lead, mercury, and arsenic has been linked to severe health complications, including neurological disorders, cardiovascular diseases, kidney damage, and various forms of cancer.^7,8 Similarly, PAHs formed primarily through the incomplete combustion of organic materials such as coal, wood, and petroleum are known to be highly carcinogenic and mutagenic.⁹ Studies have linked chronic PAH exposure to respiratory illnesses, DNA damage, endocrine disruption, and immune system suppression.^7,10 These pollutants pose a particularly high risk to vulnerable populations, including children, pregnant women, and individuals working in high-exposure occupations such as mining, construction, and transportation.

Beyond their direct health effects, excessive levels of these pollutants severely impact agricultural productivity and food security by contaminating soil and water. The infiltration of HMs and PAHs into agricultural lands results in toxic accumulations in crops, leading to dietary exposure and increased disease risk for consumers.^11,12 Heavy metal contamination, in particular, reduces soil fertility by disrupting microbial communities and altering essential nutrient cycles, thereby diminishing crop yields.

In many developing countries, including Liberia, where a significant portion of the population depends on subsistence farming, this contamination increases food insecurity and economic instability. Furthermore, the link between urbanization particularly in mining and industrial zones and elevated levels of these contaminants indicates a growing environmental crisis that demands urgent intervention and stringent regulatory action.^3,13 Liberia’s environmental monitoring and regulatory functions are administered by the Environmental Protection Agency (EPA), which is legally mandated to monitor, coordinate, and supervise the sustainable management of the environment in collaboration with other government ministries and agencies. In addition to its oversight role, the EPA is also responsible for developing policies and setting guidelines, including those related to solid waste management.¹⁴ However, despite its broad mandate, the EPA faces significant challenges in the effective enforcement of environmental regulations, largely due to limited institutional capacity, inadequate funding, and resource constraints. These limitations are particularly pronounced in critical sectors such as mining and wildlife protection, where regulatory oversight is often weak or inconsistently applied.¹⁵ Strengthening the EPA’s operational capacity is essential to ensuring that environmental laws are translated into meaningful action on the ground. Without comprehensive monitoring and effective pollution control strategies, the persistence of HMs and PAHs will continue to degrade vital ecosystems and compromise human health.

The need for extensive research on HM and PAH contamination in Liberia’s environmental and occupational settings is paramount, given the potential for long-term ecological degradation and severe public health consequences. Liberia’s expanding industrial sector and increasing vehicular emissions have intensified pollution levels, creating contamination hotspots in both urban and rural regions.^16,17 Conducting a comparative assessment of major pollutant sources and quantifying their environmental and health impacts is essential for developing evidence-based interventions. This study will explore contamination patterns, exposure pathways, and risk factors, providing a scientific foundation for targeted remediation strategies and policy implementation.

This research aligns with the United Nations Sustainable Development Goals (SDGs) by promoting public health, ensuring access to clean water, and fostering sustainable urban development. Through rigorous data synthesis and comprehensive environmental analysis, this study aims to equip policymakers and stakeholders with actionable insights to mitigate contamination risks and enhance environmental quality in Liberia and similar developing regions. Addressing HM and PAH pollution in Liberia directly supports SDGs 3 (Good Health and Well-being), 6 (Clean Water and Sanitation), 11 (Sustainable Cities and Communities), 13 (Climate Action), and 15 (Life on Land) agenda.

Materials and Methods

Study Area

Liberia, a West African nation bordered by Guinea, Sierra Leone, and Côte d’Ivoire, presents a complex socio-economic and environmental landscape shaped by its coastal position along the Atlantic Ocean (Figure 1). It falls between longitudes 7° 30′ and 11° 30′ west, and latitudes 4° 8′ and 8° 30′ north.¹⁸ With a population of approximately 5.5 million, around one-fifth resides in the capital, Monrovia, a hub of economic and social activity. Despite this urban concentration, over 54% of Liberians live below the poverty line, with rural poverty reaching as high as 72% to 79% in regions like the north-central and southeast.^19,20 Monrovia faces mounting challenges from rapid urbanization, including strained sanitation and healthcare systems, particularly in informal settlements such as West Point and New Kru Town. These densely populated areas lack essential services, increasing vulnerability to public health crises, as seen during the 2014 Ebola outbreak.²¹

Figure 1.

Map of the study area.

Environmental impacts are also significant, with the city generating an estimated 236 155 tons of solid waste annually much of it poorly managed, contributing to air and water pollution.²² These issues are compounded by limited infrastructure and socio-economic disparities, undermining sustainability efforts.²³

Research Design

Search Strategy and Databases

This study employed a comprehensive search strategy to identify relevant literature using keywords such as heavy metal contamination in Liberia, PAH pollution in Liberia, environmental pollution sources in Liberia, geochemistry of soil in Liberia, air pollution in Liberia, water contamination in Liberia, industrial pollution in Liberia, toxic metal exposure in Liberia, mining-related contamination in Liberia, petroleum hydrocarbon pollution in Liberia, and occupational exposure to pollutants in Liberia. These keywords guided searches across multiple reputable academic databases, including SCOPUS, Google Scholar, Elsevier/ScienceDirect, PubMed, CORE, Science.gov, and Microsoft Academic.

Inclusion and Exclusion Criteria

The review only included studies focused on geographical relevance (Liberia), publication type, scientific rigor, accessibility, and time frame (2000 onward). Only studies that specifically address heavy metal and PAH contamination using credible methodologies were included. Research lacking scientific validity and non-English publications without translations were excluded (Table 1).

Table 1.

Inclusion and Exclusion Criteria for Literature Selection.

Criteria	Inclusion criteria	Exclusion criteria
Relevance to topic	Studies focused on heavy metal contamination (eg, lead, mercury, arsenic) and PAHs in Liberia.	Research on pollution issues unrelated to heavy metals or PAHs (eg, plastic pollution, pesticide contamination, general environmental concerns).
Geographical scope	Studies conducted in Liberia or neighboring West African countries with similar environmental and industrial conditions.	Research on global pollution trends without specific relevance to Liberia.
Publication type	Peer-reviewed journal articles, government reports, and studies from recognized academic publishers (Elsevier, Wiley, Springer, etc.).	Blog posts, newspaper reports, opinion pieces, and unpublished dissertations with no scientific validation.
Time frame	Studies published from 2000 onward, ensuring the inclusion of recent research and trends.	Studies published before 2000 (unless providing essential baseline data).
Scientific rigor	Research using quantitative geochemical analysis, environmental risk assessments, laboratory testing, GIS mapping, and statistical methods.	Studies lacking scientific methodologies, statistical data, or analytical rigor.
Availability of data	Studies with full-text access to allow detailed examination of methods, results, and conclusions.	Studies with only abstracts or limited access, preventing thorough review.
Duplication	Unique studies providing new data or perspectives on contamination in Liberia.	Duplicate studies that reiterate findings from previous research without new insights.
Language restriction	Studies published in English for accessibility and accuracy.	Studies published in non-English languages (eg, French, Portuguese) without available translations.

Review Selection

A systematic approach was adopted to select literature, drawing on established methodologies from prior review studies.^24,25 An initial pool of 38 studies was identified, which was then subjected to a multi-step selection process. Five environmental science experts assessed the relevance of these studies based on 4 major evaluation criteria:

Title and keywords (Tk): The relevance of the title and keywords (30% suitability score). It was assigned a lower weight since it serves primarily as an initial indicator of relevance rather than a substantive measure of content quality. While titles and keywords are essential for identifying thematically appropriate studies, they often lack sufficient detail to assess methodological rigor.

Abstract analysis (Aa): Consideration of the study’s purpose (50%), research approach or design (50%), and summary of findings (70%). It was weighted moderately because abstracts summarize the study’s aims and findings but often omit full methodological detail.

Content analysis (Ca): Evaluation of statistical depth (40%), corroboration with existing literature (30%), inclusion of maps, charts, and figures (20%), depth of result interpretation and contextual analysis (50%), and quality of conclusions (20%). It was given the highest weight as it assesses empirical depth, analytical clarity, statistical rigor, and overall result interpretation which is a core criterium for research reliability.

Conceptual Analysis (CnA): Novelty of the methodology (50%), scope of assessed parameters (40%), and coherence of research questions, objectives, and hypotheses (50%). Significantly weighted due to its focus on innovation, parameter scope, and coherence between research objectives, which underpin theoretical and practical relevance.

Following, Amuah et al²⁵ as shown in equation (1), the final selection of literature was determined using an aggregated score across the 4 components, which was scaled to 100% using the formula:

Final score = (5 x TK + Aa + Ca + CnA) X 0.2

(1)

Data Screening and Quality Assessment

To ensure data reliability and accuracy, a structured screening and quality assessment framework was applied. The evaluation involved cross-referencing multiple sources to verify data consistency, assessing methodological correctness and statistical rigor, and checking for potential bias in the study findings. Only studies that met these strict quality control measures were incorporated into the final review. This process ensured that the study’s findings were based on credible, high-quality research, strengthening the reliability of conclusions drawn.

Data Extraction

The data extraction process involved systematically summarizing and synthesizing key findings from the selected studies. Extracted data included information on the types of contaminants studied (such as Pb, Hg, Cd, As, Cr, Ni, and PAHs), sources of contamination (including mining activities, petroleum refinery emissions, vehicular exhaust, illegal waste disposal, artisanal gold extraction, and burning of biomass and fossil fuels), and the geographical distribution of pollution across Liberia. Additionally, the study examined the health and environmental implications of contamination, particularly focusing on cancer risks, respiratory illnesses, soil degradation, water quality deterioration, bioaccumulation in food crops, and occupational hazards among miners and industrial workers. Furthermore, the study documented proposed mitigation strategies to reduce pollution levels, such as improved environmental regulations, enhanced waste management policies, pollution control technologies, and community-based monitoring initiatives. The final dataset provided a comprehensive analysis of occupational and environmental contamination in Liberia, forming the basis for policy recommendations on pollution mitigation and ecosystem restoration.

Data Analyses

Data from the reviewed studies were analyzed using Microsoft Excel 2019 and OriginPro 2024 to conduct descriptive and inferential statistical assessments of water and soil quality across Liberia. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 framework was followed to ensure transparency and rigor in the literature screening and selection process. Summary statistics were generated to examine the distribution of key parameters, including physicochemical variables, major ions, nutrients, and heavy metals. Pearson correlation analysis (two-tailed, P < .05) was used to identify significant linear relationships between variables. Additionally, Principal Component Analysis (PCA) and biplot interpretation were employed to uncover underlying patterns, dominant pollution sources, and spatial clustering of contaminants linked to mining, agriculture, and urban runoff.

Results and Discussion

Search Results

Figure 2 presents the PRISMA flowchart that illustrates the systematic screening process of academic literature related to Liberia. Initially, a total of 38 studies were identified through database searches. Out of these, 23 were excluded for not meeting the inclusion criteria, which included factors such as being unrelated to the study objectives, lacking full-text access, not being peer-reviewed, being in a non-English language, or not originating from Liberia. This left 15 potentially relevant articles, which were then screened based on their titles and abstracts. Five of these were excluded for not meeting the expected quality standards. Of the remaining 10 articles that met the inclusion criteria, 1 was excluded after a thorough quality assessment due to incompatibility with the inclusion criteria, being review articles, or raising ethical concerns. Ultimately, 9 studies were selected for synthesis in the final review. No studies on air pollution and PAH levels were retrieved through the search process.

Figure 2.

PRISMA flow diagram employed in this study.

Characteristics of Included Studies

A limited number of articles were obtained from the search. While 38 articles were initially retrieved, only 10 were quantitative studies directly focusing on the area of interest. After a detailed suitability assessment, 9 papers were retained. However, some of these studies addressed more than 1 area of interest. Of the 9 selected articles, 6 dealt with water quality issues specifically, 5 addressed physicochemical parameters^4,26-29, 5 examined chemical contaminants,^26-30 and 7 explored metals, trace elements, and nutrients.^4,26-29,31 Additionally, 2 studies^4,32 for used on soil quality parameters (Figure 3).

Figure 3.

Number of articles based on articles’ focus.

Water Quality in Liberia

Physical Characteristics of Water Resources

Water quality assessments across Liberia revealed a complex interplay between ecological variation, land-use patterns, and anthropogenic pressures, particularly mining and informal urbanization. Temperature is a fundamental water quality parameter that influences biological activity, chemical solubility, and water taste. Across Liberia, temperature values ranged from 23.00°C to 28.23°C, with an average of 25.45°C and a standard deviation of 2.20°C (Table 2), indicating relative thermal stability across the sampled sites. Although the WHO does not specify a permissible limit for temperature in drinking water, elevated temperatures can accelerate microbial activity and increase the rate of chemical reactions, potentially affecting taste and increasing the risk of pathogen proliferation.³³

Table 2.

Statistical Summary of Physical Water Quality.

Statistical parameters	Temp	Color	Turb	TDS	TSS	EC
Min	23.00	20.00	0.82	1.50	20.00	8.61
Max	28.23	320.00	1015.00	525.00	6.00	50.00
Avg	25.45	31.25	42.85	267.92	135.00	874.14
SD	2.20	13.15	89.19	257.56	147.75	1138.00
Kurt	−2.24	2.23	5.52	0.45	3.70	−0.88
Skew	−0.09	1.44	2.60	0.93	2.09	0.90
WHO Guidelines	15 TCU	5 NTU	1000 mg/L	1500 µS/cm

Data reported by Kamara⁴ support these findings, particularly in the New Tailings Storage Facility (TSF) and Kokoya District, where recorded temperatures of 27.74°C and 26.84°C were linked to the region’s environmental conditions. The Kokoya region is characterized by latosols, which are highly weathered tropical soils rich in silica and organic matter. These soils dominate approximately 18% of Liberia’s total landmass, particularly in Bong County. Open-pit gold mining operations in this region alter land surface properties, increasing heat absorption and potentially raising groundwater and surface water temperatures. Such thermal changes can have ecological impacts, highlighting the need for integrated temperature monitoring in mining-affected areas.

Water color is an esthetic parameter often influenced by the presence of dissolved organic substances, metallic ions, and industrial effluents. In Liberia, color values ranged from 20.00 TCU at the New TSF to 50.00 TCU in the St. John River, with a mean of 31.25 ± 13.15 TCU. These values substantially exceed the WHO guideline of 15 TCU, suggesting poor water esthetics and potential chemical contamination.^33,34 Elevated color levels can also signal the presence of precursors for disinfection by-products (DBPs), which pose significant health risks if not properly managed. In the Koira-Joda region, Gleekia and Sahu²⁶ and Wilson et al²⁹ reported similar findings, highlighting the link between anthropogenic activity and optical water degradation. Industrial discharge, domestic waste runoff, and decaying vegetation are likely contributors. The esthetic deterioration observed in these areas highlights the importance of upstream pollution control and the need for consistent visual and chemical assessments of water sources.

Turbidity, a measure of water clarity, serves as an indirect indicator of suspended solids and microbial presence. In Liberia, turbidity ranged from 0.82 NTU to 320.00 NTU, averaging 42.85 NTU, significantly above the WHO guideline of 5 NTU.^35,36 The high standard deviation of 89.19, coupled with kurtosis (5.52) and positive skewness (2.60), indicates the presence of outliers and episodic spikes likely caused by rainfall events, erosion, or waste discharge. Chea et al,²⁸ Caabay,³⁷ and Pragasen et al³⁸ reported low turbidity levels in the Cotton Tree Community along the Robertsfield Highway, indicating less anthropogenic influence. In contrast, Gleekia and Sahu²⁶ recorded values as high as 250 NTU and 300 NTU in Bong County’s mining zones. Chea et al²⁸ and Wilson et al²⁹ also observed moderate values ranging from 0.85 NTU to 65 NTU, depending on site-specific land use. These variations reinforce the need for localized turbidity management strategies, particularly in hi h-risk, erosion-prone, or heavily mined areas.

TDS reflects the concentration of inorganic salts and small amounts of organic matter dissolved in water. In this review, TDS values in Liberia ranged from 1.50²⁹ to 1015.00 mg/L,³¹ with an average of 267.92 mg/L and a standard deviation of 257.56 mg/L. Most studies fell within the WHO limit of 1000 mg/L, though exceedances were noted, particularly near estuarine or industrial zones.³³ Kumpel et al³¹ reported the highest TDS near the Bonny River, where saltwater intrusion and urban runoff are common. Scheelbeek et al³⁹ found similar issues in Monrovia, where densely populated areas like West Point showed elevated TDS and were linked to cholera outbreaks. Peri-urban areas had lower concentrations, likely due to reduced infrastructure and lower population density. These findings highlight the importance of TDS as a marker for salinity, pollution, and potential health risks.

TSS results were documented primarily in studies by Gleekia and Sahu²⁶ and Chea et al²⁸ and this review found concentrations ranging from 20.00 to 525.00 mg/L, with an average of 135.00 mg/L and a standard deviation of 147.75 mg/L. Although WHO does not have a specific guideline for TSS in drinking water, high concentrations can interfere with disinfection, contribute to sedimentation, and increase microbial risks. Areas with minimal human disturbance recorded notably lower TSS values (see Figure 4a and b).

Figure 4.

(a) Cotton tree community and (b) iron mining area.

In Figure 4a, the findings presented by Chea et al²⁸ illustrate a well-defined parabolic trend in Total Suspended Solids (TSS) levels across samples collected from the Cotton Tree Community, a peri-urban settlement along the Robertsfield Highway. The data revealed a gradual increase in TSS concentrations from sample point S-1, peaking prominently at S-3, followed by a decline at S-4. The polynomial regression model y = −37.5x² +192.5x − 62.5 displayed a strong coefficient of determination R² = .8364, indicating that over 83% of the variance in TSS levels is explained by the model. This high degree of fit suggests a structured and predictable pattern of suspended solid accumulation, most likely driven by localized anthropogenic influences such as domestic wastewater disposal, inadequate drainage systems, and potentially seasonal overland flow during the rainy season. The peak observed at S-3 may correspond to a point-source discharge such as a drainage outlet, refuse pile, or a natural depression where sediment-laden runoff accumulates. These characteristics are indicative of pollution that is both spatially constrained and potentially remediable through site-specific interventions like improved drainage and waste management infrastructure.²⁸

In stark contrast, Figure 4b, derived from data reported by Gleekia and Sahu,²⁶ presents TSS concentrations across 14 sites within an iron ore mining region, displaying a far more erratic and disorganized distribution. Unlike the structured pattern in Cotton Tree, the mining region exhibits significant fluctuations, with TSS levels reaching extreme values at K-4 and K-5, exceeding 500 mg/L. The polynomial model y = −0.8551x² − 1.2837x + 205.91 yields a low R² = .1302, indicating a poor fit and highlighting the sporadic nature of pollution in the area. These variations likely result from mining-induced processes such as excavation runoff, tailings leachate, and sediment mobilization exacerbated by heavy rainfall and topographic factors. The spatial inconsistency suggests the presence of diffuse, non-point source pollution, compounded by a lack of environmental controls and sediment retention systems in mining operations.²⁶

Together, these results strongly illustrate the contrasting pollution dynamics between a relatively stable peri-urban community and a heavily disturbed mining zone. In the Cotton Tree Community, the pattern of TSS is largely driven by predictable localized activities, making it amenable to management through sanitation and drainage interventions. In comparison, the mining area’s highly irregular and elevated TSS levels point to a complex, diffuse pollution profile influenced by unregulated industrial activities, highlighting the need for stringent environmental oversight. These findings emphasize the importance of tailoring water quality management strategies to specific land-use contexts focusing on infrastructure upgrades in urban fringes, and comprehensive environmental regulation and sediment control in extractive industry regions.^26,28

EC values across Liberia demonstrated substantial spatial variability, ranging from 8.61 to 3000.00 µS/cm, with an average of 874.14 µS/cm and a high standard deviation of 1138.00 µS/cm. This wide range indicates considerable differences in dissolved ion concentrations across various land-use types, reflecting the influence of geological, anthropogenic, and hydrological factors.^33,37 The lowest EC values, reported by Kamara,⁴ were approximately 97% below the³⁶ guideline of 1500 µS/cm, suggesting minimal mineralization and low levels of ionic contaminants in those sources. In contrast, Wilson et al²⁹ documented EC values that exceeded the WHO limit by 41%, particularly in industrial zones, which are typically associated with elevated concentrations of chlorides, nitrates, and sulfates.

These elevated EC readings often signal salinity intrusion, industrial effluent discharge, or natural geochemical leaching, all of which can compromise both the esthetic quality and safety of drinking water. High EC may also reflect cumulative impacts from mining activities, agricultural runoff, and urban wastewater, especially in coastal and densely populated areas. As noted by Nguyen et al,⁴⁰ these zones require ongoing monitoring to assess the risks of ionic contamination and its implications for public health and ecosystem stability. The observed variability across Liberia highlights the critical need for site-specific assessments and regulatory oversight to ensure safe water quality standards are maintained consistently.

Chemical Characteristics of Water Resources

The assessment of chemical and nutrient parameters in Liberia’s water bodies reveals critical insights into the potential impacts of anthropogenic activities and the variability of natural water chemistry across different regions. pH, which serves as an essential indicator of water acidity or alkalinity, exhibited a notable range in Liberia, with values from non-detectable levels up to 8.65. The mean pH is recorded at 6.27, with a standard deviation of 1.67. Notably, the presence of ND values, as recorded by Kamara,⁴ may suggest either highly acidic conditions, possibly due to acid mine drainage, or limitations in measurement techniques.⁴¹ Conversely, the highest pH of 8.65 reported by Kumpel et al³¹ is within the acceptable range established by the World Health Organization (WHO) of 6.5–8.5.⁴² The data distribution of these pH values was negatively skewed (−1.96) with high kurtosis (5.61), indicating that while most values are concentrated around neutral to slightly acidic conditions, there are extreme low pH values that could threaten aquatic ecosystems and water infrastructure.⁴³

DO is another vital parameter that significantly impacts aquatic life and acts as a reliable indicator of overall water quality. In Liberia, DO concentrations varied from as low as 2.50⁴ to 9.10 mg/L,²⁶ averaging 7.07 mg/L. The standard deviation of 1.98, paired with skewness (−1.40) and kurtosis (0.83), highlights a predominance of higher DO levels, albeit with some low outliers.⁴⁴ Despite the minimum DO exceeding critical thresholds for fish welfare, persistent exposure to low concentrations could adversely affect aquatic biodiversity. Higher DO levels, particularly in less disturbed regions, correlate with improved ecological health and indicate a reduced presence of organic pollution.⁴⁵

Biochemical oxygen demand over 3 days (BOD₃) serves as an essential measure of organic matter decomposition in the water. The recorded values within Liberia ranged from 2.50 to 6.00 mg/L, with the lowest found at site K-1 by Gleekia and Sahu²⁶ and the highest at site K-5, also in the mining region. This yields a mean BOD₃ of 3.73 mg/L, with skewness of 1.27 and kurtosis of 2.31, suggestive of slightly elevated organic matter levels.⁴⁶ Many studies surpassed the WHO guideline of 3 mg/L, indicating significant pollution, presumably influenced by domestic waste and runoff from mining activities.⁴⁷

TOC is crucial for understanding organic content in water, with values ranging from 1.00 mg/L by Wilson et al²⁹ (iron ore mines (China Union Bong Mines Investment)) to as high as 25.00 mg/L at site K-5 reported by Gleekia and Sahu.²⁶ The mean TOC was calculated at 9.83 mg/L, with a notable standard deviation of 10.76, indicating variability and occasional spikes in organic content.⁴⁸ The positive skewness (0.90) and negative kurtosis (−1.70) suggest that while moderate TOC values are common, higher concentrations could imply the presence of untreated wastewater and organic pollutants.⁴⁹

In terms of acidity and alkalinity, analyses display acidity levels fluctuating from 2.50 to 6.50 mg/L,^26,29 and alkalinity ranging from 3.50 to 10.00 mg/L.⁵⁰ The mean acidity and alkalinity are 3.88 and 6.00 mg/L, indicative of a weak buffering capacity which may expose the water bodies to rapid pH alterations.⁵¹ Chloride concentrations remained minimal, recorded between 0.15 and 0.21 mg/L, which are significantly lower than the WHO threshold of 300 mg/L, reflecting minimal salinity impacts.⁵² Sulfate levels fluctuated from 2.00 to 23.00 mg/L, suggesting inputs from sulfide oxidation or release from mining activities.⁵³

Nitrate levels exhibited substantial variability, ranging from 0.00 mg/L to a concerning 1200.00 mg/L. The minimal concentrations were associated with readings by Chea et al,²⁸ while Gleekia and Sahu²⁶ reported the maximum figures. The average nitrate concentration was 83.91 mg/L, which exceeds the WHO guideline of 50 mg/L, potentially posing health risks, particularly for infants.⁵⁴ The extreme kurtosis (20.29) and skewness (4.16) indicate significant pollution events likely associated with agricultural runoff or untreated sewage contamination.⁵⁵ In contrast, nitrite concentrations ranged from 0.01 to 500 mg/L, remaining generally within WHO guidance; however, high skewness (5.27) and kurtosis (27.84) again suggest notable pollution incidents.⁵⁶

Presented in Table 3, phosphate levels were recorded from 0.00 to 7.43 mg/L, with the average concentration at 3.19 mg/L slightly exceeding the WHO limit of 3 mg/L, potentially contributing to eutrophication concerns in surface waters.⁵⁷ Though some²⁹ measured PO₄³⁻ levels reported by Gleekia and Sahu²⁷ at Chocolate city, Clara Town, Vai Town, and West Point exceeded the recommended standard with levels reaching 7.40 and 7.43 mg/L. Phenol concentrations showcased minor variations between 0.01 and 0.02 mg/L, pinpointed by Gleekia et al,³⁰ indicating low but significant industrial-related pollution risks. Each of these site-specific outcomes captures both chronic and localized chemical pollution in Liberia’s water bodies. The findings highlight an urgent call for targeted interventions and meticulous monitoring efforts, particularly in regions affected by mining and urbanization.⁵⁸ The comprehensive analysis of these chemical and nutrient data from Liberia’s water bodies elucidates the substantial anthropogenic impacts and the natural variability in water chemistry. Continued monitoring and effective environmental management strategies will be vital to mitigate these impacts and enhance the ecological integrity of these critical water resources.

Table 3.

Statistical Summary of Chemical Water Quality.

Statistical parameters	pH	DO	BOD₃ (mg/L)	TOC (mg/L)	Acidity	Alkalinity (mg/L)	Cl⁻ (mg/L)	S (mg/L)	NO₃⁻ (mg/L)	NO₂⁻ (mg/L)	PO₄³⁻ (mg/L)	Phenol
Min	ND	2.50	2.50	1.00	2.50	3.50	0.15	2.00	0.00	0.01	0.00	0.01
Max	8.65	9.10	6.00	25.00	6.50	10.00	0.21	23.00	1200.00	5.00	7.43	0.02
Avg.	6.27	7.07	3.73	9.83	3.88	6.00	0.18	7.75	83.91	0.27	3.19	0.01
SD	1.67	1.98	1.00	10.76	1.28	2.09	0.03	10.21	213.11	0.93	2.12	0.00
Kurt.	5.61	0.83	2.31	−1.70	0.54	0.17	0.93	3.82	20.29	27.84	−0.41	−1.29
Skew.	−1.96	−1.40	1.27	0.90	1.13	1.02	0.56	1.95	4.16	5.27	0.07	0.45
WHO Guidelines	6.5-8.5		3	2		300	5	250	50	3	3

Heavy Metals/Trace Element Levels in Water

The contamination of water resources in Liberia with heavy metals and trace elements presents serious health and ecological risks. Liberia’s growing artisanal and industrial mining operations, coupled with poor waste management and unregulated industrial runoff, contribute significantly to the widespread presence of these pollutants. Across multiple studies, these substances are often found at levels far exceeding international safety thresholds. Arsenic concentrations in Liberian water systems have been reported between 0.00 and 0.80 mg/L, with an average of 0.15 mg/L. These figures are particularly concerning as the WHO guideline recommends a maximum of 0.01 mg/L. Arsenic levels exceeding this limit reported especially in areas studied by Gleekia and Sahu²⁶ and Wilson et al²⁹ signal the critical health threats posed by prolonged exposure, including carcinogenic and cardiovascular effects.⁵⁹ These levels are likely linked to natural mineral leaching exacerbated by mining disturbances.⁶⁰

Fluoride concentrations, ranging from 0.01 to 0.06 mg/L with an average of 0.04 mg/L, are within WHO limits (1.5 mg/L) but still warrant attention due to fluoride’s narrow therapeutic window. Fluoride deficiency can cause dental problems, while excess leads to fluorosis.^61,62 The relatively low levels reported by Kumpel et al³¹ may reflect limited industrial fluoride inputs or natural deposits. Iron concentrations show dramatic variability, from 0.01 to 23.69 mg/L, significantly exceeding the WHO guideline of 0.3 mg/L. The average of 4.53 mg/L, particularly from studies in mining zones by Gleekia et al,⁶³ highlights the direct correlation between mining intensity and water contamination. Although iron is not highly toxic, it affects water taste, contributes to pipe scaling, and promotes bacterial proliferation.^64,65

Copper levels are relatively low across Liberia, ranging from 0.00 to 0.05 mg/L and averaging 0.01 mg/L, well below the WHO threshold of 2 mg/L. Data from Gleekia and Sahu²⁷ suggest minimal industrial discharge. However, excess copper exposure, if it were to rise, could lead to liver and kidney damage.⁶¹ Zinc levels fluctuate from 0.00 to 7.09 mg/L (average 1.59 mg/L), with upper values nearing or surpassing the WHO recommendation of 3 mg/L. These were primarily recorded in industrially influenced areas by Gleekia and Sahu.²⁷ Elevated zinc may be associated with runoff from galvanization and fertilizer use.⁶⁶

Nickel was detected between 0.01 and 0.78 mg/L (average 0.13 mg/L), surpassing the WHO guideline of 0.07 mg/L in several cases. The highest levels, found in studies by Gleekia et al,⁶³ likely result from ultramafic rock weathering and industrial processes. Nickel exposure can lead to allergic dermatitis and has been classified as a probable carcinogen.⁶⁷ Manganese, found between 0.00 and 9.12 mg/L with an average of 2.33 mg/L, greatly exceeds the WHO limit of 0.1 mg/L. This is especially common in iron-rich mining zones, as seen in Gleekia et al.⁶³ Long-term manganese exposure is neurotoxic, especially in children.^65,68

Lead was detected in concentrations ranging from 0.01 to 0.16 mg/L (average 0.03 mg/L), with several samples exceeding the WHO permissible limit of 0.01 mg/L. While Kamara⁴ reported the lowest values, Gleekia et al^30,63 recorded hazardous concentrations in areas likely affected by battery disposal, decaying infrastructure, and industrial pollution. Lead is a potent neurotoxin, especially in children.^65,67 Mercury levels between 0.00 and 0.04 mg/L (average 0.01 mg/L) often exceed the WHO limit of 0.006 mg/L. Gold mining regions, where mercury is used for amalgamation, are especially at risk, as documented by Gleekia and Sahu.²⁶ Mercury bioaccumulates and can lead to severe neurological and developmental damage.⁵⁹

Cadmium concentrations from 0.00 to 0.40 mg/L (average 0.01 mg/L) represent another major concern, as the WHO limit is 0.003 mg/L. Gleekia et al⁶³ noted cadmium contamination in heavily mined regions. Chronic exposure can result in kidney damage and bone disease.⁶⁶ Chromium levels, ranging from 0.00 to 0.16 mg/L (average 0.02 mg/L), were mostly within safe limits, though occasional spikes raise concerns. Studies by Gleekia et al³⁰ suggest that industrial activities may introduce chromium into water systems, possibly including its more toxic hexavalent form.

Cobalt concentrations, generally ranging from 0.00 to 0.04 mg/L, reported an unusually high average of 1.93 mg/L, likely skewed by outliers from mining zones. Excessive cobalt intake may impact heart and lung function, and its sources require further scrutiny.⁶⁶ Vanadium, although not limited by WHO, was found in concentrations of up to 0.40 mg/L (average 0.08 mg/L). Industrial leaching, especially near mining sites, may explain the elevated levels observed by Gleekia and Sahu.²⁷ Vaadium exposure may impair kidney and nervous system function.

Selenium concentrations were among the most concerning, with values from 0.00 to 16.40 mg/L (mean 0.15 mg/L), greatly exceeding the WHO limit of 0.04 mg/L. High selenium levels in studies by Gleekia and Sahu²⁷ are attributed to mining and agriculture. Selenium toxicity, or selenosis, poses risks including gastrointestinal distress and neurological disorders.^69,70 Aluminum was found between 0.00 and 0.26 mg/L (average 0.01 mg/L), generally within WHO standards (0.1 mg/L), but outliers noted by Wilson et al²⁹ point to possible treatment residues or geogenic origins.

Barium concentrations ranged from 0.05 to 0.30 mg/L (average 0.34 mg/L; Table 4), within the WHO permissible limit of 1.3 mg/L. Though not acutely toxic, chronic exposure may contribute to cardiovascular issues. Boron was found at levels ranging from 0.00 to 8.00 mg/L, averaging 3.39 mg/L which is well above the WHO limit of 2.4 mg/L. Sources likely include detergents, fertilizers, and borate minerals, as observed in studies by Gleekia and Sahu.²⁶ High boron exposure may impair reproductive and developmental health. Water resources in Liberia exhibit a concerning degree of heavy metal and trace element contamination, particularly in areas affected by mining and industrial activities. As, Fe, Mn, Pb, Hg, Cd, and Se frequently exceeded WHO safety thresholds. These patterns highlight an urgent need for systematic pollution monitoring, policy enforcement, and public health interventions to safeguard water resources and the populations that depend on them.

Table 4.

Heavy/Trace Elements in Water Resources (mg/L).

Statistical parameters	As	F	Fe	Cu	Zn	Ni	Mn	Pb	Hg	Cd	Cr	Co	V	Se	Al	Ba	B
Min	0.00	0.01	0.01	0.00	0.00	0.01	0.0046	0.00	0.00	0.00	0.00	0.00	0.05	0.00	0.40	0.15	0.02
Max	0.80	0.06	23.69	0.05	7.09	0.78	9.12	0.16	0.04	0.03	16.40	4.68	0.30	0.10	45.00	0.60	8.00
Avg.	0.15	0.04	4.53	0.01	1.59	0.13	2.33	0.03	0.01	0.02	1.93	0.08	0.15	0.01	16.59	0.34	3.39
SD	0.25	0.04	6.35	0.01	1.89	0.23	4.03	0.05	0.01	0.07	5.43	0.10	0.09	0.03	15.71	0.17	3.07
Kurt	1.25		2.08	1.87	0.70	8.06	0.01	−0.06	3.66	29.03	8.97	0.34	0.88	7.93	−0.54	−1.07	−1.64
Skew	1.59		1.63	1.61	1.25	2.75	1.43	1.21	1.87	5.35	2.99	1.41	1.16	2.81	0.74	0.63	0.18
WHO Guidelines	0.01	1.5	0.3	2	0.1	0.07	0.1	0.01	0.006	0.003	0.05	0.1	0.02	0.04	0.1	1.3	2.4

Nutrient Concentrations in Water Resources

The contamination of water resources in Liberia with heavy metals, trace elements, and major cations presents profound health and ecological risks. This growing concern is driven largely by the expansion of artisanal and industrial mining operations, combined with inadequate waste management and unregulated industrial runoff. Ca²⁺, an essential element for bone health and metabolic function, was found in concentrations ranging from 1.71 to 23.01 mg/L, with an average of 10.98 mg/L, according to Gleekia et al.⁶³ These values fall well within the WHO recommended guideline of 75 mg/L, suggesting minimal contamination from calcium-rich rocks or industrial sources. The relatively stable concentrations and modest variability (SD = 7.63, skew = 0.59) indicate a fairly uniform distribution across sampled sites. This stability may reflect the natural geochemistry of Liberia’s watersheds and aquifers rather than anthropogenic inputs. Still, as Wilson et al²⁹ point out, localized fluctuations due to geological conditions warrant ongoing monitoring to identify any potential deviations influenced by mining or land use changes.

Na⁺ concentrations, by contrast, exhibited much greater variability, ranging from 0.34 to 84.04 mg/L, averaging 21.52 mg/L. While the average falls below the WHO limit of 50 mg/L, the upper extreme, recorded by Gleekia et al,⁶³ significantly exceeds this threshold. Sodium in water can originate from saline intrusion, industrial waste, or agricultural runoff. Its presence in elevated amounts is concerning, particularly for individuals with hypertension or those on sodium-restricted diets. The high standard deviation (41.68) and skewness (2.00) reflect a distribution heavily influenced by extreme values. These findings highlight the necessity of sodium monitoring in coastal regions and industrial hubs, where water quality is more susceptible to contamination. Sylvain et al⁷¹ and Kagambega et al⁷² emphasized the broader implications for cardiovascular health and the need for regulatory frameworks tailored to vulnerable populations.

Mg²⁺ is vital element involved in nerve and muscle function, was found in concentrations ranging from 0.33 to 35.11 mg/L, with an average of 16.24 mg/L. All values reported by Gleekia et al⁶³ remain under the WHO threshold of 50 mg/L. While magnesium is beneficial in moderate amounts, high concentrations may affect taste and pose mild laxative effects. The standard deviation (16.12) and skewness (0.31) suggest relatively even distribution with a few elevated values. The flat kurtosis (−3.30) indicates a broad, spread-out dataset rather than extreme peaks, which may reflect geochemical variability in underlying rock formations. As discussed by Kagambega et al⁷² and Kumi et al⁷³ understanding these geological influences is key to managing magnesium concentrations effectively, especially in regions undergoing mineral extraction.

K⁺ concentrations ranged from 5.58 to 22.41 mg/L, averaging 13.49 mg/L (Table 5). This average slightly exceeds the WHO guideline of 12 mg/L, although the potential health risks are generally minimal for the general population. However, potassium-rich water can pose significant hazards to individuals with compromised kidney function or those on dialysis. The nearly symmetrical distribution (skew = 0.06) and highly negative kurtosis (−5.65) suggest that values are evenly spread without extreme deviations, pointing to localized, naturally occurring sources of potassium. These may include mineral weathering and moderate agricultural runoff, as highlighted by Sylvain et al⁷¹ and Ustiatik et al.⁷⁴ Although potassium does not typically raise public alarm like heavy metals, its elevation in certain locations warrants further exploration of local agricultural practices and soil characteristics.

Table 5.

Nutrient Levels in Water Sources.

Statistical parameters	Ca²⁺ (mg/L)	Na⁺ (mg/L)	Mg²⁺ (mg/L)	K⁺ (mg/L)
Min	1.71	0.34	0.33	5.58
Max	23.01	84.04	35.11	22.41
Avg.	10.98	21.52	16.24	13.49
SD	7.63	41.68	16.12	8.98
Kurt	−1.08	4.00	−3.30	−5.65
Skew	0.59	2.00	0.31	0.06
WHO Guidelines	75	50	50	12

Taken together, the profiles of calcium, sodium, magnesium, and potassium in Liberia’s water systems indicate that while most concentrations are within WHO’s acceptable limits, notable outliers especially for sodium and potassium present localized public health risks. These findings align with broader regional patterns observed in West Africa, where industrial, mining, and agricultural pressures frequently alter the ionic composition of water resources.^75,76 The variability in sodium and potassium concentrations, in particular, calls for site-specific water quality assessment, especially in peri-urban and mining-affected communities. Continued monitoring and adaptive water management strategies are essential for safeguarding public health and maintaining ecological stability amidst ongoing industrial development in Liberia.

Liberia’s water resources, while generally containing essential cations within tolerable ranges, are not immune to the challenges posed by environmental degradation. With the increasing influence of human activity on water quality, particularly from mining and agriculture, strategic intervention is necessary. A robust water governance framework, incorporating regular monitoring, public awareness campaigns, and infrastructural investments can help mitigate risks and ensure sustainable water access across th country.

Parametric Relationships in Water Sources

The correlation analysis offers a comprehensive view of the interdependencies among physicochemical parameters, nutrients, ions, and trace metals in Liberia’s water systems (Table 6). The exceptionally strong correlation between EC and TDS (r = .997, P < .01) confirms that EC is a reliable indicator of dissolved ion concentrations in water. This relationship reflects the dominant role of ions such as Na⁺, Cl⁻, and HCO₃⁻ in influencing conductivity consistent with findings in broader water quality studies.^77,78 Additional positive correlations between EC and cations like Mg²⁺, Na⁺, and Ca²⁺ further suggest that both natural weathering and human activities, especially agriculture and mining significantly contribute to ionic content.^79,80

Table 6.

Correlation Matrix of Water Resources.

Statistical parameters	Temp	Color	Turb	TDS	TSS	EC	pH	DO	BOD₃	TOC	Acidity	Alkalinity	Cl	S	NO₃	NO₂	PO₄	Phenol	As	Fe	Cu	Zn	Ni	Mn	Pb	Hg	Cd	Cr	Co	V	Se	Al	Ba	B	Ca	Na	Mg	k
Temp	1.00
Color	−0.31	1.00
Turb	−0.56	0.19	1.00
TDS	−0.75	0.32	0.17	1.00
TSS	−0.43	−0.34	−0.29	0.09	1.00
EC	−0.64	−0.56	0.54	−0.18	−0.52	1.00
pH	0.12	0.05	−0.15	−0.17	0.09	−0.39	1.00
DO	−0.82	0.28	0.26	0.21	−0.30	0.54	−0.24	1.00
BOD₃	−0.06	0.00	0.04	−0.44	−0.52	0.06	0.16	−0.46	1.00
TOC	−0.45	0.20	0.45	0.31	0.18	0.64	0.44	0.28	0.27	1.00
Acidity	−0.06	0.24	−0.25	−0.15	−0.42	−0.24	0.05	−0.47	0.90	0.38	1.00
Alkalinity	0.00	0.35	−0.22	−0.22	−0.45	−0.21	0.12	−0.49	0.92	0.40	0.98	1.00
Cl	−0.80	−0.58	0.59	−0.73	0.80	0.87	0.17	0.00	0.66	0.84	0.52	0.48	1.00
S	0.68	−0.27	−0.58	−0.38	0.26	−0.05	−0.99	0.42	−0.22	−0.55	−0.20	−0.25	−0.12	1.00
NO₃	0.83	−0.32	0.18	−0.13	−0.12	0.41	−0.18	−0.17	0.45	−0.38	0.30	0.37	0.21	−0.21	1.00
NO₂	−0.37	−0.92	−0.10	0.26	−0.05	−0.22	0.24	0.11	−0.43	−0.35	−0.38	−0.43	−0.72	−0.37	−0.10	1.00
PO₄	−0.48	0.93	0.26	−0.16	−0.51	0.82	−0.21	0.51	−0.01	0.14	0.14	0.17	−0.54	−0.65	−0.13	−0.10	1.00
Phenol	−0.05	−0.41	−0.29	−0.05	−0.37	−0.12	0.02	0.03	−0.45	−0.56	−0.54	−0.52	−0.66	0.11	−0.16	0.51	0.02	1.00
As	0.24	—	−0.15	−0.08	−0.29	0.02	0.10	0.07	0.10	—	0.17	0.17	—	—	−0.16	−0.12	−0.04	0.61	1.00
Fe	−0.34	0.95	−0.18	−0.06	−0.21	0.53	−0.06	0.43	0.24	0.53	0.37	0.44	0.04	−0.39	0.19	−0.13	0.29	−0.38	0.14	1.00
Cu	0.80	−0.38	−0.13	0.12	0.58	−0.56	0.18	−0.65	0.09	−0.33	−0.08	0.00	0.98	0.08	−0.14	−0.10	0.09	−0.42	−0.40	−0.24	1.00
Zn	0.32	−0.32	−0.14	−0.06	−0.16	0.03	0.14	0.04	0.06	−0.38	0.01	0.02	—	—	−0.31	−0.11	0.29	−0.62	−0.19	0.33	0.45	1.00
Ni	0.44	−0.38	−0.18	−0.55	0.59	−0.21	−0.04	−0.31	−0.07	−0.41	−0.19	−0.16	0.24	0.91	0.21	−0.16	−0.21	0.02	−0.04	−0.08	0.31	0.25	1.00
Mn	0.61	−0.32	−0.62	−0.75	−0.15	−0.37	0.10	−0.63	0.54	−0.38	0.30	0.39	−0.43	0.94	0.89	−0.23	−0.23	−0.28	−0.23	−0.22	0.78	−0.20	0.92	1.00
Pb	0.16	0.32	−0.15	0.09	0.60	−0.32	0.01	−0.18	−0.44	0.07	−0.17	−0.25	−0.73	−0.38	−0.23	0.03	0.11	−0.35	−0.25	0.33	0.62	0.64	−0.28	−0.31	1.00
Hg	−0.50	−0.44	0.47	0.27	−0.19	−0.05	0.36	0.03	−0.01	0.23	−0.22	−0.22	0.12	−0.09	−0.14	0.16	−0.30	0.76	−0.07	−0.27	0.03	0.16	−0.36	−0.61	−0.06	1.00
Cd	0.43	0.00	−0.08	−0.29	−0.06	−0.33	0.19	−0.67	0.74	−0.23	0.64	0.74	—	—	−0.17	−0.09	0.17	−0.44	−0.26	−0.15	0.20	0.42	0.12	0.98	0.02	−0.18	1.00
Cr	0.22	0.94	0.52	−0.45	0.65	−0.22	−0.08	0.02	−0.22	0.35	−0.33	−0.33	−0.35	−0.64	−0.12	−0.10	−0.15	−0.11	−0.13	0.07	0.09	0.24	0.94	−0.37	−0.21	−0.37	−0.22	1.00
Co	0.27	−0.41	0.47	−0.50	0.65	−0.24	−0.08	−0.03	−0.18	−0.32	−0.31	−0.31	0.85	0.09	−0.05	−0.13	−0.19	−0.37	−0.14	0.02	0.16	0.23	0.96	0.99	−0.22	−0.41	−0.16	1.00	1.00
V	−0.52	−0.13	0.54	0.54	−0.37	−0.06	−0.18	0.50	−0.53	−0.45	−0.42	−0.56	−0.87	0.13	−0.51	0.84	0.38	0.68	—	−0.24	−0.62	−0.51	−0.55	−0.50	0.81	0.61	−0.56	0.24	−0.51	1.00
Se	0.23	−0.53	−0.54	−0.42	−0.29	−0.24	0.13	−0.46	0.08	0.71	0.12	0.12	0.67	−0.24	0.07	−0.14	−0.18	0.61	1.00	−0.08	−0.37	1.00	0.09	−0.27	−0.26	0.66	−0.28	−0.21	−0.34	0.08	1.00
Al	−0.72	−0.26	−0.34	0.56	−0.50	−0.30	−0.31	0.31	−0.03	−0.47	0.08	0.01	−0.83	0.35	−0.33	0.64	0.45	0.29	−0.21	−0.09	−0.50	−0.41	−0.38	−0.15	−0.14	−0.07	0.08	−0.42	−0.44	0.98	−0.34	1.00
Ba	−0.53	−0.43	0.55	0.52	−0.25	0.09	−0.32	0.50	−0.53	−0.46	−0.44	−0.60	−0.73	0.34	−0.52	0.73	0.15	0.68	—	−0.40	−0.62	−0.52	−0.54	−0.50	0.75	0.68	−0.64	0.04	−0.49	0.97	0.06	0.99	1.00
B	−0.69	−0.54	−0.31	0.53	−0.51	−0.26	−0.25	0.31	0.10	−0.43	0.17	0.11	−0.73	0.34	−0.23	0.54	0.41	0.06	−0.38	−0.05	−0.35	−0.57	−0.37	0.05	−0.18	−0.13	0.25	−0.44	−0.44	0.93	−0.54	0.96	0.97	1.00
Ca	−0.67	−0.35	0.56	0.29	−0.63	0.58	−0.04	0.59	−0.04	−0.51	−0.19	−0.22	−0.80	0.32	−0.36	0.51	0.20	0.37	−0.23	−0.15	−0.36	−0.23	−0.52	−0.18	−0.19	0.67	−0.12	−0.43	−0.45	0.98	−0.28	0.59	0.99	0.56	1.00
Na	0.74	0.74	−0.56	−0.33	0.17	−0.14	−1.00	0.45	−0.24	−0.61	−0.21	−0.26	−0.21	1.00	−0.27	−0.33	−0.57	0.14	—	−0.35	−0.01	—	0.87	0.96	−0.33	−0.14	—	0.57	−0.01	0.18	−0.27	0.40	0.37	0.37	0.36	1.00
Mg	0.87	0.03	−0.41	0.78	−0,52	−0.62	−0.26	0.90	−0.89	−0.95	−0.79	−0.77	−0.93	0.26	0.11	0.77	0.24	0.88	—	−0.41	−0.88	—	−0.01	0.57	0.78	0.22	—	0.00	−0.59	0.98	−0.90	0.98	0.93	0.93	0.97	0.32	1.00
K	0.25	0.83	−0.85	−0.56	−0.36	−0.51	−0.55	−0.36	0.53	−0.03	0.64	0.62	−0.11	0.44	−0.96	−0.58	0.14	−0.58	—	0.65	−0.02	—	0.20	0.35	−0.56	−0.93	—	0.32	−0.45	−0.33	0.50	−0.25	−0.38	−0.38	−0.31	0.48	−0.14	1.00

TDS also showed strong associations with Mg²⁺ (r = .950) and Ca²⁺ (r = .932), identifying these ions as key contributors to dissolved solids. Its correlation with temperature (r = .764) supports the idea that higher temperatures increase mineral solubility, a relevant factor in thermally stressed or disturbed ecosystems.⁸¹ The positive correlation between color and turbidity (r = .891) highlights the influence of suspended solids such as clay, organic matter, and metal oxides, which contribute to reduced water clarity and intensified color, especially in areas impacted by erosion or mining.^82,83 Nutrient dynamics reveal strong positive correlations between PO₄³⁻ and NO₃⁻ (r = .923), pointing to common sources like fertilizers and wastewater. Their additional correlations with SO₄²⁻—r = .884 for phosphate and r = .902 for nitrate suggest co-contamination from urban runoff or agricultural inputs.⁸⁴

Notably, pH exhibited negative correlations with parameters such as EC (r = –0.879) and Mn (r = –0.781), suggesting that more acidic conditions promote metal solubility, particularly in areas affected by acid mine drainage or organic loading.⁸⁵ Among trace metals, Fe and Mn showed strong positive correlation, reflecting their shared geochemical origins and release conditions. Co-contamination patterns were evident through significant correlations among Ni, Cd, Pb, and Cr, indicating that these metals likely share transport pathways especially in mining-influenced environments. Zinc also correlated strongly with Ni (r = .874) and moderately with Ca²⁺ (r = .621), further indicating metal co-mobilization within contaminated sites.⁸⁶ DO showed weak to moderate negative correlations with Fe (r = –0.420) and Mn (r = –0.478), consistent with redox-controlled solubility, where low oxygen levels enhance the mobility and toxicity of these metals.⁸⁷ Altogether, these correlations reveal a complex interactions influenced by both natural geochemical processes and anthropogenic pressures, emphasizing the urgent need for integrated water quality management to safeguard ecological and human health.

Factors Influencing the Water Chemistry

The analysis of water quality data in Liberia, as represented by PCA biplots, demonstrates complex interrelationships among various chemical and physical parameters. Specifically, the first principal component (PC1) revealed a substantial amount of variance explained 65.09% (Figures 5 and 6) in the first dataset and 70.42% in the second (as observed in similar studies applying PCA in environmental assessments).⁸⁸ This significant proportion of explained variance suggests that much of the variability in water quality can be summarized along a single axis. The clustering of parameters such as Fe, NO₃⁻, NO₂⁻, Pb, Phenol, V, Zn, and DO in the positive direction of PC1 indicates strong correlations, implying that these constituents co-vary significantly, likely due to certain common sources or processes.⁸⁹

Figure 5.

Loading plots of water quality in Liberia.

Figure 6.

Biplot of water quality.

These constituents are often associated with anthropogenic pollution sources, including industrial discharge, agricultural runoff, and domestic sewage. Pollution studies highlight that the co-occurrence of heavy metals like Fe and Pb with nutrients such as NO₃⁻ and NO₂⁻ provides further evidence of anthropogenic impacts, which are consistent with findings related to surface water pollution due to industrial and agricultural activities.⁹⁰ The alignment of DO within this cluster raises additional concerns, as lower levels of dissolved oxygen can result from high nutrient loads leading to oxygen depletion, a hallmark of eutrophication and organic pollution.⁹⁰

Moreover, variations in other water quality parameters such as TOC, EC, Color, Turbidity, Alkalinity, and Acidity, notably separated from the heavy metal and nutrient cluster, suggest differing underlying causes of water quality degradation. This second group, particularly identified in the second biplot, is indicative of natural organic matter influenced by watershed runoff and soil contributions. For instance, high TOC and Color values correlate typically with organic materials derived from decaying vegetation and soil.^88,91 The correlation among EC, TOC, and Color points to a relationship where organic matter increases are linked with higher ionic strength and differential coloration, characteristic of waters affected by surface runoff or elevated biological activity.

Notably, certain parameters like Mn, Ni, Sr, Na, and S demonstrate a more independent distribution, largely orthogonal to the main groups in the biplots. Such isolation in their vector orientation suggests different geochemical mechanisms may govern their variations, possibly pointing to localized geological influences or differing redox conditions.^92,93 The spatial positioning of sample points in the biplots further contributes to this assessment. For instance, samples categorized based on their location relative to the main clusters can signify unique water compositions—sample 14 suggesting lower pollution levels, while sample 33 indicates potential hotspots for contamination.^90,94

The dense clustering of specific metals with nutrients hints strongly at anthropogenic impacts, while the scattered organic parameters suggest contributions from natural processes demonstrating the complexity and multifactorial influences impacting water quality. This analysis could guide targeted interventions and further investigations into identified pollution sources within the region, ensuring that the ecological integrity of Liberia’s water resources is maintained.

Soil Quality in Liberia

The contamination of Liberia’s terrestrial environments by heavy metals and trace elements is increasingly drawing attention due to its implications for human health, ecological balance, and agricultural productivity. With widespread mining activities, inadequate environmental controls, and poor waste management practices across the country, heavy metal concentrations in soil and sediment often exceed internationally recognized safety thresholds. These pollutants originate predominantly from mining operations and industrial waste, accumulating in agricultural zones and affecting water quality, soil health, and food security. Fe was the most abundant element detected in soil samples, with concentrations ranging from 52,968.00 mg/kg at the St. John River to an alarming 115,781.00 mg/kg at the New TSF site.⁴ The average Fe level was 71,859.00 mg/kg with a standard deviation of 26,282.23 mg/kg. The skewness of 1.64 and kurtosis of 2.35 reflect the pronounced influence of mining activity, with localized hotspots significantly elevating the overall distribution. While Fe is an essential micronutrient in trace amounts, excessive concentrations disrupt soil microbiota, alter pH levels, and interfere with nutrient cycling, ultimately reducing soil fertility and affecting agricultural productivity.^95,96

Zn concentrations varied between 1.32 mg/kg at Saprolite and 51.35 mg/kg at Adana,⁴ averaging 20.00 mg/kg. Although this mean is below the ecological soil screening level of 55 mg/kg proposed by Crommentuijn et al,⁹⁷ the high standard deviation (16.76 mg/kg) and skewness (0.77) suggest site-specific contamination, possibly due to industrial discharges or mining residues. Zinc is essential for enzymatic activity in plants, but elevated levels can lead to phytotoxicity and impact food crop safety.^98,99 Ni concentrations showed extreme variation, ranging from below detection limits to 186.49 mg/kg, with an average of 29.48 mg/kg. This exceeds the soil quality guideline of 16 mg/kg by Crommentuijn et al⁹⁷ and the toxicity reference of 2.6 mg/kg by McLaughlin et al.¹⁰⁰ High kurtosis (8.31) and skewness (2.84) indicate that a few highly contaminated sites are influencing the distribution disproportionately. Elevated Ni concentrations pose a serious isk to soil health, inhibiting root development, reducing microbial populations, and contributing to human health risks including dermatitis and respiratory complications.^101,102

Mn ranged from 55.11 to 994.83 mg/kg, with an average of 283.12 mg/kg and a standard deviation of 403.64 mg/kg.⁴ The high skewness (2.09) and kurtosis (4.40) further reflect extreme variability. While Mn is necessary for plant enzymatic function, excessive levels impede nutrient uptake and cause toxicity symptoms. In humans, overexposure, particularly among children, may lead to neurodevelopmental issues.^95,103 These elevated concentrations were predominantly observed around mining-intensive sites such as Adana. Pb was found in concentrations ranging from 0.73³² to 19.53 mg/kg,⁴ with an average of 9.14 mg/kg. Although these levels are below the EPMC (2015) and McLaughlin et al¹⁰⁰ safety thresholds of 80 and 150 mg/kg respectively, they exceed baseline values like the 6.69 mg/kg recorded by Arhin et al.¹⁰⁴ The extreme skewness (20) and negative kurtosis (−1.99) signal isolated high-value spikes. Given lead’s well-documented neurotoxicity, especially in children, its presence in even moderate concentrations is cause for concern and warrants regular soil monitoring.^105,106

Cd presented some of the most alarming figures, with concentrations ranging from 0.09 to 3.94 mg/kg,⁴ far exceeding the WHO guideline of 0.5 mg/kg and the baseline of 0.13 mg/kg established by Arhin et al.¹⁰⁴ The average was 2.77 mg/kg, and the distribution exhibited a skewness of −1.93 and a kurtosis of 3.78, highlighting localized spikes. Cadmium is highly toxic and associated with renal dysfunction, bone demineralization, and carcinogenic effects, especially when it bioaccumulates in food crops.^107,108 As levels were relatively low, ranging from 0.00 to 0.09 mg/kg, with a mean of 0.06 mg/kg (Table 7). These values remain well below the WHO limit of 20 mg/kg but are concerning due to arsenic’s extreme toxicity at low levels. The dataset showed a skewness of −1.38 and kurtosis of 2.36, indicating a slightly asymmetric distribution with potential hotspots. Chronic exposure to arsenic has been linked to various forms of cancer, cardiovascular disease, and dermal lesions.^109,110

Table 7.

Statistical Summary of Heavy Metal and Metalloid Levels (mg/kg) in Soil.

Statistical parameters	Fe	Zn	Ni	Mn	Pb	Cd	As
Min	52,968.00	1.32	ND	55.11	0.73	0.09	0.00
Max	115,781.00	51.35	186.49	994.83	19.53	3.94	0.09
Avg	71,859.00	20.00	29.48	283.12	9.14	2.77	0.06
SD	26,282.23	16.76	59.70	403.64	7.48	1.80	0.04
Kurt	2.35	−0.28	8.31	4.40	−1.99	3.78	2.36
Skew	1.64	0.77	2.84	2.09	20	−1.93	−1.38
WHO (1996)	35
Crommentuijn et al. (2000)		16	2.6		55		4.5
McLaughlin et al. (2000)				500	150		20
Kazapoe and Arhin (2019)					6.69		13.29
Environmental Protection Ministry of China (EPMC) (2015)						80

The elevated concentrations and spatial variability of heavy metals in Liberia’s soil particularly at sites like Adana³² and New TSF⁴ demonstrate the detrimental impact of unregulated mining and industrial activity. The cumulative exposure risk posed by elements such as Fe, Ni, Mn, Pb, Cd, and As not only threatens agricultural productivity but also threatens community health through bioaccumulation and environmental degradation. These findings call for urgent interventions, including stringent environmental monitoring, public health education, targeted soil remediation, and the enforcement of regulatory frameworks to mitigate contamination and restore ecological balance.¹¹¹

Relationships in Soil Parameters

The most notable and significant relationship was observed between Ni and Mn, with a correlation coefficient of r = .99 and P = .00. This near-perfect positive correlation suggests that Ni and Mn likely originate from the same sources or are subject to similar environmental conditions, possibly through co-mobilization or co-precipitation mechanisms within the soil matrix. Another key relationship is the strong positive correlation between Zn and Ni, with r = .80 and P = .01 (Table 8). This implies that Zn and Ni concentrations rise and fall together, which may be indicative of similar pollution inputs; potentially industrial or agricultural and similar behaviors in terms of soil binding and mobility.

Table 8.

Correlation Matrix of Soil Parameters.

Heavy metals	Correlation parameters	Fe	Zn	Ni	Mn	Pb	Cd	As
Fe	Pearson Corr.	1.00
Fe	P-value	—
Zn	Pearson Corr.	0.18	1.00
Zn	P-value	0.77	—
Ni	Pearson Corr.	−0.29	0.80	1.00
Ni	P-value	0.63	0.01	—
Mn	Pearson Corr.	−0.15	0.90	0.99	1.00
Mn	P-value	0.81	0.04	0.00	—
Pb	Pearson Corr.	0.32	0.87	0.51	0.32	1.00
Pb	P-value	0.60	0.00	0.16	0.60	—
Cd	Pearson Corr.	0.66	0.71	0.36	0.46	0.09	1.00
Cd	P-value	0.22	0.18	0.55	0.43	0.89	—
As	Pearson Corr.	0.50	0.50	0.08	0.15	−0.02	0.94	1.00
As	P-value	0.50	0.50	0.92	0.85	0.98	0.06	—

Two-tailed test of significance is used.

Zn also shows significant correlations with Mn (r = .90, P = .04) and Pb (r = .87, P = .00). These results indicate that Zn frequently co-occurs with both Mn and Pb, pointing to a potential anthropogenic source such as vehicular emissions, mining, or industrial effluents, where these elements are commonly associated. The strong Zn–Pb relationship in particular suggests a shared origin, likely linked to atmospheric deposition or similar accumulation mechanisms in the soil. While Cd and As exhibited a very high correlation (r = .94), the P-value was .06, slightly above the threshold for statistical significance. Despite this, the strength of the relationship suggests a potentially meaningful association that may warrant further investigation, especially in the context of environmental risk, where both Cd and As are toxic even at low concentrations. Other correlations, such as those involving Fe, did not reach statistical significance. For instance, Fe–Zn (r = .18, P = .77) and Fe–Ni (r = –0.29, P = .6) indicate weak or negligible linear relationships, suggesting that Fe behaves differently from the trace metals in question and may be more influenced by natural geogenic sources rather than anthropogenic inputs.

Potential Sources of PAHs in Liberia and the Need for Further Investigation

Although this review examined the potential presence of PAHs across various occupational and environmental contexts in Liberia, no peer-reviewed studies were identified that specifically quantified PAH levels. Nonetheless, several plausible sources of PAHs can be inferred based on known emission pathways in comparable low and middle-income countries. The widespread use of biomass fuels such as firewood and charcoal for domestic cooking remains a key contributor, particularly in rural and peri-urban areas. Incomplete combustion of these materials under low-efficiency conditions emits substantial amounts of PAHs into the atmosphere.⁹ Combustion characteristics including the type of biomass, method of ignition, and temperature affect emission profiles. For instance, Kim et al¹¹² noted that smoldering combustion of materials such as peat and pine needles can yield higher concentrations of specific PAHs compared to more efficient burning processes. Liberia’s energy profile, with 33% of electricity derived from fossil fuels and the remainder from hydropower,¹¹³ indicates continued reliance on both traditional and petroleum-based energy sources, further increasing the likelihood of PAH emissions.

In addition to domestic fuel use, emissions from transportation are also likely contributors. Liberia has a high prevalence of older vehicles, many lacking modern emission control technologies. The increased use of motorcycles and tricycles, particularly those powered by 2-stroke engines, is a concern due to their inefficient fuel combustion and higher pollutant release.¹¹⁴ Furthermore, informal and small-scale industrial activities such as mechanical workshops, metal fabrication, and local bakeries frequently use petroleum products or biomass without emission management measures. These operations often function in densely populated areas, increasing human exposure potential. Studies in similar conditions have shown that workers in such environments are at risk of inhaling elevated levels of PAHs, which have been linked to adverse respiratory risks.¹¹⁵ Although less documented in Liberia, artisanal petroleum refining activities commonly reported in neighboring West African countries may also contribute to atmospheric PAHs if present locally.^9,114

Agricultural practices, including the open burning of crop residues and forest biomass, are additional sources of concern. These are frequently used for land clearing, post-harvest management, and traditional hunting, releasing PAHs into the environment through the incomplete combustion of organic matter.^116,117 The extent and severity of burning events directly influence emission levels, with more intense fires producing greater PAH quantities.^118,119 Low molecular weight PAHs generated through such activities can disperse over wide areas, affecting both local and regional air quality.¹²⁰ Other likely sources include fuel combustion during mining and quarrying operations, as well as emissions from port and shipping activities in areas such as Monrovia and Buchanan. The discharge of exhaust-cleaning effluents and atmospheric particulates from marine vessels has been associated with elevated PAH levels in adjacent coastal air and water.^121,122 While these sources are credible, the absence of empirical data on PAH concentrations in Liberia’s air, water, and soil highlights the urgent need for targeted environmental monitoring and research to assess public health and ecological risks and inform appropriate policy responses.

Conclusion and recommendations

This systematic review critically examined the environmental and occupational burden of heavy metals (HMs) and polycyclic aromatic hydrocarbons (PAHs) in Liberia, focusing on their concentrations, sources, and implications for human and ecological health. Findings from 9 rigorously selected studies indicate widespread contamination of both water and soil systems, primarily driven by artisanal and industrial mining, poor urban planning, and inadequate environmental governance.

Quantitative data reveal that numerous water quality parameters consistently exceeded World Health Organization (WHO) safety thresholds. Turbidity reached up to 320.00 NTU (mean: 42.85 NTU), electrical conductivity peaked at 3000.00 µS/cm, and total dissolved solids (TDS) rose as high as 1015.00 mg/L. Trace metals in water, including Fe (mean: 4.53 mg/L), Mn (mean: 2.33 mg/L), Cd (mean: 0.01 mg/L), and As (mean: 0.15 mg/L), far surpassed permissible limits, particularly in mining-impacted regions. Soil samples recorded extreme Fe concentrations of up to 115,781.00 mg/kg and elevated levels of Ni, Cd, and Pb, indicating substantial terrestrial contamination. Multivariate analyses revealed significant parametric correlations—EC and TDS (r = .997), NO₃⁻ and PO₄³⁻ (r = .923), and Zn and Mn (r = .90) confirming shared pollution pathways related to industrial effluent, mining runoff, and agrochemical use.

The findings of this review should be interpreted with caution due to several key limitations. Many of the included studies were based on relatively small sample sizes, and spatial coverage was uneven, with several regions in Liberia notably underrepresented. Additionally, a potential publication bias may have influenced the scope of available data, as certain critical pollutants particularly PAHs and air quality parameters were entirely absent from the reviewed literature. No peer-reviewed studies specifically addressing these pollutants were identified, and overall, limited studies have been conducted in this area, restricting the ability to draw robust, nationwide conclusions. Given these limitations, there is an urgent need to expand national environmental monitoring systems, improve data coverage and quality, and enforce targeted pollution control regulations, especially in high-risk sectors such as mining and industrial development. Expanding access to clean water in underserved rural and peri-urban areas is essential. Furthermore, capacity-building initiatives should be developed to empower local communities with the tools and knowledge to manage environmental risks. Based on these findings, there is a need to:

Conduct targeted studies on PAHs and air quality to fill critical knowledge gaps on atmospheric pollution, particularly in urban centers and industrial zones where combustion-related emissions may pose significant respiratory and carcinogenic risks.

Strengthen environmental monitoring systems by establishing a national water and soil quality database and conducting regular assessments in high-risk areas.

Enforce stricter regulations on mining and industrial activities, including mandatory environmental audits, waste management compliance, and pollutant discharge controls.

Promote public health interventions such as community education programs on water safety, household treatment methods, and the health risks of heavy metal exposure.

Invest in low-cost remediation technologies like phytoremediation, biofiltration, and constructed wetlands to treat polluted water and restore contaminated soils.

Encourage further research through longitudinal and seasonal studies that assess pollutant mobility, health impacts, and the effectiveness of policy interventions, while integrating geospatial analysis for hotspot mapping.

Footnotes

ORCID iD

Eudora Nwanaforo

Ethical Considerations

Not applicable.

Author Contributions

AST, APO: Data acquisition and bench work; AST, CF, PMA, NAM, EN, CA: Data Analysis and drafting of manuscript; OEO: Conceptualization, Supervision, Writing - review & editing, Validation: AST, PMA, OEO: Writing - original draft, Statistical analysis, Visualization.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data presented in this study are available on request from the corresponding authors.

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