bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.64898/2026.08.04.740606

Chemical Dynamics of Heavy Metal Translocation and Phytoaccumulation Paradoxes in Highly Leached, Acidic Xanthic Ferralsols of Fendall, Liberia

Dorbor, C. G.·Nyuma, H. T.
View original source ↗Export metadata ↓

Abstract

Heavy metal contamination in soil systems is an escalating global crisis, yet over 55% of urban agriculture literature focuses on high-tech indoor vertical farming, leaving vulnerable open-field tropical urban agriculture (UPA) severely under-researched. This study addresses this knowledge gap by evaluating the chemical mechanics of heavy metal translocation within highly acidic Xanthic Ferralsols in Fendall, Liberia, serving as a global proxy for tropical agro-ecosystem risks. Using a randomized complete block design, topsoil (0-20 cm) from five cultivated and five uncultivated sites (n = 10) and corresponding tissues of cabbage (Brassica oleracea), lettuce (Lactuca sativa), and sweet potato greens (Ipomoea batatas) were screened via handheld X-ray fluorescence spectrometry. Results revealed a compelling geochemical paradox: uncultivated soils demonstrated significantly higher geogenic background metal concentrations across all elements (p < 0.05). Systemic Nickel (Ni) above the World Health Organization (WHO) thresholds was recorded on both land-use histories. While plant tissues accumulation of Copper (Cu) and Zinc (Zn) remained within safe parameters, Lead (Pb) concentrations across all crops profoundly exceeded food safety standards, averaging 1.53 mg/kg, which is over 5 times above the WHO limit (0.30 mg/kg). Risk screening highlighted high soil-to-plant transfer factors (TF =>1) for Ni and Zn in plant tissue, in the order of cabbage > sweet potato greens > lettuce. These findings provide transferable insights into how high soil acidity accelerates toxic metal mobilization, offering a scalable framework for crop-specific exclusion policies and real-time field screening in tropical municipalities. AbstractRapid urban agricultural expansion in Montserrado County, Liberia, relies heavily on highly weathered acidic Xanthic Ferralsols. While these soils support crop growth, they present a hidden geogenic risk of heavy metal translocation to the human food chain. This study evaluated the concentrations of Copper (Cu), Nickel (Ni), Lead (Pb), and Zinc (Zn) in the soils and edible tissues of cabbage (Brassica oleracea), lettuce (Lactuca sativa), and sweet potato greens (Ipomoea batatas) in Fendall. The uncultivated soils exhibited a geochemical paradox, showing significantly higher total metal concentrations than cultivated plots. However, low soil pH (4.0-6.0) highly mobilized these geogenic metals, driving rapid plant uptake. While tissue concentrations of Cu, Ni, and Zn remained within safe physiological ranges, Lead (Pb) concentrations in all three crops reached an alarming mean of 1.53 mg/kg, exceeding the World Health Organization (WHO) permissible safety limit of 0.30 mg/kg by more than 5-fold. The soil-to-plant Transfer Factors (TF) for Lead ranged from 0.51 to 0.81, highlighting significant bioavailable translocation despite the lack of visible crop phytotoxicity. These results demonstrate that passive geogenic metal uptake poses a silent public health threat. Immediate agronomic interventions, including systematic liming and crop-exclusion frameworks, are urgently required to safeguard urban food security in Monrovia. GRAPHIC ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=157 SRC="FIGDIR/small/740606v1_ufig1.gif" ALT="Figure 1"> View larger version (111K): org.highwire.dtl.DTLVardef@11b1d37org.highwire.dtl.DTLVardef@11b3bc9org.highwire.dtl.DTLVardef@f24429org.highwire.dtl.DTLVardef@158e03b_HPS_FORMAT_FIGEXP M_FIG C_FIG

Explore related subjects

ecologyPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Dorbor, C. G., Nyuma, H. T.. 2026-08-04. Chemical Dynamics of Heavy Metal Translocation and Phytoaccumulation Paradoxes in Highly Leached, Acidic Xanthic Ferralsols of Fendall, Liberia. https://doi.org/10.64898/2026.08.04.740606

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Operationalising the context in regenerative agriculture: decision-making and farm variability

Soil degradation is a widespread challenge that requires a broad response at the individual farm level. To ensure effectivity, the practices should be tailored to the farm context: land manager objectives and farm specific challenges. These have however been difficult to quantify. Here we demonstrate that a workable farm context can be created based on a value survey, open satellite and soil data, and published models for vegetation gross primary productivity and soil erosion. Based on the findings, despite individual differences, farmers value profitability and operational efficiency, but also biodiversity and soil health. At least the regenerative farmers surveyed also value working for the greater good more than maintaining tradition or power. In spite of wide differences in farm production orientation, we also found that each farm also had a broad variation in individual fields GPP. Most fields have a stable GPP level, which is either high or low, and that there is a 2-3-fold difference between the weakest and best producing fields indicating the potential for improving GPP by improving the growing conditions on currently weak fields. In addition, soil loss was found to be highly concentrated in critical source areas, where 10% of the field area contributed to 50% of the soil loss. Overall, open data can be linked to modelling workflows to rapidly produce a decision-making context for farmers. This facilitates benchmarking and co-learning as well as enables land managers and advisors to identify the farm context for planning effective responses to soil degradation.

ecology↗
bioRxiv2026

Fly, land, listen: Autonomous intermittent locomotion enables scalable low-noise drone ecoacoustic surveys

Ecoacoustic monitoring is enabling scientists and land managers to monitor and manage biodiversity more effectively and cost-efficiently in the face of human pressures and rapidly changing climates. Currently, most ecoacoustic surveys use manually deployed static sensors to record data, limiting the scale and reach of surveying efforts. Here we present a proof-of-concept autonomous drone platform that can use intermittent locomotion to conduct ecoacoustic surveys using an onboard sensor. Our custom prototype is able to fly, navigate, and avoid obstacles autonomously, land at a pre-determined location, record audio from an onboard microphone whilst static, before taking off and moving to the next sampling site. Autonomous navigation and operation enable greater sampling flexibility, reach, and scalability. Furthermore, by recording audio only whilst landed, noise from the drone's rotors does not mask signals or disturb animals, simplifying signal processing and downstream ecological analyses. We conducted trials in a scrubland habitat at the Knepp Estate in West Sussex, where our prototype demonstrated successful autonomous navigation and obstacle avoidance. Furthermore, we found that avian biodiversity data collected from the drone platform was comparable to that from traditional static acoustic sensor deployments, and that vocalisation patterns were not significantly impacted by the noise of the drone arriving or leaving a site. While scaled deployments of our technology would require further technical and regulatory challenges to be solved, our first demonstration of autonomous intermittent robotics-assisted ecoacoustic surveys lays the foundations for more cost-effective and far-reaching biodiversity surveys, with transformative potential for conservation, agricultural management, biosecurity, and more.

ecology↗
bioRxiv2026

Do higher-order moments improve inference of population dynamics?

Fitting mathematical models of population dynamics to microbial time-series data allows us to estimate the ecological processes and interactions taking place in the microbiome. Repeated experiments of microbial systems yield replicates which slightly differ from each other. Some of this variability arises due to the fact that births and deaths occur at random. Most prior work focuses on fitting a deterministic mathematical model to the average across replicates. We use a stochastic model to fit the variability to the observed variability across replicates. Using a simulation-driven approach, we study the conditions under which our approach allows us to infer a larger fraction of ecological parameters correctly. We observe a substantial improvement in parameter inference. Lastly, our Bayesian approach not only allows us to incorporate prior information about the system, but also provides a distribution of parameters which conveys some idea of the uncertainty of the estimates.

ecology↗