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Soil Microbial Responses to Varying Environmental Conditions in a Copper Belt Region of Africa: Phytoremediation Perspectives

2024· article· en· W4405850124 on OpenAlexafffund
K. K. Nkongolo, John Banza Mukalay, Antoine K. Lubobo

Bibliographic record

VenueMicroorganisms · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsLaurentian University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhytoremediationCobaltLeucaena leucocephalaCopperEnvironmental chemistryTailingsRhizosphereSoil contaminationEnvironmental scienceAgronomySoil waterChemistryBiologyHeavy metalsSoil science

Abstract

fetched live from OpenAlex

The mining industry in the copper belt region of Africa was initiated in the early 1900s, with copper being the main ore extracted to date. The main objectives of the present study are (1) to characterize the microbial structure, abundance, and diversity in different ecological conditions in the cupriferous city of Lubumbashi and (2) to assess the metal phytoextraction potential of Leucaena leucocephala, a main plant species used in tailing. Four ecologically different sites were selected. They include a residential area (site 1), an agricultural dry field (site 2), and an agricultural wetland (site 3), all located within the vicinity of a copper/cobalt mining plant. A remediated tailing was also added as a highly stressed area (site 4). As expected, the highest levels of copper and cobalt among the sites studied were found at the remediated tailing, with 9447 mg/kg and 2228 mg/kg for copper and cobalt, respectively. The levels of these metals at the other sites were low, varying from 41 mg/kg to 579 mg/kg for copper and from 4 mg/kg to 110 mg/kg for cobalt. Interestingly, this study revealed that the Leucaena leucocephala grown on the remediated sites is a copper/cobalt excluder species as it accumulates soil bioavailable metals from the rhizosphere in its roots. Amplicon sequence analysis showed significant differences among the sites in bacterial and fungal composition and abundance. Site-specific genera were identified. Acidibacter was the most abundant bacterial genus in the residential and remediated tailing sites, with 11.1% and 4.4%, respectively. Bacillus was predominant in both dry (19.3%) and wet agricultural lands (4.8%). For fungi, Fusarium exhibited the highest proportion of the fungal genera at all the sites, with a relative abundance ranging from 15.6% to 20.3%. Shannon diversity entropy indices were high and similar, ranging from 8.3 to 9 for bacteria and 7.0 and 7.4 for fungi. Β diversity analysis confirmed the closeness of the four sites regardless of the environmental conditions. This lack of differences in the microbial community diversity and structures among the sites suggests microbial resilience and physiological adaptations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.121
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.235
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2024
Admission routes2
Has abstractyes

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