A robust assessment and treatment of selected organochlorine pesticides and heavy metals in industrial wastewater using nanoparticles: a case study in Nigeria
Bibliographic record
Abstract
The discharge of non/ill-treated industrial effluent containing organochlorine pesticides (OCPs) and potentially toxic metal ions into the aquatic ecosystem has endangered both aquatic life and man. Thus, this study presents the evaluation of potentially toxic metal ions in thirteen industrial effluents sampled from five different states in Nigeria, as well as the level of OCPs content of a pesticide industry sited in Kanu State, Nigeria. The range of concentration estimated for the analyte was noticed to be higher than the recommended concentration limits for both OCPs and potentially toxic metal ions. Thus, biozinc oxide nanoparticles (BioZnONPs), zinc oxide nanoparticles (ZnONPs), and biozinc oxide-carboxymethyl cellulose nanocomposite (BioZnONPs-CMC) were fabricated and characterized using non-destructive spectroscopic techniques and a specific surface area analyzer for remediation of industrial effluents. The incorporation of nanoparticles and nanocomposites resulted in a substantial reduction in key water quality parameters, including total dissolved solids (TDS), sulfate (SO₄²⁻), electrical conductivity (EC), total phosphorus, chemical oxygen demand (COD), chloride (Cl⁻), and pH, highlighting their potential for effective water purification applications. The OCPs mean adsorption capacities of BioZnONPs, ZnONPs, and BioZnONPs-CMC were determined as 10.2 ± 10.182 mg g –1 , 14.2 ± 4.526 mg g –1 , and 14.2 ± 4.525 mg g –1 , respectively. Conversely, BioZnONPs, ZnONPs, and BioZnONPs-CMC exhibited strong efficacy in removing cadmium (Cd), chromium (Cr), and lead (Pb) ions from industrial effluents, underscoring their potential as effective agents for heavy metal remediation. This study shows the successful fabrication of wastewater treatment agents for the removal of OCPs and potentially toxic metal ions. To maintain a safe environment, this study also highlights the necessity of routinely evaluating both treated and untreated industrial effluents.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".