Analytical Replication Improves Precision but Does Not Create Environmental Replication: Hierarchical Uncertainty and Decision Stability in Metal(loid) Soil Exposure Screening.
5 affiliations
- 1Area of Legal and Forensic Medicine, Department of Surgery, Medical and Social Sciences, Faculty of Medicine and Health Sciences, University of Alcalá, Ctra. Madrid-Barcelona, Km 33.6, 28871 Madrid, Spain.
- 2Leicester School of Allied Health Sciences, De Montfort University, Leicester LE1 9BH, UK.
- 3Scientific Computation Research Institute (SCRIUR), University of La Rioja, 26006 Logroño, Spain.
- 4Department of Biomedical Sciences, Faculty of Pharmacy, University of Alcalá, Ctra. Madrid-Barcelona, Km 33.6, 28871 Madrid, Spain.
- 5Department of Agri-Environmental Research, Institute for Rural, Agricultural and Food Research and Development (IMIDRA), Finca el Encín, Ctra. Madrid-Barcelona, Km 38.2, 28800 Madrid, Spain.
Abstract
Environmental exposure screening relies on concentration inputs shaped by hierarchical sampling and analytical design. This study investigated how analytical-unit selection and the allocation of replication across a nested soil-sampling hierarchy affect concentration precision, environmental inference, and downstream exposure-screening decisions. We reanalysed a published Leicester/Leicestershire topsoil dataset comprising 26 independent site-level composites, each represented by two physical laboratory samples, with 102 valid analytical determinations overall. Log-scale mixed models quantified variance components for 10 fully detected elements; 500 censor-compatible realisations addressed Cd and Cr; and 10,000 simulations compared reduced analytical designs with the full observed design (R4). Between-composite differences accounted for 65.6-93.8% of log-scale variability, with sensitivity estimates of 87.2% for Cd and 92.3% for Cr. At comparable analytical effort, one determination from each of two physical samples (R3) improved agreement with R4 relative to duplicating one physical sample (R2): median element MdAPE was 1.32% versus 4.27%, mean agreement within ±10% was 96.7% versus 77.5%, and mean rank displacement was 0.47 versus 1.42 positions. Treating the 102 analytical records as independent yielded five FDR-significant urban-rural signals, whereas analyses respecting the 26 independent environmental units yielded none after FDR adjustment. No hazard-index or cancer-risk-band classification switched relative to R4; mean Pb screening-classification switch probabilities were 9.57%, 7.72%, and 0.96% under R1, R2, and R3, respectively. Analytical replication improves measurement precision but does not create environmental replication.
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BibTeX
@article{penafernandez2026analytical,
title = {{Analytical Replication Improves Precision but Does Not Create Environmental Replication: Hierarchical Uncertainty and Decision Stability in Metal(loid) Soil Exposure Screening.}},
author = {Antonio Peña-Fernández and Tomás Cámara-Pastor and Gurminderjeet S Jagdev and Borja Martínez-Alonso and Mark D Evans and M Carmen Lobo-Bedmar},
journal = {Toxics},
year = {2026},
month = oct,
doi = {10.3390/toxics14100887},
url = {https://europepmc.org/article/MED/42931674},
note = {CC BY},
}
RIS
TY - JOUR
TI - Analytical Replication Improves Precision but Does Not Create Environmental Replication: Hierarchical Uncertainty and Decision Stability in Metal(loid) Soil Exposure Screening.
AU - Antonio Peña-Fernández
AU - Tomás Cámara-Pastor
AU - Gurminderjeet S Jagdev
AU - Borja Martínez-Alonso
AU - Mark D Evans
AU - M Carmen Lobo-Bedmar
PY - 2026
DA - 2026/10/02
JO - Toxics
DO - 10.3390/toxics14100887
UR - https://europepmc.org/article/MED/42931674
AB - Environmental exposure screening relies on concentration inputs shaped by hierarchical sampling and analytical design. This study investigated how analytical-unit selection and the allocation of replication across a nested soil-sampling hierarchy affect concentration precision, environmental inference, and downstream exposure-screening decisions. We reanalysed a published Leicester/Leicestershire topsoil dataset comprising 26 independent site-level composites, each represented by two physical laboratory samples, with 102 valid analytical determinations overall. Log-scale mixed models quantified variance components for 10 fully detected elements; 500 censor-compatible realisations addressed Cd and Cr; and 10,000 simulations compared reduced analytical designs with the full observed design (R4). Between-composite differences accounted for 65.6-93.8% of log-scale variability, with sensitivity estimates of 87.2% for Cd and 92.3% for Cr. At comparable analytical effort, one determination from each of two physical samples (R3) improved agreement with R4 relative to duplicating one physical sample (R2): median element MdAPE was 1.32% versus 4.27%, mean agreement within ±10% was 96.7% versus 77.5%, and mean rank displacement was 0.47 versus 1.42 positions. Treating the 102 analytical records as independent yielded five FDR-significant urban-rural signals, whereas analyses respecting the 26 independent environmental units yielded none after FDR adjustment. No hazard-index or cancer-risk-band classification switched relative to R4; mean Pb screening-classification switch probabilities were 9.57%, 7.72%, and 0.96% under R1, R2, and R3, respectively. Analytical replication improves measurement precision but does not create environmental replication.
SN - 2305-6304
ER -