Monitoring network design limits detection of sediment-mediated contaminant mobilisation in the River Mersey estuary (UK)

Nangir, D ORCID: 0009-0006-6931-3832, Andredaki, M and Carnacina, I ORCID: 0000-0001-5567-7180 (2026) Monitoring network design limits detection of sediment-mediated contaminant mobilisation in the River Mersey estuary (UK). Marine Pollution Bulletin, 231. ISSN 0025-326X

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Abstract

Suspended sediments regulate turbidity and act as major vectors for metals, organic contaminants, and nutrient fluxes in estuarine systems. Effective assessment of sediment-mediated pollution therefore depends on coordinated monitoring of sediment and contaminant dynamics. This study evaluates whether a long-term regulatory monitoring network can resolve sediment-associated pollution dynamics in the River Mersey estuary (UK). A total of 44,516 water-column observations (2015–2025) across 42 monitoring stations and 187 determinants were analysed. Monitoring frequency classification revealed pronounced structural imbalance: only 6.9% of determinants were sampled routinely (≥1000 observations), whereas 67.4% were recorded fewer than 100 times. Physical and oxygen-related variables accounted for 52% of all observations, while sediment and turbidity indicators comprised only 4.2%. Temporal separation analysis revealed a bimodal sampling structure characterised by episodic co-sampling embedded within extended periods of asynchronous measurement. While pairwise co-sampling coherence (CCR) was often moderate to high across several process groups, strict multivariate coherence (CCRₛₜᵣᵢcₜ) remained low across much of the network, indicating that complete multivariate observation states suitable for integrated sediment–contaminant assessment were uncommon. Diagnostic machine-learning reconstruction yielded moderate performance under random validation (R² = 0.588) but substantially lower spatial generalisation under grouped cross-validation (mean R² ≈ 0.26), indicating limited transferability across stations. These findings suggest that sediment-mediated pollution assessment is constrained more strongly by monitoring architecture, temporal alignment, and environmental heterogeneity than by modelling approaches alone. The study introduces a diagnostic framework combining temporal separation analysis, coherence metrics, and hierarchical validation for evaluating monitoring-network suitability in estuarine systems.

Item Type:	Article
Uncontrolled Keywords:	Estuarine pollution; Sediment contaminants; Monitoring network; Machine learning; Water quality monitoring; River Mersey; Estuarine pollution; Machine learning; Monitoring network; River Mersey; Sediment contaminants; Water quality monitoring; 37 Earth Sciences; 41 Environmental Sciences; 4105 Pollution and Contamination; Machine Learning and Artificial Intelligence; Marine Biology & Hydrobiology
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) T Technology > TC Hydraulic engineering. Ocean engineering
Divisions:	Civil Engineering and Built Environment
Publisher:	Elsevier
Date of acceptance:	9 June 2026
Date of first compliant Open Access:	5 August 2026
Date Deposited:	05 Aug 2026 10:44
Last Modified:	05 Aug 2026 10:44
DOI or ID number:	10.1016/j.marpolbul.2026.120004
URI:	https://researchonline.ljmu.ac.uk/id/eprint/29107

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