Determination of river water composition trends with uncertainty: Seasonal variation of nutrients concentration in Tagus river estuary in the dry 2017 year

Metrologically sound comparison of trace-metal levels in sea cucumber tissues from different species and habitats

Sea cucumbers are indicators of metal contamination in sea bottoms due to their low mobility and feeding behaviour. Comparing contaminations of specimens from different locations, habitats, and/or organs allows understanding of contamination processes and differences. However, the interpretation of these data is affected by the variability of contamination levels in specimens, the uncertainty of tissue analyses, and the complex correlation of mass fractions estimated by using the same calibration of the used instrumental method of analysis. This work presents a novel tool for the sound comparison of contamination levels of biota where all mentioned factors are considered to produce reliable and undisputable information on the studied system. The Monte Carlo simulation of uncertainty components, affecting the determination of mean contamination levels observed in selected types of tissues, allowed simulating mean contamination differences and determining if these are meaningful. This tool was used to assess the levels of Cd, Cu, Ni and Pb of animals collected in different locations of Sesimbra-Portugal. It was concluded that specimens that selectively consume macroalgae have larger contamination levels than animals feeding on sediment. The gut is the most contaminated organ suggesting intake from feeding is dominant. Three of the analysed animals have Pb mass fractions larger than a maximum admissible value for human consumption of 3 mg kg^-1 with a probability larger than 2.5%.

Determination of microplastic contamination levels and trends in vast oceanic sediment areas with uncertainty

Small plastic particles, designated as microplastics, are known vehicles of several contaminants desorbed from their surface after being ingested by marine organisms. The monitoring of the levels and trends of microplastics in oceanic areas is essential to identify relevant threats and respective sources whose management should be improved to protect the environmental resources. However, the assessment of contamination trends in large oceanic areas is affected by contamination heterogeneity, sampling representativeness, and the uncertainty of collected sample analyses. Only contamination variations not justifiable by system heterogeneity and their characterisation uncertainty are meaningful and should be taken seriously by the authorities. This work describes a novel methodology for the objective identification of meaningful variation of microplastic contamination in vast oceanic areas by the Monte Carlo simulation of all uncertainty components. This tool was successfully applied to the monitoring of the levels and trends of microplastic contamination in sediments from a 700 km² oceanic area from 3 km to 20 km offshore Sesimbra and Sines (Portugal). This work allowed concluding that contamination has not varied between 2018 and 2019 (difference of mean total microplastic contamination between -40 kg^-1 and 34 kg^-1) but that microparticles made of PET are the major type of studied microplastics (in 2019, mean contamination is between 36 kg^-1 and 85 kg^-1). All assessments were performed for a 99 % confidence level.

Bottom-Up Evaluation of the Uncertainty of the Quantification of Microplastics Contamination in Sediment Samples

The quantification and comparison of microplastic contamination of sediments are affected by sample heterogeneity and the systematic and random effects affecting sample analysis. The quantification and combination of these components in the measurement uncertainty allows the objective interpretation of analysis results. This work presents the first detailed evaluation of the uncertainty of microplastic contamination quantification in sediments. The random and systematic effects affecting microplastic counts are modeled by the Poisson-lognormal distribution with inputs estimated from duplicate sediment analysis and the analysis of sediments spiked with microparticles. The uncertainty from particle counting was combined with the uncertainty from the determination of the dry mass of the analytical portion by the Monte Carlo method. The developed methodology was implemented in a user-friendly spreadsheet made available as the Supporting Information. The contamination of sediment samples collected in various inland Portuguese waters was determined, ranging from [0; 160] to [361; 2932] kg^-1 for a 99% confidence level, and compared by assessing if the difference between contamination levels is equivalent to zero for the same confidence level. Several samples proved to have metrologically different microplastic contamination. This work represents a contribution to the objectivity of the assessment of environmental contamination with microplastics.

Drawing Attention to the Measurement Uncertainty Arising from Sampling in Chemical and Physicochemical Analyses: An Overview

Sometimes, analytical chemicals forget that the measurement process begins with the selection of the sample; thus, it must be understood that the measurement uncertainty is constituted by the association of the uncertainty arising from the sampling and the uncertainty arising from the traditional analytical process, that which is carried out in the laboratory. The analytical process is well-controlled, so its uncertainty is well defined; however, the uncertainty arising from sampling, for not having this controlled environment, is often not evident, so that there is still no culture to consider it for the calculation of measurement uncertainty. This study discusses the importance of the sampling uncertainty concerning the analytical uncertainty and details the current approaches available in the literature, such as the classical analysis of variance, the robust analysis of variance, and the range statistics. Moreover, this work highlights the recent manuscripts that are using these mentioned approaches, correlating them to the matrices, chemical and physical-chemical analytes, and analytical techniques. Finally, some case studies using the uncertainty information in compliance assessment show that the measurement uncertainty arising from sampling in chemical and physicochemical analyses cannot always be neglected.

Monte Carlo bottom-up evaluation of the uncertainty of complex sample preparation: Elemental determination in sediments

Many chemical analyses involve a complex sample preparation, and some, based on an instrumental method of analysis such as spectrometric or chromatographic methods, are affected by matrix effects. The objective interpretation of the results of these analyses performed in the framework of a research or a conformity assessment requires quantifying the measurement uncertainty. This work presents a novel methodology for the bottom-up modelling of the performance of complex analytical operations, such as sample digestion or extraction, by the Monte Carlo simulation of their performance independently of the performance of the other analytical steps. The simulation of between-days precision of complex sample preparation and mean measurement error observed from the analysis of various reference materials and their combination with models of instrumental quantification performance allow the detailed modelling of the measurement uncertainty. The developed methodology adapts to the complex distribution of observed measurement performance data avoiding the under evaluation of the measurement uncertainty by assuming the normal distribution of systematic and random effects. The developed methodology was successfully applied to the determination of total or acid-extractable As (following OSPAR or EPA 3051A methods, respectively) in sediments where measurement trueness was assessed from the analysis of one Certified Reference Material and two spiked samples. The evaluated uncertainty is fit for environmental monitoring considering performance criteria defined for Quasimeme proficiency tests. The developed measurement models were successfully cross-validated by randomly extracting data from the validation set subsequently used to check the compatibility between estimated and reference values for 95% or 99% confidence level. The observed success rate of these assessments is compatible with the confidence level of the tests.