Monte Carlo bottom-up evaluation of global instrumental quantification uncertainty: Flexible and user-friendly computational tool

Evaluation of fluoride exposure using disability-adjusted life years and health risk assessment in south-western Iran: A novel Monte Carlo simulation

Consumption of fluoride-contaminated water is a worldwide concern, especially in developing countries, including Iran. However, there are restricted studies of non-single-value health risk assessment and the disease burden regarding fluoride intake nationwide. Prolonged exposure to excessive fluoride has been linked to adverse health effects such as dental and skeletal fluorosis. This can lead to under-mineralization of hard tissues, causing aesthetic concerns for teeth and changes in bone structure, increasing the risk of fractures. As such, we aimed to implement probability-based frameworks using Monte Carlo methods to explore the potential adverse effects of fluoride via the ingestion route. This platform consists of two sectors: 1) health risk assessment of various age categories coupled with a variance decomposition technique to measure the contributions of predictor variables in the outcome of the health risk model, and 2) implementing Monte Carlo methods in dose-response curves to explore the fluoride-induced burden of diseases of dental fluorosis and skeletal fractures in terms of disability-adjusted life years (DALYs). For this purpose, total water samples of 8053 (N=8053) from 57 sites were analyzed in Fars and Bushehr Provinces. The mean fluoride concentrations were 0.75 mg/L and 1.09 mg/L, with maximum fluoride contents of 6.5 mg/L and 3.22 mg/L for the Fars and Bushehr provinces, respectively. The hazard quotient of the 95th percentile (HQ>1) revealed that all infants and children in the study area were potentially vulnerable to over-receiving fluoride. Sobol' sensitivity analysis indices, including first-order, second-order, and total order, disclosed that fluoride concentration (Cw), ingestion rate (IRw), and their mutual interactions were the most influential factors in the health risk model. DALYs rate of dental fluorosis was as high as 981.45 (uncertainty interval: UI 95 % 353.23-1618.40) in Lamerd, and maximum DALYs of skeletal fractures occurred in Mohr 71.61(49.75-92.71), in Fars Province, indicated severe dental fluorosis but mild hazard regarding fractures. Residents of the Tang-e Eram in Bushehr Province with a DALYs rate of 3609.40 (1296.68-5993.73) for dental fluorosis and a DALYs rate of 284.67 (199.11-367.99) for skeletal fractures were the most potentially endangered population. By evaluating the outputs of the DALYs model, the gap in scenarios of central tendency exposure and reasonable maximum exposure highlights the role of food source intake in over-receiving fluoride. This research insists on implementing defluoridation programs in fluoride-endemic zones to combat the undesirable effects of fluoride. The global measures presented in this research aim to address the root causes of contamination and help policymakers and authorities mitigate fluoride's harmful impacts on the environment and public health.

A novel uncertainty evaluation method based on the particle filter and beta distribution for data with unknown distribution

Uncertainty evaluation for unknown distribution data is a key problem to be solved in uncertainty evaluation theory. To evaluate the measurement uncertainty of data with unknown distributions, a novel uncertainty evaluation method based on the particle filter (PF) and beta distribution is proposed in this paper. A beta distribution with wide adaptability was adopted as the distribution type of measurement results, the parameters of the beta distribution were taken as the parameters to be estimated, and a state-space model was established. The PF method, suitable for non-Gaussian data, was utilized to obtain the estimates of the parameters of the beta distribution according to the measurement results. Finally, the best estimates of the measurement results and their uncertainty were calculated using the beta distribution parameters. Simulation results show that the proposed method is adaptive to accurately evaluate the measurement uncertainties of data, especially for non-Gaussian distribution data or asymmetrically distributed data. Multiple evaluation results show that the method has good robustness. The experimental results for the drift errors of a laser interferometer show that the uncertainty result of the proposed method is consistent with the Monte Carlo method. This method is suitable for a variety of distribution types that can be characterized through beta distribution and can solve the optimal estimation and uncertainty evaluation of most measurement results with unknown distribution types.

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.

Development and validation of statistically sound criteria for the match of unweathered GC-MS fingerprints in oil spill forensics

The investigation of an oil spill's origin frequently relies on determining the equivalence of oil component patterns in samples from the contaminated environment and suspected oil source. This comparison benefits if based on the ratio of the abundance of unweathered characteristic components of the oil product, Diagnostic Ratios, DR. Replicate determinations of DR from one sample are used to set limits for the second sample's DR. The composition equivalence of oil patterns in both samples is indicated if all compared DR are statistically equivalent with a high confidence level. Some studies define DR limits assuming their normality and using Student's t statistics (S-t). However, since the ratio of correlated abundances can be not normally distributed, this criterion can drive to more false comparisons than predicted by the test confidence level. This work developed a computational tool for the reliable description of the non-normal distribution of the DR based on the Monte Carlo Method (MCM), aiming to allow the accurate control of the confidence of DR comparison. This work concluded that S-t defines 95% or 98% confidence limits with probabilities of falsely rejecting samples equivalence, φ, that can be up to 4.3% higher than predicted by the confidence level of the S-t test (i.e., 5% and 2%). The fragilities of the S-t limits significantly reduce the probability (1-θ) of two samples with the same oil producing equivalent values of all compared DR. For the studied 69 DR from unweathered components, the (1-θ) for 98% confidence level limits, set by the MCM and S-t from triplicate injections of one sample, are 94.8% and 91.7%, respectively. These values are below the confidence level (P) defined for each DR because DR are correlated with a correlation coefficient lower than 1. The (1-θ) can be increased to above P by using MCM limits and accepting composition equivalence if at least one of two sample extract injections produces values within limits set from the other sample's replicate injection. The validated user-friendly MS-Excel file used to set and access comparison criteria is made available as Supplementary Material and was checked experimentally. However, it is not feasible to estimate model confidence exclusively from experimentation because it would require too much independent analysis.

Evaluation and validation of detailed and simplified models of the uncertainty of unified [Formula: see text] measurements in aqueous solutions

The use of the unified pH concept, [Formula: see text] , applicable to aqueous and non-aqueous solutions, which allows interpreting and comparison of the acidity of different types of solutions, requires reliable and objective determination. The [Formula: see text] can be determined by a single differential potentiometry measurement referenced to an aqueous reference buffer or by a ladder of differential potentiometric measurements that allows minimisation of inconsistencies of various determinations. This work describes and assesses bottom-up evaluations of the uncertainty of these measurements, where uncertainty components are combined by the Monte Carlo Method (MCM) or Taylor Series Approximation (TSM). The MCM allows a detailed simulation of the measurements, including an iterative process involving in minimising ladder deviations. On the other hand, the TSM requires the approximate determination of minimisation uncertainty. The uncertainty evaluation was successfully applied to measuring aqueous buffers with pH of 2.00, 4.00, 7.00, and 10.00, with a standard uncertainty of 0.01. The reference and estimated values from both approaches are metrologically compatible for a 95% confidence level even when a negligible contribution of liquid junction potential uncertainty is assumed. The MCM estimated pH values with an expanded uncertainty, for the 95% confidence level, between 0.26 and 0.51, depending on the pH value and ladder inconsistencies. The minimisation uncertainty is negligible or responsible for up to 87% of the measurement uncertainty. The TSM quantified measurement uncertainties on average only 0.05 units larger than the MCM estimated ones. Additional experimental tests should be performed to test these uncertainty models for analysis performed in other laboratories and on non-aqueous solutions.

Application of Measurement Uncertainty on Conformity Assessment in Pharmaceutical Drug Products

BACKGROUND

Conformity assessment in pharmaceutical drug products usually are based on analytical results. An analytical result always is associated with an uncertainty which must be considered in a compliance assessment.

OBJECTIVE

The objective of the present project aims to estimate the measurement uncertainty associated with the results of average weight, assay, uniformity of content, and dissolution testing in ranitidine tablets manufactured by two different companies (X and Y).

METHODS

The uncertainty was evaluated using the Monte Carlo method. The particular and total risks of false acceptance decisions (consumers' risks) were also estimated by the Monte Carlo method.

RESULTS

The results for ranitidine tablets manufactured by company X were (359.2 ± 2.7) mg of average weight, (108.6 ± 1.6)% of assay, (11.4 ± 2.8) of acceptance value for uniformity of content, and (89.4 ± 1.3)% for dissolution testing. The results for company Y were (312.5 ± 0.9) mg, (107.0 ± 1.4)%, (7.2 ± 1.7), and (93.6 ± 1.3)%, respectively. A 95% confidence level was adopted. According to results obtained from the Monte Carlo simulations, the results of average weight, assay of ranitidine, and uniformity of content are significantly correlated due to shared analytical steps. The correlation between values significantly affected the risk of false decisions.

CONCLUSIONS

Consumers' risks were estimated for both ranitidine tablets and their results ensure compliance for ranitidine tablets manufactured by companies X and Y.

HIGHLIGHTS

The use of measurement uncertainty is an important issue regarding the conformity/non-conformity assessment of pharmaceutical products.

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.