A statistical assessment of micropollutants occurrence, time trend, fate and human health risk using left-censored water quality data

Cantoni, Beatrice; Compagni, Riccardo Delli; Turolla, Andrea; Epifani, Ilenia; Antonelli, Manuela

doi:10.1016/j.chemosphere.2020.127095

Cited by 17 publications

(5 citation statements)

References 17 publications

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“…Concentrations below the LOQ were replaced by the value of LOQ/ 2. 25 Calculation of Placental Structure and Efficiency. After delivery, placenta record forms were completed by trained midwives, which included date and time of delivery, large or small (cross-sectional) diameter of the placenta (unit: cm), and birth weight of newborns (unit: g).…”

Section: ■ Materials and Methodsmentioning

confidence: 99%

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Impact of Gestational Exposure to Individual and Combined Per- and Polyfluoroalkyl Substances on a Placental Structure and Efficiency: Findings from the Ma’anshan Birth Cohort

Gan,

Xing,

Tong

et al. 2024

Environ. Sci. Technol.

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Prenatal exposure to perfluoroalkyl and polyfluoroalkyl substances (PFASs) is inevitable among pregnant women. Nevertheless, there is a scarcity of research investigating the connections between prenatal PFAS exposure and the placental structure and efficiency. Based on 712 maternal–fetal dyads in the Ma’anshan Birth Cohort, we analyzed associations between individual and mixed PFAS exposure and placental measures. We repeatedly measured 12 PFAS in the maternal serum during pregnancy. Placental weight, scaling exponent, chorionic disc area, and disc eccentricity were used as the outcome variables. Upon adjusting for confounders and implementing corrections for multiple comparisons, we identified positive associations between branched perfluorohexane sulfonate (br-PFHxS) and 6:2 chlorinated polyfluorinated ether sulfonate (6:2 Cl-PFESA) with placental weight. Additionally, a positive association was observed between br-PFHxS and the scaling exponent, where a higher scaling exponent signified reduced placental efficiency. Based on neonatal sex stratification, female infants were found to be more susceptible to the adverse effects of PFAS exposure. Mixed exposure modeling revealed that mixed PFAS exposure was positively associated with placental weight and scaling exponent, particularly during the second and third trimesters. Furthermore, br-PFHxS and 6:2 Cl-PFESA played major roles in the placental measures. This study provides the first epidemiological evidence of the relationship between prenatal PFAS exposure and placental measures.

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Section: ■ Materials and Methodsmentioning

confidence: 99%

“…The LOQ values ranged from 0.005–0.05 ng/mL (Table S3). Concentrations below the LOQ were replaced by the value of LOQ/2 …”

Section: Methodsmentioning

confidence: 99%

Impact of Gestational Exposure to Individual and Combined Per- and Polyfluoroalkyl Substances on a Placental Structure and Efficiency: Findings from the Ma’anshan Birth Cohort

Gan,

Xing,

Tong

et al. 2024

Environ. Sci. Technol.

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“…Trace pollutants, such as cyanobacterial toxins, heavy metals, volatile organic compounds, and pesticides, are widely observed in aquatic ecosystems. Despite their low concentrations, these pollutants can have significant impacts on human health and the environment. − As such, techniques for monitoring these pollutants have advanced rapidly, providing an increasing amount of data to evaluate their environmental risk. With the accumulation of monitoring data, predicting these trace pollutants and understanding their response relationships with other environmental drivers through data-driven methods become feasible.…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Interpretable Machine Learning Framework for Accurate Prediction of Trace Pollutants: With an Application to Microcystin

Wu,

Liang,

Qiu

2023

ACS EST Water

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Trace pollutants are widely observed in aquatic ecosystems and can significantly impact human health and the environment. Accurate prediction of trace pollutants and understanding their response to environmental drivers are key to environmental management, yet these tasks remain challenging. An important reason for this challenge is that monitoring data for trace pollutants are often left-censored, leading to biased estimation and inaccurate response-driver relationships. Here we propose a novel two-stage interpretable machine learning framework applicable to left-censored trace pollutant data. The two stages include (1) a classifier to predict the presence of the pollutant and (2) a regressor to predict the pollutant concentration if present. The two stages were followed by a model interpretation to understand the contribution of drivers to the presence and concentration of the pollutant accordingly. We take the prediction of microcystin (MICX) in lakes across the United States as a case study. Applying this framework to MICX consistently improved prediction accuracy, including prediction of its occurrence and concentration regardless of the algorithms and performance metrics used. The best-performing algorithm using the two-stage framework, compared with the baseline model, improves classification performance by 48% to 290% and the regression performance by 11% to 33%, depending on the metric used to evaluate the performance. The interpretable machine learning model also successfully revealed the impacts of the most important drivers on the presence of MICX and its concentration. Our results showed the advantages of this framework, including its interpretability to understand the driver-response relationship, ability to handle nonlinearity, better prediction performance, differentiation between the underlying processes, and potential to be generalized to other pollutants. As such, we anticipate that the framework we propose will be a starting point for using state-of-the-art interpretable machine learning models for predicting trace pollutants.

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“…In addition to enabling the analysis of water quality (Cantoni et al, 2020), the handling of censored data helps evaluate the risk of disease caused by microorganisms (Canales et al, 2018), analyze breast cancer patients (Faucheux et al, 2021), spatially interpolate measurements in riverbeds (Mohamed et al, 2021), and model genetic modifications in fish meat (Fusek et al, 2020), among other areas.…”

Section: Introductionmentioning

confidence: 99%

Assessment of left-censored data treatment methods using stochastic simulation

Silva,

Pinto

2023

RBRH

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The paper evaluates the influence of size series, percentage of censored data, and coefficients of variation used to generate synthetic series on the estimation of means, standard deviations, coefficients of variation, and medians in series with censored data. Seven techniques were applied to treat censored data in synthetic series with 180 scenarios (four size series, nine censoring percentages and five coefficients of variation): values proportional to the DL: zero, DL/2, DL/20.5 and DL - and parametric (MLE), robust (ROS) and Kaplan-Meier methods. Predictions were analyzed with four performance metrics (MPE, MAPE, KGE, and RMSE). It is found that the percentage of censored data and the coefficient of variation significantly alter forecast quality. It is also found that substitution by DL/2, by DL/20.5 and ROS are the most appropriate techniques for estimating the variables described, emphasizing ROS when estimating parametric variables and substitution by DL/20.5 for medians.

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A statistical assessment of micropollutants occurrence, time trend, fate and human health risk using left-censored water quality data

Cited by 17 publications

References 17 publications

Impact of Gestational Exposure to Individual and Combined Per- and Polyfluoroalkyl Substances on a Placental Structure and Efficiency: Findings from the Ma’anshan Birth Cohort

Impact of Gestational Exposure to Individual and Combined Per- and Polyfluoroalkyl Substances on a Placental Structure and Efficiency: Findings from the Ma’anshan Birth Cohort

A Two-Stage Interpretable Machine Learning Framework for Accurate Prediction of Trace Pollutants: With an Application to Microcystin

Assessment of left-censored data treatment methods using stochastic simulation

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