A Hybrid Bayesian Network Framework for Risk Assessment of Arsenic Exposure and Adverse Reproductive Outcomes

Association of maternal metals exposure, metabolites and birth outcomes in newborns: A prospective cohort study

BACKGROUND

Maternal exposure to metals may pose a risk to the health of newborns, however, the underlying mechanisms remain ambiguous. Herein, we aimed to investigate the influence of metals exposure on birth outcomes and reveal the importance of metabolites in the exposure-outcomes association by using metabolomics methods.

METHODS

In our study, 292 mother-pairs were included who were recruited from the affiliated hospitals of Nanjing Medical University between 2006 and 2011. We measured fifteen metals (mercury, lead, vanadium, arsenic, zinc, cadmium, rubidium, copper, cobalt, iron, molybdenum, strontium, thallium, magnesium and calcium) and metabolites in maternal second trimester serums by using inductively coupled plasma mass spectrometry and ultra-high performance liquid chromatography high resolution accurate mass spectrometry, respectively. A multi-step statistical analysis strategy including exposome-wide association study (ExWAS) model, variable selection models and multiple-exposure models were performed to systematically appraise the associations of individual and mixed metals exposure with birth outcomes. Furthermore, differential metabolites that associated with metals exposure and birth outcomes were identified using linear regression models.

RESULTS

Metal's levels in maternal serums ranged from 0.05 μg/L to 1864.76 μg/L. In the ExWAS model, maternal exposure to arsenic was negatively associated with birth weight (β = 188.83; 95% CI: -368.27, -9.39), while maternal mercury exposure showed a positive association (β = 533.65; 95%CI: 179.40, 887.90) with birth weight. Moreover, each unit increase in mercury (1 ng/mL-log transformed) was associated with a 1.82 week-increase (95%CI: 0.85, 2.79) in gestational age. These findings were subsequently validated by variable selection models and multiple exposure models. Metabolomic analysis further revealed the significant role of 3-methyladenine in the relationship between arsenic exposure and birth weight.

CONCLUSION

This study provides new epidemiological evidence indicating the associations of metals exposure and neonatal birth outcomes, and emphasizes the potential role of metabolite biomarkers and their importance in monitoring adverse birth outcomes.

Applying Bayesian Belief Networks to Assess Alpine Grassland Degradation Risks: A Case Study in Northwest Sichuan, China

Grasslands are crucial components of ecosystems. In recent years, owing to certain natural and socio-economic factors, alpine grassland ecosystems have experienced significant degradation. This study integrated the frequency ratio model (FR) and Bayesian belief networks (BBN) for grassland degradation risk assessment to mitigate several issues found in previous studies. Firstly, the identification of non-encroached degraded grasslands and shrub-encroached grasslands could help stakeholders more accurately understand the status of different types of alpine grassland degradation. In addition, the index discretization method based on the FR model can more accurately ascertain the relationship between grassland degradation and driving factors to improve the accuracy of results. On this basis, the application of BBN not only effectively expresses the complex causal relationships among various variables in the process of grassland degradation, but also solves the problem of identifying key factors and assessing grassland degradation risks under uncertain conditions caused by a lack of information. The obtained result showed that the accuracies based on the confusion matrix of the slope of NDVI change (NDVIs), shrub-encroached grasslands, and grassland degradation indicators in the BBN model were 85.27, 88.99, and 74.37%, respectively. The areas under the curve based on the ROC curve of NDVIs, shrub-encroached grasslands, and grassland degradation were 75.39% (P < 0.05), 66.57% (P < 0.05), and 66.11% (P < 0.05), respectively. Therefore, this model could be used to infer the probability of grassland degradation risk. The results obtained using the model showed that the area with a higher probability of degradation (P > 30%) was 2.22 million ha (15.94%), with 1.742 million ha (78.46%) based on NDVIs and 0.478 million ha (21.54%) based on shrub-encroached grasslands. Moreover, the higher probability of grassland degradation risk was mainly distributed in regions with lower vegetation coverage, lower temperatures, less potential evapotranspiration, and higher soil sand content. Our research can provide guidance for decision-makers when formulating scientific measures for alpine grassland restoration.

Probabilistic multi-pathway human health risk assessment due to heavy metal(loid)s in a traditional gold mining area in Ecuador

Mining operations are important causes of environmental pollution in developing countries where mining waste management is not adequate. Consequently, heavy metal(loid)s are easily released into the environment, being a potential risk to human health. This study carries out a Bayesian probabilistic human health risk assessment, related to multi-pathway exposure to heavy metal(loid)s in a gold mining area in Southern Ecuador. Concentrations of As, Cd, Cr, Cu, Ni, Pb, and Zn in tap water, surface water, and soil samples, were analyzed to assess the potential adverse human health effects based on the Hazard Index (HI) and Total cancer risk (TCR). Adults and children residents were surveyed to adjust their exposure parameters to the site-specific conditions. Exposure to heavy metal(loid)s resulted in unacceptable risk levels for human health in the two age groups, both carcinogenic (TCR > 1 × 10-5) and non-carcinogenic (HI > 1) through ingestion of tap water and incidental ingestion of surface water. Sensitivity analysis showed that As concentration in waters and exposure frequency were the main contributors to risk outcome. Exposure to soil via accidental ingestion and dermal contact was below the safety limit, not posing a risk to human health. These findings can provide a baseline for the environmental management of the mining area and indicate the need for further research on As pollution in water and its implications on the health of the inhabitants of mining communities.

Data mining for pesticide decontamination using heterogeneous photocatalytic processes

Pesticides are chemical compounds used to kill pests and weeds. Due to their nature, pesticides are potentially toxic to many organisms, including humans. Among the various methods used to decontaminate pesticides from the environment, the heterogeneous photocatalytic process is one of the most effective approaches. This study focuses on artificial intelligence (AI) techniques used to generate optimum predictive models for pesticide decontamination processes using heterogeneous photocatalytic processes. In the present study, 537 valid cases from 45 articles from January 2000 to April 2020 were filtered based on their content collected and analyzed. Based on cross-industry standard process (CRISP) methodology, a set of four classifiers were applied: Decision Trees (DT), Bayesian Network (BN), Support Vector Machines (SVM), and Feed Forward Multilayer Perceptron Neural Networks (MLP). To compare the accuracy of the selected algorithms, accuracy, and sensitivity criteria were applied. After the final analysis, the DT classification algorithm with seven factors of prediction, the accuracy of 91.06%, and sensitivity of 80.32% was selected as the optimal predictor model.

Quantifying and predicting ecological and human health risks for binary heavy metal pollution accidents at the watershed scale using Bayesian Networks

The accidental leakage of industrial wastewater containing heavy metals from enterprises poses great risks to resident health, social instability, and ecological safety. During 2005-2018, heavy metal mixed pollution accidents comprised approximately 33% of the major environmental ones in China. A Bayesian Networks-based probabilistic approach is developed to quantitatively predict ecological and human health risks for heavy metal mixed pollution accidents at the watershed scale. To estimate the probability distributions of joint ecological exposure once a heavy metal mixed pollution accident occurs, a Copula-based joint exposure calculation method, comprised of a hydro-dynamic model, emergent heavy metal pollution transport model, and the Copula functions, is embedded. This approach was applied to the risk assessment of acute Cr⁶⁺-Hg²⁺ mixed pollution accidents at 76 electroplating enterprises in 24 risk sub-watersheds of the Dongjiang River downstream watershed. The results indicated that nine sub-watersheds created high ecological risks, while only five created high human health risks. In addition, the ecological and human health risk levels were highest in the tributary (the Xizhijiang River), while the ecological risk was more critical in the river network, and the human health risk was more serious in the mainstream of the Dongjiang River. The quantitative risk assessment provides a substantial support to incident prevention and control, risk management, as well as regulatory decision making for electroplating enterprises.

Multi-pathway human exposure risk assessment using Bayesian modeling at the historically largest mercury mining district

The largest mercury (Hg) mining district in the world is located in Almadén (Spain), with well-known environmental impacts in the surrounding ecosystem. However, the impact of mercury on the health of the inhabitants of this area has not been documented accordingly. This study aims to carry out a probabilistic human health risk assessment using Bayesian modeling to estimate the non-carcinogenic risk related to Hg through multiple exposure pathways. Samples of vegetables, wild mushrooms, fish, soil, water, and air were analyzed, and adult residents were randomly surveyed to adjust the risk models to the specific population data. On the one hand, the results for the non-carcinogenic risk based on Hazard Quotient (HQ) showed unacceptable risk levels through ingestion of Hg-contaminated vegetables and fish, with HQ values 20 and 3 times higher, respectively, than the safe exposure threshold of 1 for the 97.5th percentile. On the other hand, ingestion of mushrooms, dermal contact with soil, ingestion of water, dermal contact with water and inhalation of air, were below the safety limit for the 97.5th percentile, and did not represent a risk to the health of residents. In addition, the probabilistic approach was compared with the conservative deterministic approach, and similar results were obtained. This is the first study conducted in Almadén, which clearly reveals the high levels of human health risk to which the population is exposed due to the legacy of two millennia of Hg mining.