Probabilistic human health risk assessment and Sobol sensitivity reveal the major health risk parameters of aluminum in drinking water in Shiraz, Iran.

Overuse of aluminum salts (a.k.a., alum) in coagulation and flocculation processes in water treatment raises concerns about increased levels of aluminum (Al) in drinking water. In this study, we present a probabilistic human health risk assessment (HRA) for non-cancerogenic risks, with Sobol sensitivity analysis, to vet the concern of increased health risk from Al in drinking water in Shiraz, Iran, for children, adolescents, and adults. The results show that the concentration of Al in the drinking water in Shiraz varies significantly between winter and summer seasons and varies considerably spatially across the city irrespective of the season. However, all concentrations are below the guideline concentration. The HRA results show that the highest health risk is for children in summer, and the lowest is for adolescents and adults during winter, with generally higher health risks for younger age groups. However, Monte Carlo results for all age groups suggest no adverse health effects due to Al exposure. The sensitivity analysis shows that the sensitive parameters vary across age groups. For example, the Al concentration and ingestion rate pose the most risk for adolescent and adult groups, and children group, respectively. More importantly, the interaction of Al concentration with ingestion rate and body weight is the controlling parameters for evaluating HRA rather than Al concentration alone. We conclude that while the HRA of Al in Shiraz drinking water did not indicate significant health risk, regular monitoring and optimal operation of the coagulation and flocculation processes are essential.

Comprehensive assessment of water quality and associated health risks in an arid region in south Iran.

This study aims at investigating the quality of drinking water and evaluating the non-carcinogenic risk of fluoride and nitrate ions in drinking water, and fluoride in tea in Zarrin Dasht, Iran. We focus on tea since it is the most popular drink among Iranian people and in the study region. We collected and analyzed 23 drinking water samples and 23 tea samples from different locations in the study region. Based on the water quality index, the consumed drinking water does not have a good quality in most Zarrin Dasht areas. Accordingly, the water quality index (WQI) is poor and very poor in 70% and 13% of the water samples, respectively. The average fluoride concentration of the tea samples is 2.71 mg/L. The mean values of Fluoride Hazard Index (HIfluoride) are 3.77, 2.77, and 2.33 for children, teenagers, and adults, respectively, which are higher than the safe limit of 1. The Nitrate Hazard Index (HInitrate) is higher than the safe limit of 1 in 8.7% of the samples. The results of the Monte Carlo simulation demonstrate that HIfluoride and HInitrate are higher than 1 in all the groups, except for adults. According to the results of the sensitivity analysis, ingestion rate and body weight have a large effect on HIfluoride and HInitrate, but body weight is inversely associated with sensitivity. According to the Piper diagram, saline water is the predominant type in Zarrin Dasht. Besides, the results of the principal component analysis (PCA) show a high correlation between fluoride and pH, which could be related to the effect of pH on fluoride dissolution and ion exchange. Therefore, appropriate measures are recommended to be taken in order to reduce the amount of fluoride in the drinking water resources of this region. Reduction of tea consumption can also be considered an important factor in decreasing the amount of fluoride intake.

Earth system models for regional environmental management of red tide: Prospects and limitations of current generation models and next generation development.

Earth system models (ESMs) serve as a unique research infrastructure for quality climate services, yet their application for environmental management at regional scale has not yet been fully explored. The unprecedented resolution and model fidelity of the Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations, especially of the High-Resolution Model Intercomparison Project (HighResMIP) focusing on regional phenomena, offer opportunities for such applications. This article presents the first venture into using the HighResMIP simulations to tackle a regional environmental issue, the Florida Red Tide. This is a harmful algae bloom caused by the dinoflagellate Karenia brevis, a toxic single-celled microscopic protist. We use CMIP6 historical simulations to establish a causal agreement between the position of Loop Current, a warm ocean current that moves into the Gulf of Mexico, and the occurrence of K. brevis blooms on the Western Florida shelf. Results show that the high-resolution ESMs are capable of simulating the phenomena of interest (i.e., Loop Current) at the regional spatial scale with generally adequate data-model agreement in the context of the relation between Loop Current and red tide. We use this case study to elaborate on the prospects and limitations of using publicly available CMIP data for regional environmental management. We highlight the current gaps and the developmental needs for the next generation ESMs, and discuss the role of stakeholder participation in future ESMs development to facilitate the translation of scientific understanding to better inform decision-making of regional environmental management.

Prescreening-Based Subset Selection for Improving Predictions of Earth System Models With Application to Regional Prediction of Red Tide.

We present the ensemble method of prescreening-based subset selection to improve ensemble predictions of Earth system models (ESMs). In the prescreening step, the independent ensemble members are categorized based on their ability to reproduce physically-interpretable features of interest that are regional and problem-specific. The ensemble size is then updated by selecting the subsets that improve the performance of the ensemble prediction using decision relevant metrics. We apply the method to improve the prediction of red tide along the West Florida Shelf in the Gulf of Mexico, which affects coastal water quality and has substantial environmental and socioeconomic impacts on the State of Florida. Red tide is a common name for harmful algal blooms that occur worldwide, which result from large concentrations of aquatic microorganisms, such as dinoflagellate Karenia brevis, a toxic single celled protist. We present ensemble method for improving red tide prediction using the high resolution ESMs of the Coupled Model Intercomparison Project Phase 6 (CMIP6) and reanalysis data. The study results highlight the importance of prescreening-based subset selection with decision relevant metrics in identifying non-representative models, understanding their impact on ensemble prediction, and improving the ensemble prediction. These findings are pertinent to other regional environmental management applications and climate services. Additionally, our analysis follows the FAIR Guiding Principles for scientific data management and stewardship such that data and analysis tools are findable, accessible, interoperable, and reusable. As such, the interactive Colab notebooks developed for data analysis are annotated in the paper. This allows for efficient and transparent testing of the results' sensitivity to different modeling assumptions. Moreover, this research serves as a starting point to build upon for red tide management, using the publicly available CMIP, Coordinated Regional Downscaling Experiment (CORDEX), and reanalysis data.