Assessing water quality index and health risk using deterministic and probabilistic approaches in Darab County, Iran; A machine learning for fluoride prediction.

The present study employed deterministic and probabilistic approaches to determine the Water Quality Index (WQI) and assess health risks associated with water consumption in Darab County, Iran. Additionally, pollution levels were predicted using a machine-learning algorithm. The study's findings indicate that certain physicochemical parameters of water in some locations exceeded permissible limits (WHO or EPA), with 79.00 % of total hardness (TH) and 21.74 % of Total dissolved solids (TDS) levels exceeding standard values. The WQI for drinking water was determined to be 94.56 % using the deterministic approach, and 98.4 % of samples included the excellent and good categories according to the WQI classification system using the probabilistic approach. Fluoride (F) exhibited the most substantial impact on WQI values. The Artificial Neural Network (ANN) analysis findings suggest that the pH, nitrate (NO3), and TDS are the most significant factors affecting the prediction of F concentration in water. Multivariate analysis demonstrated that anthropogenic, especially agriculture and geogenic factors, contributed to the water quality in this area. The health risk assessment (HRA) using deterministic methods revealed that water consumption posed a relatively high risk in certain areas. However, Monte Carlo simulation demonstrated that the 5th and 95th percentiles of Hazard Index (HI) for children, teenagers, and adults were within limits of (0.14-2.38), (0.09-1.29), and (0.10-1.00) respectively, with a certainty level of 70 %, 91 %, and 95 %. Interactive indices revealed that the intake of IR and NO3-IR in children, BW and F-BW in teenagers, and NO3 and NO3-IR in adults significantly impacted health risks. Based on these findings, augmenting water treatment processes, regulating fluoride concentrations, and advocating for sustainable agricultural practices complemented by continuous monitoring is imperative.

An effective magnetic nanobiocomposite: Preparation, characterization and its application for adsorption removal of P-nitroaniline from aquatic environments.

In this investigation, a magnetic nanobiocomposite, denoted as CoFe2O4/Activated Carbon integrated with Chitosan (CoFe2O4/AC@Ch), was synthesized based on a microwave-assisted for the efficacious adsorption of P-nitroaniline (PNA). The physicochemical properties of the said nano biocomposite were thoroughly characterized using a suite of analytical methodologies, namely FESEM/EDS, BET, FTIR, XRD, and VSM. The results confirm the successful synthesis of the nanobiocomposite, with its point of zero charge (pHZPC) determined to be 6.4. Adsorptive performance towards PNA was systematically examined over a spectrum of conditions, encompassing variations in PNA concentration (spanning 10-40 mg/L), adsorbent concentration (10-200 mg/L), contact periods (2.5-22.5 min), and solution pH (3-11). Upon optimization, the conditions converged to an adsorbent concentration of 200 mg/L, pH 5, PNA concentration of 10 mg/L, and a contact duration of 22.5 min, under which an impressive PNA adsorption efficacy of 98.6% was attained. Kinetic and isotherm analyses insinuated the adsorption mechanism to adhere predominantly to the pseudo-second-order kinetic and Langmuir isotherm models. The magnetic nanocomposite was recovered and used in 4 cycles, and the absorption rate reached 86%, which shows the good stability of the magnetic nanocomposite in wastewater treatment. Conclusively, these empirical outcomes underscore the viability of the formulated magnetic nanobiocomposite as a potent, recyclable adsorbent for the proficient extraction of PNA from aqueous matrices.

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.

Evaluation of water quality of Chahnimeh as natural reservoirs from Sistan region in southwestern Iran: a Monte Carlo simulation and Sobol sensitivity assessment.

Maintaining the water quality is essential because of the limitation of drinking water bodies and their significant effects on life. Recently, much scientific interest has been attracted to the ecological condition assessment of water resources. Because of numerous health issues connected to water quality, the present work aimed to define the water quality status of Chahnimeh reservoirs, Sistan and Baluchistan province, Iran via the Iran Water Quality Index (IRWQISC), the National Sanitation Foundation Water Quality Index (NSFWQI), and human risk assessment. This cross-sectional descriptive work was accomplished in 4 seasons in 2020. The samples were gathered from 5 various points of Chahnimeh reservoirs. This study led to the results that the NSFWQI index was between 29.4 to 49.32, which showed "bad" quality, and the IRWQI index was between 19.27 and 39.23, which indicated "bad" and "relatively bad" quality. The best water quality based on both indexes was observed in the spring, and the worst was in the fall and summer. The highest value of HQ related to nitrate in drinking water was 1.60 in the group of children. However, according to the Monte Carlo simulation, HQ95% was estimated as 1.29. The Sobol sensitivity analysis of the first-order effect showed that daily water's daily ingestion rate (IR) was the most sensitive input. In addition, the value of the second-order effect indicated that the interaction effect of concentration-ingestion rate was the most sensitive input parameter for HQ. Therefore, regular monitoring is necessary to ensure water safety for human consumption.