Surface water quality analysis using multivariate statistical techniques: a case study of Fars Province rivers, Iran

Spatio-temporal monitoring of water quality in the Gharni reservoir (India) by multivariate statistical tools: a case study of a reservoir located in a rainfall deficit area

The present study reported the effectiveness of multivariate statistical tools used to monitor spatio-temporal fluctuations in the water quality of Gharni reservoir. The Gharni reservoir is situated on the Gharni river sub-basin which is a tributary of the main river Manjara located in the Marathwada rainfall deficit area of Maharashtra, India. The water body is periodically sampled at five selected locations between August 2019 and January 2021 to assess water quality of 20 parameters. Different statistical techniques were employed to handle intricate data matrices, including cluster scanning/analysis (CA), factor examination (analysis)/principal component evaluation (analysis) (FA/PCA) for data reduction, and discriminant survey/analysis (DA) for data classification. This method of hierarchical CA categorized the five sampling stations into three groups and seasons into four groups/clusters based on resemblance in the recorded physico-chemical parameter readings. The FA/PCA successfully extracted a total of 14 factors, which accounted for 70% out of the total 20 measured variables. These factors were crucial in explaining 62% of the variability observed in the data. Furthermore, the analysis pointed out the specific components/factors responsible for the alteration in the quality of reservoir water. Additionally, the dominance of individual group was evaluated in relation to the comprehensive differentiability at five distinct sampled locations. The DA yielded 14 parameters with a 99% accuracy rate for assigning correct values. The varifactors (VF) developed in the factor analysis have shown that the variation in quality of surface water was interrelated to two groups. The first group included physico-chemical parameters like dissolved oxygen, temperature, and conductivity, whereas the second group covered nutrients like chlorophyll, phosphorus, and nitrogen and were deposited in the water by soil erosion during rainy season from agricultural land. This case study demonstrates the use of multivariate statistical tools as an excellent exploratory tool for analyzing and interpreting complex data sets. It highlights their effectiveness in assessing water quality and understanding its spatio-temporal variations, ultimately assisting in the management of reservoir water quality.

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.

Evaluation of potential human health risks arising from nitrate and fluoride in the groundwater of Aurangabad, Bihar using GIS and chemometric analysis

This study employed the groundwater pollution index to assess the appropriateness of groundwater for human consumption. Additionally, the hazard index was utilized to evaluate the potential non-carcinogenic risks associated with fluoride and nitrate exposure among children, women, and men in the study region. A total of 103 samples were collected from the Aurangabad district of Bihar. The analyzed samples were assessed using several physicochemical parameters. Major cations in the groundwater are Ca2+ > Mg2+ and major anions are HCO3- > Cl- > SO42- > NO3- > F- > PO43-. Around 17% of the collected groundwater samples surpassed the allowable BIS concentration limits for Nitrate, while approximately 11% surpassed the allowed limits for fluoride concentration. Principal component analysis was utilized for its efficacy and efficiency in the analytical procedure. Four principal components were recovered that explained 69.06% of the total variance. The Hazard Quotient (HQ) of nitrate varies between 0.03-1.74, 0.02-1.47, and 0.03-1.99 for females, males, and children, respectively. The HQ of fluoride varies between 0.04-1.59, 0.04-1.34, and 0.05-1.82 for females, males, and children, respectively. The central part of the district was at high risk according to the spatial distribution maps of the total hazard index (THI). Noncarcinogenic risks due to THI are 47%, 37%, and 28% for children, females, and males, respectively. According to the human health risk assessment, children are more prone to getting affected by polluted water than adults. The groundwater pollution index (GPI) value ranges from 0.46 to 2.27 in the study area. Seventy-five percent of the samples fell under minor pollution and only one fell under high pollution. The spatial distribution of GPI in the research area shows that the central region is highly affected, which means that this water is unsuitable for drinking purposes.

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.

Hydrochemical variations and driving mechanisms in a large linked river-irrigation-lake system

A linked river-irrigation-lake system exhibits intricate and dynamic hydrochemical variations, closely related to changes in natural conditions and anthropogenic activities. However, little is known about the sources, migration and transformation of hydrochemical composition, and the driving mechanisms, in such systems. In this study, the hydrochemical characteristics and processes in the linked Yellow River-Hetao Irrigation District-Lake Ulansuhai system were studied, based on a comprehensive hydrochemical and stable isotope analysis of water samples collected during spring, summer, and autumn. The results showed that the water bodies in the system were weakly alkaline with a pH range of 8.05-8.49. The concentrations of hydrochemical ions showed an increasing trend in the water flow direction. Total dissolved solids (TDS) were less than 1000 mg/L (freshwater) in the Yellow River and the irrigation canals, and increased to more than 1800 mg/L (saltwater) in the drainage ditches and Lake Ulansuhai. The dominant hydrochemical types varied from SO₄•Cl-Ca•Mg and HCO₃-Ca•Mg types in the Yellow River and the irrigation canals to Cl-Na type in the drainage ditches and Lake Ulansuhai. The ion concentrations in the Yellow River, the irrigation canals, and the drainage ditches were highest during summer, while ion concentrations in Lake Ulansuhai were highest during spring. The hydrochemistry of the Yellow River and the irrigation canals was mainly affected by rock weathering, while evaporation was the principal controlling factor in the drainage ditches and Lake Ulansuhai. Water-rock interactions including the dissolution of evaporites and silicates, the precipitation of carbonates, and cation exchange were the main sources of hydrochemical compositions in this system. Anthropogenic inputs had a low impact on the hydrochemistry. Therefore, greater attention should be paid in future to hydrochemical variations, especially salt ions, in the management of linked river-irrigation-lake system water resources.

Evaluating the habitat suitability modeling of Aceria alhagi and Alhagi maurorum in their native range using machine learning techniques.. [DOI: 10.21203/rs.3.rs-2441475/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Spatial locational modeling techniques are increasingly used in species distribution modeling. However, the implemented techniques differ in their modeling performance. In this study, we tested the predictive accuracy of three algorithms, namely "random forest (RF)," "support vector machine (SVM)," and "boosted regression trees (BRT)" to prepare habitat suitability mapping of an invasive species, Alhagi maurorum, and its potential biological control agent, Aceria alhagi. Location of this study was in Fars Province, southwest of Iran. The spatial distributions of the species were forecasted using GPS devices and GIS software. The probability values of occurrence were then checked using three algorithms. The predictive accuracy of the machine learning (ML) techniques was assessed by computing the “area under the curve (AUC)” of the “receiver-operating characteristic” plot. When the Aceria alhagi was modeled, the AUC values of RF, BRT and SVM were 0.89, 0.81, and 0.79, respectively. However, in habitat suitability models (HSMs) of Alhagi maurorum the AUC values of RF, BRT and SVM were 0.89, 0.80, and 0.73, respectively. The RF model provided significantly more accurate predictions than other algorithms. The importance of factors on the growth and development of Alhagi maurorum and Aceria alhagi was also determined using the partial least squares (PLS) algorithm, and the most crucial factors were the road and slope. Habitat suitability modeling based on algorithms may significantly increase the accuracy of species distribution forecasts, and thus it shows considerable promise for different conservation biological and biogeographical applications.