Groundwater geochemistry, quality, and pollution of the largest lake basin in the Middle East: Comparison of PMF and PCA-MLR receptor models and application of the source-oriented HHRA approach

Haghnazar, Hamed; Johannesson, Karen H.; González‐Pinzón, Ricardo; Pourakbar, Mojtaba; Aghayani, Ehsan; Rajabi, Akbar; Hashemi, Ahmad

doi:10.1016/j.chemosphere.2021.132489

Cited by 102 publications

(28 citation statements)

References 80 publications

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“…However, there was no significant difference between the mean perceived risks of these diseases. Studies have indicated adverse effects of LUD on hypertension ( 44 ), cancer ( 25 , 26 ), depression ( 41 ), stress/anxiety ( 41 ), cardiovascular diseases ( 29 , 42 , 43 , 49 ), and income and job loss ( 38 , 50 - 53 ). Moreover, several studies have shown the adverse effect of drying up of other lakes on cancer ( 11 , 54 ) and stress ( 55 ).…”

Section: Discussionmentioning

confidence: 99%

“…Due to the entry of industrial and agricultural effluents into the lake and their sediment in its bed, these dusts would be contaminated with industrial and agricultural toxins ( 10 ). Therefore, dust exposure through respiration and skin and accumulation of metals in plants and agricultural products and their concentration in the food chain could cause health problems, including respiratory diseases, cancers, cardiovascular diseases, hypertension, and eye diseases ( 25 - 29 ).…”

Section: Introductionmentioning

confidence: 99%

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Public Perception on the Health Consequences of an Environmental Disaster: The Case of Lake Urmia Drying up

Jafari-Khounigh

Sadeghi‐Bazargani²,

Haghdoost

2022

Med. J. Islam. Republ. Iran

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Public Perception on the Health Consequences of an Environmental Disaster: The Case of Lake Urmia Drying up

Jafari-Khounigh

Sadeghi‐Bazargani²,

Haghdoost

2022

Med. J. Islam. Republ. Iran

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“…On the one hand, the quantity of groundwater is decreasing [ 11 ]. Population growth, urbanization, and excessive extraction of groundwater lead to the water level declining severely [ 12 ]. Irrigation using groundwater also decreases the available quantity [ 13 ].…”

Section: Related Workmentioning

confidence: 99%

Predicting Groundwater Indicator Concentration Based on Long Short-Term Memory Neural Network: A Case Study

Liu

et al. 2022

IJERPH

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Prediction of groundwater quality is an essential step for sustainable utilization of water resources. Most of the related research in the study area focuses on water distribution and rational utilization of resources but lacks results on groundwater quality prediction. Therefore, this paper introduces a prediction model of groundwater quality based on a long short-term memory (LSTM) neural network. Based on groundwater monitoring data from October 2000 to October 2014, five indicators were screened as research objects: TDS, fluoride, nitrate, phosphate, and metasilicate. Considering the seasonality of water quality time series data, the LSTM neural network model was used to predict the groundwater index concentrations in the dry and rainy periods. The results suggest the model has high accuracy and can be used to predict groundwater quality. The mean absolute errors (MAEs) of these parameters are, respectively, 0.21, 0.20, 0.17, 0.17, and 0.20. The root mean square errors (RMSEs) are 0.31, 0.29, 0.28, 0.27, and 0.31, respectively. People can be given early warnings and take measures according to the forecast situation. It provides a reference for groundwater management and sustainable utilization in the study area in the future and also provides a new idea for coastal cities with similar hydrogeological conditions.

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“…Heavy metals (HMs) in aquatic environments are present as a result of natural rock weathering processes and are also contributed from anthropogenic sources. Studies indicate that the natural biogeochemical cycle of HMs has been perturbed by anthropogenic activities [ 1 , 2 , 3 , 4 , 5 ]. Nowadays, the presence of HMs in the aquatic environment is mainly associated with anthropogenic sources [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Heavy Metals in River Sediments: Contamination, Toxicity, and Source Identification—A Case Study from Poland

Sojka

Jaskuła

2022

IJERPH

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This study investigated the spatial distribution, contamination, potential ecological risks and quantities of pollutant sources of six heavy metals (HMs) in sediments of 47 rivers. The catchments of the investigated rivers are situated in Poland, but some of them are located in Slovakia, the Czech Republic, and Germany. Cluster analysis was applied to analyze the spatial distribution of Cd, Cr, Cu, Ni, Pb, and Zn in river sediments. Moran I and Getis-Ord Gi* statistics were calculated to reveal the distribution pattern and hotspot values. Principal component analysis (PCA) and positive matrix factorization (PMF) were used to identify pollution sources. Furthermore, geochemical indices and sediment quality guidelines allowed us to assess sediment contamination and potential toxic effects on aquatic biota. The results showed that in 1/3rd of the rivers, the HM pattern and concentrations indicate sediment contamination. The EF, PLI, and MPI indices indicate that concentrations were at a rather low level in 2/3rd of the analyzed rivers. Only in individual rivers may the HMs have toxic effects on aquatic biota. Spatial autocorrelation analysis using the Moran I statistic revealed a random and dispersed pattern of HMs in river sediments. PCA analysis identified two sources of HMs’ delivery to the aquatic environment. Cr, Cu, Ni, Pb, and Zn originate from point and non-point sources, while Cd concentrations have a dominant natural origin. The PMF identified three sources of pollution. Among them, urban pollution sources are responsible for Cu delivery, agricultural pollution for Zn, and industrial pollution for Ni and Cr. Moreover, the analysis showed no relationship between catchment land-use patterns and HM content in river sediments.

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Groundwater geochemistry, quality, and pollution of the largest lake basin in the Middle East: Comparison of PMF and PCA-MLR receptor models and application of the source-oriented HHRA approach

Cited by 102 publications

References 80 publications

Public Perception on the Health Consequences of an Environmental Disaster: The Case of Lake Urmia Drying up

Public Perception on the Health Consequences of an Environmental Disaster: The Case of Lake Urmia Drying up

Predicting Groundwater Indicator Concentration Based on Long Short-Term Memory Neural Network: A Case Study

Heavy Metals in River Sediments: Contamination, Toxicity, and Source Identification—A Case Study from Poland

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