Application of chemometric analysis and self Organizing Map-Artificial Neural Network as source receptor modeling for metal speciation in river sediment

Self-organizing map algorithm for assessing spatial and temporal patterns of pollutants in environmental compartments: A review

The evaluation of the spatial and temporal distribution of pollutants is a crucial issue to assess the anthropogenic burden on the environment. Numerous chemometric approaches are available for data exploration and they have been applied for environmental health assessment purposes. Among the unsupervised methods, Self-Organizing Map (SOM) is an artificial neural network able to handle non-linear problems that can be used for exploratory data analysis, pattern recognition, and variable relationship assessment. Much more interpretation ability is gained when the SOM-based model is merged with clustering algorithms. This review comprises: (i) a description of the algorithm operation principle with a focus on the key parameters used for the SOM initialization; (ii) a description of the SOM output features and how they can be used for data mining; (iii) a list of available software tools for performing calculations; (iv) an overview of the SOM application for obtaining spatial and temporal pollution patterns in the environmental compartments with focus on model training and result visualization; (v) advice on reporting SOM model details in a paper to attain comparability and reproducibility among published papers as well as advice for extracting valuable information from the model results is presented.

Delineating the controlling mechanisms of arsenic release into groundwater and its associated health risks in the Southern Loess Plateau, China

The mechanisms controlling arsenic (As) enrichment and mobilization associated with human health risk assessment of groundwater in the Longdong Basin, located in the southern part of the Loess Plateau, China, have been yet unexplained. This uncertainty is partly attributed to a poor understanding of groundwater arsenic management. To address this problem, this study investigated the occurrence and spatial distribution of As in unconfined groundwater (UG) and confined groundwater (CG) in the study area, integrated Self-Organizing Maps (SOM) and geochemical modeling to elucidate the mechanisms controlling As release and mobilization in groundwater, and conducted a health risk assessment of groundwater As. The results showed that 13.6% of UG samples (n = 66) and 22.4% of CG samples (n = 98) exceeded the WHO guideline limit of As (10 μg/L). The detailed hydrogeochemical studies showed that As-enrichment groundwater is dominated by Cl-Na type, and Gaillardet diagram indicated that evaporites weathering may contribute to As mobilization in CG. The SOM analysis combined with Spearman's correlation coefficient quantified the negative correlation between As and redox potential, dissolved oxygen, SO₄^2-, NO₃^-, and the positive correlation between As and HCO₃^-, Mn in UG. In CG, As is positively correlated to pH and negatively to electrical conductivity, SO₄^2-, Fe and Mn. The saturation indices of the mineral phases indicates an insignificant relationship between As and Fe. We conclude that under oxidizing conditions, evaporative controls and the desorption of Fe-oxides under alkaline and high salinity conditions are the dominant mechanisms controlling As release and mobilization in groundwater. In addition, exposure to groundwater As through drinking water posed potential risk of carcinogenic and non-carcinogenic effects on children and adults. This study contributes to groundwater As management and sustainable safe groundwater supply.

Spatial variations and potential risks of heavy metals in sediments of Yueqing Bay, China

In this study, we determined the spatial variations and potential risks of heavy metals in the sediments of Yueqing Bay by assessing the relationship between metal concentrations and sediment physiochemical factors. We found higher sediment metal concentrations in the inner bay than in the central and outer bay, particularly with respect to Hg, Cu, and Pb concentrations. According to the sediment quality guidelines, the heavy metals had a toxicity incidence probability of 21%. Assessments of heavy metal contamination using the geo-accumulation index and potential ecological risk index suggest that Cr, As, Pb, and Hg likely pose low ecological risks, while Cu, Zn, and Cd were identified as priority pollutants and may pose moderate ecological risks to the ecosystem. Multivariate statistical analysis inferred the high influence of sediment texture, total organic carbon (TOC), and petroleum hydrocarbons (PHCs) on the distribution and fate of metals in sediment.

Metallic contamination of global river sediments and latest developments for their remediation

This review article represents the comparative study of heavy metal concentration in water and sediments of 43 important global rivers. The review is a solitary effort in the area of heavy metal contamination of river-sediments during last ten years. The interpretation of heavy metal contamination in sediments has been verified with different indices, factors, codes and reference guidelines, which is based on geochemical data linked to background value of metals. It is observed that health hazards arise due to dynamics of movement of metals between water and sediments, which is primarily influenced by several factors such as physical, chemical, biological, hydrological and environmental. Also, the reason behind accumulation and assimilation of heavy metals on river water system is explained with appropriate mechanisms. Several factors e.g. pH, ORP, organic matter etc. are mainly involved in the distribution, accumulation and assimilation of metals in the sediment phase to water phase. Remediation technologies such as in-situ and ex-situ have been discussed for the removal of heavy metals from contaminated sediments. We have also compared the performance efficiencies of the technologies adopted by different researchers during the period 2003 to 2019 for the removal of metal bound sediments. Many researchers have preferred in-situ over ex-situ remediation due to low cost and time saving remediation effects. In this work we have also incorporated the safety measures and strategies which can prevent the metal accumulation in sediments of river system.

An insight into machine learning models era in simulating soil, water bodies and adsorption heavy metals: Review, challenges and solutions

The development of computer aid models for heavy metals (HMs) simulation has been remarkably advanced over the past two decades. Several machine learning (ML) models have been developed for modeling HMs over the past two decades with outstanding progress. Although there have been a noticeable number of diverse ML models investigations, it is essential to have an informative vision on the progression of those computer aid models. In the current short review covering the simulation of heavy metals in contaminated soil, water bodies and removal from aqueous solution, numerous aspects on the methodological and conceptual HMs modeling are reviewed and discussed in detail. For instance, the limitation of the classical analytical methods, types of heavy metal dataset, necessity for new versions of ML models exploration, HM input parameters selection, ML models internal parameters tuning, performance metrics selection and the types of the modelled HM. The current review provides few outlooks in understanding the underlying od the ML models application for HM simulation. Tackling these modeling aspects is significantly essential for ML developers and environmental scientists to obtain creditability and scientific consistency in the domain of environmental science. Based on the discussed modeling aspects, it was concluded several future research directions, which will promote environmental scientists for better understanding of the underlying HMs simulation.

Impact of green infrastructure on the mitigation of road-deposited sediment induced stormwater pollution

As a vital stormwater pollution source, the pollutants associated with road-deposited sediment (RDS) have become a growing concern in urban water management. Green infrastructure has exhibited great potential in stormwater pollution mitigation, but is not comprehensively understood yet due to the influences of complex RDS-associated pollutant migration processes (i.e., build-up, wash-off, and discharge). In this study, a city-scale hydraulic and water quality model was used to analyze the migration and removal processes of four RDS-associated pollutants (total suspended solids (TSS), chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP)) under different hydrological patterns, land-cover types, and green infrastructure installation locations. Results show that the antecedent dry-weather period was the main factor influencing RDS build-up, while the precipitation pattern strongly impacted RDS wash-off, discharge, and removal. The downstream-installed green infrastructures reduced the RDS-induced stormwater pollution by up to 68% and relieved the pollution-mitigation pressure of the studied drainage networks by almost 50%. The TSS and COD removal rates were higher (62.22-68.09%) near green space, while those of TN and TP were higher around buildings and roads (40.00-62.50%). Sensitivity analysis indicated that seven parameters regarding the surface layer characteristics and soil texture class strongly impacted the pollution-mitigation performance among the 31 technical parameters of green infrastructure. The results of this study would assist urban water management by optimizing green infrastructure for stormwater pollution mitigation.

Assessment of trace elements in terminal tap water of Hunan Province, South China, and the potential health risks

A total of 116 terminal tap water (TTW) samples from Xiangjiang, Zijiang, Yuanjiang, and Lishui river basins of Hunan province were collected and concentrations of As, Cd, Cr, Pb, Mn, Zn, Fe, Al, and Cu were determined using inductively coupled plasma mass spectrometry. The results showed that 10% of the water samples exceeded the limit level of Cd established by World Health Organization (WHO) of 0.003 mg L^-1. Three percent of the samples had Fe level and 1% had As level above the WHO limits of 0.3 and 0.01 mg L^-1, respectively. Multivariate statistic approach (cluster analysis and principal component analysis) results revealed that anthropogenic activities and pipeline corrosion were major sources of TTW contamination in Hunan province. The individual and total hazard quotient values estimated by deterministic and probabilistic approaches were both less than 1. However, the mean cancer risk values of Cd were 2.2 × 10^-4 and 1.4 × 10^-4 for Xiangjiang and Yuanjiang river basin, respectively, both greater than 10^-4. The 95th percentile value of cancer risk for Cr was slightly greater than 10^-4 in Xiangjiang river basins. Long-term exposure to Cd and Cr through tap water consumption poses moderate carcinogenic health risks to the local residents.

Sediment core data reconstruct the management history and usage of a heavily modified urban lake in Berlin, Germany

Urban surface waters face several stressors associated with industry and urban water management. Over much of the past century, the wastewater treatment in Berlin, Germany, relied on inefficient sewage farms, which resulted in severe eutrophication and sediment contamination in the recipient surface waterbodies. A prominent example is Lake Tegel, where a multitude of management measures were applied in the last decades for the purpose of ecosystem restoration. In this study, we analyzed sediment cores of three lakes with X-ray fluorescence spectroscopy: Lake Tegel, Lake Großer Wannsee, which is environmentally similar but has a different management history, and Lake Userin, which serves as a reference located in a nature protection area. Multivariate statistical methods (principal component analysis, k-means clustering, and self-organizing maps) were used to assess the sediment quality and to reconstruct the management history of Lake Tegel. Principal component analysis established two main gradients of sediment composition: heavy metals and lithogenic elements. The impact of the management measures was visualized in the lake sediment composition changing from high abundance of heavy metals and reducing redox conditions to less-impacted sediments in recent layers. The clustering techniques suggested heterogeneity among sites within Lake Tegel that probably reflect urban water management measures. The abundance of heavy metals in recent lake sediments of Lake Tegel is similar to a lake with low urban impact and is lower than in Lake Großer Wannsee suggesting that the management measures were successful in the reduction of heavy metals, which are still a threat for surface waters worldwide.

Level, source identification, and risk analysis of heavy metal in surface sediments from river-lake ecosystems in the Poyang Lake, China

The concentrations, sources, and risks of heavy metals (Fe, Al, Mn, Cr, Co, Ni, Cu, Zn, As, Cd, W, Pb, and Tl) in sediments in five river-lake ecosystems in the Poyang Lake region were studied. The concentrations of the heavy metals varied spatially, with most of the highest concentrations in the Raohe river-lake ecosystem (RH). All heavy metals except As, Cd, W, and Tl were enriched in sediments possessing high total organic carbon contents or in finer sediments. Based on enrichment factors and statistical methods, it was found that Cd in sediments in the Xiushui (XS), Ganjiang (GJ), Xinjiang (XJ) river-lake ecosystems, and RH; Mn in the XS, GJ, and RH; and W in the XS and GJ were greatly affected by anthropogenic inputs. Moreover, the origins of Cu, Zn, and As require more attention due to the high concentrations found. The high enrichment factor of Cd in the sediments indicated that this metal might cause significant pollution in the environment. The results of the modified potential ecological risk index revealed that the XS, GJ, RH, and XJ were at considerable ecological risk, while the sediments in the Fuhe river-lake ecosystem (FH) were at moderate ecological risk, with Cd contributing the highest proportion of risk. The hazard score fundamentally validated the modified potential ecological risk analysis and revealed a mean toxicity of 57.80% to the benthic organisms in the RH.

Identifying trace metal distribution and occurrence in sediments, inundated soils, and non-flooded soils of a reservoir catchment using Self-Organizing Maps, an artificial neural network method

The Lancang-Mekong River is a trans-boundary river which provides a livelihood for over 60 million people in Southeast Asia. Its environmental security is vital to both local and regional inhabitants. Efforts have been undertaken to identify controlling factors of the distribution of trace metals in sediments and soils of the Manwan Reservoir catchment in the Lancang-Mekong River basin. The physicochemical attributes of 63 spatially distributed soil and sediment samples, along with land-use, flooding, topographic, and location characteristics, were analyzed using the Self-Organizing Map (SOM) methodology. The SOM permits the analysis of complex multivariate datasets and gives a visual interpretation that is generally not easy to obtain using traditional statistical methods. Across the catchment, enrichments of trace metals are rare overall, despite the severely enriched cadmium (Cd). The analysis of SOM showed that flooded levels and land-use types were associated with high concentrations of Cd. Sediments and inundated soils covered with shrub and open woodlands in downstream always have a high concentration of Cd. The results demonstrate that SOM is a useful tool that can aid in the interpretation of complex datasets and help identify the environment of enriched metals on a catchment scale.

Occurrence and risk assessment of heavy metals in sediments of the Xiangjiang River, China

Sediment samples were collected from 22 typical metal-polluted sections along the Xiangjiang River (XJR). Spatial distribution and speciation characteristics of heavy metals in sediments of XJR were determined. Furthermore, ecological risk and enrichment degree of metals were assessed by different indices. The results showed that combined metal pollution occurred in sediments of XJR, with content range of Cd, Pb, Zn, Cu, As, Mn, Cr, and Hg reaching 2.95-29.15, 30.93-235.83, 61.50-3771.11, 9.56-81.81, 3.93-46.28, 774.83-8700.72, 10.64-65.16, and 0.13-5.09 mg kg^-1, respectively. Pollution levels increased in period of industrialization but decreased after thousands of pollution enterprises were banned. Sections with serious pollution and higher risk were mainly located at Hengyang and Chang-Zhu-Tan regions (Changsha, Zhuzhou, and Xiangtan) for contaminations of Cd, As, Pb, and Hg. Values of both enrichment factor and geo-accumulation index followed the order Cd > Hg > Zn > Mn > Pb > Cu > As > Cr. Bioavailable fractions followed the order Cd (66.93 %), Zn (33.80 %), Pb (30.81 %), Mn (18.38 %), Hg (17.58 %), Cu (10.20 %), As (9.81 %), and Cr (7.65 %). Considering their bioavailability, biotoxicity, or abundance, contamination of Cd was the most dominant, and pollution of other metals should not be ignored.