Spatial distribution and source identification for heavy metals in surface sediments of East Dongting Lake, China

Machine learning approaches to identify spatial factors and their influential distances for heavy metal contamination in downstream sediment

Contaminated sediments can adversely affect aquatic ecosystems, making the identification and management of pollutant sources extremely important. In this study, we proposed machine learning approaches to reveal sources and their influential distances for heavy metal contamination of downstream sediment. We employed classification models with artificial neural networks (ANN) and random forest (RF), respectively, to predict the heavy metal contamination of stream sediments using upland environmental variables as input features. A comprehensive Korean nationwide monitoring database containing 1546 datasets was used to train and test the models. These datasets encompass the concentrations of eight heavy metals (Ar, Cd, Cr, Cu, Hg, Ni, Pb, and Zn) in sediment samples collected from 160 stream sites across the nation from 2014 to 2018. Model's prediction accuracy was evaluated for input feature sets from different influential upland areas defined by different buffer radii and the watershed boundary for each site. Although both ANN and RF models were unsatisfactory in predicting heavy metal quartile classes, RF-classifiers with adaptive synthetic oversampling (ORFC) showed reasonably well-predicted classes of the sediment samples based on the Canada's Sediment Quality Guidelines (accuracy ranged from 0.67 to 0.94). The best influential distance (i.e., buffer radius) was determined for each heavy metal based on the accuracy of ORFC. The results indicated that Cd, Cu and Pb had shorter influential distances (1.5-2.0 km) than the other heavy metals with little difference in accuracy for different influential distances. Feature importance calculation revealed that upland soil contamination was the primary factor for Hg and Ni, while residential areas and roads were significant features associated with Pb and Zn contamination. This approach offers information on major contamination sources and their influential areas to be prioritized for managing contaminated stream sediments.

Spatiotemporal ecological risk evaluation and source identification of heavy metals and nutrients in the water and lake surface sediment in a protected catchment area of a volcanic lake

Indonesia has numerous lakes; however, research on the spatiotemporal sediment quality and source identification in lakes remains limited. The overaccumulation of heavy metals and nutrients in lakes severely threatens aquatic ecosystems. This study aims to identify potential sources of metallic deposits (Cu, Pb, Cr, Fe, Al, and Cd) and nutrients (TN and TP) in lake-surface sediment, utilizing enrichment factors (EF), geoaccumulation indices (Igeo), potential ecological risk indices (Er), and risk indices (RI). Multivariate statistical analyses, including principal component analysis (PCA) and Pearson's correlation analysis, were conducted to pinpoint pollution sources linked to land use. Eight sampling sites for surface sediment and water were examined in both wet and dry seasons at Menjer Lake, chosen for its diverse applications in tourism, hydropower, floating net cages, and extensive agriculture in its catchment. Correlation and PCA results indicated that Pb, Fe, and Al mainly originate from tourism, while Al, Fe, TN, and TP are associated with agriculture. The highest average loading from land use was observed in agriculture (> 0.8), floating net cages (> 0.76), and tourism (> 0.68). Furthermore, the highest loading from nutrients and all metals were TP (> 0.71) and all metals (> 0.35), respectively. Ecological risk assessment revealed low to moderately polluted EFs and Igeo in the dry season. However, Menjer Lake's Er and RI for heavy metals were generally classified as unpolluted.

The environmental impact of heavy metals in sediments of main valleys in the eastern side of Mosul City, Iraq

Analyzing the geochemical changes in stream sediments can reveal important surface processes on Earth, like weathering, transportation, and cation exchange. The study area is located on the eastern side of Mosul, where valleys named Al-Rashediya, Al-Kharrazi, Al-Khosar, Al-Danffilli, and Al-Shor flow towards the Tigris River. These valleys' sediments contain diverse components like clay minerals, organic matter, iron oxides, carbonates, and heavy metals (H.M.s), either as part of these substances or adsorbed onto them. In this study, 36 sediment samples were gathered from these valleys. They underwent chemical analysis through X-ray fluorescence to ascertain their chemical constituents of major oxides. To understand the distribution of H.M.s in these sediments, correlation coefficient analysis and factor analysis were utilized. The study employed the geoaccumulation index (Igeo) and enrichment factor (E.F.) to evaluate sediment contamination. The results of Igeo ranged from Cr = 0.24 to 1.83, Ni = -0.92 to 0.77, Cu = -2.41 to 0.05, Zn = -1.83 to 0.89, Pb = -1.54 to 0.36, and As = -2.84 to 0.80. These findings suggest that the valley sediments are generally in the range of deficiency to minimal enrichment and moderate enrichment. However, Al-Danffilli Valley shows strong contamination levels for Cu, Zn, and Pb. The E.F. values for Cr = 3.63-12.50, Ni = 1.95-4.19, Cu = 0.69-12.36, Zn = 1.08-16.19, Pb = 1.25-62.16, and As = 0.60-1.79 indicate levels ranging from deficiency to minimal and moderate enrichment. Al-Danffilli Valley, in particular, was identified as ranging from moderate to extremely high enrichment, attributed to its location near industrial areas and its tributaries.

Recent advances in instrumental techniques for heavy metal quantification

Heavy metals (HMs) are ubiquitous; they are found in soil, water, air, and all biological matrices. The toxicity, bioaccumulation potential, and deleterious effects of most of these metals on humans and the environment have been widely documented. Consequently, the detection and quantification of HMs in various environmental samples have become a pressing issue. The analysis of the concentrations of HMs is a vital component of environmental monitoring; hence, the selection of the most suitable analytical technique for their determination has become a topic of great interest in food, environment, and human health safety. Analytical techniques for the quantification of these metals have evolved. Presently, a broad range of HM analytical techniques are available with each having its outstanding merits as well as limitations. Most analytical scientists, therefore, adopt complementation of more than one method, with the choice influenced by the specific metal of interest, desired limits of detection and quantification, nature of the interference, level of sensitivity, and precision among others. Sequel to the above, this work comprehensively reviews the most recent advances in instrumental techniques for the determination of HMs. It gives a general overview of the concept of HMs, their sources, and why their accurate quantification is pertinent. It highlights various conventional and more advanced techniques for HM determination, and as one of its kind, it also gives special attention to the specific merits and demerits of the analytical techniques. Finally, it presents the most recent studies in this regard.