Speciations of metals were assessed in a tropical rain-fed river, flowing through the highly economically important part of the India. The pattern of distribution of heavy metals (Cd, Co, Cr, Cu, Mn, Ni, Pb and Zn) were evaluated in water and sediment along with mineralogical characterization, changes with different water quality parameters and their respective health hazard to the local population along the Damodar River basin during pre-monsoon and post-monsoon seasons. The outcome of the speciation analysis using MINTEQ indicated that free metal ions, carbonate, chloride and sulfate ions were predominantly in anionic inorganic fractions, while in cationic inorganic fractions metal loads were negligible. Metals loads were higher in sediment phase than in the aqueous phase. The estimated values of Igeo in river sediment during both the seasons showed that most of the metals were found in the Igeo class 0–1 which represents unpolluted to moderately polluted sediment status. The result of partition coefficient indicated the strong retention capability of Cr, Pb, Co and Mn, while Cd, Zn, Cu and Ni have resilient mobility capacity. The mineralogical analysis of sediment samples indicated that in Damodar River, quartz, kaolinite and calcite minerals were dominantly present. The hazard index values of Cd, Co and Cr were >?1 in river water, which suggested potential health risk for the children. A combination of pragmatic, computational and statistical relationship between ionic species and fractions of metals represented a strong persuasion for identifying the alikeness among the different sites of the river. 相似文献
Environmental and Ecological Statistics - Recently there has been some renewed interest in skew-normal distribution (SND) because it provides a nice and natural generalization (in terms of... 相似文献
The accessibility to clean water is essential for humans, yet nearly 250 million people die yearly due to contamination by cholera, dysentery, arsenicosis, hepatitis A, polio, typhoid fever, schistosomiasis, malaria, and lead poisoning, according to the World Health Organization. Therefore, advanced materials and techniques are needed to remove contaminants. Here, we review nanohybrids combining conducting polymers and zinc oxide for the photocatalytic purification of waters, with focus on in situ polymerization, template synthesis, sol–gel method, and mixing of semiconductors. Advantages include less corrosion of zinc oxide, less charge recombination and more visible light absorption, up to 53%.
The present study has tried to develop ecological insecurity model (EIM) in the growing stone quarrying and crushing dominated areas using robust machine learning techniques and attempted to link it with ecosystem service value (ESV). Satellite image-based landscape metrics have been used for developing machine learning-oriented EIM, and the global coefficient of Costanza et al. (Glob Environ Change 26:152–158, 2014) has been used for computing ESV. Field parameter-based ecological insecurity index (EII) has been developed for validating the EIMs along with the statistical methods. Applied Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN) revealed that 21.88 to 60.79% area has predicted as highly ecologically insecure in all the selected four stone quarrying and crushing dominated clusters and this is has inflated from 2000 to 2020. All the applied models are acceptable in terms of their performances, but the RF model is found to be the best representative in relation to EII. It causes considerable loss of ESV which ranges from 160,845.18 US$ to 757,445.17 US$ in all the clusters from 2000 to 2020. The findings of the study are useful for ecological management in this area. It further recommends applying such an approach in such similar fields to establish the general finding and provides knowledge to the state of arts.