Comparing the nutrient changes,heavy metals,and genotoxicity assessment before and after vermicomposting of thermal fly ash using Eisenia fetida

Environmental Science and Pollution Research - Fly ash (FA) is available in an unstable state and can be ameliorated by vermicomposting. The different ratios of FA viz (FA10, FA15, FA20, FA25,...     

Doped graphitic carbon nitride (g-C3N4) catalysts for efficient photodegradation of tetracycline antibiotics in aquatic environments

Environmental Science and Pollution Research - Tetracyclines (TCs) antibiotics are very common and often used in both human and veterinary medicines. More than 75% of TCs are excreted in an active...     

Conducting polymers/zinc oxide-based photocatalysts for environmental remediation: a review

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%.

     

Assessing the impact of ecological insecurity on ecosystem service value in stone quarrying and crushing dominated areas

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.