Sustainable approaches towards green synthesis of TiO2 nanomaterials and their applications in photocatalysis-mediated sensing to monitor environmental pollution.

Nanomaterials are gaining enormous interests due to their novel applications that have been explored nearly in every field of our contemporary society. In this scenario, preparations of nanomaterials following green routes have attracted widespread attention in terms of sustainable, reliable, and environmentally friendly practices to produce diverse nanostructures. In this review, we summarize the fundamental processes and mechanisms of green synthesis approaches of TiO2 nanoparticles (NPs). We explore the role of plants and microbes as natural bioresources to prepare TiO2 NPs. Particularly, focus has been made to explore the potential of TiO2 -based nanomaterials to design a variety of sensing platforms by exploiting the photocatalysis efficiency under the influence of a light source. These types of sensing are of massive importance for monitoring environmental pollution and therefore for inventing advanced strategies to remediate hazardous pollutants and offer a clean environment.

Assessment of ground and surface water quality along the river Varuna, Varanasi, India.

Multivariate statistical techniques were employed for monitoring of ground-surface water interactions in rivers. The river Varuna is situated in the Indo-Gangetic plain and is a small tributary of river Ganga. The study area was monitored at seven sampling sites for 3 years (2010-12), and eight physio-chemical parameters were taken into account for this study. The data obtained were analysed by multivariate statistical techniques so as to reveal the underlying implicit information regarding proposed interactions for the relevant area. The principal component analysis (PCA) and cluster analysis (CA), and the results of correlations were also studied for all parameters monitored at every site. Methods used in this study are essentially multivariate statistical in nature and facilitate the interpretation of data so as to extract meaningful information from the datasets. The PCA technique was able to compress the data from eight to three parameters and captured about 78.5% of the total variance by performing varimax rotation over the principal components. The varifactors, as yielded from PCA, were treated by CA which grouped them convincingly into three groups having similar characteristics and source of contamination. Moreover, the loading of variables on significant PCs showed correlations between various ground water and surface water (GW-SW) parameters. The correlation coefficients calculated for various physiochemical parameters for ground and surface water established the correlations between them. Thus, this study presents the utility of multivariate statistical techniques for evaluation of the proposed interactions and effective future monitoring of potential sites.