Novel Biocompatible Green Silver Nanoparticles Efficiently Eliminates Multidrug Resistant Nosocomial Pathogens and Mycobacterium Species.

Unlabelled: Bacterial infection is a major crisis of 21st era and the emergence of multidrug resistant (MDR) pathogens cause significant health problems. We developed, green chemistry-based silver nanoparticles (G-Ag NPs) using fruit peel extract. G-Ag NPs has a spherical shape in the range of ~ 40 nm with a surface charge of - 31 Mv.

Highly efficient visible light active doped metal oxide photocatalyst and SERS substrate for water treatment.

The development of efficient nanomaterials with promising optical and surface properties for multifunctional applications has always been a subject of novel research. In this work, the study of highly efficient TiO nanorods (NRs) and Ta-doped TiO NRs (Ta-TiO NRs) synthesized by alkaline hydrothermal treatment followed by soaking treatment has been reported. NRs were investigated for their potential applications as recyclable/reproducible visible light active photocatalysts and surface-enhanced Raman scattering (SERS) substrates in wastewater treatment.

ZIP Code-Level Estimation of Air Quality and Health Risk Due to Particulate Matter Pollution in New York City.

Exposure to PM is associated with hundreds of premature mortalities every year in New York City (NYC). Current air quality and health impact assessment tools provide county-wide estimates but are inadequate for assessing health benefits at neighborhood scales, especially for evaluating policy options related to energy efficiency or climate goals. We developed a new ZIP Code-Level Air Pollution Policy Assessment (ZAPPA) tool for NYC by integrating two reduced form models─Community Air Quality Tools (C-TOOLS) and the Co-Benefits Risk Assessment Health Impacts Screening and Mapping Tool (COBRA)─that propagate emissions changes to estimate air pollution exposures and health benefits.

Regression-based flexible models for photochemical air pollutants in the national capital territory of megacity Delhi.

Modelling photochemical pollutants, such as ground level ozone (O), nitric oxide (NO) and nitrogen dioxide (NO), in urban terrain was proven to be cardinal, chronophagous and complex. We built linear regression and random forest regression models using 4-years (2015-2018; hourly-averaged) observations for forecasting O, NO and NO levels for two scenarios (1-month prediction (for January 2019) and 1-year prediction (for 2019)) - with and without the impact of meteorology. These flexible models have been developed for, both, localised (site-specific models) and combined (indicative of city-level) cases.