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

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 Citrus pseudolimon fruit peel extract. G-Ag NPs has a spherical shape in the range of ~ 40 nm with a surface charge of - 31 Mv. This nano-bioagent is an eco-friendly tool to combat menace of MDR. Biochemical tests prove that G-Ag NPs are compatible with human red blood cells and peripheral blood mononuclear cells. There have been many reports on the synthesis of silver nanoparticles, but this study suggests a green technique for making non-cytotoxic, non-hemolytic organometallic silver nanoparticles with a high therapeutic index for possible use in the medical field. On the same line, G-Ag NPs are very effective against Mycobacterium sp. and MDR strains including Escherichia coli, Klebsiella species, Pseudomonas aeruginosa, and Acinetobacter baumannii isolated from patient samples. Based on it, we filed a patent to Indian Patent Office (reference no. 202111048797) which can revolutionize the prevention of biomedical device borne infections in hospital pre/post-operated cases. This work could be further explored in future by in vivo experimentation with mice model to direct its possible clinical utility. Supplementary Information: The online version contains supplementary material available at 10.1007/s12088-023-01061-0.

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 TiO2 nanorods (NRs) and Ta-doped TiO2 NRs (Ta-TiO2 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. NRs were characterized by various microscopic (scanning and transmission electron microscopy), spectroscopic (X-ray diffraction, X-ray photoelectron, UV-visible, photoluminescence, and Raman spectroscopy), and surface (Brunauer-Emmett-Teller) techniques. The NRs exhibited promising optical properties with a band gap of 2.95 eV (TiO2 NRs) and 2.58 eV (Ta-TiO2 NRs) showing excellent photo-degradation activities for methylene blue (MB) dye molecules under natural sunlight. Particularly, Ta-TiO2 NRs showed enhanced response as visible light active photocatalysts in normal sunlight and also as SERS substrate attributed to the additional defects introduced by Ta doping. It could be explained by the combined effect of doping-induced enhanced visible light absorption and charge transfer (CT) properties of Ta-TiO2 NRs. Furthermore, Ta-TiO2 NRs were investigated for their long-term stability, reproducibility of the data, and recyclability in view of their potential applications in water treatment.

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

Exposure to PM2.5 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. ZAPPA leverages custom higher resolution inputs for emissions, health incidences, and population. It, then, enables rapid policy evaluation with localized ZIP code tabulation area (ZCTA)-level analysis of potential health and monetary benefits stemming from air quality management decisions. We evaluated the modeled 2016 PM2.5 values against observed values at EPA and NYCCAS monitors, finding good model performance (FAC2, 1; NMSE, 0.05). We, then, applied ZAPPA to assess PM2.5 reduction-related health benefits from five illustrative policy scenarios in NYC focused on (1) commercial cooking, (2) residential and commercial building fuel regulations, (3) fleet electrification, (4) congestion pricing in Manhattan, and (5) these four combined as a "citywide sustainable policy implementation" scenario. The citywide scenario estimates an average reduction in PM2.5 of 0.9 µg/m3. This change translates to avoiding 210-475 deaths, 340 asthma emergency department visits, and monetized health benefits worth $2B to $5B annually, with significant variation across NYC's 192 ZCTAs. ZCTA-level assessments can help prioritize interventions in neighborhoods that would see the most health benefits from air pollution reduction. ZAPPA can provide quantitative insights on health and monetary benefits for future sustainability policy development in NYC.

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

Modelling photochemical pollutants, such as ground level ozone (O3), nitric oxide (NO) and nitrogen dioxide (NO2), 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 O3, NO and NO2 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. Both models were aided with machine learning, to reduce their time-intensity compared to models built over high-performance computing. O3 prediction performance of linear regression model at the city level, under both cases of meteorological consideration, was found to be significantly poor. However, the site-specific model with meteorology performed satisfactorily (r = 0.87; RK Puram site). Further, during testing, linear regression models (site-specific and combined) for NO and NO2 with meteorology, show a slight improvement in their prediction accuracies when compared to the corresponding equivalent linear models without meteorology. Random forest regression with meteorology performed satisfactorily for indicative city-level NO (r = 0.90), NO2 (r = 0.89) and O3 (r = 0.85). In both regression techniques, increased uncertainty in modelling O3 is attributed to it being a secondary pollutant, non-linear dependency on NOx, VOCs, CO, radicals, and micro-climatic meteorological parameters. Analysis of importance among various precursors and meteorology have also been computed. The study holistically concludes that site-specific models with meteorology perform satisfactorily for both linear regression and random forest regression.