Predicting the combustion behaviour of compression ignition engine fuelled with biodiesel from Stoechospermum marginatum,a macro algae

Environmental Science and Pollution Research - Biodiesel is one among the recent developments in the field of renewable energy research. To enhance the combustion characteristics of compression...     

Assimilation of conventional and satellite wind observations in a mesoscale atmospheric model for studying atmospheric dispersion

A mesoscale atmospheric model PSU/NCAR MM5 is used to provide operational weather forecasts for a nuclear emergency response decision support system on the southeast coast of India. In this study the performance of the MM5 model with assimilation of conventional surface and upper-air observations along with satellite derived 2-d surface wind data from QuickSCAT sources is examined. Two numerical experiments with MM5 are conducted: one with static initialization using NCEP FNL data and second with dynamic initialization by assimilation of observations using four dimensional data assimilation (FDDA) analysis nudging for a pre-forecast period of 12 h. Dispersion simulations are conducted for a hypothetical source at Kalpakkam location with the HYSPLIT Lagrangian particle model using simulated wind field from the above experiments. The present paper brings out the differences in the atmospheric model predictions and the differences in dispersion model results from control and assimilation runs. An improvement is noted in the atmospheric fields from the assimilation experiment which has led to significant alteration in the trajectory positions, plume orientation and its distribution pattern. Sensitivity tests using different PBL and surface parameterizations indicated the simple first order closure schemes (Blackadar, MRF) coupled with the simple soil model have given better results for various atmospheric fields. The study illustrates the impact of the assimilation of the scatterometer wind and automated weather stations (AWS) observations on the meteorological model predictions and the dispersion results.     

Study of the performance of a coolant mixed with an Al2O3/SiC nanomaterial in an engine radiator

One of the important components of a car to control the temperature of a car's engine is the radiator. To increase the heat absorption capacity of the coolant/fluid used in the radiator with minimum pumping power, innovative fluids called nanofluids have become the main area of research these days. Therefore, with the development of new technologies in the field of “nano-materials” and “nano-fluids,” the physical and chemical properties of coolant/fluid can be improved which in turn improves the radiator and engine efficiency, and reduces radiator weight and size. In this article, the heat transfer by forced convection in nanofluids based on Al₂O₃ and SiC was studied experimentally and compared to that of base fluid in an automotive radiator. The nanofluid is mixed with ethylene glycol and the fluid is prepared by the sonication method. The nanofluids were prepared by varying the nanomaterials and the amounts of nanomaterials in the base fluid and their heat transfer performance in the radiator was analyzed using ANSYS FLUENT software. Approximately 15% and 12% increase in radiator efficiency by using Al₂O₃ mixed nanofluid and SiC mixed nanofluid, respectively.     

Correction to: Impact of precipitation disparity on groundwater fluctuation in a semi-arid region (Vellore district) of southern India using geospatial techniques

Environmental Science and Pollution Research - A Correction to this paper has been published: https://doi.org/10.1007/s11356-021-13406-7     

Impact of precipitation disparity on groundwater fluctuation in a semi-arid region (Vellore district) of southern India using geospatial techniques

Environmental Science and Pollution Research - In the present study, impact of precipitation disparity on groundwater level fluctuation was carried out in Vellore district, Tamil Nadu, India, using...     

Valorization of agro-industrial fruit peel waste to fluorescent nanocarbon sensor: Ultrasensitive detection of potentially hazardous tropane alkaloid

• Transformation of agro-industrial waste to value-added material via green chemistry. • Orange peel is valorized into fluorescent nanodiamond-like carbon (fNDC) sensor. • fNDC detects potentially hazardous drug atropine sulfate (AS). • fNDC recognizes AS in biological fluids and pharmaceuticals. • fNDC assures applications in clinical and forensic toxicology. Millions of tonnes of agro-industrial waste are generated each year globally, with the vast majority of it going untreated, underutilized, and disposed of by burning or landfilling, causing severe environmental distress and economic downturn. A practical solution to this global issue is to use green chemistry to convert this waste into value-added products. Accordingly, in the present study, agro-industrial orange peel waste was valorized into fluorescent nanodiamond-like carbon sensor via a green route involving hydrothermal treatment of microwave carbonized orange peel waste. The developed sensor, used for the fluorescence detection of potentially hazardous drug atropine sulfate, exhibits unique dual linearity over concentration ranges of 300 nM to 1 M and from 1 M to 10 M, as well as ultra-low sensitivity of 34.42 nM and 356.46 nM, respectively. Additionally, the sensor demonstrates excellent reproducibility, high stability, and satisfactory recovery when used to identify and quantify atropine sulfate in biological samples and commercially available pharmaceuticals, indicating promising multidisciplinary applications.     

Novel carbon nano-onions from paraffinum liquidum for rapid and efficient removal of industrial dye from wastewater

Environmental Science and Pollution Research - Carbon nano-onions (CNOs) are fascinating zero-dimensional carbon materials owning distinct multi-shell architecture. Their physicochemical properties...     

Physicochemical and toxicological characteristics of urban aerosols during a recent Indonesian biomass burning episode

Air particulate matter (PM) samples were collected in Singapore from 21 to 29 October 2010. During this time period, a severe regional smoke haze episode lasted for a few days (21–23 October). Physicochemical and toxicological characteristics of both haze and non-haze aerosols were evaluated. The average mass concentration of PM_2.5 (PM with aerodynamic diameter of ≤2.5 μm) increased by a factor of 4 during the smoke haze period (107.2 μg/m³) as compared to that during the non-smoke haze period (27.0 μg/m³). The PM_2.5 samples were analyzed for 16 priority polycyclic aromatic hydrocarbons (PAHs) listed by the United States Environmental Protection Agency and 10 transition metals. Out of the seven PAHs known as potential or suspected carcinogens, five were found in significantly higher levels in smoke haze aerosols as compared to those in the background air. Metal concentrations were also found to be higher in haze aerosols. Additionally, the toxicological profile of the PM_2.5 samples was evaluated using a human epithelial lung cell line (A549). Cell viability and death counts were measured after a direct exposure of PM_2.5 samples to A459 cells for a period of 48 h. The percentage of metabolically active cells decreased significantly following a direct exposure to PM samples collected during the haze period. To provide further insights into the toxicological characteristics of the aerosol particles, glutathione levels, as an indirect measure of oxidative stress and caspase-3/7 levels as a measure of apoptotic death, were also evaluated.     

Odor emission rate estimation of indoor industrial sources using a modified inverse modeling method

Odor emission rates are commonly measured in the laboratory or occasionally estimated with inverse modeling techniques. A modified inverse modeling approach is used to estimate source emission rates inside of a postdigestion centrifuge building of a water reclamation plant. Conventionally, inverse modeling methods divide an indoor environment in zones on the basis of structural design and estimate source emission rates using models that assume homogeneous distribution of agent concentrations within a zone and experimentally determined link functions to simulate airflows among zones. The modified approach segregates zones as a function of agent distribution rather than building design and identifies near and far fields. Near-field agent concentrations do not satisfy the assumption of homogeneous odor concentrations; far-field concentrations satisfy this assumption and are the only ones used to estimate emission rates. The predictive ability of the modified inverse modeling approach was validated with measured emission rate values; the difference between corresponding estimated and measured odor emission rates is not statistically significant. Similarly, the difference between measured and estimated hydrogen sulfide emission rates is also not statistically significant. The modified inverse modeling approach is easy to perform because it uses odor and odorant field measurements instead of complex chamber emission rate measurements.