Efficacy of biochar in removal of organic pesticide,Bentazone from watershed systems

Abstract

Bentazone is one of the toxic insecticides used to control forest tent caterpillar moths, boll weevils, gypsy moths, and other types of moths in various field crops. We report the efficacy of biochar prepared from the Azardirachta Indica waste biomass as adsorbent for removal of Bentazone. Biochar material was prepared by pyrolysis process under limited oxygen conditions. Biochar material was characterized by proximate and ultimate analysis, SEM analysis, FTIR analysis and TG/DTA analyses. The Bentazone adsorption capacity by biochar from aqueous solutions was assessed. Effect of time, adsorbent dosage, insecticide concentration and pH on the adsorption characteristics of the biochar were evaluated. Adsorption parameters were obtained at equilibrium contact time of 150?min, with biochar dosage of 0.5?g at pH 8. From the optimization studies, desirability of 0.952 was obtained with response (adsorption uptake) of 79.40?mg/g, for initial concentration of insecticide (50?mg/L), adsorbent dosage (0.448?g), time 30.0?min and pH 2. The adsorption isotherm data for the removal of Bentazone fitted well with the Freundlich isotherm. This study indicates that the biochar produced from the bark of Azardirachta Indica biomass could be employed as a potential adsorbent for removal of synthetic organic pollutants from the water streams.     

The Development of a Model to Examine Source-Receptor Relationships for Visibility on the Colorado Plateau

This paper describes the development and application of the Visibility and Haze in the Western Atmosphere (VISHWA) model to understand the source-receptor relationships that govern chemical species relevant to visibility degradation in the western United States. The model was developed as part of a project referred to as Visibility Assessment for Regional Emission Distributions (VARED), the objective of which is to estimate the contributions of various geographical regions, compounds, and emission

sources to light scattering and absorption by particles on the Colorado Plateau.

The VISHWA model is a modified version of a comprehensive Eulerian model, known as the Acid Deposition and Oxidant Model.1 The modifications were designed to obtain the computational efficiency required to simulate a one-year period at about 1/25th of real time, and at the same time incorporate mechanistic features relevant to realistic modeling of the fate and transport of visibility degrading species. The modifications included use of a condensed chemical mechanism; incorporation of reactions to simulate the formation of secondary organic particles; and use of a semi-Lagrangian advection scheme to preserve concentration peaks during advection.

The model was evaluated with 1992 air quality data from Project MOHAVE (Measurements of Haze and Visual Effects) intensive experiments. An important conclusion of this evaluation is that aqueous-phase oxidation of SO2 to sulfate in nonprecipitating clouds makes a significant contribution to observed sulfate levels during winter as well as summer. Model estimates of ambient sulfate

for the winter intensive were within a factor of 2 of the observations for 75% of the values. The corresponding statistic for the summer intensive was 90%. Model estimates of carbon were within a factor of 2 of the limited set of observations.     

Effects of untreated hospital effluents on the accumulation of toxic metals in sediments of receiving system under tropical conditions: Case of South India and Democratic Republic of Congo

Physicochemical and ecotoxicological analyses have been performed to assess the quality of sediments receiving untreated hospital effluents from Indian and Democratic Republic of Congo (DRC) hospitals. The sediments were collected monthly and characterized for grain size, organic matter, total organic carbon, total carbon, nitrogen, phosphorus, toxic metals and ecotoxicity. The results highlight the high concentration of toxic metals from the Indian hospital effluent receiving systems, especially for Cr, Cu, As, Zn and Hg. On the other hand, the metal concentrations in the sediment receiving system from DRC are low (e.g. maximum Hg and Zn concentration were 0.46 and 48.84 mg kg⁻¹ respectively). Ostracods exposed to sediment samples H2 (September month sample) and H3 (June and September month samples) were found dead after 6 d of exposure whereas the higher mortality rate for Congo sediments was 23% but was accompanied with 33 ± 7% of growth inhibition. The results of this study show the variation of sediment composition on toxic metal levels as well as toxicity related to both, the type of hospitals and the sampling period. Additionally, hospital effluent disposal practices at the study sites can lead to the pollution of water resources and may generate risks for aquatic organisms and human health.     

Carbon dissipation from surgical cotton production wastewater using macroalgae,microalgae, and activated sludge microbes

Surgical cotton production has drastically been increased in the past few years due to excessive use by medical health professionals especially in countries like India, which is among the top three exporters of cotton worldwide. The effluent generated from surgical cotton industries differ from textile effluents by the conspicuous absence of dyeing chemicals. This wastewater has a high concentration of suspended particles, COD, dissolved ions, organic carbon, and alkaline pH. Several studies have been published on the treatment of textile effluents and the degradation of dyeing chemicals, while the treatment studies on surgical cotton wastewater have been rarely reported in spite of their potential to cause pollution in receiving land/water bodies. Activated sludge microbes have been extensively studied and well documented in the treatment of several industrial effluent but does not match to the production of valuable biomass from algae. The global energy demand has prompted the scientific community to investigate and explore the possibility of using algae for energy production with simultaneous wastewater treatment. To the best of the authors’ knowledge, no research articles have been published which compare the effectiveness of activated sludge microorganisms, microalgae, and macroalgae in removing contaminants from real wastewater. To date, there is a knowledge gap in understanding and selecting the right choice of biological system for effective and economical effluent treatment. In an attempt to minimize this gap, carbon removal by microalgae, macroalgae, and activated sludge microbes were investigated on real effluent from surgical cotton industries. It was observed that the strain of Chlorella vulgaris could dissipate 83% of COD from real wastewater, while consortia of macroalgae (consisting predominantly of Ulvaceae and Chaetomorpha) and activated sludge microbes could remove 81% and 69% of the carbon, respectively. The microalgal growth (in terms of wet weight) increased from 0.15 to 0.3 g, whereas the macroalgal wet weight increased from 1.5 to 3 g in over 7 days of batch experiments conducted in triplicates. This indicated the superlative performance of microalgae over activated sludge microbes in carbon dissipation.

     

新型自来水复合絮凝剂的研制与应用

研究用壳聚糖、聚铝和助剂A复合制备出一种成本低、絮凝效果好、出水Al3 含量少的新型自来水处理絮凝剂,并确定其最佳配比(体积比)为:0.1%壳聚糖:2%聚合氯化铝:0.1%助剂A=1:6:5.与传统聚铝类絮凝剂相比,其出水浊度和Al3 含量分别下降了2.97%和10.7%,并且药剂成本下降6.25%,有明显的经济和环境效益.     

Numerical and experimental investigation of state of health of Li-ion battery

ABSTRACT

Estimation of State of Health (SoH) of Lithium-ion (Li-ion) battery is essential to predict the lifespan of batteries of an electric vehicle (EV). The efficient prediction of battery health indicates to the effective and safe operation of EV. However, delivering an effective and accurate method for the estimation of SoH in the real condition is truly a challenging task. The present study proposed a holistic procedure of combining both experimental and numerical investigations to conduct the fundamental study on coupled mechanical-electrochemical behavior of Li-ion battery. The proposed investigation highlighted the effect of stress on the capacity of the battery, considering capacity fade as an equivalent parameter to its health for real-time estimation of SoH. Finally, a simple model of Artificial Neural Network (ANN) is provided, which shows the linear dependency of stress with the SoH. The results obtained from the ANN model are validated with a Linear Regression (LR) model for a better understanding of the inspection. The predicted value of mean Square Error (MSE) and R square error in the ANN training model are found to be 0.000309 and 0.849687, respectively. Whereas for the test model, these predicted values are found to be 0.000438 and 0.819347, respectively.     

Revised algorithm for estimating light extinction from IMPROVE particle speciation data

The Interagency Monitoring of Protected Visual Environments (IMPROVE) particle monitoring network consists of approximately 160 sites at which fine particulate matter (PM2.5) mass and major species concentrations and course particulate matter (PM10) mass concentrations are determined by analysis of 24-hr duration sampling conducted on a 1-day-in-3 schedule A simple algorithm to estimate light extinction from the measured species concentrations was incorporated in the 1999 Regional Haze Rule as the basis for the haze metric used to track haze trends. A revised algorithm was developed that is more consistent with the recent atmospheric aerosol literature and reduces bias for high and low light extinction extremes. The revised algorithm differs from the original algorithm in having a term for estimating sea salt light scattering from Cl(-) ion data, using 1.8 instead of 1.4 for the mean ratio of organic mass to measured organic carbon, using site-specific Rayleigh scattering based on site elevation and mean temperature, employing a split component extinction efficiency associated with large and small size mode sulfate, nitrate and organic mass species, and adding a term for nitrogen dioxide (NO2) absorption for sites with NO2 concentration information. Light scattering estimates using the original and the revised algorithms are compared with nephelometer measurements at 21 IMPROVE monitoring sites. The revised algorithm reduces the underprediction of high haze periods and the overprediction of low haze periods compared with the performance of the original algorithm. This is most apparent at the hazier monitoring sites in the eastern United States. For each site, the PM10 composition for days selected as the best 20% and the worst 20% haze condition days are nearly identical regardless of whether the basis of selection was light scattering from the original or revised algorithms, or from nephelometer-measured light scattering.     

Variation of Mass Scattering Efficiencies in IMPROVE

Abstract

The Interagency Monitoring of Protected Visual Environments (IMPROVE) equation used to assess compliance under the U.S. Environmental Protection Agency (EPA) Haze Rule assumes that dry mass scattering efficiencies for aerosol chemical components are constant. However, examination of aerosol size distributions and chemical composition during the Big Bend Regional Aerosol and Visibility Observational Study and the Southeastern Aerosol and Visibility Study suggests that volume and mass scattering efficiencies vary directly with increasing particle light scattering and aerosol mass concentration. This is consistent with the observation that particle distributions were shifted to larger sizes under more polluted conditions and appears to be related to aging of the aerosol during transport to remote locations.