Confirming anthropogenic influences on the major organic and inorganic constituents of rainwater in an urban area

Recently, rainwater composition affected by atmospheric pollutants has been the topic of intense study in East Asia because of its adverse environmental and human health effects. In the present study, the chemical composition and organic compounds of rainwater were investigated from June to December 2012 at Gwangju in Korea. The aim of this study is to determine the seasonal variation of rainwater chemical composition and to identify possible sources of inorganic and organic compounds. The volume-weighted mean of pH ranged from 3.83 to 8.90 with an average of 5.78. Of rainwater samples, 50 % had pH values below 5.6. The volume-weighted mean concentration (VWMC) of major ions followed the order Cl- > SO4 2- > NH4+ > Na+ > NO3- > Ca2+ > Mg2+ > K+. The VWMC of trace metals decreased in the order Zn > Al > Fe > Mn > Pb > Cu > Ni > Cd > Cr. The VWMCs of major ions and trace metals were higher in winter than in summer. The high enrichment factors indicate that Zn, Pb, Cu, and Cd originated predominantly from anthropogenic sources. Factor analysis (principal component analysis) indicates the influence of anthropogenic pollutants, sea salt, and crustal materials on the chemical compositions of rainwater. Benzoic acids, 1H-isoindole-1,3(2H)-dione, phthalic anhydride, benzene, acetic acids, 1,2-benzenedicarboxylic acids, benzonitrile, acetaldehyde, and acetamide were the most prominent pyrolysis fragments for rainwater organic compounds identified by pyrolysis gas chromatography/mass spectrometry (Py-GC/MS). The results indicate that anthropogenic sources are the most important factors affecting the organic composition of rainwater in an urban area. © 2015 Author(s)open

Magnetic Steel Slag Biochar for Ammonium Nitrogen Removal from Aqueous Solution

In this study, magnetic steel slag biochar (MSSB) was synthesized from low-cost steel slag waste to investigate the effectiveness of steel slag biochar composite for NH4-N removal and magnetic properties in aqueous solution. The maximum adsorption capacity of NH4-N by MSSB was 4.366 mg/g according to the Langmuir model. The magnetic properties of MSSB indicated paramagnetic behavior and a saturation magnetic moment of 2.30 emu/g at 2 Tesla. The NH4-N adsorption process was well characterized by the pseudo-second order kinetic model and Temkin isotherm model. This study demonstrated the potential of magnetic biochar synthesized from steel slag waste for NH4-N removal in aqueous solution.11Nsciescopu

Comparison of knife-edge and multi-slit camera for proton beam range verification by Monte Carlo simulation

The mechanical-collimation imaging is the most mature technology in prompt gamma (PG) imaging which is considered the most promising technology for beam range verification in proton therapy. The purpose of the present study is to compare the performances of two mechanical-collimation PG cameras, knife-edge (KE) camera and multi-slit (MS) camera. For this, the PG cameras were modeled by Geant4 Monte Carlo code, and the performances of the cameras were compared for imaginary point and line sources and for proton beams incident on a cylindrical PMMA phantom. From the simulation results, the KE camera was found to show higher counting efficiency than the MS camera, being able to estimate the beam range even for 107 protons. Our results, however, confirmed that in order to estimate the beam range correctly, the KE camera should be aligned, at least approximately, to the location of the proton beam range. The MS camera was found to show lower efficiency, being able to estimate the beam range correctly only when the number of the protons is at least 108. For enough number of protons, however, the MS camera estimated the beam range correctly, errors being less than 1.2 mm, regardless of the location of the camera. Keywords: Proton therapy, Beam range verification, Prompt gamma, Knife-edge camera, Multi-slit camer

Tackling range uncertainty in proton therapy: Development and evaluation of a new multi-slit prompt-gamma camera (MSPGC) system

In theory, the sharp dose falloff at the distal end of a proton beam allows for high conformal dose to the target. However, conformity has not been fully achieved in practice, primarily due to beam range uncertainty, which is approximately 4% and varies slightly across institutions. To address this issue, we developed a new range verification system prototype: a multi-slit prompt-gamma camera (MSPGC). This system features high prompt-gamma detection sensitivity, an advanced range estimation algorithm, and a precise camera positioning system. We evaluated the range measurement precision of the prototype for single spot beams with varying energies, proton quantities, and positions, as well as for spot-scanning proton beams in a simulated SSPT treatment using a phantom. Our results demonstrated high accuracy (<0.4 mm) in range measurement for the tested beam energies and positions. Measurement precision increased significantly with the number of protons, achieving 1% precision with 5 × 108 protons. For spot-scanning proton beams, the prototype ensured more than 5 × 108 protons per spot with a 7 mm or larger spot aggregation, achieving 1% range measurement precision. Based on these findings, we anticipate that the clinical application of the new prototype will reduce range uncertainty (currently approximately 4%) to 1% or less

Media 3: Real-time integral imaging system for light field microscopy

Originally published in Optics Express on 05 May 2014 (oe-22-9-10210

Visualization 1: Real-time depth controllable integral imaging pickup and reconstruction method with a light field camera

Raw light field according to focal planes in real-time Originally published in Applied Optics on 10 December 2015 (ao-54-35-10333

Visualization 2: Real-time depth controllable integral imaging pickup and reconstruction method with a light field camera

Synchronized video of reconstruction window and overall proposed system Originally published in Applied Optics on 10 December 2015 (ao-54-35-10333

Identification of Chemical Vapor Mixture Assisted by Artificially Extended Database for Environmental Monitoring

A fully integrated sensor array assisted by pattern recognition algorithm has been a primary candidate for the assessment of complex vapor mixtures based on their chemical fingerprints. Diverse prototypes of electronic nose systems consisting of a multisensory device and a post processing engine have been developed. However, their precision and validity in recognizing chemical vapors are often limited by the collected database and applied classifiers. Here, we present a novel way of preparing the database and distinguishing chemical vapor mixtures with small data acquisition for chemical vapors and their mixtures of interest. The database for individual vapor analytes is expanded and the one for their mixtures is prepared in the first-order approximation. Recognition of individual target vapors of NO2, HCHO, and NH3 and their mixtures was evaluated by applying the support vector machine (SVM) classifier in different conditions of temperature and humidity. The suggested method demonstrated the recognition accuracy of 95.24%. The suggested method can pave a way to analyze gas mixtures in a variety of industrial and safety applications