Long-term sensor measurements of lung deposited surface area of particulate matter emitted from local vehicular and residential wood combustion sources

Abstract

Lung deposited surface area (LDSA) is a relatively new metric that has been argued to be more accurate at predicting health effects from aerosol exposure. For typical atmospheric aerosol, the LDSA concentration depends mainly on the concentration of ultrafine particles (e.g. vehicular exhaust emissions and residential wood combustion) and therefore optical methods cannot be used to measure and quantify it. The objective of this study was to investigate and describe typical characteristics of LDSA under different urban environments and evaluate how a diffusion charging-based Pegasor AQ Urban sensor (Pegasor Ltd., Finland) can be used as an alternative to optical sensors when assessing local combustion emissions and respective LDSA concentrations. Long-term (12?months) sensor measurements of LDSA were carried out at three distinctly different measurement sites (four sensor nodes) in the Helsinki metropolitan area, Finland. The sites were affected mainly by vehicular exhaust emission (street canyon and urban background stations) and by residential wood combustion (two detached housing area stations). The results showed that the accuracy of the AQ Urban was good (R² = 0.90) for the measurement of LDSA when compared to differential mobility particle sizer. The mean concentrations of LDSA were more than twice as high at the street canyon (mean 22 µm² cm^?3) site when compared to the urban background site (mean 9.4 µm² cm^?3). In the detached housing area, the mean concentrations were 12 µm² cm^?3, and wood combustion typically caused high LDSA peaks in the evenings. High correlations and similar diurnal cycles were observed for the LDSA and black carbon at street canyon and urban background stations. The utilization of a small-scale sensor network (four nodes) showed that the cross-station variability in hourly LDSA concentrations was significant in every site, even within the same detached housing area (distance between the two sites ～670?m).     

Effect of 100 KeV Ar+ ion beam irradiation on ZnO thin films – Influence of morphology vis-a-vis electrode/electrolyte interface and its impact on photoelectrochemical water splitting

The present study attempts quantitative determination of changes in the morphological surface features viz. fractal dimension, lower and upper cut off length scale through Power Spectral Density analysis prior to and after irradiation of 100 KeV Ar⁺ ion beam at incidence angles of 0°, 40° and 60° on ZnO thin films. All the unirradiated and irradiated samples are subjected to photoelectrochemical characterization and a correlation between photoelectrochemical performance and morphological parameters is established. Sample irradiated at 40° angle at the fluence of 5 × 10¹⁶ ions/cm² is found to possess maximum fractal dimension of 2.72, lower and upper cut off length scale of 3.16 nm and 63.00 nm respectively. This sample exhibits maximum photocurrent density of 3.19 mA/cm² and applied bias photon-to-current efficiency of 1.12% at 1.23 V/RHE. Hydrogen gas collected for duration of 1 h for the same sample was ~4.83 mLcm^?2.     

Carbon-based TiO2-x heterostructure nanocomposites for enhanced photocatalytic degradation of dye molecules

Application of brown titanium dioxide (TiO_2-x) and its modified composite forms in the photocatalytic decomposition of organic pollutants in the environment is a promising way to provide solutions for environmental redemption. Herein, we report the synthesis of effective and stable TiO_2-x nanoparticles with g-C₃N₄, RGO, and multiwalled carbon nanotubes (CNTs) using a simple hydrothermal method. Among all the as-synthesized samples, excellent photocatalytic degradation activity was observed for RGO-TiO_2-x nanocomposite with high rate constants of 0.075 min^?1_, 0.083 min^?1 and 0.093 min^?1 for methylene blue, rhodamine-B, and rosebengal dyes under UV–Visible light irradiation, respectively. The altered bandgap (1.8 eV) and the large surface area of RGO-TiO_2-x nanocomposite impacts on both absorption of visible light and efficiency of photogenerated charge electron (e^?)/hole (h⁺) pair separation. This resulted in enhanced photocatalytic property of carbon-based TiO_2-x nanocomposites. A systematic study on the influence of different carbon nanostructures on the photocatalytic activity of brown TiO_2-x is carried out.     

A machine learning system for identifying transmembrane domains from amino acid sequences

We present our machine learning system, that uses inductive logic programming techniques to learn how to identify transmembrane domains from amino acid sequences. Our system facilitates the use of operators such as ‘contains’, that act on entire sequences, rather than on individual elements of a sequence. The prediction accuracy of our new system is around 93%, and this compares favourably with earlier results. This work was carried out with the support of a research grant from ISIS, Fujitsu Laboratories.     

Analysis of lipid classes by solid-phase extraction and high-performance size-exclusion chromatography

An improved method to analyze lipid classes of edible oils and fats by solid-phase extraction (SPE) and high-performance size-exclusion chromatography (HPSEC) is presented. A mixture of lipid standards was fractionated by the solid-phase extraction procedure (NH₂ phase) into polar and nonpolar fractions; these were then submitted to analysis by HPSEC. The size-exclusion chromatographic columns were three styrene/divinylbenzene columns with pore sizes of 100 Å and 50 Å. Light-scattering was used for the detection system, and the parameters of the detector were optimized to minimize the difference between the responses of the compounds studied. With this procedure it was possible to separate the following lipid classes: triacylglycerols, diacylglycerols, monoacylglycerols and free fatty acids, sterols, sterol esters, tocopherols and carotenoids. Quantitative analysis was studied for a light-scattering detector with several lipid standards of different molecular weights and unsaturation levels.     

Oxidation of some aliphatic polyols on anodically deposited MnO2

Galvanostatic steady state current potential measurements were carried out for oxidation of a series of aliphatic alcohols having varying number of hydroxyl groups. The anodically deposited layer of MnO₂ on platinum was used as the electrode material. The deposit was characterised by scanning electron microscopy (SEM), energy dispersive X-ray analysis (EDAX) and electrode potential measurements. The catalytic role of MnO₂ in the electro-oxidation of alcohols was indicated by the chronopotentiograms and the cyclic voltammograms. An analysis of the electrochemical data indicated a catalytic EC mechanism in which Mn (V) is generated electrochemically and consumed chemically in succession. Based on this and the hydrogen bonding interaction between alcoholic hydroxyl groups and MnO₂ layer, a mechanism was proposed which accounts for the variation in the observed electrochemical reaction orders. Tafel behaviour was found to be followed only approximately. Current efficiency of the electrochemical oxidation of polyols was studied. Replacement of platinum by carbon as current collector was found to leave the electrocatalytic activity of the MnO₂ deposit practically unaltered.     

Mapping surface temperature in large lakes with MODIS data

Satellite sensor MODIS on two platforms can produce Sea Surface Temperature over certain regions about three to four times per day. Our objective was to test if the MODIS SST product can be applied for lakes whose surface areas are large enough to be observed at the MODIS spatial resolution and to compare the satellite-derived lake surface temperatures with in situ measurements. Surface temperatures for Lakes Vänern and Vättern in Sweden, two of the largest European lakes, are extracted from the MODIS/Terra images for period 2001-2003. The results are analyzed on different quality levels, as all MODIS L2 products are equipped with an additional quality flag. We present temperature development over 2001-2003, and show the capability of the MODIS SST product to couple the known thermodynamical features in the lakes under study, where temperature varies greatly with space and time. These results can complement lake monitoring programs anywhere.     

Realization of a temperature sensor using both two- and three-dimensional photonic structures through a machine learning technique

A temperature sensor based on photonic crystal structures with two- and three-dimensional geometries is proposed, and its measurement performance is estimated using a machine learning technique. The temperature characteristics of the photonic crystal structures are studied by mathematical modeling. The physics of the structure is investigated based on the effective electrical permittivity of the substrate (silicon) and column (air) materials for a signal at 1200 nm, whereas the mathematical principle of its operation is studied using the plane-wave expansion method. Moreover, the intrinsic characteristics are investigated based on the absorption and reflection losses as frequently considered for such photonic structures. The output signal (transmitted energy) passing through the structures determines the magnitude of the corresponding temperature variation. Furthermore, the numerical interpretation indicates that the output signal varies nonlinearly with temperature for both the two- and three-dimensional photonic structures. The relation between the transmitted energy and the temperature is found through polynomial-regression-based machine learning techniques. Moreover, rigorous mathematical computations indicate that a second-order polynomial regression could be an appropriate candidate to establish this relation. Polynomial regression is implemented using the Numpy and Scikit-learn library on the Google Colab platform.

     

Cover Feature: Superior Proton-Transfer Catalytic Promiscuity of Cytochrome c in Self-Organized Media (7/2021)

Evolutionarily elderly proteins commonly feature greater catalytic promiscuity. Cytochrome c is among the first set of proteins in evolution to have known prospects in electron transport and peroxidative properties. Here, we report that cyt c is also a proficient proton-transfer catalyst and enhances the Kemp elimination (KE; model reaction to show proton transfer catalytic property) by ∼750-fold on self-organized systems like micelles and vesicles. The self-organized systems mimic the mitochondrial environment in vitro for cyt c. Using an array of biophysical and biochemical mutational assays, both acid–base and redox mechanistic pathways have been explored. The histidine moiety close to hemin group (His18) is mainly responsible for proton abstraction to promote the concerted E2 pathway for KE catalysis when cyt c is in its oxidized form; this has also been confirmed by a H18A mutant of cyt c. However, the redox pathway is predominant under reducing conditions in the presence of dithiothreitol over the pH range 6–7.4. Interestingly, we found almost 750-fold enhanced KE catalysis by cyt c compared to aqueous buffer. Overall, in addition to providing mechanistic insights, the data reveal an unprecedented catalytic property of cyt c that could be of high importance in an evolutionary perspective considering its role in delineating the phylogenic tree and also towards generating programmable designer biocatalysts.