The collection efficiency of a particle-loaded single filter fiber

The collection efficiency η(M) of single fibers, both isolated and within a parallel array, was determined as a function of accumulated particle mass for Stokes numbers St=0.3–3 and interception parameters In=0.04–0.32. This regime is dominated by inertia, interception and particle bounce. Measurements were made with an optical in-situ particle counting technique. Monodisperse polystyrene aerosols (1.3, 2.6, 3.6 and 5.2 μm) were deposited onto steel wires of 8 and 30 μm, respectively. Particle accumulation reached values on the order of M=0.5–1 μg/mm fiber, corresponding to 10⁴–10⁶ particles per mm, depending on size.η(M) was found to increase for all operating conditions between about 2 and nearly 50 fold, most strongly however for those conditions were the bare fiber is ineffective as a particle collector, i.e. for very low or very high values of St number, corresponding to low collision or low adhesion probabilities, respectively. The absolute efficiency increased to values approaching or exceeding unity, i.e. the effective fiber collision cross-section exceeded the diameter of the bare fiber. For fibers within an array of parallel fibers (equivalent packing density ∼0.004), the initial efficiency η₀ was higher than for an isolated fiber by almost an order of magnitude. However the increase with loading was substantially smaller, typically by an order of magnitude.The efficiency increase can be described by a power law of the type where b and c are empirical fit coefficients. Within an array, the exponent c is on the order of 0.7±0.05, but lower than reported in earlier work on fiber arrays which suggest a value near unity. For isolated fibers, c→1 as interception becomes dominant (at very low flow velocities) while at high impact velocities and significant particle bounce c≈0.5. The coefficient c correlates with fiber Reynolds number, but not with other parameters. The coefficient b is inversely proportional to η₀ (consistent with earlier work, however with significantly lower values than previously published) and a function of (St/R)².The experimental section of the paper is preceded by a literature review on single-fiber efficiency data and models for the inertia-interception regime, including both information on bare fibers and dust loaded fibers. An improved, general fit function with physically meaningful limits for St→0 and St→∞ is proposed for the efficiency of bare fibers.     

Sources and dynamics of semivolatile organic compounds in a single‐family residence in northern California

Semivolatile organic compounds (SVOCs) emitted from building materials, consumer products, and occupant activities alter the composition of air in residences where people spend most of their time. Exposures to specific SVOCs potentially pose risks to human health. However, little is known about the chemical complexity, total burden, and dynamic behavior of SVOCs in residential environments. Furthermore, little is known about the influence of human occupancy on the emissions and fates of SVOCs in residential air. Here, we present the first‐ever hourly measurements of airborne SVOCs in a residence during normal occupancy. We employ state‐of‐the‐art semivolatile thermal‐desorption aerosol gas chromatography (SV‐TAG). Indoor air is shown consistently to contain much higher levels of SVOCs than outdoors, in terms of both abundance and chemical complexity. Time‐series data are characterized by temperature‐dependent elevated background levels for a broad suite of chemicals, underlining the importance of continuous emissions from static indoor sources. Substantial increases in SVOC concentrations were associated with episodic occupant activities, especially cooking and cleaning. The number of occupants within the residence showed little influence on the total airborne SVOC concentration. Enhanced ventilation was effective in reducing SVOCs in indoor air, but only temporarily; SVOCs recovered to previous levels within hours.     

From Single Molecules to Thin Film Electronics,Nanofibers, e‐Textiles and Power Cables: Bridging Length Scales with Organic Semiconductors

Organic semiconductors are the centerpiece of several vibrant research fields from single‐molecule to organic electronics, and they are finding increasing use in bioelectronics and even classical polymer technology. The versatile chemistry and broad range of electronic functionalities of conjugated materials enable the bridging of length scales 15 orders of magnitude apart, ranging from a single nanometer (10^?9 m) to the size of continents (10⁶ m). This work provides a taste of the diverse applications that can be realized with organic semiconductors. The reader will embark on a journey from single molecular junctions to thin film organic electronics, supramolecular assemblies, biomaterials such as amyloid fibrils and nanofibrillated cellulose, conducting fibers and yarns for e‐textiles, and finally to power cables that shuffle power across thousands of kilometers.     

Observable Dictionary Learning for High-Dimensional Statistical Inference

This paper introduces a method for efficiently inferring a high-dimensional distributed quantity from a few observations. The quantity of interest (QoI) is approximated in a basis (dictionary) learned from a training set. The coefficients associated with the approximation of the QoI in the basis are determined by minimizing the misfit with the observations. To obtain a probabilistic estimate of the quantity of interest, a Bayesian approach is employed. The QoI is treated as a random field endowed with a hierarchical prior distribution so that closed-form expressions can be obtained for the posterior distribution. The main contribution of the present work lies in the derivation of a representation basis consistent with the observation chain used to infer the associated coefficients. The resulting dictionary is then tailored to be both observable by the sensors and accurate in approximating the posterior mean. An algorithm for deriving such an observable dictionary is presented. The method is illustrated with the estimation of the velocity field of an open cavity flow from a handful of wall-mounted point sensors. Comparison with standard estimation approaches relying on Principal Component Analysis and K-SVD dictionaries is provided and illustrates the superior performance of the present approach.     

A method for mapping Australian woody vegetation cover by linking continental-scale field data and long-term Landsat time series

There is a significant need to provide nationwide consistent information for land managers and scientists to assist with property planning, vegetation monitoring applications, risk assessment, and conservation activities at an appropriate spatial scale. We created maps of woody vegetation cover of Australia using a consistent method applied across the continent, and made them accessible. We classified pixels as woody or not woody, quantified their foliage projective cover, and classed them as forest or other wooded lands based on their cover density. The maps provide, for the first time, cover density estimates of Australian forests and other wooded lands with the spatial detail required for local-scale studies. The maps were created by linking field data, collected by a network of collaborators across the continent, to a time series of Landsat-5 TM and Landsat-7 ETM+ images for the period 2000–2010. The fractions of green vegetation cover, non-green vegetation cover, and bare ground were calculated for each pixel using a previously developed spectral unmixing approach. Time series statistics, for the green vegetation cover, were used to classify each pixel as either woody or not using a random forest classifier. An estimate of woody foliage projective cover was made by calibration with field measurements, and woody pixels classified as forest where the foliage cover was at least 0.1. Validation of the foliage projective cover with field measurements gave a coefficient of determination, R²,of 0.918 and root mean square error of 0.070. The user’s and producer’s accuracies for areas mapped as forest were high at 92.2% and 95.9%, respectively. The user’s and producers’s accuracies were lower for other wooded lands at 75.7% and 61.3%, respectively. Further research into methods to better separate areas with sparse woody vegetation from those without woody vegetation is needed. The maps provide information that will assist in gaining a better understanding of our natural environment. Applications range from the continental-scale activity of estimating national carbon stocks, to the local scale activities of assessing habitat suitability and property planning.     

Comparing the input, output, and validation maps for several models of land change

This paper applies methods of multiple resolution map comparison to quantify characteristics for 13 applications of 9 different popular peer-reviewed land change models. Each modeling application simulates change of land categories in raster maps from an initial time to a subsequent time. For each modeling application, the statistical methods compare: (1) a reference map of the initial time, (2) a reference map of the subsequent time, and (3) a prediction map of the subsequent time. The three possible two-map comparisons for each application characterize: (1) the dynamics of the landscape, (2) the behavior of the model, and (3) the accuracy of the prediction. The three-map comparison for each application specifies the amount of the prediction’s accuracy that is attributable to land persistence versus land change. Results show that the amount of error is larger than the amount of correctly predicted change for 12 of the 13 applications at the resolution of the raw data. The applications are summarized and compared using two statistics: the null resolution and the figure of merit. According to the figure of merit, the more accurate applications are the ones where the amount of observed net change in the reference maps is larger. This paper facilitates communication among land change modelers, because it illustrates the range of results for a variety of models using scientifically rigorous, generally applicable, and intellectually accessible statistical techniques.