Effect of ambient humidity and temperature on transmission probability.

<p>The predicted probability of transmission at varied temperatures versus (A, C) relative humidity and (B, D) absolute humidity for the pulmonary (A–B) and NPTB (C–D) deposition efficiencies at 10 cm and 30 cm downstream, respectively. The experimental observations by Lowen <i>et al. </i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0037088#pone.0037088-Lowen2" target="_blank">[4]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0037088#pone.0037088-Lowen3" target="_blank">[5]</a> are shown as discrete points. Blue circles: T = 5°C; gray triangles: T = 20°C; red squares: T = 30°C.</p

Probability of transmission at different positions for rPan99 and Tx91 experiments.

<p>Contour plot of transmission probability in (A) rPan99 experiment with χ = 1, (B) Tx91 experiment with χ = 1, and (C) Tx91 experiment with χ = .135 using the NPTB deposition efficiency. At x≈7 cm, transmission probabilities match the findings of Mubareka <i>et al. </i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0037088#pone.0037088-Mubareka1" target="_blank">[6]</a> (A, C).</p

Droplet size evolution and deposition efficiencies.

<p>(A) Aerosol size versus time for droplets in air at 50% RH. Solid lines, <i>a₀</i> = 5 µm; dotted lines, <i>a₀</i> = 15 µm. Blue curves: T = 5°C; red curves: T = 30°C. (B) The deposition efficiency of a unit-density particle of radius <i>a</i> depositing in the pulmonary (P) and nasopharyngeal-tracheobronchial (NPTB) regions of a guinea pig. Purple: Pulmonary; black: NPTB. Reproduced from Schreider <i>et al. </i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0037088#pone.0037088-Schreider1" target="_blank">[34]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0037088#pone.0037088-Schreider2" target="_blank">[35]</a>.</p

Sensitivity analysis of viral kinetics and airflow parameters for NPTB deposition efficiency 30 cm downstream.

<p>(A) Contour plot of transmission probability as a function of <i>n_p^{drop, max}</i> and <i>t_peak</i>. The animals are assumed to be brought into contact one day post-inoculation and removed seven days later. (B) Contour plot of transmission probability as a function of the turbulent dispersivity coefficients <i>i_y</i>, <i>i_z</i> (assumed equal) and mean airflow velocity <i>U</i>. Small changes in either the degree of turbulence or the flow velocity yield large changes in the transmission probability.</p

Guinea pig viral growth kinetics at different temperatures.

<p>The measurements by Lowen <i>et al. </i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0037088#pone.0037088-Lowen2" target="_blank">[4]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0037088#pone.0037088-Lowen3" target="_blank">[5]</a> of the influenza concentration observed in nasal titers obtained from inoculated guinea pigs maintained at different temperatures. Blue circles: T = 5°C; red squares: T = 30°C. Dashed lines are fits to a numerical model for influenza viral dynamics <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0037088#pone.0037088-Baccam1" target="_blank">[18]</a>; solid lines are analytical estimates given by Equation 1.</p

A Comprehensive Breath Plume Model for Disease Transmission via Expiratory Aerosols

<div><p>The peak in influenza incidence during wintertime in temperate regions represents a longstanding, unresolved scientific question. One hypothesis is that the efficacy of airborne transmission via aerosols is increased at lower humidities and temperatures, conditions that prevail in wintertime. Recent work with a guinea pig model by Lowen <em>et al.</em> indicated that humidity and temperature do modulate airborne influenza virus transmission, and several investigators have interpreted the observed humidity dependence in terms of airborne virus survivability. This interpretation, however, neglects two key observations: the effect of ambient temperature on the viral growth kinetics within the animals, and the strong influence of the background airflow on transmission. Here we provide a comprehensive theoretical framework for assessing the probability of disease transmission via expiratory aerosols between test animals in laboratory conditions. The spread of aerosols emitted from an infected animal is modeled using dispersion theory for a homogeneous turbulent airflow. The concentration and size distribution of the evaporating droplets in the resulting “Gaussian breath plume” are calculated as functions of position, humidity, and temperature. The overall transmission probability is modeled with a combination of the time-dependent viral concentration in the infected animal and the probability of droplet inhalation by the exposed animal downstream. We demonstrate that the breath plume model is broadly consistent with the results of Lowen <em>et al.,</em> without invoking airborne virus survivability. The results also suggest that, at least for guinea pigs, variation in viral kinetics within the infected animals is the dominant factor explaining the increased transmission probability observed at lower temperatures.</p> </div

Guinea pig viral growth kinetics of rPan99 and Tx91.

<p>The measurements by Mubareka <i>et al. </i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0037088#pone.0037088-Mubareka1" target="_blank">[6]</a> of the influenza concentration observed in nasal titers obtained from inoculated guinea pigs infected with rPan99 and Tx91. Black circles: rPan99; purple squares: Tx91. Dashed lines are fits to a numerical model for influenza viral dynamics <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0037088#pone.0037088-Baccam1" target="_blank">[18]</a>.</p

Probability of transmission at different positions.

<p>Contour plots of the probability of transmission as a function of position downstream from an infected animal located at the origin for the pulmonary (A–F) and NPTB (G–L) deposition efficiencies. Red denotes high probability of transmission, blue denotes low probability. (A–C, G–I) Fixed relative humidity and varying temperature. (D–F, J–L) Fixed temperature and varying relative humidity. Note that the transmission probability depends strongly on temperature but more weakly on humidity.</p

Growth of Ammonium Bisulfate Clusters by Adsorption of Oxygenated Organic Molecules

Quantum chemical calculations were employed to model the interactions of the [(NH₄⁺)₄(HSO₄^–)₄] ammonium bisulfate cluster with one or more molecular products of monoterpene oxidation. A strong interaction was found between the bisulfate ion of this cluster and a carboxylic acid, aldehyde, or ketone functionality of the organic molecule. Free energies of adsorption for carboxylic acids were in the −70 to −73 kJ/mol range, while those for aldehydes and ketones were in the −46 to −50 kJ/mol range. These values suggest that a small ambient [(NH₄⁺)₄(SO₄⁻)₄]­cluster is able to adsorb an oxygenated organic molecule. While adsorption of the first molecule is highly favorable, adsorption of subsequent molecules is less so, suggesting that sustained uptake of organic molecules does not occur, and thus is not a pathway for continuing growth of the cluster. This result is consistent with ambient measurements showing that particles below ∼1 nm grow slowly, while those above 1 nm grow at an increasing rate presumably due to a lower surface energy barrier enabling the uptake of organic molecules. This work provides insight into the molecular level interactions which affect sustained cluster growth by uptake of organic molecules

Direct Surface Analysis of Time-Resolved Aerosol Impactor Samples with Ultrahigh-Resolution Mass Spectrometry

Aerosol particles in the atmosphere strongly influence the Earth’s climate and human health, but the quantification of their effects is highly uncertain. The complex and variable composition of atmospheric particles is a main reason for this uncertainty. About half of the particle mass is organic material, which is very poorly characterized on a molecular level, and therefore it is challenging to identify sources and atmospheric transformation processes. We present here a new combination of techniques for highly time-resolved aerosol sampling using a rotating drum impactor (RDI) and organic chemical analysis using direct liquid extraction surface analysis (LESA) combined with ultrahigh-resolution mass spectrometry. This minimizes sample preparation time and potential artifacts during sample workup compared to conventional off-line filter or impactor sampling. Due to the high time resolution of about 2.5 h intensity correlations of compounds detected in the high-resolution mass spectra were used to identify groups of compounds with likely common sources or atmospheric history