Contribution of bioaerosols to airborne particulate matter.

Fine and coarse particulate matter (PM), as measured, for example, in regulatory air pollution monitoring networks, contains biological entities such as fungal spores, pollen, animal dander, leaf wax, and human skin cells, to mention but a few types. Although these bioaerosols come in a wide range of particle size, of 14 common types nine fall into the 0- 10 µm range and four are in the 0- 2.5 µm range. These bioaerosols contribute to the concentrations of particulates determined by both filter-based and continuous instruments. This paper reviews bioaerosol research conducted worldwide in the last twenty years. Such studies have been conducted in Toronto, Canada, central Germany, Phoenix, Arizona, Davis, California, Dallas, Texas, and at many other sites worldwide. Notwithstanding the wide variety of climates, ecological systems, and urban and rural environments in which these measurements have been made, a reasonable, first-order estimate of the overall bioaerosol contribution to particles 2.5 microns and smaller (PM2.5) is 16.5% and to particles 10 microns and smaller (PM10) is 16.3%. A percentage contribution of this magnitude from unregulated emissions means that achieving PM standards will require greater reductions in the better understood anthropogenic and natural emissions of geological and combustion particles. In one such case the emission reductions necessary to achieve the standard increase from 25% (with bioaerosols ignored) to 36% (with bioaerosols accounted for). Although to the uninitiated this difference may not appear to be substantial, it can only be considered vast and nearly regulatorily impossible to those policy makers and regulators responsible for enacting emission-reduction regulations. Emissions of airborne biological materials are unregulated. Ignoring this natural component in attempting to achieve national ambient air quality standards for particulates can lead to overly optimistic predictions of attainment.Implications: For those officials still striving to meet federal air quality standards for particulate matter, either PM10 or PM2.5, it would be prudent to acknowledge the presence of unregulated bioaerosols. Ignoring this portion of PM may lead to over-optimistic projections of attainment.

Simulating the meteorology and PM10 concentrations in Arizona dust storms using the Weather Research and Forecasting model with Chemistry (Wrf-Chem).

Nine dust storms in south-central Arizona were simulated with the Weather Research and Forecasting with Chemistry model (WRF-Chem) at 2 km resolution. The windblown dust emission algorithm was the Air Force Weather Agency model. In comparison with ground-based PM10 observations, the model unevenly reproduces the dust-storm events. The model adequately estimates the location and timing of the events, but it is unable to precisely replicate the magnitude and timing of the elevated hourly concentrations of particles 10 µm and smaller ([PM10]).Furthermore, the model underestimated [PM10] in highly agricultural Pinal County because it underestimated surface wind speeds and because the model's erodible fractions of the land surface data were too coarse to effectively resolve the active and abandoned agricultural lands. In contrast, the model overestimated [PM10] in western Arizona along the Colorado River because it generated daytime sea breezes (from the nearby Gulf of California) for which the surface-layer speeds were too strong. In Phoenix, AZ, the model's performance depended on the event, with both under- and overestimations partly due to incorrect representation of urban features. Sensitivity tests indicate that [PM10] highly relies on meteorological forcing. Increasing the fraction of erodible surfaces in the Pinal County agricultural areas improved the simulation of [PM10] in that region. Both 24-hr and 1-hr measured [PM10] were, for the most part, and especially in Pinal County, extremely elevated, with the former exceeding the health standard by as much as 10-fold and the latter exceeding health-based guidelines by as much as 70-fold. Monsoonal thunderstorms not only produce elevated [PM10], but also cause urban flash floods and disrupt water resource deliveries. Given the severity and frequency of these dust storms, and conceding that the modeling system applied in this work did not produce the desired agreement between simulations and observations, additional research in both the windblown dust emissions model and the weather research/physicochemical model is called for. IMPLICATIONS: While many dust storms can be considered to be natural, in semi-arid climates such storms often have an anthropogenic component in their sources of dust. Applying the natural, exceptional events policy to these storms with strong signatures of anthropogenic sources would appear not only to be misguided but also to stifle genuine regulatory efforts at remediation. Those dust storms that have resulted, in part, from passage over abandoned farm land should no longer be considered "natural"; policymakers and lawmakers need to compel the owners of such land to reduce its potential for windblown dust.

Stochastic mirage phenomenon in a random medium.

In the framework of geometric optics, we consider the problem of characterizing the ray trajectory in a random medium with a mean refractive index gradient. Such a gradient results in the mirage phenomenon where an object's observed location is displaced from its actual location. We derive formulas for the mean ray path in both the situation of isotropic stochastic fluctuations and an important anisotropic case. For the isotropic model, the mean squared displacement is also given by a simple formula. Our results could be useful for applications involving the propagation of electromagnetic waves through the atmosphere, where larger-scale mean gradients and smaller-scale stochastic fluctuations are both present.

Kalman filter data assimilation: targeting observations and parameter estimation.

This paper studies the effect of targeted observations on state and parameter estimates determined with Kalman filter data assimilation (DA) techniques. We first provide an analytical result demonstrating that targeting observations within the Kalman filter for a linear model can significantly reduce state estimation error as opposed to fixed or randomly located observations. We next conduct observing system simulation experiments for a chaotic model of meteorological interest, where we demonstrate that the local ensemble transform Kalman filter (LETKF) with targeted observations based on largest ensemble variance is skillful in providing more accurate state estimates than the LETKF with randomly located observations. Additionally, we find that a hybrid ensemble Kalman filter parameter estimation method accurately updates model parameters within the targeted observation context to further improve state estimation.

Spiral laser beams in inhomogeneous media.

Explicit solutions of the inhomogeneous paraxial wave equation in a linear and quadratic approximation are applied to wave fields with invariant features, such as oscillating laser beams in a parabolic waveguide and spiral light beams in varying media. A similar effect of superfocusing of particle beams in a thin monocrystal film, harmonic oscillations of cold trapped atoms, and motion in magnetic field are also mentioned.