Engineering Papers⌕ Search

DOE OSTI · 1042963

Predicting reaerosolization

Abstract

Outdoor studies of the environmental persistence of bacteria have led to many interesting results. It turns out that the initial deposition of bacteria is not the end of the story. We examined both the ongoing daily deposition and aerosolization of bacteria for two weeks following an initial deposition event. Differences between samples collected in a clearing and those collected beneath a forest canopy were also examined. There were two important results: first, bacteria were still moving about in significant quantities after two weeks, though the local environment where they were most prevalent appeared to shift over time; second, we were able to develop a simple mathematical model that could fairly accurately estimate the average daily airborne concentration of bacteria over the duration of the experiment using readily available environmental information. The implication is that deposition patterns are very likely to shift over an extended period of time following a release, possibly quite significantly, but there is hope that we may be able to estimate these changes fairly accurately.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Daniel, William Brent, Omberg, Kristin M. 2010-11-29. Predicting reaerosolization. https://www.osti.gov/biblio/1042963

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

Water4Energy Step-1 Band-M Ready-to-Train Samples for TVA Weeks-to-Years Prediction, Version 0

AI-ready Band-M (monthly) labelled training pack for the Water4Energy Genesis Task-1 project on weeks-to-years prediction of Tennessee Valley temperature and precipitation. The deposit includes leakage-aware issue-time samples (samples_M_v0.nc; N=486), train-only scalers, issue-time split table, supporting monthly panels, and Python generation scripts to recreate the pack from the companion Tier-1 raw observation collection (https://doi.org/10.13139/ORNLNCCS/3398576). Each sample pairs a 12-month lookback of teleconnection indices and SST box anomalies with TVA-mean ERA5 anomaly targets (t2m, tp, msl) at leads 1–3 months.

54 ENVIRONMENTAL SCIENCES↗

Multi-Angle Snowflake Camera, particle analysis

The c1 level data product for the Mutli-Angle Snowflake Camera contains snowflake fall speeds and particle size, among other analysis for images associated with each hydrometeor.

54 ENVIRONMENTAL SCIENCES↗

Multi-Angle Snowflake Camera, time bins

The c1 level data product for the Mutli-Angle Snowflake Camera contains snowflake fall speeds and particle size, among other analysis for images associated with each hydrometeor.

54 ENVIRONMENTAL SCIENCES↗