Lighthart B, Mohr A J
Environmental Research Laboratory, U.S. Environmental Protection Agency, Corvallis, Oregon 97333.
Appl Environ Microbiol. 1987 Jul;53(7):1580-3. doi: 10.1128/aem.53.7.1580-1583.1987.
A Gaussian plume model has been modified to include an airborne microbial survival term that is a best-fit function of laboratory experimental data of weather variables. The model has been included in an algorithm using microbial source strength and local hourly mean weather data to drive the model through a summer- and winter-day cycle. For illustrative purposes, a composite airborne "virus" (developed using actual characteristics from two viruses) was used to show how wind speed could have a major modulating effect on near-source viable concentrations. For example, at high wind speeds such as those occurring during the day, or with short travel times, near-source locations experience high viable concentrations because the microorganisms have not had time to become inactivated. As the travel time increases, because of slow wind speed or longer distances, die-off modulation by sunshine, relative humidity, temperature, etc., potentially becomes increasingly predominant.
高斯烟羽模型已被修改，纳入了一个空气传播微生物存活项，该项是天气变量实验室实验数据的最佳拟合函数。该模型已被纳入一种算法中，利用微生物源强和当地每小时平均天气数据，驱动模型完成夏季和冬季日循环。为便于说明，使用了一种复合空气传播“病毒”（根据两种病毒的实际特征开发）来展示风速如何对源附近的存活浓度产生主要调节作用。例如，在白天出现的高风速或短传播时间情况下，源附近位置的存活浓度较高，因为微生物没有时间失活。随着传播时间增加，由于风速缓慢或距离较长，阳光、相对湿度、温度等因素导致的微生物死亡调节作用可能会变得越来越显著。
