Quantifying aerial concentrations of maize pollen in the atmospheric surface layer using remote-piloted airplanes and Lagrangian stochastic modeling
Quantifying aerial concentrations of maize pollen in the atmospheric surface layer using remote-piloted airplanes and Lagrangian stochastic modeling
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DOI：
10.1175/jam2381.1
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发表时间：
2006-07-01
影响因子：
3
通讯作者：
Shields, Elson J.
中科院分区：
文献类型：
--
作者：
Aylor, Donald E.;Boehm, Matthew T.;Shields, Elson J.
转基因作物的广泛采用导致需要更好地了解花粉在大气中的传播，因为环境中的花粉流动可能会导致不必要的遗传性状移动。通过比较拉格朗日随机（LS）模型的结果与花粉浓度测量玉米田使用基于塔的旋翼采样器和机载无线电遥控遥控飞行器（RPV）的组合，配备了远程操作的花粉采样器的空中传播的玉米花粉进行了研究。模型和测量值之间的比较分两步进行。在第一步中，LS模型与旋转杆采样器结合使用，以估计每个采样周期的花粉释放速率Q。在第二步中，通过模拟RPV飞行路径通过与大气条件、场几何形状、风向和源强度相对应的LS模型花粉羽流，计算对应于每个RPV测量值C-measure的浓度C-model的建模值。在所有的采样周期，除了那些被确定为逆风的领域，比C-模型/C-措施的几何平均值和几何标准差分别为1.42和4.53，和对数正态分布对应于这些值被发现，以密切配合的PDF的C-模型/C-措施。模型输出对湍流参数敏感，在实验过程中遇到的值范围内，C模型的平均值相差100倍。在与这种大的潜在变异性相比，得出的结论是，在这里发现的C-模型和C-措施之间的平均因子为1.4，表明LS模型能够准确地预测，平均而言，在一系列大气条件下的浓度。
The extensive adoption of genetically modified crops has led to a need to understand better the dispersal of pollen in the atmosphere because of the potential for unwanted movement of genetic traits via pollen flow in the environment. The aerial dispersal of maize pollen was studied by comparing the results of a Lagrangian stochastic (LS) model with pollen concentration measurements made over cornfields using a combination of tower-based rotorod samplers and airborne radio-controlled remote-piloted vehicles (RPVs) outfitted with remotely operated pollen samplers. The comparison between model and measurements was conducted in two steps. In the first step, the LS model was used in combination with the rotorod samplers to estimate the pollen release rate Q for each sampling period. In the second step, a modeled value for the concentration C-model, corresponding to each RPV measured value C-measure, was calculated by simulating the RPV flight path through the LS model pollen plume corresponding to the atmospheric conditions, field geometry, wind direction, and source strength. The geometric mean and geometric standard deviation of the ratio C-model/C-measure over all of the sampling periods, except those determined to be upwind of the field, were 1.42 and 4.53, respectively, and the lognormal distribution corresponding to these values was found to fit closely the PDF of C-model/C-measure. Model output was sensitive to the turbulence parameters, with a factor-of-100 difference in the average value of C-model over the range of values encountered during the experiment. In comparison with this large potential variability, it is concluded that the average factor of 1.4 between C-model and C-measure found here indicates that the LS model is capable of accurately predicting, on average, concentrations over a range of atmospheric conditions.