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This could be for any number of reasons. Here are a few:
- There could be an error in the report of the pollen count. There are many factors going into a truly volumetric pollen count, meaning that it represents the number of pollen grains per cubic meter of air. The pollen analyst must go through a series of calculations to figure out how what they see in the microscope corresponds to an estimate of pollen concentration in the atmosphere. Human error with this calculation can easily skew the pollen count.
- There are times when certain trees pollinate in unexpected "booms", sending pollen counts sky high for a day or two. There is no way that a forecasting model can take this into account. IMS Health knows when most of the trees, grasses, and weeds that affect allergy sufferers tend to pollinate in each area of the country. The existence and duration of such unique and short-lived phenomena as in this example are impossible to predict.
- The location of the actual sampling device used to establish the pollen count may distort the pollen count. For instance, if a sampler is placed on the second story of a building, right next to an elm tree, when that particular tree starts to pollinate, that sampler will indicate huge numbers for elm pollen. Does that represent what that community is experiencing? Probably not, at least to that outrageous degree. Remember, one sampler is typically used to represent the pollen count for an entire community. If it is placed in such a way where very local vegetation (i.e. immediately surrounding the sampler) has an "unfair advantage" of being represented within a sample, it is not a very effective count during the time when that vegetation is most active.
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