科研之友
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摘要
Time-varying parametric linear and time-varying nonparametric regression models as well as a time-varying nonparametric median regression model are developed to predict the daily pollen concentration for Szeged in Hungary using previous-day meteorological parameters and the daily pollen concentration. The models are applied to rainy days and non-rainy days, respectively. The most important predictor is the previous-day pollen concentration level, and the only other predictor retained by a stepwise regression procedure is the daily mean global solar flux for rainy days and the daily mean temperature for non-rainy days. Although the variance percentage explained by these two predictors is higher for non-rainy (55.2%) days than for rainy (51.9%) days, the prediction rate is slightly better for rainy than for non-rainy days. Nonparametric regression yields substantially better estimates, especially for rainy days indicating a nonlinear relationship between the predictors and the pollen concentration. The explained variance percentage is 71.4 and 64.6% for rainy and non-rainy days, respectively. Concerning the mean absolute error, the nonparametric median regression provides the best estimate. The quantile regression shows that probability distribution of daily ragweed concentration is much more skewed for non-rainy days, while the more concentrated probability distribution for rainy days exhibits relatively stable ragweed pollen concentrations. The possible lowest limits of concentrations are also calculated. Under highly favorable conditions for peak concentrations, the pollen level reaches at least 350 grains m(-3) and 450 grains m(-3) for rainy and non-rainy days, respectively. These values again underline the excessive ragweed pollen load over the area of Szeged.
- 出版日期2011-9
