Myszkowska Dorota
Department of Clinical and Environmental Allergology, Jagiellonian University Medical College, Śniadeckich 10, 31-531, Kraków, Poland,
Int J Biometeorol. 2014 Jul;58(5):975-86. doi: 10.1007/s00484-013-0682-7. Epub 2013 Jun 21.
The relationship between the meteorological elements, especially the thermal conditions and the Poaceae pollen appearance in the air, were analysed as a basis to construct a useful model predicting the grass season start. Poaceae pollen concentrations were monitored in 1991-2012 in Kraków using the volumetric method. Cumulative temperature and effective cumulative temperature significantly influenced the season start in this period. The strongest correlation was seen as the sum of mean daily temperature amplitudes from April 1 to April 14, with mean daily temperature>15 °C and effective cumulative temperature>3 °C during that period. The proposed model, based on multiple regression, explained 57% of variation of the Poaceae season starts in 1991-2010. When cumulative mean daily temperature increased by 10 °C, the season start was accelerated by 1 day. The input of the interaction between these two independent variables into the factor regression model caused the increase in goodness of model fitting. In 2011 the season started 5 days earlier in comparison with the predicted value, while in 2012 the season start was observed 2 days later compared to the predicted day. Depending on the value of mean daily temperature from March 18th to the 31st and the sum of mean daily temperature amplitudes from April 1st to the 14th, the grass pollen seasons were divided into five groups referring to the time of season start occurrence, whereby the early and moderate season starts were the most frequent in the studied period and they were especially related to mean daily temperature in the second half of March.
分析了气象要素,特别是热条件与空气中禾本科花粉出现之间的关系,以此为基础构建一个预测草花粉季开始的有用模型。1991 - 2012年期间,在克拉科夫采用容积法监测了禾本科花粉浓度。累积温度和有效累积温度对这一时期的花粉季开始有显著影响。相关性最强的是4月1日至4月14日的日平均温度振幅之和,在此期间日平均温度>15°C且有效累积温度>3°C。所提出的基于多元回归的模型解释了1991 - 2010年禾本科花粉季开始时间变化的57%。当累积日平均温度升高10°C时,花粉季开始时间提前1天。将这两个自变量之间的相互作用纳入因子回归模型,提高了模型拟合优度。2011年花粉季开始时间比预测值提前了5天,而2012年花粉季开始时间比预测日期晚了2天。根据3月18日至31日的日平均温度值以及4月1日至14日的日平均温度振幅之和,将草花粉季分为五组,以花粉季开始时间划分,其中早期和中期开始在研究期间最为常见,且它们尤其与3月下旬的日平均温度有关。