## 2 Materials and Methods
### Methods  
This study was approached in two distinct phases. The first phase involved calculating the correlation between the two types of variables (airborne pollen and meteorological) described in the previous section. In this first stage, monthly meteorological data were used, which were initially provided by the AEMET. In the case of atmospheric pollen, the data provided by the Palinocam Network are daily data, which is why these were taken as the basis for calculating the monthly values, finding the average values month by month, as shown in Table 1 (Appendix A). In a second phase, it was decided to carry out an analysis of the inter-relationships of these variables within the main pollination periods (MPPs) of each of the 6 pollen types. This required the request of new data, this time daily, to the AEMET. It was considered of interest to study the inter-relationships between pollen grain concentration and meteorological factors throughout the year, in addition to the MPPs, since the analysis of the results obtained from monthly data reveals relevant information on the behavior of the variables that would remain uncovered if only their correlations were studied from daily data. The MPPs were calculated, ignoring the lowest and null pollen concentration values of the whole year, namely 10%, according to the criteria of [PERSON] and [PERSON] [32], i.e., the main pollen period was defined as the period from which the sum of the (daily mean) concentrations reaches 5% of the total sum until the time when the sum reaches 95%, i.e., the main pollen season with 90% of the total amount of pollen. For the two phases indicated, a rank correlation coefficient was used because airborne pollen concentrations do not have a normal distribution [31]. [PERSON]'s linear correlation coefficients were calculated for each of the six variables, corresponding to the pollen types, with each of the 15 meteorological sub-variables under study, in the case of the monthly values. Statgraphics Centurion 18 was used, because it was the one available at the time. And in the case of daily values, the 14 sub-variables were analyzed, together with the daily values of pollen types, with a more advanced version of Statgraphics, Centurion 19. Although AEMET was asked to ensure that the sub-variables, with daily data, which make up the 6 meteorological variables, were exactly the same as those initially provided with monthly data, this coincidence did not occur precisely in all cases. Thus, with regard to precipitation, the sub-variable'maximum daily precipitation intensity' (dPMax) was included, replacing'maximum daily monthly precipitation' (Pmax). On the other hand, the monthly total precipitation (TP) is the result of the sum of the daily total precipitation (dPT), the latter taken from 7:00 h of each day to 7:00 h of the following day. As far as temperature is concerned, there is a precise equivalence between the monthly sub-variables TMax, TMin and TMed, and the daily ones, dTMax, dTMin and dTMed, since the former are the result of finding the monthly average value of the latter. Exactly the same is true for the radiation sub-variables. Also, the values of the daily humidity subtypes coincide with those of the monthly results, which is why they are comparable with each other. In fact, in the case of HMax and HMin, their values are the result of the monthly average of the daily values of the dHMaxand dHMin sub-variables, respectively. And, as for the average monthly humidity (HMed), it results from finding the average value of the relative humidities quantified at 7:00 h, at 13:00 h, and at 18:00 h, which, in turn, are the result of the calculation of the average of the daily relative humidities, taken at those times. With regard to wind, there was no equivalence, with daily values, with the variable V36, so only the'maximum daily wind gust speed' (dVMax) was studied.