Abstract Background Pollen is naturally emitted and is relevant for health, crop sciences and monitoring climate change, among others. Despite their relevance, pollen is often insufficiently monitored resulting in a lack of data. Thus, spatial modelling of pollen concentrations for unmonitored areas is necessary. The aim of this study was to develop an automatic system for calculating daily pollen concentrations at sites without regular pollen monitoring. Method We used data from 14 pollen taxa collected during 2015 at 26 stations distributed across Bavaria, Germany. The proposed system was based on the Kriging interpolation method to spatially model pollen concentrations for unmonitored areas, in combination with regression of environmental parameters. The method also took into account weather effects on daily pollen concentrations. Results An automatic system was developed for calculating current pollen concentrations at any location of the county. The results were displayed as daily pollen concentrations per m3 in maps of 1 km2 resolution. The models are trained automatically for every day by using the pollen and weather inputs. Automatic inputs will increase the usability of the model. In 50% of the cases, Gaussian Kriging was selected as the optimal model. An R2 of 0.5 is reached in external validation without considering the effect of the weather. An R2 of 0.7 is reached after considering the effect of daily weather parameters. Conclusions A fully automatic pollen network (ePIN) was built in Bavaria during 2018 that delivers data on-line without delay. The proposed method allows for a comparably small number of automatic devices per study area, but still providing information on pollen on any location in the study area.

中文翻译：

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在没有监测的情况下，当前空气中花粉浓度的空间插值

摘要背景花粉是自然排放的，与健康、作物科学和监测气候变化等有关。尽管它们具有相关性，但花粉通常没有得到充分监测，从而导致缺乏数据。因此，需要对未监测区域的花粉浓度进行空间建模。本研究的目的是开发一种自动系统，用于在没有定期花粉监测的地点计算每日花粉浓度。方法 我们使用了 2015 年在德国巴伐利亚州分布的 26 个站点收集的 14 个花粉类群的数据。所提出的系统基于克里金插值法，结合环境参数的回归，对未监测区域的花粉浓度进行空间建模。该方法还考虑了天气对每日花粉浓度的影响。结果开发了一个自动系统，用于计算该县任何位置的当前花粉浓度。结果在 1 平方公里分辨率的地图中显示为每立方米每日花粉浓度。这些模型每天都会使用花粉和天气输入自动训练。自动输入将增加模型的可用性。在 50% 的情况下，高斯克里金法被选为最佳模型。在不考虑天气影响的情况下，在外部验证中达到了 0.5 的 R2。考虑到日常天气参数的影响后，R2 达到了 0.7。结论 2018 年，巴伐利亚州建立了一个全自动花粉网络 (ePIN)，可立即在线提供数据。所提出的方法允许每个研究区域的自动设备数量相对较少，