Correlation between airborne pollen data and the risk of tick-borne encephalitis in northern Italy.

Tick-borne encephalitis (TBE) is caused by a flavivirus that infects animals including humans. In Europe, the TBE virus circulates enzootically in natural foci among ticks and rodent hosts. The abundance of ticks depends on the abundance of rodent hosts, which in turn depends on the availability of food resources, such as tree seeds. Trees can exhibit large inter-annual fluctuations in seed production (masting), which influences the abundance of rodents the following year, and the abundance of nymphal ticks two years later. Thus, the biology of this system predicts a 2-year time lag between masting and the incidence of tick-borne diseases such as TBE. As airborne pollen abundance is related to masting, we investigated whether inter-annual variation in pollen load could be directly correlated with inter-annual variation in the incidence of TBE in human populations with a 2-year time lag. We focused our study on the province of Trento (northern Italy), where 206 TBE cases were notified between 1992 and 2020. We tested the relationship between TBE incidence and pollen load collected from 1989 to 2020 for 7 different tree species common in our study area. Through univariate analysis we found that the pollen quantities recorded two years prior for two tree species, hop-hornbeam (Ostrya carpinifolia) and downy oak (Quercus pubescens), were positively correlated with TBE emergence (R2 = 0.2) while a multivariate model with both tree species better explained the variation in annual TBE incidence (R2 = 0.34). To the best of our knowledge, this is the first attempt at quantifying the correlation between pollen quantities and the incidence of TBE in human populations. As pollen loads are collected by widespread aerobiological networks using standardized procedures, our study could be easily replicated to test their potential as early warning system for TBE and other tick-borne diseases.

Integration of reference data from different Rapid-E devices supports automatic pollen detection in more locations.

Pollen is the most common cause of seasonal allergies, affecting over 33 % of the European population, even when considering only grasses. Informing the population and clinicians in real-time about the actual presence of pollen in the atmosphere is essential to reduce its harmful health and economic impact. Thus, there is a growing network of automatic particle analysers, and the reproducibility and transferability of implemented models are recommended since a reference dataset for local pollen of interest needs to be collected for each device to classify pollen, which is complex and time-consuming. Therefore, it would be beneficial to incorporate the reference dataset collected from other devices in different locations. However, it must be considered that laser-induced data are prone to device-specific noise due to laser and detector sensibility. This study collected data from two Rapid-E bioaerosol identifiers in Serbia and Italy and implemented a multi-modal convolutional neural network for pollen classification. We showed that models lost their performance when trained on data from one and tested on another device, not only in terms of the recognition ability but also in comparison with the manual measurements from Hirst-type traps. To enable pollen classification with just one model in both study locations, we first included the missing pollen classes in the dataset from the other study location, but it showed poor results, implying that data of one pollen class from different devices are more different than data of different pollen classes from one device. Combining all available reference data in a single model enabled the classification of a higher number of pollen classes in both study locations. Finally, we implemented a domain adaptation method, which improved the recognition ability and the correlations of transferred models only for several pollen classes.

Sampling bias and sampling errors in pollen counting in aerobiological monitoring in Italy.

Information about airborne pollen concentration is of concern for health authorities across Europe. The reliability of data estimates depends on the accuracy and precision of pollen counts. In Italy, pollen counts are carried out on slides for microscopic evaluation and are regulated by the national Standard UNI 11108:2004. Our results showed that counts performed according to the Italian standard may result in a significant bias in the number of pollen grains counted and this will have an impact on final estimates of pollen concentration. For the same sample size, confidence intervals vary in relation to pollen abundance, either in terms of number of grains or of number of species. The sample size suggested by the standard (20% of the target surface) may result in errors in pollen counts ranging from 7-55% of the mean value, and in missing 22-54% of the taxa present on the slide.

Biomolecular identification of allergenic pollen: a new perspective for aerobiological monitoring?

BACKGROUND: Accurate and updated information on airborne pollen in specific areas can help allergic patients. Current monitoring systems are based on a morphologic identification approach, a time-consuming method that may represent a limiting factor for sampling network enhancement. OBJECTIVE: To verify the feasibility of developing a real-time polymerase chain reaction (PCR) approach, an alternative to optical analysis, as a rapid, accurate, and automated tool for the detection and quantification of airborne allergenic pollen taxa. METHODS: The traditional cetyl trimethyl ammonium bromide-based method was modified for DNA isolation from pollen. Taxon-specific DNA sequences were identified via bioinformatics or literature searches and were PCR amplified from the matching allergenic taxa; based on the sequences of PCR products, complementary or degenerate TaqMan probes were developed. The accuracy of the quantitative real-time PCR assay was tested on 3 plant species. RESULTS: The setup of a modified DNA extraction protocol allowed us to achieve good-quality pollen DNA. Taxon-specific nuclear gene fragments were identified and sequenced. Designed primer pairs and probes identified selected pollen taxa, mostly at the required classification level. Pollen was properly identified even when collected on routine aerobiological tape. Preliminary quantification assays on pollen grains were successfully performed on test species and in mixes. CONCLUSIONS: The real-time PCR approach revealed promising results in pollen identification and quantification, even when analyzing pollen mixes. Future perspectives could concern the development of multiplex real-time PCR for the simultaneous detection of different taxa in the same reaction tube and the application of high-throughput molecular methods.

Ambient levels of nitrogen dioxide (NO2) may reduce pollen viability in Austrian pine (Pinus nigra Arnold) trees--correlative evidence from a field study.

A fully randomized sampling design was adopted to test whether pollen viability of Austrian pine (Pinus nigra Arnold) was impacted by NO(2) pollution. Spatial strata (500500 m each) with high (41.9-44.6 microg m(-3)) and low (15.4-21.0 microg m(-3)) NO(2) were selected from a defined population in a small area (236.5 km(2), <200 m range in elevation) in Northern Italy. Pollen viability was measured by means of the Tetrazolium (TTC) test. Analysis of variance by means of a generalised linear model showed that NO(2) was a significant factor (P=0.0425) affecting pollen viability. Within the treatment, no significant differences were detected among replicates. Within each replicate, sampling unit data were significantly different (P=0.000) and this suggested some improvement in the applied sampling design was needed. Pollen viability was significantly related to pollen germination (P<0.01) and tube length (P<0.01). This suggested a possible impact of NO(2) on the regeneration of Austrian pine in polluted environments.