Large cities are typically characterized by a mosaic of green spaces that hold a remarkable variety of native and “exotic” plants. Urban beekeeping has gained increasing popularity. In order to characterize the “urban” in the honey, pollen diversity in 50 honey samples from 18 apiary locations in Vienna, Austria, was microscopically analyzed. The relative abundances of each plant taxon were determined by counting out 500 individual pollen grains per sample. In total, 202 taxa could be identified, with a median of 46 per sample. Taxa richness and diversity differed significantly across three years but did not so between urban and suburban apiaries. Despite trees comprising only roughly a quarter of all taxa, the amount of tree pollen was disproportionally high. The invasive Ailanthus altissima was predominant in 15 out of 50 samples. Other important non-native and/or ornamental trees included Sophora japonica, Gleditsia triacanthos, Castanea sativa, Koelreuteria paniculata, and Liriodendron tulipifera. Urban honey from Central Europe may typically comprise pollen taxa from Europe, East Asia, and North America alike. The results of this study show that intentionally planted, managed urban green spaces can support stable foraging resources for pollinators in cities.

Wastewater data can be very useful for epidemic control during a disease outbreak and proper synthesis of different sources of information can be integrated towards an alerting system, that can be used for decision support. Wastewater data are considered to be of high quality, since they do not depend on testing and can take into account asymptomatic cases. However, little effort has been given into utilizing such information in statistical process control procedures, usually aimed at industrial problems. In this article, we demonstrate how wastewater data can be utilized in health surveillance and propose a statistical framework that can act as a decision support tool. Specifically, we analyze SARS-CoV-2 wastewater data from Austria, constructing summary variables to implicitly describe the Covid-19 prevalence and, based on them, we assess the effectiveness of the current sampling strategy of Austria. We propose a framework of a statistical process monitoring system to aid epidemic management procedures in case SARS-CoV-2 concentration gets dangerously high.