Erratum: "Climate Change and Future Pollen Allergy in Europe"

Current and future threats to human health in the Anthropocene

It has been widely recognised that the threats to human health from global environmental changes (GECs) are increasing in the Anthropocene epoch, and urgent actions are required to tackle these pressing challenges. A scoping review was conducted to provide an overview of the nine planetary boundaries and the threats to population health posed by human activities that are exceeding these boundaries in the Anthropocene. The research progress and key knowledge gaps were identified in this emerging field. Over the past three decades, there has been a great deal of research progress on health risks from climate change, land-use change and urbanisation, biodiversity loss and other GECs. However, several significant challenges remain, including the misperception of the relationship between human and nature; assessment of the compounding risks of GECs; strategies to reduce and prevent the potential health impacts of GECs; and uncertainties in fulfilling the commitments to the Paris Agreement. Confronting these challenges will require rigorous scientific research that is well-coordinated across different disciplines and various sectors. It is imperative for the international community to work together to develop informed policies to avert crises and ensure a safe and sustainable planet for the present and future generations.

Predicting the severity of the grass pollen season and the effect of climate change in Northwest Europe

Allergic rhinitis is an inflammation in the nose caused by overreaction of the immune system to allergens in the air. Managing allergic rhinitis symptoms is challenging and requires timely intervention. The following are major questions often posed by those with allergic rhinitis: How should I prepare for the forthcoming season? How will the season's severity develop over the years? No country yet provides clear guidance addressing these questions. We propose two previously unexplored approaches for forecasting the severity of the grass pollen season on the basis of statistical and mechanistic models. The results suggest annual severity is largely governed by preseasonal meteorological conditions. The mechanistic model suggests climate change will increase the season severity by up to 60%, in line with experimental chamber studies. These models can be used as forecasting tools for advising individuals with hay fever and health care professionals how to prepare for the grass pollen season.

Geographical Imputation of Missing Poaceae Pollen Data via Convolutional Neural Networks

Airborne pollen monitoring datasets sometimes exhibit gaps, even very long, either because of maintenance or because of a lack of expert personnel. Despite the numerous imputation techniques available, not all of them effectively include the spatial relations of the data since the assumption of missing-at-random is made. However, there are several techniques in geostatistics that overcome this limitation such as the inverse distance weighting and Gaussian processes or kriging. In this paper, a new method is proposed that utilizes convolutional neural networks. This method not only shows a competitive advantage in terms of accuracy when compared to the aforementioned techniques by improving the error by 5% on average, but also reduces execution training times by 90% when compared to a Gaussian process. To show the advantages of the proposal, 10%, 20%, and 30% of the data points are removed in the time series of a Poaceae pollen observation station in the region of Madrid, and the airborne concentrations from the remaining available stations in the network are used to impute the data removed. Even though the improvements in terms of accuracy are not significantly large, even if consistent, the gain in computational time and the flexibility of the proposed convolutional neural network allow field experts to adapt and extend the solution, for instance including meteorological variables, with the potential decrease of the errors reported in this paper.