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

Direct assessment of health impacts on hospital admission from traffic intensity in Madrid.

In this paper we establish the attributable risk on respiratory and cardiovascular disorders related to traffic intensity in Madrid. In contrast to previous related studies, the proposed approach directly associates road traffic counts to patient emergency admission rates instead of using primary air pollutants. By applying Shapley values over gradient boosting machines, a first selection step is performed among all traffic observation points based on their influence on patient emergency admissions at Gregorio Marañon hospital. A subsequent quantification of the relative risk associated to traffic intensity of the selected point is calculated via ARIMA and log-linear Poisson regression models. The results obtained show that 13% of respiratory cases are related to traffic intensity while, in the case of cardiovascular disorders, the percentage increases to 39%.S