Bioaerosols in urban environments: Trends and interactions with pollutants and meteorological variables based on quasi-climatological series.

Pollen grains emitted by urban vegetation are the main primary biological airborne particles (PBAPs) which alter the biological quality of urban air and have a significant impact on human health. This work analyses the interactions which exist between pollen-type PBAPs, meteorological variables, and air pollutants in the urban atmosphere so that the complex relationships and trends in future scenarios of changing environmental conditions can be assessed. For this study, the 1992-2018 pollen data series from the city of Granada (southeast Spain) was used, in which the dynamics of the total pollen as well as the 8 main pollen types (Cupressaceae, Olea, Pinus, Platanus, Poaceae, Populus, Quercus and Urticaceae) were analysed. The trend analysis showed that all except Urticaceae trended upward throughout the series. Spearman's correlations with meteorological variables showed that, in general, the most influential variables on the pollen concentrations were the daily maximum temperature, relative humidity, water vapor pressure, global radiation, and insolation, with different effects on different pollen types. Parallel analysis by neural networks (ANN) confirmed these variables as the predominant ones, especially global radiation. The correlation with atmospheric pollutants revealed that ozone was the pollutant with the highest influence, although some pollen types also showed correlation with NO2, SO2, CO and PM10. The Generalized Linear Models (GLM) between pollen and pollutants also indicated O3 as the most prominent variable. These results highlight the active role that pollen-type PBAPs have on urban air quality by establishing their interactions with meteorological variables and pollutants, thereby providing information on the behaviour of pollen emissions under changing environmental conditions.

Estimating UV erythemal irradiance by means of neural networks.

In recent years, there has been a substantial increase in attempts to model the flux of ultraviolet radiation (UV). UV irradiance at surface level is a result of the combined effects of solar zenith angle, surface elevation, cloud cover, aerosol load and optical properties, surface albedo and the vertical profile of ozone. In this study, we present the development of an artificial neural network (ANN) model that can be used to estimate solar UV irradiance on the basis of optical air mass, ozone columnar content, latitude, horizontal visibility data and cloud information such as type, coverage and height. ANN are widely accepted as a technology offering an alternative way to tackle complex and ill-defined problems. They can learn from examples, are fault tolerant in the sense that they are able to handle noisy and incomplete data, are able to deal with nonlinear problems and, once trained, can perform prediction and generalization at high speed. In this study, a multilayer perceptron network (MLP) consisting of an input layer, an output layer and one hidden layer was used. Training of the neural network was done using the Bayesian regulation back propagation algorithm. The study was developed using data from three stations on the Iberian Peninsula: Madrid and Murcia during the period 2000-2001 and Zaragoza in 2001. To train and validate the MPL neural networks, independent subsets of data were extracted from the complete database at each station. The results suggest that a MLP neural network using optical air mass, ozone columnar content, latitude and total cloud coverage provides the best estimates, with mean bias deviation and root mean square deviation of -0.1% and 18.0%, 1.6% and 19.6%, 0.1% and 14.6% at Madrid, Murcia and Zaragoza, respectively. Despite the dependence of the cloud radiative effect on cloud type, the use of additional information such as cloud type or cloud elevation did not improve these results. The performance of the developed ANN has been checked regarding its ability to estimate the UV index (UVI); results indicate that in more than 95% of the cases, the difference between estimated and measured values does not exceed one unit of UVI.