Near surface ozone predictions

Human beings, animals and crops are directly exposed to the ambient airmass of the lower part of the atmosphere (planetary boundary layer). This airmass may contain several pollutants like ozone which is harmful to living beings (WHO, 2013; Bell et al., 2014; Lefohn et al., 2017; Fleming et al., 2018 ) and certain crops (Avnery et al., 2011; Mills et al., 2018). Therefore, the prediction of ozone concentrations is of significant importance to issue warnings for the public if high ozone concentrations are foreseeable.

We develop deep neural networks to predict near-surface ozone concentrations for more than 300 German measurement sites. Our recent model consists of multiple convolutional layers which we group into individual inception blocks (extension of Szegedy, 2015). Each inception block consists of three convolutional, and two pooling towers (max, mean) [Figure 6]. We use chemical precursors and meteorological variables of the seven previous days as input to predict the daily maximum eight-hour average (dma8eu) for a lead time of up to four days.

We create a workflow which ensures that the full workflow, including downloading the data, preprocessing, training, and postprocessing steps, is reproducible.

Figure 7:Inception block with dimensionality reduction and batch normalisation before activation. Successive layers are grouped to towers. The input and output layer of an inception block are colour coded in green, pooling layers in red, filter reduction layers

WHO, 2013:,-ozone-and-nitrogen-dioxide
Bell et al., Who is More Affected by Ozone Pollution? A Systematic Review and Meta-Analysis, 2014:
Lefohn et al., Responses of human health and vegetation exposure metrics to changes in ozone concentration distributions in the European Union, United States, and China, 2017:
Fleming et al., Tropospheric Ozone Assessment Report: Present-day ozone distribution and trends relevant to human health, 2018:
Avnery et al., Global crop yield reductions due to surface ozone exposure: 1. Year 2000 crop production losses and economic damage, 2011;
Mills et al., Ozone pollution will compromise efforts to increase global wheat production, 2018:
Szegedy et al., Going deeper with convolutions, 2015: