A Deep Learning approach to de-bias Air Quality forecasts, using heterogeneous Open Data sources as reference
Authors: Pérez Antonio , Santa Cruz Mario , Flemming Johannes , Razinger Miha 
Published: March 2022

&lt;p&gt;The degradation of air quality is a challenge that policy-makers face all over the world. According to the World Health Organisation, air pollution causes an estimate of 7 million premature deaths every year. In this context, air quality forecasts are crucial tools for decision- and policy-makers, to achieve data-informed decisions.&lt;/p&gt;&lt;p&gt;Global forecasts, such as the Copernicus Atmosphere monitoring service model (CAMS), usually exhibit biases: systematic deviations from observations. Adjusting these biases is typically the first step towards obtaining actionable air quality forecasts. It is especially relevant in health-related decisions, when the metrics of interest depend on specific thresholds.&lt;/p&gt;&lt;p&gt;AQ (Air quality) - Bias correction was a project funded by the ECMWF Summer of Weather Code (ESOWC) 2021 whose aim is to improve CAMS model forecasts for air quality variables (NO&lt;sub&gt;2&lt;/sub&gt;, O&lt;sub&gt;3&lt;/sub&gt;, PM&lt;sub&gt;2.5&lt;/sub&gt;), using as a reference the in-situ observations provided by OpenAQ. The adjustment, based on machine learning methods, was performed over a set of specific interesting locations provided by the ECMWF, for the period June 2019 to March 2021.&lt;/p&gt;&lt;p&gt;The machine learning approach uses three different deep learning based models, and an extra neural network that gathers the output of the three previous models. From the three DL-based models, two of them are independent and follow the same structure built upon the InceptionTime module: they use both meteorological and air quality variables, to exploit the temporal variability and to extract the most meaningful features of the past [t-24h, t-23h, &amp;#8230; t-1h] and future [t, t+1h, &amp;#8230;, t+23h] CAMS predictions. The third model uses the station static attributes (longitude, latitude and elevation), and a multilayer perceptron interacts with the station attributes. The extracted features from these three models are fed into another multilayer perceptron, to predict the upcoming errors with hourly resolution [t, t+1h, &amp;#8230;, t+23h]. As a final step, 5 different initializations are considered, assembling them with equal weights to have a more stable regressor.&lt;/p&gt;&lt;p&gt;Previous to the modelisation, CAMS forecasts of air quality variables were actually biassed independently from the location of interest and the variable (on average: bias&lt;sub&gt;NO2&lt;/sub&gt; = -22.76, bias&lt;sub&gt;O3&lt;/sub&gt; = 44.30, bias&lt;sub&gt;PM2.5&lt;/sub&gt; = 12.70). In addition, the skill of the model, measured by the Pearson correlation, did not reach 0.5 for any of the variables&amp;#8212;with remarkable low values for NO&lt;sub&gt;2&lt;/sub&gt; and O&lt;sub&gt;3&lt;/sub&gt; (on average: pearson&lt;sub&gt;NO2&lt;/sub&gt; = 0.10, pearson&lt;sub&gt;O3&lt;/sub&gt; = 0.14).&lt;/p&gt;&lt;p&gt;AQ-BiasCorrection modelisation properly corrects these biases. Overall, the number of stations that improve the biases both in train and test sets are: 52 out of 61 (85%) for NO&lt;sub&gt;2&lt;/sub&gt;, 62 out of 67 (92%) for O&lt;sub&gt;3,&lt;/sub&gt;&amp;#160;and 80 out of 102 (78%) for PM&lt;sub&gt;2.5&lt;/sub&gt;. Furthermore, the bias improves with declines of -1.1%, -9.7% and -13.9% for NO&lt;sub&gt;2&lt;/sub&gt;, O&lt;sub&gt;3&lt;/sub&gt; and PM&lt;sub&gt;2.5&lt;/sub&gt; respectively. In addition, there is an increase in the model skill measured through the Pearson correlation, reaching values in the range of 100-400% for the overall improvement of the variable skill.&lt;/p&gt;