Long Short-Term Memory Approach for Short-Term Air Quality Forecasting in the Bay of Algeciras (Spain)

Predicting air quality is a very important task, as it is known to have a significant impact on health. The Bay of Algeciras (Spain) is a highly industrialised area with one of the largest superports in Europe. During the period 2017-2019, different data were recorded in the monitoring stations of the bay, forming a database of 131 variables (air pollutants, meteorological information, and vessel data), which were predicted in the Algeciras station using long short-term memory models. Four different approaches have been developed to make SO2 and NO2 forecasts 1 h and 4 h in Algeciras. The first uses the remaining 130 exogenous variables. The second uses only the time series data without exogenous variables. The third approach consists of using an autoregressive time series arrangement as input, and the fourth one is similar, using the time series together with wind and ship data. The results showed that SO2 is better predicted with autoregressive information and NO2 is better predicted with ships and wind autoregressive time series, indicating that NO2 is closely related to combustion engines and can be better predicted. The interest of this study is based on the fact that it can serve as a resource for making informed decisions for authorities, companies, and citizens alike.

Automated summary

Predicting air quality is crucial for health. This study uses long short-term memory models to forecast SO2 and NO2 pollutants in the Bay of Algeciras, Spain. The results show that SO2 is better predicted using autoregressive information, while NO2 is closely related to combustion engines and can be better predicted using ship and wind autoregressive time series. This study is important as it provides valuable information for decision-making by authorities, companies, and citizens.