# Space-Time Prediction of PM\({}_{2.5}\) Concentrations in Santiago de Chile Using LSTM Networks
###### Abstract  
Currently, air pollution is a highly important issue in society due to its harmful effects on human health and the environment. The prediction of pollutant concentrations in Santiago de Chile is typically based on statistical methods or classical neural networks. Existing methods often assume that historical values are known at a fixed geographic point, such that air pollution can be predicted at a future hour using time series analysis. However, these methods are inapplicable when it is necessary to know the pollutant concentrations at every point of the space. This work proposes a method that addresses the space-time prediction of PM\({}_{2.5}\) concentration in Santiago de Chile at any spatial points through the use of the LSTM recurrent network model. In particular, by considering historical values of air pollutants (PM\({}_{2.5}\), PM\({}_{10}\) and nitrogen dioxide) and meteorological variables (temperature, wind speed and direction and relative humidity), measured at fixed monitoring stations, the proposed model can predict PM\({}_{2.5}\) concentrations for the next 24 h in a new location where measurements are not available. This work describes the experiments carried out, with particular emphasis on the pre-processing step, which constitutes an important factor for obtaining relatively good results. The proposed multilayer LSTM model obtained \(R^{2}\) values equal to 0.74 and 0.38 in seven stations when considering forecasts of 1 and 24 h, respectively. As future work, we plan to include more input variables in the proposed model and to use attention-based networks. space-time prediction; pollution model; PM\({}_{2.5}\); recurrent neural networks +
Footnote †: journal: Medical  
in Santiago. In the last few decades, the Ministry of Environment has been applying several restriction measures to emission sources in order to protect human health. Theses measures are based on the Chilean National Standards, which are not so stringent as the international guidelines proposed by WHO [12; 13]. For example, for the PM\({}_{2.5}\), four ranges of increasing concentrations define air quality, starting from the first level (good to moderate) including a maximum 24 h average of PM\({}_{2.5}\) less than 80 \(\mathrm{\SIUnitSymbolMicro g}/\mathrm{m}^{3}\), to the last level (hazardous) where the 24 h average is greater than 170 \(\mathrm{\SIUnitSymbolMicro g}/\mathrm{m}^{3}\)[14; 15]. Instead, the latest WHO guidelines recommended 25 \(\mathrm{\SIUnitSymbolMicro g}/\mathrm{m}^{3}\) (24 h mean) and an annual level of 10 \(\mathrm{\SIUnitSymbolMicro g}/\mathrm{m}^{3}\)[13]. However, some temporary measures implemented by the Chilean government allowed us to reduce the particular matter concentrations of 20%, with a consequent benefit on the the population health in the short run [16]. Measures that could reduce pollution in the short run refer, mainly, to driving restrictions and temporarily shutting down or reducing the usage of stationary emissions sources. A result of [16] was that after three days of an episode of a high-pollution announcement, there were approximately fifteen fewer (cumulative) deaths above the age of 64, and most of that reduction was due to decreases in deaths due to respiratory causes. The impacts of short term exposure to air pollutants were also demonstrated by [17; 18] Hence, a good prediction of the pollution levels for the next few hours or days could be very useful for environmental management, since it allows one to alert the population of forthcoming high pollution episodes and to support decision makers to implement restriction measures [14; 16]. For example, [PERSON] et al. [19] integrated two prediction pollution models in traditional traffic management support systems for a sustainable mobility of road vehicles in urban areas; and [PERSON] and [PERSON] [20] reviewed the evidence on greenhouse gas (GHG) mitigation measures and the related health co-benefits, and provided recommendations for further development and implementation of climate change response policies.