# A Spatial-Temporal Approach for Air Quality Forecast in Urban Areas
###### Abstract  
The diameter of PM2.5 is less than that of 2.5 \(\mathrm{\SIUnitSymbolMicro m}\)/\(\mathrm{m}^{3}\) particulate matter; PM2.5 is small enough to enter the body through the alveolar microvasculature and has a major impact on human health. Therefore, people are interested in the establishment of air quality monitoring and forecasting. The historical and current air quality indices (AQI) can now be easily obtained from air quality sensors. However, people are more likely to need the PM2.5 forecasting information. Based on the literature, air quality varies because of a variety of factors, such as the meteorology in urban areas. In this paper, a spatial-temporal approach is proposed to forecast PM2.5 for 48 h using temporal and spatial features. From the temporal perspective, it is considered that the AQI in a few hours may be very similar because AQI is continuous. In addition, this research reveals the relationship between weather similarities and PM2.5 similarity. It is found that the more similar the weather is, the more similar the PM2.5 value is. From a spatial perspective, it is also considered that the air quality may be similar to that of the adjacent monitoring stations. Finally, the experimental results, based on AirBox data, show that the proposed approach outperforms the two methods based on well-established measurements in terms of the PM2.5 forecast error.  
air quality forecast; spatial-temporal; urban computing; data mining; AirBox +
Footnote †: journal: Applied  
the cause-and-effect relationship of air quality and other features. This study is dedicated to constructing a construable and efficient method.  
In this paper, a spatial-temporal approach is proposed based on meteorological factors for PM2.5 forecasting. In this approach, spatial information is considered, including the location of a geographic region, and temporal information, such as meteorology data and air quality for the forecasting model. It is found that meteorology features are related to PM2.5. To easily understand the cause-and-effect relationship of meteorology and PM2.5, a weight-average-based method is proposed for forecasting. It was found using PM2.5 at different times, which was monitored over close time intervals, and under very similar weather conditions. Additionally, PM2.5 values, at the same time, are similar when the monitored locations are close to each other. In the temporal part, data that have close times and have similar weather conditions are selected as references. These references are used for PM2.5 forecasting using similar weather conditions. In the spatial part, the forecast value in the temporal part is adjacent to the smooth part. Therefore, the time series of the PM2.5 forecast values for devices that are near to each other are likely to be similar. In this way, a PM2.5 value is forecasted over the following 48 h. Temporal and spatial data are collected and several internal and external experiments are conducted. The internal experimental results show that spatial or temporal feature combinations reduce forecast errors. In external experiments, we tried to compare our approach to two methods, based on the well-established Pearson correlation coefficient (PCC) [22] and inverse distance weighting (IDW) [23] measurements. The experimental results show that the proposed approach performs well and more stably in comparison to the other two methods.