A Spatial-Temporal Approach for Air Quality Forecast in Urban Areas

Abstract

The diameter of PM2.5 is less than that of 2.5 μg/m3 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.

Keywords

• air quality forecast
• spatial-temporal
• urban computing
• data mining
• AirBox