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.
Highlights
In recent years, the awareness of air pollution has been increasing
We discussed the relationship between need for air quality forecasting
We discussed the relationship between spaspatial-temporal data and PM2.5 values, and forecasted air quality using a spatial-temporal tial-temporal data and PM2.5 values, and forecasted air quality using a spatial-temporal combination approach
Summary
The awareness of air pollution has been increasing. Air pollution seriously affects human health, especially atmospheric particulate matter. Based on the collected references, air quality analysis issues are classified into real time prediction and future forecasting. The literature on these two topics emphasized the importance of feature selection and has proposed some methods based on feature combinations. The majority of references propose an air quality prediction algorithm with multiple features, such as meteorological, traffic, human mobility, road networks, points of interest, and time [9,13,31,32]. The authors in above works collected meteorology, traffic flow, human mobility, points of interest, road networks, and city data to forecast air quality. Temporal and spatial features are prepared for PM2.5 forecasting
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