# A Novel Spatiotemporal Analysis Framework for Air Pollution Episode Association in Puli, Taiwan
###### Abstract  
Air pollution has been a global issue that solicits proposals for sustainable development of social economics. Though the sources emitting pollutants are thoroughly investigated, the transportation, dispersion, scattering, and diminishing of pollutants in the spatiotemporal domain are underexplored, and the relationship between these activities and atmospheric and anthropogenic conditions is hardly known. This paper proposes machine learning approaches for the spatiotemporal analysis of air pollution episode associations. We deployed an internet of low-cost sensors for acquiring the hourly time series data of PM\({}_{2.5}\) concentrations in Puli, Taiwan. The region is resolved into 10 \(\times\) 10 grids, and each grid has an area size of 400 \(\times\) 400 m\({}^{2}\). We consider the monitored PM\({}_{2.5}\) concentration at a grid as its gray intensity, such that a 10 \(\times\) 10 PM\({}_{2.5}\) image is obtained every hour or a PM\(  
phenomena, anthropogenic activities, and precursor emissions [3]. Natural phenomena such as soiling, crustal elements, volcanic eruptions, and sea salt drifting, result in coarser particles [4]. Anthropogenic activities ranging from vehicle exhaust, biomass burning, coal, and gasoline combustion to petrochemical production and steel refineries generate finer-sized particles. The last source is contributed by the photochemical transformation of precursor emissions such as sulfur dioxide (SO\({}_{2}\)) and nitrogen oxides (NO\({}_{x}\)). Most of the existing research on air pollution places its focus on source apportionment, PM\({}_{2.5}\) concentration prediction, and spatiotemporal analysis [5; 6]. The source apportionment research intends to identify the chemical compositions of the captured pollutants so that the suspicious emitting site may be detected. The receptor model, source model, and tracer model are the most prevalent apportionment methodologies [5]. The research for PM\({}_{2.5}\) concentration prediction is categorized into short-term (in hours or days), middle-term (in months or seasons), and longer-term (in years). The most commonly applied methodologies are mathematical regression, machine learning, and meta-evolution [7]. As compared to source apportionment and concentration prediction, the research for spatiotemporal analysis is less contemplated. This is mainly because of the high computational effort incurred by combining spatial and temporal data for pattern discovery. Most researchers applied classic statistical approaches to describe the general properties of the data, and they conducted regression separately on spatial and temporal axes to save computational time [8; 9]. Only a few studies [10; 11] employed machine learning techniques to identify spatiotemporal clusters of similar PM\({}_{2.5}\) dispersion patterns, leaving a large room for deeper exploitation. This paper proposes an innovative spatiotemporal analysis framework for air pollution episode association. Our methodology is unique in the sense that we develop the air pollution spatiotemporal analysis from the perspective of video analysis. The crossover between the two fields creates new and innovative techniques that bring many insights unseen before. If we take a bird's-eye view of the land from above and monitor the hourly PM\({}_{2.5}\) concentrations, the geography becomes an image map if we consider the quantity of PM\({}_{2.5}\) concentrations as the gray value of pixels. The air pollution time series then becomes a video that can be analyzed using novel video processing techniques. We developed the shot boundary detection algorithms to separate the air pollution time series into meaningful shots (video clips). Each shot implicitly corresponds to an air pollution episode that may contain patterns of pollution accumulation, transportation, and dispersion. To represent the pollution episode efficiently and effectively, the gait energy image (GEI) scheme is adopted to digest the shot characteristics into a single image.