# Improving PM2.5 Concentration Forecast with the Identification of Temperature Inversion
## 1 Introduction  
The growth of economic and industrial development inevitably threatens the preservation of the natural environment. One of the sustainable development goals is to promote green trade, which produces low carbon levels during product manufacturing, transportation, use, and end-use disposal. Some of the production emissions are toxic, especially for the particulate matter of diameter \(\leq\) 2.5 \(\mu\)m (PM2.5), and they can significantly spoil human health. Many researches have shown that PM2.5 concentrations are greatly related to respiratory diseases [1] and cancer development [2]. It is reported by World Health Organization (WHO) that nearly 3 million people were killed due to ambient PM2.5 in 2012 [3]. The apportionment of PM2.5 covers a wide range of chemicals such as sulfate, nitrate, ammonium, carbonaceous compounds, crustal elements, organic compounds, and free radicals [4]. These chemical compositions come from three main sources [5]: natural, anthropogenic, and secondary transformation. The natural source comes from environmental soil dust, crustal elements, volcanic eruptions, sea salt, biomass, etc. The anthropogenic source is emitted from the economic activities ranging from vehicle exhaust, burning, coaland gasoline combustion, to petrochemical production and steel refinery. The secondary transformation source is contributed by photochemical reactions of precursors such as SO\({}_{2}\) and NO\({}_{\rm x}\). The air pollution incurred by these sources may influence different-sized over various durations. The size of the pollution area could be small such as incense burned during temple worship. Vehicle traffic usually causes a line pollution along the roads. For pollution resulting from volcanic eruptions or large petrochemical complexes, the area of pollution coverage can be as large as a city. PM\({}_{2.5}\) pollution, once emitted from various sources, will drift in the air and transport to downstream zones. Some particular geographic terrains such as valleys, basins, and hillside could easily trap the PM\({}_{2.5}\) flow to collectively concentrate at local regions. Usually, the concentration lasts for several hours and scatters away with wind flows. However, it may last for several days if the weather condition prevents the concentration from scattering. One such notorious weather condition is the temperature inversion which forms a warm-air cap above the cooler ground surface, trapping the PM\({}_{2.5}\) concentrations under the warm-air cap. Temperature inversion is a commonly appearing scenario in Taiwan and frequently causes high PM\({}_{2.5}\) concentrations at particular places. This paper chooses Puli township as the study site because it is a basin surrounded by mountains and often sees temperature inversion. We observe that there is high association between the occurrence of high PM\({}_{2.5}\) incurred by temperature inversion and some weather patterns. One such pattern is that there is no precipitation and the wind is relatively calm. If the temperature inversion phenomenon can be recognized by analyzing the weather patterns, we speculate that the estimation of PM\({}_{2.5}\) concentration based on regression of weather variables would be more accurate. The contributions of this paper include the following. (1) Many researches have shown that temperature inversion is one of the leading causes for observing high PM\({}_{2.5}\) concentrations. Puli is a basin township which easily seduces temperature inversion. To the best of our knowledge, there is no existing research which analyzes the meteorological scenarios of temperature inversion for improving the PM\({}_{2.5}\) prediction performance. This paper presents the first proposal in this research direction. (2) To construct a more effective model for PM\({}_{2.5}\) concentration forecasting, two machine-learning-based classifiers, namely, the CART (classification and regression trees) and CNN (convolutional neural networks), are developed to distinguish the test days into two classes.