# A Novel Interpretable Deep Learning Model for Ozone Prediction
## 1 Introduction  
Ozone (O\({}_{3}\)) is formed through photochemical reactions of compounds (VOCs) and NO\({}_{\text{x}}\)[1]. This disparity in spatiotemporal distribution is primarily caused by variations in emission characteristics, synoptic conditions, topographic distribution and land-use types [2; 3; 4; 5; 6]. Generally, the emission source and meteorological characteristics are the fundamental and essential factors for the formation, transport and dispersion of O\({}_{3}\)[7]. The source of O\({}_{3}\) precursor emissions can be categorized as anthropogenic sources and natural sources [8]. Additionally, meteorological variables such as solar radiation, wind direction, wind speed, atmospheric pressure, temperature and relative humidity have a complex relationship with O\({}_{3}\)[9; 10; 11; 12; 13]. Recently, ozone pollution has gradually increased and become a primary pollutant of great concern in air pollution control [14; 15; 16] due to its detrimental impacts on both human health and agriculture [17; 18; 19; 20]. Considering the complexity of the O\({}_{3}\) formation mechanism, the exacerbated combined atmospheric pollution increases the difficulty in ozone control [21]. One of the most important tasks for assessing efficient prevention and control strategies for O\({}_{3}\) pollution is predicting O\({}_{3}\)Building precise O\({}_{3}\)-prediction models can strongly support decision-makers in efficiently reducing heavy ozone pollution peaks, which is an urgent and necessary task. Generally, ozone-prediction approaches can be classified into two types: numerical and statistical approaches. Numerical approaches simulate the real atmospheric environment by utilizing accurate estimations of anthropogenic emissions and incorporating specific atmospheric physics and chemistry reactions. Some numerical approaches [22; 23; 24] have been widely used in ozone prediction. Unfortunately, numerical models suffer from an imperfect understanding of complex ozone formation and thus sacrifice spatiotemporal resolution. Therefore, the spatiotemporal representativeness, emission and modeling mechanisms still need to be perfected [25]. In contrast, statistical models do not take into account the complicated reaction mechanisms. However, they offer greater flexibility and more computing advantages [26]. Classical statistical ozone-prediction models mainly consist of basic regression models [27; 28; 29; 30; 31], which can limit the model's capacity to describe nonlinear and complex internal physicochemical processes. Therefore, they often fail to meet practical requirements [32; 33]. Machine learning (ML) methods, as a promising approach, have inspired advancements in the field of ozone forecasting. Basic ML algorithms [34; 35], advanced ensemble algorithms [36; 37; 38; 39] and artificial neural network (ANN) [40; 41; 42] have been intensively studied in ozone forecasting. Nevertheless, these models fail to capture spatial and temporal information simultaneously and normally ignore the interaction between elements in the sequence due to their independent and identically distributed (i.i.d.) premise. Therefore, more powerful methods are needed. To capture spatiotemporal information, emerging deep learning (DL) models are a good choice because of their powerful representative capabilities. Theoretically, a deep learning neural network (DNN) is capable of fitting any form of function. However, training such a deep neural network can be extremely challenging. Considering the No Free Lunch theorem [43; 44], the majority of sequence-oriented neural networks have been implemented using the recurrent neural network (RNN) [45]. RNNs are specifically designed for sequence forecasting by learning temporal patterns. Nonetheless, RNNs have issues with gradient explosion and gradient vanishing and they also lack long-term memory. Accordingly, the Long Short-Term Memory (LSTM) [46] and Gated Recurrent Unit (GRU) [47] were proposed to address these issues by incorporating memory units and a gating mechanism [48].