## 2 Related Work and Contributions
### Related Work
The EEMD method has also been used in [21] to decompose the PM\({}_{2.5}\) historical data and the least square support vector machine (LSSVM) is employed to predict all reconstructed components and integrate them to produce the final forecasting result. A specific type of ANN named multiple layer perceptron (MLP) is adopted in [22] to learn the relationship between PM\({}_{2.5}\) concentration and a set of meteorological factors with satellite-derived AOD data. An ensemble model was established in [23] to combine PM\({}_{2.5}\) estimates from three machine learning methods, namely, neural network, random forest, and gradient boosting. An improved deep learning model for predicting daily PM\({}_{2.5}\) concentration in the Beijing-Tianjin-Hebei area has been proposed in [24]. The improved deep learning model considers spatiotemporal correlations among surrounding monitoring sites with spatialsite-density information. Firstly, an MLP is applied to generate weighted PM\({}_{2.5}\) time series data based on air pollution concentration, wind condition, and the distance between the focused site and its neighboring sites. Second, both the original and weighted PM\({}_{2.5}\) time series are fed into a long short-term memory (LSTM) to extract spatiotemporal features. Finally, these spatiotemporal features and meteorological data are input to another MLP to predict the daily PM\({}_{2.5}\) concentration. Vast amounts of meteorological and pollutant data have been dealt with by [25] based on deep learning techniques. A deep learning network consisting of a convolutional neural network (CNN) and an LSTM is deployed. The CNN extracts meteorological and pollutant features from 14 sites in Shanghai and the LSTM is used to model time dependence of pollutants. The experimental results showed that the proposed combined model outperforms classic deep learning models. (3) _Meta-evolution._ Evolutionary algorithms are a family of nature-inspired optimization algorithms based on evolution in biology. Among them, genetic algorithm (GA), particle swarm optimization (PSO), ant cuckoo search algorithm (CSA) are notable ones. Meta-evolution is a framework where the evolutionary algorithms are applied to fine tune the hyperparameters of a machine learning model to improve its performance. Meta-evolution frameworks have been developed to predict PM\({}_{2.5}\) concentrations. For example, GA [26], PSO [27], and CSA [28] have been applied to optimize the parameter settings of artificial neural networks and support vector machines to estimate the PM\({}_{2.5}\) mass by using meteorological variables as inputs. (4) _Receptor model._ In this model, the relationship between the pollution and its particulate sources is estimated according to the pollutants' conservation on chemical mass balance (CMB) or the chemical transportation by the profiles of particulate mass sources along the dispersion route. The receptor model is commonly used for PM\({}_{2.5}\) dispersion prediction of line or point sources such as roadways or burning sites. The dispersion of PM\({}_{2.5}\) along urban highway in India has been analyzed in [29]. The vehicle types, sizes, and ages are considered in the emission mass balance and the molecular settling velocity and meteorological conditions are incorporated into the line source dispersion prediction model. The impact of Taiwan's barbecue festival on air quality is studied in [30], which monitored the PM\({}_{2.5}\) mass before, in, and after, the barbecue festival and compared the contribution from different chemical species. Another type of receptor model is based on statistical analysis, such as positive matrix factorization (PMF) [31]. PMF justifies the number of factors for finding the best-fit model to interpret the solution. PMF is used to analyze the contribution of various source apportionments in a mixture of PM\({}_{2.5}\) samples. (5) _Hybrid approaches._ In order to obtain better prediction performance, hybrid methods combining multiple types of prediction approaches are proposed.