# Construction of Air Quality Level Prediction Model Based on STEPDISC-PCA-BP
## 1 Introduction  
Green water and green mountains are reservoirs, grain depots, money depots, and carbon depots [1]. Bayannur city in Inner Mongolia relies on its rich resource advantages to develop an ecological tourism economy. Air pollution has a significant impact on tourism. Accurate and effective prediction of ambient air quality can provide an important guarantee for the development of local tourism [2]. Air quality index (AQI) and AQI-based air quality level (AQL) is a new national ambient air quality evaluation standard released in 2012 which states that the main evaluation factors of AQI are six air pollutants, namely sulfur dioxide (SO\({}_{2}\)), nitrogen dioxide (NO\({}_{2}\)), carbon monoxide (CO), ozone (O\({}_{3}\)), and particulate matter of median aerometric diameter \(\leq\) 10 \(\upmu\)m or \(\leq\) 2.5 \(\upmu\)m (PM\({}_{10}\), PM\({}_{2.5}\)). There are six grades of AQL, i.e., level 1 (0 \(\leq\) AQI \(\leq\) 50, excellent), level 2 (51 \(\leq\) AQI \(\leq\) 100, good), level 3 (101 \(\leq\) AQI \(\leq\) 150, light pollution), level 4 (151 \(\leq\) AQI \(\leq\) 200, moderate pollution), level 5 (201 \(\leq\) AQI \(\leq\) 300, heavy pollution), and level 6 (AQI \(\geq\) 300, severe pollution). The greater the AQI value, the higher the AQL and the more serious the air pollution condition [3, 4]. The complex composition and extensive sources of air pollutants make the prediction of air quality levels full of uncertainty. With its simple structure and strong self-learning ability, BP neural networks have shown promise in improving the accuracy of air quality predictions by allowing models to learn from complex and non-linear relationships in the data [5]. It hasbeen applied to AQL prediction by domestic and foreign scholars in recent years. [PERSON] et al. established the BP prediction model evaluated by air pollutants has good generalization and strong stability [6]. [PERSON] et al. confirmed that the BP neural network is optimal in detecting the best predictors of air pollution-induced cardiopulmonary disease admissions [7]. The selection of evaluation factors is directly related to the robustness of the AQL prediction model. The Ambient Air Quality Standard states that the AQI value depends on six air pollutants, which have become the necessary parameters for detecting air quality. Not only that, the accumulation and dispersion of air pollutants are affected by weather conditions such as air pressure, temperature, humidity, wind speed, wind direction, sunshine, etc. [8]. Some scholars have modeled both air pollutant factors and meteorological factors. [PERSON] et al. modeled based on the previous day's AQI value and the current day's meteorological factor values [9]. [PERSON] et al. added not only the AQI value of the previous day and the meteorological value of the day, but also entered the concentration values of six air pollutants in the model [10]. Given the issues of multiple factors, overlapping information, and difficulty solving equations, it is necessary to integrate multiple methods in practice to retain significant factors, remove correlation, reduce data dimensionality, simplify structure, and improve model fitting performance. However, most of the existing modeling studies only used a single BP neural network and had the following problems: some did not add meteorological evaluation factors; some lacked six air pollutants; some did not screen out factors with strong discrimination ability and were missing to perform factor dimensionality reduction treatment.