基于PCA-BP神经网络的城市空气质量统计研究

Statistical Study on Urban Air Quality Based on PCA-BP Neural Network

摘要: 本研究基于A市历史数据，深入剖析了PM_2.5污染特征及其短期浓度变化，从空气污染物和气象因素两大维度出发，通过描述性统计、相关性分析及逐步回归分析等手段，揭示了PM_2.5污染浓度的主要成因及其时间变化规律。为提高预测精度，研究融合了主成分分析与BP神经网络理论，运用SPSS软件将11项指标精简至4个主成分，并以此构建了PCA-BP神经网络预测模型，同时利用Python软件进行仿真验证。这一系列工作不仅深化了对PM_2.5污染特征的理解，也为环境污染分析提供了科学依据，对未来环境保护策略的制定与实施具有重要参考价值，有助于推动空气质量改善和环境保护事业的持续发展。

Abstract: This study utilizes historical data from City A to conduct an in-depth analysis of the characteristics of PM_2.5 pollution and its short-term concentration fluctuations. It approaches the issue from two perspectives: air pollutants and meteorological factors, employing descriptive statistics, correlation analysis, and stepwise regression to elucidate the primary determinants of PM_2.5 concentration levels and their temporal variations. To enhance predictive accuracy, this research integrates principal component analysis (PCA) with BP neural network theory, utilizing SPSS software to distill 11 indicators into 4 principal components before constructing a PCA-BP neural network prediction model. Additionally, validation of the model is performed using Python software. This comprehensive approach not only enriches our understanding of PM_2.5 pollution characteristics but also provides robust scientific evidence for environmental pollution assessment. The findings hold significant reference value for developing and implementing future environmental protection strategies, thereby contributing to the ongoing advancement of air quality improvement and environmental conservation efforts.