Policymakers frequently analyze air quality and climate change in isolation,
disregarding their interactions. This study explores the influence of specific
climate factors on air quality by contrasting a regression model with K-Means
Clustering, Hierarchical Clustering, and Random Forest techniques. We employ
Physics-based Deep Learning (PBDL) and Long Short-Term Memory (LSTM) to examine
the air pollution predictions. Our analysis utilizes ten years (2009-2018) of
daily traffic, weather, and air pollution data from three major cities in
Norway. Findings from feature selection reveal a correlation between rising
heating degree days and heightened air pollution levels, suggesting increased
heating activities in Norway are a contributing factor to worsening air
quality. PBDL demonstrates superior accuracy in air pollution predictions
compared to LSTM. This paper contributes to the growing literature on PBDL
methods for more accurate air pollution predictions using environmental
variables, aiding policymakers in formulating effective data-driven climate
policies.

研究探讨了气候因素对空气质量的影响，通过对比回归模型与 K-Means 聚类、层次聚类和随机森林技术来分析。我们使用基于物理学的深度学习 (Physics-based Deep Learning, PBDL) 和长短期记忆 (Long Short-Term Memory, LSTM) 方法来研究空气污染预测。分析使用了挪威三个主要城市 2009 年至 2018 年的每日交通、天气和空气污染数据。特征选择的结果显示，采暖度日增加与空气污染水平增加之间存在相关性，这表明挪威的采暖活动是空气质量恶化的一个因素。PBDL 方法在空气污染预测方面表现出较高的准确性，相较于 LSTM 方法更为优越。本文对于基于环境变量的 PBDL 方法进行更准确的空气污染预测的增长文献做出了贡献，有助于决策者制定有效的数据驱动型气候政策。